diff --git a/.gitignore b/.gitignore index 5e3a68f95..2cbb0a96e 100644 --- a/.gitignore +++ b/.gitignore @@ -1,5 +1,11 @@ -# Project-specific -benchmark_output/ +# Benchmark outputs, only track docs/results.csv and docs/config.yaml +glum_benchmarks/results/* +!glum_benchmarks/results/docs/ +!glum_benchmarks/results/docs/** +glum_benchmarks/results/docs/* +!glum_benchmarks/results/docs/results.csv +!glum_benchmarks/results/docs/config.yaml +glum_benchmarks/.cache/ # Files created by templating dense.cpp @@ -137,9 +143,6 @@ mlruns *.pdf *.lprof -# GLM_BENCHMARKS_CACHE -cache - # pkgs pkgs/* diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index cd710d995..a71eafec5 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -41,7 +41,7 @@ repos: - id: mypy name: mypy entry: pixi run -e default mypy - exclude: (^tests/|^src/glum_benchmarks/orig_sklearn_fork/) + exclude: ^tests/ language: system types: [python] require_serial: true diff --git a/README.md b/README.md index 2e127d992..131c5aef5 100644 --- a/README.md +++ b/README.md @@ -13,19 +13,23 @@ Generalized linear models (GLM) are a core statistical tool that include many common methods like least-squares regression, Poisson regression and logistic regression as special cases. At QuantCo, we have used GLMs in e-commerce pricing, insurance claims prediction and more. We have developed `glum`, a fast Python-first GLM library. The development was based on [a fork of scikit-learn](https://github.com/scikit-learn/scikit-learn/pull/9405), so it has a scikit-learn-like API. We are thankful for the starting point provided by Christian Lorentzen in that PR! -The goal of `glum` is to be at least as feature-complete as existing GLM libraries like `glmnet` or `h2o`. It supports +We believe that for GLM development, broad support for distributions, regularization, and statistical inference, along with fast formula-based specification, is key. `glum` supports * Built-in cross validation for optimal regularization, efficiently exploiting a “regularization path” * L1 regularization, which produces sparse and easily interpretable solutions * L2 regularization, including variable matrix-valued (Tikhonov) penalties, which are useful in modeling correlated effects * Elastic net regularization * Normal, Poisson, logistic, gamma, and Tweedie distributions, plus varied and customizable link functions +* Built-in formula-based model specification using `formulaic` +* Classical statistical inference for unregularized models * Box constraints, linear inequality constraints, sample weights, offsets -This repo also includes tools for benchmarking GLM implementations in the `glum_benchmarks` module. For details on the benchmarking, [see here](src/glum_benchmarks/README.md). Although the performance of `glum` relative to `glmnet` and `h2o` depends on the specific problem, we find that when N >> K (there are more observations than predictors), it is consistently much faster for a wide range of problems. +Performance also matters, so we conducted extensive benchmarks against other modern libraries. Although performance depends on the specific problem, we find that when N >> K (there are more observations than predictors), `glum` is consistently much faster for a wide range of problems. This repo includes the benchmarking tools in the `glum_benchmarks` module. For details, [see here](glum_benchmarks/README.md). -![Performance benchmarks](docs/_static/headline_benchmark.png#gh-light-mode-only) -![Performance benchmarks](docs/_static/headline_benchmark_dark.png#gh-dark-mode-only) + +Benchmark results +Benchmark results + For more information on `glum`, including tutorials and API reference, please see [the documentation](https://glum.readthedocs.io/en/latest/). diff --git a/docs/_static/intermediate-housing-gamma-normalized.png b/docs/_static/intermediate-housing-gamma-normalized.png new file mode 100644 index 000000000..697d800d8 Binary files /dev/null and b/docs/_static/intermediate-housing-gamma-normalized.png differ diff --git a/docs/_static/intermediate-housing-gaussian-normalized.png b/docs/_static/intermediate-housing-gaussian-normalized.png new file mode 100644 index 000000000..e9db309d6 Binary files /dev/null and b/docs/_static/intermediate-housing-gaussian-normalized.png differ diff --git a/docs/_static/simulated-glm-gaussian-k-over-n-0.01-normalized.png b/docs/_static/simulated-glm-gaussian-k-over-n-0.01-normalized.png new file mode 100644 index 000000000..ae4108482 Binary files /dev/null and b/docs/_static/simulated-glm-gaussian-k-over-n-0.01-normalized.png differ diff --git a/docs/_static/simulated-glm-gaussian-k-over-n-0.1-normalized.png b/docs/_static/simulated-glm-gaussian-k-over-n-0.1-normalized.png new file mode 100644 index 000000000..d2d45fa8c Binary files /dev/null and b/docs/_static/simulated-glm-gaussian-k-over-n-0.1-normalized.png differ diff --git a/docs/_static/simulated-glm-gaussian-k-over-n-0.5-normalized.png b/docs/_static/simulated-glm-gaussian-k-over-n-0.5-normalized.png new file mode 100644 index 000000000..64d0dae8b Binary files /dev/null and b/docs/_static/simulated-glm-gaussian-k-over-n-0.5-normalized.png differ diff --git a/docs/_static/simulated-glm-gaussian-k-over-n-1-normalized.png b/docs/_static/simulated-glm-gaussian-k-over-n-1-normalized.png new file mode 100644 index 000000000..b5ae4c448 Binary files /dev/null and b/docs/_static/simulated-glm-gaussian-k-over-n-1-normalized.png differ diff --git a/docs/_static/simulated-glm-gaussian-k-over-n-2.png b/docs/_static/simulated-glm-gaussian-k-over-n-2.png new file mode 100644 index 000000000..50e0f8e96 Binary files /dev/null and b/docs/_static/simulated-glm-gaussian-k-over-n-2.png differ diff --git a/docs/_static/wide-insurance-gamma-normalized.png b/docs/_static/wide-insurance-gamma-normalized.png new file mode 100644 index 000000000..d884dc720 Binary files /dev/null and b/docs/_static/wide-insurance-gamma-normalized.png differ diff --git a/docs/_static/wide-insurance-gamma-normalized_dark.png b/docs/_static/wide-insurance-gamma-normalized_dark.png new file mode 100644 index 000000000..2a814c211 Binary files /dev/null and b/docs/_static/wide-insurance-gamma-normalized_dark.png differ diff --git a/docs/_static/wide-insurance-poisson-normalized.png b/docs/_static/wide-insurance-poisson-normalized.png new file mode 100644 index 000000000..b85a19ff2 Binary files /dev/null and b/docs/_static/wide-insurance-poisson-normalized.png differ diff --git a/docs/_static/wide-insurance-tweedie-p=1.5-normalized.png b/docs/_static/wide-insurance-tweedie-p=1.5-normalized.png new file mode 100644 index 000000000..299461de8 Binary files /dev/null and b/docs/_static/wide-insurance-tweedie-p=1.5-normalized.png differ diff --git a/docs/benchmarks.rst b/docs/benchmarks.rst index e50b36adf..9169614c2 100644 --- a/docs/benchmarks.rst +++ b/docs/benchmarks.rst @@ -1,30 +1,132 @@ -Benchmarks against glmnet and H2O +Benchmarks ================================= -The following benchmarks were run on a MacBook Pro laptop with a quad-core Intel Core i5. +The following benchmarks were run on a MacBook Pro laptop with an Apple M4 Max chip. -The title of each plot refers to both which dataset the benchmark was run on and whether a L2 ridge regression penalty or an L1 lasso penalty was included. For example "Narrow-Insurance-Ridge" was run on the ``narrow-insurance`` dataset with a ridge regression penalty. Each dataset/penalty pair is tested on five distributions that cover most of the common GLM types. The outcome variable is modified appropriately so that the behavior is similar to that expected for the distribution. For example, for the Poisson regression, we predict the number of claims per person. And for the binomial regression, we predict whether any given individual has ever had a claim. For the ``housing`` dataset, we only test three distributions because it does not contain count data that can be used as an outcome. +Each plot title indicates the dataset and distribution used. For example, "Wide-Insurance-Gamma" refers to the ``wide-insurance`` dataset fit with a gamma distribution. Further information about the datasets can be found at the end of the document. -Note that glum was originally developed to solve problems where N >> K (number of observations is larger than the number of predictors), which is the case for the following benchmarks. +For each dataset/distribution pair, we benchmark three regularization types: -If a bar goes out of the range of the chart, the exact runtime is printed on the bar with an arrow indicating that the bar is truncated. +- Elastic net (``l1_ratio=0.5``): ``elastic-net`` +- Ridge (``l1_ratio=0.0``): ``ridge`` +- Lasso (``l1_ratio=1.0``): ``lasso`` -.. image:: _static/narrow-insurance-l2.png +We extract target variables and benchmark them under typical distributions (for example, insurance claim counts using Poisson models). + +Runtime plots are reported relative to ``glum``: for each benchmark case, ``glum``'s runtime is normalized to 1.0 and other libraries' runtimes are scaled accordingly. If a bar exceeds the plotting range, the exact runtime is printed on the bar and an arrow indicates truncation. + +We compare ``glum`` against ``sklearn``, ``skglm``, ``glmnet``, ``h2o`` and ``celer``. As some libraries do not support all benchmark cases, these combinations are shown as ``N/A`` (not supported). If a library does not converge (either it reaches ``max_iter`` or exceeds the 100s timeout), it is shown as ``NC`` (not converged) at the maximum bar height. + +glum was designed for settings with N >> K —that is, many more observations than predictors, apart from high-cardinality categorical features. This regime is well illustrated by the wide-insurance benchmark. For insurance data, we evaluate gamma, Poisson, and Tweedie distributions. + +.. BENCHMARK_FIGURES_START + +.. image:: _static/wide-insurance-poisson-normalized.png :width: 700 -.. image:: _static/narrow-insurance-lasso.png + +.. image:: _static/wide-insurance-gamma-normalized.png :width: 700 -.. image:: _static/intermediate-insurance-l2.png + +.. image:: _static/wide-insurance-tweedie-p=1.5-normalized.png :width: 700 -.. image:: _static/intermediate-insurance-lasso.png + +.. BENCHMARK_FIGURES_END + +To showcase ``glum’s`` performance on another dataset, we also report results for ``intermediate-housing``, which has N >> K and only numerical (no categorical) features. For this dataset, we benchmark gamma and Gaussian distributions. + +.. BENCHMARK_FIGURES_START + +.. image:: _static/intermediate-housing-gamma-normalized.png :width: 700 -.. image:: _static/wide-insurance-l2.png + +.. image:: _static/intermediate-housing-gaussian-normalized.png :width: 700 -Note that the ``r-glmnet`` result for the ``wide-insurance-ridge`` Poisson benchmark is missing because ``glmnet`` did not converge after several hours of runtime. +.. BENCHMARK_FIGURES_END + + +``glum`` is primarily optimized for N >> K settings, and is not tuned for N ~ K or N < K. This is illustrated by the simulated benchmark with varying K/N ratios: ``glum`` performs best when N >> K, and relative performance decreases as K/N increases. -.. image:: _static/wide-insurance-lasso.png +For K/N = 2, we include an unnormalized runtime plot, because in the normalized version the ``glmnet`` bar becomes too small to read clearly. + +.. BENCHMARK_FIGURES_START + +.. image:: _static/simulated-glm-gaussian-k-over-n-0.01-normalized.png :width: 700 -.. image:: _static/intermediate-housing-l2.png + +.. image:: _static/simulated-glm-gaussian-k-over-n-0.1-normalized.png :width: 700 -.. image:: _static/intermediate-housing-lasso.png + +.. image:: _static/simulated-glm-gaussian-k-over-n-0.5-normalized.png :width: 700 + +.. image:: _static/simulated-glm-gaussian-k-over-n-1-normalized.png + :width: 700 + +.. image:: _static/simulated-glm-gaussian-k-over-n-2.png + :width: 700 + +.. BENCHMARK_FIGURES_END + +In the following table more information about the used datasets can be found. After filtering for ``ClaimAmountCut > 0`` in the "Wide-Insurance-Gamma" dataset, only about 25,000 rows are left. We, therefore, artificially increase the dataset by sampling with replacement and adding noise. The filter is also why the number of columns after one-hot-encoding is smaller compared to the other distributions on this dataset because some category levels only exist in the dropped rows. + +For ``simulated-glm`` we reduce N from 10 000 to 1 000 for K/N = 1 and K/N = 2 in order to speed things up (with N = 10 000 nearly no library converges within the 100s limit). + +.. list-table:: Dataset Overview + :header-rows: 1 + :widths: 30 10 5 5 10 40 + + * - (Dataset, Distribution) + - (N, K) + - Cat. Columns + - Num. Columns + - Columns (OHE) + - Source + * - (wide-insurance, poisson), (wide-insurance, tweedie) + - (600 000, 9) + - 8 + - 1 + - 322 + - `freMTPL2 `_ + feature engineering/preprocessing + * - (wide-insurance, gamma) + - (600 000, 9) + - 8 + - 1 + - 256 + - `freMTPL2 `_ + feature engineering/preprocessing + * - (intermediate-housing, poisson), (intermediate-housing, gamma) + - (21 613, 10) + - 0 + - 10 + - 10 + - `house_sales `_ + feature engineering/preprocessing + * - (simulated-glm, gaussian) with K/N = 0.01 + - (10 000, 100) + - 0 + - 100 + - 100 + - simulated + * - (simulated-glm, gaussian) with K/N = 0.1 + - (10 000, 1 000) + - 0 + - 1 000 + - 1 000 + - simulated + * - (simulated-glm, gaussian) with K/N = 0.5 + - (10 000, 5 000) + - 0 + - 5 000 + - 5 000 + - simulated + * - (simulated-glm, gaussian) with K/N = 1 + - (1 000, 1 000) + - 0 + - 1 000 + - 1 000 + - simulated + * - (simulated-glm, gaussian) with K/N = 2 + - (1 000, 2 000) + - 0 + - 2 000 + - 2 000 + - simulated diff --git a/docs/benchmarks/benchmark_data.csv b/docs/benchmarks/benchmark_data.csv deleted file mode 100644 index cfca811fc..000000000 --- a/docs/benchmarks/benchmark_data.csv +++ /dev/null @@ -1,108 +0,0 @@ -problem_name,num_rows,regularization_strength,offset,library_name,storage,threads,single_precision,cv,hessian_approx,diagnostics_level,runtime,intercept,n_iter,runtime per iter,l1,l2,num_nonzero_coef,obj_val,rel_obj_val -intermediate-housing-no-weights-l2-binomial,500000,0.001,False,glum,auto,6,False,False,0.0,basic,1.3652839660644531,69341.4879203022,23,0.059360172437584915,0.4877111308088241,0.044681992095028794,12,0.0005213970913546075,0.0 -intermediate-housing-no-weights-l2-binomial,500000,0.001,False,h2o,sparse,6,False,False,0.0,basic,9.333569049835205,445.45091544849527,50,0.1866713809967041,1.7344550029758241,1.7526630040141455,12,0.004013210430335418,0.0034918133389808103 -intermediate-housing-no-weights-l2-binomial,500000,0.001,False,r-glmnet,sparse,6,False,False,0.0,basic,3.2920618057250977,40.46007369880116,109,0.030202401887386217,3.354212530891049,10.295010554277946,12,0.011902524295372181,0.011381127204017573 -intermediate-housing-no-weights-l2-gamma,500000,0.001,False,glum,auto,6,False,False,0.0,basic,0.31121373176574707,13.016136204637288,5,0.062242746353149414,0.5380656358695788,0.12572504427472417,12,14.059529622824732,0.0 -intermediate-housing-no-weights-l2-gamma,500000,0.001,False,h2o,sparse,6,False,False,0.0,basic,2.263485908508301,13.012301432549195,8,0.2829357385635376,0.5369660881887359,0.12526628798805908,12,14.059529724472153,1.0164742114682213e-07 -intermediate-housing-no-weights-l2-gamma,500000,0.001,False,r-glmnet,sparse,6,False,False,0.0,basic,6.559195041656494,13.017370776464762,43,0.15253941957340683,0.5378326788708638,0.12567200268590992,12,14.059529639251656,1.6426923821200035e-08 -intermediate-housing-no-weights-l2-gaussian,500000,0.001,False,glum,auto,6,False,False,0.0,basic,0.12359094619750977,-6.0289166867733e-06,1,0.12359094619750977,1.0000012823539342,0.9999999999996267,8,0.0005000000000636909,6.370132286243058e-14 -intermediate-housing-no-weights-l2-gaussian,500000,0.001,False,h2o,sparse,6,False,False,0.0,basic,1.5631258487701416,0.0,1,1.5631258487701416,1.0000000180885078,0.9999999999999604,1,0.0004999999999999896,0.0 -intermediate-housing-no-weights-l2-gaussian,500000,0.001,False,r-glmnet,sparse,6,False,False,0.0,basic,0.42995715141296387,-121148.39006375556,12,0.03582976261774699,15049.399614699496,47395731.28886296,12,9091035.021098096,9091035.020598097 -intermediate-housing-no-weights-lasso-binomial,500000,0.001,False,glum,auto,6,False,False,0.0,basic,0.9577291011810303,348.36801630476117,15,0.06384860674540202,2.337573054867256,5.435283019569339,5,0.004627077628184912,3.6951255944489905e-05 -intermediate-housing-no-weights-lasso-binomial,500000,0.001,False,h2o,sparse,6,False,False,0.0,basic,11.166002988815308,360.9778631029436,50,0.22332005977630615,2.344980461925311,5.469830140858556,5,0.0045901263722404224,0.0 -intermediate-housing-no-weights-lasso-binomial,500000,0.001,False,r-glmnet,sparse,6,False,False,0.0,basic,2.2993080615997314,21.056819532372693,71,0.03238462058591171,6.761309634019512,45.694408257688764,5,0.008784460444105363,0.00419433407186494 -intermediate-housing-no-weights-lasso-gamma,500000,0.001,False,glum,auto,6,False,False,0.0,basic,0.3847339153289795,12.987910308518288,5,0.0769467830657959,0.4098993729194777,0.11112838041472135,10,14.059936432429105,0.0 -intermediate-housing-no-weights-lasso-gamma,500000,0.001,False,h2o,sparse,6,False,False,0.0,basic,2.6593220233917236,12.985366626817822,9,0.29548022482130265,0.4095731114254718,0.11097424724351537,10,14.059936446157986,1.3728881143038052e-08 -intermediate-housing-no-weights-lasso-gamma,500000,0.001,False,r-glmnet,sparse,6,False,False,0.0,basic,6.474939823150635,12.988174245609281,44,0.14715772325342352,0.4096123472422231,0.11106287293820276,10,14.059936445106779,1.267767402168829e-08 -intermediate-housing-no-weights-lasso-gaussian,500000,0.001,False,glum,auto,6,False,False,0.0,basic,0.1570429801940918,1.7800950445234776e-05,1,0.1570429801940918,1.000000050939643,0.9999999999120377,3,0.0010000002082510176,2.0781104463109268e-10 -intermediate-housing-no-weights-lasso-gaussian,500000,0.001,False,h2o,sparse,6,False,False,0.0,basic,1.2889678478240967,-9.34607920412156e-07,1,1.2889678478240967,0.9999999999999936,0.9999999999999871,1,0.001000000000439973,0.0 -intermediate-housing-no-weights-lasso-gaussian,500000,0.001,False,r-glmnet,sparse,6,False,False,0.0,basic,0.461179256439209,-121148.17237972468,12,0.03843160470326742,15049.245776775055,47394170.1850359,12,9067337.951865943,9067337.950865943 -intermediate-insurance-no-weights-l2-binomial,500000,0.001,False,glum,auto,6,False,False,0.0,basic,0.3378269672393799,-3.932807351284047,3,0.11260898907979329,9.256632307391504,1.7858402804518303,105,0.19449939260575022,2.7755575615628914e-16 -intermediate-insurance-no-weights-l2-binomial,500000,0.001,False,h2o,sparse,6,False,False,0.0,basic,3.6067357063293457,-3.932807351283909,3,1.2022452354431152,9.25663230739059,1.7858402804517826,105,0.19449939260574994,0.0 -intermediate-insurance-no-weights-l2-binomial,500000,0.001,False,r-glmnet,sparse,6,False,False,0.0,basic,0.938262939453125,-3.9455816775537245,10,0.0938262939453125,9.230290491485048,1.802360809140097,105,0.19450391243582998,4.519830080040865e-06 -intermediate-insurance-no-weights-l2-gamma,500000,0.001,False,glum,auto,6,False,False,0.0,basic,0.27772998809814453,7.86579325469752,3,0.09257666269938152,9.962570595160145,1.666382250624836,105,8.502289625261978,0.0 -intermediate-insurance-no-weights-l2-gamma,500000,0.001,False,h2o,sparse,6,False,False,0.0,basic,3.055985927581787,7.868284881884337,3,1.0186619758605957,9.966871936480448,1.6679278575432115,105,8.502289978288497,3.530265182405401e-07 -intermediate-insurance-no-weights-l2-gamma,500000,0.001,False,r-glmnet,sparse,6,False,False,0.0,basic,13.998401165008545,7.823589354513357,47,0.2978383226597563,9.945582275807602,1.6563266778580759,105,8.502293302259204,3.6769972258099415e-06 -intermediate-insurance-no-weights-l2-gaussian,500000,0.001,False,glum,auto,6,False,False,0.0,basic,0.1420118808746338,2745.4802204258326,1,0.1420118808746338,19290.84881816882,6380818.704562034,105,14517871.094367057,1.1175870895385742e-08 -intermediate-insurance-no-weights-l2-gaussian,500000,0.001,False,h2o,sparse,6,False,False,0.0,basic,2.1197590827941895,2745.4802204505954,1,2.1197590827941895,19290.848818373965,6380818.704666495,105,14517871.094367046,0.0 -intermediate-insurance-no-weights-l2-gaussian,500000,0.001,False,r-glmnet,sparse,6,False,False,0.0,basic,1.41050124168396,1688.321791454727,3,0.47016708056132,28887.219602411566,17718284.232755415,105,14525980.780754795,8109.686387749389 -intermediate-insurance-no-weights-l2-poisson,500000,0.001,False,glum,auto,6,False,False,0.0,basic,0.41778087615966797,-1.8855435691549802,4,0.10444521903991699,17.54068100366305,7.456522432870721,105,0.4917490423020855,0.0 -intermediate-insurance-no-weights-l2-poisson,500000,0.001,False,h2o,sparse,6,False,False,0.0,basic,3.558475971221924,-1.884742570607226,3,1.1861586570739746,17.543520731688453,7.462396903795074,105,0.4917491539905156,1.1168843011555296e-07 -intermediate-insurance-no-weights-l2-poisson,500000,0.001,False,r-glmnet,sparse,6,False,False,0.0,basic,1.7636909484863281,-1.9384673533650738,29,0.060816929258149244,17.243294604174285,7.6473677313942865,105,0.49184048195859287,9.143965650737496e-05 -intermediate-insurance-no-weights-l2-tweedie-p=1.5,500000,0.001,False,glum,auto,6,False,False,0.0,basic,0.4201068878173828,4.639420860909255,4,0.1050267219543457,39.757694487606834,27.410634545617008,105,59.870197245954245,0.0 -intermediate-insurance-no-weights-l2-tweedie-p=1.5,500000,0.001,False,h2o,sparse,6,False,False,0.0,basic,3.075371026992798,4.639357743741159,3,1.0251236756642659,39.75749992307433,27.410316063056296,105,59.87019724681042,8.561755748814903e-10 -intermediate-insurance-no-weights-l2-tweedie-p=1.5,500000,0.001,False,r-glmnet,sparse,6,False,False,0.0,basic,26.588134050369263,4.617501409901606,141,0.18856832659836356,39.54713125935264,27.319641734303485,105,59.87084669550669,0.0006494495524478339 -intermediate-insurance-no-weights-lasso-binomial,500000,0.001,False,glum,auto,6,False,False,0.0,basic,0.3555409908294678,-3.852486266492804,3,0.11851366360982259,1.1139284415691963,0.29947282675370246,10,0.1973457868311363,0.0 -intermediate-insurance-no-weights-lasso-binomial,500000,0.001,False,h2o,sparse,6,False,False,0.0,basic,4.267688035964966,-3.878053595560125,5,0.8535376071929932,1.0119645774298665,0.2874333786234385,10,0.19734867432583092,2.8874946946255697e-06 -intermediate-insurance-no-weights-lasso-binomial,500000,0.001,False,r-glmnet,sparse,6,False,False,0.0,basic,0.6762068271636963,-3.8512351746691507,11,0.06147334792397239,1.1132899701110648,0.2990075246802091,10,0.19734579855539008,1.1724253790035277e-08 -intermediate-insurance-no-weights-lasso-gamma,500000,0.001,False,glum,auto,6,False,False,0.0,basic,0.3477509021759033,7.509368030580318,3,0.11591696739196777,3.500645843254284,0.49225307039009003,50,8.507477698078239,0.0 -intermediate-insurance-no-weights-lasso-gamma,500000,0.001,False,h2o,sparse,6,False,False,0.0,basic,2.699298143386841,7.531880589262129,3,0.8997660477956136,3.1520876571113745,0.4715586987149958,39,8.507651686798825,0.00017398872058649317 -intermediate-insurance-no-weights-lasso-gamma,500000,0.001,False,r-glmnet,sparse,6,False,False,0.0,basic,5.039093017578125,7.513832169022204,17,0.296417236328125,3.5054336803510173,0.4936175577556672,51,8.50747798479991,2.8672167218246614e-07 -intermediate-insurance-no-weights-lasso-gaussian,500000,0.001,False,glum,auto,6,False,False,0.0,basic,0.4860198497772217,3110.1706567421843,1,0.4860198497772217,27433.04097823872,24253345.69309193,100,14512879.198146721,0.0 -intermediate-insurance-no-weights-lasso-gaussian,500000,0.001,False,h2o,sparse,6,False,False,0.0,basic,2.3440258502960205,3072.859981509211,1,2.3440258502960205,27433.14875914769,24189316.39621429,101,14512879.198287832,0.00014111027121543884 -intermediate-insurance-no-weights-lasso-gaussian,500000,0.001,False,r-glmnet,sparse,6,False,False,0.0,basic,1.4172799587249756,1688.1676454705955,3,0.4724266529083252,28836.682418824563,17639246.619587004,103,14517149.7939078,4270.595761079341 -intermediate-insurance-no-weights-lasso-poisson,500000,0.001,False,glum,auto,6,False,False,0.0,basic,0.5265569686889648,-2.4917608878494946,5,0.10531139373779297,8.672618937821719,7.0068383159052035,39,0.4999329027838563,0.0 -intermediate-insurance-no-weights-lasso-poisson,500000,0.001,False,h2o,sparse,6,False,False,0.0,basic,6.343238830566406,-2.660918779107629,6,1.0572064717610676,8.813338910678684,7.208516396333103,38,0.49997375752418716,4.085474033083125e-05 -intermediate-insurance-no-weights-lasso-poisson,500000,0.001,False,r-glmnet,sparse,6,False,False,0.0,basic,1.9376027584075928,-2.5713138749068736,46,0.04212179909581724,8.662917210486127,6.932115473672988,42,0.4999360949628418,3.1921789854783e-06 -intermediate-insurance-no-weights-lasso-tweedie-p=1.5,500000,0.001,False,glum,auto,6,False,False,0.0,basic,0.5623600482940674,4.515881476418223,4,0.14059001207351685,37.41229600278945,29.106673367120504,94,59.894888727156435,0.0 -intermediate-insurance-no-weights-lasso-tweedie-p=1.5,500000,0.001,False,h2o,sparse,6,False,False,0.0,basic,3.2149980068206787,4.423778998634875,3,1.0716660022735596,36.81992970145965,28.980491073763883,92,59.89529123268652,0.00040250553008291945 -intermediate-insurance-no-weights-lasso-tweedie-p=1.5,500000,0.001,False,r-glmnet,sparse,6,False,False,0.0,basic,21.632970094680786,4.419243104942215,131,0.16513717629527316,37.372767140704525,28.742863773267572,100,59.89511703028867,0.00022830313223209941 -narrow-insurance-no-weights-l2-binomial,500000,0.001,False,glum,auto,6,False,False,0.0,basic,0.27996397018432617,-4.180151762184284,3,0.09332132339477539,5.060356469202336,0.9832189204498677,54,0.1952604610413139,1.942890293094024e-16 -narrow-insurance-no-weights-l2-binomial,500000,0.001,False,h2o,sparse,6,False,False,0.0,basic,3.64421010017395,-4.180151762181152,3,1.2147367000579834,5.0603564692007925,0.9832189204496945,54,0.1952604610413137,0.0 -narrow-insurance-no-weights-l2-binomial,500000,0.001,False,r-glmnet,sparse,6,False,False,0.0,basic,0.8502748012542725,-4.16040735349332,10,0.08502748012542724,5.028368054500571,0.9992407559579505,54,0.19526413703679926,3.6759954855636323e-06 -narrow-insurance-no-weights-l2-gamma,500000,0.001,False,glum,auto,6,False,False,0.0,basic,0.2661159038543701,7.355154583351965,3,0.08870530128479004,4.131381561686681,0.5534496875675825,54,8.50675102925574,0.0 -narrow-insurance-no-weights-l2-gamma,500000,0.001,False,h2o,sparse,6,False,False,0.0,basic,2.59419322013855,7.35544836771941,3,0.8647310733795166,4.132024611604063,0.5535393456155183,54,8.506751065123318,3.586757735263291e-08 -narrow-insurance-no-weights-l2-gamma,500000,0.001,False,r-glmnet,sparse,6,False,False,0.0,basic,8.858185052871704,7.306725715703681,31,0.2857479049313453,4.0455745227159605,0.5661223524123943,54,8.506758286673623,7.257417882655659e-06 -narrow-insurance-no-weights-l2-gaussian,500000,0.001,False,glum,auto,6,False,False,0.0,basic,0.11331510543823242,1622.4685360113594,1,0.11331510543823242,7796.293666766565,1965710.1813652876,54,14535740.277365696,0.0 -narrow-insurance-no-weights-l2-gaussian,500000,0.001,False,h2o,sparse,6,False,False,0.0,basic,2.1790730953216553,1622.4685361478698,1,2.1790730953216553,7796.293666889359,1965710.1813671356,54,14535740.277365701,5.587935447692871e-09 -narrow-insurance-no-weights-l2-gaussian,500000,0.001,False,r-glmnet,sparse,6,False,False,0.0,basic,0.9746627807617188,1557.1267474629233,3,0.32488759358723956,8290.550598737389,2743919.227293954,54,14536146.525204191,406.2478384952992 -narrow-insurance-no-weights-l2-poisson,500000,0.001,False,glum,auto,6,False,False,0.0,basic,0.32416486740112305,-2.2794554902930875,4,0.08104121685028076,10.19475423242634,4.04638411584928,54,0.4973397652827558,5.260947233409752e-11 -narrow-insurance-no-weights-l2-poisson,500000,0.001,False,h2o,sparse,6,False,False,0.0,basic,2.9681179523468018,-2.279476792303359,4,0.7420294880867004,10.194921029964622,4.046511019447791,54,0.49733976523014634,0.0 -narrow-insurance-no-weights-l2-poisson,500000,0.001,False,r-glmnet,sparse,6,False,False,0.0,basic,1.409796953201294,-2.3338522767480048,27,0.0522147019704183,9.918941561798443,4.25405056700107,54,0.4974376826180215,9.791738787517934e-05 -narrow-insurance-no-weights-l2-tweedie-p=1.5,500000,0.001,False,glum,auto,6,False,False,0.0,basic,0.3993680477142334,2.8480328292709247,4,0.09984201192855835,14.253548970526953,6.614587773546725,54,60.64156464159215,0.0 -narrow-insurance-no-weights-l2-tweedie-p=1.5,500000,0.001,False,h2o,sparse,6,False,False,0.0,basic,2.9729480743408203,2.84802484788491,3,0.9909826914469401,14.25352639353286,6.614550893829492,54,60.64156464177111,1.7896439885589643e-10 -narrow-insurance-no-weights-l2-tweedie-p=1.5,500000,0.001,False,r-glmnet,sparse,6,False,False,0.0,basic,9.020383358001709,3.073101018240435,47,0.19192305017024913,14.076631472656903,7.433287184225484,54,60.64205110193866,0.00048646034651511627 -narrow-insurance-no-weights-lasso-binomial,500000,0.001,False,glum,auto,6,False,False,0.0,basic,0.3195929527282715,-3.8511442308157138,3,0.10653098424275716,1.0121958665108395,0.2890464798120844,9,0.19734867429582134,0.0 -narrow-insurance-no-weights-lasso-binomial,500000,0.001,False,h2o,sparse,6,False,False,0.0,basic,3.1397287845611572,-3.876959073549801,5,0.6279457569122314,1.0070937837020404,0.2838248131498391,10,0.19734871836122317,4.406540182744223e-08 -narrow-insurance-no-weights-lasso-binomial,500000,0.001,False,r-glmnet,sparse,6,False,False,0.0,basic,0.6365761756896973,-3.8499353707004453,11,0.05787056142633611,1.0119544520173915,0.28867722840701215,9,0.19734868577071196,1.1474890621032685e-08 -narrow-insurance-no-weights-lasso-gamma,500000,0.001,False,glum,auto,6,False,False,0.0,basic,0.3304741382598877,7.31606647743836,3,0.11015804608662923,2.3187831291684318,0.2889408620915385,38,8.509605081206425,0.0 -narrow-insurance-no-weights-lasso-gamma,500000,0.001,False,h2o,sparse,6,False,False,0.0,basic,3.652060031890869,7.304636743178588,6,0.6086766719818115,2.2032055869362175,0.27791339999450687,36,8.50962769531576,2.2614109335350463e-05 -narrow-insurance-no-weights-lasso-gamma,500000,0.001,False,r-glmnet,sparse,6,False,False,0.0,basic,4.194249868392944,7.318373048782557,13,0.32263460526099574,2.3155440341967504,0.2887248429511649,39,8.509605612581186,5.31374761081338e-07 -narrow-insurance-no-weights-lasso-gaussian,500000,0.001,False,glum,auto,6,False,False,0.0,basic,0.2415480613708496,1494.0493589138002,1,0.2415480613708496,7973.913061185697,2527684.4294455703,49,14534680.227043832,0.0 -narrow-insurance-no-weights-lasso-gaussian,500000,0.001,False,h2o,sparse,6,False,False,0.0,basic,1.813011884689331,1469.779248486943,1,1.813011884689331,7973.911099327892,2516841.270539224,51,14534680.227043895,6.332993507385254e-08 -narrow-insurance-no-weights-lasso-gaussian,500000,0.001,False,r-glmnet,sparse,6,False,False,0.0,basic,0.9876160621643066,1557.0841270987871,3,0.32920535405476886,8287.102713435255,2742159.0152984816,53,14534782.76105566,102.5340118277818 -narrow-insurance-no-weights-lasso-poisson,500000,0.001,False,glum,auto,6,False,False,0.0,basic,0.40797901153564453,-2.544078367443045,4,0.10199475288391113,5.804898515594923,4.199195707661827,29,0.5028252020633025,0.0 -narrow-insurance-no-weights-lasso-poisson,500000,0.001,False,h2o,sparse,6,False,False,0.0,basic,5.422412872314453,-2.7169963529328434,6,0.9037354787190756,5.781952081983785,4.127270083679544,30,0.5028260710450684,8.689817658780186e-07 -narrow-insurance-no-weights-lasso-poisson,500000,0.001,False,r-glmnet,sparse,6,False,False,0.0,basic,1.2878801822662354,-2.5178511524900373,30,0.042929339408874514,5.7958100410627535,4.164613649758953,31,0.5028439764561256,1.8774392823051045e-05 -narrow-insurance-no-weights-lasso-tweedie-p=1.5,500000,0.001,False,glum,auto,6,False,False,0.0,basic,0.37630510330200195,2.952248551883776,4,0.09407627582550049,13.848661646560725,7.755392891335311,50,60.65217511351814,0.0 -narrow-insurance-no-weights-lasso-tweedie-p=1.5,500000,0.001,False,h2o,sparse,6,False,False,0.0,basic,3.0462992191314697,2.800231312754611,3,1.0154330730438232,13.848626021212423,6.951533672061727,51,60.65217511363744,1.1930012533412082e-10 -narrow-insurance-no-weights-lasso-tweedie-p=1.5,500000,0.001,False,r-glmnet,sparse,6,False,False,0.0,basic,8.152156829833984,3.038473679707259,46,0.17722080064856488,13.925756932386248,7.503772312811216,54,60.6523389740007,0.00016386048255867536 -wide-insurance-no-weights-l2-binomial,500000,0.001,False,glum,auto,6,False,False,0.0,basic,4.215606927871704,-5.918508617336183,11,0.3832369934428822,11.159818181019002,21.603972314434618,318,0.01782568026660583,0.0 -wide-insurance-no-weights-l2-binomial,500000,0.001,False,h2o,sparse,6,False,False,0.0,basic,14.187084913253784,-5.920088090515427,18,0.7881713840696547,11.107363506030417,21.64020564113237,321,0.017858869162273708,3.318889566787664e-05 -wide-insurance-no-weights-l2-binomial,500000,0.001,False,r-glmnet,sparse,6,False,False,0.0,basic,1746.3902020454407,-64823731.83146797,34982,0.0499225373633709,755979.5993718462,206954508776.8952,320,107925651.32235527,107925651.30452959 -wide-insurance-no-weights-l2-gamma,500000,0.001,False,glum,auto,6,False,False,0.0,basic,1.1700620651245117,7.175365651037758,9,0.13000689612494576,7.339501472942532,0.4149920032373346,269,8.12204963352236,0.0 -wide-insurance-no-weights-l2-gamma,500000,0.001,False,h2o,sparse,6,False,False,0.0,basic,19.572278022766113,7.16822980979415,43,0.455169256343398,7.289293103105495,0.41280455292966034,269,8.122142104575758,9.247105339760253e-05 -wide-insurance-no-weights-l2-gamma,500000,0.001,False,r-glmnet,sparse,6,False,False,0.0,basic,44.02700638771057,7.130453129548489,99,0.4447172362395007,7.08948700731966,0.42423638267982205,269,8.126099676722658,0.0040500432002978926 -wide-insurance-no-weights-l2-gaussian,500000,0.001,False,glum,auto,6,False,False,0.0,basic,0.15034031867980957,2264.9547069928763,1,0.15034031867980957,14301.636865890545,4585549.785197817,269,656265.7979189006,6.984919309616089e-10 -wide-insurance-no-weights-l2-gaussian,500000,0.001,False,h2o,sparse,6,False,False,0.0,basic,3.0654048919677734,2264.954706699715,1,3.0654048919677734,14301.636866715638,4585549.7852022555,269,656265.7979188999,0.0 -wide-insurance-no-weights-l2-gaussian,500000,0.001,False,r-glmnet,sparse,6,False,False,0.0,basic,3.41753888130188,2336.1242826644043,6,0.5695898135503134,28834.45004031686,18670134.133964155,269,662359.1090619661,6093.311143066152 -wide-insurance-no-weights-l2-poisson,500000,0.001,False,glum,auto,6,False,False,0.0,basic,3.1020541191101074,-1.0039837194290375,12,0.2585045099258423,43.727440391941656,38.41398070919996,320,-0.09069293091273091,0.0 -wide-insurance-no-weights-l2-poisson,500000,0.001,False,h2o,sparse,6,False,False,0.0,basic,7.727496862411499,-0.9997856054798493,6,1.2879161437352498,43.793197484034145,38.599300343620015,320,-0.09065564189538636,3.7289017344552255e-05 -wide-insurance-no-weights-l2-tweedie-p=1.5,500000,0.001,False,glum,auto,6,False,False,0.0,basic,1.6995139122009277,3.049327207806064,6,0.2832523187001546,86.48354109988361,100.65970779501377,320,19.99338779120593,0.0 -wide-insurance-no-weights-l2-tweedie-p=1.5,500000,0.001,False,h2o,sparse,6,False,False,0.0,basic,6.410604000091553,3.0493120116926344,5,1.2821208000183106,86.46808714064487,100.51359846888303,320,19.99338799596454,2.0475861006730156e-07 -wide-insurance-no-weights-l2-tweedie-p=1.5,500000,0.001,False,r-glmnet,sparse,6,False,False,0.0,basic,209.39575815200806,3.0934279054780243,495,0.42302173364042034,82.53438075735215,64.76492445198161,320,21.508556330000197,1.515168538794267 -wide-insurance-no-weights-lasso-binomial,500000,0.001,False,glum,auto,6,False,False,0.0,basic,4.2330238819122314,-6.863795697837583,12,0.3527519901593526,9.382245164991804,45.710311235264385,5,0.01142955372142668,0.0 -wide-insurance-no-weights-lasso-binomial,500000,0.001,False,h2o,sparse,6,False,False,0.0,basic,15.270301818847656,-6.8600630178035304,50,0.3054060363769531,9.582500197720083,43.48127783969452,5,0.011631793919210997,0.00020224019778431607 -wide-insurance-no-weights-lasso-binomial,500000,0.001,False,r-glmnet,sparse,6,False,False,0.0,basic,978.0747902393341,0.0,100001,0.009780650095892383,0.0,0.0,0,0.6931471805603778,0.6817176268389511 -wide-insurance-no-weights-lasso-gamma,500000,0.001,False,glum,auto,6,False,False,0.0,basic,1.6683471202850342,7.108632425910281,9,0.1853719022538927,0.9498959850061601,0.11338859702590669,34,8.124203845085558,0.0 -wide-insurance-no-weights-lasso-gamma,500000,0.001,False,h2o,sparse,6,False,False,0.0,basic,17.53027105331421,7.11618482167628,43,0.4076807221700979,0.6565589492341464,0.05803044094453133,21,8.124523509488359,0.00031966440280051245 -wide-insurance-no-weights-lasso-gamma,500000,0.001,False,r-glmnet,sparse,6,False,False,0.0,basic,19.150114059448242,7.0513578427166985,104,0.18413571211007926,0.9357051773625767,0.14124513505008962,30,8.12815221882619,0.0039483737406307284 -wide-insurance-no-weights-lasso-gaussian,500000,0.001,False,glum,auto,6,False,False,0.0,basic,1.0905101299285889,2357.86487467387,1,1.0905101299285889,27114.65614222386,16187764.6409055,263,653040.5845412787,0.0 -wide-insurance-no-weights-lasso-gaussian,500000,0.001,False,h2o,sparse,6,False,False,0.0,basic,3.0921239852905273,2364.825739351348,1,3.0921239852905273,27114.656146136087,13904399.864690054,263,653040.5845412812,2.561137080192566e-09 -wide-insurance-no-weights-lasso-gaussian,500000,0.001,False,r-glmnet,sparse,6,False,False,0.0,basic,3.8003342151641846,2336.1616806051093,6,0.6333890358606974,28110.699244447183,18392862.761907972,264,653052.4896719343,11.905130655621178 -wide-insurance-no-weights-lasso-poisson,500000,0.001,False,glum,auto,6,False,False,0.0,basic,3.378067970275879,-0.6484275884038628,13,0.25985138232891375,24.42887242506683,38.616666353164675,71,-0.07863825391226621,0.0 -wide-insurance-no-weights-lasso-poisson,500000,0.001,False,h2o,sparse,6,False,False,0.0,basic,8.872656106948853,-0.9535071270654356,7,1.2675223009926933,16.166465168795057,25.562586384297397,44,-0.051186009809016136,0.027452244103250076 -wide-insurance-no-weights-lasso-poisson,500000,0.001,False,r-glmnet,sparse,6,False,False,0.0,basic,2728.6244990825653,0.0,100001,0.02728597213110434,0.0,0.0,0,1.0000000000006695,1.0786382539129358 -wide-insurance-no-weights-lasso-tweedie-p=1.5,500000,0.001,False,glum,auto,6,False,False,0.0,basic,6.557005882263184,3.290808834962732,9,0.7285562091403537,71.78912666519844,172.42337269077885,192,20.00964588214773,0.0 -wide-insurance-no-weights-lasso-tweedie-p=1.5,500000,0.001,False,h2o,sparse,6,False,False,0.0,basic,7.450127124786377,3.2393271452117287,6,1.2416878541310628,67.94307599363889,163.2669035773099,181,20.019489821072938,0.009843938925207851 -wide-insurance-no-weights-lasso-tweedie-p=1.5,500000,0.001,False,r-glmnet,sparse,6,False,False,0.0,basic,208.8611500263214,3.2824850054768104,777,0.26880456888844456,60.65105771807204,58.96369507797802,192,20.064111216515634,0.05446533436790446 diff --git a/docs/benchmarks/benchmark_figure.py b/docs/benchmarks/benchmark_figure.py deleted file mode 100644 index a5094576b..000000000 --- a/docs/benchmarks/benchmark_figure.py +++ /dev/null @@ -1,310 +0,0 @@ -# --- -# jupyter: -# jupytext: -# formats: ipynb,py:percent -# text_representation: -# extension: .py -# format_name: percent -# format_version: '1.3' -# jupytext_version: 1.13.0 -# kernelspec: -# display_name: Python 3 (ipykernel) -# language: python -# name: python3 -# --- - -# %% -import subprocess - -import matplotlib.pyplot as plt -import pandas as pd - -# %% -# !rm -r benchmark_output - -# %% -base_cmd = ( - "glm_benchmarks_run --threads 6 --num_rows {n} --storage {s} " - "--problem_name {p} --library_name {lib}" -) - -problems = [] -for p in ["narrow-insurance", "intermediate-insurance", "wide-insurance"]: - for reg in ["l2", "lasso"]: - for dist in ["tweedie-p=1.5", "poisson", "gaussian", "gamma", "binomial"]: - problems.append(f"{p}-no-weights-{reg}-{dist}") - -p = "intermediate-housing" -for reg in ["l2", "lasso"]: - for dist in ["gaussian", "gamma", "binomial"]: - problems.append(f"{p}-no-weights-{reg}-{dist}") - -n = 500000 - -# %% -# run h2o benchmark, sparse storage works best. -s = "sparse" -for p in problems: - cmd = base_cmd.format(n=n, s=s, p=p, lib="h2o") - print(cmd) - subprocess.run(cmd.split(" ")) - -# run glum benchmarks where auto storage works best. -lib = "glum" -s = "auto" -for p in problems: - cmd = base_cmd.format(n=n, s=s, p=p, lib=lib) - print(cmd) - subprocess.run(cmd.split(" ")) - -analyze_cmd = "glm_benchmarks_analyze --export benchmark_data.csv" -subprocess.run(analyze_cmd.split(" ")) - -# %% -df = pd.read_csv("benchmark_data.csv") -df.drop( - [ - "storage", - "num_rows", - "regularization_strength", - "offset", - "threads", - "single_precision", - "cv", - "hessian_approx", - "diagnostics_level", - ], - axis=1, - inplace=True, -) -df["distribution"] = ( - df["problem_name"].str.split("-").apply(lambda x: x[-2] if "5" in x[-1] else x[-1]) -) - -# %% -# %config InlineBackend.figure_format='retina' - -# %% -for prob_name in ["narrow-insurance", "intermediate-insurance", "intermediate-housing"]: - for reg in ["l2", "lasso"]: - plot_df = ( - df[ - df["problem_name"].str.contains(reg) - & df["problem_name"].str.contains(prob_name) - ] - .copy() - .set_index(["distribution"])[["runtime", "library_name"]] - ) - plot_df = plot_df.pivot(columns="library_name") - plot_df.columns = plot_df.columns.get_level_values(1) - plot_df = plot_df.sort_index(axis=1) - plot_df.index = [x.title() for x in plot_df.index] - - title = prob_name.title() + "-" + ("Lasso" if reg == "lasso" else "Ridge") - plot_df.plot.bar( - ylim=[0, 4], - title=title, - legend=False, - figsize=(6, 3), - width=0.8, - ylabel="run time (s)", - yticks=[0, 1, 2, 3, 4], - cmap="Paired", - ) - plt.legend(bbox_to_anchor=(1, 1), loc="upper left", ncol=1) - plt.xticks(rotation=45, ha="right") - - ax = plt.gca() - - # Hide the right and top spines - ax.spines["right"].set_visible(False) - ax.spines["top"].set_visible(False) - - # Only show ticks on the left and bottom spines - ax.yaxis.set_ticks_position("left") - ax.xaxis.set_ticks_position("bottom") - - for p in ax.patches: - x = p.get_x() # type: ignore - y = p.get_height() # type: ignore - if y > 4.1: - text_x = x + 0.02 - text_y = 2.75 if y > 10 else 2.95 - ax.annotate( - f"{y:.1f}", - (text_x, text_y), - fontsize=14, - rotation="vertical", - ) - arrow_x = text_x + 0.11 - arrow_y = 3.5 - ax.annotate( - "", - xy=(arrow_x, arrow_y + 0.5), - xytext=(arrow_x, arrow_y), - arrowprops=dict(arrowstyle="->"), - ) - - plt.tight_layout() - fp = f"../_static/{prob_name}-{reg}.png" - plt.savefig(fp, dpi=300) - fp = f"../_static/{prob_name}-{reg}.pdf" - plt.savefig(fp) - plt.show() - -# %% -for prob_name in ["wide-insurance"]: - for reg in ["l2", "lasso"]: - plot_df = ( - df[ - df["problem_name"].str.contains(reg) - & df["problem_name"].str.contains(prob_name) - ] - .copy() - .set_index(["distribution"])[["runtime", "library_name"]] - ) - plot_df = plot_df.pivot(columns="library_name") - plot_df.columns = plot_df.columns.get_level_values(1) - plot_df = plot_df.sort_index(axis=1) - plot_df.index = [x.title() for x in plot_df.index] - - title = prob_name.title() + "-" + ("Lasso" if reg == "lasso" else "Ridge") - plot_df.plot.bar( - ylim=[0, 15], - title=title, - legend=False, - figsize=(6, 3), - width=0.8, - ylabel="run time (s)", - yticks=[0, 3, 6, 9, 12, 15], - cmap="Paired", - ) - plt.legend(bbox_to_anchor=(1, 1), loc="upper left", ncol=1) - plt.xticks(rotation=45, ha="right") - - ax = plt.gca() - - # Hide the right and top spines - ax.spines["right"].set_visible(False) - ax.spines["top"].set_visible(False) - - # Only show ticks on the left and bottom spines - ax.yaxis.set_ticks_position("left") - ax.xaxis.set_ticks_position("bottom") - - for p in ax.patches: - x = p.get_x() # type: ignore - y = p.get_height() # type: ignore - if y > 15.1: - text_x = x + 0.01 - text_y = 8 if y > 1000 else (9 if y > 100 else 10) - ax.annotate( - f"{y:.1f}", - (text_x, text_y), - fontsize=14, - rotation="vertical", - ) - arrow_x = text_x + 0.13 - arrow_y = 13 - ax.annotate( - "", - xy=(arrow_x, arrow_y + 2), - xytext=(arrow_x, arrow_y), - arrowprops=dict(arrowstyle="->"), - ) - - plt.tight_layout() - fp = f"../_static/{prob_name}-{reg}.png" - plt.savefig(fp, dpi=300) - fp = f"../_static/{prob_name}-{reg}.pdf" - plt.savefig(fp) - plt.show() - -# %% -prob_name = "intermediate-insurance" -reg = "lasso" -plot_df = ( - df[ - df["problem_name"].str.contains(reg) - & df["problem_name"].str.contains(prob_name) - ] - .copy() - .set_index(["distribution"])[["runtime", "library_name"]] -) -plot_df = plot_df.pivot(columns="library_name") -plot_df.columns = plot_df.columns.get_level_values(1) -plot_df = plot_df.sort_index(axis=1) -plot_df.index = [x.title() for x in plot_df.index] - -plot_df.plot.bar( - ylim=[0, 5], - legend=False, - figsize=(6, 3), - width=0.8, - ylabel="run time (s)", - yticks=[0, 1, 2, 3, 4, 5], - cmap="Paired", -) -plt.legend(bbox_to_anchor=(0, 1), loc="upper left", ncol=1) -plt.xticks(rotation=45, ha="right") - -ax = plt.gca() - -# Hide the right and top spines -ax.spines["right"].set_visible(False) -ax.spines["top"].set_visible(False) - -# Only show ticks on the left and bottom spines -ax.yaxis.set_ticks_position("left") -ax.xaxis.set_ticks_position("bottom") - -for p in ax.patches: - x = p.get_x() # type: ignore - y = p.get_height() # type: ignore - if y > 5.1: - text_x = x + 0.04 - text_y = 3.2 - ax.annotate( - f"{y:.1f}", - (text_x, text_y), - fontsize=14, - rotation="vertical", - ) - arrow_x = text_x + 0.15 - arrow_y = 4.2 - ax.annotate( - "", - xy=(arrow_x, arrow_y + 0.8), - xytext=(arrow_x, arrow_y), - arrowprops=dict(arrowstyle="->"), - ) - -# Dark mode version -ax.set_facecolor((0, 0, 0)) -ax.xaxis.label.set_color("white") -ax.yaxis.label.set_color("white") -ax.spines["bottom"].set_color("white") -ax.spines["left"].set_color("white") -ax.tick_params(axis="x", colors="white") -ax.tick_params(axis="y", colors="white") - -plt.tight_layout() -fp = "../_static/headline_benchmark_dark.png" -plt.savefig(fp, dpi=300, facecolor=(0, 0, 0, 0)) - -# Light mode version -ax.set_facecolor((1, 1, 1)) -ax.xaxis.label.set_color("black") -ax.yaxis.label.set_color("black") -ax.spines["bottom"].set_color("black") -ax.spines["left"].set_color("black") -ax.tick_params(axis="x", colors="black") -ax.tick_params(axis="y", colors="black") - -fp = "../_static/headline_benchmark.png" -plt.savefig(fp, dpi=300, facecolor=(0, 0, 0, 0)) -fp = "../_static/headline_benchmark.pdf" -plt.savefig(fp) -plt.show() - -# %% diff --git a/docs/contributing.rst b/docs/contributing.rst index dee75653f..a4409fb43 100644 --- a/docs/contributing.rst +++ b/docs/contributing.rst @@ -157,13 +157,12 @@ What follows is a high-level summary of the source code structure. For more deta The GLM benchmark suite ------------------------ -Before deciding to build a library custom built for our purposes, we did an thorough investigation of the various open source GLM implementations available. This resulted in an extensive suite of benchmarks for comparing the correctness, runtime and availability of features for these libraries. +We maintain an extensive suite of benchmarks for comparing ``glum`` against other modern GLM libraries in terms of correctness, runtime and feature availability. These benchmarks help highlight the use cases where ``glum`` is particularly well suited. -The benchmark suite has two command line entrypoints: +The benchmark suite is configured via ``glum_benchmarks/config.yaml`` and can be run with: -* ``glm_benchmarks_run`` -* ``glm_benchmarks_analyze`` +:: -Both of these CLI tools take a range of arguments that specify the details of the benchmark problems and which libraries to benchmark. + pixi run -e benchmark run-benchmarks -For more details on the benchmark suite, see the README in the source at ``src/glum_benchmarks/README.md``. +For more details on the benchmark suite, see the README at ``glum_benchmarks/README.md``. diff --git a/docs/index.rst b/docs/index.rst index 16ea1ce1e..c916b36c3 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -7,15 +7,20 @@ Welcome to glum's documentation! * L1 and elastic net regularization, which produce sparse and easily interpretable solutions * L2 regularization, including variable matrix-valued (Tikhonov) penalties, which are useful in modeling correlated effects * Normal, Poisson, logistic, gamma, and Tweedie distributions, plus varied and customizable link functions -* Dispersion and standard errors -* Box and linear inequality constraints, sample weights, offsets. -* A scikit-learn-like API to fit smoothly into existing workflows. +* Built-in formula-based model specification using ``formulaic`` +* Classical statistical inference for unregularized models using dispersion and standard errors +* Box and linear inequality constraints, sample weights, offsets +* A scikit-learn-like API to fit smoothly into existing workflows ``glum`` was also built with performance in mind. The following figure shows the runtime of a realistic example using an insurance dataset. For more details and other benchmarks, see the :doc:`Benchmarks` section. -.. image:: _static/headline_benchmark.png +.. BENCHMARK_FIGURES_START + +.. image:: _static/wide-insurance-gamma-normalized.png :width: 600 +.. BENCHMARK_FIGURES_END + We suggest visiting the :doc:`Installation` and :doc:`Getting Started` sections first. .. toctree:: @@ -24,7 +29,7 @@ We suggest visiting the :doc:`Installation` and :doc:`Getting Started Getting Started Motivation - Benchmarks vs glmnet/H2O + Benchmarks .. toctree:: :maxdepth: 2 diff --git a/glum_benchmarks/README.md b/glum_benchmarks/README.md new file mode 100644 index 000000000..eca1e5ae5 --- /dev/null +++ b/glum_benchmarks/README.md @@ -0,0 +1,174 @@ +# glum_benchmarks + +Module to benchmark glum against similar libraries. + +## Benchmarked Libraries + +- [glum](https://github.com/Quantco/glum) +- [scikit-learn](https://scikit-learn.org/) +- [H2O](https://h2o.ai/) +- [skglm](https://contrib.scikit-learn.org/skglm/) +- [celer](https://mathurinm.github.io/celer/) +- [glmnet (R)](https://cran.r-project.org/package=glmnet) + +## Running the benchmarks + +```bash +pixi run -e benchmark run-benchmarks +``` + +The benchmark script runs in four steps that can be controlled independently. + +### Step 1: Run Benchmarks (`run_benchmarks`) + +Executes each problem/library combination and saves raw results. + +- **Input:** Configuration from `config.yaml` +- **Output:** Pickle files in `results//pickles/` +- **Set `run_benchmarks: false`** in config.yaml to skip + +### Step 2: Analyze Results (`analyze_results`) + +Reads pickle files, prints a summary table, and exports CSV for plotting. + +- **Input:** Pickle files from `results//pickles/` +- **Output:** Summary printed to console + `results//results.csv` +- **Set `analyze_results: false`** in config.yaml to skip + +### Step 3: Generate Plots (`generate_plots`) + +Creates comparison charts from the CSV produced by Step 2. + +- **Input:** `results//results.csv` +- **Output:** PNG files in `results//figures/` +- **Set `generate_plots: false`** in config.yaml to skip + +### Step 4: Update documentation and README (`update_docs`) + +Copies figures to `docs/_static/` and updates figure references in documentation files. + +- **Input:** PNG files from `results//figures/` +- **Output:** Copies to `docs/_static/` + updates `docs/benchmarks.rst` and `README.md` +- **Set `update_docs: false`** in config.yaml to skip + +Use `docs_figures` and `readme_figures` in config.yaml to control which figures are included (default: all figures for docs, first non-normalized figure for README). Figure names are usually `-.png`. For `simulated-glm` they include the ratio suffix, e.g. `simulated-glm-gaussian-k-over-n-0.7.png`. + +### Workflow examples + +**Full run (default):** + +```yaml +run_benchmarks: true +analyze_results: true +generate_plots: true +update_docs: false # Set to true to update docs/_static and documentation +``` + +**Regenerate plots without re-running benchmarks:** + +```yaml +run_benchmarks: false +analyze_results: true # Rebuild CSV from existing pickles +generate_plots: true +update_docs: false +``` + +**Update documentation with existing figures:** + +```yaml +run_benchmarks: false +analyze_results: false +generate_plots: false +update_docs: true # Only copy figures and update docs +``` + +## Output structure + +Results are organized by `run_name` (default: `"docs"`): + +``` +glum_benchmarks/ +└── results/ + └── docs/ # run_name = "docs" + ├── config.yaml # Snapshot of config used for this run (tracked) + ├── pickles/ # Step 1 output (gitignored) + ├── figures/ # Step 3 output (gitignored) + └── results.csv # Step 2 output (tracked) +``` + +The configuration is automatically saved to `results//config.yaml` for full reproducibility. You can re-run old benchmarks by copying their config back to the main directory. + +For the `docs` run, both `results.csv` and `config.yaml` are tracked in git. Change `run_name` for experiments. + +## Configuration + +Edit `config.yaml` to customize benchmark parameters. + +### General Options + +| Option | Description | +| ----------------- | -------------------------------------------------- | +| `run_benchmarks` | Run Step 1 (execute benchmarks) | +| `analyze_results` | Run Step 2 (analyze pickles, write CSV) | +| `generate_plots` | Run Step 3 (generate figures from CSV) | +| `update_docs` | Run Step 4 (copy figures to docs and update files) | +| `docs_figures` | List of figures for docs (null = all) | +| `readme_figures` | List of figures for README (null = first figure) | +| `run_name` | Subfolder in `results/` (`"docs"` is git-tracked) | +| `clear_output` | Clear entire `run_name` directory before running | + +### Benchmark Settings + +| Option | Description | +| ------------- | --------------------------------------------------------------------- | +| `iterations` | Runs per benchmark (>=2; first is warmup, median of rest reported) | +| `num_threads` | Number of threads for parallel execution | +| `timeout` | Timeout in seconds (benchmarks timing out are marked "not converged") | +| `storage` | Storage format per library: (`auto`, `dense`, `cat`, `csr`, `csc`) | + +**Notes:** + +- **Standardization**: Continuous features are always pre-standardized in the data loader before OHE/format conversion. Libraries are told not to standardize again internally. +- **glmnet dependency**: R + `glmnet` are required for the `glmnet` benchmark (via `rpy2`). If missing, the benchmark is skipped. +- **Convergence**: A benchmark is marked "not converged" if it either (1) hits the timeout, or (2) reaches the library's internal `max_iter` limit. + +### Parameter Grid + +The `param_grid` section defines which benchmark combinations to run using a sklearn-style parameter grid: + +```yaml +param_grid: + - libraries: ["glum", "sklearn"] + datasets: ["intermediate-insurance"] + regularizations: ["lasso", "l2"] + distributions: ["gaussian", "poisson"] + alphas: [0.001] + - datasets: ["simulated-glm"] + distributions: ["gaussian", "poisson"] + num_rows: [1000, 5000] + k_over_n_ratios: [0.5, 0.7, 1.2] +``` + +Each entry computes a Cartesian product. Multiple entries are unioned (not crossed). + +**Available values:** + +- `libraries`: `["glum", "sklearn", "h2o", "skglm", "celer", "zeros", "glmnet"]` +- `datasets`: `["intermediate-housing", "intermediate-insurance", "narrow-insurance", "wide-insurance", "simulated-glm", "categorical-simulated"]` +- `regularizations`: `["lasso", "l2", "net"]` +- `distributions`: `["gaussian", "gamma", "binomial", "poisson", "tweedie-p=1.5"]` +- `alphas`: `[0.0001, 0.001, 0.01]` +- `num_rows`: list of row limits, where `null` means full dataset (e.g., `[1000, null]`) +- `k_over_n_ratios`: any positive float values (e.g., `[0.5, 0.7, 1.2]`, applies to `simulated-glm` only) + +When an entry is omitted or set to `null`, all available values are used. For `libraries`, the default excludes `zeros` (include it explicitly if you want it). For `k_over_n_ratios`, the default is `[1.0]` when omitted. For `num_rows`, the default is `[null]` (full dataset). If you want to run all default combinations, leave the `param_grid` entry empty. + +**Alpha note:** `alphas` are per-observation values for unweighted data, when weights are present, the benchmark runner adjusts internally to keep the penalty comparable. + +See `problems.py` for available datasets and problem definitions. + +## Testing + +```bash +pixi run -e benchmark test-benchmarks +``` diff --git a/glum_benchmarks/__init__.py b/glum_benchmarks/__init__.py new file mode 100644 index 000000000..e0f472738 --- /dev/null +++ b/glum_benchmarks/__init__.py @@ -0,0 +1 @@ +"""Benchmark utilities for comparing GLM implementations.""" diff --git a/glum_benchmarks/config.yaml b/glum_benchmarks/config.yaml new file mode 100644 index 000000000..941ec2ec2 --- /dev/null +++ b/glum_benchmarks/config.yaml @@ -0,0 +1,99 @@ +# Benchmark Configuration +# Edit this file to configure which benchmarks to run and how to run them + +# Steps to run +run_benchmarks: true # Run the benchmarks (can be slow) +analyze_results: true # Analyze and print results (writes CSV) +generate_plots: true # Generate comparison plots (reads CSV and writes PNGs) +update_docs: true # Copy figures to docs/_static and update benchmarks.rst and README.md + +# Figures to include in docs/README (null = use defaults) +# Default for readme: first non-normalized figure only +# Default for docs: all figures in a single block +# +# Figure name format: +# Standard: "-.png" +# Simulated with K/N: "--k-over-n-.png" +# Normalized: append "-normalized" before ".png" +# +# docs_figures is a list of lists: each inner list maps to one +# BENCHMARK_FIGURES_START/END block in benchmarks.rst (in order). +# Example with 3 blocks: +# docs_figures: +# - ["wide-insurance-gamma-normalized.png", "wide-insurance-poisson-normalized.png"] +# - ["simulated-glm-gaussian-k-over-n-0.01-normalized.png"] +# - ["wide-insurance-gamma.png"] +docs_figures: + - ["wide-insurance-poisson-normalized.png", "wide-insurance-gamma-normalized.png", "wide-insurance-tweedie-p=1.5-normalized.png"] + - ["intermediate-housing-gamma-normalized.png", "intermediate-housing-gaussian-normalized.png"] + - ["simulated-glm-gaussian-k-over-n-0.01-normalized.png", "simulated-glm-gaussian-k-over-n-0.1-normalized.png", "simulated-glm-gaussian-k-over-n-0.5-normalized.png","simulated-glm-gaussian-k-over-n-1-normalized.png", "simulated-glm-gaussian-k-over-n-2.png"] +readme_figures: ["wide-insurance-gamma-normalized.png"] + +# Output settings +run_name: "docs" # Subfolder name within results/ ("docs" CSV is git-tracked) +clear_output: True # Clear entire run_name directory before running + +# Benchmark settings +iterations: 5 # Run each benchmark N times; first is warmup, report median of rest (>=2) +num_threads: 16 # Number of threads for parallel execution +timeout: 100 # Timeout in seconds per benchmark run (reports as "not converged" if exceeded) + +# Storage format per library [auto, dense, cat, csr, csc] +storage: + glum: auto + sklearn: csr + h2o: csr + celer: csc + skglm: csc + glmnet: csc + +# PARAMETER GRID +# Each entry specifies a parameter set. Within each entry, the Cartesian +# product is computed. Across entries, results are unioned (not crossed). +# +# Available values: +# libraries: ["glum", "sklearn", "h2o", "skglm", "celer", "zeros", "glmnet"] +# datasets: ["intermediate-housing", "intermediate-insurance", "narrow-insurance", "wide-insurance","simulated-glm", "categorical-simulated"] +# regularizations: ["lasso", "l2", "net"] +# alphas: [0.001, 0.01, 0.1] +# num_rows: [1000, null] # null = full dataset +# k_over_n_ratios: [0.5, 0.7, 1.2] # only used for dataset "simulated-glm" +# distributions: ["gaussian", "gamma", "binomial", "poisson", "tweedie-p=1.5"] +# +# Use null for any field (or omit) to use its default value. +# For libraries, default means all available EXCEPT "zeros" (include it explicitly if needed). +# For num_rows in param_grid, default is [null] (full dataset). +# +# Example: Run all regularizations for gaussian, but only lasso for gamma: +# param_grid: +# - distributions: [gaussian] +# regularizations: [lasso, l2, net] +# - distributions: [gamma] +# regularizations: [lasso] +# +# Example: Run simulated-glm with multiple K/N ratios in one run: +# param_grid: +# - datasets: [simulated-glm] +# distributions: [gaussian, poisson] +# num_rows: [1000, 5000] +# k_over_n_ratios: [0.5, 0.7, 1.2] + +param_grid: +- datasets: [intermediate-housing] + distributions: [gamma, gaussian] + alphas: [0.001] +- datasets: [wide-insurance] + distributions: [gamma, poisson, tweedie-p=1.5] + alphas: [0.001] + num_rows: [600000] +- datasets: [simulated-glm] + alphas: [0.001] + num_rows: [10000] + k_over_n_ratios: [0.01,0.1,0.5] + distributions: [gaussian] +- datasets: [simulated-glm] + regularizations: [lasso, net] + alphas: [0.001] + num_rows: [1000] + k_over_n_ratios: [1,2] + distributions: [gaussian] diff --git a/src/glum_benchmarks/data/__init__.py b/glum_benchmarks/data/__init__.py similarity index 73% rename from src/glum_benchmarks/data/__init__.py rename to glum_benchmarks/data/__init__.py index 7474e1514..bfc44a849 100644 --- a/src/glum_benchmarks/data/__init__.py +++ b/glum_benchmarks/data/__init__.py @@ -6,7 +6,11 @@ generate_real_insurance_dataset, generate_wide_insurance_dataset, ) -from .simulated_glm import simulate_glm_data +from .simulated_glm import ( + simulate_categorical_dataset, + simulate_glm_dataset, + simulate_mixed_data, +) __all__ = [ "generate_intermediate_insurance_dataset", @@ -14,7 +18,9 @@ "generate_wide_insurance_dataset", "generate_real_insurance_dataset", "create_insurance_raw_data", - "simulate_glm_data", + "simulate_categorical_dataset", + "simulate_glm_dataset", + "simulate_mixed_data", "generate_housing_dataset", "create_housing_raw_data", ] diff --git a/src/glum_benchmarks/data/create_housing.py b/glum_benchmarks/data/create_housing.py similarity index 100% rename from src/glum_benchmarks/data/create_housing.py rename to glum_benchmarks/data/create_housing.py diff --git a/src/glum_benchmarks/data/create_insurance.py b/glum_benchmarks/data/create_insurance.py similarity index 98% rename from src/glum_benchmarks/data/create_insurance.py rename to glum_benchmarks/data/create_insurance.py index ee7c6bf25..2f9636af3 100644 --- a/src/glum_benchmarks/data/create_insurance.py +++ b/glum_benchmarks/data/create_insurance.py @@ -482,6 +482,14 @@ def generate_wide_insurance_dataset( ) -> tuple[pd.DataFrame, np.ndarray, np.ndarray]: """Generate a version of the tutorial data set with many features.""" df = _read_insurance_data(num_rows, noise, distribution) + drop_cols = { + "ClaimNb", + "ClaimAmountCut", + "ClaimNb_pos", + "Exposure", + "HasClaim", + "ClaimAmount", + } cat_cols = [ "Area", "VehPower", @@ -501,7 +509,7 @@ def generate_wide_insurance_dataset( lambda x: [ elmt for elmt in x.select_dtypes(["number"]).columns - if elmt not in cat_cols + if elmt not in cat_cols and elmt not in drop_cols ], ), ( diff --git a/glum_benchmarks/data/simulated_glm.py b/glum_benchmarks/data/simulated_glm.py new file mode 100644 index 000000000..6d945aa2b --- /dev/null +++ b/glum_benchmarks/data/simulated_glm.py @@ -0,0 +1,309 @@ +from functools import partial +from typing import Optional + +import numpy as np +import pandas as pd +import scipy.sparse as sps + +from glum._distribution import TweedieDistribution +from glum._glm import get_family, get_link + + +def _resolve_family(family: str): + """Convert benchmark family strings like 'tweedie_p=1.5' to glum family objects.""" + if "tweedie" in family and "=" in family: + p = float(family.split("=")[1]) + return TweedieDistribution(p) + return get_family(family) + + +def tweedie_rv(mu, sigma2=1, p=1.5): + """Generate draws from a tweedie distribution with power p. + + mu is the location parameter and sigma2 is the dispersion coefficient. + """ + n = len(mu) + rand = np.random.default_rng(1) + + # transform tweedie parameters into poisson and gamma + lambda_ = (mu ** (2 - p)) / ((2 - p) * sigma2) + alpha_ = (2 - p) / (p - 1) + beta_ = (mu ** (1 - p)) / ((p - 1) * sigma2) + + arr_N = rand.poisson(lambda_) + out: np.ndarray = np.empty(n, dtype=np.float64) + for i, N in enumerate(arr_N): # type: ignore + out[i] = np.sum(rand.gamma(alpha_, 1 / beta_[i], size=N)) + + return out + + +def _gamma_rv(mu, rand: np.random._generator.Generator, shape: float = 2.0): + """Generate gamma random variates with specified mean. + + Parameters + ---------- + mu : array-like + The desired mean values. Must be positive. + rand : np.random.Generator + Random number generator. + shape : float + Shape parameter (k). Higher values give less variance. Default 2.0. + + Returns + ------- + array + Gamma random variates with E[y] = mu. + """ + mu = np.asarray(mu) + scale = mu / shape + y = rand.gamma(shape, scale) + # Ensure strictly positive (gamma GLM requires y > 0) + return np.maximum(y, np.finfo(float).eps) + + +def _get_family_rv(family, rand: np.random._generator.Generator): + family_rv = { + "poisson": rand.poisson, + "gamma": rand.gamma, + "normal": rand.normal, + "binomial": partial(rand.binomial, 1), + } + + if family in family_rv.keys(): + return family_rv[family] + elif "tweedie" in family: + p = float(family.split("=")[1]) + return partial(tweedie_rv, p=p) + else: + raise ValueError( + 'family must take the value "poisson", "gamma", "normal", "binomial", or ' + '"tweedie_p=XX". ' + f"Currently {family}." + ) + + +def simulate_mixed_data( + family: str = "poisson", + link: str = "auto", + n_rows: int = 5000, + dense_features: int = 10, + sparse_features: int = 0, + sparse_density: float = 0.05, + categorical_features: int = 2, + categorical_levels: int = 10, + ohe_categorical: bool = True, + intercept: float = 0.2, + drop_first: bool = False, + random_seed: int = 1, +): + """ + Simulate the data we will use for benchmarks. + + Parameters + ---------- + family + link + n_rows + dense_features + sparse_features + sparse_density + categorical_features + categorical_levels + ohe_categorical + intercept + drop_first + random_seed + + Returns + ------- + dict + """ + rand = np.random.default_rng(random_seed) + + # Creating dense component + if dense_features > 0: + dense_feature_names = [f"dense{i}" for i in range(dense_features)] + X_dense = rand.normal( + rand.integers(-2, 2, size=dense_features), size=(n_rows, dense_features) + ) + X_dense = pd.DataFrame(data=X_dense, columns=dense_feature_names) + coefs_dense = np.concatenate( + [ + [1, 0.5, 0.1, -0.1, -0.5, -1, 0, 0, 0, 0], + rand.choice([0, 1, -1], size=dense_features), + ] + )[:dense_features] + coefs_dense = pd.Series(data=coefs_dense, index=dense_feature_names) + + # Creating sparse component + sparse_feature_names = [f"sparse{i}" for i in range(sparse_features)] + X_sparse = sps.random(n_rows, sparse_features, density=sparse_density).toarray() + X_sparse = pd.DataFrame(data=X_sparse, columns=sparse_feature_names) + coefs_sparse = rand.choice([0, 1, -1], size=sparse_features) + coefs_sparse = pd.Series(data=coefs_sparse, index=sparse_feature_names) + + # Creating categorical component + cat_feature_names = [f"cat{i}" for i in range(categorical_features)] + fixed_effects = rand.choice( + np.arange(categorical_levels), size=(n_rows, categorical_features) + ) + X_cat = pd.DataFrame(data=fixed_effects, columns=cat_feature_names) + # Convert categorical columns to dtype 'category' + for col in X_cat.columns: + X_cat[col] = X_cat[col].astype("category") + X_cat_ohe = pd.get_dummies( + X_cat, columns=cat_feature_names, drop_first=drop_first, dtype=float + ) + + coefs_cat = pd.Series( + data=rand.uniform(size=len(X_cat_ohe.columns)), index=X_cat_ohe.columns + ) + + # Merging + X = pd.concat([X_dense, X_sparse, X_cat_ohe], axis=1) + coefs = pd.concat([coefs_dense, coefs_sparse, coefs_cat]) + + intercept = intercept + + link_inst = get_link(link=link, family=_resolve_family(family)) + family_rv = _get_family_rv(family, rand) + + y = family_rv(link_inst.inverse(intercept + X.to_numpy() @ coefs.to_numpy())) + + weights = rand.uniform(size=n_rows) + offset = np.log(rand.uniform(size=n_rows)) + + if not ohe_categorical: + X = pd.concat([X_dense, X_sparse, X_cat], axis=1) + + data = { + "X": X, + "y": y, + "sample_weight": weights, + "offset": offset, + "intercept": intercept, + "coefs": coefs, + } + return data + + +def simulate_glm_dataset( + num_rows: Optional[int] = None, + noise: Optional[float] = None, # unused, required by load_data signature + distribution: str = "poisson", + k_over_n_ratio: Optional[float] = 1.0, +) -> tuple[pd.DataFrame, np.ndarray, np.ndarray]: + """Generate a simulated GLM dataset with configurable K/N ratio. + + Parameters + ---------- + num_rows + Number of rows. Defaults to 1000. + noise + Unused, present to match load_data signature. + distribution + The GLM family: "gaussian", "poisson", "gamma", or "binomial". + k_over_n_ratio + Ratio of number of features to number of rows (K/N). + - 1.0 gives a square design matrix + - > 1.0 gives high-dimensional data (K > N) + - < 1.0 gives low-dimensional data (K < N) + + Returns + ------- + tuple[pd.DataFrame, np.ndarray, np.ndarray] + (X, y, exposure). + """ + n_rows = num_rows if num_rows is not None else 1000 + ratio = 1.0 if k_over_n_ratio is None else float(k_over_n_ratio) + if ratio <= 0: + raise ValueError("k_over_n_ratio must be > 0.") + n_features = max(1, int(round(n_rows * ratio))) + rand = np.random.default_rng(42) + + # Generate standardized features + X = pd.DataFrame( + data=rand.normal(0, 1, size=(n_rows, n_features)), + columns=[f"x{i}" for i in range(n_features)], + ) + + # Sparse coefficients (~10% non-zero) + coefs = rand.choice([0] * 9 + [1], size=n_features) * rand.normal( + 0, 1, size=n_features + ) + + # Linear predictor + eta = X.to_numpy() @ coefs + + # Map distribution names (_get_family_rv uses "normal" not "gaussian") + family = "normal" if distribution == "gaussian" else distribution + + # Get the link function for the distribution + family_inst = _resolve_family(family) + link_inst = get_link(link="auto", family=family_inst) + + # Compute mu using the inverse link + mu = link_inst.inverse(np.clip(eta, -5, 5)) + + # Generate y based on distribution using the family's random variate generator. + # Use improved gamma parameterization for benchmarks. + if family == "gamma": + y = _gamma_rv(mu, rand=rand) + else: + family_rv = _get_family_rv(family, rand) + y = family_rv(mu) + + exposure = np.ones(n_rows) + return X, y, exposure + + +def simulate_categorical_dataset( + num_rows: Optional[int] = None, + noise: Optional[float] = None, # unused, required by load_data signature + distribution: str = "gaussian", + categorical_ratio: float = 0.9, +) -> tuple[pd.DataFrame, np.ndarray, np.ndarray]: + """Generate a dataset with a high-cardinality categorical feature. + + This creates a dataset with a single high-cardinality categorical + feature (stored as categorical values, not one-hot encoded). + + Parameters + ---------- + num_rows + Number of rows. Defaults to 1000. + noise + Unused, present to match load_data signature. + distribution + The GLM family: "gaussian", "poisson", "gamma", or "binomial". + categorical_ratio + Controls the number of categorical levels relative to rows. + Default 0.9 means ~0.9 * n levels for the categorical feature. + + Returns + ------- + tuple[pd.DataFrame, np.ndarray, np.ndarray] + (X, y, exposure) where X has dense columns and one categorical column. + """ + n = num_rows if num_rows is not None else 1000 + + # Number of categorical levels (creates this many one-hot columns) + n_cat_levels = int(n * categorical_ratio) + + # Map distribution names (simulate_mixed_data uses "normal" not "gaussian") + family = "normal" if distribution == "gaussian" else distribution + + data = simulate_mixed_data( + family=family, + link="auto", + n_rows=n, + dense_features=5, + sparse_features=0, + categorical_features=1, + categorical_levels=n_cat_levels, + ohe_categorical=False, + ) + + exposure = np.ones(n) + return data["X"], data["y"], exposure diff --git a/glum_benchmarks/libraries/__init__.py b/glum_benchmarks/libraries/__init__.py new file mode 100644 index 000000000..918592c53 --- /dev/null +++ b/glum_benchmarks/libraries/__init__.py @@ -0,0 +1,43 @@ +"""Benchmark functions for different GLM libraries.""" + +# Always available +from .bench_glum import glum_bench +from .bench_zeros import zeros_bench + +__all__ = ["glum_bench", "zeros_bench"] + +# Optional dependencies, only import if available +try: + from .bench_celer import celer_bench +except ImportError: + celer_bench = None # type: ignore +else: + __all__.append("celer_bench") + +try: + from .bench_h2o import h2o_bench +except ImportError: + h2o_bench = None # type: ignore +else: + __all__.append("h2o_bench") + +try: + from .bench_skglm import skglm_bench +except ImportError: + skglm_bench = None # type: ignore +else: + __all__.append("skglm_bench") + +try: + from .bench_sklearn import sklearn_bench +except ImportError: + sklearn_bench = None # type: ignore +else: + __all__.append("sklearn_bench") + +try: + from .bench_glmnet import glmnet_bench +except ImportError: + glmnet_bench = None # type: ignore +else: + __all__.append("glmnet_bench") diff --git a/glum_benchmarks/libraries/bench_celer.py b/glum_benchmarks/libraries/bench_celer.py new file mode 100644 index 000000000..13a70fe8c --- /dev/null +++ b/glum_benchmarks/libraries/bench_celer.py @@ -0,0 +1,75 @@ +import warnings +from typing import Any, Optional, Union + +import numpy as np +from celer import ElasticNet, Lasso +from scipy import sparse as sps + +from glum_benchmarks.util import ( + benchmark_convergence_tolerance, + runtime, +) + + +def _build_and_fit(model_class, model_args, fit_args): + return model_class(**model_args).fit(**fit_args) + + +def celer_bench( + dat: dict[str, Union[np.ndarray, sps.spmatrix]], + distribution: str, + alpha: float, + l1_ratio: float, + iterations: int, + timeout: Optional[float] = None, + **kwargs, +): + result: dict[str, Any] = {} + fit_args = {"X": dat["X"], "y": dat["y"]} + + # LogisticRegression doesn't support fitting an intercept yet, so we also skip it + if distribution not in ["gaussian"]: + warnings.warn(f"Celer doesn't support {distribution}, skipping.") + return {} + + if distribution == "gaussian": + if l1_ratio == 0.0: + warnings.warn("Celer doesn't support Ridge Regression, skipping.") + return {} + elif l1_ratio < 1.0: + model_class = ElasticNet + else: + model_class = Lasso + + model_args = { + "tol": benchmark_convergence_tolerance, + "fit_intercept": True, + "alpha": alpha, + } + + if model_class == ElasticNet: + model_args["l1_ratio"] = l1_ratio + + try: + result["runtime"], m = runtime( + _build_and_fit, + iterations, + model_class, + model_args, + fit_args, + timeout=timeout, + ) + except TimeoutError: + # Re-raise TimeoutError to allow proper timeout handling at higher level + raise + except Exception as e: + warnings.warn(f"Celer failed: {e}") + return {} + + result["intercept"] = np.array(m.intercept_).ravel()[0] + result["coef"] = np.array(m.coef_).ravel() + + result["n_iter"] = getattr(m, "n_iter_", None) + result["max_iter"] = m.max_iter # For convergence detection + + return result diff --git a/glum_benchmarks/libraries/bench_glmnet.py b/glum_benchmarks/libraries/bench_glmnet.py new file mode 100644 index 000000000..99cdc478d --- /dev/null +++ b/glum_benchmarks/libraries/bench_glmnet.py @@ -0,0 +1,194 @@ +import multiprocessing as mp +import time +import warnings +from typing import Optional, Union + +import numpy as np +import pandas as pd +from scipy import sparse as sps + +from glum_benchmarks.util import benchmark_convergence_tolerance + +_GLMNET_MAXIT = 100_000 + + +def _setup_and_fit(X_np, y, distribution, l1_ratio, alpha): + """ + Run in a child process: set up R/glmnet, fit, return results. + + All arguments must be picklable (no rpy2 objects). + """ + import rpy2.robjects as ro + from rpy2.robjects.packages import importr + from rpy2.robjects.vectors import FloatVector, IntVector + + # Packages must be pre-installed. Fail fast if missing. + # Use suppressPackageStartupMessages to avoid noisy loading messages. + required_pkgs = ["glmnet", "Matrix"] + if distribution.startswith("tweedie"): + required_pkgs += ["tweedie", "statmod"] + for pkg in required_pkgs: + ro.r(f'suppressPackageStartupMessages(library("{pkg}"))') + + glmnet_pkg = importr("glmnet") + ro.r["glmnet.control"](epsnr=benchmark_convergence_tolerance * 10.0) + + # Build R family object + if distribution in ("gaussian", "binomial", "poisson"): + family = distribution + elif distribution == "gamma": + family = ro.r["Gamma"](link="log") + elif distribution.startswith("tweedie-p="): + p = float(distribution.split("tweedie-p=")[1]) + family = ro.r["tweedie"](link_power=0, var_power=p) + else: + raise ValueError(f"Unknown distribution: {distribution}") + + # Convert data + fit (timed: matches what .fit() does in other libs). + n, p = X_np.shape + lambda_vec = FloatVector([alpha]) + + start = time.time() + + if sps.issparse(X_np): + matrix_pkg = importr("Matrix") + coo = sps.coo_matrix(X_np) + X_r = matrix_pkg.sparseMatrix( + i=IntVector(coo.row + 1), + j=IntVector(coo.col + 1), + x=FloatVector(coo.data), + dims=IntVector([n, p]), + ) + else: + X_f = np.asarray(X_np, dtype=float, order="F") + X_r = ro.r.matrix(FloatVector(X_f.ravel(order="F")), nrow=n, ncol=p) + + fit = glmnet_pkg.glmnet( + x=X_r, + y=FloatVector(y), + family=family, + alpha=l1_ratio, + intercept=True, + standardize=False, # Data is pre-standardized in the data loader + maxit=_GLMNET_MAXIT, + thresh=benchmark_convergence_tolerance, + **{"lambda": lambda_vec}, + ) + fit_runtime = time.time() - start + + # Extract results + coef_mat = ro.r["as.matrix"](ro.r["coef"](fit, s=lambda_vec)) + coef_vec = np.asarray(coef_mat).ravel() + + n_iter = None + try: + npasses = fit.rx2("npasses") + if len(npasses) > 0: + n_iter = int(npasses[0]) + except Exception: + pass + + return { + "intercept": float(coef_vec[0]), + "coef": coef_vec[1:], + "n_iter": n_iter, + "max_iter": _GLMNET_MAXIT, + "fit_runtime": fit_runtime, + } + + +def _worker(queue, *args): + """Target for child process. Must be module-level to be picklable.""" + try: + queue.put(("ok", _setup_and_fit(*args))) + except Exception as e: + queue.put(("error", str(e))) + + +def _run_with_hard_timeout(args, timeout): + """ + Run _setup_and_fit in a child process with a hard kill timeout. + + Uses 'spawn' so R is initialized fresh (fork is unsafe with R). + """ + ctx = mp.get_context("spawn") + queue = ctx.Queue() + proc = ctx.Process(target=_worker, args=(queue, *args)) + proc.start() + proc.join(timeout) + + if proc.is_alive(): + proc.kill() + proc.join() + raise TimeoutError(f"glmnet exceeded {timeout}s hard timeout") + + if queue.empty(): + raise RuntimeError("glmnet child process died without returning results") + + status, payload = queue.get_nowait() + if status == "error": + raise RuntimeError(f"glmnet failed in child process: {payload}") + return payload + + +def glmnet_bench( + dat: dict[str, Union[np.ndarray, sps.spmatrix]], + distribution: str, + alpha: float, + l1_ratio: float, + iterations: int, + timeout: Optional[float] = None, + **kwargs, +): + """ + Run the glmnet benchmark via rpy2 in a child process. + + Each call spawns a fresh process so that: + 1. R is cleanly initialized (no fork issues). + 2. A hard timeout can kill R mid-computation. + """ + # Prepare X as a picklable numpy/scipy object + X = dat["X"] + if sps.issparse(X): + X = sps.csc_matrix(X) + elif isinstance(X, pd.DataFrame): + X = X.to_numpy(dtype=float) + elif not isinstance(X, np.ndarray): + warnings.warn( + "glmnet requires data as scipy.sparse matrix, pandas dataframe, or " + "numpy array. Skipping." + ) + return {} + + y = np.asarray(dat["y"], dtype=float).ravel() + + fit_args = (X, y, distribution, l1_ratio, alpha) + + # Run iterations, keeping the fastest. + # The child process times data conversion + glmnet() (excludes R startup). + # The hard timeout covers the whole child process (startup + fit). + successful_runs: list[tuple[float, dict]] = [] + + for i in range(iterations): + try: + fit_result = _run_with_hard_timeout(fit_args, timeout) + successful_runs.append((fit_result["fit_runtime"], fit_result)) + except TimeoutError: + pass + except Exception as e: + warnings.warn(f"glmnet iteration {i + 1} failed: {e}") + + if not successful_runs: + raise TimeoutError( + f"All {iterations} glmnet iterations exceeded {timeout}s timeout" + ) + + best_time, best_result = min(successful_runs, key=lambda x: x[0]) + + return { + "runtime": best_time, + "intercept": best_result["intercept"], + "coef": best_result["coef"], + "n_iter": best_result["n_iter"], + "max_iter": best_result["max_iter"], + } diff --git a/src/glum_benchmarks/bench_glum.py b/glum_benchmarks/libraries/bench_glum.py similarity index 69% rename from src/glum_benchmarks/bench_glum.py rename to glum_benchmarks/libraries/bench_glum.py index 098c637d3..fe6f45bdb 100644 --- a/src/glum_benchmarks/bench_glum.py +++ b/glum_benchmarks/libraries/bench_glum.py @@ -5,16 +5,17 @@ import pandas as pd from scipy import sparse as sps -from glum import GeneralizedLinearRegressor, GeneralizedLinearRegressorCV - -from .util import benchmark_convergence_tolerance, get_sklearn_family, runtime +from glum import GeneralizedLinearRegressor +from glum_benchmarks.util import ( + benchmark_convergence_tolerance, + get_sklearn_family, + runtime, +) random_seed = 110 -def _build_and_fit(model_args, fit_args, cv: bool): - if cv: - return GeneralizedLinearRegressorCV(**model_args).fit(**fit_args) +def _build_and_fit(model_args, fit_args): return GeneralizedLinearRegressor(**model_args).fit(**fit_args) @@ -24,10 +25,9 @@ def glum_bench( alpha: float, l1_ratio: float, iterations: int, - cv: bool, diagnostics_level: str = "basic", - reg_multiplier: Optional[float] = None, hessian_approx: float = 0.0, + timeout: Optional[float] = None, **kwargs, ): """ @@ -40,9 +40,7 @@ def glum_bench( alpha l1_ratio iterations - cv diagnostics_level - reg_multiplier hessian_approx kwargs @@ -63,7 +61,6 @@ def glum_bench( model_args = dict( family=get_sklearn_family(distribution), l1_ratio=l1_ratio, - max_iter=1000, random_state=random_seed, copy_X=False, selection="cyclic", @@ -74,25 +71,19 @@ def glum_bench( verbose=False, ) - if not cv: - model_args["alpha"] = ( - alpha if reg_multiplier is None else alpha * reg_multiplier - ) + model_args["alpha"] = alpha + + result["runtime"], m = runtime( + _build_and_fit, iterations, model_args, fit_args, timeout=timeout + ) - result["runtime"], m = runtime(_build_and_fit, iterations, model_args, fit_args, cv) - if not cv: - # Just check that predict works here... This doesn't take very long. - m.predict(**{k: v for k, v in fit_args.items() if k != "y"}) + # Just check that predict works here... This doesn't take very long. + m.predict(**{k: v for k, v in fit_args.items() if k != "y"}) result["intercept"] = m.intercept_ result["coef"] = m.coef_ result["n_iter"] = m.n_iter_ - if cv: - alphas: np.ndarray = m.alphas_ - result["n_alphas"] = len(alphas) - result["max_alpha"] = alphas.max() - result["min_alpha"] = alphas.min() - result["best_alpha"] = m.alpha_ + result["max_iter"] = m.max_iter # For convergence detection with pd.option_context( "display.expand_frame_repr", diff --git a/src/glum_benchmarks/bench_h2o.py b/glum_benchmarks/libraries/bench_h2o.py similarity index 54% rename from src/glum_benchmarks/bench_h2o.py rename to glum_benchmarks/libraries/bench_h2o.py index 2094f99db..5a27320f1 100644 --- a/src/glum_benchmarks/bench_h2o.py +++ b/glum_benchmarks/libraries/bench_h2o.py @@ -1,3 +1,4 @@ +import logging import os import warnings from typing import Optional, Union @@ -8,7 +9,13 @@ from h2o.estimators.glm import H2OGeneralizedLinearEstimator from scipy import sparse as sps -from .util import benchmark_convergence_tolerance, runtime +from glum_benchmarks.util import ( + benchmark_convergence_tolerance, + runtime, +) + +# Suppress H2O's "Closing connection" messages at exit +logging.getLogger("h2o").setLevel(logging.WARNING) def _build_and_fit(model_args, train_args): @@ -30,8 +37,7 @@ def h2o_bench( alpha: float, l1_ratio: float, iterations: int, - cv: bool, - reg_multiplier: Optional[float] = None, + timeout: Optional[float] = None, **kwargs, ): """ @@ -44,14 +50,13 @@ def h2o_bench( alpha l1_ratio iterations - cv - reg_multiplier kwargs Returns ------- dict of data about this run """ + result: dict = {} if not isinstance(dat["X"], (np.ndarray, sps.spmatrix, pd.DataFrame)): @@ -61,24 +66,17 @@ def h2o_bench( ) return result - h2o.init(nthreads=int(os.environ.get("OMP_NUM_THREADS", os.cpu_count()))) # type: ignore + h2o.init( + nthreads=int(os.environ.get("OMP_NUM_THREADS", os.cpu_count())), # type: ignore + verbose=False, + ) + h2o.no_progress() # Suppress progress bars train_mat = _hstack_sparse_or_dense((dat["X"], dat["y"][:, np.newaxis])) - - use_weights = "sample_weight" in dat.keys() - if use_weights: - train_mat = _hstack_sparse_or_dense( - (train_mat, dat["sample_weight"][:, np.newaxis]) - ) - if "offset" in dat.keys(): - train_mat = _hstack_sparse_or_dense((train_mat, dat["offset"][:, np.newaxis])) - train_h2o = h2o.H2OFrame(train_mat) - # Determine the y column index (it's right after X columns) - n_extra_cols = int(use_weights) + int("offset" in dat.keys()) - y_col_idx = -(1 + n_extra_cols) - y_col = train_h2o.col_names[y_col_idx] + # y column is the last column + y_col = train_h2o.col_names[-1] # For binomial, convert target to categorical if distribution == "binomial": @@ -89,21 +87,15 @@ def h2o_bench( model_args = dict( model_id="glm", - # not sure if this is right family="tweedie" if tweedie else distribution, alpha=l1_ratio, - lambda_=alpha if reg_multiplier is None else alpha * reg_multiplier, - standardize=False, + lambda_=alpha, solver="IRLSM", objective_epsilon=benchmark_convergence_tolerance, beta_epsilon=benchmark_convergence_tolerance, gradient_epsilon=benchmark_convergence_tolerance, - max_iterations=1000, gainslift_bins=0, ) - if cv: - model_args["lambda_search"] = True - model_args["nfolds"] = 5 if tweedie: p = float(distribution.split("=")[-1]) @@ -112,35 +104,20 @@ def h2o_bench( if "gamma" in distribution: model_args["link"] = "Log" - if use_weights: - train_args = dict( - x=train_h2o.col_names[:y_col_idx], - y=y_col, - training_frame=train_h2o, - weights_column=train_h2o.col_names[y_col_idx + 1], - ) - if "offset" in dat.keys(): - train_args["offset_column"] = train_h2o.col_names[-1] - elif "offset" in dat.keys(): - train_args = dict( - x=train_h2o.col_names[:y_col_idx], - y=y_col, - training_frame=train_h2o, - offset_column=train_h2o.col_names[-1], - ) - else: - train_args = dict( - x=train_h2o.col_names[:-1], - y=y_col, - training_frame=train_h2o, - ) + train_args = dict( + x=train_h2o.col_names[:-1], + y=y_col, + training_frame=train_h2o, + ) - result["runtime"], m = runtime(_build_and_fit, iterations, model_args, train_args) + result["runtime"], m = runtime( + _build_and_fit, iterations, model_args, train_args, timeout=timeout + ) # un-standardize standardized_intercept = m.coef()["Intercept"] - # Number of X columns (excluding y, weights, offset) - n_x_cols = train_mat.shape[1] - (1 + n_extra_cols) + # Number of X columns (excluding y) + n_x_cols = train_mat.shape[1] - 1 standardized_coefs = np.array( [ # h2o automatically removes zero-variance columns; impute to 1 @@ -148,12 +125,10 @@ def h2o_bench( for i in range(n_x_cols) ] ) - if cv: - result["best_alpha"] = m._model_json["output"]["lambda_best"] - result["n_alphas"] = m.parms["nlambdas"]["actual_value"] result["intercept"] = standardized_intercept result["coef"] = standardized_coefs - result["n_iter"] = m.score_history().iloc[-1]["iteration" if cv else "iterations"] + result["n_iter"] = m.score_history().iloc[-1]["iterations"] + result["max_iter"] = m.actual_params.get("max_iterations", 100) return result diff --git a/glum_benchmarks/libraries/bench_skglm.py b/glum_benchmarks/libraries/bench_skglm.py new file mode 100644 index 000000000..c965b4ff8 --- /dev/null +++ b/glum_benchmarks/libraries/bench_skglm.py @@ -0,0 +1,90 @@ +import warnings +from typing import Any, Optional, Union + +import numpy as np +from scipy import sparse as sps +from skglm import GeneralizedLinearEstimator +from skglm.datafits import Gamma, Logistic, Poisson, Quadratic +from skglm.penalties import L1, L1_plus_L2 +from skglm.solvers import AndersonCD, ProxNewton + +from glum_benchmarks.util import ( + benchmark_convergence_tolerance, + runtime, +) + + +def _build_and_fit(model_args, fit_args): + return GeneralizedLinearEstimator(**model_args).fit(**fit_args) + + +def skglm_bench( + dat: dict[str, Union[np.ndarray, sps.spmatrix]], + distribution: str, + alpha: float, + l1_ratio: float, + iterations: int, + timeout: Optional[float] = None, + **kwargs, +): + result: dict[str, Any] = {} + if "tweedie" in distribution: + warnings.warn("skglm doesn't support Tweedie, skipping.") + return result + + DATAFITS = { + "gaussian": Quadratic(), + "poisson": Poisson(), + "binomial": Logistic(), + "gamma": Gamma(), + } + + datafit = DATAFITS[distribution] + + if l1_ratio == 1: + penalty = L1(alpha=alpha) + else: + # We use L1_plus_L2 (l1_ratio=0) for pure L2 to ensure prox_1d is available + penalty = L1_plus_L2(alpha=alpha, l1_ratio=l1_ratio) + + # Note on GramCD: + # In skglm==0.5, GramCD requires datafit=None, but GeneralizedLinearEstimator.fit() + # materializes datafit to Quadratic(). This mismatch makes GramCD fail when used via + # GeneralizedLinearEstimator, so we route Gaussian through AndersonCD/ProxNewton. + # ProxNewton is faster for non-Gaussian distributions (Poisson, Gamma, Binomial) + # and required for L2 problems. AndersonCD is better for Gaussian. + if distribution in ["poisson", "gamma", "binomial"] or l1_ratio == 0: + solver = ProxNewton(tol=benchmark_convergence_tolerance, fit_intercept=True) + else: + solver = AndersonCD(tol=benchmark_convergence_tolerance, fit_intercept=True) + + model_args = { + "datafit": datafit, + "penalty": penalty, + "solver": solver, + } + + fit_args = {"X": dat["X"], "y": dat["y"]} + + # skglm's Logistic datafit expects labels in {-1, 1}, not {0, 1} + if distribution == "binomial": + fit_args["y"] = 2 * fit_args["y"] - 1 + + try: + result["runtime"], m = runtime( + _build_and_fit, iterations, model_args, fit_args, timeout=timeout + ) + except TimeoutError: + # Re-raise TimeoutError to allow proper timeout handling at higher level + raise + except Exception as e: + warnings.warn(f"skglm failed: {e}") + return {} + + result["intercept"] = np.array(m.intercept_).ravel()[0] + result["coef"] = np.array(m.coef_).ravel() + + result["n_iter"] = getattr(m, "n_iter_", None) + result["max_iter"] = solver.max_iter # For convergence detection + + return result diff --git a/glum_benchmarks/libraries/bench_sklearn.py b/glum_benchmarks/libraries/bench_sklearn.py new file mode 100644 index 000000000..296093eae --- /dev/null +++ b/glum_benchmarks/libraries/bench_sklearn.py @@ -0,0 +1,158 @@ +import warnings +from typing import Any, Optional, Union + +import numpy as np +from scipy import sparse as sps +from sklearn.linear_model import ( + ElasticNet, + Lasso, + LogisticRegression, + Ridge, + TweedieRegressor, +) + +from glum_benchmarks.util import ( + benchmark_convergence_tolerance, + runtime, +) + + +def _build_and_fit(model_class, model_args, fit_args): + """Build and fit a sklearn regressor.""" + return model_class(**model_args).fit(**fit_args) + + +def sklearn_bench( + dat: dict[str, Union[np.ndarray, sps.spmatrix]], + distribution: str, + alpha: float, + l1_ratio: float, + iterations: int, + timeout: Optional[float] = None, + **kwargs, +): + """ + Benchmark scikit-learn GLM regressors. + + Parameters + ---------- + dat + distribution + alpha + l1_ratio + iterations + kwargs + + Returns + ------- + Dict of + """ + result: dict[str, Any] = {} + n_samples = dat["X"].shape[0] + model_class = None + + if distribution == "gaussian": + if l1_ratio == 0.0: + # Pure L2 (Ridge): sklearn uses sum-of-squared-errors loss while glum + # uses mean-squared-error loss. To get the same optimal coefficients, + # we scale alpha by n_samples to compensate for the 1/n factor in glum. + model_class = Ridge + model_args = { + "alpha": alpha * n_samples, + "fit_intercept": True, + "tol": benchmark_convergence_tolerance, + "solver": "auto", + } + elif l1_ratio == 1.0: + # Pure L1 + model_class = Lasso + model_args = { + "alpha": alpha, + "fit_intercept": True, + "tol": benchmark_convergence_tolerance, + "precompute": True, + } + else: + model_class = ElasticNet + model_args = { + "alpha": alpha, + "l1_ratio": l1_ratio, + "fit_intercept": True, + "tol": benchmark_convergence_tolerance, + "precompute": True, + } + elif distribution == "binomial": + # # sklearn uses sum(loss), glum uses mean(loss), so C = 1 / (alpha * n_samples) + C_value = 1.0 / (alpha * n_samples) if alpha > 0 else 1e10 + + # Newton-Cholesky is best choice for n_samples >> n_features + # For saga to work, we need to scale the features + solver = "newton-cholesky" if l1_ratio == 0.0 else "saga" + + model_class = LogisticRegression + model_args = { + "C": C_value, + "l1_ratio": l1_ratio, + "solver": solver, + "fit_intercept": True, + "tol": benchmark_convergence_tolerance, + } + else: + if "tweedie" in distribution: + power = float(distribution.split("-p=")[1]) + elif distribution == "poisson": + power = 1.0 + elif distribution == "gamma": + power = 2.0 + + # sklearn's TweedieRegressor only supports L2 regularization + if l1_ratio > 0: + warnings.warn( + f"sklearn only supports L2 regularization for {distribution}, skipping." + ) + return result + + # Use Newton-Cholesky as it is best choice for n_samples >> n_features and + # one-hot encoded categorical features + model_class = TweedieRegressor + model_args = { + "power": power, + "alpha": alpha, + "fit_intercept": True, + "tol": benchmark_convergence_tolerance, + "solver": "newton-cholesky", + } + + fit_args = {"X": dat["X"], "y": dat["y"]} + + try: + result["runtime"], m = runtime( + _build_and_fit, + iterations, + model_class, + model_args, + fit_args, + timeout=timeout, + ) + except ValueError as e: + warnings.warn(f"Problem failed with this error: {e}") + return result + + if distribution == "binomial": + intercept = m.intercept_[0] if m.intercept_.ndim > 0 else m.intercept_ + coef = m.coef_.ravel() + n_iter = m.n_iter_[0] if hasattr(m.n_iter_, "__len__") else m.n_iter_ + else: + intercept = m.intercept_ + coef = m.coef_ + # Ridge has n_iter_=None (closed-form), treat as 0 iterations (direct solve) + n_iter = getattr(m, "n_iter_", None) + n_iter = 0 if n_iter is None else n_iter + + result["intercept"] = intercept + result["coef"] = coef + result["n_iter"] = n_iter + # For convergence detection: get max_iter from model + result["max_iter"] = getattr(m, "max_iter", None) + + return result diff --git a/src/glum_benchmarks/zeros_benchmark.py b/glum_benchmarks/libraries/bench_zeros.py similarity index 77% rename from src/glum_benchmarks/zeros_benchmark.py rename to glum_benchmarks/libraries/bench_zeros.py index 80b4288c3..366157c32 100644 --- a/src/glum_benchmarks/zeros_benchmark.py +++ b/glum_benchmarks/libraries/bench_zeros.py @@ -5,7 +5,7 @@ def zeros_bench( - dat: dict[str, Union[np.ndarray, sps.spmatrix]], *, cv: bool = False, **kwargs + dat: dict[str, Union[np.ndarray, sps.spmatrix]], **kwargs ) -> dict[str, Any]: """ Run a "benchmark" of how long it takes to return all zero coefficients. @@ -13,7 +13,6 @@ def zeros_bench( Parameters ---------- dat - cv kwargs """ result = { @@ -23,6 +22,4 @@ def zeros_bench( "coef": np.zeros(dat["X"].shape[1]), "n_iter": 1, } - if cv: - result["alpha"] = 0 return result diff --git a/src/glum_benchmarks/problems.py b/glum_benchmarks/problems.py similarity index 63% rename from src/glum_benchmarks/problems.py rename to glum_benchmarks/problems.py index ca53af4ae..e827012ca 100644 --- a/src/glum_benchmarks/problems.py +++ b/glum_benchmarks/problems.py @@ -1,6 +1,7 @@ +import inspect import os from functools import partial -from typing import Callable, Optional, Union +from typing import Any, Callable, Optional, Union, cast import attr import numpy as np @@ -8,7 +9,8 @@ import tabmat as tm from git_root import git_root from joblib import Memory -from scipy.sparse import csc_matrix +from scipy.sparse import csc_matrix, csr_matrix +from sklearn.preprocessing import StandardScaler from .data import ( generate_housing_dataset, @@ -16,6 +18,8 @@ generate_narrow_insurance_dataset, generate_real_insurance_dataset, generate_wide_insurance_dataset, + simulate_categorical_dataset, + simulate_glm_dataset, ) from .util import cache_location, exposure_and_offset_to_weights, get_tweedie_p @@ -28,7 +32,7 @@ class Problem: data_loader = attr.ib(type=Callable) distribution = attr.ib(type=str) - regularization_strength = attr.ib(type=float) + alpha = attr.ib(type=float) l1_ratio = attr.ib(type=float) @@ -40,47 +44,62 @@ def load_data( ], num_rows: Optional[int] = None, storage: str = "dense", - single_precision: bool = False, noise: Optional[float] = None, distribution: str = "poisson", - data_setup: str = "weights", + data_setup: str = "no-weights", + standardize: bool = True, + k_over_n_ratio: Optional[float] = None, ) -> dict[str, np.ndarray]: """ Load the data. + By default, continuous features are pre-standardized before OHE and + format conversion. Pass ``standardize=False`` to skip (e.g. for + golden master tests that compare against stored coefficients). + A note about weights and exposures: Due to the way we have set up this problem, by rescaling the target variable, it is appropriate to pass what is modeled as an 'exposure' as a weight. Everywhere else, exposures will be referred to as weights. """ - # TODO: add a weights_and_offset option # Step 1) Load the data. if data_setup not in ["weights", "offset", "no-weights"]: raise NotImplementedError - X_in, y, exposure = loader_func(num_rows, noise, distribution) + loader_kwargs = dict(num_rows=num_rows, noise=noise, distribution=distribution) + if "k_over_n_ratio" in inspect.signature(loader_func).parameters: + loader_kwargs["k_over_n_ratio"] = k_over_n_ratio + X_in, y, exposure = cast(Any, loader_func)(**loader_kwargs) + + # Step 1.5) Standardize continuous columns BEFORE OHE/format conversion. + # At this point we still have dtype information, so we can reliably + # distinguish continuous columns from categoricals. + if standardize: + X_in = X_in.copy() - # Step 2) Convert to needed precision level. - if single_precision: - X_in = X_in.astype(np.float32) - y = y.astype(np.float32) - if exposure is not None: - exposure = exposure.astype(np.float32) + continuous_cols = [ + c + for c in X_in.select_dtypes(include=[np.number]).columns + if X_in[c].nunique(dropna=False) > 2 + ] - # Step 3) One hot encode columns if we are not using CategoricalMatrix + if continuous_cols: + scaler = StandardScaler() + X_in[continuous_cols] = scaler.fit_transform(X_in[continuous_cols]) + + # Step 2) One hot encode columns if we are not using CategoricalMatrix def transform_col(i: int, dtype) -> Union[pd.DataFrame, tm.CategoricalMatrix]: if dtype.name == "category": if storage == "cat": return tm.CategoricalMatrix(X_in.iloc[:, i]) - return pd.get_dummies(X_in.iloc[:, i], drop_first=False) + return pd.get_dummies(X_in.iloc[:, i], drop_first=True) return X_in.iloc[:, [i]] mat_parts = [transform_col(i, dtype) for i, dtype in enumerate(X_in.dtypes)] # TODO: add a threshold for the number of categories needed to make a categorical # matrix - # Step 4) Convert the matrix to the appropriate storage type. + # Step 3) Convert the matrix to the appropriate storage type. if storage == "auto": - dtype = np.float32 if single_precision else np.float64 - X = tm.from_pandas(X_in, dtype, sparse_threshold=0.1, cat_threshold=3) + X = tm.from_pandas(X_in, np.float64, sparse_threshold=0.1, cat_threshold=3) elif storage == "cat": cat_indices_in_expanded_arr: list[np.ndarray] = [] dense_indices_in_expanded_arr: list[int] = [] @@ -110,12 +129,21 @@ def transform_col(i: int, dtype) -> Union[pd.DataFrame, tm.CategoricalMatrix]: indices=[np.array(dense_indices_in_expanded_arr)] + cat_indices_in_expanded_arr, ) - elif storage == "sparse": - X = csc_matrix(pd.concat(mat_parts, axis=1, ignore_index=True)) + elif storage == "csr": + X = csr_matrix( + pd.concat(mat_parts, axis=1, ignore_index=True).to_numpy(dtype=np.float64) + ) + elif storage == "csc": + X = csc_matrix( + pd.concat(mat_parts, axis=1, ignore_index=True).to_numpy(dtype=np.float64) + ) elif storage.startswith("split"): threshold = float(storage.split("split")[1]) X = tm.from_csc( - csc_matrix(pd.concat(mat_parts, axis=1, ignore_index=True)), threshold + csc_matrix( + pd.concat(mat_parts, axis=1, ignore_index=True).astype(np.float64) + ), + threshold, ) else: # Fall back to using a dense matrix. X = pd.concat(mat_parts, axis=1, ignore_index=True) @@ -152,7 +180,7 @@ def get_all_problems() -> dict[str, Problem]: Dict mapping problem names to Problem instances. """ - regularization_strength = 0.001 + alpha = 0.001 housing_distributions = ["gaussian", "gamma", "binomial"] housing_load_funcs = { @@ -163,8 +191,8 @@ def get_all_problems() -> dict[str, Problem]: "gaussian", "poisson", "gamma", - "tweedie-p=1.5", "binomial", + "tweedie-p=1.5", ] insurance_load_funcs = { "intermediate-insurance": generate_intermediate_insurance_dataset, @@ -174,6 +202,12 @@ def get_all_problems() -> dict[str, Problem]: if os.path.isfile(git_root("data", "X.parquet")): insurance_load_funcs["real-insurance"] = generate_real_insurance_dataset + # Simulated dataset with configurable K/N ratio. + simulated_distributions = ["gaussian", "poisson", "gamma", "binomial"] + simulated_load_funcs = { + "simulated-glm": simulate_glm_dataset, + } + problems = {} for penalty_str, l1_ratio in [("l2", 0.0), ("net", 0.5), ("lasso", 1.0)]: # Add housing problems @@ -187,7 +221,7 @@ def get_all_problems() -> dict[str, Problem]: load_data, load_fn, distribution=dist, data_setup=data_setup ), distribution=distribution, - regularization_strength=regularization_strength, + alpha=alpha, l1_ratio=l1_ratio, ) # Add insurance problems @@ -201,8 +235,38 @@ def get_all_problems() -> dict[str, Problem]: load_data, load_fn, distribution=dist, data_setup=data_setup ), distribution=distribution, - regularization_strength=regularization_strength, + alpha=alpha, l1_ratio=l1_ratio, ) + # Add simulated problems + for distribution in simulated_distributions: + suffix = penalty_str + "-" + distribution + dist = distribution + for problem_name, load_fn in simulated_load_funcs.items(): + problems["-".join((problem_name, "no-weights", suffix))] = Problem( + data_loader=partial( + load_data, load_fn, distribution=dist, data_setup="no-weights" + ), + distribution=distribution, + alpha=alpha, + l1_ratio=l1_ratio, + ) + + # Add categorical-simulated problems + for distribution in simulated_distributions: + suffix = penalty_str + "-" + distribution + problems["-".join(("categorical-simulated", "no-weights", suffix))] = ( + Problem( + data_loader=partial( + load_data, + simulate_categorical_dataset, + distribution=distribution, + data_setup="no-weights", + ), + distribution=distribution, + alpha=alpha, + l1_ratio=l1_ratio, + ) + ) return problems diff --git a/glum_benchmarks/results/docs/config.yaml b/glum_benchmarks/results/docs/config.yaml new file mode 100644 index 000000000..941ec2ec2 --- /dev/null +++ b/glum_benchmarks/results/docs/config.yaml @@ -0,0 +1,99 @@ +# Benchmark Configuration +# Edit this file to configure which benchmarks to run and how to run them + +# Steps to run +run_benchmarks: true # Run the benchmarks (can be slow) +analyze_results: true # Analyze and print results (writes CSV) +generate_plots: true # Generate comparison plots (reads CSV and writes PNGs) +update_docs: true # Copy figures to docs/_static and update benchmarks.rst and README.md + +# Figures to include in docs/README (null = use defaults) +# Default for readme: first non-normalized figure only +# Default for docs: all figures in a single block +# +# Figure name format: +# Standard: "-.png" +# Simulated with K/N: "--k-over-n-.png" +# Normalized: append "-normalized" before ".png" +# +# docs_figures is a list of lists: each inner list maps to one +# BENCHMARK_FIGURES_START/END block in benchmarks.rst (in order). +# Example with 3 blocks: +# docs_figures: +# - ["wide-insurance-gamma-normalized.png", "wide-insurance-poisson-normalized.png"] +# - ["simulated-glm-gaussian-k-over-n-0.01-normalized.png"] +# - ["wide-insurance-gamma.png"] +docs_figures: + - ["wide-insurance-poisson-normalized.png", "wide-insurance-gamma-normalized.png", "wide-insurance-tweedie-p=1.5-normalized.png"] + - ["intermediate-housing-gamma-normalized.png", "intermediate-housing-gaussian-normalized.png"] + - ["simulated-glm-gaussian-k-over-n-0.01-normalized.png", "simulated-glm-gaussian-k-over-n-0.1-normalized.png", "simulated-glm-gaussian-k-over-n-0.5-normalized.png","simulated-glm-gaussian-k-over-n-1-normalized.png", "simulated-glm-gaussian-k-over-n-2.png"] +readme_figures: ["wide-insurance-gamma-normalized.png"] + +# Output settings +run_name: "docs" # Subfolder name within results/ ("docs" CSV is git-tracked) +clear_output: True # Clear entire run_name directory before running + +# Benchmark settings +iterations: 5 # Run each benchmark N times; first is warmup, report median of rest (>=2) +num_threads: 16 # Number of threads for parallel execution +timeout: 100 # Timeout in seconds per benchmark run (reports as "not converged" if exceeded) + +# Storage format per library [auto, dense, cat, csr, csc] +storage: + glum: auto + sklearn: csr + h2o: csr + celer: csc + skglm: csc + glmnet: csc + +# PARAMETER GRID +# Each entry specifies a parameter set. Within each entry, the Cartesian +# product is computed. Across entries, results are unioned (not crossed). +# +# Available values: +# libraries: ["glum", "sklearn", "h2o", "skglm", "celer", "zeros", "glmnet"] +# datasets: ["intermediate-housing", "intermediate-insurance", "narrow-insurance", "wide-insurance","simulated-glm", "categorical-simulated"] +# regularizations: ["lasso", "l2", "net"] +# alphas: [0.001, 0.01, 0.1] +# num_rows: [1000, null] # null = full dataset +# k_over_n_ratios: [0.5, 0.7, 1.2] # only used for dataset "simulated-glm" +# distributions: ["gaussian", "gamma", "binomial", "poisson", "tweedie-p=1.5"] +# +# Use null for any field (or omit) to use its default value. +# For libraries, default means all available EXCEPT "zeros" (include it explicitly if needed). +# For num_rows in param_grid, default is [null] (full dataset). +# +# Example: Run all regularizations for gaussian, but only lasso for gamma: +# param_grid: +# - distributions: [gaussian] +# regularizations: [lasso, l2, net] +# - distributions: [gamma] +# regularizations: [lasso] +# +# Example: Run simulated-glm with multiple K/N ratios in one run: +# param_grid: +# - datasets: [simulated-glm] +# distributions: [gaussian, poisson] +# num_rows: [1000, 5000] +# k_over_n_ratios: [0.5, 0.7, 1.2] + +param_grid: +- datasets: [intermediate-housing] + distributions: [gamma, gaussian] + alphas: [0.001] +- datasets: [wide-insurance] + distributions: [gamma, poisson, tweedie-p=1.5] + alphas: [0.001] + num_rows: [600000] +- datasets: [simulated-glm] + alphas: [0.001] + num_rows: [10000] + k_over_n_ratios: [0.01,0.1,0.5] + distributions: [gaussian] +- datasets: [simulated-glm] + regularizations: [lasso, net] + alphas: [0.001] + num_rows: [1000] + k_over_n_ratios: [1,2] + distributions: [gaussian] diff --git a/glum_benchmarks/results/docs/results.csv b/glum_benchmarks/results/docs/results.csv new file mode 100644 index 000000000..78c387f7e --- /dev/null +++ b/glum_benchmarks/results/docs/results.csv @@ -0,0 +1,142 @@ +problem_name,num_rows,k_over_n_ratio,alpha,library_name,storage,threads,n_iter,converged,runtime,runtime per iter,intercept,l1,l2,num_nonzero_coef,obj_val,obj_gap,rel_obj_val +intermediate-housing-no-weights-l2-gamma,21613,1.0,0.001,glmnet,csc,16,21,True,0.16077685356140137,0.0076560406457810174,13.094335394372376,1.0560992334997028,0.2083343834990184,10,14.096592929091601,4.825613739711798e-06,3.4232494446803335e-07 +intermediate-housing-no-weights-l2-gamma,21613,1.0,0.001,glum,auto,16,4,True,0.009757041931152344,0.002439260482788086,13.094319104440412,1.0627080252143677,0.20912988250924588,10,14.096588103477862,0.0,0.0 +intermediate-housing-no-weights-l2-gamma,21613,1.0,0.001,h2o,csr,16,5,True,0.22510385513305664,0.045020771026611325,13.093973377526403,1.1058618154056836,0.23692759612792813,10,14.096595547501312,7.444023450986492e-06,5.280727078313318e-07 +intermediate-housing-no-weights-l2-gamma,21613,1.0,0.001,skglm,csc,16,17,True,0.04574704170227051,0.0026910024530747358,13.09430525853679,1.0624196436200823,0.20897666797090905,10,14.096588104595012,1.1171508162988175e-09,7.924973107664244e-11 +intermediate-housing-no-weights-l2-gamma,21613,1.0,0.001,sklearn,csr,16,4,True,0.010890007019042969,0.002722501754760742,13.094319104440412,1.0627080252143675,0.20912988250924583,10,14.096588103477862,0.0,0.0 +intermediate-housing-no-weights-l2-gaussian,21613,1.0,0.001,glmnet,csc,16,11,True,0.06523704528808594,0.0059306404807350855,535755.9249355074,1114755.3660310605,390968251465.56775,10,23688960667.571003,29491448.76910019,0.0012464966350835833 +intermediate-housing-no-weights-l2-gaussian,21613,1.0,0.001,glum,auto,16,1,True,0.0022840499877929688,0.0022840499877929688,536348.086827908,1039089.1461450121,306136096863.6541,10,23659469218.801903,0.0,0.0 +intermediate-housing-no-weights-l2-gaussian,21613,1.0,0.001,h2o,csr,16,1,True,0.22382378578186035,0.22382378578186035,535753.552186993,1114444.9984665243,390337908623.24817,10,23681953917.51728,22484698.715377808,0.0009503467092790688 +intermediate-housing-no-weights-l2-gaussian,21613,1.0,0.001,skglm,csc,16,15,True,0.06793808937072754,0.004529205958048502,536348.0868276724,1039089.1462413827,306136096894.50226,10,23659469218.801903,0.0,0.0 +intermediate-housing-no-weights-l2-gaussian,21613,1.0,0.001,sklearn,csr,16,0,True,0.003448963165283203,0.003448963165283203,536348.0868275819,1039089.1577254538,306136099364.34436,10,23659469218.802097,0.000194549560546875,8.222904696115024e-15 +intermediate-housing-no-weights-lasso-gamma,21613,1.0,0.001,glmnet,csc,16,21,True,0.16451191902160645,0.007833900905790784,13.095170590798567,0.9488371944085633,0.15921946675903895,10,14.097512369979095,4.835927907009818e-06,3.4303424880810276e-07 +intermediate-housing-no-weights-lasso-gamma,21613,1.0,0.001,glum,auto,16,4,True,0.00952911376953125,0.0023822784423828125,13.09514553239568,0.956709840322823,0.1603250778737891,10,14.097507535269946,1.2187584275125118e-09,8.645205009245194e-11 +intermediate-housing-no-weights-lasso-gamma,21613,1.0,0.001,h2o,csr,16,5,True,0.22377681732177734,0.04475536346435547,13.094030332719514,1.0891677324799582,0.2310115595380003,10,14.097571827939676,6.429388848872009e-05,4.560656437559925e-06 +intermediate-housing-no-weights-lasso-gamma,21613,1.0,0.001,skglm,csc,16,17,True,0.04600691795349121,0.002706289291381836,13.095132632999158,0.9563913615218042,0.16023382575985065,10,14.097507534051188,0.0,0.0 +intermediate-housing-no-weights-lasso-gaussian,21613,1.0,0.001,celer,csc,16,3,True,0.027535200119018555,0.009178400039672852,535749.8263428942,1115564.2221287554,391085528715.8024,10,23486740189.009758,1.1444091796875e-05,4.872575634072064e-16 +intermediate-housing-no-weights-lasso-gaussian,21613,1.0,0.001,glmnet,csc,16,11,True,0.06455564498901367,0.005868694999001243,535755.9242040479,1114755.4495251803,390968361892.9666,10,23493477657.566967,6737468.557220459,0.0002868626511384987 +intermediate-housing-no-weights-lasso-gaussian,21613,1.0,0.001,glum,auto,16,1,True,0.003591775894165039,0.003591775894165039,535749.8263428425,1115564.2222216285,391085528718.9581,10,23486740189.009754,7.62939453125e-06,3.248383756048043e-16 +intermediate-housing-no-weights-lasso-gaussian,21613,1.0,0.001,h2o,csr,16,1,True,0.22641515731811523,0.22641515731811523,535749.8252379455,1115564.3621644985,391085696914.2446,10,23486740189.00982,7.2479248046875e-05,3.0859645682456405e-15 +intermediate-housing-no-weights-lasso-gaussian,21613,1.0,0.001,skglm,csc,16,1,True,0.009266853332519531,0.009266853332519531,535749.8263447076,1115564.2219154884,391085528465.5109,10,23486740189.00975,3.814697265625e-06,1.6241918780240214e-16 +intermediate-housing-no-weights-lasso-gaussian,21613,1.0,0.001,sklearn,csr,16,52,True,0.021435976028442383,0.0004122303082392766,535749.8263428791,1115564.2221933692,391085528700.5606,10,23486740189.009747,0.0,0.0 +intermediate-housing-no-weights-net-gamma,21613,1.0,0.001,glmnet,csc,16,21,True,0.1628890037536621,0.007756619226364862,13.094720958469038,1.004539186801711,0.1822323414280771,10,14.097062862331804,4.862245704728707e-06,3.449120876638949e-07 +intermediate-housing-no-weights-net-gamma,21613,1.0,0.001,glum,auto,16,4,True,0.009462118148803711,0.0023655295372009277,13.094701055521872,1.0122359227290287,0.1834469098575443,10,14.09705800136506,1.2789609371566257e-09,9.072537951881977e-11 +intermediate-housing-no-weights-net-gamma,21613,1.0,0.001,h2o,csr,16,5,True,0.23186683654785156,0.04637336730957031,13.094000664945543,1.097527952884283,0.23395603137290621,10,14.09708588444488,2.7884358781093965e-05,1.9780268181434493e-06 +intermediate-housing-no-weights-net-gamma,21613,1.0,0.001,skglm,csc,16,17,True,0.04561305046081543,0.002683120615342084,13.09468772926472,1.0118996515875778,0.18332214953279674,10,14.097058000086099,0.0,0.0 +intermediate-housing-no-weights-net-gaussian,21613,1.0,0.001,celer,csc,16,3,True,0.027132749557495117,0.009044249852498373,536071.1910117024,1074508.6894273285,343893612452.4904,10,23578384761.814896,0.0,0.0 +intermediate-housing-no-weights-net-gaussian,21613,1.0,0.001,glmnet,csc,16,11,True,0.0641946792602539,0.005835879932750355,535755.9245697777,1114755.4077781101,390968306679.25244,10,23591219176.372375,12834414.557479858,0.0005443296768260879 +intermediate-housing-no-weights-net-gaussian,21613,1.0,0.001,glum,auto,16,1,True,0.0035359859466552734,0.0035359859466552734,536071.1910116494,1074508.6895251912,343893612456.41754,10,23578384761.814907,1.1444091796875e-05,4.853636885003532e-16 +intermediate-housing-no-weights-net-gaussian,21613,1.0,0.001,h2o,csr,16,1,True,0.22376394271850586,0.22376394271850586,535751.6904783726,1115004.1615117632,390711325830.3882,10,23584428959.845562,6044198.030666351,0.0002563448722940049 +intermediate-housing-no-weights-net-gaussian,21613,1.0,0.001,skglm,csc,16,1,True,0.011924266815185547,0.011924266815185547,536071.191011708,1074508.689417384,343893612449.7887,10,23578384761.81491,1.52587890625e-05,6.471515846671377e-16 +intermediate-housing-no-weights-net-gaussian,21613,1.0,0.001,sklearn,csr,16,53,True,0.0217740535736084,0.00041083119950204527,536071.19101165,1074508.6895252054,343893612456.4325,10,23578384761.814903,7.62939453125e-06,3.2357579233356883e-16 +simulated-glm-no-weights-l2-gaussian,10000,0.01,0.001,glmnet,csc,16,4,True,0.18618988990783691,0.04654747247695923,-0.0022812548854366124,9.302288470041454,8.332242692764567,100,0.6647465066104008,1.917097866233064e-06,2.8839615943905547e-06 +simulated-glm-no-weights-l2-gaussian,10000,0.01,0.001,glum,auto,16,1,True,0.002374887466430664,0.002374887466430664,-0.002281254885436599,9.296232017913745,8.320972921195633,100,0.6647445895125347,1.1102230246251565e-16,1.6701497720188995e-16 +simulated-glm-no-weights-l2-gaussian,10000,0.01,0.001,h2o,csr,16,1,True,0.23220396041870117,0.23220396041870117,-0.0022812548854365664,9.296231125404155,8.320971265210495,100,0.664744589512576,4.141131881851834e-14,6.229658649630495e-14 +simulated-glm-no-weights-l2-gaussian,10000,0.01,0.001,skglm,csc,16,4,True,0.02570486068725586,0.006426215171813965,-0.0022812548854364624,9.296232017836383,8.320972921295798,100,0.6647445895125346,0.0,0.0 +simulated-glm-no-weights-l2-gaussian,10000,0.01,0.001,sklearn,csr,16,0,True,0.007047891616821289,0.007047891616821289,-0.0022812548854365556,9.296450698152526,8.320972483690692,100,0.6647446137050604,2.4192525871491455e-08,3.639371610264949e-08 +simulated-glm-no-weights-l2-gaussian,10000,0.1,0.001,glmnet,csc,16,4,True,0.5528931617736816,0.1382232904434204,-0.034059969694517654,48.62924390793651,15.118509264867782,1000,2.047417073843288,4.998005233014524e-05,2.441186657816253e-05 +simulated-glm-no-weights-l2-gaussian,10000,0.1,0.001,glum,auto,16,1,True,0.04870915412902832,0.04870915412902832,-0.03405996969451767,48.592501826186215,15.097667559374846,1000,2.047367093790958,0.0,0.0 +simulated-glm-no-weights-l2-gaussian,10000,0.1,0.001,h2o,csr,16,1,True,0.2288200855255127,0.2288200855255127,-0.03405996969451762,48.59249622363308,15.097664185023001,1000,2.047367093791042,8.393286066166183e-14,4.0995511218386135e-14 +simulated-glm-no-weights-l2-gaussian,10000,0.1,0.001,skglm,csc,16,6,True,0.2537200450897217,0.04228667418162028,-0.03405996969451749,48.59238652366331,15.09766362536609,1000,2.04736709408523,2.942721621934652e-10,1.437319975913959e-10 +simulated-glm-no-weights-l2-gaussian,10000,0.1,0.001,sklearn,csr,16,0,True,0.12804889678955078,0.12804889678955078,-0.03405996969451765,48.59266976991273,15.097665579131291,1000,2.047367147290275,5.3499316976513e-08,2.6130788728001035e-08 +simulated-glm-no-weights-l2-gaussian,10000,0.5,0.001,glmnet,csc,16,4,True,1.9127001762390137,0.4781750440597534,-0.0043429012035277595,194.67332641388433,20.509553904020535,5000,1.9153119086542179,0.058278227028305984,0.03138242865755721 +simulated-glm-no-weights-l2-gaussian,10000,0.5,0.001,glum,auto,16,1,True,0.951524019241333,0.951524019241333,-0.004342901203527675,210.08601346423717,22.68933184652654,5000,1.8570336816259119,0.0,0.0 +simulated-glm-no-weights-l2-gaussian,10000,0.5,0.001,h2o,csr,16,1,True,8.513091087341309,8.513091087341309,-0.004342901203527773,210.08594428744672,22.689320072331245,5000,1.8570336816262063,2.944311461305915e-13,1.5854916851739843e-13 +simulated-glm-no-weights-l2-gaussian,10000,0.5,0.001,skglm,csc,16,8,True,2.648055076599121,0.33100688457489014,-0.004342901203527461,210.04808732861417,22.685993791767284,5000,1.8570340739364048,3.9231049298038556e-07,2.1125653070379482e-07 +simulated-glm-no-weights-l2-gaussian,10000,0.5,0.001,sklearn,csr,16,0,True,1.5846309661865234,1.5846309661865234,-0.004342901203527898,210.08775634857676,22.689285828346016,5000,1.8570339421942728,2.6056836088805824e-07,1.403142891085958e-07 +simulated-glm-no-weights-lasso-gaussian,1000,1.0,0.001,celer,csc,16,13,True,1.9246349334716797,0.14804884103628305,-0.18993517668200524,164.52485589680475,51.60360688720317,939,0.2557311405985473,6.286411651790225e-07,2.458217286499573e-06 +simulated-glm-no-weights-lasso-gaussian,1000,1.0,0.001,glmnet,csc,16,12,True,0.09877610206604004,0.00823134183883667,-0.18993517668200505,108.49983730644972,25.875742576859416,991,0.36746632045161676,0.11173580849423465,0.43692795059533446 +simulated-glm-no-weights-lasso-gaussian,1000,1.0,0.001,glum,auto,16,1,True,0.6457421779632568,0.6457421779632568,-0.18993517668200488,164.61309763711165,51.64999370472596,939,0.2557305119573821,0.0,0.0 +simulated-glm-no-weights-lasso-gaussian,1000,1.0,0.001,h2o,csr,16,4,True,0.6556029319763184,0.1639007329940796,-0.18993517668200519,164.59796335405022,51.64258520783053,939,0.2557305750489348,6.309155270445288e-08,2.467110874707324e-07 +simulated-glm-no-weights-lasso-gaussian,1000,1.0,0.001,skglm,csc,16,8,True,0.24224376678466797,0.030280470848083496,-0.1899351766819543,163.86381060877923,51.26379853425601,943,0.2557571546052537,2.664264787161086e-05,0.00010418251489697442 +simulated-glm-no-weights-lasso-gaussian,1000,1.0,0.001,sklearn,csr,16,893,True,1.4703631401062012,0.001646543269995746,-0.18993517668200524,164.51509661303047,51.590203032897094,941,0.2557317177401446,1.2057827624700757e-06,4.715052393400054e-06 +simulated-glm-no-weights-lasso-gaussian,1000,2.0,0.001,celer,csc,16,13,True,10.125555992126465,0.7788889224712665,-0.3923010691472551,98.3769130855911,19.19075136641936,1020,0.09929083316323092,8.354475899058356e-06,8.414854271889125e-05 +simulated-glm-no-weights-lasso-gaussian,1000,2.0,0.001,glmnet,csc,16,6,True,0.18593716621398926,0.030989527702331543,-0.39230106914725504,149.92970807387053,21.103913288239546,1969,0.15574764375123873,0.056465165063906864,0.5687324270149556 +simulated-glm-no-weights-lasso-gaussian,1000,2.0,0.001,glum,auto,16,1,True,8.3595609664917,8.3595609664917,-0.39230106914725527,98.37175137194332,19.348186888945396,988,0.09928247868733187,0.0,0.0 +simulated-glm-no-weights-lasso-gaussian,1000,2.0,0.001,h2o,csr,16,4,True,3.287881851196289,0.8219704627990723,-0.3923010691472554,98.37157585164383,19.331125993563585,992,0.09928440621854773,1.9275312158617552e-06,1.9414616167391297e-05 +simulated-glm-no-weights-lasso-gaussian,1000,2.0,0.001,skglm,csc,16,9,True,1.026670217514038,0.1140744686126709,-0.39230106914728696,98.54495671834968,18.910841558668395,1081,0.09948217704678908,0.00019969835945721215,0.002011415932574748 +simulated-glm-no-weights-lasso-gaussian,1000,2.0,0.001,sklearn,csr,16,1000,False,2.2064249515533447,0.0022064249515533446,-0.3923010691472551,98.48946150089223,18.41390184374141,1074,0.09940980559180647,0.00012732690447460338,0.0012824710478431063 +simulated-glm-no-weights-lasso-gaussian,10000,0.01,0.001,celer,csc,16,6,True,0.06896686553955078,0.01149447758992513,-0.002281254885436556,9.205685025281035,8.319749732616224,97,0.6698350575935113,0.0,0.0 +simulated-glm-no-weights-lasso-gaussian,10000,0.01,0.001,glmnet,csc,16,4,True,0.18959689140319824,0.04739922285079956,-0.002281254885436613,9.204336438102436,8.320192279985767,97,0.6698360565835206,9.989900093820836e-07,1.491397020889163e-06 +simulated-glm-no-weights-lasso-gaussian,10000,0.01,0.001,glum,auto,16,1,True,0.0034952163696289062,0.0034952163696289062,-0.00228125488543664,9.20568453009658,8.319750124515025,97,0.6698350575948433,1.3320455849452628e-12,1.9886173018934662e-12 +simulated-glm-no-weights-lasso-gaussian,10000,0.01,0.001,h2o,csr,16,2,True,0.22474384307861328,0.11237192153930664,-0.0022812548854365777,9.205598366299354,8.31973984014341,97,0.6698350605966898,3.00317859469601e-09,4.483459861723878e-09 +simulated-glm-no-weights-lasso-gaussian,10000,0.01,0.001,skglm,csc,16,5,True,0.022512197494506836,0.004502439498901367,-0.0022812548854367946,9.205685025281042,8.319749732616222,97,0.6698350575935113,0.0,0.0 +simulated-glm-no-weights-lasso-gaussian,10000,0.01,0.001,sklearn,csr,16,5,True,0.012853860855102539,0.002570772171020508,-0.002281254885436556,9.205684530096578,8.319750124515025,97,0.6698350575948434,1.3321566072477253e-12,1.988783047626246e-12 +simulated-glm-no-weights-lasso-gaussian,10000,0.1,0.001,celer,csc,16,9,True,0.7240152359008789,0.08044613732231988,-0.03405996969451765,47.49253976590552,15.021568163926743,964,2.0878758619595112,3.552713678800501e-15,1.7015923903953837e-15 +simulated-glm-no-weights-lasso-gaussian,10000,0.1,0.001,glmnet,csc,16,8,True,0.6130728721618652,0.07663410902023315,-0.034059969694517654,47.49025913991842,15.021539257564534,964,2.087876018522138,1.5656263041563534e-07,7.498656087182243e-08 +simulated-glm-no-weights-lasso-gaussian,10000,0.1,0.001,glum,auto,16,1,True,0.0470430850982666,0.0470430850982666,-0.03405996969451761,47.492543063095134,15.021568744820819,964,2.0878758619619013,2.3936408410918375e-12,1.1464478730288898e-12 +simulated-glm-no-weights-lasso-gaussian,10000,0.1,0.001,h2o,csr,16,3,True,0.7077131271362305,0.2359043757120768,-0.034059969694517626,47.492517928633696,15.021596050822655,963,2.0878758654785883,3.5190805824925064e-09,1.6854836279345595e-09 +simulated-glm-no-weights-lasso-gaussian,10000,0.1,0.001,skglm,csc,16,8,True,0.24083995819091797,0.030104994773864746,-0.034059969694518306,47.49253993096129,15.021568165965089,964,2.0878758619595077,0.0,0.0 +simulated-glm-no-weights-lasso-gaussian,10000,0.1,0.001,sklearn,csr,16,7,True,0.164780855178833,0.023540122168404714,-0.03405996969451765,47.492803825398944,15.021598012548237,964,2.087875870153575,8.194067202538236e-09,3.9245950163186244e-09 +simulated-glm-no-weights-lasso-gaussian,10000,0.5,0.001,celer,csc,16,13,True,5.913524150848389,0.4548864731421837,-0.004342901203527908,196.27954971441505,21.53174641288995,4828,2.0490276623320125,2.2917244990594554e-08,1.1184448930684047e-08 +simulated-glm-no-weights-lasso-gaussian,10000,0.5,0.001,glmnet,csc,16,9,True,2.268550157546997,0.252061128616333,-0.0043429012035277196,193.38066167777575,21.152983844368805,4865,2.0517059359935628,0.0026782965787952584,0.0013071061254987363 +simulated-glm-no-weights-lasso-gaussian,10000,0.5,0.001,glum,auto,16,1,True,1.290815830230713,1.290815830230713,-0.004342901203527542,196.28616862969633,21.532308355721835,4827,2.0490276394147675,0.0,0.0 +simulated-glm-no-weights-lasso-gaussian,10000,0.5,0.001,h2o,csr,16,3,True,31.05604887008667,10.35201629002889,-0.00434290120352778,196.2854480768024,21.53227356225487,4828,2.0490276478443095,8.429541953347552e-09,4.11392300972336e-09 +simulated-glm-no-weights-lasso-gaussian,10000,0.5,0.001,skglm,csc,16,10,True,2.2787411212921143,0.22787411212921144,-0.004342901203531326,196.28531614127783,21.532217738327894,4828,2.0490276412754933,1.860725795665985e-09,9.081018527390074e-10 +simulated-glm-no-weights-lasso-gaussian,10000,0.5,0.001,sklearn,csr,16,30,True,2.6046009063720703,0.08682003021240234,-0.004342901203527908,196.27775306199035,21.53122384870416,4828,2.049027704490937,6.507616934214866e-08,3.175953710450454e-08 +simulated-glm-no-weights-net-gaussian,1000,1.0,0.001,celer,csc,16,13,True,2.9079649448394775,0.22368961114149827,-0.18993517668200524,194.40973795339642,66.63242362432453,971,0.1811354848723096,1.1317347632933128e-06,6.2480404389879476e-06 +simulated-glm-no-weights-net-gaussian,1000,1.0,0.001,glmnet,csc,16,8,True,0.09328317642211914,0.011660397052764893,-0.18993517668200507,103.07015429149118,23.478999722734134,998,0.37175236870167744,0.19061801556413113,1.0523570612769588 +simulated-glm-no-weights-net-gaussian,1000,1.0,0.001,glum,auto,16,1,True,1.0357780456542969,1.0357780456542969,-0.18993517668200485,194.6929282504854,66.78900750674099,973,0.1811343531375463,0.0,0.0 +simulated-glm-no-weights-net-gaussian,1000,1.0,0.001,h2o,csr,16,2,True,0.2222001552581787,0.11110007762908936,-0.1899351766820051,194.6117336284198,66.74737301774161,971,0.18113464349923455,2.9036168824880804e-07,1.6030183298709704e-06 +simulated-glm-no-weights-net-gaussian,1000,1.0,0.001,skglm,csc,16,8,True,0.25818681716918945,0.03227335214614868,-0.1899351766818791,192.03580632940714,65.28081367802545,970,0.18118994478912698,5.559165158067203e-05,0.0003069083838473087 +simulated-glm-no-weights-net-gaussian,1000,1.0,0.001,sklearn,csr,16,1000,False,1.5717947483062744,0.0015717947483062744,-0.18993517668200524,193.99187538597408,66.38537480965948,972,0.18113875367832633,4.400540780019613e-06,2.4294346731003672e-05 +simulated-glm-no-weights-net-gaussian,1000,2.0,0.001,celer,csc,16,12,True,6.34620213508606,0.5288501779238383,-0.3923010691472551,99.61699915556592,17.380094556926686,1115,0.05443972739880544,1.6041029775717974e-05,0.0002947435362417174 +simulated-glm-no-weights-net-gaussian,1000,2.0,0.001,glmnet,csc,16,7,True,0.18584609031677246,0.026549441473824636,-0.39230106914725504,154.51344767834394,21.749251973351967,1986,0.08484152226983172,0.030417835900801993,0.5589080404173343 +simulated-glm-no-weights-net-gaussian,1000,2.0,0.001,glum,auto,16,1,True,4.6530539989471436,4.6530539989471436,-0.39230106914725527,99.5863061044772,17.380412921296813,1086,0.05442368636902972,0.0,0.0 +simulated-glm-no-weights-net-gaussian,1000,2.0,0.001,h2o,csr,16,2,True,1.3102149963378906,0.6551074981689453,-0.3923010691472553,99.58706948123712,17.385179584839626,1094,0.05442584202976058,2.1556607308556575e-06,3.9608870230487644e-05 +simulated-glm-no-weights-net-gaussian,1000,2.0,0.001,skglm,csc,16,9,True,1.1150269508361816,0.12389188342624241,-0.3923010691472883,99.8572574799046,17.626442423150408,1179,0.05462727590396018,0.00020358953493045812,0.003740825888749655 +simulated-glm-no-weights-net-gaussian,1000,2.0,0.001,sklearn,csr,16,1000,False,2.1810050010681152,0.0021810050010681154,-0.3923010691472551,99.93150219958918,17.037185745419876,1161,0.05451261008473364,8.892371570391744e-05,0.0016339157017213788 +simulated-glm-no-weights-net-gaussian,10000,0.01,0.001,celer,csc,16,6,True,0.06835818290710449,0.011393030484517416,-0.0022812548854365556,9.250487227969552,8.32034124836432,98,0.6673010546110977,0.0,0.0 +simulated-glm-no-weights-net-gaussian,10000,0.01,0.001,glmnet,csc,16,4,True,0.18716788291931152,0.04679197072982788,-0.002281254885436613,9.253508332683536,8.325975384370139,98,0.6673015340499803,4.794388825946072e-07,7.184746364203241e-07 +simulated-glm-no-weights-net-gaussian,10000,0.01,0.001,glum,auto,16,1,True,0.0034410953521728516,0.0034410953521728516,-0.00228125488543664,9.250486739016575,8.320341657038202,98,0.667301054612459,1.361355472795367e-12,2.040091894637816e-12 +simulated-glm-no-weights-net-gaussian,10000,0.01,0.001,h2o,csr,16,2,True,0.22037601470947266,0.11018800735473633,-0.002281254885436572,9.250478956521867,8.32033843192244,98,0.6673010546482426,3.714495377948879e-11,5.566446137438944e-11 +simulated-glm-no-weights-net-gaussian,10000,0.01,0.001,skglm,csc,16,5,True,0.022979021072387695,0.004595804214477539,-0.002281254885436242,9.250487227969522,8.320341248364244,98,0.6673010546110979,2.220446049250313e-16,3.3275026824952143e-16 +simulated-glm-no-weights-net-gaussian,10000,0.01,0.001,sklearn,csr,16,5,True,0.013373136520385742,0.0026746273040771486,-0.0022812548854365556,9.250486739016573,8.320341657038197,98,0.6673010546124593,1.361577517400292e-12,2.0404246449060656e-12 +simulated-glm-no-weights-net-gaussian,10000,0.1,0.001,celer,csc,16,9,True,0.7154581546783447,0.07949535051981609,-0.03405996969451765,48.0362291109397,15.059160232943087,981,2.06775417366076,6.217248937900877e-15,3.006764061752009e-15 +simulated-glm-no-weights-net-gaussian,10000,0.1,0.001,glmnet,csc,16,7,True,0.5910780429840088,0.08443972042628697,-0.034059969694517654,48.05201471791891,15.071599539755221,982,2.067756359658992,2.185998238424247e-06,1.057184778669388e-06 +simulated-glm-no-weights-net-gaussian,10000,0.1,0.001,glum,auto,16,1,True,0.04757356643676758,0.04757356643676758,-0.03405996969451761,48.0362333943998,15.059161049923546,981,2.067754173663573,2.8195223933380476e-12,1.363567502004536e-12 +simulated-glm-no-weights-net-gaussian,10000,0.1,0.001,h2o,csr,16,2,True,0.47034502029418945,0.23517251014709473,-0.03405996969451761,48.03623961623492,15.059166248104646,981,2.06775417510253,1.4417764759855345e-09,6.972668677693984e-10 +simulated-glm-no-weights-net-gaussian,10000,0.1,0.001,skglm,csc,16,8,True,0.2435770034790039,0.03044712543487549,-0.0340599696945189,48.03622939594965,15.059160226609993,981,2.0677541736607536,0.0,0.0 +simulated-glm-no-weights-net-gaussian,10000,0.1,0.001,sklearn,csr,16,8,True,0.18522882461547852,0.023153603076934814,-0.03405996969451765,48.036304704584545,15.05917243894907,981,2.067754174275906,6.151523734843067e-10,2.974978270242059e-10 +simulated-glm-no-weights-net-gaussian,10000,0.5,0.001,celer,csc,16,12,True,5.7226197719573975,0.47688498099644977,-0.0043429012035279034,202.96003257058172,22.084706969819827,4918,1.954683736459981,4.214402293811759e-08,2.156053332857309e-08 +simulated-glm-no-weights-net-gaussian,10000,0.5,0.001,glmnet,csc,16,9,True,2.253176689147949,0.25035296546088326,-0.004342901203527717,199.88993873837433,21.69514204629718,4932,1.9574684011409191,0.002784706824961125,0.001424632963920863 +simulated-glm-no-weights-net-gaussian,10000,0.5,0.001,glum,auto,16,1,True,1.3294620513916016,1.3294620513916016,-0.004342901203527538,202.9704062771516,22.085594654333256,4918,1.954683694315958,0.0,0.0 +simulated-glm-no-weights-net-gaussian,10000,0.5,0.001,h2o,csr,16,3,True,31.433804988861084,10.477934996287027,-0.004342901203527776,202.96995051695777,22.08554929679054,4918,1.954683696869232,2.5532740366429607e-09,1.3062338648793403e-09 +simulated-glm-no-weights-net-gaussian,10000,0.5,0.001,skglm,csc,16,10,True,2.366034984588623,0.2366034984588623,-0.004342901203531851,202.9697717175261,22.085550182632865,4919,1.9546836969448271,2.6288691223896876e-09,1.3449076850818367e-09 +simulated-glm-no-weights-net-gaussian,10000,0.5,0.001,sklearn,csr,16,32,True,2.794651746749878,0.08733286708593369,-0.0043429012035279034,202.96154472548358,22.084507648731602,4918,1.954683749475726,5.515976808645462e-08,2.8219280821165182e-08 +wide-insurance-no-weights-l2-gamma,600000,1.0,0.001,glmnet,csc,16,9,True,3.5717549324035645,0.3968616591559516,7.497419021386908,25.366963131944573,4.823251565230799,256,8.490677674525973,0.0005491833233897125,6.46849248463993e-05 +wide-insurance-no-weights-l2-gamma,600000,1.0,0.001,glum,auto,16,3,True,0.09991812705993652,0.03330604235331217,7.414197552984377,25.214549543467786,4.771856011905555,264,8.490128491202583,0.0,0.0 +wide-insurance-no-weights-l2-gamma,600000,1.0,0.001,h2o,csr,16,4,True,0.43921399116516113,0.10980349779129028,7.934585254509618,85.78045498680585,59.80200755283245,256,8.51599905544741,0.025870564244826255,0.0030471345953872395 +wide-insurance-no-weights-l2-gamma,600000,1.0,0.001,skglm,csc,16,15,True,2.3812060356140137,0.15874706904093425,7.50837696522423,25.67143381271818,4.784864088309819,256,8.490646003913024,0.0005175127104415367,6.0954638198677444e-05 +wide-insurance-no-weights-l2-gamma,600000,1.0,0.001,sklearn,csr,16,3,True,0.3132781982421875,0.10442606608072917,7.510844033441776,25.68298137194127,4.785988694702942,256,8.490645912715086,0.0005174215125034465,6.094389655463935e-05 +wide-insurance-no-weights-l2-poisson,600000,1.0,0.001,glmnet,csc,16,18,True,0.5345830917358398,0.029699060651991103,-1.2755113833683906,53.42720526046824,34.07101680766372,307,0.5121190946929294,0.022278011136894205,0.04548007891695291 +wide-insurance-no-weights-l2-poisson,600000,1.0,0.001,glum,auto,16,4,True,0.12481689453125,0.0312042236328125,-1.752263303793848,36.199957047173996,12.531106003047356,315,0.4898410835560352,0.0,0.0 +wide-insurance-no-weights-l2-poisson,600000,1.0,0.001,h2o,csr,16,5,True,0.6514129638671875,0.1302825927734375,-0.5986366815857769,426.1892251415504,1339.9698984769764,307,1.1494803593956342,0.659639275839599,1.3466393448481335 +wide-insurance-no-weights-l2-poisson,600000,1.0,0.001,skglm,csc,16,8,True,0.92327880859375,0.11540985107421875,-1.284471601295827,53.57514704370188,34.140195640370244,307,0.5121139219416838,0.02227283838564864,0.04546951885692689 +wide-insurance-no-weights-l2-poisson,600000,1.0,0.001,sklearn,csr,16,4,True,0.4034397602081299,0.10085994005203247,-1.2870690815604002,53.587984660972,34.15393733139729,307,0.5121138834635779,0.022272799907542717,0.04546944030470411 +wide-insurance-no-weights-l2-tweedie-p=1.5,600000,1.0,0.001,glmnet,csc,16,52,True,7.246757984161377,0.13936073046464187,5.016313445688354,156.58415492351048,169.91198322948713,307,60.583174140668866,0.11282778247959868,0.0018658365508819159 +wide-insurance-no-weights-l2-tweedie-p=1.5,600000,1.0,0.001,glum,auto,16,4,True,0.12420105934143066,0.031050264835357666,4.7095215672990784,126.05194926596438,89.42598566067127,315,60.47034635818927,0.0,0.0 +wide-insurance-no-weights-l2-tweedie-p=1.5,600000,1.0,0.001,h2o,csr,16,4,True,0.44124269485473633,0.11031067371368408,6.139155262269141,744.3216370278548,4201.671243232769,307,62.48833863962389,2.0179922814346227,0.033371601172602405 +wide-insurance-no-weights-l2-tweedie-p=1.5,600000,1.0,0.001,sklearn,csr,16,4,True,0.40207982063293457,0.10051995515823364,5.234959208923594,165.78914099428476,168.05347942387414,307,60.545101894698455,0.07475553650918698,0.0012362346341855065 +wide-insurance-no-weights-lasso-gamma,600000,1.0,0.001,glmnet,csc,16,13,True,3.3504488468170166,0.2577268343705397,7.46224938371326,8.859356141148092,1.7842207173423752,99,8.503279647852633,0.000534436681810746,6.285460384119336e-05 +wide-insurance-no-weights-lasso-gamma,600000,1.0,0.001,glum,auto,16,3,True,0.11461615562438965,0.03820538520812988,7.458122366966873,8.684006630320807,1.5461967029042714,102,8.502745211170822,0.0,0.0 +wide-insurance-no-weights-lasso-gamma,600000,1.0,0.001,h2o,csr,16,6,True,0.4316740036010742,0.0719456672668457,7.4847094735341555,54.27932309918353,33.15468321316712,236,8.541015702338594,0.038270491167772036,0.0045009570694289 +wide-insurance-no-weights-lasso-gamma,600000,1.0,0.001,skglm,csc,16,14,True,1.7817778587341309,0.1272698470524379,7.46250264976596,8.420354852725525,1.5238212585851185,101,8.503189604787723,0.0004443936169007401,5.226472225898292e-05 +wide-insurance-no-weights-lasso-poisson,600000,1.0,0.001,glmnet,csc,16,35,True,0.6393070220947266,0.018265914916992188,-1.2016336251262212,29.50430279033865,25.854697961952194,72,0.5354118641420852,0.029306272336029004,0.057905450582848746 +wide-insurance-no-weights-lasso-poisson,600000,1.0,0.001,glum,auto,16,4,True,0.13782000541687012,0.03445500135421753,-1.6376130413201526,12.945220683347127,7.6044806141628865,68,0.5061055918060562,0.0,0.0 +wide-insurance-no-weights-lasso-poisson,600000,1.0,0.001,h2o,csr,16,10,True,1.0691649913787842,0.10691649913787842,-0.8674638880956712,167.9287752912917,251.88478879881305,237,0.6485113100703431,0.14240571826428694,0.281375508529963 +wide-insurance-no-weights-lasso-poisson,600000,1.0,0.001,skglm,csc,16,7,True,0.5816588401794434,0.08309412002563477,-1.1815352275871067,30.224859400419145,27.1458984647938,72,0.5353902095270632,0.02928461772100699,0.05786266382970354 +wide-insurance-no-weights-lasso-tweedie-p=1.5,600000,1.0,0.001,glmnet,csc,16,43,True,5.714865207672119,0.1329038420388865,4.948554147628833,170.51970522098122,1596.4954098238238,216,4807.155999355063,4746.616640368523,78.40546579662097 +wide-insurance-no-weights-lasso-tweedie-p=1.5,600000,1.0,0.001,glum,auto,16,6,True,0.25235986709594727,0.04205997784932455,4.849913246384293,106.6426963429158,90.34045028464394,221,60.53935898654003,0.0,0.0 +wide-insurance-no-weights-lasso-tweedie-p=1.5,600000,1.0,0.001,h2o,csr,16,4,True,0.6433579921722412,0.1608394980430603,6.027234481995983,663.2736278950679,3183.581053847547,307,61.05160611278306,0.5122471262430324,0.008461389991871609 +wide-insurance-no-weights-net-gamma,600000,1.0,0.001,glmnet,csc,16,12,True,3.444072961807251,0.28700608015060425,7.458395665026475,12.939078318809774,2.5748305149785495,146,8.498529135053142,0.0005807860978261914,6.834427252050662e-05 +wide-insurance-no-weights-net-gamma,600000,1.0,0.001,glum,auto,16,4,True,0.13893795013427734,0.034734487533569336,7.455068648663861,12.875594502991692,2.4560071246395543,143,8.497948348955315,0.0,0.0 +wide-insurance-no-weights-net-gamma,600000,1.0,0.001,h2o,csr,16,8,True,0.6425271034240723,0.08031588792800903,7.523323102848009,59.36067733566276,37.88155822867532,247,8.52570184370091,0.02775349474559441,0.003265905322783734 +wide-insurance-no-weights-net-gamma,600000,1.0,0.001,skglm,csc,16,14,True,1.6589100360870361,0.11849357400621686,7.455097803317209,12.686591376120965,2.389422446212486,143,8.498492993780213,0.0005446448248971336,6.40913315228715e-05 +wide-insurance-no-weights-net-poisson,600000,1.0,0.001,glmnet,csc,16,23,True,0.5674479007720947,0.024671647859656292,-1.2223250810811517,36.547152691537754,28.8080120293397,110,0.5259020484653575,0.02564064353922102,0.0512544907257174 +wide-insurance-no-weights-net-poisson,600000,1.0,0.001,glum,auto,16,4,True,0.13807320594787598,0.034518301486968994,-1.8385831511071609,19.51596800880474,8.706242262103284,112,0.5002614049261365,0.0,0.0 +wide-insurance-no-weights-net-poisson,600000,1.0,0.001,h2o,csr,16,8,True,0.8753888607025146,0.10942360758781433,-0.8519450124948105,206.26641860642525,341.01656750197054,258,0.6683705239819937,0.16810911905585724,0.3360425517548741 +wide-insurance-no-weights-net-poisson,600000,1.0,0.001,skglm,csc,16,7,True,0.6095468997955322,0.08707812854221889,-1.21743221130842,36.82728959106525,29.28998588561841,110,0.5258960468505254,0.025634641924388912,0.05124249376818078 +wide-insurance-no-weights-net-tweedie-p=1.5,600000,1.0,0.001,glmnet,csc,16,58,True,6.865434885025024,0.11836956698319008,4.988734364525118,142.98299162257783,164.7252477402239,236,60.600447635193824,0.0935340943596259,0.001545841440028216 +wide-insurance-no-weights-net-tweedie-p=1.5,600000,1.0,0.001,glum,auto,16,5,True,0.2799680233001709,0.05599360466003418,4.7406153728234575,111.25673909142866,86.38619730557012,238,60.5069135408342,0.0,0.0 +wide-insurance-no-weights-net-tweedie-p=1.5,600000,1.0,0.001,h2o,csr,16,4,True,0.453563928604126,0.1133909821510315,6.08371556563129,700.0209332488993,3616.519892470872,307,61.64195875151962,1.1350452106854192,0.01875893421533414 diff --git a/glum_benchmarks/run_benchmarks.py b/glum_benchmarks/run_benchmarks.py new file mode 100644 index 000000000..cfec17dfb --- /dev/null +++ b/glum_benchmarks/run_benchmarks.py @@ -0,0 +1,1321 @@ +#!/usr/bin/env python +""" +Benchmark runner script for comparing GLM libraries. + +Usage: + pixi run -e benchmark run-benchmarks + +Configuration: + Edit config.yaml to configure which benchmarks to run. Use param_grid to + specify combinations of libraries, datasets, regularizations, distributions, + and alphas. You can also control which steps to run (run_benchmarks, + analyze_results, generate_plots, update_docs). + +Output: + - glum_benchmarks/results/RUN_NAME/pickles/: Pickle files with detailed results + - glum_benchmarks/results/RUN_NAME/figures/: PNG plots comparing library performance + - glum_benchmarks/results/RUN_NAME/results.csv: Summary CSV for reproducibility +""" + +from __future__ import annotations + +import pickle +import re +import shutil +import warnings +from contextlib import nullcontext +from itertools import product +from pathlib import Path +from typing import Literal, cast + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +from matplotlib.patches import Patch +from pydantic import ( + BaseModel, + ConfigDict, + Field, + model_validator, +) +from rich import box +from rich.console import Console +from rich.table import Table +from ruamel.yaml import YAML + +from glum_benchmarks.problems import get_all_problems +from glum_benchmarks.util import ( + BenchmarkParams, + execute_problem_library, + get_all_libraries, + get_params_from_fname, +) + +_RICH_CONSOLE = Console() + +# Type aliases for configuration options +Library = Literal["glum", "sklearn", "h2o", "skglm", "celer", "zeros", "glmnet"] +Dataset = Literal[ + "intermediate-insurance", + "intermediate-housing", + "narrow-insurance", + "wide-insurance", + "simulated-glm", + "categorical-simulated", +] +Regularization = Literal["lasso", "l2", "net"] +Alpha = float # Valid values: 0.0001, 0.001, 0.01 +ALPHA_VALUES = (0.001, 0.01, 0.1) +Distribution = Literal["gaussian", "gamma", "binomial", "poisson", "tweedie-p=1.5"] +StorageFormat = Literal["auto", "dense", "cat", "csr", "csc"] + +# Internal benchmark-only distribution restrictions by dataset. +# This is intentionally not config-exposed, so test suites that rely on +# get_all_problems() remain unaffected. +ALLOWED_DISTRIBUTIONS_BY_DATASET: dict[Dataset, set[Distribution]] = { + "intermediate-housing": {"gaussian", "gamma"}, + "intermediate-insurance": {"gamma", "poisson", "tweedie-p=1.5"}, + "narrow-insurance": {"gamma", "poisson", "tweedie-p=1.5"}, + "wide-insurance": {"gamma", "poisson", "tweedie-p=1.5"}, + "simulated-glm": {"binomial", "gaussian", "gamma", "poisson"}, + "categorical-simulated": {"binomial", "gamma", "poisson"}, +} + + +class ParamGridEntry(BaseModel): + """A single entry in the parameter grid. + + Each entry specifies a set of parameter values. The Cartesian product + is computed within each entry, but entries are unioned (not crossed). + """ + + model_config = ConfigDict(extra="forbid") + + libraries: list[Library] | None = Field( + default=None, description="Libraries to benchmark (None = all)" + ) + datasets: list[Dataset] | None = Field( + default=None, description="Datasets to include (None = all)" + ) + regularizations: list[Regularization] | None = Field( + default=None, description="Regularization types (None = all)" + ) + alphas: list[Alpha] | None = Field( + default=None, description="Per-observation alpha values (None = all)" + ) + num_rows: list[int | None] | None = Field( + default=None, + description=( + "Default is None; None means full dataset. " + "Inside the list, null means full dataset." + ), + ) + k_over_n_ratios: list[float] | None = Field( + default=None, + description=( + "Feature-to-row ratios (K/N) for simulated-glm. Ignored for other datasets." + ), + ) + distributions: list[Distribution] | None = Field( + default=None, description="Distributions (None = all)" + ) + + +class BenchmarkConfig(BaseModel): + """Configuration for benchmark runs.""" + + model_config = ConfigDict( + arbitrary_types_allowed=True, + extra="forbid", + validate_default=True, + ) + + # Steps to run + run_benchmarks: bool = Field(default=True, description="Whether to run benchmarks") + analyze_results: bool = Field( + default=True, description="Whether to analyze and print results to CSV" + ) + generate_plots: bool = Field( + default=True, description="Whether to generate comparison plots" + ) + update_docs: bool = Field( + default=False, + description="Whether to copy figures to docs/_static and update benchmarks.rst", + ) + docs_figures: list[list[str]] | None = Field( + default=None, + description=( + "Figure groups for docs/benchmarks.rst. Each inner list maps to one " + "BENCHMARK_FIGURES_START/END block (in order). None = all figures in " + "a single block." + ), + ) + readme_figures: list[str] | None = Field( + default=None, + description="Figure names to include in README.md (None = first figure only)", + ) + + # Output settings + run_name: str = Field( + default="docs", description="Subfolder name within results/ directory" + ) + clear_output: bool = Field( + default=True, description="Clear entire run_name directory before running" + ) + + # Parameter grid for benchmark selection + # Each entry in the list specifies a parameter set. + # Within each entry: Cartesian product of the lists. + # Across entries: Union (not product). + param_grid: list[ParamGridEntry] = Field( + default_factory=lambda: [ParamGridEntry()], + description="List of parameter sets. Each entry defines a Cartesian product, " + "entries are unioned. Default runs all combinations.", + ) + + # Benchmark settings + num_threads: int = Field( + default=16, ge=1, description="Number of threads for parallel execution" + ) + iterations: int = Field( + default=3, + ge=1, + description=( + "Run each benchmark N times. When >= 2, the first iteration is " + "discarded as warmup and the median of the rest is reported." + ), + ) + timeout: int = Field( + default=100, ge=1, description="Timeout in seconds per benchmark run" + ) + storage: dict[Library, StorageFormat] = Field( + default_factory=dict, + description="Storage format per library (missing libraries default to 'dense')", + ) + + # Path for computing derived paths + script_dir: Path = Field( + default=Path("."), + description="Directory containing config.yaml", + ) + + @property + def results_dir(self) -> Path: + """Directory for all results.""" + return self.script_dir / "results" / self.run_name + + @property + def pickle_dir(self) -> Path: + """Directory for pickle files.""" + return self.results_dir / "pickles" + + @property + def figure_dir(self) -> Path: + """Directory for generated figures.""" + return self.results_dir / "figures" + + @property + def csv_file(self) -> Path: + """Path to results CSV file.""" + return self.results_dir / "results.csv" + + @property + def docs_static_dir(self) -> Path: + """Directory for docs static files (figures).""" + return self.script_dir.parent / "docs" / "_static" + + @property + def index_rst(self) -> Path: + """Path to index.rst docs file.""" + return self.script_dir.parent / "docs" / "index.rst" + + @property + def benchmarks_rst(self) -> Path: + """Path to benchmarks.rst docs file.""" + return self.script_dir.parent / "docs" / "benchmarks.rst" + + @property + def readme_file(self) -> Path: + """Path to README.md file.""" + return self.script_dir.parent / "README.md" + + @model_validator(mode="after") + def validate_config(self) -> BenchmarkConfig: + """Validate cross-field constraints.""" + # Ensure run_name is not empty + if not self.run_name or not self.run_name.strip(): + raise ValueError("run_name cannot be empty") + return self + + @classmethod + def from_yaml(cls, yaml_path: Path) -> BenchmarkConfig: + """Load configuration from YAML file.""" + yaml = YAML(typ="safe", pure=True) + with open(yaml_path) as f: + data = yaml.load(f) + + # Add script_dir for path computation + data["script_dir"] = yaml_path.parent + + return cls.model_validate(data) + + +def _parse_problem_name(name: str) -> tuple[str, str, str]: + """Parse problem name into (dataset, regularization, distribution). + + Problem name format: {dataset}-no-weights-{regularization}-{distribution} + where dataset is 2 parts (e.g., "intermediate-insurance"), + regularization is 1 part (lasso/l2/net), + and distribution may contain hyphens (e.g., "tweedie-p=1.5"). + """ + parts = name.split("-") + dataset = "-".join(parts[:2]) + reg = parts[4] + dist = "-".join(parts[5:]) + return dataset, reg, dist + + +def get_benchmark_combinations( + config: BenchmarkConfig, +) -> list[tuple[str, str, float, int | None, float]]: + """Get list of (problem_name, library, alpha, num_rows, k_over_n_ratio) tuples. + + Cartesian product within each entry, union across entries. + + Returns: + List of (problem_name, library_name, alpha, num_rows, k_over_n_ratio) + tuples to run. + """ + + all_problems = get_all_problems() + available_libraries = list(get_all_libraries()) + default_libraries = [lib for lib in available_libraries if lib != "zeros"] + + # Filter to "-no-weights-" problems only + base_problems = [name for name in all_problems.keys() if "-no-weights-" in name] + + # Build a lookup: (dataset, reg, dist) -> problem_name + problem_lookup = {} + for name in base_problems: + dataset, reg, dist = _parse_problem_name(name) + problem_lookup[(dataset, reg, dist)] = name + + # Get all valid values for each dimension + all_datasets = sorted({_parse_problem_name(n)[0] for n in base_problems}) + all_regs = sorted({_parse_problem_name(n)[1] for n in base_problems}) + all_dists = sorted({_parse_problem_name(n)[2] for n in base_problems}) + all_alphas = list(ALPHA_VALUES) + allowed_dist_by_dataset = ALLOWED_DISTRIBUTIONS_BY_DATASET + + combinations: set[tuple[str, str, float, int | None, float]] = set() + + # Print configuration per parameter grid entry + print("=" * 70) + print("BENCHMARK CONFIGURATION") + print("=" * 70) + for i, entry in enumerate(config.param_grid, 1): + # Use entry values or defaults (all) + libraries = entry.libraries if entry.libraries else default_libraries + datasets = entry.datasets if entry.datasets else all_datasets + regs = entry.regularizations if entry.regularizations else all_regs + dists = entry.distributions if entry.distributions else all_dists + alphas = entry.alphas if entry.alphas else all_alphas + num_rows_values = entry.num_rows if entry.num_rows is not None else [None] + k_over_n_ratios = ( + entry.k_over_n_ratios if entry.k_over_n_ratios is not None else [1.0] + ) + + print(f"Parameter Set {i}:") + print(f" Libraries: {libraries}") + print(f" Datasets: {datasets}") + print(f" Regularizations: {regs}") + print(f" Distributions: {dists}") + print(f" Alphas: {alphas}") + print(f" num_rows: {num_rows_values}") + print(f" K/N ratios (simulated-glm): {k_over_n_ratios}") + print() + + # Cartesian product within this entry + for lib, dataset, reg, dist, alpha, num_rows in product( + libraries, datasets, regs, dists, alphas, num_rows_values + ): + dataset_dist_allowlist = allowed_dist_by_dataset.get(cast(Dataset, dataset)) + if ( + dataset_dist_allowlist is not None + and dist not in dataset_dist_allowlist + ): + continue + key = (dataset, reg, dist) + if key in problem_lookup: + # Only add if library is actually available + if lib in available_libraries: + ratios_for_dataset = ( + k_over_n_ratios if dataset == "simulated-glm" else [1.0] + ) + for k_over_n_ratio in ratios_for_dataset: + combinations.add( + (problem_lookup[key], lib, alpha, num_rows, k_over_n_ratio) + ) + + print(f"Total benchmark runs: {len(combinations)}") + print("=" * 70) + print() + + return sorted( + combinations, + key=lambda combo: ( + combo[0], # problem_name + combo[1], # library_name + combo[2], # alpha + -1 if combo[3] is None else combo[3], # num_rows (None first) + combo[4], # k_over_n_ratio + ), + ) + + +def run_single_benchmark( + problem_name: str, + library_name: str, + alpha: float, + num_rows: int | None, + k_over_n_ratio: float, + config: BenchmarkConfig, +) -> tuple[dict, BenchmarkParams]: + """Run a single benchmark and return results. + + If the benchmark exceeds the configured timeout, returns a result with + runtime=timeout and timed_out=True. + """ + # Get library-specific settings from config + lib_key: Library = library_name # type: ignore[assignment] + storage = config.storage.get(lib_key, "dense") + + params = BenchmarkParams( + problem_name=problem_name, + library_name=library_name, + num_rows=num_rows, + k_over_n_ratio=k_over_n_ratio, + storage=storage, + threads=config.num_threads, + alpha=alpha, + ) + + # Pass timeout to execute_problem_library for per-iteration timeout handling + try: + result, _ = execute_problem_library( + params, + iterations=config.iterations, + diagnostics_level=None, + timeout=config.timeout, + ) + result["timed_out"] = False + except TimeoutError: + # All iterations timed out + result = { + "runtime": float(config.timeout), + "timed_out": True, + "intercept": None, + "coef": None, + "n_iter": None, + } + + return result, params + + +def run_all_benchmarks(config: BenchmarkConfig): + """Run all configured benchmarks.""" + # Set up output directory + if config.clear_output and config.results_dir.exists(): + print(f"Clearing output directory: {config.results_dir}") + shutil.rmtree(config.results_dir) + config.pickle_dir.mkdir(parents=True, exist_ok=True) + + # Get benchmark combinations (problem, library, alpha, num_rows, k_over_n_ratio) + # from param_grid. + combinations = get_benchmark_combinations(config) + + total = len(combinations) + current = 0 + + for problem_name, library_name, alpha, num_rows, k_over_n_ratio in combinations: + current += 1 + rows_label = ( + f", num_rows={num_rows}" if num_rows is not None else ", num_rows=full" + ) + ratio_label = ( + f", K/N={k_over_n_ratio:g}" + if problem_name.startswith("simulated-glm-") + else "" + ) + label = f"{library_name} / {problem_name} (α={alpha}{rows_label}{ratio_label})" + print(f"[{current}/{total}] {label}", end=" ", flush=True) + + try: + # Capture warnings to display after the result + with warnings.catch_warnings(record=True) as caught_warnings: + warnings.simplefilter("always") + # Ignore deprecation warnings from third-party libraries + warnings.filterwarnings( + "ignore", + message=".*asyncio.iscoroutinefunction.*", + ) + result, params = run_single_benchmark( + problem_name, + library_name, + alpha, + num_rows, + k_over_n_ratio, + config, + ) + + # Save result + fname = params.get_result_fname() + ".pkl" + with open(config.pickle_dir / fname, "wb") as f: + pickle.dump(result, f) + + if result.get("timed_out"): + print(f"-> TIMEOUT ({config.timeout}s)") + elif len(result) > 0 and "runtime" in result: + print(f"-> {result['runtime']:.4f}s") + else: + print("-> (skipped)") + + # Print captured warnings + for w in caught_warnings: + print(f" Warning: {w.message}") + + except Exception as e: + print(f"-> ERROR: {e}") + + +def analyze_results(config: BenchmarkConfig) -> pd.DataFrame: + """Analyze benchmark results and print summary.""" + print() + print("=" * 60) + print("ANALYZING RESULTS") + print("=" * 60) + + display_precision = 4 + np.set_printoptions(precision=display_precision, suppress=True) + pd.set_option("display.precision", display_precision) + + results = [] + + for fname in config.pickle_dir.glob("*.pkl"): + with open(fname, "rb") as f: + data = pickle.load(f) + + if not data: + continue + + params = get_params_from_fname(fname.name) + if params.problem_name is None: + continue + problem = get_all_problems()[params.problem_name] + + # Handle timed out runs (no coef) + timed_out = data.get("timed_out", False) + coefs = data.get("coef") + + if coefs is None and not timed_out: + # Skipped run (not supported by library) + continue + + # Calculate runtime per iteration + n_iter = data.get("n_iter") + runtime = data.get("runtime") + if n_iter is not None and n_iter > 0: + runtime_per_iter = runtime / n_iter + else: + runtime_per_iter = runtime + + # Calculate coefficient norms (0 for timed out) + if coefs is not None: + l1_norm: float = np.sum(np.abs(coefs)) + l2_norm: float = np.sum(coefs**2) + num_nonzero_coef: int = np.sum(np.abs(coefs) > 1e-8) + else: + l1_norm = 0.0 + l2_norm = 0.0 + num_nonzero_coef = 0 + + # Get regularization strength from params or problem default + alpha = problem.alpha if params.alpha is None else params.alpha + + # Check convergence: + # 1. timed_out=True means we hit the benchmark timeout + # 2. n_iter >= max_iter means the library hit its internal iteration limit + max_iter = data.get("max_iter") + hit_max_iter = ( + n_iter is not None and max_iter is not None and n_iter >= max_iter + ) + converged = not timed_out and not hit_max_iter + + results.append( + { + "problem_name": params.problem_name, + "library_name": params.library_name, + "num_rows": data.get("num_rows"), + "k_over_n_ratio": params.k_over_n_ratio, + "alpha": alpha, + "storage": params.storage, + "threads": params.threads, + "n_iter": n_iter, + "converged": converged, + "runtime": runtime, + "runtime per iter": runtime_per_iter, + "intercept": data.get("intercept"), + "l1": l1_norm, + "l2": l2_norm, + "num_nonzero_coef": num_nonzero_coef, + "obj_val": data.get("obj_val"), + } + ) + + if not results: + print("No results found!") + return pd.DataFrame() + + df = pd.DataFrame(results) + + # Format: set index and sort + problem_id_cols = ["problem_name", "num_rows", "k_over_n_ratio", "alpha"] + df = df.set_index(problem_id_cols).sort_values("library_name").sort_index() + + # Calculate objective gaps within each + # (problem_name, num_rows, k_over_n_ratio, alpha) group. + # Use transform to preserve alignment when index has duplicates. + best_obj = df.groupby(level=problem_id_cols)["obj_val"].transform("min") + df["obj_gap"] = df["obj_val"] - best_obj + + # rel_obj_val is a true relative gap from best objective: + # (obj_val - best_obj) / abs(best_obj) + # If best_obj is exactly 0, mark rows as NaN except exact ties, which are 0. + safe_denom = best_obj.abs().replace(0, np.nan) + df["rel_obj_val"] = df["obj_gap"] / safe_denom + df.loc[df["obj_gap"] == 0, "rel_obj_val"] = 0.0 + + # Display columns + cols_to_show = [ + "library_name", + "storage", + "threads", + "n_iter", + "runtime", + "intercept", + "num_nonzero_coef", + "obj_val", + "obj_gap", + "rel_obj_val", + ] + + # Group by problem_name and k_over_n_ratio so simulated-glm gets one table + # per K/N value, while alpha/num_rows remain row values. + table_df = df.loc[:, cols_to_show].reset_index() + _print_dataframe_table( + table_df, + title="Benchmark summary", + group_by=["problem_name", "k_over_n_ratio"], + ) + + # Export to CSV for figure generation and reproducibility + config.csv_file.parent.mkdir(parents=True, exist_ok=True) + df.reset_index().to_csv(config.csv_file, index=False) + print(f"\nExported results to: {config.csv_file}") + + return df.reset_index() + + +def _print_dataframe_table( + df: pd.DataFrame, + *, + title: str, + max_rows: int | None = None, + group_by: list[str] | None = None, +) -> None: + """Print a DataFrame as a rich table.""" + rows = df.copy() if max_rows is None else df.head(max_rows).copy() + grouping_cols = [column for column in (group_by or []) if column in rows.columns] + if grouping_cols: + # Sort comparisons by problem settings first, use library as tie-breaker + compare_cols = [ + c + for c in ["alpha", "k_over_n_ratio", "num_rows", "library_name"] + if c in rows.columns and c not in grouping_cols + ] + sort_cols = grouping_cols + compare_cols + rows = rows.sort_values(sort_cols, kind="mergesort") + + def format_cell(value: object) -> str: + is_na = pd.isna(value) + if isinstance(is_na, (bool, np.bool_)) and is_na: + return "" + if isinstance(value, float): + return f"{value:.6g}" + return str(value) + + def render_table(table_df: pd.DataFrame, table_title: str) -> None: + table = Table( + title=table_title, + box=box.SIMPLE_HEAVY, + header_style="bold cyan", + row_styles=["", "dim"], + show_lines=False, + ) + for column in table_df.columns: + justify = ( + "right" if pd.api.types.is_numeric_dtype(table_df[column]) else "left" + ) + table.add_column(str(column), justify=justify, overflow="fold") + + for row in table_df.itertuples(index=False, name=None): + table.add_row(*[format_cell(value) for value in row]) + + _RICH_CONSOLE.print(table) + + if not grouping_cols: + render_table(rows, title) + else: + value_cols = [column for column in rows.columns if column not in grouping_cols] + for group_values, group_df in rows.groupby( + grouping_cols, sort=False, dropna=False + ): + if not isinstance(group_values, tuple): + group_values = (group_values,) + group_items = dict(zip(grouping_cols, group_values)) + problem_name = str(group_items.get("problem_name", "")) + is_simulated_glm = problem_name.startswith("simulated-glm-") + group_label_parts = [] + for column, value in zip(grouping_cols, group_values): + if column == "k_over_n_ratio" and not is_simulated_glm: + continue + group_label_parts.append(f"{column}={format_cell(value)}") + group_label = ", ".join(group_label_parts) + if not isinstance(group_df, pd.DataFrame): + continue + render_table(group_df.loc[:, value_cols], f"{title} - {group_label}") + + if max_rows is not None and len(df) > max_rows: + _RICH_CONSOLE.print(f"[dim]Showing first {max_rows} of {len(df)} rows.[/dim]") + + +def _render_bar_chart( + pivot: pd.DataFrame, + unsupported: pd.DataFrame, + not_converged: pd.DataFrame, + colors: dict, + y_max: float, + title: str, + ylabel: str, + dark_mode: bool = False, + x_labels: list[str] | None = None, + show_baseline: bool = False, +) -> plt.Figure: + """Render a bar chart with support for light/dark mode.""" + # Apply dark mode style if requested + style_context = plt.style.context("dark_background") if dark_mode else nullcontext() + + with style_context: + plot_colors = [colors.get(lib, "#999999") for lib in pivot.columns] + pivot_clipped = pivot.clip(upper=y_max) + pivot_clipped = pivot_clipped.mask(not_converged, y_max) + + fig, ax = plt.subplots(figsize=(10, 5)) + pivot_clipped.plot(kind="bar", ax=ax, color=plot_colors) + + # Colors for dark mode + na_bg = "#1a1a1a" if dark_mode else "white" + na_text = "#888888" if dark_mode else "#666666" + nc_edge = "white" if dark_mode else "black" + nc_text = "#ff6666" if dark_mode else "#cc0000" + arrow_color = "white" if dark_mode else "black" + + # The edge stroke is centred on the bar boundary, so half the + # linewidth extends above the geometric top. Subtract that offset + # so hatched bars visually align with the clipped bars at y_max. + hatch_lw = 1 + inv = ax.transData.inverted() + lw_px = hatch_lw * fig.dpi / 72.0 + lw_offset = abs(inv.transform((0, lw_px / 2))[1] - inv.transform((0, 0))[1]) + + # Draw hatched bars for unsupported library/reg_combo combos + n_combos = len(pivot.index) + bars = ax.patches + for i, combo in enumerate(pivot.index): + for j, lib in enumerate(pivot.columns): + if unsupported.loc[combo, lib]: + bar_idx = j * n_combos + i + bar = bars[bar_idx] + x = bar.get_x() + width = bar.get_width() + lib_color = colors.get(lib, "#999999") + ax.bar( + x + width / 2, + y_max - lw_offset, + width=width, + color=na_bg, + edgecolor=lib_color, + linewidth=hatch_lw, + hatch="//", + ) + ax.text( + x + width / 2, + y_max / 2, + "N/A", + ha="center", + va="center", + fontsize=8, + color=na_text, + fontweight="bold", + ) + elif not_converged.loc[combo, lib]: + bar_idx = j * n_combos + i + bar = bars[bar_idx] + x = bar.get_x() + width = bar.get_width() + ax.bar( + x + width / 2, + y_max - lw_offset, + width=width, + color="none", + edgecolor=nc_edge, + linewidth=hatch_lw, + hatch="//", + alpha=0.5, + ) + ax.text( + x + width / 2, + y_max + y_max * 0.02, + "NC", + ha="center", + va="bottom", + fontsize=7, + color=nc_text, + fontweight="bold", + ) + + # Add annotations for clipped bars + for i, combo in enumerate(pivot.index): + for j, lib in enumerate(pivot.columns): + original_val = pivot.loc[combo, lib] + if ( + original_val > y_max + and not unsupported.loc[combo, lib] + and not not_converged.loc[combo, lib] + ): + bar_idx = j * n_combos + i + bar = bars[bar_idx] + x = bar.get_x() + bar.get_width() / 2 + # Format: use .1f for normalized (ratios), .4f for absolute + fmt = ( + f"{original_val:.1f}x" + if show_baseline + else f"{original_val:.4f}" + ) + ax.text( + x, + y_max * 0.75, + fmt, + ha="center", + va="center", + fontsize=9, + fontweight="bold", + rotation=90, + color=arrow_color, + ) + ax.annotate( + "", + xy=(x, y_max), + xytext=(x, y_max * 0.88), + arrowprops=dict(arrowstyle="->", color=arrow_color, lw=1.5), + ) + + ax.set_ylim(0, y_max * 1.08) + ax.set_title(title) + ax.set_ylabel(ylabel) + ax.set_xlabel("") + + if show_baseline: + ax.axhline(y=1.0, color="gray", linestyle="--", linewidth=1, alpha=0.7) + + # Legend + handles, labels = ax.get_legend_handles_labels() + handles.append(Patch(facecolor=na_bg, edgecolor="gray", hatch="//")) + labels.append("N/A (not supported)") + handles.append(Patch(facecolor=na_bg, edgecolor=nc_edge, hatch="//")) + labels.append("NC (not converged)") + ax.legend(handles, labels, title="", bbox_to_anchor=(1.02, 1), loc="upper left") + + if x_labels: + ax.set_xticklabels(x_labels) + plt.xticks(rotation=45, ha="right") + plt.tight_layout() + + return fig + + +def plot_results(config: BenchmarkConfig): + """Generate benchmark comparison plots from CSV file. + + Reads from CSV_FILE, which allows regenerating figures without + re-running benchmarks. The CSV can be committed to the repository. + """ + print() + print("=" * 60) + print("GENERATING PLOTS") + print("=" * 60) + + if not config.csv_file.exists(): + print(f"CSV file not found: {config.csv_file}") + print("Run ANALYZE_RESULTS first to generate the CSV.") + return + + df = pd.read_csv(config.csv_file) + print(f"Reading results from: {config.csv_file}") + + if df["converged"].dtype == object: # string type from CSV + df["converged"] = df["converged"] == "True" + + if df.empty: + print("No data to plot!") + return + + config.figure_dir.mkdir(exist_ok=True) + + # Extract distribution, regularization and dataset from problem_name + df = df.copy() + parsed = df["problem_name"].apply(_parse_problem_name) + df["dataset"] = parsed.apply(lambda x: x[0]) + reg_map = {"lasso": "lasso", "l2": "ridge", "net": "elastic-net"} + df["regularization"] = parsed.apply(lambda x: reg_map.get(x[1], x[1])) + df["distribution"] = parsed.apply(lambda x: x[2]) + + # Create a combined regularization column (type + strength) + df["reg_combo"] = df.apply( + lambda row: f"{row['regularization']} (α={row['alpha']})", + axis=1, + ) + + # Drop duplicates (keep latest result for each unique combo) + dedupe_cols = [ + "dataset", + "distribution", + "regularization", + "alpha", + "library_name", + ] + if "k_over_n_ratio" in df.columns: + dedupe_cols.append("k_over_n_ratio") + df = df.drop_duplicates(subset=dedupe_cols, keep="last") + + # Ensure library colors are consistent across plots + colors = { + "glum": "#a6cee3", + "h2o": "#fdbf6f", + "glmnet": "#b15928", + "sklearn": "#1f78b4", + "skglm": "#33a02c", + "celer": "#fb9a99", + } + + # Generate one plot per dataset/distribution combo. For simulated-glm, + # split plots by K/N ratio so each figure shows a single ratio. + for dataset in df["dataset"].unique(): + for dist in df["distribution"].unique(): + base_subset = df[(df["dataset"] == dataset) & (df["distribution"] == dist)] + if base_subset.empty: + continue + + split_by_ratio = ( + dataset == "simulated-glm" and "k_over_n_ratio" in df.columns + ) + ratio_groups = ( + base_subset.groupby("k_over_n_ratio", sort=True, dropna=False) + if split_by_ratio + else [(None, base_subset)] + ) + + for ratio_value, subset in ratio_groups: + if subset.empty: + continue + + ratio_suffix = "" + ratio_title_suffix = "" + if split_by_ratio: + ratio_text = f"{float(ratio_value):g}" + ratio_suffix = f"-k-over-n-{ratio_text}" + ratio_title_suffix = f" (K/N={ratio_text})" + + print(f" Plotting {dataset}-{dist}{ratio_suffix}: {len(subset)} rows") + print(f" reg_combos: {subset['reg_combo'].unique().tolist()}") + + # Pivot for plotting: reg_combo on x-axis, libraries as bars + pivot_raw = subset.pivot( + index="reg_combo", + columns="library_name", + values="runtime", + ) + # Track which cells are unsupported (NaN) before filling + unsupported = pivot_raw.isna() + pivot = pivot_raw.fillna(0) + + # Track which cells did not converge + pivot_converged = ( + subset.pivot( + index="reg_combo", + columns="library_name", + values="converged", + ) + .fillna(True) + .astype(bool) + ) + not_converged = ~pivot_converged + + # Calculate y-axis limit (10x fastest runtime) + min_runtime = pivot.values[pivot.values > 0].min() + y_max = min_runtime * 10 + + # Title + title_dataset = dataset.replace(" ", "-").title() + title_dist = dist.replace(" ", "-").title() + title = f"{title_dataset}-{title_dist}{ratio_title_suffix}" + + # Determine if this figure needs a dark mode version (for README) + fname = f"{dataset}-{dist}{ratio_suffix}" + readme_figs = config.readme_figures or [] + needs_dark = f"{fname}.png" in readme_figs + + # Generate light mode plot + fig = _render_bar_chart( + pivot=pivot, + unsupported=unsupported, + not_converged=not_converged, + colors=colors, + y_max=y_max, + title=title, + ylabel="run time (s)", + dark_mode=False, + ) + fig.savefig(config.figure_dir / f"{fname}.png", dpi=300) + plt.close(fig) + print(f"Saved: {fname}.png") + + # Generate dark mode version if needed for README + if needs_dark: + fig = _render_bar_chart( + pivot=pivot, + unsupported=unsupported, + not_converged=not_converged, + colors=colors, + y_max=y_max, + title=title, + ylabel="run time (s)", + dark_mode=True, + ) + fig.savefig(config.figure_dir / f"{fname}_dark.png", dpi=300) + plt.close(fig) + print(f"Saved: {fname}_dark.png") + + # Generate normalized plot (glum = 1.0) + if "glum" in pivot.columns: + pivot_norm = pivot.div(pivot["glum"], axis=0) + norm_y_max = 10.0 + + # X-tick labels with glum runtime + x_labels = [] + for combo in pivot.index: + glum_runtime = pivot.loc[combo, "glum"] + if glum_runtime > 0: + x_labels.append(f"{combo}\n(glum = {glum_runtime:.3f}s)") + else: + x_labels.append(combo) + + fname_norm = f"{dataset}-{dist}{ratio_suffix}-normalized" + needs_dark_norm = f"{fname_norm}.png" in readme_figs + + # Generate light mode normalized plot + fig = _render_bar_chart( + pivot=pivot_norm, + unsupported=unsupported, + not_converged=not_converged, + colors=colors, + y_max=norm_y_max, + title=f"{title} (normalized)", + ylabel="run time relative to glum", + dark_mode=False, + x_labels=x_labels, + show_baseline=True, + ) + fig.savefig(config.figure_dir / f"{fname_norm}.png", dpi=300) + plt.close(fig) + print(f"Saved: {fname_norm}.png") + + # Generate dark mode version if needed for README + if needs_dark_norm: + fig = _render_bar_chart( + pivot=pivot_norm, + unsupported=unsupported, + not_converged=not_converged, + colors=colors, + y_max=norm_y_max, + title=f"{title} (normalized)", + ylabel="run time relative to glum", + dark_mode=True, + x_labels=x_labels, + show_baseline=True, + ) + fig.savefig( + config.figure_dir / f"{fname_norm}_dark.png", dpi=300 + ) + plt.close(fig) + print(f"Saved: {fname_norm}_dark.png") + + +# Markers for auto-generated content in docs +RST_START_MARKER = ".. BENCHMARK_FIGURES_START" +RST_END_MARKER = ".. BENCHMARK_FIGURES_END" +MD_START_MARKER = "" +MD_END_MARKER = "" + + +def update_docs(config: BenchmarkConfig): + """Copy figures to docs/_static and update index.rst, benchmarks.rst and README.md. + + Uses marker comments to safely replace only the auto-generated figure + references in each file. Figures to include can be specified via + docs_figures and readme_figures config options. + """ + print() + print("=" * 60) + print("UPDATING DOCS") + print("=" * 60) + + if not config.figure_dir.exists(): + print(f"Figure directory not found: {config.figure_dir}") + print("Run GENERATE_PLOTS first to create figures.") + return + + # Find all generated figures + all_figures = sorted(config.figure_dir.glob("*.png")) + if not all_figures: + print("No figures found to copy.") + return + + # Get available figure names (without path) + available = {f.name for f in all_figures} + + # Determine which figure groups to use for docs + # Each group maps to one BENCHMARK_FIGURES_START/END block in benchmarks.rst + if config.docs_figures is not None: + docs_figure_groups = [] + for group in config.docs_figures: + valid = [f for f in group if f in available] + missing = set(group) - available + if missing: + print(f"Warning: docs_figures not found: {missing}") + docs_figure_groups.append(valid) + else: + # Default: all generated figures in a single group + docs_figure_groups = [sorted(f.name for f in all_figures)] + + # Determine which figures to use for README + if config.readme_figures is not None: + # Use explicitly specified figures + readme_fig_names = [f for f in config.readme_figures if f in available] + missing = set(config.readme_figures) - available + if missing: + print(f"Warning: readme_figures not found: {missing}") + else: + # Default: first non-normalized figure only + non_norm = sorted(f.name for f in all_figures if "normalized" not in f.name) + readme_fig_names = [non_norm[0]] if non_norm else [] + + # Collect all figures needed for copying + all_docs_figs = {f for group in docs_figure_groups for f in group} + all_needed = all_docs_figs | set(readme_fig_names) + + # Copy figures to docs/_static + config.docs_static_dir.mkdir(parents=True, exist_ok=True) + for fig_name in sorted(all_needed): + src = config.figure_dir / fig_name + dest = config.docs_static_dir / fig_name + shutil.copy2(src, dest) + print(f"Copied: {fig_name} -> docs/_static/") + + # Update benchmarks.rst + # Each figure group replaces one BENCHMARK_FIGURES_START/END block (in order) + if any(docs_figure_groups): + if config.benchmarks_rst.exists(): + with open(config.benchmarks_rst) as f: + rst_content = f.read() + + block_pattern = re.compile( + rf"{re.escape(RST_START_MARKER)}.*?{re.escape(RST_END_MARKER)}", + re.DOTALL, + ) + blocks = list(block_pattern.finditer(rst_content)) + + if not blocks: + print(f"\nNo marker blocks found in {config.benchmarks_rst}") + print(f"Add: {RST_START_MARKER} and {RST_END_MARKER}") + else: + if len(docs_figure_groups) != len(blocks): + print( + f"\nWarning: {len(docs_figure_groups)} figure group(s) in " + f"config but {len(blocks)} marker block(s) in " + f"{config.benchmarks_rst}. " + f"Replacing min({len(docs_figure_groups)}, " + f"{len(blocks)}) blocks." + ) + + # Replace blocks from last to first to preserve positions + total_figs = 0 + for group, match in reversed(list(zip(docs_figure_groups, blocks))): + rst_lines = [RST_START_MARKER, ""] + for fig_name in group: + rst_lines.append(f".. image:: _static/{fig_name}") + rst_lines.append(" :width: 700") + rst_lines.append("") + rst_lines.append(RST_END_MARKER) + replacement = "\n".join(rst_lines) + rst_content = ( + rst_content[: match.start()] + + replacement + + rst_content[match.end() :] + ) + total_figs += len(group) + + with open(config.benchmarks_rst, "w") as f: + f.write(rst_content) + print(f"\nUpdated: {config.benchmarks_rst}") + print( + f"Replaced {min(len(docs_figure_groups), len(blocks))} block(s) " + f"with {total_figs} total figure references." + ) + else: + print(f"\nbenchmarks.rst not found: {config.benchmarks_rst}") + + # Update README.md + # Use tags with width for controlled display size and GitHub light/dark mode + if readme_fig_names: + md_lines = [MD_START_MARKER] + for fig_name in readme_fig_names: + # Check if dark mode version exists + base_name = fig_name.replace(".png", "") + dark_name = f"{base_name}_dark.png" + dark_exists = (config.figure_dir / dark_name).exists() + + if dark_exists: + # Copy dark version too + shutil.copy2( + config.figure_dir / dark_name, + config.docs_static_dir / dark_name, + ) + print(f"Copied: {dark_name} -> docs/_static/") + light_ref = f"docs/_static/{fig_name}#gh-light-mode-only" + dark_ref = f"docs/_static/{dark_name}#gh-dark-mode-only" + else: + light_ref = f"docs/_static/{fig_name}#gh-light-mode-only" + dark_ref = f"docs/_static/{fig_name}#gh-dark-mode-only" + + md_lines.append( + f'Benchmark results' + ) + md_lines.append( + f'Benchmark results' + ) + md_lines.append(MD_END_MARKER) + md_new_content = "\n".join(md_lines) + + if config.readme_file.exists(): + with open(config.readme_file) as f: + md_content = f.read() + + if MD_START_MARKER in md_content and MD_END_MARKER in md_content: + pattern = re.compile( + rf"{re.escape(MD_START_MARKER)}.*?{re.escape(MD_END_MARKER)}", + re.DOTALL, + ) + updated_md = pattern.sub(md_new_content, md_content) + with open(config.readme_file, "w") as f: + f.write(updated_md) + print(f"\nUpdated: {config.readme_file}") + print(f"Inserted {len(readme_fig_names)} figure references.") + else: + print(f"\nMarkers not found in {config.readme_file}") + print(f"Add: {MD_START_MARKER} and {MD_END_MARKER}") + else: + print(f"\nREADME.md not found: {config.readme_file}") + + # Update index.rst (headline figure, same as README) + if readme_fig_names: + idx_lines = [RST_START_MARKER, ""] + for fig_name in readme_fig_names: + idx_lines.append(f".. image:: _static/{fig_name}") + idx_lines.append(" :width: 600") + idx_lines.append("") + idx_lines.append(RST_END_MARKER) + idx_new_content = "\n".join(idx_lines) + + if config.index_rst.exists(): + with open(config.index_rst) as f: + idx_content = f.read() + + if RST_START_MARKER in idx_content and RST_END_MARKER in idx_content: + pattern = re.compile( + rf"{re.escape(RST_START_MARKER)}.*?{re.escape(RST_END_MARKER)}", + re.DOTALL, + ) + updated_idx = pattern.sub(idx_new_content, idx_content) + with open(config.index_rst, "w") as f: + f.write(updated_idx) + print(f"\nUpdated: {config.index_rst}") + print(f"Inserted {len(readme_fig_names)} headline figure(s).") + else: + print(f"\nMarkers not found in {config.index_rst}") + print(f"Add: {RST_START_MARKER} and {RST_END_MARKER}") + else: + print(f"\nindex.rst not found: {config.index_rst}") + + +def main(): + # Load configuration + script_dir = Path(__file__).parent + config_file = script_dir / "config.yaml" + config = BenchmarkConfig.from_yaml(config_file) + + # Run benchmark steps + if config.run_benchmarks: + run_all_benchmarks(config) + + if config.analyze_results: + analyze_results(config) + + if config.generate_plots: + plot_results(config) + + if config.update_docs: + update_docs(config) + + # Print summary + print() + print("=" * 60) + print("DONE") + print("=" * 60) + if config.run_benchmarks or config.analyze_results: + print(f"Results saved to: {config.results_dir}/") + if config.generate_plots: + print(f"Figures saved to: {config.figure_dir}/") + if config.update_docs: + print(f"Docs updated: {config.index_rst}, {config.benchmarks_rst}") + print(f"README updated: {config.readme_file}") + + # Snapshot config for reproducibility only when new benchmarks are run. + if config.run_benchmarks: + config.results_dir.mkdir(parents=True, exist_ok=True) + shutil.copy2(config_file, config.results_dir / "config.yaml") + print(f"Config snapshot saved to: {config.results_dir / 'config.yaml'}") + + +if __name__ == "__main__": + main() diff --git a/glum_benchmarks/tests/test_benchmarks.py b/glum_benchmarks/tests/test_benchmarks.py new file mode 100644 index 000000000..12438b192 --- /dev/null +++ b/glum_benchmarks/tests/test_benchmarks.py @@ -0,0 +1,125 @@ +""" +Basic tests for the benchmark infrastructure. +These tests verify the benchmark pipeline works, not specific problem results. +""" + +import numpy as np +import pytest + +from glum_benchmarks.problems import get_all_problems +from glum_benchmarks.util import ( + BenchmarkParams, + execute_problem_library, + get_all_libraries, + get_params_from_fname, + get_tweedie_p, +) + + +def test_get_all_problems_returns_dict(): + problems = get_all_problems() + assert isinstance(problems, dict) + assert len(problems) > 0 + + +def test_all_problems_have_required_attributes(): + problems = get_all_problems() + for name, problem in problems.items(): + assert hasattr(problem, "data_loader") + assert hasattr(problem, "distribution") + assert hasattr(problem, "alpha") + + +def test_get_all_libraries_returns_dict(): + libraries = get_all_libraries() + assert isinstance(libraries, dict) + + +def test_glum_always_available(): + libraries = get_all_libraries() + assert "glum" in libraries + + +def test_params_creation(): + params = BenchmarkParams( + problem_name="test-problem", + library_name="glum", + num_rows=100, + storage="dense", + threads=1, + alpha=0.01, + ) + assert params.problem_name == "test-problem" + assert params.library_name == "glum" + + +def test_get_result_fname(): + params = BenchmarkParams( + problem_name="test-problem", + library_name="glum", + num_rows=100, + storage="dense", + threads=1, + alpha=0.01, + ) + fname = params.get_result_fname() + assert "test-problem" in fname + assert "glum" in fname + + +def test_params_roundtrip(): + """Test that params can be serialized to filename and back.""" + params = BenchmarkParams( + problem_name="intermediate-housing-no-weights-lasso-gaussian", + library_name="glum", + num_rows=1000, + storage="dense", + threads=4, + alpha=0.001, + ) + fname = params.get_result_fname() + ".pkl" + recovered = get_params_from_fname(fname) + assert recovered.problem_name == params.problem_name + assert recovered.library_name == params.library_name + + +def test_execute_simple_problem(): + """Test that a simple problem runs without error.""" + problems = get_all_problems() + problem_name = next( + (n for n in problems if "gaussian" in n and "narrow" not in n), None + ) + if problem_name is None: + pytest.skip("No suitable test problem found") + + params = BenchmarkParams( + problem_name=problem_name, + library_name="glum", + num_rows=100, + storage="dense", + threads=1, + alpha=0.1, + ) + + result, _ = execute_problem_library(params, iterations=1) + + assert "coef" in result + assert "intercept" in result + assert "runtime" in result + assert isinstance(result["coef"], np.ndarray) + + +def test_get_tweedie_p_poisson(): + assert get_tweedie_p("poisson") == 1 + + +def test_get_tweedie_p_gamma(): + assert get_tweedie_p("gamma") == 2 + + +def test_get_tweedie_p_gaussian(): + assert get_tweedie_p("gaussian") == 0 + + +def test_get_tweedie_p_explicit(): + assert get_tweedie_p("tweedie-p=1.5") == 1.5 diff --git a/src/glum_benchmarks/util.py b/glum_benchmarks/util.py similarity index 50% rename from src/glum_benchmarks/util.py rename to glum_benchmarks/util.py index c023d2438..a64865ae5 100644 --- a/src/glum_benchmarks/util.py +++ b/glum_benchmarks/util.py @@ -1,11 +1,9 @@ -import glob import os -import shutil +import signal +import statistics import time -from functools import reduce -from typing import Callable, Optional, Union +from typing import Optional, Union -import click import numpy as np import pandas as pd import tabmat as tm @@ -18,29 +16,80 @@ cache_location = os.environ.get("GLM_BENCHMARKS_CACHE", None) -def runtime(f, iterations, *args, **kwargs): +def runtime(f, iterations, *args, timeout=None, **kwargs): """ - Measure how long it tales to run function f. + Measure how long it takes to run function f. + + When iterations >= 2, the first iteration is treated as warmup and + discarded. The median runtime of the remaining iterations is reported. + This avoids JIT/cache warmup effects and is more robust to outliers + than the minimum. + + When iterations == 1 (e.g. in tests), the single run is returned + directly with no warmup discard. Parameters ---------- f: function - iterations + iterations: int + Total number of times to run the function. Use >= 2 for + benchmarking (1 warmup + measured runs). Use 1 for tests. args: Passed to f + timeout: float, optional + Timeout in seconds for each iteration. kwargs: Passed to f Returns ------- - Tuple: (Minimimum runtime across iterations, output of f) + Tuple: (Median runtime after warmup, output from the run closest to + the median runtime) + If all iterations timeout, raises TimeoutError. """ - rs = [] - for _ in range(iterations): - start = time.time() - out = f(*args, **kwargs) - end = time.time() - rs.append(end - start) - return np.min(rs), out + + successful_runs = [] # (runtime, output, iteration_index) tuples + + for i in range(iterations): + # Set up timeout for this iteration if requested + if timeout is not None: + + def _iter_timeout_handler(signum, frame): + raise TimeoutError(f"Iteration {i + 1} exceeded {timeout}s timeout") + + old_handler = signal.signal(signal.SIGALRM, _iter_timeout_handler) + signal.alarm(int(timeout)) + + try: + start = time.time() + out = f(*args, **kwargs) + end = time.time() + runtime_val = end - start + successful_runs.append((runtime_val, out, i)) + except TimeoutError: + # This iteration timed out, continue to next iteration + pass + finally: + # Cancel alarm and restore handler + if timeout is not None: + signal.alarm(0) + signal.signal(signal.SIGALRM, old_handler) + + if not successful_runs: + # All iterations timed out + raise TimeoutError(f"All {iterations} iterations exceeded {timeout}s timeout") + + # Discard the first successful iteration (warmup) + if len(successful_runs) > 1: + measured_runs = successful_runs[1:] + else: + # If only the warmup succeeded use it as a fallback + measured_runs = successful_runs + + # Return the run closest to the median runtime + runtimes = [r[0] for r in measured_runs] + median_runtime = statistics.median(runtimes) + closest_run = min(measured_runs, key=lambda x: abs(x[0] - median_runtime)) + return closest_run[0], closest_run[1] def get_sklearn_family(distribution): @@ -195,22 +244,20 @@ def __init__( problem_name: Optional[str] = None, library_name: Optional[str] = None, num_rows: Optional[int] = None, + k_over_n_ratio: Optional[float] = None, storage: Optional[str] = None, threads: Optional[int] = None, - single_precision: Optional[bool] = None, - regularization_strength: Optional[float] = None, - cv: Optional[bool] = None, + alpha: Optional[float] = None, hessian_approx: Optional[float] = None, diagnostics_level: Optional[str] = None, ): self.problem_name = problem_name self.library_name = library_name self.num_rows = num_rows + self.k_over_n_ratio = k_over_n_ratio self.storage = storage self.threads = threads - self.single_precision = single_precision - self.regularization_strength = regularization_strength - self.cv = cv + self.alpha = alpha self.hessian_approx = hessian_approx self.diagnostics_level = diagnostics_level @@ -218,11 +265,10 @@ def __init__( "problem_name", "library_name", "num_rows", + "k_over_n_ratio", "storage", "threads", - "single_precision", - "regularization_strength", - "cv", + "alpha", "hessian_approx", "diagnostics_level", ] @@ -263,121 +309,21 @@ def get_result_fname(self) -> str: problem_name=None, library_name=None, num_rows=None, - regularization_strength=None, + k_over_n_ratio=1.0, + alpha=None, storage="dense", - cv=False, - single_precision=False, hessian_approx=0.0, diagnostics_level="basic", ) -def benchmark_params_cli(func: Callable) -> Callable: - """ - Decorate a function so that options given via click CLI get mapped into a \ - BenchmarkParams instance. - - Parameters - ---------- - func - - Returns - ------- - Callable: - wrapped function that takes a BenchmarkParams instance as an argument. - - """ - - @click.option( - "--problem_name", - type=str, - help="Specify a comma-separated list of benchmark problems you want to run. " - "Leaving this blank will default to running all problems.", - ) - @click.option( - "--library_name", - help="Specify a comma-separated list of libraries to benchmark. Leaving this " - "blank will default to running all problems.", - ) - @click.option( - "--num_rows", - type=int, - help="Pass an integer number of rows. This is useful for testing and " - "development. The default is to use the full dataset.", - ) - @click.option( - "--storage", - type=str, - help="Specify the storage format. Currently supported: dense, sparse. Leaving " - "this black will default to dense.", - ) - @click.option( - "--threads", - type=int, - help="Specify the number of threads. If not set, it will use OMP_NUM_THREADS. " - "If that's not set either, it will default to os.cpu_count().", - ) - @click.option("--cv", type=bool, help="Cross-validation") - @click.option("--single_precision", type=bool, help="Whether to use 32-bit data") - @click.option( - "--regularization_strength", - type=float, - help="Regularization strength. Set to None to use the default value of the " - "problem.", - ) - @click.option( - "--hessian_approx", - type=float, - help="Threshold for dropping rows in the IRLS approximate Hessian update.", - ) - @click.option( - "--diagnostics_level", - type=str, - help="Choose 'basic' for brief glum diagnostics or 'full' for more " - "extensive diagnostics. Any other string will result in no diagnostic " - "output at all.", - ) - def wrapped_func( - problem_name: Optional[str], - library_name: Optional[str], - num_rows: Optional[int], - storage: Optional[str], - threads: Optional[int], - cv: Optional[bool], - single_precision: Optional[bool], - regularization_strength: Optional[float], - hessian_approx: Optional[float], - diagnostics_level: Optional[str], - *args, - **kwargs, - ): - params = BenchmarkParams( - problem_name, - library_name, - num_rows, - storage, - threads, - single_precision, - regularization_strength, - cv, - hessian_approx, - diagnostics_level, - ) - return func(params, *args, **kwargs) - - return wrapped_func - - -@click.command() -@benchmark_params_cli -def _get_params(params: BenchmarkParams): - _get_params.out = params # type: ignore - - def get_params_from_fname(fname: str) -> BenchmarkParams: """ Map file name to a BenchmarkParams instance. + File names are formatted as: + problem_library_numrows_storage_threads_alpha_hessian_diag.pkl + Parameters ---------- fname: file name @@ -385,35 +331,29 @@ def get_params_from_fname(fname: str) -> BenchmarkParams: Returns ------- BenchmarkParams - """ - cli_list = reduce( - lambda x, y: x + y, - [ - ["--" + elt[0], elt[1]] - for elt in zip(BenchmarkParams.param_names, fname.strip(".pkl").split("_")) - if elt[1] != "None" - ], - ) - _get_params(cli_list, standalone_mode=False) - return _get_params.out # type: ignore - - -def _get_size_of_cache_directory(): - return sum( - os.path.getsize(x) for x in glob.glob(f"{cache_location}/**", recursive=True) - ) + parts = fname.replace(".pkl", "").split("_") + # Parse each part, converting "None" strings to actual None + def parse_value(value: str, dtype=str): + if value == "None": + return None + if dtype is int: + return int(value) + if dtype is float: + return float(value) + return value -def clear_cache(force=False): - """Clear the cache directory if its size exceeds a threshold.""" - if cache_location is None: - return + # Map parts to parameter names with appropriate types + # Order matches BenchmarkParams.param_names + param_types = [str, str, int, float, str, int, float, float, str] - cache_size_limit = float(os.environ.get("GLM_BENCHMARKS_CACHE_SIZE_LIMIT", 1024**3)) + kwargs = {} + for i, (name, dtype) in enumerate(zip(BenchmarkParams.param_names, param_types)): + if i < len(parts): + kwargs[name] = parse_value(parts[i], dtype) - if force or _get_size_of_cache_directory() > cache_size_limit: - shutil.rmtree(cache_location) + return BenchmarkParams(**kwargs) def get_tweedie_p(distribution: str) -> float: @@ -438,3 +378,130 @@ def get_tweedie_p(distribution: str) -> float: return 0 else: raise ValueError("Not a Tweedie distribution.") + + +def get_all_libraries() -> dict: + """ + Get the names of all available libraries and the functions to benchmark them. + + Libraries with missing dependencies are excluded from the result. + + Returns + ------- + dict + Mapping of library name to benchmark function. + """ + from glum_benchmarks.libraries import ( + celer_bench, + glmnet_bench, + glum_bench, + h2o_bench, + skglm_bench, + sklearn_bench, + zeros_bench, + ) + + all_libraries = { + "glum": glum_bench, + "zeros": zeros_bench, + "celer": celer_bench, + "h2o": h2o_bench, + "glmnet": glmnet_bench, + "skglm": skglm_bench, + "sklearn": sklearn_bench, + } + + # Filter out libraries that aren't available (None due to missing deps) + return {k: v for k, v in all_libraries.items() if v is not None} + + +def execute_problem_library( + params: BenchmarkParams, + iterations: int = 1, + diagnostics_level: Optional[str] = "basic", + standardize: bool = True, + timeout: Optional[float] = None, + **kwargs, +): + """ + Run the benchmark problem specified by 'params', 'iterations' times. + + By default, continuous features are pre-standardized in the data loader + before OHE and format conversion. Pass ``standardize=False`` to skip + (e.g. for golden master tests). + + Parameters + ---------- + params + iterations + diagnostics_level + standardize + Whether to pre-standardize continuous features in the data loader. + kwargs + + Returns + ------- + Tuple: Result data on this run, and the alpha applied + """ + from glum_benchmarks.problems import get_all_problems + + assert params.problem_name is not None + assert params.library_name is not None + P = get_all_problems()[params.problem_name] + L = get_all_libraries()[params.library_name] + + for k in params.param_names: + if getattr(params, k) is None: + params.update_params(**{k: defaults[k]}) + + dat = P.data_loader( + num_rows=params.num_rows, + k_over_n_ratio=params.k_over_n_ratio, + storage=params.storage, + standardize=standardize, + ) + + os.environ["OMP_NUM_THREADS"] = str(params.threads) + + if params.alpha is None: + params.alpha = P.alpha + + alpha = ( + params.alpha / np.asarray(dat["sample_weight"]).mean() + if "sample_weight" in dat + else params.alpha + ) + + result = L( + dat, + distribution=P.distribution, + alpha=alpha, + l1_ratio=P.l1_ratio, + iterations=iterations, + diagnostics_level=diagnostics_level, + hessian_approx=params.hessian_approx, + timeout=timeout, + **kwargs, + ) + + if len(result) > 0: + # Use best_alpha from CV if available, otherwise use base alpha + alpha_for_obj = result.get("best_alpha", P.alpha) + alpha_for_obj = ( + alpha_for_obj / np.asarray(dat["sample_weight"]).mean() + if "sample_weight" in dat + else alpha_for_obj + ) + obj_val = get_obj_val( + dat, + P.distribution, + alpha_for_obj, + P.l1_ratio, + result["intercept"], + result["coef"], + ) + + result["obj_val"] = obj_val + result["num_rows"] = dat["y"].shape[0] + + return result, params.alpha diff --git a/pixi.lock b/pixi.lock index b76022fe8..9e7a8321c 100644 --- a/pixi.lock +++ b/pixi.lock @@ -5,12 +5,12 @@ environments: - url: https://conda.anaconda.org/conda-forge/ indexes: - https://pypi.org/simple - options: - pypi-prerelease-mode: if-necessary-or-explicit packages: linux-64: - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-7_kmp_llvm.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/_r-mutex-1.0.1-anacondar_1.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.15.3-hb03c661_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/annotated-types-0.7.0-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/attrs-25.4.0-pyhcf101f3_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-auth-0.9.3-hef928c7_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-cal-0.9.13-h2c9d079_1.conda @@ -30,20 +30,28 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-storage-blobs-cpp-12.16.0-h75daedc_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-storage-common-cpp-12.12.0-h3d7a050_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-storage-files-datalake-cpp-12.14.0-hd454692_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils-2.45-default_h4852527_105.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.45-default_hfdba357_105.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_linux-64-2.45-default_h4852527_105.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils-2.45.1-default_h4852527_101.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.45.1-default_hfdba357_101.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_linux-64-2.45.1-default_h4852527_101.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/blas-2.305-mkl.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/blas-devel-3.11.0-5_hcf00494_mkl.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-1.2.0-hed03a55_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-bin-1.2.0-hb03c661_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/bwidget-1.10.1-ha770c72_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_8.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.6-hb03c661_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/c-compiler-1.11.0-h4d9bdce_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2026.1.4-hbd8a1cb_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.4-he90730b_1.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/click-8.3.1-pyh8f84b5b_1.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/conda-gcc-specs-14.3.0-he8ccf15_16.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.4-h3394656_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/cffi-2.0.0-py313hf46b229_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/conda-gcc-specs-14.3.0-he8ccf15_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.3.3-py313hc8edb43_4.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/curl-8.18.0-h4e3cde8_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/cxx-compiler-1.11.0-hfcd1e18_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/cython-3.2.4-py314h1807b08_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/cycler-0.12.1-pyhcf101f3_2.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/cython-3.2.4-py313hc80a56d_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.1-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2 @@ -51,29 +59,36 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/fontconfig-2.15.0-h7e30c49_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-hc364b38_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.61.1-py313h3dea7bd_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/formulaic-1.2.1-pyhd8ed1ab_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/freetype-2.14.1-ha770c72_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc-14.3.0-h0dff253_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_impl_linux-64-14.3.0-he8b2097_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_linux-64-14.3.0-h298d278_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/fribidi-1.0.16-hb03c661_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc-14.3.0-h0dff253_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_impl_linux-64-14.3.0-hb1e0a52_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_linux-64-14.3.0-h298d278_20.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/gflags-2.2.2-h5888daf_1005.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gfortran_impl_linux-64-14.3.0-h1a219da_17.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/giflib-5.2.2-hd590300_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/git_root-0.2-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/glog-0.7.1-hbabe93e_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/graphite2-1.3.14-hecca717_2.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx-14.3.0-h76987e4_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_impl_linux-64-14.3.0-h2185e75_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_linux-64-14.3.0-h310e576_17.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-12.3.2-h6083320_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/icu-78.2-h33c6efd_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gsl-2.7-he838d99_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx-14.3.0-h76987e4_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_impl_linux-64-14.3.0-h2185e75_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_linux-64-14.3.0-h3c3a7a4_20.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-12.2.0-h15599e2_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/icu-75.1-he02047a_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-8.7.0-pyhe01879c_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.3.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/interface_meta-1.3.0-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jinja2-3.1.6-pyhcf101f3_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/joblib-1.5.3-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/kernel-headers_linux-64-4.18.0-he073ed8_9.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.3-hb9d3cd8_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/kiwisolver-1.4.9-py313hc8edb43_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/krb5-1.21.3-h659f571_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/lcms2-2.18-h0c24ade_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.45-default_hbd61a6d_105.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.45.1-default_hbd61a6d_101.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/lerc-4.0.0-h0aef613_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libabseil-20250512.1-cxx17_hba17884_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-23.0.0-h2c50142_1_cpu.conda @@ -97,13 +112,14 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/libffi-3.5.2-h3435931_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libfreetype-2.14.1-ha770c72_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libfreetype6-2.14.1-h73754d4_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.2.0-he0feb66_16.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/libgcc-devel_linux-64-14.3.0-hf649bbc_116.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.2.0-h69a702a_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.2.0-h69a702a_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.2.0-h68bc16d_16.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.2.0-he0feb66_17.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/libgcc-devel_linux-64-14.3.0-hf649bbc_117.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.2.0-h69a702a_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.2.0-h69a702a_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-ng-15.2.0-h69a702a_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.2.0-h68bc16d_17.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libglib-2.86.3-h6548e54_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.2.0-he0feb66_16.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.2.0-he0feb66_17.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-2.39.0-hdb79228_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-storage-2.39.0-hdbdcf42_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libgrpc-1.73.1-h3288cfb_1.conda @@ -122,67 +138,106 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.54-h421ea60_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libprotobuf-6.31.1-h49aed37_4.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libre2-11-2025.11.05-h7b12aa8_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libsanitizer-14.3.0-h8f1669f_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.51.2-hf4e2dac_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libsanitizer-14.3.0-h8f1669f_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.51.2-h0c1763c_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libssh2-1.11.1-hcf80075_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.2.0-h934c35e_16.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/libstdcxx-devel_linux-64-14.3.0-h9f08a49_116.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-15.2.0-hdf11a46_16.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.2.0-h934c35e_17.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/libstdcxx-devel_linux-64-14.3.0-h9f08a49_117.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-15.2.0-hdf11a46_17.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libthrift-0.22.0-h454ac66_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libtiff-4.7.1-h9d88235_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libutf8proc-2.11.3-hfe17d71_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.41.3-h5347b49_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.6.0-hd42ef1d_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libxcb-1.17.0-h8a09558_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libxml2-16-2.15.1-hca6bf5a_1.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.15.1-he237659_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libxml2-16-2.15.1-ha9997c6_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.15.1-h26afc86_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-hb9d3cd8_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/llvm-openmp-21.1.8-h4922eb0_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/llvmlite-0.46.0-py313hdd307be_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/lz4-c-1.10.0-h5888daf_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/make-4.4.1-hb9d3cd8_2.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/markdown-it-py-4.0.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/markupsafe-3.0.3-py313h3dea7bd_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.10.8-py313h683a580_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/mdurl-0.1.2-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/mkl-2025.3.0-h0e700b2_463.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/mkl-devel-2025.3.0-ha770c72_463.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/mkl-include-2025.3.0-hf2ce2f3_463.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.15.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/munkres-1.1.4-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.16.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h2d0b736_3.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/nlohmann_json-3.12.0-h54a6638_1.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/numexpr-2.14.1-mkl_py314h9a883a4_1.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-2.4.1-py314h2b28147_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/numba-0.63.1-py313h5dce7c4_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/numexpr-2.14.1-mkl_py313hddce2c6_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-2.3.5-py313hf6604e3_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/openjdk-25.0.1-h5755bd7_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/openjpeg-2.5.4-h55fea9a_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/openssl-3.6.1-h35e630c_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/orc-2.2.2-h19cb568_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/packaging-26.0-pyhcf101f3_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/pandas-3.0.0-py314hb4ffadd_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/pandas-3.0.0-py313hbfd7664_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/pango-1.56.4-hadf4263_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/pcre2-10.47-haa7fec5_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0-pyh145f28c_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/pillow-12.1.0-py313h80991f8_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0.1-pyh145f28c_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/pixman-0.46.4-h54a6638_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhf9edf01_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/prometheus-cpp-1.3.0-ha5d0236_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-hb9d3cd8_1002.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/pyarrow-23.0.0-py314hdafbbf9_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/pyarrow-core-23.0.0-py314h52d6ec5_0_cpu.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/python-3.14.2-h32b2ec7_101_cp314.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/pyarrow-23.0.0-py313h78bf25f_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/pyarrow-core-23.0.0-py313he109ebe_0_cpu.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pycparser-2.22-pyh29332c3_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pydantic-2.12.5-pyhcf101f3_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/pydantic-core-2.41.5-py313h843e2db_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pygments-2.19.2-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.3.2-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-9.0.2-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/python-3.13.12-hc97d973_100_cp313.conda - conda: https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhe01879c_2.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/python_abi-3.14-8_cp314.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/python_abi-3.13-8_cp313.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/qhull-2020.2-h434a139_5.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/r-base-4.5.2-h835929b_3.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/r-codetools-0.2_20-r45hc72bb7e_2.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/r-foreach-1.5.2-r45hc72bb7e_4.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/r-glmnet-4.1_10-r45ha36cffa_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/r-iterators-1.0.14-r45hc72bb7e_4.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/r-lattice-0.22_7-r45h54b55ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/r-matrix-1.7_4-r45h0e4624f_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/r-rcpp-1.1.1-r45h3697838_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/r-rcppeigen-0.3.4.0.2-r45h3704496_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/r-shape-1.4.6.1-r45ha770c72_2.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/r-survival-3.8_6-r45h54b55ab_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/re2-2025.11.05-h5301d42_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/readline-8.3-h853b02a_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/rich-14.3.2-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/rpy2-3.6.4-py313r45h67dc6d7_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/ruamel.yaml-0.19.1-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/ruamel.yaml.clib-0.2.15-py313h54dd161_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/s2n-1.6.2-he8a4886_1.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/scikit-learn-1.8.0-np2py314hf09ca88_1.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/scipy-1.17.0-py314hf07bd8e_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/scikit-learn-1.8.0-np2py313h16d504d_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/scipy-1.17.0-py313h4b8bb8b_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/sed-4.9-h6688a6e_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/setuptools-80.10.2-pyh332efcf_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/setuptools-scm-9.2.2-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/six-1.17.0-pyhe01879c_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/skglm-0.5-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/snappy-1.2.2-h03e3b7b_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/sysroot_linux-64-2.28-h4ee821c_9.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/tabmat-4.2.0-py314ha0b5721_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/tabmat-4.2.1-py313h08cd8bf_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/tbb-2022.3.0-hb700be7_2.conda - conda: https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.6.0-pyhecae5ae_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.13-noxft_h366c992_103.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/tktable-2.10-h8d826fa_7.conda - conda: https://conda.anaconda.org/conda-forge/noarch/tomli-2.4.0-pyhcf101f3_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.2-pyh8f84b5b_2.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.3-pyh8f84b5b_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.15.0-h396c80c_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/typing-inspection-0.4.2-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.15.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2025c-hc9c84f9_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/tzlocal-5.3.1-pyh8f84b5b_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/wheel-0.46.3-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/wrapt-2.1.0-py314h5bd0f2a_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/wrapt-2.1.1-py313h07c4f96_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libice-1.1.2-hb9d3cd8_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libsm-1.2.6-he73a12e_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libx11-1.8.12-h4f16b4b_0.conda @@ -197,16 +252,24 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxtst-1.2.5-hb9d3cd8_3.conda - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.23.0-pyhcf101f3_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/zlib-1.3.1-hb9d3cd8_2.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/zlib-ng-2.3.3-hceb46e0_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.7-hb78ec9c_6.conda + - pypi: https://files.pythonhosted.org/packages/3e/51/d460992bf50b2657bf525f855fdba91207eff09ae2f06cae70095b9bc4eb/celer-0.7.4-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl - pypi: https://files.pythonhosted.org/packages/e6/ad/3cc14f097111b4de0040c83a525973216457bbeeb63739ef1ed275c1c021/certifi-2026.1.4-py3-none-any.whl - - pypi: https://files.pythonhosted.org/packages/67/ff/f6b948ca32e4f2a4576aa129d8bed61f2e0543bf9f5f2b7fc3758ed005c9/charset_normalizer-3.4.4-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl + - pypi: https://files.pythonhosted.org/packages/f5/83/6ab5883f57c9c801ce5e5677242328aa45592be8a00644310a008d04f922/charset_normalizer-3.4.4-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl + - pypi: https://files.pythonhosted.org/packages/37/45/01e7455a9659528e77a414b222326d4c525796e4f571bbabcb2e0ff3d1f4/download-0.3.5-py3-none-any.whl - pypi: https://files.pythonhosted.org/packages/5e/44/6294e10f0931cf31576202349ac7fb9879646083ef29f2a6f00db26ec0c0/h2o-3.46.0.9-py2.py3-none-any.whl - pypi: https://files.pythonhosted.org/packages/0e/61/66938bbb5fc52dbdf84594873d5b51fb1f7c7794e9c0f5bd885f30bc507b/idna-3.11-py3-none-any.whl + - pypi: https://files.pythonhosted.org/packages/f9/38/e2d64862f9d7c6e761f6d61ee9cd11badc98abd484f28a5dfb0310489334/libsvmdata-0.4.1-py3-none-any.whl - pypi: https://files.pythonhosted.org/packages/1e/db/4254e3eabe8020b458f1a747140d32277ec7a271daf1d235b70dc0b4e6e3/requests-2.32.5-py3-none-any.whl + - pypi: https://files.pythonhosted.org/packages/83/11/00d3c3dfc25ad54e731d91449895a79e4bf2384dc3ac01809010ba88f6d5/seaborn-0.13.2-py3-none-any.whl - pypi: https://files.pythonhosted.org/packages/40/44/4a5f08c96eb108af5cb50b41f76142f0afa346dfa99d5296fe7202a11854/tabulate-0.9.0-py3-none-any.whl - pypi: https://files.pythonhosted.org/packages/39/08/aaaad47bc4e9dc8c725e68f9d04865dbcb2052843ff09c97b08904852d84/urllib3-2.6.3-py3-none-any.whl + - pypi: https://files.pythonhosted.org/packages/7d/8e/952a351c10df395d9bab850f611f4368834ae9104d6449049f5a49e00925/xarray-2026.1.0-py3-none-any.whl osx-arm64: - conda: https://conda.anaconda.org/conda-forge/osx-arm64/_openmp_mutex-4.5-7_kmp_llvm.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/_r-mutex-1.0.1-anacondar_1.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/annotated-types-0.7.0-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/attrs-25.4.0-pyhcf101f3_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-auth-0.9.3-h1ddaa69_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-cal-0.9.13-h6ee9776_1.conda @@ -226,13 +289,18 @@ environments: - conda: https://conda.anaconda.org/conda-forge/osx-arm64/azure-storage-blobs-cpp-12.16.0-h6507aac_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/azure-storage-common-cpp-12.12.0-ha416c23_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/azure-storage-files-datalake-cpp-12.14.0-hcfc4f22_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/brotli-1.2.0-h7d5ae5b_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/brotli-bin-1.2.0-hc919400_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/bwidget-1.10.1-hce30654_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/bzip2-1.0.8-hd037594_8.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/c-ares-1.34.6-hc919400_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/c-compiler-1.11.0-h61f9b84_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2026.1.4-hbd8a1cb_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/cairo-1.18.4-h6a3b0d2_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/cctools-1030.6.3-llvm19_1_hd01ab73_4.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/cctools_impl_osx-arm64-1030.6.3-llvm19_1_he8a363d_4.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/cctools_osx-arm64-1030.6.3-llvm19_1_h6d92914_4.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/cffi-2.0.0-py314h44086f9_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/clang-19-19.1.7-default_hf3020a7_7.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/clang-19.1.7-default_hf9bcbb7_7.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/clang_impl_osx-arm64-19.1.7-default_hc11f16d_7.conda @@ -240,28 +308,55 @@ environments: - conda: https://conda.anaconda.org/conda-forge/osx-arm64/clangxx-19.1.7-default_hc995acf_7.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/clangxx_impl_osx-arm64-19.1.7-default_hc11f16d_7.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/clangxx_osx-arm64-19.1.7-h75f8d18_31.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/click-8.3.1-pyh8f84b5b_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/compiler-rt-19.1.7-h855ad52_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/compiler-rt_osx-arm64-19.1.7-he32a8d3_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/contourpy-1.3.3-py314hf8a3a22_4.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/curl-8.18.0-he38603e_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/cxx-compiler-1.11.0-h88570a1_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/cycler-0.12.1-pyhcf101f3_2.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/cython-3.2.4-py314hc6117b3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_3.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/fontconfig-2.15.0-h1383a14_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-hc364b38_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/fonttools-4.61.1-pyh7db6752_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/formulaic-1.2.1-pyhd8ed1ab_2.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/freetype-2.14.1-hce30654_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/fribidi-1.0.16-hc919400_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/gflags-2.2.2-hf9b8971_1005.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/gfortran_impl_osx-arm64-14.3.0-h6d03799_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/gfortran_osx-arm64-14.3.0-h3c33bd0_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/git_root-0.2-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/glog-0.7.1-heb240a5_0.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/icu-78.2-h38cb7af_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/gmp-6.3.0-h7bae524_2.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/graphite2-1.3.14-hec049ff_2.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/gsl-2.7-h6e638da_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/harfbuzz-12.2.0-haf38c7b_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/icu-75.1-hfee45f7_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-8.7.0-pyhe01879c_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.3.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/interface_meta-1.3.0-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/isl-0.26-imath32_h347afa1_101.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jinja2-3.1.6-pyhcf101f3_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/joblib-1.5.3-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/kiwisolver-1.4.9-py314h42813c9_2.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/krb5-1.21.3-h237132a_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/lcms2-2.18-hdfa7624_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/ld64-956.6-llvm19_1_he86490a_4.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/ld64_osx-arm64-956.6-llvm19_1_ha2625f7_4.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/lerc-4.0.0-hd64df32_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libabseil-20250512.1-cxx17_hd41c47c_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-23.0.0-h4365f54_1_cpu.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-acero-23.0.0-h6de58dd_1_cpu.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-compute-23.0.0-h45df96a_1_cpu.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-dataset-23.0.0-h6de58dd_1_cpu.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-substrait-23.0.0-hb5627e6_1_cpu.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libasprintf-0.25.1-h493aca8_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libblas-3.11.0-5_h51639a9_openblas.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libbrotlicommon-1.2.0-hc919400_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libbrotlidec-1.2.0-hc919400_1.conda @@ -270,22 +365,30 @@ environments: - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libclang-cpp19.1-19.1.7-default_hf3020a7_7.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcrc32c-1.1.2-hbdafb3b_0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcurl-8.18.0-he38603e_0.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-21.1.8-hf598326_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-21.1.8-h55c6f16_2.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-devel-19.1.7-h6dc3340_2.conda - conda: https://conda.anaconda.org/conda-forge/noarch/libcxx-headers-19.1.7-h707e725_2.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libdeflate-1.25-hc11a715_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libedit-3.1.20250104-pl5321hafb1f1b_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libev-4.33-h93a5062_2.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libevent-2.1.12-h2757513_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libexpat-2.7.3-haf25636_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libffi-3.5.2-hcf2aa1b_0.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgcc-15.2.0-hcbb3090_16.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran-15.2.0-h07b0088_16.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran5-15.2.0-hdae7583_16.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libfreetype-2.14.1-hce30654_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libfreetype6-2.14.1-h6da58f4_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgcc-15.2.0-hcbb3090_17.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgettextpo-0.25.1-h493aca8_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran-15.2.0-h07b0088_17.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/libgfortran-devel_osx-arm64-14.3.0-hc965647_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran5-15.2.0-hdae7583_17.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libglib-2.86.3-hfe11c1f_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgoogle-cloud-2.39.0-head0a95_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgoogle-cloud-storage-2.39.0-hfa3a374_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgrpc-1.73.1-h3063b79_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libiconv-1.18-h23cfdf5_2.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libintl-0.25.1-h493aca8_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libjemalloc-local-5.3.0-hf9b8971_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libjpeg-turbo-3.1.2-hc919400_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/liblapack-3.11.0-5_hd9741b5_openblas.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libllvm19-19.1.7-h8e0c9ce_2.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/liblzma-5.8.2-h8088a28_0.conda @@ -295,39 +398,82 @@ environments: - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libopentelemetry-cpp-1.21.0-he15edb5_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libopentelemetry-cpp-headers-1.21.0-hce30654_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libparquet-23.0.0-hcc2992d_1_cpu.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libpng-1.6.54-h132b30e_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libprotobuf-6.31.1-h98f38fd_4.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libre2-11-2025.11.05-h91c62da_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libsigtool-0.1.3-h98dc951_0.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libsqlite-3.51.2-h1ae2325_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libsqlite-3.51.2-h1b79a29_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libssh2-1.11.1-h1590b86_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libthrift-0.22.0-h14a376c_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libtiff-4.7.1-h4030677_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libutf8proc-2.11.3-h2431656_0.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libxml2-16-2.15.1-h5ef1a60_1.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libxml2-2.15.1-h8d039ee_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libwebp-base-1.6.0-h07db88b_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libxcb-1.17.0-hdb1d25a_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libxml2-16-2.15.1-h0ff4647_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libxml2-2.15.1-h9329255_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libzlib-1.3.1-h8359307_2.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/llvm-openmp-21.1.8-h4a912ad_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/llvm-tools-19-19.1.7-h91fd4e7_2.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/llvm-tools-19.1.7-h855ad52_2.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/llvmlite-0.46.0-py314ha398f32_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/lz4-c-1.10.0-h286801f_1.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.15.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/make-4.4.1-hc9fafa5_2.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/markdown-it-py-4.0.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/markupsafe-3.0.3-pyh7db6752_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/matplotlib-base-3.10.8-py314hd63e3f0_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/mdurl-0.1.2-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/mpc-1.3.1-h8f1351a_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/mpfr-4.2.1-hb693164_3.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/munkres-1.1.4-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.16.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/ncurses-6.5-h5e97a16_3.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/nlohmann_json-3.12.0-h784d473_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/numba-0.63.1-py314h945de62_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/numexpr-2.14.1-py314hc5bb990_1.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/numpy-2.4.1-py314hae46ccb_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/numpy-2.3.5-py314hae46ccb_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/openjdk-25.0.1-hde7fb7b_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/openjpeg-2.5.4-hbfb3c88_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/openssl-3.6.1-hd24854e_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/orc-2.2.2-hac85105_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/packaging-26.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pandas-3.0.0-py314h5e21a50_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0-pyh145f28c_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pango-1.56.4-h875632e_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pcre2-10.47-h30297fc_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pillow-12.1.0-py314hab283cf_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0.1-pyh145f28c_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pixman-0.46.4-h81086ad_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhf9edf01_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/prometheus-cpp-1.3.0-h0967b3e_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pthread-stubs-0.4-hd74edd7_1002.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyarrow-23.0.0-py314he55896b_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyarrow-core-23.0.0-py314hd0436b2_0_cpu.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/python-3.14.2-h40d2674_101_cp314.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pycparser-2.22-pyh29332c3_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pydantic-2.12.5-pyhcf101f3_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pydantic-core-2.41.5-py314haad56a0_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pygments-2.19.2-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.3.2-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-9.0.2-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/python-3.14.3-h4c637c5_100_cp314.conda - conda: https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhe01879c_2.conda - conda: https://conda.anaconda.org/conda-forge/noarch/python_abi-3.14-8_cp314.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/qhull-2020.2-h420ef59_5.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/r-base-4.5.2-hb2e0d2d_3.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/r-codetools-0.2_20-r45hc72bb7e_2.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/r-foreach-1.5.2-r45hc72bb7e_4.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/r-glmnet-4.1_10-r45hbf3f414_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/r-iterators-1.0.14-r45hc72bb7e_4.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/r-lattice-0.22_7-r45h6168396_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/r-matrix-1.7_4-r45hb2d3ebe_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/r-rcpp-1.1.1-r45h1380947_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/r-rcppeigen-0.3.4.0.2-r45hd057375_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/r-shape-1.4.6.1-r45ha770c72_2.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/r-survival-3.8_6-r45hbe92478_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/re2-2025.11.05-h64b956e_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/readline-8.3-h46df422_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/rich-14.3.2-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/rpy2-3.6.4-py314r45h77b1809_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/ruamel.yaml-0.19.1-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/ruamel.yaml.clib-0.2.15-py314ha14b1ff_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/scikit-learn-1.8.0-np2py314h15f0f0f_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/scipy-1.17.0-py314hfc1f868_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/sdkroot_env_osx-arm64-26.0-ha3f98da_6.conda @@ -335,29 +481,45 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/setuptools-scm-9.2.2-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/sigtool-codesign-0.1.3-h98dc951_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/six-1.17.0-pyhe01879c_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/skglm-0.5-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/snappy-1.2.2-hada39a4_1.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tabmat-4.2.0-py314h759a76e_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tabmat-4.2.1-py314h759a76e_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tapi-1600.0.11.8-h997e182_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.6.0-pyhecae5ae_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tk-8.6.13-h010d191_3.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tktable-2.10-h3c7de25_7.conda - conda: https://conda.anaconda.org/conda-forge/noarch/tomli-2.4.0-pyhcf101f3_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.2-pyh8f84b5b_2.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.3-pyh8f84b5b_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.15.0-h396c80c_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/typing-inspection-0.4.2-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.15.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2025c-hc9c84f9_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/tzlocal-5.3.1-pyh8f84b5b_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/unicodedata2-17.0.0-py314h0612a62_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/wheel-0.46.3-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/wrapt-2.1.0-py314h6c2aa35_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/wrapt-2.1.1-py314h6c2aa35_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/xorg-libxau-1.0.12-hc919400_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/xorg-libxdmcp-1.1.5-hc919400_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.23.0-pyhcf101f3_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/zlib-1.3.1-h8359307_2.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/zlib-ng-2.3.3-hed4e4f5_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/zstd-1.5.7-hbf9d68e_6.conda + - pypi: https://files.pythonhosted.org/packages/de/cd/094d08cf59158a7f180020778096b4b1b36cfa843218681134fc7112508d/celer-0.7.4.tar.gz - pypi: https://files.pythonhosted.org/packages/e6/ad/3cc14f097111b4de0040c83a525973216457bbeeb63739ef1ed275c1c021/certifi-2026.1.4-py3-none-any.whl - pypi: https://files.pythonhosted.org/packages/2a/35/7051599bd493e62411d6ede36fd5af83a38f37c4767b92884df7301db25d/charset_normalizer-3.4.4-cp314-cp314-macosx_10_13_universal2.whl + - pypi: https://files.pythonhosted.org/packages/37/45/01e7455a9659528e77a414b222326d4c525796e4f571bbabcb2e0ff3d1f4/download-0.3.5-py3-none-any.whl - pypi: https://files.pythonhosted.org/packages/5e/44/6294e10f0931cf31576202349ac7fb9879646083ef29f2a6f00db26ec0c0/h2o-3.46.0.9-py2.py3-none-any.whl - pypi: https://files.pythonhosted.org/packages/0e/61/66938bbb5fc52dbdf84594873d5b51fb1f7c7794e9c0f5bd885f30bc507b/idna-3.11-py3-none-any.whl + - pypi: https://files.pythonhosted.org/packages/f9/38/e2d64862f9d7c6e761f6d61ee9cd11badc98abd484f28a5dfb0310489334/libsvmdata-0.4.1-py3-none-any.whl - pypi: https://files.pythonhosted.org/packages/1e/db/4254e3eabe8020b458f1a747140d32277ec7a271daf1d235b70dc0b4e6e3/requests-2.32.5-py3-none-any.whl + - pypi: https://files.pythonhosted.org/packages/83/11/00d3c3dfc25ad54e731d91449895a79e4bf2384dc3ac01809010ba88f6d5/seaborn-0.13.2-py3-none-any.whl - pypi: https://files.pythonhosted.org/packages/40/44/4a5f08c96eb108af5cb50b41f76142f0afa346dfa99d5296fe7202a11854/tabulate-0.9.0-py3-none-any.whl - pypi: https://files.pythonhosted.org/packages/39/08/aaaad47bc4e9dc8c725e68f9d04865dbcb2052843ff09c97b08904852d84/urllib3-2.6.3-py3-none-any.whl + - pypi: https://files.pythonhosted.org/packages/7d/8e/952a351c10df395d9bab850f611f4368834ae9104d6449049f5a49e00925/xarray-2026.1.0-py3-none-any.whl win-64: + - conda: https://conda.anaconda.org/conda-forge/win-64/_openmp_mutex-4.5-2_gnu.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/_r-mutex-1.0.1-anacondar_1.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/annotated-types-0.7.0-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/attrs-25.4.0-pyhcf101f3_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/aws-c-auth-0.9.3-h2970c50_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/aws-c-cal-0.9.13-h46f3b43_1.conda @@ -372,23 +534,51 @@ environments: - conda: https://conda.anaconda.org/conda-forge/win-64/aws-checksums-0.2.7-hcb3a2da_5.conda - conda: https://conda.anaconda.org/conda-forge/win-64/aws-crt-cpp-0.35.4-hca034e6_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/aws-sdk-cpp-1.11.606-hac16450_10.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/binutils_impl_win-64-2.45.1-default_ha84baeb_101.conda - conda: https://conda.anaconda.org/conda-forge/win-64/blas-2.305-mkl.conda - conda: https://conda.anaconda.org/conda-forge/win-64/blas-devel-3.11.0-5_h85df5b5_mkl.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/brotli-1.2.0-h2d644bc_1.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/brotli-bin-1.2.0-hfd05255_1.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/bwidget-1.10.1-h57928b3_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/bzip2-1.0.8-h0ad9c76_8.conda - conda: https://conda.anaconda.org/conda-forge/win-64/c-ares-1.34.6-hfd05255_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/c-compiler-1.11.0-h528c1b4_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2026.1.4-h4c7d964_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/click-8.3.1-pyha7b4d00_1.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/cairo-1.18.4-h5782bbf_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/cffi-2.0.0-py314h5a2d7ad_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/contourpy-1.3.3-py314hf309875_4.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/curl-8.18.0-h43ecb02_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/cxx-compiler-1.11.0-h1c1089f_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/cycler-0.12.1-pyhcf101f3_2.conda - conda: https://conda.anaconda.org/conda-forge/win-64/cython-3.2.4-py314h344ed54_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_3.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/fontconfig-2.15.0-h765892d_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-hc364b38_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/fonttools-4.61.1-pyh7db6752_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/formulaic-1.2.1-pyhd8ed1ab_2.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/freetype-2.14.1-h57928b3_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/gcc_impl_win-64-15.2.0-h58d629f_17.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/gfortran_impl_win-64-15.2.0-h0e079bb_17.conda - conda: https://conda.anaconda.org/conda-forge/noarch/git_root-0.2-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/win-64/icu-78.2-h637d24d_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/gsl-2.7-hdfb1a43_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/win-64/gxx_impl_win-64-15.2.0-h22fd5bf_17.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/icu-75.1-he0c23c2_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-8.7.0-pyhe01879c_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.3.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/interface_meta-1.3.0-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jinja2-3.1.6-pyhcf101f3_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/joblib-1.5.3-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/kiwisolver-1.4.9-py314hf309875_2.conda - conda: https://conda.anaconda.org/conda-forge/win-64/krb5-1.21.3-hdf4eb48_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/lcms2-2.18-hf2c6c5f_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/ld_impl_win-64-2.45.1-default_hfd38196_101.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/lerc-4.0.0-h6470a55_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/libabseil-20250512.1-cxx17_habfad5f_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/libarrow-23.0.0-hcf7e2ff_1_cpu.conda - conda: https://conda.anaconda.org/conda-forge/win-64/libarrow-acero-23.0.0-h7d8d6a5_1_cpu.conda @@ -402,67 +592,130 @@ environments: - conda: https://conda.anaconda.org/conda-forge/win-64/libcblas-3.11.0-5_h2a3cdd5_mkl.conda - conda: https://conda.anaconda.org/conda-forge/win-64/libcrc32c-1.1.2-h0e60522_0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/win-64/libcurl-8.18.0-h43ecb02_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libdeflate-1.25-h51727cc_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/libevent-2.1.12-h3671451_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/libexpat-2.7.3-hac47afa_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/libffi-3.5.2-h3d046cb_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libfreetype-2.14.1-h57928b3_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libfreetype6-2.14.1-hdbac1cb_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libgcc-15.2.0-h8ee18e1_17.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/libgcc-devel_win-64-15.2.0-hbb59886_117.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libgfortran-15.2.0-h719f0c7_17.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libgfortran5-15.2.0-h44d81a7_17.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libglib-2.86.3-h0c9aed9_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libgomp-15.2.0-h8ee18e1_17.conda - conda: https://conda.anaconda.org/conda-forge/win-64/libgoogle-cloud-2.39.0-h19ee442_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/libgoogle-cloud-storage-2.39.0-he04ea4c_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/libgrpc-1.73.1-h317e13b_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/libhwloc-2.12.2-default_h4379cf1_1000.conda - conda: https://conda.anaconda.org/conda-forge/win-64/libiconv-1.18-hc1393d2_2.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libintl-0.22.5-h5728263_3.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libjpeg-turbo-3.1.2-hfd05255_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/liblapack-3.11.0-5_hf9ab0e9_mkl.conda - conda: https://conda.anaconda.org/conda-forge/win-64/liblapacke-3.11.0-5_h3ae206f_mkl.conda - conda: https://conda.anaconda.org/conda-forge/win-64/liblzma-5.8.2-hfd05255_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/libmpdec-4.0.0-hfd05255_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/libparquet-23.0.0-h7051d1f_1_cpu.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libpng-1.6.54-h7351971_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/libprotobuf-6.31.1-hdcda5b4_4.conda - conda: https://conda.anaconda.org/conda-forge/win-64/libre2-11-2025.11.05-h0eb2380_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/libsqlite-3.51.2-hf5d6505_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/libssh2-1.11.1-h9aa295b_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libstdcxx-15.2.0-hae5796f_17.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/libstdcxx-devel_win-64-15.2.0-h0a72980_117.conda - conda: https://conda.anaconda.org/conda-forge/win-64/libthrift-0.22.0-h23985f6_1.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libtiff-4.7.1-h8f73337_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/libutf8proc-2.11.3-hb980946_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libwebp-base-1.6.0-h4d5522a_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/libwinpthread-12.0.0.r4.gg4f2fc60ca-h57928b3_10.conda - - conda: https://conda.anaconda.org/conda-forge/win-64/libxml2-16-2.15.1-h3cfd58e_1.conda - - conda: https://conda.anaconda.org/conda-forge/win-64/libxml2-2.15.1-h779ef1b_1.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libxcb-1.17.0-h0e4246c_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libxml2-16-2.15.1-h06f855e_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libxml2-2.15.1-ha29bfb0_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/libzlib-1.3.1-h2466b09_2.conda - conda: https://conda.anaconda.org/conda-forge/win-64/llvm-openmp-21.1.8-h4fa8253_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/llvmlite-0.46.0-py314hb492ee6_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/lz4-c-1.10.0-h2466b09_1.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/m2-conda-epoch-20250515-0_x86_64.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/m2w64-sysroot_win-64-12.0.0.r4.gg4f2fc60ca-hd8ed1ab_10.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/markdown-it-py-4.0.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/markupsafe-3.0.3-pyh7db6752_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/matplotlib-base-3.10.8-py314hfa45d96_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/mdurl-0.1.2-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/mingw-w64-ucrt-x86_64-crt-git-12.0.0.r4.gg4f2fc60ca-hd8ed1ab_10.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/mingw-w64-ucrt-x86_64-headers-git-12.0.0.r4.gg4f2fc60ca-hd8ed1ab_10.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/mingw-w64-ucrt-x86_64-windows-default-manifest-6.4-he206cdd_7.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/mingw-w64-ucrt-x86_64-winpthreads-git-12.0.0.r4.gg4f2fc60ca-hd8ed1ab_10.conda - conda: https://conda.anaconda.org/conda-forge/win-64/mkl-2025.3.0-hac47afa_455.conda - conda: https://conda.anaconda.org/conda-forge/win-64/mkl-devel-2025.3.0-h57928b3_455.conda - conda: https://conda.anaconda.org/conda-forge/win-64/mkl-include-2025.3.0-h57928b3_455.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.15.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/munkres-1.1.4-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.16.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/numba-0.63.1-py314h36f8cf2_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/numexpr-2.14.1-mkl_py314h220b711_1.conda - - conda: https://conda.anaconda.org/conda-forge/win-64/numpy-2.4.1-py314h06c3c77_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/numpy-2.3.5-py314h06c3c77_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/openjdk-25.0.1-hda6743d_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/openjpeg-2.5.4-h24db6dd_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/openssl-3.6.1-hf411b9b_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/orc-2.2.2-hbd3206f_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/packaging-26.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/pandas-3.0.0-py314hf700ef7_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0-pyh145f28c_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/pcre2-10.47-hd2b5f0e_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/pillow-12.1.0-py314h61b30b5_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0.1-pyh145f28c_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/pixman-0.46.4-h5112557_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhf9edf01_1.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/pthread-stubs-0.4-h0e40799_1002.conda - conda: https://conda.anaconda.org/conda-forge/win-64/pyarrow-23.0.0-py314h86ab7b2_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/pyarrow-core-23.0.0-py314hb5be3fa_0_cpu.conda - - conda: https://conda.anaconda.org/conda-forge/win-64/python-3.14.2-h4b44e0e_101_cp314.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pycparser-2.22-pyh29332c3_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pydantic-2.12.5-pyhcf101f3_1.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/pydantic-core-2.41.5-py314h9f07db2_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pygments-2.19.2-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.3.2-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-9.0.2-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/python-3.14.3-h4b44e0e_100_cp314.conda - conda: https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhe01879c_2.conda - conda: https://conda.anaconda.org/conda-forge/noarch/python-tzdata-2025.3-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/python_abi-3.14-8_cp314.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/qhull-2020.2-hc790b64_5.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/r-base-4.4.3-h347933c_7.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/r-codetools-0.2_20-r44hc72bb7e_2.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/r-foreach-1.5.2-r44hc72bb7e_4.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/r-glmnet-4.1_10-r44hf64fc22_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/r-iterators-1.0.14-r44hc72bb7e_4.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/r-lattice-0.22_7-r44heceb674_1.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/r-matrix-1.7_4-r44h5ea86f4_1.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/r-rcpp-1.1.1-r44hd8a2815_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/r-rcppeigen-0.3.4.0.2-r44hac2c72c_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/r-shape-1.4.6.1-r44ha770c72_2.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/r-survival-3.8_6-r44h2a2a84f_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/re2-2025.11.05-ha104f34_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/rich-14.3.2-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/rpy2-3.6.4-py314r44h83472a1_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/ruamel.yaml-0.19.1-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/ruamel.yaml.clib-0.2.15-py314hc5dbbe4_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/scikit-learn-1.8.0-np2py314h1b5b07a_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/scipy-1.17.0-py314h221f224_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/setuptools-80.10.2-pyh332efcf_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/setuptools-scm-9.2.2-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/six-1.17.0-pyhe01879c_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/skglm-0.5-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/snappy-1.2.2-h7fa0ca8_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/symlink-exe-runtime-1.0-hcfcfb64_0.tar.bz2 - - conda: https://conda.anaconda.org/conda-forge/win-64/tabmat-4.2.0-py314hd8fd7ce_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/tabmat-4.2.1-py314hd8fd7ce_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/tbb-2022.3.0-h3155e25_2.conda - conda: https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.6.0-pyhecae5ae_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/tk-8.6.13-h6ed50ae_3.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/tktable-2.10-h7e9e0db_7.conda - conda: https://conda.anaconda.org/conda-forge/noarch/tomli-2.4.0-pyhcf101f3_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.2-pyha7b4d00_2.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.3-pyha7b4d00_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.15.0-h396c80c_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/typing-inspection-0.4.2-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.15.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2025c-hc9c84f9_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/tzlocal-5.3.1-pyha7b4d00_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/ucrt-10.0.26100.0-h57928b3_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/unicodedata2-17.0.0-py314h5a2d7ad_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/vc-14.3-h41ae7f8_34.conda - conda: https://conda.anaconda.org/conda-forge/win-64/vc14_runtime-14.44.35208-h818238b_34.conda - conda: https://conda.anaconda.org/conda-forge/win-64/vcomp14-14.44.35208-h818238b_34.conda @@ -470,21 +723,27 @@ environments: - conda: https://conda.anaconda.org/conda-forge/win-64/vs2022_win-64-19.44.35207-ha74f236_34.conda - conda: https://conda.anaconda.org/conda-forge/noarch/vswhere-3.1.7-h40126e0_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/wheel-0.46.3-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/win-64/wrapt-2.1.0-py314h5a2d7ad_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/wrapt-2.1.1-py314h5a2d7ad_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/xorg-libxau-1.0.12-hba3369d_1.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/xorg-libxdmcp-1.1.5-hba3369d_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.23.0-pyhcf101f3_1.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/zlib-ng-2.3.3-h0261ad2_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/zstd-1.5.7-h534d264_6.conda + - pypi: https://files.pythonhosted.org/packages/de/cd/094d08cf59158a7f180020778096b4b1b36cfa843218681134fc7112508d/celer-0.7.4.tar.gz - pypi: https://files.pythonhosted.org/packages/e6/ad/3cc14f097111b4de0040c83a525973216457bbeeb63739ef1ed275c1c021/certifi-2026.1.4-py3-none-any.whl - pypi: https://files.pythonhosted.org/packages/4b/51/8ade005e5ca5b0d80fb4aff72a3775b325bdc3d27408c8113811a7cbe640/charset_normalizer-3.4.4-cp314-cp314-win_amd64.whl + - pypi: https://files.pythonhosted.org/packages/37/45/01e7455a9659528e77a414b222326d4c525796e4f571bbabcb2e0ff3d1f4/download-0.3.5-py3-none-any.whl - pypi: https://files.pythonhosted.org/packages/5e/44/6294e10f0931cf31576202349ac7fb9879646083ef29f2a6f00db26ec0c0/h2o-3.46.0.9-py2.py3-none-any.whl - pypi: https://files.pythonhosted.org/packages/0e/61/66938bbb5fc52dbdf84594873d5b51fb1f7c7794e9c0f5bd885f30bc507b/idna-3.11-py3-none-any.whl + - pypi: https://files.pythonhosted.org/packages/f9/38/e2d64862f9d7c6e761f6d61ee9cd11badc98abd484f28a5dfb0310489334/libsvmdata-0.4.1-py3-none-any.whl - pypi: https://files.pythonhosted.org/packages/1e/db/4254e3eabe8020b458f1a747140d32277ec7a271daf1d235b70dc0b4e6e3/requests-2.32.5-py3-none-any.whl + - pypi: https://files.pythonhosted.org/packages/83/11/00d3c3dfc25ad54e731d91449895a79e4bf2384dc3ac01809010ba88f6d5/seaborn-0.13.2-py3-none-any.whl - pypi: https://files.pythonhosted.org/packages/40/44/4a5f08c96eb108af5cb50b41f76142f0afa346dfa99d5296fe7202a11854/tabulate-0.9.0-py3-none-any.whl - pypi: https://files.pythonhosted.org/packages/39/08/aaaad47bc4e9dc8c725e68f9d04865dbcb2052843ff09c97b08904852d84/urllib3-2.6.3-py3-none-any.whl + - pypi: https://files.pythonhosted.org/packages/7d/8e/952a351c10df395d9bab850f611f4368834ae9104d6449049f5a49e00925/xarray-2026.1.0-py3-none-any.whl default: channels: - url: https://conda.anaconda.org/conda-forge/ - options: - pypi-prerelease-mode: if-necessary-or-explicit packages: linux-64: - conda: https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2 @@ -510,9 +769,9 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-storage-blobs-cpp-12.16.0-h75daedc_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-storage-common-cpp-12.12.0-h3d7a050_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-storage-files-datalake-cpp-12.14.0-hd454692_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils-2.45-default_h4852527_105.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.45-default_hfdba357_105.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_linux-64-2.45-default_h4852527_105.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils-2.45.1-default_h4852527_101.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.45.1-default_hfdba357_101.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_linux-64-2.45.1-default_h4852527_101.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_8.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.6-hb03c661_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/c-compiler-1.11.0-h4d9bdce_0.conda @@ -520,8 +779,8 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/click-8.3.1-pyh8f84b5b_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/comm-0.2.3-pyhe01879c_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/conda-gcc-specs-14.3.0-he8ccf15_16.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/cpython-3.14.2-py314hd8ed1ab_101.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/conda-gcc-specs-14.3.0-he8ccf15_17.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/cpython-3.14.3-py314hd8ed1ab_100.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/cxx-compiler-1.11.0-hfcd1e18_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/cython-3.2.4-py314h1807b08_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/debugpy-1.8.20-py314h42812f9_0.conda @@ -530,22 +789,22 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/execnet-2.1.2-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/executing-2.2.1-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/formulaic-1.2.1-pyhd8ed1ab_2.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc-14.3.0-h0dff253_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_impl_linux-64-14.3.0-he8b2097_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_linux-64-14.3.0-h298d278_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc-14.3.0-h0dff253_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_impl_linux-64-14.3.0-hb1e0a52_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_linux-64-14.3.0-h298d278_20.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/gflags-2.2.2-h5888daf_1005.conda - conda: https://conda.anaconda.org/conda-forge/noarch/git_root-0.2-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/glog-0.7.1-hbabe93e_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx-14.3.0-h76987e4_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_impl_linux-64-14.3.0-h2185e75_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_linux-64-14.3.0-h310e576_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx-14.3.0-h76987e4_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_impl_linux-64-14.3.0-h2185e75_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_linux-64-14.3.0-h3c3a7a4_20.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/icu-78.2-h33c6efd_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-8.7.0-pyhe01879c_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.3.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/interface_meta-1.3.0-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/ipdb-0.13.13-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/ipykernel-7.1.0-pyha191276_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/ipython-9.9.0-pyh53cf698_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/ipython-9.10.0-pyh53cf698_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/ipython_pygments_lexers-1.1.1-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/jedi-0.19.2-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/joblib-1.5.3-pyhd8ed1ab_0.conda @@ -554,7 +813,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/kernel-headers_linux-64-4.18.0-he073ed8_9.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.3-hb9d3cd8_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/krb5-1.21.3-h659f571_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.45-default_hbd61a6d_105.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.45.1-default_hbd61a6d_101.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libabseil-20250512.1-cxx17_hba17884_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-23.0.0-h2c50142_1_cpu.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-acero-23.0.0-h635bf11_1_cpu.conda @@ -573,12 +832,12 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/libevent-2.1.12-hf998b51_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.7.3-hecca717_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libffi-3.5.2-h3435931_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.2.0-he0feb66_16.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/libgcc-devel_linux-64-14.3.0-hf649bbc_116.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.2.0-h69a702a_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.2.0-h69a702a_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.2.0-h68bc16d_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.2.0-he0feb66_16.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.2.0-he0feb66_17.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/libgcc-devel_linux-64-14.3.0-hf649bbc_117.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.2.0-h69a702a_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.2.0-h69a702a_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.2.0-h68bc16d_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.2.0-he0feb66_17.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-2.39.0-hdb79228_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-storage-2.39.0-hdbdcf42_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libgrpc-1.73.1-h3288cfb_1.conda @@ -594,13 +853,13 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/libparquet-23.0.0-h7376487_1_cpu.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libprotobuf-6.31.1-h49aed37_4.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libre2-11-2025.11.05-h7b12aa8_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libsanitizer-14.3.0-h8f1669f_16.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libsanitizer-14.3.0-h8f1669f_17.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libsodium-1.0.20-h4ab18f5_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.51.2-hf4e2dac_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libssh2-1.11.1-hcf80075_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.2.0-h934c35e_16.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/libstdcxx-devel_linux-64-14.3.0-h9f08a49_116.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-15.2.0-hdf11a46_16.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.2.0-h934c35e_17.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/libstdcxx-devel_linux-64-14.3.0-h9f08a49_117.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-15.2.0-hdf11a46_17.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libthrift-0.22.0-h454ac66_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libutf8proc-2.11.3-hfe17d71_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.41.3-h5347b49_0.conda @@ -613,13 +872,13 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/memory_profiler-0.61.0-pyhcf101f3_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/mypy-1.19.1-py314h5bd0f2a_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/mypy_extensions-1.1.0-pyha770c72_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.15.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.16.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h2d0b736_3.conda - conda: https://conda.anaconda.org/conda-forge/noarch/nest-asyncio-1.6.0-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/nlohmann_json-3.12.0-h54a6638_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/nomkl-1.0-h5ca1d4c_0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/linux-64/numexpr-2.14.1-py314heb044ea_101.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-2.4.1-py314h2b28147_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-2.4.2-py314h2b28147_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/openssl-3.6.1-h35e630c_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/orc-2.2.2-h19cb568_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/packaging-26.0-pyhcf101f3_0.conda @@ -628,7 +887,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/pathspec-1.0.4-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/patsy-1.0.2-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pexpect-4.9.0-pyhd8ed1ab_1.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0-pyh145f28c_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0.1-pyh145f28c_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/platformdirs-4.5.1-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhf9edf01_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/prometheus-cpp-1.3.0-ha5d0236_0.conda @@ -641,9 +900,9 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/pygments-2.19.2-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-9.0.2-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.8.0-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/python-3.14.2-h32b2ec7_101_cp314.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/python-3.14.3-h32b2ec7_100_cp314.conda - conda: https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhe01879c_2.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/python-gil-3.14.2-h4df99d1_101.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/python-gil-3.14.3-h4df99d1_100.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/python-librt-0.7.8-py314h0f05182_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/python_abi-3.14-8_cp314.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/pyzmq-27.1.0-py312hfb55c3c_0.conda @@ -659,20 +918,20 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/stack_data-0.6.3-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/statsmodels-0.14.6-py314hc02f841_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/sysroot_linux-64-2.28-h4ee821c_9.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/tabmat-4.2.0-py314ha0b5721_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/tabmat-4.2.1-py314ha0b5721_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.6.0-pyhecae5ae_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.13-noxft_h366c992_103.conda - conda: https://conda.anaconda.org/conda-forge/noarch/toml-0.10.2-pyhcf101f3_3.conda - conda: https://conda.anaconda.org/conda-forge/noarch/tomli-2.4.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/tornado-6.5.3-py314h5bd0f2a_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.2-pyh8f84b5b_2.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.3-pyh8f84b5b_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/traitlets-5.14.3-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.15.0-h396c80c_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.15.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2025c-hc9c84f9_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/wcwidth-0.5.3-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/wheel-0.46.3-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/wrapt-2.1.0-py314h5bd0f2a_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/wrapt-2.1.1-py314h5bd0f2a_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/zeromq-4.3.5-h387f397_9.conda - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.23.0-pyhcf101f3_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/zlib-1.3.1-hb9d3cd8_2.conda @@ -720,7 +979,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/comm-0.2.3-pyhe01879c_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/compiler-rt-19.1.7-h855ad52_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/compiler-rt_osx-arm64-19.1.7-he32a8d3_1.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/cpython-3.14.2-py314hd8ed1ab_101.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/cpython-3.14.3-py314hd8ed1ab_100.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/cxx-compiler-1.11.0-h88570a1_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/cython-3.2.4-py314hc6117b3_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/debugpy-1.8.20-py314he609de1_0.conda @@ -738,7 +997,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/interface_meta-1.3.0-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/ipdb-0.13.13-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/ipykernel-7.1.0-pyh5552912_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/ipython-9.9.0-pyh53cf698_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/ipython-9.10.0-pyh53cf698_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/ipython_pygments_lexers-1.1.1-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/jedi-0.19.2-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/joblib-1.5.3-pyhd8ed1ab_0.conda @@ -761,7 +1020,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libclang-cpp19.1-19.1.7-default_hf3020a7_7.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcrc32c-1.1.2-hbdafb3b_0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcurl-8.18.0-he38603e_0.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-21.1.8-hf598326_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-21.1.8-h55c6f16_2.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-devel-19.1.7-h6dc3340_2.conda - conda: https://conda.anaconda.org/conda-forge/noarch/libcxx-headers-19.1.7-h707e725_2.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libedit-3.1.20250104-pl5321hafb1f1b_0.conda @@ -769,9 +1028,9 @@ environments: - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libevent-2.1.12-h2757513_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libexpat-2.7.3-haf25636_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libffi-3.5.2-hcf2aa1b_0.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgcc-15.2.0-hcbb3090_16.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran-15.2.0-h07b0088_16.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran5-15.2.0-hdae7583_16.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgcc-15.2.0-hcbb3090_17.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran-15.2.0-h07b0088_17.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran5-15.2.0-hdae7583_17.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgoogle-cloud-2.39.0-head0a95_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgoogle-cloud-storage-2.39.0-hfa3a374_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgrpc-1.73.1-h3063b79_1.conda @@ -806,12 +1065,12 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/memory_profiler-0.61.0-pyhcf101f3_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/mypy-1.19.1-py314hbdd0d06_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/mypy_extensions-1.1.0-pyha770c72_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.15.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.16.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/ncurses-6.5-h5e97a16_3.conda - conda: https://conda.anaconda.org/conda-forge/noarch/nest-asyncio-1.6.0-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/nlohmann_json-3.12.0-h784d473_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/numexpr-2.14.1-py314hc5bb990_1.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/numpy-2.4.1-py314hae46ccb_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/numpy-2.4.2-py314hae46ccb_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/openssl-3.6.1-hd24854e_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/orc-2.2.2-hac85105_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/packaging-26.0-pyhcf101f3_0.conda @@ -820,7 +1079,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/pathspec-1.0.4-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/patsy-1.0.2-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pexpect-4.9.0-pyhd8ed1ab_1.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0-pyh145f28c_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0.1-pyh145f28c_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/platformdirs-4.5.1-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhf9edf01_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/prometheus-cpp-1.3.0-h0967b3e_0.conda @@ -833,9 +1092,9 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/pygments-2.19.2-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-9.0.2-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.8.0-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/python-3.14.2-h40d2674_101_cp314.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/python-3.14.3-h4c637c5_100_cp314.conda - conda: https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhe01879c_2.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/python-gil-3.14.2-h4df99d1_101.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/python-gil-3.14.3-h4df99d1_100.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/python-librt-0.7.8-py314ha14b1ff_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/python_abi-3.14-8_cp314.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyzmq-27.1.0-py312hd65ceae_0.conda @@ -851,21 +1110,21 @@ environments: - conda: https://conda.anaconda.org/conda-forge/osx-arm64/snappy-1.2.2-hada39a4_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/stack_data-0.6.3-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/statsmodels-0.14.6-py314hdcf55e8_0.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tabmat-4.2.0-py314h759a76e_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tabmat-4.2.1-py314h759a76e_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tapi-1600.0.11.8-h997e182_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.6.0-pyhecae5ae_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tk-8.6.13-h010d191_3.conda - conda: https://conda.anaconda.org/conda-forge/noarch/toml-0.10.2-pyhcf101f3_3.conda - conda: https://conda.anaconda.org/conda-forge/noarch/tomli-2.4.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tornado-6.5.4-py314h0612a62_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.2-pyh8f84b5b_2.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.3-pyh8f84b5b_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/traitlets-5.14.3-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.15.0-h396c80c_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.15.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2025c-hc9c84f9_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/wcwidth-0.5.3-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/wheel-0.46.3-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/wrapt-2.1.0-py314h6c2aa35_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/wrapt-2.1.1-py314h6c2aa35_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/zeromq-4.3.5-h888dc83_9.conda - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.23.0-pyhcf101f3_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/zlib-1.3.1-h8359307_2.conda @@ -894,7 +1153,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/click-8.3.1-pyha7b4d00_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/comm-0.2.3-pyhe01879c_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/cpython-3.14.2-py314hd8ed1ab_101.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/cpython-3.14.3-py314hd8ed1ab_100.conda - conda: https://conda.anaconda.org/conda-forge/win-64/cxx-compiler-1.11.0-h1c1089f_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/cython-3.2.4-py314h344ed54_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/debugpy-1.8.20-py314hb98de8c_0.conda @@ -909,7 +1168,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/interface_meta-1.3.0-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/ipdb-0.13.13-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/ipykernel-7.1.0-pyh6dadd2b_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/ipython-9.9.0-pyhe2676ad_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/ipython-9.10.0-pyhe2676ad_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/ipython_pygments_lexers-1.1.1-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/jedi-0.19.2-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/joblib-1.5.3-pyhd8ed1ab_0.conda @@ -953,11 +1212,11 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/memory_profiler-0.61.0-pyhcf101f3_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/mypy-1.19.1-py314h5a2d7ad_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/mypy_extensions-1.1.0-pyha770c72_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.15.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.16.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/nest-asyncio-1.6.0-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/nomkl-1.0-h5ca1d4c_0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/win-64/numexpr-2.14.1-py314h5a6676c_101.conda - - conda: https://conda.anaconda.org/conda-forge/win-64/numpy-2.4.1-py314h06c3c77_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/numpy-2.4.2-py314h06c3c77_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/openssl-3.6.1-hf411b9b_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/orc-2.2.2-hbd3206f_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/packaging-26.0-pyhcf101f3_0.conda @@ -965,7 +1224,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/parso-0.8.5-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pathspec-1.0.4-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/patsy-1.0.2-pyhcf101f3_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0-pyh145f28c_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0.1-pyh145f28c_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/platformdirs-4.5.1-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhf9edf01_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/prompt-toolkit-3.0.52-pyha770c72_0.conda @@ -976,9 +1235,9 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/pygments-2.19.2-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-9.0.2-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.8.0-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/win-64/python-3.14.2-h4b44e0e_101_cp314.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/python-3.14.3-h4b44e0e_100_cp314.conda - conda: https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhe01879c_2.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/python-gil-3.14.2-h4df99d1_101.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/python-gil-3.14.3-h4df99d1_100.conda - conda: https://conda.anaconda.org/conda-forge/win-64/python-librt-0.7.8-py314hc5dbbe4_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/python-tzdata-2025.3-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/python_abi-3.14-8_cp314.conda @@ -993,13 +1252,13 @@ environments: - conda: https://conda.anaconda.org/conda-forge/win-64/snappy-1.2.2-h7fa0ca8_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/stack_data-0.6.3-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/statsmodels-0.14.6-py314h2dcd201_0.conda - - conda: https://conda.anaconda.org/conda-forge/win-64/tabmat-4.2.0-py314hd8fd7ce_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/tabmat-4.2.1-py314hd8fd7ce_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.6.0-pyhecae5ae_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/tk-8.6.13-h6ed50ae_3.conda - conda: https://conda.anaconda.org/conda-forge/noarch/toml-0.10.2-pyhcf101f3_3.conda - conda: https://conda.anaconda.org/conda-forge/noarch/tomli-2.4.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/tornado-6.5.4-py314h5a2d7ad_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.2-pyha7b4d00_2.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.3-pyha7b4d00_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/traitlets-5.14.3-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.15.0-h396c80c_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.15.0-pyhcf101f3_0.conda @@ -1013,15 +1272,13 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/vswhere-3.1.7-h40126e0_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/wcwidth-0.5.3-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/wheel-0.46.3-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/win-64/wrapt-2.1.0-py314h5a2d7ad_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/wrapt-2.1.1-py314h5a2d7ad_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/zeromq-4.3.5-h5bddc39_9.conda - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.23.0-pyhcf101f3_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/zstd-1.5.7-h534d264_6.conda docs: channels: - url: https://conda.anaconda.org/conda-forge/ - options: - pypi-prerelease-mode: if-necessary-or-explicit packages: linux-64: - conda: https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2 @@ -1031,7 +1288,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/altair-6.0.0-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/anyio-4.12.1-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/argon2-cffi-25.1.0-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/argon2-cffi-bindings-25.1.0-py314h5bd0f2a_2.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/argon2-cffi-bindings-25.1.0-py313h07c4f96_2.conda - conda: https://conda.anaconda.org/conda-forge/noarch/arrow-1.4.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/asttokens-3.0.1-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/async-lru-2.1.0-pyhcf101f3_0.conda @@ -1056,12 +1313,12 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-storage-blobs-cpp-12.16.0-h75daedc_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-storage-common-cpp-12.12.0-h3d7a050_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-storage-files-datalake-cpp-12.14.0-hd454692_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/babel-2.17.0-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/backports.zstd-1.3.0-py314h680f03e_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/babel-2.18.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/backports.zstd-1.3.0-py313h18e8e13_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/beautifulsoup4-4.14.3-pyha770c72_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils-2.45-default_h4852527_105.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.45-default_hfdba357_105.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_linux-64-2.45-default_h4852527_105.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils-2.45.1-default_h4852527_101.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.45.1-default_hfdba357_101.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_linux-64-2.45.1-default_h4852527_101.conda - conda: https://conda.anaconda.org/conda-forge/noarch/bleach-6.3.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/bleach-with-css-6.3.0-h5f6438b_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/blosc-1.21.6-he440d0b_1.conda @@ -1069,7 +1326,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/branca-0.8.2-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-1.2.0-hed03a55_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-bin-1.2.0-hb03c661_1.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.2.0-py314h3de4e8d_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.2.0-py313hf159716_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_8.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.6-hb03c661_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/c-compiler-1.11.0-h4d9bdce_0.conda @@ -1077,24 +1334,24 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/cached-property-1.5.2-hd8ed1ab_1.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/noarch/cached_property-1.5.2-pyha770c72_1.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/noarch/certifi-2026.1.4-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/cffi-2.0.0-py314h4a8dc5f_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/cffi-2.0.0-py313hf46b229_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.4.4-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/click-8.3.1-pyh8f84b5b_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/cloudpickle-3.1.2-pyhcf101f3_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/comm-0.2.3-pyhe01879c_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/conda-gcc-specs-14.3.0-he8ccf15_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.3.3-py314h97ea11e_4.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/cpython-3.14.2-py314hd8ed1ab_101.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/conda-gcc-specs-14.3.0-he8ccf15_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.3.3-py313hc8edb43_4.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/cpython-3.13.12-py313hd8ed1ab_100.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/cxx-compiler-1.11.0-hfcd1e18_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/cycler-0.12.1-pyhcf101f3_2.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/cython-3.2.4-py314h1807b08_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/cytoolz-1.1.0-py314h5bd0f2a_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/cython-3.2.4-py313hc80a56d_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/cytoolz-1.1.0-py313h07c4f96_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/dask-2026.1.2-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/dask-core-2026.1.2-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/dask-glm-0.3.2-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/dask-ml-2025.1.0-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/debugpy-1.8.20-py314h42812f9_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/debugpy-1.8.20-py313h5d5ffb9_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/decorator-5.2.1-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/defusedxml-0.7.1-pyhd8ed1ab_0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/noarch/distributed-2026.1.2-pyhcf101f3_0.conda @@ -1102,15 +1359,15 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.1-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/executing-2.2.1-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/folium-0.20.0-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/fonttools-4.61.1-pyh7db6752_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.61.1-py313h3dea7bd_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/formulaic-1.2.1-pyhd8ed1ab_2.conda - conda: https://conda.anaconda.org/conda-forge/noarch/fqdn-1.5.1-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/freetype-2.14.1-ha770c72_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/freexl-2.0.0-h9dce30a_2.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/fsspec-2026.1.0-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc-14.3.0-h0dff253_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_impl_linux-64-14.3.0-he8b2097_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_linux-64-14.3.0-h298d278_17.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/fsspec-2026.2.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc-14.3.0-h0dff253_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_impl_linux-64-14.3.0-hb1e0a52_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_linux-64-14.3.0-h298d278_20.conda - conda: https://conda.anaconda.org/conda-forge/noarch/geopandas-1.1.2-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/geopandas-base-1.1.2-pyha770c72_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/geos-3.14.1-h480dda7_0.conda @@ -1118,9 +1375,9 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/giflib-5.2.2-hd590300_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/glog-0.7.1-hbabe93e_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/gmp-6.3.0-hac33072_2.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx-14.3.0-h76987e4_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_impl_linux-64-14.3.0-h2185e75_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_linux-64-14.3.0-h310e576_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx-14.3.0-h76987e4_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_impl_linux-64-14.3.0-h2185e75_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_linux-64-14.3.0-h3c3a7a4_20.conda - conda: https://conda.anaconda.org/conda-forge/noarch/h11-0.16.0-pyhcf101f3_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/h2-4.3.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/hpack-4.1.0-pyhd8ed1ab_0.conda @@ -1133,7 +1390,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-8.7.0-pyhe01879c_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/interface_meta-1.3.0-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/ipykernel-7.1.0-pyha191276_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/ipython-9.9.0-pyh53cf698_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/ipython-9.10.0-pyh53cf698_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/ipython_genutils-0.2.0-pyhd8ed1ab_2.conda - conda: https://conda.anaconda.org/conda-forge/noarch/ipython_pygments_lexers-1.1.1-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/isoduration-20.11.0-pyhd8ed1ab_1.conda @@ -1158,11 +1415,11 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/jupytext-1.19.1-pyhbbac1ac_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/kernel-headers_linux-64-4.18.0-he073ed8_9.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.3-hb9d3cd8_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/kiwisolver-1.4.9-py314h97ea11e_2.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/kiwisolver-1.4.9-py313hc8edb43_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/krb5-1.21.3-h659f571_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/lark-1.3.1-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/lcms2-2.18-h0c24ade_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.45-default_hbd61a6d_105.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.45.1-default_hbd61a6d_101.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/lerc-4.0.0-h0aef613_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/liac-arff-2.5.0-pyhd8ed1ab_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libabseil-20250512.1-cxx17_hba17884_0.conda @@ -1187,13 +1444,13 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/libffi-3.5.2-h3435931_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libfreetype-2.14.1-ha770c72_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libfreetype6-2.14.1-h73754d4_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.2.0-he0feb66_16.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/libgcc-devel_linux-64-14.3.0-hf649bbc_116.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.2.0-h69a702a_16.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.2.0-he0feb66_17.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/libgcc-devel_linux-64-14.3.0-hf649bbc_117.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.2.0-h69a702a_17.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libgdal-core-3.12.1-hf05ffb4_1.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.2.0-h69a702a_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.2.0-h68bc16d_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.2.0-he0feb66_16.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.2.0-h69a702a_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.2.0-h68bc16d_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.2.0-he0feb66_17.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-2.39.0-hdb79228_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-storage-2.39.0-hdbdcf42_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libgrpc-1.73.1-h3288cfb_1.conda @@ -1217,14 +1474,14 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/libpysal-4.14.1-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libre2-11-2025.11.05-h7b12aa8_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/librttopo-1.1.0-h46dd2a8_20.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libsanitizer-14.3.0-h8f1669f_16.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libsanitizer-14.3.0-h8f1669f_17.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libsodium-1.0.20-h4ab18f5_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libspatialite-5.1.0-gpl_h2abfd87_119.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.51.2-hf4e2dac_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libssh2-1.11.1-hcf80075_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.2.0-h934c35e_16.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/libstdcxx-devel_linux-64-14.3.0-h9f08a49_116.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-15.2.0-hdf11a46_16.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.2.0-h934c35e_17.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/libstdcxx-devel_linux-64-14.3.0-h9f08a49_117.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-15.2.0-hdf11a46_17.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libthrift-0.22.0-h454ac66_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libtiff-4.7.1-h9d88235_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libutf8proc-2.11.3-hfe17d71_0.conda @@ -1236,27 +1493,27 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/libxml2-devel-2.15.1-he237659_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-hb9d3cd8_2.conda - conda: https://conda.anaconda.org/conda-forge/noarch/lifelines-0.30.0-pyhd8ed1ab_1.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/llvmlite-0.46.0-py314h946fb2a_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/llvmlite-0.46.0-py313hdd307be_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/locket-1.0.0-pyhd8ed1ab_0.tar.bz2 - - conda: https://conda.anaconda.org/conda-forge/linux-64/lz4-4.4.5-py314hd4c109c_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/lz4-4.4.5-py313h28739b2_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/lz4-c-1.10.0-h5888daf_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/lzo-2.10-h280c20c_1002.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/make-4.4.1-hb9d3cd8_2.conda - conda: https://conda.anaconda.org/conda-forge/noarch/mapclassify-2.10.0-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/markdown-it-py-4.0.0-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/markupsafe-3.0.3-pyh7db6752_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.10.8-py314h1194b4b_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/markupsafe-3.0.3-py313h3dea7bd_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.10.8-py313h683a580_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/matplotlib-inline-0.2.1-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/mdit-py-plugins-0.5.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/mdurl-0.1.2-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/minio-7.2.20-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/minizip-4.0.10-h05a5f5f_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/mistune-3.2.0-pyhcf101f3_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/msgpack-python-1.1.2-py314h9891dd4_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/msgpack-python-1.1.2-py313h7037e92_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/multipledispatch-0.6.0-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/munkres-1.1.4-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/muparser-2.3.5-h5888daf_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.15.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.16.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/nbclassic-1.3.3-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/nbclient-0.10.4-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/nbconvert-7.17.0-h14065e2_0.conda @@ -1270,52 +1527,52 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/nlohmann_json-3.12.0-h54a6638_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/nomkl-1.0-h5ca1d4c_0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/noarch/notebook-shim-0.2.4-pyhd8ed1ab_1.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/numba-0.63.1-py314h8169c2f_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/numexpr-2.14.1-py314heb044ea_101.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-2.3.5-py314h2b28147_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/numba-0.63.1-py313h5dce7c4_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/numexpr-2.14.1-py313h24ae7f9_101.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-2.3.5-py313hf6604e3_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/openjpeg-2.5.4-h55fea9a_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/openml-0.12.2-pyhd8ed1ab_0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/linux-64/openssl-3.6.1-h35e630c_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/orc-2.2.2-h19cb568_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/overrides-7.7.0-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/packaging-26.0-pyhcf101f3_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/pandas-3.0.0-py314hb4ffadd_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/pandoc-3.8.3-ha770c72_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/pandas-3.0.0-py313hbfd7664_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/pandoc-3.9-ha770c72_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pandocfilters-1.5.0-pyhd8ed1ab_0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/noarch/parso-0.8.5-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/partd-1.4.2-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pbr-7.0.3-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/pcre2-10.47-haa7fec5_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pexpect-4.9.0-pyhd8ed1ab_1.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/pillow-12.1.0-py314h8ec4b1a_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0-pyh145f28c_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/pillow-12.1.0-py313h80991f8_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0.1-pyh145f28c_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/platformdirs-4.5.1-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/proj-9.7.1-he0df7b0_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/prometheus-cpp-1.3.0-ha5d0236_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/prometheus_client-0.24.1-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/prompt-toolkit-3.0.52-pyha770c72_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/psutil-7.2.2-py314h0f05182_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/psutil-7.2.2-py313h54dd161_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-hb9d3cd8_1002.conda - conda: https://conda.anaconda.org/conda-forge/noarch/ptyprocess-0.7.0-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pure_eval-0.2.3-pyhd8ed1ab_1.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/pyarrow-23.0.0-py314hdafbbf9_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/pyarrow-core-23.0.0-py314h52d6ec5_0_cpu.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/pyarrow-23.0.0-py313h78bf25f_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/pyarrow-core-23.0.0-py313he109ebe_0_cpu.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pycparser-2.22-pyh29332c3_1.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/pycryptodome-3.23.0-py314h11b9afc_2.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/pycryptodome-3.23.0-py313h6123c0d_2.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pygments-2.19.2-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/pyogrio-0.12.1-py314hbcf5174_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/pyogrio-0.12.1-py313hae45665_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.3.2-pyhcf101f3_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/pyproj-3.7.2-py314h24aeaa0_2.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/pyproj-3.7.2-py313h77f6078_2.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pysocks-1.7.1-pyha55dd90_7.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/python-3.14.2-h32b2ec7_101_cp314.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/python-3.13.12-hc97d973_100_cp313.conda - conda: https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhe01879c_2.conda - conda: https://conda.anaconda.org/conda-forge/noarch/python-fastjsonschema-2.21.2-pyhe01879c_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/python-gil-3.14.2-h4df99d1_101.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/python-gil-3.13.12-h4df99d1_100.conda - conda: https://conda.anaconda.org/conda-forge/noarch/python-json-logger-2.0.7-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/python-tzdata-2025.3-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/python_abi-3.14-8_cp314.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/python_abi-3.13-8_cp313.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pytz-2025.2-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/pyyaml-6.0.3-pyh7db6752_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/pyyaml-6.0.3-py313h3dea7bd_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/pyzmq-27.1.0-py312hfb55c3c_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/qhull-2020.2-h434a139_5.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/re2-2025.11.05-h5301d42_0.conda @@ -1327,14 +1584,14 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/rfc3987-syntax-1.1.0-pyhe01879c_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/roman-numerals-4.1.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/roman-numerals-py-4.1.0-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/rpds-py-0.30.0-py314h2e6c369_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/rpds-py-0.30.0-py313h843e2db_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/s2n-1.6.2-he8a4886_1.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/scikit-learn-1.8.0-np2py314hf09ca88_1.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/scipy-1.17.0-py314hf07bd8e_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/scikit-learn-1.8.0-np2py313h16d504d_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/scipy-1.17.0-py313h4b8bb8b_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/send2trash-2.1.0-pyha191276_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/setuptools-80.10.2-pyh332efcf_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/setuptools-scm-9.2.2-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/shapely-2.1.2-py314hbe3edd8_2.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/shapely-2.1.2-py313had47c43_2.conda - conda: https://conda.anaconda.org/conda-forge/noarch/six-1.17.0-pyhe01879c_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/snappy-1.2.2-h03e3b7b_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/sniffio-1.3.1-pyhd8ed1ab_2.conda @@ -1356,7 +1613,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/sqlite-3.51.2-h04a0ce9_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/stack_data-0.6.3-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/sysroot_linux-64-2.28-h4ee821c_9.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/tabmat-4.2.0-py314ha0b5721_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/tabmat-4.2.1-py313h08cd8bf_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/tblib-3.2.2-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/terminado-0.18.1-pyhc90fa1f_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.6.0-pyhecae5ae_0.conda @@ -1364,14 +1621,13 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.13-noxft_h366c992_103.conda - conda: https://conda.anaconda.org/conda-forge/noarch/tomli-2.4.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/toolz-1.1.0-pyhd8ed1ab_1.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/tornado-6.5.3-py314h5bd0f2a_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.2-pyh8f84b5b_2.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/tornado-6.5.3-py313h07c4f96_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.3-pyh8f84b5b_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/traitlets-5.14.3-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.15.0-h396c80c_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.15.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/typing_utils-0.1.0-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2025c-hc9c84f9_1.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/unicodedata2-17.0.0-py314h5bd0f2a_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/uri-template-1.3.0-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/uriparser-0.9.8-hac33072_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/urllib3-2.6.3-pyhd8ed1ab_0.conda @@ -1380,7 +1636,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/webencodings-0.5.1-pyhd8ed1ab_3.conda - conda: https://conda.anaconda.org/conda-forge/noarch/websocket-client-1.9.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/wheel-0.46.3-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/wrapt-2.1.0-py314h5bd0f2a_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/wrapt-2.1.1-py313h07c4f96_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/xerces-c-3.3.0-hd9031aa_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/xmltodict-1.0.2-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxau-1.0.12-hb03c661_1.conda @@ -1391,7 +1647,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/zict-3.0.0-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.23.0-pyhcf101f3_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/zlib-1.3.1-hb9d3cd8_2.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/zlib-ng-2.3.2-hceb46e0_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/zlib-ng-2.3.3-hceb46e0_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.7-hb78ec9c_6.conda osx-arm64: - conda: https://conda.anaconda.org/conda-forge/osx-arm64/_openmp_mutex-4.5-7_kmp_llvm.conda @@ -1426,7 +1682,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/osx-arm64/azure-storage-blobs-cpp-12.16.0-h6507aac_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/azure-storage-common-cpp-12.12.0-ha416c23_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/azure-storage-files-datalake-cpp-12.14.0-hcfc4f22_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/babel-2.17.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/babel-2.18.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/backports.zstd-1.3.0-py314h680f03e_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/beautifulsoup4-4.14.3-pyha770c72_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/bleach-6.3.0-pyhcf101f3_0.conda @@ -1463,7 +1719,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/osx-arm64/compiler-rt-19.1.7-h855ad52_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/compiler-rt_osx-arm64-19.1.7-he32a8d3_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/contourpy-1.3.3-py314hf8a3a22_4.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/cpython-3.14.2-py314hd8ed1ab_101.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/cpython-3.14.3-py314hd8ed1ab_100.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/cxx-compiler-1.11.0-h88570a1_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/cycler-0.12.1-pyhcf101f3_2.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/cython-3.2.4-py314hc6117b3_0.conda @@ -1485,7 +1741,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/fqdn-1.5.1-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/freetype-2.14.1-hce30654_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/freexl-2.0.0-h3ab3353_2.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/fsspec-2026.1.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/fsspec-2026.2.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/geopandas-1.1.2-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/geopandas-base-1.1.2-pyha770c72_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/geos-3.14.1-h5afe852_0.conda @@ -1505,7 +1761,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-8.7.0-pyhe01879c_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/interface_meta-1.3.0-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/ipykernel-7.1.0-pyh5552912_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/ipython-9.9.0-pyh53cf698_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/ipython-9.10.0-pyh53cf698_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/ipython_genutils-0.2.0-pyhd8ed1ab_2.conda - conda: https://conda.anaconda.org/conda-forge/noarch/ipython_pygments_lexers-1.1.1-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/isoduration-20.11.0-pyhd8ed1ab_1.conda @@ -1551,7 +1807,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libclang-cpp19.1-19.1.7-default_hf3020a7_7.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcrc32c-1.1.2-hbdafb3b_0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcurl-8.18.0-he38603e_0.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-21.1.8-hf598326_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-21.1.8-h55c6f16_2.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-devel-19.1.7-h6dc3340_2.conda - conda: https://conda.anaconda.org/conda-forge/noarch/libcxx-headers-19.1.7-h707e725_2.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libdeflate-1.25-hc11a715_0.conda @@ -1562,10 +1818,10 @@ environments: - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libffi-3.5.2-hcf2aa1b_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libfreetype-2.14.1-hce30654_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libfreetype6-2.14.1-h6da58f4_0.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgcc-15.2.0-hcbb3090_16.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgcc-15.2.0-hcbb3090_17.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgdal-core-3.12.1-ha937536_1.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran-15.2.0-h07b0088_16.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran5-15.2.0-hdae7583_16.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran-15.2.0-h07b0088_17.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran5-15.2.0-hdae7583_17.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgoogle-cloud-2.39.0-head0a95_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgoogle-cloud-storage-2.39.0-hfa3a374_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgrpc-1.73.1-h3063b79_1.conda @@ -1627,7 +1883,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/multipledispatch-0.6.0-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/munkres-1.1.4-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/muparser-2.3.5-h11e0b38_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.15.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.16.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/nbclassic-1.3.3-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/nbclient-0.10.4-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/nbconvert-7.17.0-h14065e2_0.conda @@ -1650,7 +1906,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/overrides-7.7.0-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/packaging-26.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pandas-3.0.0-py314h5e21a50_0.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pandoc-3.8.3-hce30654_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pandoc-3.9-hce30654_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pandocfilters-1.5.0-pyhd8ed1ab_0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/noarch/parso-0.8.5-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/partd-1.4.2-pyhd8ed1ab_0.conda @@ -1658,7 +1914,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pcre2-10.47-h30297fc_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pexpect-4.9.0-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pillow-12.1.0-py314hab283cf_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0-pyh145f28c_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0.1-pyh145f28c_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/platformdirs-4.5.1-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/proj-9.7.1-hfb14a63_2.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/prometheus-cpp-1.3.0-h0967b3e_0.conda @@ -1679,15 +1935,15 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.3.2-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyproj-3.7.2-py314h87291f3_2.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pysocks-1.7.1-pyha55dd90_7.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/python-3.14.2-h40d2674_101_cp314.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/python-3.14.3-h4c637c5_100_cp314.conda - conda: https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhe01879c_2.conda - conda: https://conda.anaconda.org/conda-forge/noarch/python-fastjsonschema-2.21.2-pyhe01879c_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/python-gil-3.14.2-h4df99d1_101.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/python-gil-3.14.3-h4df99d1_100.conda - conda: https://conda.anaconda.org/conda-forge/noarch/python-json-logger-2.0.7-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/python-tzdata-2025.3-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/python_abi-3.14-8_cp314.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pytz-2025.2-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/pyyaml-6.0.3-pyh7db6752_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyyaml-6.0.3-py314h6e9b3f0_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyzmq-27.1.0-py312hd65ceae_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/qhull-2020.2-h420ef59_5.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/re2-2025.11.05-h64b956e_0.conda @@ -1728,7 +1984,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxext-altair-0.2.0-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/sqlite-3.51.2-h77b7338_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/stack_data-0.6.3-pyhd8ed1ab_1.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tabmat-4.2.0-py314h759a76e_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tabmat-4.2.1-py314h759a76e_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tapi-1600.0.11.8-h997e182_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/tblib-3.2.2-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/terminado-0.18.1-pyhc90fa1f_1.conda @@ -1738,7 +1994,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/tomli-2.4.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/toolz-1.1.0-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tornado-6.5.4-py314h0612a62_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.2-pyh8f84b5b_2.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.3-pyh8f84b5b_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/traitlets-5.14.3-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.15.0-h396c80c_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.15.0-pyhcf101f3_0.conda @@ -1753,7 +2009,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/webencodings-0.5.1-pyhd8ed1ab_3.conda - conda: https://conda.anaconda.org/conda-forge/noarch/websocket-client-1.9.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/wheel-0.46.3-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/wrapt-2.1.0-py314h6c2aa35_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/wrapt-2.1.1-py314h6c2aa35_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/xerces-c-3.3.0-h25f632f_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/xmltodict-1.0.2-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/xorg-libxau-1.0.12-hc919400_1.conda @@ -1764,7 +2020,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/zict-3.0.0-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.23.0-pyhcf101f3_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/zlib-1.3.1-h8359307_2.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/zlib-ng-2.3.2-hed4e4f5_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/zlib-ng-2.3.3-hed4e4f5_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/zstd-1.5.7-hbf9d68e_6.conda win-64: - conda: https://conda.anaconda.org/conda-forge/win-64/_openmp_mutex-4.5-2_gnu.conda @@ -1793,7 +2049,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/win-64/aws-checksums-0.2.7-hcb3a2da_5.conda - conda: https://conda.anaconda.org/conda-forge/win-64/aws-crt-cpp-0.35.4-hca034e6_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/aws-sdk-cpp-1.11.606-hac16450_10.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/babel-2.17.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/babel-2.18.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/backports.zstd-1.3.0-py314h680f03e_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/beautifulsoup4-4.14.3-pyha770c72_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/bleach-6.3.0-pyhcf101f3_0.conda @@ -1818,7 +2074,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/comm-0.2.3-pyhe01879c_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/contourpy-1.3.3-py314hf309875_4.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/cpython-3.14.2-py314hd8ed1ab_101.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/cpython-3.14.3-py314hd8ed1ab_100.conda - conda: https://conda.anaconda.org/conda-forge/win-64/cxx-compiler-1.11.0-h1c1089f_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/cycler-0.12.1-pyhcf101f3_2.conda - conda: https://conda.anaconda.org/conda-forge/win-64/cython-3.2.4-py314h344ed54_0.conda @@ -1840,7 +2096,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/fqdn-1.5.1-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/freetype-2.14.1-h57928b3_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/freexl-2.0.0-hf297d47_2.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/fsspec-2026.1.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/fsspec-2026.2.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/geopandas-1.1.2-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/geopandas-base-1.1.2-pyha770c72_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/geos-3.14.1-hdade9fe_0.conda @@ -1856,7 +2112,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-8.7.0-pyhe01879c_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/interface_meta-1.3.0-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/ipykernel-7.1.0-pyh6dadd2b_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/ipython-9.9.0-pyhe2676ad_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/ipython-9.10.0-pyhe2676ad_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/ipython_genutils-0.2.0-pyhd8ed1ab_2.conda - conda: https://conda.anaconda.org/conda-forge/noarch/ipython_pygments_lexers-1.1.1-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/isoduration-20.11.0-pyhd8ed1ab_1.conda @@ -1904,9 +2160,9 @@ environments: - conda: https://conda.anaconda.org/conda-forge/win-64/libffi-3.5.2-h3d046cb_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/libfreetype-2.14.1-h57928b3_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/libfreetype6-2.14.1-hdbac1cb_0.conda - - conda: https://conda.anaconda.org/conda-forge/win-64/libgcc-15.2.0-h8ee18e1_16.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libgcc-15.2.0-h8ee18e1_17.conda - conda: https://conda.anaconda.org/conda-forge/win-64/libgdal-core-3.12.1-h4c6072a_1.conda - - conda: https://conda.anaconda.org/conda-forge/win-64/libgomp-15.2.0-h8ee18e1_16.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libgomp-15.2.0-h8ee18e1_17.conda - conda: https://conda.anaconda.org/conda-forge/win-64/libgoogle-cloud-2.39.0-h19ee442_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/libgoogle-cloud-storage-2.39.0-he04ea4c_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/libgrpc-1.73.1-h317e13b_1.conda @@ -1959,7 +2215,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/multipledispatch-0.6.0-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/munkres-1.1.4-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/muparser-2.3.5-he0c23c2_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.15.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.16.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/nbclassic-1.3.3-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/nbclient-0.10.4-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/nbconvert-7.17.0-h14065e2_0.conda @@ -1981,14 +2237,14 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/overrides-7.7.0-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/packaging-26.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/pandas-3.0.0-py314hf700ef7_0.conda - - conda: https://conda.anaconda.org/conda-forge/win-64/pandoc-3.8.3-h57928b3_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/pandoc-3.9-h57928b3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pandocfilters-1.5.0-pyhd8ed1ab_0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/noarch/parso-0.8.5-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/partd-1.4.2-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pbr-7.0.3-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/pcre2-10.47-hd2b5f0e_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/pillow-12.1.0-py314h61b30b5_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0-pyh145f28c_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0.1-pyh145f28c_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/platformdirs-4.5.1-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/proj-9.7.1-hd30e2cd_2.conda - conda: https://conda.anaconda.org/conda-forge/noarch/prometheus_client-0.24.1-pyhd8ed1ab_0.conda @@ -2005,17 +2261,17 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.3.2-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/pyproj-3.7.2-py314h422fe16_2.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pysocks-1.7.1-pyh09c184e_7.conda - - conda: https://conda.anaconda.org/conda-forge/win-64/python-3.14.2-h4b44e0e_101_cp314.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/python-3.14.3-h4b44e0e_100_cp314.conda - conda: https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhe01879c_2.conda - conda: https://conda.anaconda.org/conda-forge/noarch/python-fastjsonschema-2.21.2-pyhe01879c_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/python-gil-3.14.2-h4df99d1_101.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/python-gil-3.14.3-h4df99d1_100.conda - conda: https://conda.anaconda.org/conda-forge/noarch/python-json-logger-2.0.7-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/python-tzdata-2025.3-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/python_abi-3.14-8_cp314.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pytz-2025.2-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/pywin32-311-py314h8f8f202_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/pywinpty-2.0.15-py314h51f0985_1.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/pyyaml-6.0.3-pyh7db6752_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/pyyaml-6.0.3-py314h2359020_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/pyzmq-27.1.0-py312hbb5da91_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/qhull-2020.2-hc790b64_5.conda - conda: https://conda.anaconda.org/conda-forge/win-64/re2-2025.11.05-ha104f34_0.conda @@ -2053,7 +2309,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/sphinxext-altair-0.2.0-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/sqlite-3.51.2-hdb435a2_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/stack_data-0.6.3-pyhd8ed1ab_1.conda - - conda: https://conda.anaconda.org/conda-forge/win-64/tabmat-4.2.0-py314hd8fd7ce_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/tabmat-4.2.1-py314hd8fd7ce_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/tblib-3.2.2-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/terminado-0.18.1-pyh6dadd2b_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.6.0-pyhecae5ae_0.conda @@ -2062,7 +2318,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/tomli-2.4.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/toolz-1.1.0-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/tornado-6.5.4-py314h5a2d7ad_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.2-pyha7b4d00_2.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.3-pyha7b4d00_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/traitlets-5.14.3-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.15.0-h396c80c_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.15.0-pyhcf101f3_0.conda @@ -2086,7 +2342,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/wheel-0.46.3-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/win_inet_pton-1.1.0-pyh7428d3b_8.conda - conda: https://conda.anaconda.org/conda-forge/win-64/winpty-0.4.3-4.tar.bz2 - - conda: https://conda.anaconda.org/conda-forge/win-64/wrapt-2.1.0-py314h5a2d7ad_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/wrapt-2.1.1-py314h5a2d7ad_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/xerces-c-3.3.0-hac47afa_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/xmltodict-1.0.2-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/xorg-libxau-1.0.12-hba3369d_1.conda @@ -2097,13 +2353,11 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/zict-3.0.0-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.23.0-pyhcf101f3_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/zlib-1.3.1-h2466b09_2.conda - - conda: https://conda.anaconda.org/conda-forge/win-64/zlib-ng-2.3.2-h0261ad2_1.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/zlib-ng-2.3.3-h0261ad2_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/zstd-1.5.7-h534d264_6.conda glum-tabmat: channels: - url: https://conda.anaconda.org/conda-forge/ - options: - pypi-prerelease-mode: if-necessary-or-explicit packages: linux-64: - conda: https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2 @@ -2129,9 +2383,9 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-storage-blobs-cpp-12.16.0-h75daedc_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-storage-common-cpp-12.12.0-h3d7a050_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-storage-files-datalake-cpp-12.14.0-hd454692_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils-2.45-default_h4852527_105.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.45-default_hfdba357_105.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_linux-64-2.45-default_h4852527_105.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils-2.45.1-default_h4852527_101.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.45.1-default_hfdba357_101.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_linux-64-2.45.1-default_h4852527_101.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_8.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.6-hb03c661_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/c-compiler-1.11.0-h4d9bdce_0.conda @@ -2139,32 +2393,32 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/click-8.3.1-pyh8f84b5b_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/comm-0.2.3-pyhe01879c_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/conda-gcc-specs-14.3.0-he8ccf15_16.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/cpython-3.14.2-py314hd8ed1ab_101.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/conda-gcc-specs-14.3.0-he8ccf15_17.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/cpython-3.12.12-py312hd8ed1ab_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/cxx-compiler-1.11.0-hfcd1e18_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/cython-3.2.4-py314h1807b08_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/debugpy-1.8.20-py314h42812f9_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/cython-3.2.4-py312h68e6be4_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/debugpy-1.8.20-py312h8285ef7_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/decorator-5.2.1-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.1-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/execnet-2.1.2-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/executing-2.2.1-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/formulaic-1.2.1-pyhd8ed1ab_2.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc-14.3.0-h0dff253_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_impl_linux-64-14.3.0-he8b2097_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_linux-64-14.3.0-h298d278_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc-14.3.0-h0dff253_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_impl_linux-64-14.3.0-hb1e0a52_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_linux-64-14.3.0-h298d278_20.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/gflags-2.2.2-h5888daf_1005.conda - conda: https://conda.anaconda.org/conda-forge/noarch/git_root-0.2-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/glog-0.7.1-hbabe93e_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx-14.3.0-h76987e4_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_impl_linux-64-14.3.0-h2185e75_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_linux-64-14.3.0-h310e576_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx-14.3.0-h76987e4_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_impl_linux-64-14.3.0-h2185e75_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_linux-64-14.3.0-h3c3a7a4_20.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/icu-78.2-h33c6efd_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-8.7.0-pyhe01879c_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.3.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/interface_meta-1.3.0-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/ipdb-0.13.13-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/ipykernel-7.1.0-pyha191276_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/ipython-9.9.0-pyh53cf698_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/ipython-9.10.0-pyh53cf698_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/ipython_pygments_lexers-1.1.1-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/jedi-0.19.2-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/jemalloc-local-5.3.0-h5888daf_1.conda @@ -2174,7 +2428,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/kernel-headers_linux-64-4.18.0-he073ed8_9.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.3-hb9d3cd8_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/krb5-1.21.3-h659f571_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.45-default_hbd61a6d_105.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.45.1-default_hbd61a6d_101.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libabseil-20250512.1-cxx17_hba17884_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-23.0.0-h2c50142_1_cpu.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-acero-23.0.0-h635bf11_1_cpu.conda @@ -2193,12 +2447,12 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/libevent-2.1.12-hf998b51_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.7.3-hecca717_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libffi-3.5.2-h3435931_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.2.0-he0feb66_16.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/libgcc-devel_linux-64-14.3.0-hf649bbc_116.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.2.0-h69a702a_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.2.0-h69a702a_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.2.0-h68bc16d_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.2.0-he0feb66_16.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.2.0-he0feb66_17.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/libgcc-devel_linux-64-14.3.0-hf649bbc_117.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.2.0-h69a702a_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.2.0-h69a702a_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.2.0-h68bc16d_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.2.0-he0feb66_17.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-2.39.0-hdb79228_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-storage-2.39.0-hdbdcf42_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libgrpc-1.73.1-h3288cfb_1.conda @@ -2206,94 +2460,96 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/libjemalloc-local-5.3.0-h5888daf_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.11.0-5_h47877c9_openblas.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/liblzma-5.8.2-hb03c661_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libmpdec-4.0.0-hb03c661_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libnghttp2-1.67.0-had1ee68_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libnsl-2.0.1-hb9d3cd8_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.30-pthreads_h94d23a6_4.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libopentelemetry-cpp-1.21.0-hb9b0907_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libopentelemetry-cpp-headers-1.21.0-ha770c72_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libparquet-23.0.0-h7376487_1_cpu.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libprotobuf-6.31.1-h49aed37_4.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libre2-11-2025.11.05-h7b12aa8_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libsanitizer-14.3.0-h8f1669f_16.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libsanitizer-14.3.0-h8f1669f_17.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libsodium-1.0.20-h4ab18f5_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.51.2-hf4e2dac_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libssh2-1.11.1-hcf80075_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.2.0-h934c35e_16.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/libstdcxx-devel_linux-64-14.3.0-h9f08a49_116.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-15.2.0-hdf11a46_16.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.2.0-h934c35e_17.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/libstdcxx-devel_linux-64-14.3.0-h9f08a49_117.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-15.2.0-hdf11a46_17.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libthrift-0.22.0-h454ac66_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libutf8proc-2.11.3-hfe17d71_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.41.3-h5347b49_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libxcrypt-4.4.36-hd590300_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libxml2-16-2.15.1-hca6bf5a_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.15.1-he237659_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-hb9d3cd8_2.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/line_profiler-5.0.0-py314hc43b2c7_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/line_profiler-5.0.0-py312h0a2e395_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/lz4-c-1.10.0-h5888daf_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/make-4.4.1-hb9d3cd8_2.conda - conda: https://conda.anaconda.org/conda-forge/noarch/mako-1.3.10-pyhcf101f3_1.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/markupsafe-3.0.3-pyh7db6752_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/markupsafe-3.0.3-py312h8a5da7c_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/matplotlib-inline-0.2.1-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/memory_profiler-0.61.0-pyhcf101f3_1.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/mypy-1.19.1-py314h5bd0f2a_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/mypy-1.19.1-py312h4c3975b_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/mypy_extensions-1.1.0-pyha770c72_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.15.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.16.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h2d0b736_3.conda - conda: https://conda.anaconda.org/conda-forge/noarch/nest-asyncio-1.6.0-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/nlohmann_json-3.12.0-h54a6638_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/nomkl-1.0-h5ca1d4c_0.tar.bz2 - - conda: https://conda.anaconda.org/conda-forge/linux-64/numexpr-2.14.1-py314heb044ea_101.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-2.4.1-py314h2b28147_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/numexpr-2.14.1-py312h88efc94_101.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-2.4.2-py312h33ff503_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/openssl-3.6.1-h35e630c_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/orc-2.2.2-h19cb568_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/packaging-26.0-pyhcf101f3_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/pandas-3.0.0-py314hb4ffadd_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/pandas-3.0.0-py312h8ecdadd_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/parso-0.8.5-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pathspec-1.0.4-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/patsy-1.0.2-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pexpect-4.9.0-pyhd8ed1ab_1.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0-pyh145f28c_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0.1-pyh8b19718_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/platformdirs-4.5.1-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhf9edf01_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/prometheus-cpp-1.3.0-ha5d0236_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/prompt-toolkit-3.0.52-pyha770c72_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/psutil-7.2.2-py314h0f05182_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/psutil-7.2.2-py312h5253ce2_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/ptyprocess-0.7.0-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pure_eval-0.2.3-pyhd8ed1ab_1.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/pyarrow-23.0.0-py314hdafbbf9_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/pyarrow-core-23.0.0-py314h52d6ec5_0_cpu.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/pyarrow-23.0.0-py312h7900ff3_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/pyarrow-core-23.0.0-py312hc195796_0_cpu.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pygments-2.19.2-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-9.0.2-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.8.0-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/python-3.14.2-h32b2ec7_101_cp314.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/python-3.12.12-hd63d673_2_cpython.conda - conda: https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhe01879c_2.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/python-gil-3.14.2-h4df99d1_101.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/python-librt-0.7.8-py314h0f05182_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/python_abi-3.14-8_cp314.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/python-gil-3.12.12-hd8ed1ab_2.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/python-librt-0.7.8-py312h5253ce2_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/python_abi-3.12-8_cp312.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/pyzmq-27.1.0-py312hfb55c3c_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/re2-2025.11.05-h5301d42_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/readline-8.3-h853b02a_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/s2n-1.6.2-he8a4886_1.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/scikit-learn-1.8.0-np2py314hf09ca88_1.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/scipy-1.17.0-py314hf07bd8e_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/scikit-learn-1.8.0-np2py312h3226591_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/scipy-1.17.0-py312h54fa4ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/setuptools-80.10.2-pyh332efcf_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/setuptools-scm-9.2.2-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/six-1.17.0-pyhe01879c_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/snappy-1.2.2-h03e3b7b_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/stack_data-0.6.3-pyhd8ed1ab_1.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/statsmodels-0.14.6-py314hc02f841_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/statsmodels-0.14.6-py312h4f23490_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/sysroot_linux-64-2.28-h4ee821c_9.conda - conda: https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.6.0-pyhecae5ae_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.13-noxft_h366c992_103.conda - conda: https://conda.anaconda.org/conda-forge/noarch/toml-0.10.2-pyhcf101f3_3.conda - conda: https://conda.anaconda.org/conda-forge/noarch/tomli-2.4.0-pyhcf101f3_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/tornado-6.5.3-py314h5bd0f2a_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.2-pyh8f84b5b_2.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/tornado-6.5.3-py312h4c3975b_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.3-pyh8f84b5b_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/traitlets-5.14.3-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.15.0-h396c80c_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.15.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2025c-hc9c84f9_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/wcwidth-0.5.3-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/wrapt-2.1.0-py314h5bd0f2a_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/wheel-0.46.3-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/wrapt-2.1.1-py312h4c3975b_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/xsimd-14.0.0-h171cf75_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/zeromq-4.3.5-h387f397_9.conda - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.23.0-pyhcf101f3_1.conda @@ -2342,7 +2598,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/comm-0.2.3-pyhe01879c_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/compiler-rt-19.1.7-h855ad52_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/compiler-rt_osx-arm64-19.1.7-he32a8d3_1.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/cpython-3.14.2-py314hd8ed1ab_101.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/cpython-3.14.3-py314hd8ed1ab_100.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/cxx-compiler-1.11.0-h88570a1_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/cython-3.2.4-py314hc6117b3_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/debugpy-1.8.20-py314he609de1_0.conda @@ -2360,7 +2616,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/interface_meta-1.3.0-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/ipdb-0.13.13-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/ipykernel-7.1.0-pyh5552912_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/ipython-9.9.0-pyh53cf698_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/ipython-9.10.0-pyh53cf698_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/ipython_pygments_lexers-1.1.1-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/jedi-0.19.2-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/jemalloc-local-5.3.0-hf9b8971_1.conda @@ -2384,7 +2640,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libclang-cpp19.1-19.1.7-default_hf3020a7_7.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcrc32c-1.1.2-hbdafb3b_0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcurl-8.18.0-he38603e_0.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-21.1.8-hf598326_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-21.1.8-h55c6f16_2.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-devel-19.1.7-h6dc3340_2.conda - conda: https://conda.anaconda.org/conda-forge/noarch/libcxx-headers-19.1.7-h707e725_2.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libedit-3.1.20250104-pl5321hafb1f1b_0.conda @@ -2392,9 +2648,9 @@ environments: - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libevent-2.1.12-h2757513_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libexpat-2.7.3-haf25636_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libffi-3.5.2-hcf2aa1b_0.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgcc-15.2.0-hcbb3090_16.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran-15.2.0-h07b0088_16.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran5-15.2.0-hdae7583_16.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgcc-15.2.0-hcbb3090_17.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran-15.2.0-h07b0088_17.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran5-15.2.0-hdae7583_17.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgoogle-cloud-2.39.0-head0a95_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgoogle-cloud-storage-2.39.0-hfa3a374_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgrpc-1.73.1-h3063b79_1.conda @@ -2432,12 +2688,12 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/memory_profiler-0.61.0-pyhcf101f3_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/mypy-1.19.1-py314hbdd0d06_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/mypy_extensions-1.1.0-pyha770c72_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.15.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.16.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/ncurses-6.5-h5e97a16_3.conda - conda: https://conda.anaconda.org/conda-forge/noarch/nest-asyncio-1.6.0-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/nlohmann_json-3.12.0-h784d473_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/numexpr-2.14.1-py314hc5bb990_1.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/numpy-2.4.1-py314hae46ccb_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/numpy-2.4.2-py314hae46ccb_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/openssl-3.6.1-hd24854e_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/orc-2.2.2-hac85105_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/packaging-26.0-pyhcf101f3_0.conda @@ -2446,7 +2702,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/pathspec-1.0.4-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/patsy-1.0.2-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pexpect-4.9.0-pyhd8ed1ab_1.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0-pyh145f28c_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0.1-pyh145f28c_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/platformdirs-4.5.1-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhf9edf01_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/prometheus-cpp-1.3.0-h0967b3e_0.conda @@ -2459,9 +2715,9 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/pygments-2.19.2-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-9.0.2-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.8.0-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/python-3.14.2-h40d2674_101_cp314.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/python-3.14.3-h4c637c5_100_cp314.conda - conda: https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhe01879c_2.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/python-gil-3.14.2-h4df99d1_101.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/python-gil-3.14.3-h4df99d1_100.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/python-librt-0.7.8-py314ha14b1ff_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/python_abi-3.14-8_cp314.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyzmq-27.1.0-py312hd65ceae_0.conda @@ -2483,13 +2739,13 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/toml-0.10.2-pyhcf101f3_3.conda - conda: https://conda.anaconda.org/conda-forge/noarch/tomli-2.4.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tornado-6.5.4-py314h0612a62_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.2-pyh8f84b5b_2.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.3-pyh8f84b5b_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/traitlets-5.14.3-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.15.0-h396c80c_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.15.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2025c-hc9c84f9_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/wcwidth-0.5.3-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/wrapt-2.1.0-py314h6c2aa35_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/wrapt-2.1.1-py314h6c2aa35_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/xsimd-14.0.0-h49c215f_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/zeromq-4.3.5-h888dc83_9.conda - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.23.0-pyhcf101f3_1.conda @@ -2520,7 +2776,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/click-8.3.1-pyha7b4d00_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/comm-0.2.3-pyhe01879c_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/cpython-3.14.2-py314hd8ed1ab_101.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/cpython-3.14.3-py314hd8ed1ab_100.conda - conda: https://conda.anaconda.org/conda-forge/win-64/cxx-compiler-1.11.0-h1c1089f_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/cython-3.2.4-py314h344ed54_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/debugpy-1.8.20-py314hb98de8c_0.conda @@ -2535,7 +2791,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/interface_meta-1.3.0-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/ipdb-0.13.13-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/ipykernel-7.1.0-pyh6dadd2b_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/ipython-9.9.0-pyhe2676ad_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/ipython-9.10.0-pyhe2676ad_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/ipython_pygments_lexers-1.1.1-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/jedi-0.19.2-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/joblib-1.5.3-pyhd8ed1ab_0.conda @@ -2558,8 +2814,8 @@ environments: - conda: https://conda.anaconda.org/conda-forge/win-64/libevent-2.1.12-h3671451_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/libexpat-2.7.3-hac47afa_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/libffi-3.5.2-h3d046cb_0.conda - - conda: https://conda.anaconda.org/conda-forge/win-64/libgcc-15.2.0-h8ee18e1_16.conda - - conda: https://conda.anaconda.org/conda-forge/win-64/libgomp-15.2.0-h8ee18e1_16.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libgcc-15.2.0-h8ee18e1_17.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libgomp-15.2.0-h8ee18e1_17.conda - conda: https://conda.anaconda.org/conda-forge/win-64/libgoogle-cloud-2.39.0-h19ee442_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/libgoogle-cloud-storage-2.39.0-he04ea4c_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/libgrpc-1.73.1-h317e13b_1.conda @@ -2585,11 +2841,11 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/memory_profiler-0.61.0-pyhcf101f3_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/mypy-1.19.1-py314h5a2d7ad_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/mypy_extensions-1.1.0-pyha770c72_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.15.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.16.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/nest-asyncio-1.6.0-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/nomkl-1.0-h5ca1d4c_0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/win-64/numexpr-2.14.1-py314h5a6676c_101.conda - - conda: https://conda.anaconda.org/conda-forge/win-64/numpy-2.4.1-py314h06c3c77_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/numpy-2.4.2-py314h06c3c77_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/openssl-3.6.1-hf411b9b_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/orc-2.2.2-hbd3206f_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/packaging-26.0-pyhcf101f3_0.conda @@ -2597,7 +2853,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/parso-0.8.5-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pathspec-1.0.4-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/patsy-1.0.2-pyhcf101f3_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0-pyh145f28c_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0.1-pyh145f28c_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/platformdirs-4.5.1-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhf9edf01_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/prompt-toolkit-3.0.52-pyha770c72_0.conda @@ -2608,9 +2864,9 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/pygments-2.19.2-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-9.0.2-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.8.0-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/win-64/python-3.14.2-h4b44e0e_101_cp314.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/python-3.14.3-h4b44e0e_100_cp314.conda - conda: https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhe01879c_2.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/python-gil-3.14.2-h4df99d1_101.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/python-gil-3.14.3-h4df99d1_100.conda - conda: https://conda.anaconda.org/conda-forge/win-64/python-librt-0.7.8-py314hc5dbbe4_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/python-tzdata-2025.3-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/python_abi-3.14-8_cp314.conda @@ -2630,7 +2886,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/toml-0.10.2-pyhcf101f3_3.conda - conda: https://conda.anaconda.org/conda-forge/noarch/tomli-2.4.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/tornado-6.5.4-py314h5a2d7ad_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.2-pyha7b4d00_2.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.3-pyha7b4d00_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/traitlets-5.14.3-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.15.0-h396c80c_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.15.0-pyhcf101f3_0.conda @@ -2643,7 +2899,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/win-64/vs2022_win-64-19.44.35207-ha74f236_34.conda - conda: https://conda.anaconda.org/conda-forge/noarch/vswhere-3.1.7-h40126e0_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/wcwidth-0.5.3-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/win-64/wrapt-2.1.0-py314h5a2d7ad_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/wrapt-2.1.1-py314h5a2d7ad_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/xsimd-14.0.0-h477610d_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/zeromq-4.3.5-h5bddc39_9.conda - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.23.0-pyhcf101f3_1.conda @@ -2651,8 +2907,6 @@ environments: lint: channels: - url: https://conda.anaconda.org/conda-forge/ - options: - pypi-prerelease-mode: if-necessary-or-explicit packages: linux-64: - conda: https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2 @@ -2662,20 +2916,20 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/cffi-2.0.0-py314h4a8dc5f_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/cfgv-3.5.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/cython-3.2.4-py314h1807b08_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/cython-lint-0.18.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/cython-lint-0.19.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/distlib-0.4.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/filelock-3.20.3-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/icu-78.2-h33c6efd_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/identify-2.6.16-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.45-default_hbd61a6d_105.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.45.1-default_hbd61a6d_101.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.7.3-hecca717_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libffi-3.5.2-h3435931_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.2.0-he0feb66_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.2.0-he0feb66_16.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.2.0-he0feb66_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.2.0-he0feb66_17.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/liblzma-5.8.2-hb03c661_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libmpdec-4.0.0-hb03c661_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.51.2-hf4e2dac_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.2.0-h934c35e_16.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.2.0-h934c35e_17.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.41.3-h5347b49_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-hb9d3cd8_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h2d0b736_3.conda @@ -2686,13 +2940,13 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/pre-commit-hooks-5.0.0-pyhd8ed1ab_2.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pycodestyle-2.14.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pycparser-2.22-pyh29332c3_1.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/python-3.14.2-h32b2ec7_101_cp314.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/python-3.14.3-h32b2ec7_100_cp314.conda - conda: https://conda.anaconda.org/conda-forge/noarch/python_abi-3.14-8_cp314.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/pyyaml-6.0.3-pyh7db6752_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/pyyaml-6.0.3-py314h67df5f8_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/readline-8.3-h853b02a_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/ruamel.yaml-0.19.1-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/ruamel.yaml.clib-0.2.15-py314h0f05182_1.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/ruff-0.14.14-h40fa522_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/ruff-0.15.0-h40fa522_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/setuptools-80.10.2-pyh332efcf_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.13-noxft_h366c992_103.conda - conda: https://conda.anaconda.org/conda-forge/noarch/tokenize-rt-6.2.0-pyhd8ed1ab_0.conda @@ -2709,12 +2963,12 @@ environments: - conda: https://conda.anaconda.org/conda-forge/osx-arm64/cffi-2.0.0-py314h44086f9_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/cfgv-3.5.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/cython-3.2.4-py314hc6117b3_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/cython-lint-0.18.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/cython-lint-0.19.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/distlib-0.4.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/filelock-3.20.3-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/icu-78.2-h38cb7af_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/identify-2.6.16-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-21.1.8-hf598326_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-21.1.8-h55c6f16_2.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libexpat-2.7.3-haf25636_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libffi-3.5.2-hcf2aa1b_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/liblzma-5.8.2-h8088a28_0.conda @@ -2729,13 +2983,13 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/pre-commit-hooks-5.0.0-pyhd8ed1ab_2.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pycodestyle-2.14.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pycparser-2.22-pyh29332c3_1.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/python-3.14.2-h40d2674_101_cp314.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/python-3.14.3-h4c637c5_100_cp314.conda - conda: https://conda.anaconda.org/conda-forge/noarch/python_abi-3.14-8_cp314.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/pyyaml-6.0.3-pyh7db6752_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyyaml-6.0.3-py314h6e9b3f0_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/readline-8.3-h46df422_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/ruamel.yaml-0.19.1-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/ruamel.yaml.clib-0.2.15-py314ha14b1ff_1.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/ruff-0.14.14-h279115b_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/ruff-0.15.0-h279115b_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/setuptools-80.10.2-pyh332efcf_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tk-8.6.13-h010d191_3.conda - conda: https://conda.anaconda.org/conda-forge/noarch/tokenize-rt-6.2.0-pyhd8ed1ab_0.conda @@ -2752,7 +3006,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/win-64/cffi-2.0.0-py314h5a2d7ad_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/cfgv-3.5.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/cython-3.2.4-py314h344ed54_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/cython-lint-0.18.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/cython-lint-0.19.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/distlib-0.4.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/filelock-3.20.3-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/identify-2.6.16-pyhd8ed1ab_0.conda @@ -2769,12 +3023,12 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/pre-commit-hooks-5.0.0-pyhd8ed1ab_2.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pycodestyle-2.14.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pycparser-2.22-pyh29332c3_1.conda - - conda: https://conda.anaconda.org/conda-forge/win-64/python-3.14.2-h4b44e0e_101_cp314.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/python-3.14.3-h4b44e0e_100_cp314.conda - conda: https://conda.anaconda.org/conda-forge/noarch/python_abi-3.14-8_cp314.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/pyyaml-6.0.3-pyh7db6752_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/pyyaml-6.0.3-py314h2359020_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/ruamel.yaml-0.19.1-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/ruamel.yaml.clib-0.2.15-py314hc5dbbe4_1.conda - - conda: https://conda.anaconda.org/conda-forge/win-64/ruff-0.14.14-h213852a_1.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/ruff-0.15.0-h213852a_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/setuptools-80.10.2-pyh332efcf_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/tk-8.6.13-h6ed50ae_3.conda - conda: https://conda.anaconda.org/conda-forge/noarch/tokenize-rt-6.2.0-pyhd8ed1ab_0.conda @@ -2792,8 +3046,6 @@ environments: nightly: channels: - url: https://conda.anaconda.org/conda-forge/ - options: - pypi-prerelease-mode: if-necessary-or-explicit packages: linux-64: - conda: https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2 @@ -2817,30 +3069,30 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-storage-blobs-cpp-12.16.0-h75daedc_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-storage-common-cpp-12.12.0-h3d7a050_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-storage-files-datalake-cpp-12.14.0-hd454692_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils-2.45-default_h4852527_105.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.45-default_hfdba357_105.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_linux-64-2.45-default_h4852527_105.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils-2.45.1-default_h4852527_101.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.45.1-default_hfdba357_101.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_linux-64-2.45.1-default_h4852527_101.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_8.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.6-hb03c661_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/c-compiler-1.11.0-h4d9bdce_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2026.1.4-hbd8a1cb_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/click-8.3.1-pyh8f84b5b_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_1.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/conda-gcc-specs-14.3.0-he8ccf15_16.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/conda-gcc-specs-14.3.0-he8ccf15_17.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/cxx-compiler-1.11.0-hfcd1e18_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/cython-3.2.4-py313hc80a56d_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.1-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/execnet-2.1.2-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/formulaic-1.2.1-pyhd8ed1ab_2.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc-14.3.0-h0dff253_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_impl_linux-64-14.3.0-he8b2097_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_linux-64-14.3.0-h298d278_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc-14.3.0-h0dff253_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_impl_linux-64-14.3.0-hb1e0a52_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_linux-64-14.3.0-h298d278_20.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/gflags-2.2.2-h5888daf_1005.conda - conda: https://conda.anaconda.org/conda-forge/noarch/git_root-0.2-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/glog-0.7.1-hbabe93e_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx-14.3.0-h76987e4_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_impl_linux-64-14.3.0-h2185e75_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_linux-64-14.3.0-h310e576_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx-14.3.0-h76987e4_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_impl_linux-64-14.3.0-h2185e75_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_linux-64-14.3.0-h3c3a7a4_20.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/icu-78.2-h33c6efd_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-8.7.0-pyhe01879c_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.3.0-pyhd8ed1ab_0.conda @@ -2850,7 +3102,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/kernel-headers_linux-64-4.18.0-he073ed8_9.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.3-hb9d3cd8_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/krb5-1.21.3-h659f571_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.45-default_hbd61a6d_105.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.45.1-default_hbd61a6d_101.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libabseil-20250512.1-cxx17_hba17884_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-23.0.0-h2c50142_1_cpu.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-acero-23.0.0-h635bf11_1_cpu.conda @@ -2869,12 +3121,12 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/libevent-2.1.12-hf998b51_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.7.3-hecca717_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libffi-3.5.2-h3435931_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.2.0-he0feb66_16.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/libgcc-devel_linux-64-14.3.0-hf649bbc_116.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.2.0-h69a702a_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.2.0-h69a702a_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.2.0-h68bc16d_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.2.0-he0feb66_16.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.2.0-he0feb66_17.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/libgcc-devel_linux-64-14.3.0-hf649bbc_117.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.2.0-h69a702a_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.2.0-h69a702a_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.2.0-h68bc16d_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.2.0-he0feb66_17.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-2.39.0-hdb79228_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-storage-2.39.0-hdbdcf42_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libgrpc-1.73.1-h3288cfb_1.conda @@ -2890,12 +3142,12 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/libparquet-23.0.0-h7376487_1_cpu.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libprotobuf-6.31.1-h49aed37_4.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libre2-11-2025.11.05-h7b12aa8_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libsanitizer-14.3.0-h8f1669f_16.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libsanitizer-14.3.0-h8f1669f_17.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.51.2-hf4e2dac_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libssh2-1.11.1-hcf80075_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.2.0-h934c35e_16.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/libstdcxx-devel_linux-64-14.3.0-h9f08a49_116.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-15.2.0-hdf11a46_16.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.2.0-h934c35e_17.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/libstdcxx-devel_linux-64-14.3.0-h9f08a49_117.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-15.2.0-hdf11a46_17.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libthrift-0.22.0-h454ac66_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libutf8proc-2.11.3-hfe17d71_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.41.3-h5347b49_0.conda @@ -2908,19 +3160,19 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/markupsafe-3.0.3-py313h3dea7bd_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/mypy-1.19.1-py313h07c4f96_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/mypy_extensions-1.1.0-pyha770c72_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.15.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.16.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h2d0b736_3.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/nlohmann_json-3.12.0-h54a6638_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/nomkl-1.0-h5ca1d4c_0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/linux-64/numexpr-2.14.1-py313h24ae7f9_101.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-2.4.1-py313hf6604e3_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-2.4.2-py313hf6604e3_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/openssl-3.6.1-h35e630c_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/orc-2.2.2-h19cb568_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/packaging-26.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/pandas-3.0.0-py313hbfd7664_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pathspec-1.0.4-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/patsy-1.0.2-pyhcf101f3_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0-pyh145f28c_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0.1-pyh145f28c_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhf9edf01_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/prometheus-cpp-1.3.0-ha5d0236_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/psutil-7.2.2-py313h54dd161_0.conda @@ -2929,7 +3181,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/pygments-2.19.2-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-9.0.2-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.8.0-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/python-3.13.11-hc97d973_101_cp313.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/python-3.13.12-hc97d973_100_cp313.conda - conda: https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhe01879c_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/python-librt-0.7.8-py313h54dd161_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/python_abi-3.13-8_cp313.conda @@ -2944,16 +3196,16 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/snappy-1.2.2-h03e3b7b_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/statsmodels-0.14.6-py313h29aa505_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/sysroot_linux-64-2.28-h4ee821c_9.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/tabmat-4.2.0-py313h08cd8bf_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/tabmat-4.2.1-py313h08cd8bf_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.6.0-pyhecae5ae_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.13-noxft_h366c992_103.conda - conda: https://conda.anaconda.org/conda-forge/noarch/tomli-2.4.0-pyhcf101f3_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.2-pyh8f84b5b_2.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.3-pyh8f84b5b_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.15.0-h396c80c_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.15.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2025c-hc9c84f9_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/wheel-0.46.3-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/wrapt-2.1.0-py313h07c4f96_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/wrapt-2.1.1-py313h07c4f96_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/xsimd-14.0.0-h171cf75_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.23.0-pyhcf101f3_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/zlib-1.3.1-hb9d3cd8_2.conda @@ -3028,7 +3280,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libclang-cpp19.1-19.1.7-default_hf3020a7_7.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcrc32c-1.1.2-hbdafb3b_0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcurl-8.18.0-he38603e_0.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-21.1.8-hf598326_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-21.1.8-h55c6f16_2.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-devel-19.1.7-h6dc3340_2.conda - conda: https://conda.anaconda.org/conda-forge/noarch/libcxx-headers-19.1.7-h707e725_2.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libedit-3.1.20250104-pl5321hafb1f1b_0.conda @@ -3036,9 +3288,9 @@ environments: - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libevent-2.1.12-h2757513_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libexpat-2.7.3-haf25636_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libffi-3.5.2-hcf2aa1b_0.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgcc-15.2.0-hcbb3090_16.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran-15.2.0-h07b0088_16.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran5-15.2.0-hdae7583_16.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgcc-15.2.0-hcbb3090_17.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran-15.2.0-h07b0088_17.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran5-15.2.0-hdae7583_17.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgoogle-cloud-2.39.0-head0a95_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgoogle-cloud-storage-2.39.0-hfa3a374_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgrpc-1.73.1-h3063b79_1.conda @@ -3072,18 +3324,18 @@ environments: - conda: https://conda.anaconda.org/conda-forge/osx-arm64/markupsafe-3.0.3-py313h7d74516_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/mypy-1.19.1-py313hd3e6d80_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/mypy_extensions-1.1.0-pyha770c72_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.15.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.16.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/ncurses-6.5-h5e97a16_3.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/nlohmann_json-3.12.0-h784d473_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/numexpr-2.14.1-py313h73ed539_1.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/numpy-2.4.1-py313h16eae64_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/numpy-2.4.2-py313h16eae64_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/openssl-3.6.1-hd24854e_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/orc-2.2.2-hac85105_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/packaging-26.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pandas-3.0.0-py313h6974306_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pathspec-1.0.4-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/patsy-1.0.2-pyhcf101f3_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0-pyh145f28c_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0.1-pyh145f28c_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhf9edf01_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/prometheus-cpp-1.3.0-h0967b3e_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/psutil-7.2.2-py313h6688731_0.conda @@ -3092,7 +3344,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/pygments-2.19.2-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-9.0.2-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.8.0-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/python-3.13.11-hfc2f54d_101_cp313.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/python-3.13.12-h20e6be0_100_cp313.conda - conda: https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhe01879c_2.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/python-librt-0.7.8-py313h6688731_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/python_abi-3.13-8_cp313.conda @@ -3107,17 +3359,17 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/six-1.17.0-pyhe01879c_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/snappy-1.2.2-hada39a4_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/statsmodels-0.14.6-py313hc577518_0.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tabmat-4.2.0-py313h5e3876c_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tabmat-4.2.1-py313h5e3876c_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tapi-1600.0.11.8-h997e182_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.6.0-pyhecae5ae_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tk-8.6.13-h010d191_3.conda - conda: https://conda.anaconda.org/conda-forge/noarch/tomli-2.4.0-pyhcf101f3_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.2-pyh8f84b5b_2.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.3-pyh8f84b5b_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.15.0-h396c80c_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.15.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2025c-hc9c84f9_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/wheel-0.46.3-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/wrapt-2.1.0-py313h0997733_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/wrapt-2.1.1-py313h0997733_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/xsimd-14.0.0-h49c215f_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.23.0-pyhcf101f3_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/zlib-1.3.1-h8359307_2.conda @@ -3171,8 +3423,8 @@ environments: - conda: https://conda.anaconda.org/conda-forge/win-64/libevent-2.1.12-h3671451_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/libexpat-2.7.3-hac47afa_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/libffi-3.5.2-h3d046cb_0.conda - - conda: https://conda.anaconda.org/conda-forge/win-64/libgcc-15.2.0-h8ee18e1_16.conda - - conda: https://conda.anaconda.org/conda-forge/win-64/libgomp-15.2.0-h8ee18e1_16.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libgcc-15.2.0-h8ee18e1_17.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libgomp-15.2.0-h8ee18e1_17.conda - conda: https://conda.anaconda.org/conda-forge/win-64/libgoogle-cloud-2.39.0-h19ee442_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/libgoogle-cloud-storage-2.39.0-he04ea4c_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/libgrpc-1.73.1-h317e13b_1.conda @@ -3194,17 +3446,17 @@ environments: - conda: https://conda.anaconda.org/conda-forge/win-64/markupsafe-3.0.3-py313hd650c13_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/mypy-1.19.1-py313h5ea7bf4_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/mypy_extensions-1.1.0-pyha770c72_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.15.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.16.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/nomkl-1.0-h5ca1d4c_0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/win-64/numexpr-2.14.1-py313h7034ea3_101.conda - - conda: https://conda.anaconda.org/conda-forge/win-64/numpy-2.4.1-py313hce7ae62_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/numpy-2.4.2-py313hce7ae62_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/openssl-3.6.1-hf411b9b_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/orc-2.2.2-hbd3206f_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/packaging-26.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/pandas-3.0.0-py313h26f5e95_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pathspec-1.0.4-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/patsy-1.0.2-pyhcf101f3_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0-pyh145f28c_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0.1-pyh145f28c_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhf9edf01_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/psutil-7.2.2-py313h5fd188c_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/pyarrow-23.0.0-py313hfa70ccb_0.conda @@ -3212,7 +3464,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/pygments-2.19.2-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-9.0.2-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.8.0-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/win-64/python-3.13.11-h09917c8_101_cp313.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/python-3.13.12-h09917c8_100_cp313.conda - conda: https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhe01879c_2.conda - conda: https://conda.anaconda.org/conda-forge/win-64/python-librt-0.7.8-py313h5fd188c_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/python-tzdata-2025.3-pyhd8ed1ab_0.conda @@ -3225,11 +3477,11 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/six-1.17.0-pyhe01879c_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/snappy-1.2.2-h7fa0ca8_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/statsmodels-0.14.6-py313h0591002_0.conda - - conda: https://conda.anaconda.org/conda-forge/win-64/tabmat-4.2.0-py313hc90dcd4_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/tabmat-4.2.1-py313hc90dcd4_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.6.0-pyhecae5ae_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/tk-8.6.13-h6ed50ae_3.conda - conda: https://conda.anaconda.org/conda-forge/noarch/tomli-2.4.0-pyhcf101f3_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.2-pyha7b4d00_2.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.3-pyha7b4d00_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.15.0-h396c80c_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.15.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2025c-hc9c84f9_1.conda @@ -3241,15 +3493,13 @@ environments: - conda: https://conda.anaconda.org/conda-forge/win-64/vs2022_win-64-19.44.35207-ha74f236_34.conda - conda: https://conda.anaconda.org/conda-forge/noarch/vswhere-3.1.7-h40126e0_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/wheel-0.46.3-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/win-64/wrapt-2.1.0-py313h5ea7bf4_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/wrapt-2.1.1-py313h5ea7bf4_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/xsimd-14.0.0-h477610d_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.23.0-pyhcf101f3_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/zstd-1.5.7-h534d264_6.conda oldies: channels: - url: https://conda.anaconda.org/conda-forge/ - options: - pypi-prerelease-mode: if-necessary-or-explicit packages: linux-64: - conda: https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2 @@ -3274,9 +3524,9 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-storage-blobs-cpp-12.16.0-h75daedc_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-storage-common-cpp-12.12.0-h3d7a050_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-storage-files-datalake-cpp-12.14.0-hd454692_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils-2.45-default_h4852527_105.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.45-default_hfdba357_105.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_linux-64-2.45-default_h4852527_105.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils-2.45.1-default_h4852527_101.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.45.1-default_hfdba357_101.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_linux-64-2.45.1-default_h4852527_101.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_8.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.6-hb03c661_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/c-compiler-1.11.0-h4d9bdce_0.conda @@ -3284,22 +3534,22 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/cached_property-1.5.2-pyha770c72_1.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/noarch/click-8.1.8-pyh707e725_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_1.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/conda-gcc-specs-14.3.0-he8ccf15_16.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/conda-gcc-specs-14.3.0-he8ccf15_17.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/cxx-compiler-1.11.0-hfcd1e18_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/cython-3.1.3-py39h6bc127c_2.conda - conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/execnet-2.1.1-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/formulaic-0.6.6-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc-14.3.0-h0dff253_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_impl_linux-64-14.3.0-he8b2097_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_linux-64-14.3.0-h298d278_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc-14.3.0-h0dff253_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_impl_linux-64-14.3.0-hb1e0a52_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_linux-64-14.3.0-h298d278_20.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/gflags-2.2.2-h5888daf_1005.conda - conda: https://conda.anaconda.org/conda-forge/noarch/git_root-0.2-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/glog-0.7.1-hbabe93e_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/graphlib-backport-1.0.3-pyhd8ed1ab_0.tar.bz2 - - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx-14.3.0-h76987e4_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_impl_linux-64-14.3.0-h2185e75_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_linux-64-14.3.0-h310e576_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx-14.3.0-h76987e4_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_impl_linux-64-14.3.0-h2185e75_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_linux-64-14.3.0-h3c3a7a4_20.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/icu-78.2-h33c6efd_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-8.7.0-pyhe01879c_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.0.0-pyhd8ed1ab_1.conda @@ -3308,7 +3558,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/kernel-headers_linux-64-4.18.0-he073ed8_9.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.3-hb9d3cd8_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/krb5-1.21.3-h659f571_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.45-default_hbd61a6d_105.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.45.1-default_hbd61a6d_101.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libabseil-20250512.1-cxx17_hba17884_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-21.0.0-h2c50142_16_cpu.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-acero-21.0.0-h635bf11_16_cpu.conda @@ -3327,13 +3577,13 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/libevent-2.1.12-hf998b51_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.7.3-hecca717_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libffi-3.5.2-h3435931_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.2.0-he0feb66_16.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/libgcc-devel_linux-64-14.3.0-hf649bbc_116.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.2.0-h69a702a_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.2.0-h69a702a_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-ng-15.2.0-h69a702a_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.2.0-h68bc16d_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.2.0-he0feb66_16.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.2.0-he0feb66_17.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/libgcc-devel_linux-64-14.3.0-hf649bbc_117.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.2.0-h69a702a_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.2.0-h69a702a_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-ng-15.2.0-h69a702a_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.2.0-h68bc16d_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.2.0-he0feb66_17.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-2.39.0-hdb79228_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-storage-2.39.0-hdbdcf42_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libgrpc-1.73.1-h3288cfb_1.conda @@ -3349,12 +3599,12 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/libparquet-21.0.0-h7376487_16_cpu.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libprotobuf-6.31.1-h49aed37_4.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libre2-11-2025.11.05-h7b12aa8_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libsanitizer-14.3.0-h8f1669f_16.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libsanitizer-14.3.0-h8f1669f_17.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.51.2-hf4e2dac_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libssh2-1.11.1-hcf80075_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.2.0-h934c35e_16.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/libstdcxx-devel_linux-64-14.3.0-h9f08a49_116.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-15.2.0-hdf11a46_16.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.2.0-h934c35e_17.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/libstdcxx-devel_linux-64-14.3.0-h9f08a49_117.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-15.2.0-hdf11a46_17.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libthrift-0.22.0-h454ac66_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libutf8proc-2.11.3-hfe17d71_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.41.3-h5347b49_0.conda @@ -3485,7 +3735,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libclang-cpp19.1-19.1.7-default_hf3020a7_7.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcrc32c-1.1.2-hbdafb3b_0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcurl-8.18.0-he38603e_0.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-21.1.8-hf598326_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-21.1.8-h55c6f16_2.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-devel-19.1.7-h6dc3340_2.conda - conda: https://conda.anaconda.org/conda-forge/noarch/libcxx-headers-19.1.7-h707e725_2.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libedit-3.1.20250104-pl5321hafb1f1b_0.conda @@ -3493,9 +3743,9 @@ environments: - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libevent-2.1.12-h2757513_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libexpat-2.7.3-haf25636_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libffi-3.5.2-hcf2aa1b_0.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgcc-15.2.0-hcbb3090_16.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran-15.2.0-h07b0088_16.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran5-15.2.0-hdae7583_16.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgcc-15.2.0-hcbb3090_17.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran-15.2.0-h07b0088_17.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran5-15.2.0-hdae7583_17.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgoogle-cloud-2.39.0-head0a95_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgoogle-cloud-storage-2.39.0-hfa3a374_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgrpc-1.73.1-h3063b79_1.conda @@ -3697,8 +3947,6 @@ environments: py310: channels: - url: https://conda.anaconda.org/conda-forge/ - options: - pypi-prerelease-mode: if-necessary-or-explicit packages: linux-64: - conda: https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2 @@ -3722,30 +3970,30 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-storage-blobs-cpp-12.16.0-h75daedc_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-storage-common-cpp-12.12.0-h3d7a050_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-storage-files-datalake-cpp-12.14.0-hd454692_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils-2.45-default_h4852527_105.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.45-default_hfdba357_105.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_linux-64-2.45-default_h4852527_105.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils-2.45.1-default_h4852527_101.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.45.1-default_hfdba357_101.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_linux-64-2.45.1-default_h4852527_101.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_8.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.6-hb03c661_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/c-compiler-1.11.0-h4d9bdce_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2026.1.4-hbd8a1cb_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/click-8.3.1-pyh8f84b5b_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_1.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/conda-gcc-specs-14.3.0-he8ccf15_16.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/conda-gcc-specs-14.3.0-he8ccf15_17.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/cxx-compiler-1.11.0-hfcd1e18_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/cython-3.2.4-py310ha58568a_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.1-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/execnet-2.1.2-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/formulaic-1.2.1-pyhd8ed1ab_2.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc-14.3.0-h0dff253_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_impl_linux-64-14.3.0-he8b2097_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_linux-64-14.3.0-h298d278_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc-14.3.0-h0dff253_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_impl_linux-64-14.3.0-hb1e0a52_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_linux-64-14.3.0-h298d278_20.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/gflags-2.2.2-h5888daf_1005.conda - conda: https://conda.anaconda.org/conda-forge/noarch/git_root-0.2-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/glog-0.7.1-hbabe93e_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx-14.3.0-h76987e4_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_impl_linux-64-14.3.0-h2185e75_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_linux-64-14.3.0-h310e576_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx-14.3.0-h76987e4_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_impl_linux-64-14.3.0-h2185e75_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_linux-64-14.3.0-h3c3a7a4_20.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/icu-78.2-h33c6efd_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-8.7.0-pyhe01879c_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.3.0-pyhd8ed1ab_0.conda @@ -3754,7 +4002,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/kernel-headers_linux-64-4.18.0-he073ed8_9.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.3-hb9d3cd8_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/krb5-1.21.3-h659f571_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.45-default_hbd61a6d_105.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.45.1-default_hbd61a6d_101.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libabseil-20250512.1-cxx17_hba17884_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-23.0.0-h2c50142_1_cpu.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-acero-23.0.0-h635bf11_1_cpu.conda @@ -3773,12 +4021,12 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/libevent-2.1.12-hf998b51_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.7.3-hecca717_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libffi-3.5.2-h3435931_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.2.0-he0feb66_16.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/libgcc-devel_linux-64-14.3.0-hf649bbc_116.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.2.0-h69a702a_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.2.0-h69a702a_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.2.0-h68bc16d_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.2.0-he0feb66_16.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.2.0-he0feb66_17.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/libgcc-devel_linux-64-14.3.0-hf649bbc_117.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.2.0-h69a702a_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.2.0-h69a702a_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.2.0-h68bc16d_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.2.0-he0feb66_17.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-2.39.0-hdb79228_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-storage-2.39.0-hdbdcf42_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libgrpc-1.73.1-h3288cfb_1.conda @@ -3794,12 +4042,12 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/libparquet-23.0.0-h7376487_1_cpu.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libprotobuf-6.31.1-h49aed37_4.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libre2-11-2025.11.05-h7b12aa8_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libsanitizer-14.3.0-h8f1669f_16.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libsanitizer-14.3.0-h8f1669f_17.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.51.2-hf4e2dac_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libssh2-1.11.1-hcf80075_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.2.0-h934c35e_16.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/libstdcxx-devel_linux-64-14.3.0-h9f08a49_116.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-15.2.0-hdf11a46_16.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.2.0-h934c35e_17.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/libstdcxx-devel_linux-64-14.3.0-h9f08a49_117.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-15.2.0-hdf11a46_17.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libthrift-0.22.0-h454ac66_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libutf8proc-2.11.3-hfe17d71_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.41.3-h5347b49_0.conda @@ -3810,7 +4058,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/lz4-c-1.10.0-h5888daf_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/mypy-1.19.1-py310h7c4b9e2_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/mypy_extensions-1.1.0-pyha770c72_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.15.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.16.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h2d0b736_3.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/nlohmann_json-3.12.0-h54a6638_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/nomkl-1.0-h5ca1d4c_0.tar.bz2 @@ -3822,7 +4070,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/pandas-2.3.3-py310h0158d43_2.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pathspec-1.0.4-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/patsy-1.0.2-pyhcf101f3_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0-pyh8b19718_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0.1-pyh8b19718_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhf9edf01_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/prometheus-cpp-1.3.0-ha5d0236_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/psutil-7.2.2-py310h139afa4_0.conda @@ -3848,16 +4096,16 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/snappy-1.2.2-h03e3b7b_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/statsmodels-0.14.6-py310hf779ad0_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/sysroot_linux-64-2.28-h4ee821c_9.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/tabmat-4.2.0-py310h0158d43_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/tabmat-4.2.1-py310h0158d43_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.6.0-pyhecae5ae_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.13-noxft_h366c992_103.conda - conda: https://conda.anaconda.org/conda-forge/noarch/tomli-2.4.0-pyhcf101f3_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.2-pyh8f84b5b_2.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.3-pyh8f84b5b_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.15.0-h396c80c_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.15.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2025c-hc9c84f9_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/wheel-0.46.3-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/wrapt-2.1.0-py310h7c4b9e2_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/wrapt-2.1.1-py310h7c4b9e2_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.23.0-pyhcf101f3_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/zlib-1.3.1-hb9d3cd8_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.7-hb78ec9c_6.conda @@ -3930,7 +4178,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libclang-cpp19.1-19.1.7-default_hf3020a7_7.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcrc32c-1.1.2-hbdafb3b_0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcurl-8.18.0-he38603e_0.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-21.1.8-hf598326_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-21.1.8-h55c6f16_2.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-devel-19.1.7-h6dc3340_2.conda - conda: https://conda.anaconda.org/conda-forge/noarch/libcxx-headers-19.1.7-h707e725_2.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libedit-3.1.20250104-pl5321hafb1f1b_0.conda @@ -3938,9 +4186,9 @@ environments: - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libevent-2.1.12-h2757513_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libexpat-2.7.3-haf25636_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libffi-3.5.2-hcf2aa1b_0.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgcc-15.2.0-hcbb3090_16.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran-15.2.0-h07b0088_16.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran5-15.2.0-hdae7583_16.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgcc-15.2.0-hcbb3090_17.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran-15.2.0-h07b0088_17.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran5-15.2.0-hdae7583_17.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgoogle-cloud-2.39.0-head0a95_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgoogle-cloud-storage-2.39.0-hfa3a374_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgrpc-1.73.1-h3063b79_1.conda @@ -3970,7 +4218,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/osx-arm64/lz4-c-1.10.0-h286801f_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/mypy-1.19.1-py310h230e4be_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/mypy_extensions-1.1.0-pyha770c72_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.15.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.16.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/ncurses-6.5-h5e97a16_3.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/nlohmann_json-3.12.0-h784d473_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/numexpr-2.14.1-py310h2d91af3_1.conda @@ -3981,7 +4229,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pandas-2.3.3-py310h25f4b65_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pathspec-1.0.4-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/patsy-1.0.2-pyhcf101f3_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0-pyh8b19718_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0.1-pyh8b19718_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhf9edf01_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/prometheus-cpp-1.3.0-h0967b3e_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/psutil-7.2.2-py310haea493c_0.conda @@ -4007,17 +4255,17 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/six-1.17.0-pyhe01879c_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/snappy-1.2.2-hada39a4_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/statsmodels-0.14.6-py310hfdc7867_0.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tabmat-4.2.0-py310h01cd53d_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tabmat-4.2.1-py310h01cd53d_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tapi-1600.0.11.8-h997e182_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.6.0-pyhecae5ae_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tk-8.6.13-h010d191_3.conda - conda: https://conda.anaconda.org/conda-forge/noarch/tomli-2.4.0-pyhcf101f3_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.2-pyh8f84b5b_2.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.3-pyh8f84b5b_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.15.0-h396c80c_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.15.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2025c-hc9c84f9_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/wheel-0.46.3-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/wrapt-2.1.0-py310h72544b6_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/wrapt-2.1.1-py310h72544b6_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.23.0-pyhcf101f3_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/zlib-1.3.1-h8359307_2.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/zstd-1.5.7-hbf9d68e_6.conda @@ -4085,7 +4333,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/win-64/lz4-c-1.10.0-h2466b09_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/mypy-1.19.1-py310h29418f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/mypy_extensions-1.1.0-pyha770c72_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.15.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.16.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/nomkl-1.0-h5ca1d4c_0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/win-64/numexpr-2.14.1-py310h76e3bba_101.conda - conda: https://conda.anaconda.org/conda-forge/win-64/numpy-2.2.6-py310h4987827_0.conda @@ -4095,7 +4343,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/win-64/pandas-2.3.3-py310hed136d8_2.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pathspec-1.0.4-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/patsy-1.0.2-pyhcf101f3_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0-pyh8b19718_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0.1-pyh8b19718_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhf9edf01_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/psutil-7.2.2-py310h1637853_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/pyarrow-23.0.0-py310h5588dad_0.conda @@ -4117,11 +4365,11 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/six-1.17.0-pyhe01879c_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/snappy-1.2.2-h7fa0ca8_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/statsmodels-0.14.6-py310h8f3aa81_0.conda - - conda: https://conda.anaconda.org/conda-forge/win-64/tabmat-4.2.0-py310hed136d8_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/tabmat-4.2.1-py310hed136d8_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.6.0-pyhecae5ae_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/tk-8.6.13-h6ed50ae_3.conda - conda: https://conda.anaconda.org/conda-forge/noarch/tomli-2.4.0-pyhcf101f3_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.2-pyha7b4d00_2.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.3-pyha7b4d00_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.15.0-h396c80c_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.15.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2025c-hc9c84f9_1.conda @@ -4133,14 +4381,12 @@ environments: - conda: https://conda.anaconda.org/conda-forge/win-64/vs2022_win-64-19.44.35207-ha74f236_34.conda - conda: https://conda.anaconda.org/conda-forge/noarch/vswhere-3.1.7-h40126e0_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/wheel-0.46.3-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/win-64/wrapt-2.1.0-py310h29418f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/wrapt-2.1.1-py310h29418f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.23.0-pyhcf101f3_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/zstd-1.5.7-h534d264_6.conda py311: channels: - url: https://conda.anaconda.org/conda-forge/ - options: - pypi-prerelease-mode: if-necessary-or-explicit packages: linux-64: - conda: https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2 @@ -4164,30 +4410,30 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-storage-blobs-cpp-12.16.0-h75daedc_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-storage-common-cpp-12.12.0-h3d7a050_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-storage-files-datalake-cpp-12.14.0-hd454692_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils-2.45-default_h4852527_105.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.45-default_hfdba357_105.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_linux-64-2.45-default_h4852527_105.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils-2.45.1-default_h4852527_101.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.45.1-default_hfdba357_101.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_linux-64-2.45.1-default_h4852527_101.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_8.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.6-hb03c661_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/c-compiler-1.11.0-h4d9bdce_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2026.1.4-hbd8a1cb_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/click-8.3.1-pyh8f84b5b_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_1.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/conda-gcc-specs-14.3.0-he8ccf15_16.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/conda-gcc-specs-14.3.0-he8ccf15_17.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/cxx-compiler-1.11.0-hfcd1e18_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/cython-3.2.4-py311h0daaf2c_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.1-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/execnet-2.1.2-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/formulaic-1.2.1-pyhd8ed1ab_2.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc-14.3.0-h0dff253_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_impl_linux-64-14.3.0-he8b2097_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_linux-64-14.3.0-h298d278_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc-14.3.0-h0dff253_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_impl_linux-64-14.3.0-hb1e0a52_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_linux-64-14.3.0-h298d278_20.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/gflags-2.2.2-h5888daf_1005.conda - conda: https://conda.anaconda.org/conda-forge/noarch/git_root-0.2-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/glog-0.7.1-hbabe93e_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx-14.3.0-h76987e4_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_impl_linux-64-14.3.0-h2185e75_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_linux-64-14.3.0-h310e576_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx-14.3.0-h76987e4_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_impl_linux-64-14.3.0-h2185e75_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_linux-64-14.3.0-h3c3a7a4_20.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/icu-78.2-h33c6efd_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-8.7.0-pyhe01879c_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.3.0-pyhd8ed1ab_0.conda @@ -4196,7 +4442,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/kernel-headers_linux-64-4.18.0-he073ed8_9.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.3-hb9d3cd8_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/krb5-1.21.3-h659f571_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.45-default_hbd61a6d_105.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.45.1-default_hbd61a6d_101.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libabseil-20250512.1-cxx17_hba17884_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-23.0.0-h2c50142_1_cpu.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-acero-23.0.0-h635bf11_1_cpu.conda @@ -4215,12 +4461,12 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/libevent-2.1.12-hf998b51_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.7.3-hecca717_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libffi-3.5.2-h3435931_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.2.0-he0feb66_16.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/libgcc-devel_linux-64-14.3.0-hf649bbc_116.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.2.0-h69a702a_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.2.0-h69a702a_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.2.0-h68bc16d_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.2.0-he0feb66_16.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.2.0-he0feb66_17.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/libgcc-devel_linux-64-14.3.0-hf649bbc_117.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.2.0-h69a702a_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.2.0-h69a702a_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.2.0-h68bc16d_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.2.0-he0feb66_17.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-2.39.0-hdb79228_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-storage-2.39.0-hdbdcf42_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libgrpc-1.73.1-h3288cfb_1.conda @@ -4236,12 +4482,12 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/libparquet-23.0.0-h7376487_1_cpu.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libprotobuf-6.31.1-h49aed37_4.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libre2-11-2025.11.05-h7b12aa8_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libsanitizer-14.3.0-h8f1669f_16.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libsanitizer-14.3.0-h8f1669f_17.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.51.2-hf4e2dac_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libssh2-1.11.1-hcf80075_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.2.0-h934c35e_16.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/libstdcxx-devel_linux-64-14.3.0-h9f08a49_116.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-15.2.0-hdf11a46_16.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.2.0-h934c35e_17.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/libstdcxx-devel_linux-64-14.3.0-h9f08a49_117.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-15.2.0-hdf11a46_17.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libthrift-0.22.0-h454ac66_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libutf8proc-2.11.3-hfe17d71_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.41.3-h5347b49_0.conda @@ -4252,19 +4498,19 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/lz4-c-1.10.0-h5888daf_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/mypy-1.19.1-py311h49ec1c0_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/mypy_extensions-1.1.0-pyha770c72_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.15.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.16.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h2d0b736_3.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/nlohmann_json-3.12.0-h54a6638_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/nomkl-1.0-h5ca1d4c_0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/linux-64/numexpr-2.14.1-py311h3143de2_101.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-2.4.1-py311h2e04523_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-2.4.2-py311h2e04523_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/openssl-3.6.1-h35e630c_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/orc-2.2.2-h19cb568_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/packaging-26.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/pandas-3.0.0-py311h8032f78_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pathspec-1.0.4-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/patsy-1.0.2-pyhcf101f3_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0-pyh8b19718_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0.1-pyh8b19718_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhf9edf01_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/prometheus-cpp-1.3.0-ha5d0236_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/psutil-7.2.2-py311haee01d2_0.conda @@ -4288,16 +4534,16 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/snappy-1.2.2-h03e3b7b_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/statsmodels-0.14.6-py311h0372a8f_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/sysroot_linux-64-2.28-h4ee821c_9.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/tabmat-4.2.0-py311hed34c8f_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/tabmat-4.2.1-py311hed34c8f_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.6.0-pyhecae5ae_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.13-noxft_h366c992_103.conda - conda: https://conda.anaconda.org/conda-forge/noarch/tomli-2.4.0-pyhcf101f3_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.2-pyh8f84b5b_2.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.3-pyh8f84b5b_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.15.0-h396c80c_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.15.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2025c-hc9c84f9_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/wheel-0.46.3-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/wrapt-2.1.0-py311h49ec1c0_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/wrapt-2.1.1-py311h49ec1c0_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.23.0-pyhcf101f3_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/zlib-1.3.1-hb9d3cd8_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.7-hb78ec9c_6.conda @@ -4370,7 +4616,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libclang-cpp19.1-19.1.7-default_hf3020a7_7.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcrc32c-1.1.2-hbdafb3b_0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcurl-8.18.0-he38603e_0.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-21.1.8-hf598326_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-21.1.8-h55c6f16_2.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-devel-19.1.7-h6dc3340_2.conda - conda: https://conda.anaconda.org/conda-forge/noarch/libcxx-headers-19.1.7-h707e725_2.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libedit-3.1.20250104-pl5321hafb1f1b_0.conda @@ -4378,9 +4624,9 @@ environments: - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libevent-2.1.12-h2757513_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libexpat-2.7.3-haf25636_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libffi-3.5.2-hcf2aa1b_0.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgcc-15.2.0-hcbb3090_16.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran-15.2.0-h07b0088_16.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran5-15.2.0-hdae7583_16.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgcc-15.2.0-hcbb3090_17.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran-15.2.0-h07b0088_17.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran5-15.2.0-hdae7583_17.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgoogle-cloud-2.39.0-head0a95_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgoogle-cloud-storage-2.39.0-hfa3a374_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgrpc-1.73.1-h3063b79_1.conda @@ -4410,18 +4656,18 @@ environments: - conda: https://conda.anaconda.org/conda-forge/osx-arm64/lz4-c-1.10.0-h286801f_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/mypy-1.19.1-py311h8b270aa_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/mypy_extensions-1.1.0-pyha770c72_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.15.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.16.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/ncurses-6.5-h5e97a16_3.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/nlohmann_json-3.12.0-h784d473_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/numexpr-2.14.1-py311h5890ad2_1.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/numpy-2.4.1-py311had1e860_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/numpy-2.4.2-py311had1e860_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/openssl-3.6.1-hd24854e_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/orc-2.2.2-hac85105_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/packaging-26.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pandas-3.0.0-py311h4a068b9_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pathspec-1.0.4-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/patsy-1.0.2-pyhcf101f3_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0-pyh8b19718_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0.1-pyh8b19718_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhf9edf01_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/prometheus-cpp-1.3.0-h0967b3e_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/psutil-7.2.2-py311he363849_0.conda @@ -4445,17 +4691,17 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/six-1.17.0-pyhe01879c_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/snappy-1.2.2-hada39a4_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/statsmodels-0.14.6-py311h09efe57_0.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tabmat-4.2.0-py311hf45e3a0_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tabmat-4.2.1-py311hf45e3a0_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tapi-1600.0.11.8-h997e182_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.6.0-pyhecae5ae_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tk-8.6.13-h010d191_3.conda - conda: https://conda.anaconda.org/conda-forge/noarch/tomli-2.4.0-pyhcf101f3_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.2-pyh8f84b5b_2.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.3-pyh8f84b5b_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.15.0-h396c80c_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.15.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2025c-hc9c84f9_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/wheel-0.46.3-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/wrapt-2.1.0-py311hc949640_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/wrapt-2.1.1-py311hc949640_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.23.0-pyhcf101f3_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/zlib-1.3.1-h8359307_2.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/zstd-1.5.7-hbf9d68e_6.conda @@ -4523,17 +4769,17 @@ environments: - conda: https://conda.anaconda.org/conda-forge/win-64/lz4-c-1.10.0-h2466b09_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/mypy-1.19.1-py311h3485c13_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/mypy_extensions-1.1.0-pyha770c72_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.15.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.16.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/nomkl-1.0-h5ca1d4c_0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/win-64/numexpr-2.14.1-py311h670de69_101.conda - - conda: https://conda.anaconda.org/conda-forge/win-64/numpy-2.4.1-py311h80b3fa1_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/numpy-2.4.2-py311h80b3fa1_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/openssl-3.6.1-hf411b9b_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/orc-2.2.2-hbd3206f_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/packaging-26.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/pandas-3.0.0-py311h0610301_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pathspec-1.0.4-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/patsy-1.0.2-pyhcf101f3_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0-pyh8b19718_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0.1-pyh8b19718_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhf9edf01_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/psutil-7.2.2-py311hf893f09_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/pyarrow-23.0.0-py311h1ea47a8_0.conda @@ -4554,11 +4800,11 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/six-1.17.0-pyhe01879c_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/snappy-1.2.2-h7fa0ca8_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/statsmodels-0.14.6-py311h17033d2_0.conda - - conda: https://conda.anaconda.org/conda-forge/win-64/tabmat-4.2.0-py311h11fd7f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/tabmat-4.2.1-py311h11fd7f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.6.0-pyhecae5ae_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/tk-8.6.13-h6ed50ae_3.conda - conda: https://conda.anaconda.org/conda-forge/noarch/tomli-2.4.0-pyhcf101f3_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.2-pyha7b4d00_2.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.3-pyha7b4d00_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.15.0-h396c80c_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.15.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2025c-hc9c84f9_1.conda @@ -4570,14 +4816,12 @@ environments: - conda: https://conda.anaconda.org/conda-forge/win-64/vs2022_win-64-19.44.35207-ha74f236_34.conda - conda: https://conda.anaconda.org/conda-forge/noarch/vswhere-3.1.7-h40126e0_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/wheel-0.46.3-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/win-64/wrapt-2.1.0-py311h3485c13_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/wrapt-2.1.1-py311h3485c13_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.23.0-pyhcf101f3_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/zstd-1.5.7-h534d264_6.conda py312: channels: - url: https://conda.anaconda.org/conda-forge/ - options: - pypi-prerelease-mode: if-necessary-or-explicit packages: linux-64: - conda: https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2 @@ -4601,30 +4845,30 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-storage-blobs-cpp-12.16.0-h75daedc_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-storage-common-cpp-12.12.0-h3d7a050_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-storage-files-datalake-cpp-12.14.0-hd454692_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils-2.45-default_h4852527_105.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.45-default_hfdba357_105.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_linux-64-2.45-default_h4852527_105.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils-2.45.1-default_h4852527_101.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.45.1-default_hfdba357_101.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_linux-64-2.45.1-default_h4852527_101.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_8.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.6-hb03c661_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/c-compiler-1.11.0-h4d9bdce_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2026.1.4-hbd8a1cb_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/click-8.3.1-pyh8f84b5b_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_1.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/conda-gcc-specs-14.3.0-he8ccf15_16.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/conda-gcc-specs-14.3.0-he8ccf15_17.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/cxx-compiler-1.11.0-hfcd1e18_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/cython-3.2.4-py312h68e6be4_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.1-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/execnet-2.1.2-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/formulaic-1.2.1-pyhd8ed1ab_2.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc-14.3.0-h0dff253_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_impl_linux-64-14.3.0-he8b2097_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_linux-64-14.3.0-h298d278_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc-14.3.0-h0dff253_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_impl_linux-64-14.3.0-hb1e0a52_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_linux-64-14.3.0-h298d278_20.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/gflags-2.2.2-h5888daf_1005.conda - conda: https://conda.anaconda.org/conda-forge/noarch/git_root-0.2-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/glog-0.7.1-hbabe93e_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx-14.3.0-h76987e4_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_impl_linux-64-14.3.0-h2185e75_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_linux-64-14.3.0-h310e576_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx-14.3.0-h76987e4_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_impl_linux-64-14.3.0-h2185e75_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_linux-64-14.3.0-h3c3a7a4_20.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/icu-78.2-h33c6efd_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-8.7.0-pyhe01879c_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.3.0-pyhd8ed1ab_0.conda @@ -4633,7 +4877,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/kernel-headers_linux-64-4.18.0-he073ed8_9.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.3-hb9d3cd8_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/krb5-1.21.3-h659f571_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.45-default_hbd61a6d_105.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.45.1-default_hbd61a6d_101.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libabseil-20250512.1-cxx17_hba17884_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-23.0.0-h2c50142_1_cpu.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-acero-23.0.0-h635bf11_1_cpu.conda @@ -4652,12 +4896,12 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/libevent-2.1.12-hf998b51_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.7.3-hecca717_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libffi-3.5.2-h3435931_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.2.0-he0feb66_16.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/libgcc-devel_linux-64-14.3.0-hf649bbc_116.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.2.0-h69a702a_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.2.0-h69a702a_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.2.0-h68bc16d_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.2.0-he0feb66_16.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.2.0-he0feb66_17.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/libgcc-devel_linux-64-14.3.0-hf649bbc_117.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.2.0-h69a702a_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.2.0-h69a702a_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.2.0-h68bc16d_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.2.0-he0feb66_17.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-2.39.0-hdb79228_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-storage-2.39.0-hdbdcf42_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libgrpc-1.73.1-h3288cfb_1.conda @@ -4673,12 +4917,12 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/libparquet-23.0.0-h7376487_1_cpu.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libprotobuf-6.31.1-h49aed37_4.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libre2-11-2025.11.05-h7b12aa8_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libsanitizer-14.3.0-h8f1669f_16.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libsanitizer-14.3.0-h8f1669f_17.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.51.2-hf4e2dac_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libssh2-1.11.1-hcf80075_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.2.0-h934c35e_16.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/libstdcxx-devel_linux-64-14.3.0-h9f08a49_116.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-15.2.0-hdf11a46_16.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.2.0-h934c35e_17.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/libstdcxx-devel_linux-64-14.3.0-h9f08a49_117.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-15.2.0-hdf11a46_17.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libthrift-0.22.0-h454ac66_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libutf8proc-2.11.3-hfe17d71_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.41.3-h5347b49_0.conda @@ -4689,19 +4933,19 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/lz4-c-1.10.0-h5888daf_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/mypy-1.19.1-py312h4c3975b_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/mypy_extensions-1.1.0-pyha770c72_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.15.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.16.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h2d0b736_3.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/nlohmann_json-3.12.0-h54a6638_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/nomkl-1.0-h5ca1d4c_0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/linux-64/numexpr-2.14.1-py312h88efc94_101.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-2.4.1-py312h33ff503_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-2.4.2-py312h33ff503_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/openssl-3.6.1-h35e630c_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/orc-2.2.2-h19cb568_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/packaging-26.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/pandas-3.0.0-py312h8ecdadd_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pathspec-1.0.4-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/patsy-1.0.2-pyhcf101f3_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0-pyh8b19718_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0.1-pyh8b19718_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhf9edf01_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/prometheus-cpp-1.3.0-ha5d0236_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/psutil-7.2.2-py312h5253ce2_0.conda @@ -4725,16 +4969,16 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/snappy-1.2.2-h03e3b7b_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/statsmodels-0.14.6-py312h4f23490_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/sysroot_linux-64-2.28-h4ee821c_9.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/tabmat-4.2.0-py312hf79963d_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/tabmat-4.2.1-py312hf79963d_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.6.0-pyhecae5ae_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.13-noxft_h366c992_103.conda - conda: https://conda.anaconda.org/conda-forge/noarch/tomli-2.4.0-pyhcf101f3_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.2-pyh8f84b5b_2.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.3-pyh8f84b5b_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.15.0-h396c80c_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.15.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2025c-hc9c84f9_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/wheel-0.46.3-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/wrapt-2.1.0-py312h4c3975b_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/wrapt-2.1.1-py312h4c3975b_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.23.0-pyhcf101f3_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/zlib-1.3.1-hb9d3cd8_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.7-hb78ec9c_6.conda @@ -4807,7 +5051,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libclang-cpp19.1-19.1.7-default_hf3020a7_7.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcrc32c-1.1.2-hbdafb3b_0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcurl-8.18.0-he38603e_0.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-21.1.8-hf598326_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-21.1.8-h55c6f16_2.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-devel-19.1.7-h6dc3340_2.conda - conda: https://conda.anaconda.org/conda-forge/noarch/libcxx-headers-19.1.7-h707e725_2.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libedit-3.1.20250104-pl5321hafb1f1b_0.conda @@ -4815,9 +5059,9 @@ environments: - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libevent-2.1.12-h2757513_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libexpat-2.7.3-haf25636_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libffi-3.5.2-hcf2aa1b_0.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgcc-15.2.0-hcbb3090_16.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran-15.2.0-h07b0088_16.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran5-15.2.0-hdae7583_16.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgcc-15.2.0-hcbb3090_17.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran-15.2.0-h07b0088_17.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran5-15.2.0-hdae7583_17.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgoogle-cloud-2.39.0-head0a95_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgoogle-cloud-storage-2.39.0-hfa3a374_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgrpc-1.73.1-h3063b79_1.conda @@ -4847,18 +5091,18 @@ environments: - conda: https://conda.anaconda.org/conda-forge/osx-arm64/lz4-c-1.10.0-h286801f_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/mypy-1.19.1-py312hefc2c51_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/mypy_extensions-1.1.0-pyha770c72_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.15.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.16.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/ncurses-6.5-h5e97a16_3.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/nlohmann_json-3.12.0-h784d473_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/numexpr-2.14.1-py312h3de7d89_1.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/numpy-2.4.1-py312he281c53_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/numpy-2.4.2-py312he281c53_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/openssl-3.6.1-hd24854e_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/orc-2.2.2-hac85105_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/packaging-26.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pandas-3.0.0-py312hae6be28_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pathspec-1.0.4-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/patsy-1.0.2-pyhcf101f3_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0-pyh8b19718_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0.1-pyh8b19718_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhf9edf01_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/prometheus-cpp-1.3.0-h0967b3e_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/psutil-7.2.2-py312hb3ab3e3_0.conda @@ -4882,17 +5126,17 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/six-1.17.0-pyhe01879c_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/snappy-1.2.2-hada39a4_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/statsmodels-0.14.6-py312ha11c99a_0.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tabmat-4.2.0-py312h31e0735_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tabmat-4.2.1-py312h31e0735_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tapi-1600.0.11.8-h997e182_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.6.0-pyhecae5ae_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tk-8.6.13-h010d191_3.conda - conda: https://conda.anaconda.org/conda-forge/noarch/tomli-2.4.0-pyhcf101f3_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.2-pyh8f84b5b_2.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.3-pyh8f84b5b_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.15.0-h396c80c_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.15.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2025c-hc9c84f9_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/wheel-0.46.3-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/wrapt-2.1.0-py312h2bbb03f_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/wrapt-2.1.1-py312h2bbb03f_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.23.0-pyhcf101f3_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/zlib-1.3.1-h8359307_2.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/zstd-1.5.7-hbf9d68e_6.conda @@ -4960,17 +5204,17 @@ environments: - conda: https://conda.anaconda.org/conda-forge/win-64/lz4-c-1.10.0-h2466b09_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/mypy-1.19.1-py312he06e257_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/mypy_extensions-1.1.0-pyha770c72_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.15.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.16.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/nomkl-1.0-h5ca1d4c_0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/win-64/numexpr-2.14.1-py312h0c4f959_101.conda - - conda: https://conda.anaconda.org/conda-forge/win-64/numpy-2.4.1-py312ha72d056_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/numpy-2.4.2-py312ha72d056_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/openssl-3.6.1-hf411b9b_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/orc-2.2.2-hbd3206f_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/packaging-26.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/pandas-3.0.0-py312h95189c4_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pathspec-1.0.4-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/patsy-1.0.2-pyhcf101f3_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0-pyh8b19718_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0.1-pyh8b19718_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhf9edf01_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/psutil-7.2.2-py312he5662c2_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/pyarrow-23.0.0-py312h2e8e312_0.conda @@ -4991,11 +5235,11 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/six-1.17.0-pyhe01879c_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/snappy-1.2.2-h7fa0ca8_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/statsmodels-0.14.6-py312h196c9fc_0.conda - - conda: https://conda.anaconda.org/conda-forge/win-64/tabmat-4.2.0-py312hc128f0a_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/tabmat-4.2.1-py312hc128f0a_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.6.0-pyhecae5ae_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/tk-8.6.13-h6ed50ae_3.conda - conda: https://conda.anaconda.org/conda-forge/noarch/tomli-2.4.0-pyhcf101f3_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.2-pyha7b4d00_2.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.3-pyha7b4d00_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.15.0-h396c80c_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.15.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2025c-hc9c84f9_1.conda @@ -5007,14 +5251,12 @@ environments: - conda: https://conda.anaconda.org/conda-forge/win-64/vs2022_win-64-19.44.35207-ha74f236_34.conda - conda: https://conda.anaconda.org/conda-forge/noarch/vswhere-3.1.7-h40126e0_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/wheel-0.46.3-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/win-64/wrapt-2.1.0-py312he06e257_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/wrapt-2.1.1-py312he06e257_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.23.0-pyhcf101f3_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/zstd-1.5.7-h534d264_6.conda py313: channels: - url: https://conda.anaconda.org/conda-forge/ - options: - pypi-prerelease-mode: if-necessary-or-explicit packages: linux-64: - conda: https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2 @@ -5038,30 +5280,30 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-storage-blobs-cpp-12.16.0-h75daedc_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-storage-common-cpp-12.12.0-h3d7a050_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-storage-files-datalake-cpp-12.14.0-hd454692_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils-2.45-default_h4852527_105.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.45-default_hfdba357_105.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_linux-64-2.45-default_h4852527_105.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils-2.45.1-default_h4852527_101.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.45.1-default_hfdba357_101.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_linux-64-2.45.1-default_h4852527_101.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_8.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.6-hb03c661_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/c-compiler-1.11.0-h4d9bdce_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2026.1.4-hbd8a1cb_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/click-8.3.1-pyh8f84b5b_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_1.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/conda-gcc-specs-14.3.0-he8ccf15_16.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/conda-gcc-specs-14.3.0-he8ccf15_17.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/cxx-compiler-1.11.0-hfcd1e18_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/cython-3.2.4-py313hc80a56d_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.1-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/execnet-2.1.2-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/formulaic-1.2.1-pyhd8ed1ab_2.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc-14.3.0-h0dff253_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_impl_linux-64-14.3.0-he8b2097_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_linux-64-14.3.0-h298d278_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc-14.3.0-h0dff253_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_impl_linux-64-14.3.0-hb1e0a52_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_linux-64-14.3.0-h298d278_20.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/gflags-2.2.2-h5888daf_1005.conda - conda: https://conda.anaconda.org/conda-forge/noarch/git_root-0.2-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/glog-0.7.1-hbabe93e_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx-14.3.0-h76987e4_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_impl_linux-64-14.3.0-h2185e75_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_linux-64-14.3.0-h310e576_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx-14.3.0-h76987e4_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_impl_linux-64-14.3.0-h2185e75_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_linux-64-14.3.0-h3c3a7a4_20.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/icu-78.2-h33c6efd_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-8.7.0-pyhe01879c_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.3.0-pyhd8ed1ab_0.conda @@ -5070,7 +5312,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/kernel-headers_linux-64-4.18.0-he073ed8_9.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.3-hb9d3cd8_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/krb5-1.21.3-h659f571_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.45-default_hbd61a6d_105.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.45.1-default_hbd61a6d_101.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libabseil-20250512.1-cxx17_hba17884_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-23.0.0-h2c50142_1_cpu.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-acero-23.0.0-h635bf11_1_cpu.conda @@ -5089,12 +5331,12 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/libevent-2.1.12-hf998b51_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.7.3-hecca717_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libffi-3.5.2-h3435931_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.2.0-he0feb66_16.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/libgcc-devel_linux-64-14.3.0-hf649bbc_116.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.2.0-h69a702a_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.2.0-h69a702a_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.2.0-h68bc16d_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.2.0-he0feb66_16.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.2.0-he0feb66_17.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/libgcc-devel_linux-64-14.3.0-hf649bbc_117.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.2.0-h69a702a_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.2.0-h69a702a_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.2.0-h68bc16d_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.2.0-he0feb66_17.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-2.39.0-hdb79228_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-storage-2.39.0-hdbdcf42_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libgrpc-1.73.1-h3288cfb_1.conda @@ -5110,12 +5352,12 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/libparquet-23.0.0-h7376487_1_cpu.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libprotobuf-6.31.1-h49aed37_4.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libre2-11-2025.11.05-h7b12aa8_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libsanitizer-14.3.0-h8f1669f_16.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libsanitizer-14.3.0-h8f1669f_17.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.51.2-hf4e2dac_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libssh2-1.11.1-hcf80075_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.2.0-h934c35e_16.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/libstdcxx-devel_linux-64-14.3.0-h9f08a49_116.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-15.2.0-hdf11a46_16.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.2.0-h934c35e_17.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/libstdcxx-devel_linux-64-14.3.0-h9f08a49_117.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-15.2.0-hdf11a46_17.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libthrift-0.22.0-h454ac66_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libutf8proc-2.11.3-hfe17d71_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.41.3-h5347b49_0.conda @@ -5125,19 +5367,19 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/lz4-c-1.10.0-h5888daf_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/mypy-1.19.1-py313h07c4f96_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/mypy_extensions-1.1.0-pyha770c72_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.15.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.16.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h2d0b736_3.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/nlohmann_json-3.12.0-h54a6638_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/nomkl-1.0-h5ca1d4c_0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/linux-64/numexpr-2.14.1-py313h24ae7f9_101.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-2.4.1-py313hf6604e3_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-2.4.2-py313hf6604e3_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/openssl-3.6.1-h35e630c_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/orc-2.2.2-h19cb568_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/packaging-26.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/pandas-3.0.0-py313hbfd7664_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pathspec-1.0.4-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/patsy-1.0.2-pyhcf101f3_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0-pyh145f28c_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0.1-pyh145f28c_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhf9edf01_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/prometheus-cpp-1.3.0-ha5d0236_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/psutil-7.2.2-py313h54dd161_0.conda @@ -5146,7 +5388,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/pygments-2.19.2-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-9.0.2-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.8.0-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/python-3.13.11-hc97d973_101_cp313.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/python-3.13.12-hc97d973_100_cp313.conda - conda: https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhe01879c_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/python-librt-0.7.8-py313h54dd161_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/python_abi-3.13-8_cp313.conda @@ -5161,16 +5403,16 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/snappy-1.2.2-h03e3b7b_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/statsmodels-0.14.6-py313h29aa505_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/sysroot_linux-64-2.28-h4ee821c_9.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/tabmat-4.2.0-py313h08cd8bf_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/tabmat-4.2.1-py313h08cd8bf_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.6.0-pyhecae5ae_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.13-noxft_h366c992_103.conda - conda: https://conda.anaconda.org/conda-forge/noarch/tomli-2.4.0-pyhcf101f3_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.2-pyh8f84b5b_2.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.3-pyh8f84b5b_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.15.0-h396c80c_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.15.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2025c-hc9c84f9_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/wheel-0.46.3-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/wrapt-2.1.0-py313h07c4f96_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/wrapt-2.1.1-py313h07c4f96_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.23.0-pyhcf101f3_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/zlib-1.3.1-hb9d3cd8_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.7-hb78ec9c_6.conda @@ -5243,7 +5485,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libclang-cpp19.1-19.1.7-default_hf3020a7_7.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcrc32c-1.1.2-hbdafb3b_0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcurl-8.18.0-he38603e_0.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-21.1.8-hf598326_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-21.1.8-h55c6f16_2.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-devel-19.1.7-h6dc3340_2.conda - conda: https://conda.anaconda.org/conda-forge/noarch/libcxx-headers-19.1.7-h707e725_2.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libedit-3.1.20250104-pl5321hafb1f1b_0.conda @@ -5251,9 +5493,9 @@ environments: - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libevent-2.1.12-h2757513_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libexpat-2.7.3-haf25636_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libffi-3.5.2-hcf2aa1b_0.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgcc-15.2.0-hcbb3090_16.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran-15.2.0-h07b0088_16.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran5-15.2.0-hdae7583_16.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgcc-15.2.0-hcbb3090_17.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran-15.2.0-h07b0088_17.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran5-15.2.0-hdae7583_17.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgoogle-cloud-2.39.0-head0a95_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgoogle-cloud-storage-2.39.0-hfa3a374_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgrpc-1.73.1-h3063b79_1.conda @@ -5284,18 +5526,18 @@ environments: - conda: https://conda.anaconda.org/conda-forge/osx-arm64/lz4-c-1.10.0-h286801f_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/mypy-1.19.1-py313hd3e6d80_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/mypy_extensions-1.1.0-pyha770c72_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.15.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.16.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/ncurses-6.5-h5e97a16_3.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/nlohmann_json-3.12.0-h784d473_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/numexpr-2.14.1-py313h73ed539_1.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/numpy-2.4.1-py313h16eae64_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/numpy-2.4.2-py313h16eae64_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/openssl-3.6.1-hd24854e_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/orc-2.2.2-hac85105_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/packaging-26.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pandas-3.0.0-py313h6974306_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pathspec-1.0.4-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/patsy-1.0.2-pyhcf101f3_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0-pyh145f28c_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0.1-pyh145f28c_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhf9edf01_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/prometheus-cpp-1.3.0-h0967b3e_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/psutil-7.2.2-py313h6688731_0.conda @@ -5304,7 +5546,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/pygments-2.19.2-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-9.0.2-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.8.0-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/python-3.13.11-hfc2f54d_101_cp313.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/python-3.13.12-h20e6be0_100_cp313.conda - conda: https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhe01879c_2.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/python-librt-0.7.8-py313h6688731_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/python_abi-3.13-8_cp313.conda @@ -5319,17 +5561,17 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/six-1.17.0-pyhe01879c_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/snappy-1.2.2-hada39a4_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/statsmodels-0.14.6-py313hc577518_0.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tabmat-4.2.0-py313h5e3876c_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tabmat-4.2.1-py313h5e3876c_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tapi-1600.0.11.8-h997e182_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.6.0-pyhecae5ae_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tk-8.6.13-h010d191_3.conda - conda: https://conda.anaconda.org/conda-forge/noarch/tomli-2.4.0-pyhcf101f3_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.2-pyh8f84b5b_2.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.3-pyh8f84b5b_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.15.0-h396c80c_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.15.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2025c-hc9c84f9_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/wheel-0.46.3-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/wrapt-2.1.0-py313h0997733_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/wrapt-2.1.1-py313h0997733_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.23.0-pyhcf101f3_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/zlib-1.3.1-h8359307_2.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/zstd-1.5.7-hbf9d68e_6.conda @@ -5398,17 +5640,17 @@ environments: - conda: https://conda.anaconda.org/conda-forge/win-64/lz4-c-1.10.0-h2466b09_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/mypy-1.19.1-py313h5ea7bf4_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/mypy_extensions-1.1.0-pyha770c72_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.15.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.16.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/nomkl-1.0-h5ca1d4c_0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/win-64/numexpr-2.14.1-py313h7034ea3_101.conda - - conda: https://conda.anaconda.org/conda-forge/win-64/numpy-2.4.1-py313hce7ae62_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/numpy-2.4.2-py313hce7ae62_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/openssl-3.6.1-hf411b9b_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/orc-2.2.2-hbd3206f_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/packaging-26.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/pandas-3.0.0-py313h26f5e95_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pathspec-1.0.4-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/patsy-1.0.2-pyhcf101f3_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0-pyh145f28c_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0.1-pyh145f28c_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhf9edf01_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/psutil-7.2.2-py313h5fd188c_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/pyarrow-23.0.0-py313hfa70ccb_0.conda @@ -5416,7 +5658,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/pygments-2.19.2-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-9.0.2-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.8.0-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/win-64/python-3.13.11-h09917c8_101_cp313.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/python-3.13.12-h09917c8_100_cp313.conda - conda: https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhe01879c_2.conda - conda: https://conda.anaconda.org/conda-forge/win-64/python-librt-0.7.8-py313h5fd188c_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/python-tzdata-2025.3-pyhd8ed1ab_0.conda @@ -5429,11 +5671,11 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/six-1.17.0-pyhe01879c_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/snappy-1.2.2-h7fa0ca8_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/statsmodels-0.14.6-py313h0591002_0.conda - - conda: https://conda.anaconda.org/conda-forge/win-64/tabmat-4.2.0-py313hc90dcd4_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/tabmat-4.2.1-py313hc90dcd4_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.6.0-pyhecae5ae_0.conda - conda: https://conda.anaconda.org/conda-forge/win-64/tk-8.6.13-h6ed50ae_3.conda - conda: https://conda.anaconda.org/conda-forge/noarch/tomli-2.4.0-pyhcf101f3_0.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.2-pyha7b4d00_2.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.3-pyha7b4d00_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.15.0-h396c80c_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.15.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2025c-hc9c84f9_1.conda @@ -5445,14 +5687,12 @@ environments: - conda: https://conda.anaconda.org/conda-forge/win-64/vs2022_win-64-19.44.35207-ha74f236_34.conda - conda: https://conda.anaconda.org/conda-forge/noarch/vswhere-3.1.7-h40126e0_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/wheel-0.46.3-pyhd8ed1ab_0.conda - - conda: https://conda.anaconda.org/conda-forge/win-64/wrapt-2.1.0-py313h5ea7bf4_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/wrapt-2.1.1-py313h5ea7bf4_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.23.0-pyhcf101f3_1.conda - conda: https://conda.anaconda.org/conda-forge/win-64/zstd-1.5.7-h534d264_6.conda py39: channels: - url: https://conda.anaconda.org/conda-forge/ - options: - pypi-prerelease-mode: if-necessary-or-explicit packages: linux-64: - conda: https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2 @@ -5476,30 +5716,30 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-storage-blobs-cpp-12.16.0-h75daedc_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-storage-common-cpp-12.12.0-h3d7a050_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-storage-files-datalake-cpp-12.14.0-hd454692_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils-2.45-default_h4852527_105.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.45-default_hfdba357_105.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_linux-64-2.45-default_h4852527_105.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils-2.45.1-default_h4852527_101.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.45.1-default_hfdba357_101.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_linux-64-2.45.1-default_h4852527_101.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_8.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.6-hb03c661_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/c-compiler-1.11.0-h4d9bdce_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2026.1.4-hbd8a1cb_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/click-8.1.8-pyh707e725_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_1.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/conda-gcc-specs-14.3.0-he8ccf15_16.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/conda-gcc-specs-14.3.0-he8ccf15_17.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/cxx-compiler-1.11.0-hfcd1e18_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/cython-3.1.3-py39h6bc127c_2.conda - conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/execnet-2.1.1-pyhd8ed1ab_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/formulaic-1.2.0-py39hf3d152e_2.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc-14.3.0-h0dff253_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_impl_linux-64-14.3.0-he8b2097_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_linux-64-14.3.0-h298d278_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc-14.3.0-h0dff253_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_impl_linux-64-14.3.0-hb1e0a52_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_linux-64-14.3.0-h298d278_20.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/gflags-2.2.2-h5888daf_1005.conda - conda: https://conda.anaconda.org/conda-forge/noarch/git_root-0.2-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/glog-0.7.1-hbabe93e_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx-14.3.0-h76987e4_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_impl_linux-64-14.3.0-h2185e75_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_linux-64-14.3.0-h310e576_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx-14.3.0-h76987e4_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_impl_linux-64-14.3.0-h2185e75_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_linux-64-14.3.0-h3c3a7a4_20.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/icu-78.2-h33c6efd_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-8.7.0-pyhe01879c_1.conda - conda: https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.0.0-pyhd8ed1ab_1.conda @@ -5508,7 +5748,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/kernel-headers_linux-64-4.18.0-he073ed8_9.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.3-hb9d3cd8_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/krb5-1.21.3-h659f571_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.45-default_hbd61a6d_105.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.45.1-default_hbd61a6d_101.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libabseil-20250512.1-cxx17_hba17884_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-21.0.0-h2c50142_16_cpu.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-acero-21.0.0-h635bf11_16_cpu.conda @@ -5527,13 +5767,13 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/libevent-2.1.12-hf998b51_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.7.3-hecca717_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libffi-3.5.2-h3435931_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.2.0-he0feb66_16.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/libgcc-devel_linux-64-14.3.0-hf649bbc_116.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.2.0-h69a702a_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.2.0-h69a702a_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-ng-15.2.0-h69a702a_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.2.0-h68bc16d_16.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.2.0-he0feb66_16.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.2.0-he0feb66_17.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/libgcc-devel_linux-64-14.3.0-hf649bbc_117.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.2.0-h69a702a_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.2.0-h69a702a_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-ng-15.2.0-h69a702a_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.2.0-h68bc16d_17.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.2.0-he0feb66_17.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-2.39.0-hdb79228_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-storage-2.39.0-hdbdcf42_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libgrpc-1.73.1-h3288cfb_1.conda @@ -5549,12 +5789,12 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/libparquet-21.0.0-h7376487_16_cpu.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libprotobuf-6.31.1-h49aed37_4.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libre2-11-2025.11.05-h7b12aa8_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libsanitizer-14.3.0-h8f1669f_16.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libsanitizer-14.3.0-h8f1669f_17.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.51.2-hf4e2dac_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libssh2-1.11.1-hcf80075_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.2.0-h934c35e_16.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/libstdcxx-devel_linux-64-14.3.0-h9f08a49_116.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-15.2.0-hdf11a46_16.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.2.0-h934c35e_17.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/libstdcxx-devel_linux-64-14.3.0-h9f08a49_117.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-15.2.0-hdf11a46_17.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libthrift-0.22.0-h454ac66_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libutf8proc-2.11.3-hfe17d71_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.41.3-h5347b49_0.conda @@ -5687,7 +5927,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libclang-cpp19.1-19.1.7-default_hf3020a7_7.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcrc32c-1.1.2-hbdafb3b_0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcurl-8.18.0-he38603e_0.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-21.1.8-hf598326_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-21.1.8-h55c6f16_2.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-devel-19.1.7-h6dc3340_2.conda - conda: https://conda.anaconda.org/conda-forge/noarch/libcxx-headers-19.1.7-h707e725_2.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libedit-3.1.20250104-pl5321hafb1f1b_0.conda @@ -5695,9 +5935,9 @@ environments: - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libevent-2.1.12-h2757513_1.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libexpat-2.7.3-haf25636_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libffi-3.5.2-hcf2aa1b_0.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgcc-15.2.0-hcbb3090_16.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran-15.2.0-h07b0088_16.conda - - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran5-15.2.0-hdae7583_16.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgcc-15.2.0-hcbb3090_17.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran-15.2.0-h07b0088_17.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran5-15.2.0-hdae7583_17.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgoogle-cloud-2.39.0-head0a95_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgoogle-cloud-storage-2.39.0-hfa3a374_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgrpc-1.73.1-h3063b79_1.conda @@ -5945,6 +6185,7 @@ packages: - msys2-conda-epoch <0.0a0 license: BSD-3-Clause license_family: BSD + purls: [] size: 49468 timestamp: 1718213032772 - conda: https://conda.anaconda.org/conda-forge/noarch/_python_abi3_support-1.0-hd8ed1ab_2.conda @@ -5957,6 +6198,13 @@ packages: license_family: MIT size: 8191 timestamp: 1744137672556 +- conda: https://conda.anaconda.org/conda-forge/noarch/_r-mutex-1.0.1-anacondar_1.tar.bz2 + sha256: e58f9eeb416b92b550e824bcb1b9fb1958dee69abfe3089dfd1a9173e3a0528a + md5: 19f9db5f4f1b7f5ef5f6d67207f25f38 + license: BSD + purls: [] + size: 3566 + timestamp: 1562343890778 - conda: https://conda.anaconda.org/conda-forge/noarch/alabaster-1.0.0-pyhd8ed1ab_1.conda sha256: 6c4456a138919dae9edd3ac1a74b6fbe5fd66c05675f54df2f8ab8c8d0cc6cea md5: 1fd9696649f65fd6611fcdb4ffec738a @@ -5992,6 +6240,18 @@ packages: license_family: BSD size: 565953 timestamp: 1763476026233 +- conda: https://conda.anaconda.org/conda-forge/noarch/annotated-types-0.7.0-pyhd8ed1ab_1.conda + sha256: e0ea1ba78fbb64f17062601edda82097fcf815012cf52bb704150a2668110d48 + md5: 2934f256a8acfe48f6ebb4fce6cde29c + depends: + - python >=3.9 + - typing-extensions >=4.0.0 + license: MIT + license_family: MIT + purls: + - pkg:pypi/annotated-types?source=hash-mapping + size: 18074 + timestamp: 1733247158254 - conda: https://conda.anaconda.org/conda-forge/noarch/anyio-4.12.1-pyhcf101f3_0.conda sha256: eb0c4e2b24f1fbefaf96ce6c992c6bd64340bc3c06add4d7415ab69222b201da md5: 11a2b8c732d215d977998ccd69a9d5e8 @@ -6030,19 +6290,19 @@ packages: license_family: MIT size: 18715 timestamp: 1749017288144 -- conda: https://conda.anaconda.org/conda-forge/linux-64/argon2-cffi-bindings-25.1.0-py314h5bd0f2a_2.conda - sha256: 39234a99df3d2e3065383808ed8bfda36760de5ef590c54c3692bb53571ef02b - md5: 3cca1b74b2752917b5b65b81f61f0553 +- conda: https://conda.anaconda.org/conda-forge/linux-64/argon2-cffi-bindings-25.1.0-py313h07c4f96_2.conda + sha256: ad188ccc06a06c633dc124b09e9e06fb9df4c32ffc38acc96ecc86e506062090 + md5: 27bbec9f2f3a15d32b60ec5734f5b41c depends: - __glibc >=2.17,<3.0.a0 - - cffi >=2.0.0b1 + - cffi >=1.0.1 - libgcc >=14 - - python >=3.14,<3.15.0a0 - - python_abi 3.14.* *_cp314 + - python >=3.13,<3.14.0a0 + - python_abi 3.13.* *_cp313 license: MIT license_family: MIT - size: 35598 - timestamp: 1762509505285 + size: 35943 + timestamp: 1762509452935 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/argon2-cffi-bindings-25.1.0-py314h0612a62_2.conda sha256: aab60bbaea5cc49dff37438d1ad469d64025cda2ce58103cf68da61701ed2075 md5: a240a79a49a95b388ef81ccda27a5e51 @@ -6857,16 +7117,29 @@ packages: purls: [] size: 197881 timestamp: 1768502314584 -- conda: https://conda.anaconda.org/conda-forge/noarch/babel-2.17.0-pyhd8ed1ab_0.conda - sha256: 1c656a35800b7f57f7371605bc6507c8d3ad60fbaaec65876fce7f73df1fc8ac - md5: 0a01c169f0ab0f91b26e77a3301fbfe4 +- conda: https://conda.anaconda.org/conda-forge/noarch/babel-2.18.0-pyhcf101f3_0.conda + sha256: 7377bce9fcc03fecd3607843d20b50546c30a923a3517a322a2a784fa6e380eb + md5: ea5be9abc2939c8431893b4e123a2065 depends: - - python >=3.9 + - python >=3.10 - pytz >=2015.7 + - python license: BSD-3-Clause license_family: BSD - size: 6938256 - timestamp: 1738490268466 + size: 7684373 + timestamp: 1770326844118 +- conda: https://conda.anaconda.org/conda-forge/linux-64/backports.zstd-1.3.0-py313h18e8e13_0.conda + sha256: 9552afbec37c4d8d0e83a5c4c6b3c7f4b8785f935094ce3881e0a249045909ce + md5: d9e90792551a527200637e23a915dd79 + depends: + - python + - libgcc >=14 + - __glibc >=2.17,<3.0.a0 + - python_abi 3.13.* *_cp313 + - zstd >=1.5.7,<1.6.0a0 + license: BSD-3-Clause AND MIT AND EPL-2.0 + size: 240943 + timestamp: 1767044981366 - conda: https://conda.anaconda.org/conda-forge/noarch/backports.zstd-1.3.0-py314h680f03e_0.conda noarch: generic sha256: c31ab719d256bc6f89926131e88ecd0f0c5d003fe8481852c6424f4ec6c7eb29 @@ -6887,38 +7160,50 @@ packages: license_family: MIT size: 90399 timestamp: 1764520638652 -- conda: https://conda.anaconda.org/conda-forge/linux-64/binutils-2.45-default_h4852527_105.conda - sha256: fe2580dfa3711d7de59ae7e044f7eea6bfdd969cc5c36d814a569225d7f7f243 - md5: 1bc3e6c577a1a206c36456bdeae406de +- conda: https://conda.anaconda.org/conda-forge/linux-64/binutils-2.45.1-default_h4852527_101.conda + sha256: 2851d34944b056d028543f0440fb631aeeff204151ea09589d8d9c13882395de + md5: 9902aeb08445c03fb31e01beeb173988 depends: - - binutils_impl_linux-64 >=2.45,<2.46.0a0 + - binutils_impl_linux-64 >=2.45.1,<2.45.2.0a0 license: GPL-3.0-only license_family: GPL purls: [] - size: 35432 - timestamp: 1766513140840 -- conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.45-default_hfdba357_105.conda - sha256: 17fbb32191430310d3eb8309f80a8df54f0d66eda9cf84b2ae5113e6d74e24d8 - md5: e410a8f80e22eb6d840e39ac6a34bd0e + size: 35128 + timestamp: 1770267175160 +- conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.45.1-default_hfdba357_101.conda + sha256: 74341b26a2b9475dc14ba3cf12432fcd10a23af285101883e720216d81d44676 + md5: 83aa53cb3f5fc849851a84d777a60551 depends: - - ld_impl_linux-64 2.45 default_hbd61a6d_105 + - ld_impl_linux-64 2.45.1 default_hbd61a6d_101 - sysroot_linux-64 - zstd >=1.5.7,<1.6.0a0 license: GPL-3.0-only license_family: GPL purls: [] - size: 3719982 - timestamp: 1766513109980 -- conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_linux-64-2.45-default_h4852527_105.conda - sha256: 0eae8088e00edc7fe7a728d64f6614d2cf17a2df010e835eccefe30bfc726759 - md5: 4b1e4ae87a52e9724a9ec0c7b822bc89 + size: 3744895 + timestamp: 1770267152681 +- conda: https://conda.anaconda.org/conda-forge/win-64/binutils_impl_win-64-2.45.1-default_ha84baeb_101.conda + sha256: 31211bd89e77203f731f31871ff13b5828fbd99f02ae2fc56ae15fcd568c4466 + md5: 84d2e3fd656b05705b7cfe7a92a8c840 depends: - - binutils_impl_linux-64 2.45 default_hfdba357_105 + - ld_impl_win-64 2.45.1 default_hfd38196_101 + - m2w64-sysroot_win-64 >=12.0.0.r0 + - zstd >=1.5.7,<1.6.0a0 + license: GPL-3.0-only + license_family: GPL + purls: [] + size: 5830940 + timestamp: 1770267725685 +- conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_linux-64-2.45.1-default_h4852527_101.conda + sha256: 4826f97d33cbe54459970a1e84500dbe0cccf8326aaf370e707372ae20ec5a47 + md5: dec96579f9a7035a59492bf6ee613b53 + depends: + - binutils_impl_linux-64 2.45.1 default_hfdba357_101 license: GPL-3.0-only license_family: GPL purls: [] - size: 36310 - timestamp: 1766513143566 + size: 36060 + timestamp: 1770267177798 - conda: https://conda.anaconda.org/conda-forge/linux-64/blas-2.305-mkl.conda build_number: 5 sha256: 7e5137d9ddb42dcd362854087e5f2f28bbdbfc504755b38d01847326b7804c99 @@ -7078,6 +7363,7 @@ packages: - libgcc >=14 license: MIT license_family: MIT + purls: [] size: 20103 timestamp: 1764017231353 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/brotli-1.2.0-h7d5ae5b_1.conda @@ -7090,6 +7376,7 @@ packages: - libbrotlienc 1.2.0 hc919400_1 license: MIT license_family: MIT + purls: [] size: 20237 timestamp: 1764018058424 - conda: https://conda.anaconda.org/conda-forge/win-64/brotli-1.2.0-h2d644bc_1.conda @@ -7104,6 +7391,7 @@ packages: - vc14_runtime >=14.44.35208 license: MIT license_family: MIT + purls: [] size: 20342 timestamp: 1764017988883 - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-bin-1.2.0-hb03c661_1.conda @@ -7116,6 +7404,7 @@ packages: - libgcc >=14 license: MIT license_family: MIT + purls: [] size: 21021 timestamp: 1764017221344 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/brotli-bin-1.2.0-hc919400_1.conda @@ -7127,6 +7416,7 @@ packages: - libbrotlienc 1.2.0 hc919400_1 license: MIT license_family: MIT + purls: [] size: 18628 timestamp: 1764018033635 - conda: https://conda.anaconda.org/conda-forge/win-64/brotli-bin-1.2.0-hfd05255_1.conda @@ -7140,23 +7430,24 @@ packages: - vc14_runtime >=14.44.35208 license: MIT license_family: MIT + purls: [] size: 22714 timestamp: 1764017952449 -- conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.2.0-py314h3de4e8d_1.conda - sha256: 3ad3500bff54a781c29f16ce1b288b36606e2189d0b0ef2f67036554f47f12b0 - md5: 8910d2c46f7e7b519129f486e0fe927a +- conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.2.0-py313hf159716_1.conda + sha256: dadec2879492adede0a9af0191203f9b023f788c18efd45ecac676d424c458ae + md5: 6c4d3597cf43f3439a51b2b13e29a4ba depends: - __glibc >=2.17,<3.0.a0 - libgcc >=14 - libstdcxx >=14 - - python >=3.14,<3.15.0a0 - - python_abi 3.14.* *_cp314 + - python >=3.13,<3.14.0a0 + - python_abi 3.13.* *_cp313 constrains: - libbrotlicommon 1.2.0 hb03c661_1 license: MIT license_family: MIT - size: 367376 - timestamp: 1764017265553 + size: 367721 + timestamp: 1764017371123 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/brotli-python-1.2.0-py314h3daef5d_1.conda sha256: 5c2e471fd262fcc3c5a9d5ea4dae5917b885e0e9b02763dbd0f0d9635ed4cb99 md5: f9501812fe7c66b6548c7fcaa1c1f252 @@ -7187,6 +7478,33 @@ packages: license_family: MIT size: 335782 timestamp: 1764018443683 +- conda: https://conda.anaconda.org/conda-forge/linux-64/bwidget-1.10.1-ha770c72_1.conda + sha256: c88dd33c89b33409ebcd558d78fdc66a63c18f8b06e04d170668ffb6c8ecfabd + md5: 983b92277d78c0d0ec498e460caa0e6d + depends: + - tk + license: TCL + purls: [] + size: 129594 + timestamp: 1750261567920 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/bwidget-1.10.1-hce30654_1.conda + sha256: 66ccefd46364f1ef536c42e7ee24d0377c2ece073734df614c6509b08e2bdf62 + md5: c42706e35f3bb26537b065a5f9ae764d + depends: + - tk + license: TCL + purls: [] + size: 129989 + timestamp: 1750261536876 +- conda: https://conda.anaconda.org/conda-forge/win-64/bwidget-1.10.1-h57928b3_1.conda + sha256: 539b9df7e02288e0a978f59616abe87c973debc1d65fee3caab3586c0d961547 + md5: f99aeb077e200ba813e2096d56efb4ae + depends: + - tk + license: TCL + purls: [] + size: 126266 + timestamp: 1750261570600 - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_8.conda sha256: c30daba32ddebbb7ded490f0e371eae90f51e72db620554089103b4a6934b0d5 md5: 51a19bba1b8ebfb60df25cde030b7ebc @@ -7325,33 +7643,71 @@ packages: license_family: BSD size: 11065 timestamp: 1615209567874 -- conda: https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.4-he90730b_1.conda - sha256: 06525fa0c4e4f56e771a3b986d0fdf0f0fc5a3270830ee47e127a5105bde1b9a - md5: bb6c4808bfa69d6f7f6b07e5846ced37 +- conda: https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.4-h3394656_0.conda + sha256: 3bd6a391ad60e471de76c0e9db34986c4b5058587fbf2efa5a7f54645e28c2c7 + md5: 09262e66b19567aff4f592fb53b28760 depends: - __glibc >=2.17,<3.0.a0 - fontconfig >=2.15.0,<3.0a0 - fonts-conda-ecosystem - - icu >=78.1,<79.0a0 - - libexpat >=2.7.3,<3.0a0 - - libfreetype >=2.14.1 - - libfreetype6 >=2.14.1 - - libgcc >=14 - - libglib >=2.86.3,<3.0a0 - - libpng >=1.6.53,<1.7.0a0 - - libstdcxx >=14 + - freetype >=2.12.1,<3.0a0 + - icu >=75.1,<76.0a0 + - libexpat >=2.6.4,<3.0a0 + - libgcc >=13 + - libglib >=2.82.2,<3.0a0 + - libpng >=1.6.47,<1.7.0a0 + - libstdcxx >=13 - libxcb >=1.17.0,<2.0a0 - libzlib >=1.3.1,<2.0a0 - - pixman >=0.46.4,<1.0a0 + - pixman >=0.44.2,<1.0a0 - xorg-libice >=1.1.2,<2.0a0 - - xorg-libsm >=1.2.6,<2.0a0 - - xorg-libx11 >=1.8.12,<2.0a0 + - xorg-libsm >=1.2.5,<2.0a0 + - xorg-libx11 >=1.8.11,<2.0a0 - xorg-libxext >=1.3.6,<2.0a0 - xorg-libxrender >=0.9.12,<0.10.0a0 license: LGPL-2.1-only or MPL-1.1 purls: [] - size: 989514 - timestamp: 1766415934926 + size: 978114 + timestamp: 1741554591855 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/cairo-1.18.4-h6a3b0d2_0.conda + sha256: 00439d69bdd94eaf51656fdf479e0c853278439d22ae151cabf40eb17399d95f + md5: 38f6df8bc8c668417b904369a01ba2e2 + depends: + - __osx >=11.0 + - fontconfig >=2.15.0,<3.0a0 + - fonts-conda-ecosystem + - freetype >=2.12.1,<3.0a0 + - icu >=75.1,<76.0a0 + - libcxx >=18 + - libexpat >=2.6.4,<3.0a0 + - libglib >=2.82.2,<3.0a0 + - libpng >=1.6.47,<1.7.0a0 + - libzlib >=1.3.1,<2.0a0 + - pixman >=0.44.2,<1.0a0 + license: LGPL-2.1-only or MPL-1.1 + purls: [] + size: 896173 + timestamp: 1741554795915 +- conda: https://conda.anaconda.org/conda-forge/win-64/cairo-1.18.4-h5782bbf_0.conda + sha256: b9f577bddb033dba4533e851853924bfe7b7c1623d0697df382eef177308a917 + md5: 20e32ced54300292aff690a69c5e7b97 + depends: + - fontconfig >=2.15.0,<3.0a0 + - fonts-conda-ecosystem + - freetype >=2.12.1,<3.0a0 + - icu >=75.1,<76.0a0 + - libexpat >=2.6.4,<3.0a0 + - libglib >=2.82.2,<3.0a0 + - libpng >=1.6.47,<1.7.0a0 + - libzlib >=1.3.1,<2.0a0 + - pixman >=0.44.2,<1.0a0 + - ucrt >=10.0.20348.0 + - vc >=14.2,<15 + - vc14_runtime >=14.29.30139 + license: LGPL-2.1-only or MPL-1.1 + purls: [] + size: 1524254 + timestamp: 1741555212198 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/cctools-1030.6.3-llvm19_1_hd01ab73_4.conda sha256: 4f408036b5175be0d2c7940250d00dae5ea7a71d194a1ffb35881fb9df6211fc md5: caf7c8e48827c2ad0c402716159fe0a2 @@ -7397,6 +7753,42 @@ packages: purls: [] size: 23211 timestamp: 1768852915341 +- pypi: https://files.pythonhosted.org/packages/3e/51/d460992bf50b2657bf525f855fdba91207eff09ae2f06cae70095b9bc4eb/celer-0.7.4-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl + name: celer + version: 0.7.4 + sha256: b06e1811481ef1b9b740cecd2c5dbe91931e8891a1adfd33c2f5317b9653ea0c + requires_dist: + - seaborn>=0.7 + - matplotlib>=2.0.0 + - libsvmdata>=0.3 + - scikit-learn>=1.0 + - xarray + - download + - tqdm + - numpydoc ; extra == 'doc' + - pandas ; extra == 'doc' + - pillow ; extra == 'doc' + - furo ; extra == 'doc' + - sphinx-copybutton ; extra == 'doc' + - sphinx-gallery ; extra == 'doc' +- pypi: https://files.pythonhosted.org/packages/de/cd/094d08cf59158a7f180020778096b4b1b36cfa843218681134fc7112508d/celer-0.7.4.tar.gz + name: celer + version: 0.7.4 + sha256: 2e3e65b5218eb2455155f3c54d2c6b33b486fd113b67fbe1f31723fe1ec14363 + requires_dist: + - seaborn>=0.7 + - matplotlib>=2.0.0 + - libsvmdata>=0.3 + - scikit-learn>=1.0 + - xarray + - download + - tqdm + - numpydoc ; extra == 'doc' + - pandas ; extra == 'doc' + - pillow ; extra == 'doc' + - furo ; extra == 'doc' + - sphinx-copybutton ; extra == 'doc' + - sphinx-gallery ; extra == 'doc' - pypi: https://files.pythonhosted.org/packages/e6/ad/3cc14f097111b4de0040c83a525973216457bbeeb63739ef1ed275c1c021/certifi-2026.1.4-py3-none-any.whl name: certifi version: 2026.1.4 @@ -7410,6 +7802,22 @@ packages: license: ISC size: 150969 timestamp: 1767500900768 +- conda: https://conda.anaconda.org/conda-forge/linux-64/cffi-2.0.0-py313hf46b229_1.conda + sha256: 2162a91819945c826c6ef5efe379e88b1df0fe9a387eeba23ddcf7ebeacd5bd6 + md5: d0616e7935acab407d1543b28c446f6f + depends: + - __glibc >=2.17,<3.0.a0 + - libffi >=3.5.2,<3.6.0a0 + - libgcc >=14 + - pycparser + - python >=3.13,<3.14.0a0 + - python_abi 3.13.* *_cp313 + license: MIT + license_family: MIT + purls: + - pkg:pypi/cffi?source=hash-mapping + size: 298357 + timestamp: 1761202966461 - conda: https://conda.anaconda.org/conda-forge/linux-64/cffi-2.0.0-py314h4a8dc5f_1.conda sha256: c6339858a0aaf5d939e00d345c98b99e4558f285942b27232ac098ad17ac7f8e md5: cf45f4278afd6f4e6d03eda0f435d527 @@ -7436,6 +7844,8 @@ packages: - python_abi 3.14.* *_cp314 license: MIT license_family: MIT + purls: + - pkg:pypi/cffi?source=hash-mapping size: 292983 timestamp: 1761203354051 - conda: https://conda.anaconda.org/conda-forge/win-64/cffi-2.0.0-py314h5a2d7ad_1.conda @@ -7450,6 +7860,8 @@ packages: - vc14_runtime >=14.44.35208 license: MIT license_family: MIT + purls: + - pkg:pypi/cffi?source=hash-mapping size: 294731 timestamp: 1761203441365 - conda: https://conda.anaconda.org/conda-forge/noarch/cfgv-3.5.0-pyhd8ed1ab_0.conda @@ -7471,10 +7883,10 @@ packages: version: 3.4.4 sha256: 8a6562c3700cce886c5be75ade4a5db4214fda19fede41d9792d100288d8f94c requires_python: '>=3.7' -- pypi: https://files.pythonhosted.org/packages/67/ff/f6b948ca32e4f2a4576aa129d8bed61f2e0543bf9f5f2b7fc3758ed005c9/charset_normalizer-3.4.4-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl +- pypi: https://files.pythonhosted.org/packages/f5/83/6ab5883f57c9c801ce5e5677242328aa45592be8a00644310a008d04f922/charset_normalizer-3.4.4-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl name: charset-normalizer version: 3.4.4 - sha256: ecaae4149d99b1c9e7b88bb03e3221956f68fd6d50be2ef061b2381b61d20838 + sha256: a8a8b89589086a25749f471e6a900d3f662d1d3b6e2e59dcecf787b1cc3a1894 requires_python: '>=3.7' - conda: https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.4.4-pyhd8ed1ab_0.conda sha256: b32f8362e885f1b8417bac2b3da4db7323faa12d5db62b7fd6691c02d60d6f59 @@ -7607,8 +8019,6 @@ packages: - python license: BSD-3-Clause license_family: BSD - purls: - - pkg:pypi/click?source=hash-mapping size: 97676 timestamp: 1764518652276 - conda: https://conda.anaconda.org/conda-forge/noarch/click-8.3.1-pyha7b4d00_1.conda @@ -7621,8 +8031,6 @@ packages: - python license: BSD-3-Clause license_family: BSD - purls: - - pkg:pypi/click?source=hash-mapping size: 96620 timestamp: 1764518654675 - conda: https://conda.anaconda.org/conda-forge/noarch/cloudpickle-3.1.2-pyhcf101f3_1.conda @@ -7681,30 +8089,32 @@ packages: purls: [] size: 10490535 timestamp: 1757411851093 -- conda: https://conda.anaconda.org/conda-forge/linux-64/conda-gcc-specs-14.3.0-he8ccf15_16.conda - sha256: 387cd20bc18c9cabae357fec1b73f691b8b6a6bafbf843b8ff17241eae0dd1d5 - md5: 77e54ea3bd0888e65ed821f19f5d23ad +- conda: https://conda.anaconda.org/conda-forge/linux-64/conda-gcc-specs-14.3.0-he8ccf15_17.conda + sha256: 522e7a22da3e8f30c8e8c80831c4d7399d8797fce154acbdf904111501d637f6 + md5: 4e58f090f75b2941346da3685564e7a7 depends: - gcc_impl_linux-64 >=14.3.0,<14.3.1.0a0 license: GPL-3.0-only WITH GCC-exception-3.1 license_family: GPL purls: [] - size: 31314 - timestamp: 1765256147792 -- conda: https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.3.3-py314h97ea11e_4.conda - sha256: b0314a7f1fb4a294b1a8bcf5481d4a8d9412a9fee23b7e3f93fb10e4d504f2cc - md5: 95bede9cdb7a30a4b611223d52a01aa4 + size: 31646 + timestamp: 1770252240343 +- conda: https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.3.3-py313hc8edb43_4.conda + sha256: 7f86eb205d2d7fcf2c82654a08c6a240623ac34cb406206b4b1f1afa5cda8e49 + md5: 33639459bc29437315d4bff9ed5bc7a7 depends: - numpy >=1.25 - python + - __glibc >=2.17,<3.0.a0 - libstdcxx >=14 - libgcc >=14 - - __glibc >=2.17,<3.0.a0 - - python_abi 3.14.* *_cp314 + - python_abi 3.13.* *_cp313 license: BSD-3-Clause license_family: BSD - size: 324013 - timestamp: 1769155968691 + purls: + - pkg:pypi/contourpy?source=compressed-mapping + size: 321850 + timestamp: 1769155964333 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/contourpy-1.3.3-py314hf8a3a22_4.conda sha256: 754ab72f1c1ae99ef7c57995f59224dc9632cbd6731fe7e6277437fd01d43156 md5: cddc851000ce131d757678c2f329eaad @@ -7717,6 +8127,8 @@ packages: - python_abi 3.14.* *_cp314 license: BSD-3-Clause license_family: BSD + purls: + - pkg:pypi/contourpy?source=hash-mapping size: 290405 timestamp: 1769156069514 - conda: https://conda.anaconda.org/conda-forge/win-64/contourpy-1.3.3-py314hf309875_4.conda @@ -7731,18 +8143,89 @@ packages: - python_abi 3.14.* *_cp314 license: BSD-3-Clause license_family: BSD + purls: + - pkg:pypi/contourpy?source=hash-mapping size: 247437 timestamp: 1769155978556 -- conda: https://conda.anaconda.org/conda-forge/noarch/cpython-3.14.2-py314hd8ed1ab_101.conda +- conda: https://conda.anaconda.org/conda-forge/noarch/cpython-3.12.12-py312hd8ed1ab_2.conda + noarch: generic + sha256: ccb90d95bac9f1f4f6629a4addb44d36433e4ad1fe4ac87a864f90ff305dbf6d + md5: ef3e093ecfd4533eee992cdaa155b47e + depends: + - python >=3.12,<3.13.0a0 + - python_abi * *_cp312 + license: Python-2.0 + size: 46644 + timestamp: 1769471040321 +- conda: https://conda.anaconda.org/conda-forge/noarch/cpython-3.13.12-py313hd8ed1ab_100.conda + noarch: generic + sha256: 7636809bda35add7af66cda1fee156136fcba0a1e24bbef1d591ee859df755a8 + md5: 9a4b8a37303b933b847c14a310f0557b + depends: + - python >=3.13,<3.14.0a0 + - python_abi * *_cp313 + license: Python-2.0 + size: 48648 + timestamp: 1770270374831 +- conda: https://conda.anaconda.org/conda-forge/noarch/cpython-3.14.3-py314hd8ed1ab_100.conda noarch: generic - sha256: 2831632c7a1a8d406739cab7e45e3a22a4109542baf0b486dffc104d14e3d3c3 - md5: ff215afbcf27f6267d6ec09209a4fe0f + sha256: 8484d2c9433fdfaf9a49d45803a5946bddfda2c07dc6653ee367dd3865f8e136 + md5: 32a3e6470f571e373e3d723cccb0b521 depends: - python >=3.14,<3.15.0a0 - python_abi * *_cp314 license: Python-2.0 - size: 49613 - timestamp: 1769457358023 + size: 49389 + timestamp: 1770271734795 +- conda: https://conda.anaconda.org/conda-forge/linux-64/curl-8.18.0-h4e3cde8_0.conda + sha256: f6f74fcb3a5a5239d8b876e9193df04dfcb1c5866e172797da657fdee9282b84 + md5: 261410cab40c7142adce3a09e24cae41 + depends: + - __glibc >=2.17,<3.0.a0 + - krb5 >=1.21.3,<1.22.0a0 + - libcurl 8.18.0 h4e3cde8_0 + - libgcc >=14 + - libssh2 >=1.11.1,<2.0a0 + - libzlib >=1.3.1,<2.0a0 + - openssl >=3.5.4,<4.0a0 + - zstd >=1.5.7,<1.6.0a0 + license: curl + license_family: MIT + purls: [] + size: 190096 + timestamp: 1767821756587 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/curl-8.18.0-he38603e_0.conda + sha256: 5bcd5c8b51a6a6141cbb56170b7497fc2008d404eeb89688789d281e63f9f80d + md5: 77fd129d3083a587962552b573b45791 + depends: + - __osx >=11.0 + - krb5 >=1.21.3,<1.22.0a0 + - libcurl 8.18.0 he38603e_0 + - libssh2 >=1.11.1,<2.0a0 + - libzlib >=1.3.1,<2.0a0 + - openssl >=3.5.4,<4.0a0 + - zstd >=1.5.7,<1.6.0a0 + license: curl + license_family: MIT + purls: [] + size: 176495 + timestamp: 1767822747479 +- conda: https://conda.anaconda.org/conda-forge/win-64/curl-8.18.0-h43ecb02_0.conda + sha256: 864b6de0a7e23abe273d1164a600486413b31502e99ae4fce77ea6c46832c3cd + md5: 2a26c0d3fcb56232c577c113bbc02ea9 + depends: + - krb5 >=1.21.3,<1.22.0a0 + - libcurl 8.18.0 h43ecb02_0 + - libssh2 >=1.11.1,<2.0a0 + - libzlib >=1.3.1,<2.0a0 + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + license: curl + license_family: MIT + purls: [] + size: 183318 + timestamp: 1767821999612 - conda: https://conda.anaconda.org/conda-forge/linux-64/cxx-compiler-1.11.0-hfcd1e18_0.conda sha256: 3fcc97ae3e89c150401a50a4de58794ffc67b1ed0e1851468fcc376980201e25 md5: 5da8c935dca9186673987f79cef0b2a5 @@ -7784,6 +8267,8 @@ packages: - python license: BSD-3-Clause license_family: BSD + purls: + - pkg:pypi/cycler?source=hash-mapping size: 14778 timestamp: 1764466758386 - conda: https://conda.anaconda.org/conda-forge/linux-64/cython-3.1.3-py39h6bc127c_2.conda @@ -7849,6 +8334,8 @@ packages: - python_abi 3.13.* *_cp313 license: Apache-2.0 license_family: APACHE + purls: + - pkg:pypi/cython?source=hash-mapping size: 3759950 timestamp: 1767577328564 - conda: https://conda.anaconda.org/conda-forge/linux-64/cython-3.2.4-py314h1807b08_0.conda @@ -7862,8 +8349,6 @@ packages: - python_abi 3.14.* *_cp314 license: Apache-2.0 license_family: APACHE - purls: - - pkg:pypi/cython?source=hash-mapping size: 3806945 timestamp: 1767576996860 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/cython-3.1.3-py39hba8e057_2.conda @@ -8026,9 +8511,9 @@ packages: - pkg:pypi/cython?source=hash-mapping size: 3332872 timestamp: 1767577440799 -- conda: https://conda.anaconda.org/conda-forge/noarch/cython-lint-0.18.0-pyhcf101f3_0.conda - sha256: 57cba981b4fffb434eac896a39efc591a12fd258fae5a9d004b741d2450a1329 - md5: 4749d005f865787f9e18ee74f29788a3 +- conda: https://conda.anaconda.org/conda-forge/noarch/cython-lint-0.19.0-pyhcf101f3_0.conda + sha256: 2685e955138d35387013402c4aa08ee0da3f882f02e1fb7c4a6494758f399927 + md5: 91c82bb23b360a26b3523f5bf909f703 depends: - python >=3.10 - cython >=0.29.32 @@ -8038,21 +8523,21 @@ packages: - python license: MIT license_family: MIT - size: 20986 - timestamp: 1760809141185 -- conda: https://conda.anaconda.org/conda-forge/linux-64/cytoolz-1.1.0-py314h5bd0f2a_1.conda - sha256: 7d0c7ac736f944ae1e97a2f066d5529d280d7d014bbf181c1d6d48d5efb1488d - md5: 51b0391b0ce96be49b1174e9a3e4a279 + size: 21215 + timestamp: 1770059998960 +- conda: https://conda.anaconda.org/conda-forge/linux-64/cytoolz-1.1.0-py313h07c4f96_1.conda + sha256: a8ffc7cf31a698a57a46bf7977185ed1e644c5e35d4e166d8f260dca93af6ffb + md5: bcca9afd203fe05d9582249ac12762da depends: - __glibc >=2.17,<3.0.a0 - libgcc >=14 - - python >=3.14,<3.15.0a0 - - python_abi 3.14.* *_cp314 + - python >=3.13,<3.14.0a0 + - python_abi 3.13.* *_cp313 - toolz >=0.10.0 license: BSD-3-Clause license_family: BSD - size: 589994 - timestamp: 1760905949247 + size: 590435 + timestamp: 1760905824293 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/cytoolz-1.1.0-py314h0612a62_1.conda sha256: ca385fbae190e16b603072213894b8079abb238b2c41b0a475186ae81f7248f7 md5: 78f23afe83dc301c716fb254ace8172e @@ -8098,6 +8583,7 @@ packages: constrains: - openssl !=1.1.1e license: BSD-3-Clause + license_family: BSD size: 11425 timestamp: 1769901592404 - conda: https://conda.anaconda.org/conda-forge/noarch/dask-core-2026.1.2-pyhcf101f3_0.conda @@ -8152,6 +8638,32 @@ packages: license_family: BSD size: 113608 timestamp: 1739051599005 +- conda: https://conda.anaconda.org/conda-forge/linux-64/debugpy-1.8.20-py312h8285ef7_0.conda + sha256: f20121b67149ff80bf951ccae7442756586d8789204cd08ade59397b22bfd098 + md5: ee1b48795ceb07311dd3e665dd4f5f33 + depends: + - python + - libgcc >=14 + - libstdcxx >=14 + - __glibc >=2.17,<3.0.a0 + - python_abi 3.12.* *_cp312 + license: MIT + license_family: MIT + size: 2858582 + timestamp: 1769744978783 +- conda: https://conda.anaconda.org/conda-forge/linux-64/debugpy-1.8.20-py313h5d5ffb9_0.conda + sha256: 8d76d4eeb5a8e3c5666880b465593fdf4a44f47fbbe89ff5b8f9abbe43026326 + md5: e94dbbec2589f3b1d821f43a4bf2ab45 + depends: + - python + - libstdcxx >=14 + - libgcc >=14 + - __glibc >=2.17,<3.0.a0 + - python_abi 3.13.* *_cp313 + license: MIT + license_family: MIT + size: 2872698 + timestamp: 1769744980407 - conda: https://conda.anaconda.org/conda-forge/linux-64/debugpy-1.8.20-py314h42812f9_0.conda sha256: d9e89e351d7189c41615cfceca76b3bcacaa9c81d9945ac1caa6fb9e5184f610 md5: 57e6fad901c05754d5256fe3ab9f277b @@ -8243,6 +8755,7 @@ packages: constrains: - openssl !=1.1.1e license: BSD-3-Clause + license_family: BSD size: 844862 timestamp: 1769888496327 - conda: https://conda.anaconda.org/conda-forge/noarch/docutils-0.21.2-pyhd8ed1ab_1.conda @@ -8253,6 +8766,23 @@ packages: license: CC-PDDC AND BSD-3-Clause AND BSD-2-Clause AND ZPL-2.1 size: 402700 timestamp: 1733217860944 +- pypi: https://files.pythonhosted.org/packages/37/45/01e7455a9659528e77a414b222326d4c525796e4f571bbabcb2e0ff3d1f4/download-0.3.5-py3-none-any.whl + name: download + version: 0.3.5 + sha256: 8b2f7695745254b0bebdfb789d426b35768366054538b4f8e7f9377dd5a9499d + requires_dist: + - tqdm + - six + - requests + - numpy ; extra == 'dev' + - codecov ; extra == 'dev' + - pytest ; extra == 'dev' + - pytest-cov ; extra == 'dev' + - matplotlib ; extra == 'sphinx' + - pandas ; extra == 'sphinx' + - sphinx ; extra == 'sphinx' + - sphinx-gallery ; extra == 'sphinx' + - pillow ; extra == 'sphinx' - conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.0-pyhd8ed1ab_0.conda sha256: ce61f4f99401a4bd455b89909153b40b9c823276aefcbb06f2044618696009ca md5: 72e42d28960d875c7654614f8b50939a @@ -8269,6 +8799,8 @@ packages: - python >=3.10 - typing_extensions >=4.6.0 license: MIT and PSF-2.0 + purls: + - pkg:pypi/exceptiongroup?source=hash-mapping size: 21333 timestamp: 1763918099466 - conda: https://conda.anaconda.org/conda-forge/noarch/execnet-2.1.1-pyhd8ed1ab_1.conda @@ -8367,6 +8899,35 @@ packages: purls: [] size: 265599 timestamp: 1730283881107 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/fontconfig-2.15.0-h1383a14_1.conda + sha256: f79d3d816fafbd6a2b0f75ebc3251a30d3294b08af9bb747194121f5efa364bc + md5: 7b29f48742cea5d1ccb5edd839cb5621 + depends: + - __osx >=11.0 + - freetype >=2.12.1,<3.0a0 + - libexpat >=2.6.3,<3.0a0 + - libzlib >=1.3.1,<2.0a0 + license: MIT + license_family: MIT + purls: [] + size: 234227 + timestamp: 1730284037572 +- conda: https://conda.anaconda.org/conda-forge/win-64/fontconfig-2.15.0-h765892d_1.conda + sha256: ed122fc858fb95768ca9ca77e73c8d9ddc21d4b2e13aaab5281e27593e840691 + md5: 9bb0026a2131b09404c59c4290c697cd + depends: + - freetype >=2.12.1,<3.0a0 + - libexpat >=2.6.3,<3.0a0 + - libiconv >=1.17,<2.0a0 + - libzlib >=1.3.1,<2.0a0 + - ucrt >=10.0.20348.0 + - vc >=14.2,<15 + - vc14_runtime >=14.29.30139 + license: MIT + license_family: MIT + purls: [] + size: 192355 + timestamp: 1730284147944 - conda: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2 sha256: a997f2f1921bb9c9d76e6fa2f6b408b7fa549edd349a77639c9fe7a23ea93e61 md5: fee5683a3f04bd15cbd8318b096a27ab @@ -8390,6 +8951,22 @@ packages: purls: [] size: 4059 timestamp: 1762351264405 +- conda: https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.61.1-py313h3dea7bd_0.conda + sha256: 97f225199e6e5dfb93f551087c0951fee92db2d29a9dcb6a0346d66bff06fea4 + md5: c0f36dfbb130da4f6ce2df31f6b25ea8 + depends: + - __glibc >=2.17,<3.0.a0 + - brotli + - libgcc >=14 + - munkres + - python >=3.13,<3.14.0a0 + - python_abi 3.13.* *_cp313 + license: MIT + license_family: MIT + purls: + - pkg:pypi/fonttools?source=hash-mapping + size: 2988776 + timestamp: 1765633043435 - conda: https://conda.anaconda.org/conda-forge/noarch/fonttools-4.61.1-pyh7db6752_0.conda sha256: bb74f1732065eb95c3ea4ae7f7ab29d6ddaafe6da32f009106bf9a335147cb77 md5: d5da976e963e70364b9e3ff270842b9f @@ -8402,6 +8979,8 @@ packages: - fonttools_no_compile license: MIT license_family: MIT + purls: + - pkg:pypi/fonttools?source=hash-mapping size: 834764 timestamp: 1765632669874 - conda: https://conda.anaconda.org/conda-forge/linux-64/formulaic-1.2.0-py39hf3d152e_2.conda @@ -8537,6 +9116,7 @@ packages: - libfreetype 2.14.1 hce30654_0 - libfreetype6 2.14.1 h6da58f4_0 license: GPL-2.0-only OR FTL + purls: [] size: 173399 timestamp: 1757947175403 - conda: https://conda.anaconda.org/conda-forge/win-64/freetype-2.14.1-h57928b3_0.conda @@ -8546,6 +9126,7 @@ packages: - libfreetype 2.14.1 h57928b3_0 - libfreetype6 2.14.1 hdbac1cb_0 license: GPL-2.0-only OR FTL + purls: [] size: 184553 timestamp: 1757946164012 - conda: https://conda.anaconda.org/conda-forge/linux-64/freexl-2.0.0-h9dce30a_2.conda @@ -8587,46 +9168,80 @@ packages: license_family: MOZILLA size: 77528 timestamp: 1734015193826 -- conda: https://conda.anaconda.org/conda-forge/noarch/fsspec-2026.1.0-pyhd8ed1ab_0.conda - sha256: bfba6c280366f48b00a6a7036988fc2bc3fea5ac1d8303152c9da69d72a22936 - md5: 1daaf94a304a27ba3446a306235a37ea +- conda: https://conda.anaconda.org/conda-forge/linux-64/fribidi-1.0.16-hb03c661_0.conda + sha256: 858283ff33d4c033f4971bf440cebff217d5552a5222ba994c49be990dacd40d + md5: f9f81ea472684d75b9dd8d0b328cf655 + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + license: LGPL-2.1-or-later + purls: [] + size: 61244 + timestamp: 1757438574066 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/fribidi-1.0.16-hc919400_0.conda + sha256: d856dc6744ecfba78c5f7df3378f03a75c911aadac803fa2b41a583667b4b600 + md5: 04bdce8d93a4ed181d1d726163c2d447 + depends: + - __osx >=11.0 + license: LGPL-2.1-or-later + purls: [] + size: 59391 + timestamp: 1757438897523 +- conda: https://conda.anaconda.org/conda-forge/noarch/fsspec-2026.2.0-pyhd8ed1ab_0.conda + sha256: 239b67edf1c5e5caed52cf36e9bed47cb21b37721779828c130e6b3fd9793c1b + md5: 496c6c9411a6284addf55c898d6ed8d7 depends: - python >=3.10 license: BSD-3-Clause - license_family: BSD - size: 148116 - timestamp: 1768000866082 -- conda: https://conda.anaconda.org/conda-forge/linux-64/gcc-14.3.0-h0dff253_16.conda - sha256: 4581ce836a04a591a2622c2a0f15b81d7a87cec614facb3a405c070c8fdb7ac8 - md5: dcaf539ffe75649239192101037f1406 + size: 148757 + timestamp: 1770387898414 +- conda: https://conda.anaconda.org/conda-forge/linux-64/gcc-14.3.0-h0dff253_17.conda + sha256: e3eb2b4655d8a65488fdfbe470705a290121c4265f9559933a8071aa9aac5b91 + md5: dfcfcc0c20762eeb840771eda366940e depends: - conda-gcc-specs - - gcc_impl_linux-64 14.3.0 he8b2097_16 + - gcc_impl_linux-64 14.3.0 hb1e0a52_17 license: BSD-3-Clause license_family: BSD purls: [] - size: 29022 - timestamp: 1765256332962 -- conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_impl_linux-64-14.3.0-he8b2097_16.conda - sha256: 4acf50b7d5673250d585a256a40aabdd922e0947ca12cdbad0cef960ee1a9509 - md5: d274bf1343507683e6eb2954d1871569 + size: 29381 + timestamp: 1770252396987 +- conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_impl_linux-64-14.3.0-hb1e0a52_17.conda + sha256: bc7014fcc7fcd54ae922fc3453ad8d88a26f439570bb6a89f785f8b5793306b2 + md5: f5c501fe2a016ed0103f7a89d2ac0412 depends: - binutils_impl_linux-64 >=2.45 - libgcc >=14.3.0 - - libgcc-devel_linux-64 14.3.0 hf649bbc_116 + - libgcc-devel_linux-64 14.3.0 hf649bbc_117 - libgomp >=14.3.0 - - libsanitizer 14.3.0 h8f1669f_16 + - libsanitizer 14.3.0 h8f1669f_17 - libstdcxx >=14.3.0 - - libstdcxx-devel_linux-64 14.3.0 h9f08a49_116 + - libstdcxx-devel_linux-64 14.3.0 h9f08a49_117 - sysroot_linux-64 license: GPL-3.0-only WITH GCC-exception-3.1 license_family: GPL purls: [] - size: 75290045 - timestamp: 1765256021903 -- conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_linux-64-14.3.0-h298d278_17.conda - sha256: 7b9585c201c175c024c56b46658d9e4b5db85a32df54517798109281a90d03bb - md5: 50dc15ac993bb5859f923979c81fafc8 + size: 74850589 + timestamp: 1770252142196 +- conda: https://conda.anaconda.org/conda-forge/win-64/gcc_impl_win-64-15.2.0-h58d629f_17.conda + sha256: 8f382c1a9ac6e227e91a85bdd7e33b171b71ec32853951f89fcf48d67961d729 + md5: 4ee5ae035327c2c2e426d5a027f308a6 + depends: + - binutils_impl_win-64 >=2.45 + - libgcc >=15.2.0 + - libgcc-devel_win-64 15.2.0 hbb59886_117 + - libgomp >=15.2.0 + - libstdcxx >=15.2.0 + - libstdcxx-devel_win-64 15.2.0 h0a72980_117 + - m2w64-sysroot_win-64 + license: GPL-3.0-only WITH GCC-exception-3.1 + license_family: GPL + purls: [] + size: 63775343 + timestamp: 1770256801479 +- conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_linux-64-14.3.0-h298d278_20.conda + sha256: 5dd1fc1757e6d0354b6fd8f1917b334d46f01995401da02d7c4d5185edc0d895 + md5: 6a7d74012f6ff0b58fb8fcb5286fa584 depends: - gcc_impl_linux-64 14.3.0.* - binutils_linux-64 @@ -8634,8 +9249,8 @@ packages: license: BSD-3-Clause license_family: BSD purls: [] - size: 28913 - timestamp: 1766347929374 + size: 28918 + timestamp: 1770277530099 - conda: https://conda.anaconda.org/conda-forge/noarch/geopandas-1.1.2-pyhd8ed1ab_0.conda sha256: 7c3e5dc62c0b3d067a6f517ea9176e9d52682499d4afb78704354a60f37c5444 md5: 3b9d40bef27d094e48bb1a821e86a252 @@ -8717,6 +9332,73 @@ packages: purls: [] size: 82090 timestamp: 1726600145480 +- conda: https://conda.anaconda.org/conda-forge/linux-64/gfortran_impl_linux-64-14.3.0-h1a219da_17.conda + sha256: 04593b314f7ab8619536cdc23f3bf1e826d0b2dcf6adbf2f39932a7aec65b25a + md5: ea4724804b89ddc81d16cabe3f4719b5 + depends: + - gcc_impl_linux-64 >=14.3.0 + - libgcc >=14.3.0 + - libgfortran5 >=14.3.0 + - libstdcxx >=14.3.0 + - sysroot_linux-64 + license: GPL-3.0-only WITH GCC-exception-3.1 + license_family: GPL + purls: [] + size: 18360922 + timestamp: 1770252307342 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/gfortran_impl_osx-arm64-14.3.0-h6d03799_1.conda + sha256: c05c634388e180f79c70a5989d2b25977716b7f6d5e395119ad0007cf4a7bcbf + md5: 1e9ec88ecc684d92644a45c6df2399d0 + depends: + - __osx >=11.0 + - cctools_osx-arm64 + - clang + - gmp >=6.3.0,<7.0a0 + - isl 0.26.* + - libcxx >=17 + - libgfortran-devel_osx-arm64 14.3.0.* + - libgfortran5 >=14.3.0 + - libiconv >=1.18,<2.0a0 + - libzlib >=1.3.1,<2.0a0 + - mpc >=1.3.1,<2.0a0 + - mpfr >=4.2.1,<5.0a0 + - zlib + license: GPL-3.0-only WITH GCC-exception-3.1 + license_family: GPL + purls: [] + size: 20286770 + timestamp: 1759712171482 +- conda: https://conda.anaconda.org/conda-forge/win-64/gfortran_impl_win-64-15.2.0-h0e079bb_17.conda + sha256: 67fec172f1848ca2ff359025580e9f213548e1c27f5744dff2f9b4a76d645f34 + md5: 276dafc0c5d229f84fe5882f3f05a4bb + depends: + - gcc_impl_win-64 >=15.2.0 + - libgcc >=15.2.0 + - libgfortran5 >=15.2.0 + - libstdcxx >=15.2.0 + - m2w64-sysroot_win-64 + license: GPL-3.0-only WITH GCC-exception-3.1 + license_family: GPL + purls: [] + size: 16785708 + timestamp: 1770257018351 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/gfortran_osx-arm64-14.3.0-h3c33bd0_0.conda + sha256: 2644e5f4b4eed171b12afb299e2413be5877db92f30ec03690621d1ae648502c + md5: 8db8c0061c0f3701444b7b9cc9966511 + depends: + - cctools_osx-arm64 + - clang + - clang_osx-arm64 + - gfortran_impl_osx-arm64 14.3.0 + - ld64_osx-arm64 + - libgfortran + - libgfortran-devel_osx-arm64 14.3.0 + - libgfortran5 >=14.3.0 + license: GPL-3.0-or-later WITH GCC-exception-3.1 + license_family: GPL + purls: [] + size: 35951 + timestamp: 1751220424258 - conda: https://conda.anaconda.org/conda-forge/linux-64/giflib-5.2.2-hd590300_0.conda sha256: aac402a8298f0c0cc528664249170372ef6b37ac39fdc92b40601a6aed1e32ff md5: 3bf7b9fd5a7136126e0234db4b87c8b6 @@ -8785,6 +9467,7 @@ packages: - __osx >=11.0 - libcxx >=16 license: GPL-2.0-or-later OR LGPL-3.0-or-later + purls: [] size: 365188 timestamp: 1718981343258 - conda: https://conda.anaconda.org/conda-forge/linux-64/graphite2-1.3.14-hecca717_2.conda @@ -8799,6 +9482,17 @@ packages: purls: [] size: 99596 timestamp: 1755102025473 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/graphite2-1.3.14-hec049ff_2.conda + sha256: c507ae9989dbea7024aa6feaebb16cbf271faac67ac3f0342ef1ab747c20475d + md5: 0fc46fee39e88bbcf5835f71a9d9a209 + depends: + - __osx >=11.0 + - libcxx >=19 + license: LGPL-2.0-or-later + license_family: LGPL + purls: [] + size: 81202 + timestamp: 1755102333712 - conda: https://conda.anaconda.org/conda-forge/noarch/graphlib-backport-1.0.3-pyhd8ed1ab_0.tar.bz2 sha256: 1a417887d6f2b770eae6154441be7a7819e9966ce495150e529e07922a5adb08 md5: 33c122658a309cc9fc0b1dda47a02a84 @@ -8808,43 +9502,92 @@ packages: license_family: PSF size: 10655 timestamp: 1635566130699 -- conda: https://conda.anaconda.org/conda-forge/linux-64/gxx-14.3.0-h76987e4_16.conda - sha256: 5a4174e7723a95eca2305f4e4b3d19fa8c714eadd921b993e1a893fe47e5d3d7 - md5: a3aa64ee3486f51eb61018939c88ef12 +- conda: https://conda.anaconda.org/conda-forge/linux-64/gsl-2.7-he838d99_0.tar.bz2 + sha256: 132a918b676dd1f533d7c6f95e567abf7081a6ea3251c3280de35ef600e0da87 + md5: fec079ba39c9cca093bf4c00001825de + depends: + - libblas >=3.8.0,<4.0a0 + - libcblas >=3.8.0,<4.0a0 + - libgcc-ng >=9.3.0 + license: GPL-3.0-or-later + license_family: GPL + purls: [] + size: 3376423 + timestamp: 1626369596591 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/gsl-2.7-h6e638da_0.tar.bz2 + sha256: 979c2976adcfc70be997abeab2ed8395f9ac2b836bdcd25ed5d2efbf1fed226b + md5: 2a2126a940e033e7225a5dc7215eea9a + depends: + - libblas >=3.9.0,<4.0a0 + - libcblas >=3.9.0,<4.0a0 + license: GPL-3.0-or-later + license_family: GPL + purls: [] + size: 2734398 + timestamp: 1626369562748 +- conda: https://conda.anaconda.org/conda-forge/win-64/gsl-2.7-hdfb1a43_0.tar.bz2 + sha256: 4bb43ff81eca1631a3738dee073763cbff2d27a47ac3c60d7b7233941d7ab202 + md5: ca5c581b3659140455cf6ae00f6a2ea9 depends: - - gcc 14.3.0 h0dff253_16 - - gxx_impl_linux-64 14.3.0 h2185e75_16 + - libblas >=3.8.0,<4.0a0 + - libcblas >=3.8.0,<4.0a0 + - vc >=14.1,<15.0a0 + - vs2015_runtime >=14.16.27012 + license: GPL-3.0-or-later + license_family: GPL + purls: [] + size: 1739909 + timestamp: 1626371462874 +- conda: https://conda.anaconda.org/conda-forge/linux-64/gxx-14.3.0-h76987e4_17.conda + sha256: 13280aa6d2e8313e7bf15d066d1a6767b749e8a3485116fb8744d1a3db93c279 + md5: eae8e3fb1f5eecb829dd7347d33ecacb + depends: + - gcc 14.3.0 h0dff253_17 + - gxx_impl_linux-64 14.3.0 h2185e75_17 license: BSD-3-Clause license_family: BSD purls: [] - size: 28403 - timestamp: 1765256369945 -- conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_impl_linux-64-14.3.0-h2185e75_16.conda - sha256: 71a6672af972c4d072d79514e9755c9e9ea359d46613fd9333adcb3b08c0c008 - md5: 8729b9d902631b9ee604346a90a50031 + size: 28708 + timestamp: 1770252431123 +- conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_impl_linux-64-14.3.0-h2185e75_17.conda + sha256: d43556d0cc5bc636ef02a21fdfc08167430846538a5a92b4ee9a0dedad13ba8f + md5: 8f02f68c780b0a6eeba034af3ed1c00a depends: - - gcc_impl_linux-64 14.3.0 he8b2097_16 - - libstdcxx-devel_linux-64 14.3.0 h9f08a49_116 + - gcc_impl_linux-64 14.3.0 hb1e0a52_17 + - libstdcxx-devel_linux-64 14.3.0 h9f08a49_117 - sysroot_linux-64 - tzdata license: GPL-3.0-only WITH GCC-exception-3.1 license_family: GPL purls: [] - size: 15255410 - timestamp: 1765256273332 -- conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_linux-64-14.3.0-h310e576_17.conda - sha256: 90ccb0df50254feb5b4e539b06e3d2c3baf5c37e40579224a277ab164566a6a0 - md5: 94474857477981fedf74cf7c47c88ba5 + size: 15251260 + timestamp: 1770252349885 +- conda: https://conda.anaconda.org/conda-forge/win-64/gxx_impl_win-64-15.2.0-h22fd5bf_17.conda + sha256: 58f9ac73f26e18befcd5a9d1c78a1b1e6f4913d676cdcfe87bce22a2663a8db9 + md5: a06aa6eee9bf8b9df14c8b5fe0b4aa00 + depends: + - gcc_impl_win-64 15.2.0 h58d629f_17 + - libstdcxx-devel_win-64 15.2.0 h0a72980_117 + - m2w64-sysroot_win-64 + - tzdata + license: GPL-3.0-only WITH GCC-exception-3.1 + license_family: GPL + purls: [] + size: 14814320 + timestamp: 1770257064278 +- conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_linux-64-14.3.0-h3c3a7a4_20.conda + sha256: 88e2ca2a6da454a11d1971e00c6e94f020fe9137f61838daba48b15886eaae84 + md5: 4b46597b58a2495ec48c215a40e42f0c depends: - gxx_impl_linux-64 14.3.0.* - - gcc_linux-64 ==14.3.0 h298d278_17 + - gcc_linux-64 ==14.3.0 h298d278_20 - binutils_linux-64 - sysroot_linux-64 license: BSD-3-Clause license_family: BSD purls: [] - size: 27464 - timestamp: 1766347929379 + size: 27482 + timestamp: 1770277530104 - conda: https://conda.anaconda.org/conda-forge/noarch/h11-0.16.0-pyhcf101f3_1.conda sha256: 96cac6573fd35ae151f4d6979bab6fbc90cb6b1fb99054ba19eb075da9822fcb md5: b8993c19b0c32a2f7b66cbb58ca27069 @@ -8878,26 +9621,45 @@ packages: - gssapi ; extra == 'kerberos' - pykerberos>=1.1.8,<2.0.0 ; sys_platform != 'win32' and extra == 'kerberos' - winkerberos>=0.5.0 ; sys_platform == 'win32' and extra == 'kerberos' -- conda: https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-12.3.2-h6083320_0.conda - sha256: 92015faf283f9c0a8109e2761042cd47ae7a4505e24af42a53bc3767cb249912 - md5: d170a70fc1d5c605fcebdf16851bd54a +- conda: https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-12.2.0-h15599e2_0.conda + sha256: 6bd8b22beb7d40562b2889dc68232c589ff0d11a5ad3addd41a8570d11f039d9 + md5: b8690f53007e9b5ee2c2178dd4ac778c depends: - __glibc >=2.17,<3.0.a0 - cairo >=1.18.4,<2.0a0 - graphite2 >=1.3.14,<2.0a0 - - icu >=78.2,<79.0a0 - - libexpat >=2.7.3,<3.0a0 + - icu >=75.1,<76.0a0 + - libexpat >=2.7.1,<3.0a0 - libfreetype >=2.14.1 - libfreetype6 >=2.14.1 - libgcc >=14 - - libglib >=2.86.3,<3.0a0 + - libglib >=2.86.1,<3.0a0 - libstdcxx >=14 - libzlib >=1.3.1,<2.0a0 license: MIT license_family: MIT purls: [] - size: 2035859 - timestamp: 1769445400168 + size: 2411408 + timestamp: 1762372726141 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/harfbuzz-12.2.0-haf38c7b_0.conda + sha256: 2f8d95fe1cb655fe3bac114062963f08cc77b31b042027ef7a04ebde3ce21594 + md5: 1c7ff9d458dd8220ac2ee71dd4af1be5 + depends: + - __osx >=11.0 + - cairo >=1.18.4,<2.0a0 + - graphite2 >=1.3.14,<2.0a0 + - icu >=75.1,<76.0a0 + - libcxx >=19 + - libexpat >=2.7.1,<3.0a0 + - libfreetype >=2.14.1 + - libfreetype6 >=2.14.1 + - libglib >=2.86.1,<3.0a0 + - libzlib >=1.3.1,<2.0a0 + license: MIT + license_family: MIT + purls: [] + size: 1537764 + timestamp: 1762373922469 - conda: https://conda.anaconda.org/conda-forge/noarch/hpack-4.1.0-pyhd8ed1ab_0.conda sha256: 6ad78a180576c706aabeb5b4c8ceb97c0cb25f1e112d76495bff23e3779948ba md5: 0a802cb9888dd14eeefc611f05c40b6e @@ -8944,18 +9706,39 @@ packages: license_family: MIT size: 17397 timestamp: 1737618427549 -- conda: https://conda.anaconda.org/conda-forge/linux-64/icu-78.2-h33c6efd_0.conda - sha256: 142a722072fa96cf16ff98eaaf641f54ab84744af81754c292cb81e0881c0329 - md5: 186a18e3ba246eccfc7cff00cd19a870 +- conda: https://conda.anaconda.org/conda-forge/linux-64/icu-75.1-he02047a_0.conda + sha256: 71e750d509f5fa3421087ba88ef9a7b9be11c53174af3aa4d06aff4c18b38e8e + md5: 8b189310083baabfb622af68fd9d3ae3 + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc-ng >=12 + - libstdcxx-ng >=12 + license: MIT + license_family: MIT + purls: [] + size: 12129203 + timestamp: 1720853576813 +- conda: https://conda.anaconda.org/conda-forge/linux-64/icu-78.2-h33c6efd_0.conda + sha256: 142a722072fa96cf16ff98eaaf641f54ab84744af81754c292cb81e0881c0329 + md5: 186a18e3ba246eccfc7cff00cd19a870 depends: - __glibc >=2.17,<3.0.a0 - libgcc >=14 - libstdcxx >=14 license: MIT license_family: MIT - purls: [] size: 12728445 timestamp: 1767969922681 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/icu-75.1-hfee45f7_0.conda + sha256: 9ba12c93406f3df5ab0a43db8a4b4ef67a5871dfd401010fbe29b218b2cbe620 + md5: 5eb22c1d7b3fc4abb50d92d621583137 + depends: + - __osx >=11.0 + license: MIT + license_family: MIT + purls: [] + size: 11857802 + timestamp: 1720853997952 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/icu-78.2-h38cb7af_0.conda sha256: d4cefbca587429d1192509edc52c88de52bc96c2447771ddc1f8bee928aed5ef md5: 1e93aca311da0210e660d2247812fa02 @@ -8963,9 +9746,20 @@ packages: - __osx >=11.0 license: MIT license_family: MIT - purls: [] size: 12358010 timestamp: 1767970350308 +- conda: https://conda.anaconda.org/conda-forge/win-64/icu-75.1-he0c23c2_0.conda + sha256: 1d04369a1860a1e9e371b9fc82dd0092b616adcf057d6c88371856669280e920 + md5: 8579b6bb8d18be7c0b27fb08adeeeb40 + depends: + - ucrt >=10.0.20348.0 + - vc >=14.2,<15 + - vc14_runtime >=14.29.30139 + license: MIT + license_family: MIT + purls: [] + size: 14544252 + timestamp: 1720853966338 - conda: https://conda.anaconda.org/conda-forge/win-64/icu-78.2-h637d24d_0.conda sha256: 5a41fb28971342e293769fc968b3414253a2f8d9e30ed7c31517a15b4887246a md5: 0ee3bb487600d5e71ab7d28951b2016a @@ -8975,7 +9769,6 @@ packages: - vc14_runtime >=14.44.35208 license: MIT license_family: MIT - purls: [] size: 13222158 timestamp: 1767970128854 - conda: https://conda.anaconda.org/conda-forge/noarch/identify-2.6.16-pyhd8ed1ab_0.conda @@ -9045,6 +9838,8 @@ packages: - python >=3.10 license: MIT license_family: MIT + purls: + - pkg:pypi/iniconfig?source=compressed-mapping size: 13387 timestamp: 1760831448842 - conda: https://conda.anaconda.org/conda-forge/noarch/interface_meta-1.3.0-pyhd8ed1ab_1.conda @@ -9148,9 +9943,9 @@ packages: license_family: BSD size: 133820 timestamp: 1761567932044 -- conda: https://conda.anaconda.org/conda-forge/noarch/ipython-9.9.0-pyh53cf698_0.conda - sha256: 4ff1733c59b72cf0c8ed9ddb6e948e99fc6b79b76989282c0c7a46aab56e6176 - md5: 8481978caa2f108e6ddbf8008a345546 +- conda: https://conda.anaconda.org/conda-forge/noarch/ipython-9.10.0-pyh53cf698_0.conda + sha256: 12cb4db242ea1a2e5e60a51b20f16e9c8120a9eb5d013c641cbf827bf3bb78e1 + md5: 441ca4e203a62f7db2f29f190c02b9cf depends: - __unix - pexpect >4.3 @@ -9167,11 +9962,11 @@ packages: - python license: BSD-3-Clause license_family: BSD - size: 646242 - timestamp: 1767621166614 -- conda: https://conda.anaconda.org/conda-forge/noarch/ipython-9.9.0-pyhe2676ad_0.conda - sha256: 1697fae5859f61938ab44af38126115ad18fc059462bb370c5f8740d7bc4a803 - md5: fe785355648dec69d2f06fa14c9e6e84 + size: 647436 + timestamp: 1770040907512 +- conda: https://conda.anaconda.org/conda-forge/noarch/ipython-9.10.0-pyhe2676ad_0.conda + sha256: 89e39c69cb3b8b0d11930968d66dca6f7c3dff3ad8c520ac10af11f53a10fae4 + md5: d44777fc7219cb62865dfdcba308ea0d depends: - __win - colorama >=0.4.4 @@ -9188,8 +9983,8 @@ packages: - python license: BSD-3-Clause license_family: BSD - size: 645119 - timestamp: 1767621201570 + size: 646337 + timestamp: 1770040952821 - conda: https://conda.anaconda.org/conda-forge/noarch/ipython_genutils-0.2.0-pyhd8ed1ab_2.conda sha256: 45821a8986b4cb2421f766b240dbe6998a3c3123f012dd566720c1322e9b6e18 md5: 2f0ba4bc12af346bc6c99bdc377e8944 @@ -9209,6 +10004,18 @@ packages: license_family: BSD size: 13993 timestamp: 1737123723464 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/isl-0.26-imath32_h347afa1_101.conda + sha256: fc9272371750c56908b8e535755b1e23cf7803a2cc4a7d9ae539347baa14f740 + md5: e80e44a3f4862b1da870dc0557f8cf3b + depends: + - libcxx >=14.0.6 + track_features: + - isl_imath-32 + license: MIT + license_family: MIT + purls: [] + size: 819937 + timestamp: 1680649567633 - conda: https://conda.anaconda.org/conda-forge/noarch/isoduration-20.11.0-pyhd8ed1ab_1.conda sha256: 08e838d29c134a7684bca0468401d26840f41c92267c4126d7b43a6b533b0aed md5: 0b0154421989637d424ccf0f104be51a @@ -9264,6 +10071,8 @@ packages: - python license: BSD-3-Clause license_family: BSD + purls: + - pkg:pypi/jinja2?source=compressed-mapping size: 120685 timestamp: 1764517220861 - conda: https://conda.anaconda.org/conda-forge/noarch/joblib-1.5.1-pyhd8ed1ab_0.conda @@ -9572,19 +10381,21 @@ packages: purls: [] size: 134088 timestamp: 1754905959823 -- conda: https://conda.anaconda.org/conda-forge/linux-64/kiwisolver-1.4.9-py314h97ea11e_2.conda - sha256: a707d08c095d02148201f2da9fba465054fb750e33117e215892a4fefcc1b54a - md5: 57f1ce4f7ba6bcd460be8f83c8f04c69 +- conda: https://conda.anaconda.org/conda-forge/linux-64/kiwisolver-1.4.9-py313hc8edb43_2.conda + sha256: 60d7d525db89401f88f5c91bdbb79d3afbf005e7d7c1326318659fa097607e51 + md5: 3e0e65595330e26515e31b7fc6d933c7 depends: - python + - __glibc >=2.17,<3.0.a0 - libstdcxx >=14 - libgcc >=14 - - __glibc >=2.17,<3.0.a0 - - python_abi 3.14.* *_cp314 + - python_abi 3.13.* *_cp313 license: BSD-3-Clause license_family: BSD - size: 78071 - timestamp: 1762488742381 + purls: + - pkg:pypi/kiwisolver?source=hash-mapping + size: 77616 + timestamp: 1762488778882 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/kiwisolver-1.4.9-py314h42813c9_2.conda sha256: c4d7e6653d343e768110ec77ac1c6c89f313f77a19a1f2cd60b7c7b8b0758bdf md5: 9aa431bf603c231e8c77a1b0842a85ed @@ -9596,6 +10407,8 @@ packages: - python_abi 3.14.* *_cp314 license: BSD-3-Clause license_family: BSD + purls: + - pkg:pypi/kiwisolver?source=hash-mapping size: 68534 timestamp: 1762489024029 - conda: https://conda.anaconda.org/conda-forge/win-64/kiwisolver-1.4.9-py314hf309875_2.conda @@ -9612,6 +10425,8 @@ packages: - python_abi 3.14.* *_cp314 license: BSD-3-Clause license_family: BSD + purls: + - pkg:pypi/kiwisolver?source=hash-mapping size: 73670 timestamp: 1762488752873 - conda: https://conda.anaconda.org/conda-forge/linux-64/krb5-1.21.3-h659f571_0.conda @@ -9687,6 +10502,7 @@ packages: - libtiff >=4.7.1,<4.8.0a0 license: MIT license_family: MIT + purls: [] size: 211756 timestamp: 1768184994800 - conda: https://conda.anaconda.org/conda-forge/win-64/lcms2-2.18-hf2c6c5f_0.conda @@ -9700,6 +10516,7 @@ packages: - vc14_runtime >=14.44.35208 license: MIT license_family: MIT + purls: [] size: 522238 timestamp: 1768184858107 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/ld64-956.6-llvm19_1_he86490a_4.conda @@ -9735,19 +10552,31 @@ packages: purls: [] size: 1040464 timestamp: 1768852821767 -- conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.45-default_hbd61a6d_105.conda - sha256: 1027bd8aa0d5144e954e426ab6218fd5c14e54a98f571985675468b339c808ca - md5: 3ec0aa5037d39b06554109a01e6fb0c6 +- conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.45.1-default_hbd61a6d_101.conda + sha256: 565941ac1f8b0d2f2e8f02827cbca648f4d18cd461afc31f15604cd291b5c5f3 + md5: 12bd9a3f089ee6c9266a37dab82afabd depends: - __glibc >=2.17,<3.0.a0 - zstd >=1.5.7,<1.6.0a0 constrains: - - binutils_impl_linux-64 2.45 + - binutils_impl_linux-64 2.45.1 + license: GPL-3.0-only + license_family: GPL + purls: [] + size: 725507 + timestamp: 1770267139900 +- conda: https://conda.anaconda.org/conda-forge/win-64/ld_impl_win-64-2.45.1-default_hfd38196_101.conda + sha256: 6e0294b26a796436c0e449cc55d45ec518904c6e666ca882a74000407f25aed5 + md5: 6e84306d2deb7e69d0bc90a6b36d5ebb + depends: + - zstd >=1.5.7,<1.6.0a0 + constrains: + - binutils_impl_win-64 2.45.1 license: GPL-3.0-only license_family: GPL purls: [] - size: 730831 - timestamp: 1766513089214 + size: 876736 + timestamp: 1770267709635 - conda: https://conda.anaconda.org/conda-forge/linux-64/lerc-4.0.0-h0aef613_1.conda sha256: 412381a43d5ff9bbed82cd52a0bbca5b90623f62e41007c9c42d3870c60945ff md5: 9344155d33912347b37f0ae6c410a835 @@ -9768,6 +10597,7 @@ packages: - libcxx >=18 license: Apache-2.0 license_family: Apache + purls: [] size: 188306 timestamp: 1745264362794 - conda: https://conda.anaconda.org/conda-forge/win-64/lerc-4.0.0-h6470a55_1.conda @@ -9779,6 +10609,7 @@ packages: - vc14_runtime >=14.29.30139 license: Apache-2.0 license_family: Apache + purls: [] size: 164701 timestamp: 1745264384716 - conda: https://conda.anaconda.org/conda-forge/noarch/liac-arff-2.5.0-pyhd8ed1ab_2.conda @@ -10519,6 +11350,16 @@ packages: purls: [] size: 375573 timestamp: 1769494893495 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libasprintf-0.25.1-h493aca8_0.conda + sha256: 7265547424e978ea596f51cc8e7b81638fb1c660b743e98cc4deb690d9d524ab + md5: 0deb80a2d6097c5fb98b495370b2435b + depends: + - __osx >=11.0 + - libcxx >=18 + license: LGPL-2.1-or-later + purls: [] + size: 52316 + timestamp: 1751558366611 - conda: https://conda.anaconda.org/conda-forge/linux-64/libblas-3.11.0-5_h4a7cf45_openblas.conda build_number: 5 sha256: 18c72545080b86739352482ba14ba2c4815e19e26a7417ca21a95b76ec8da24c @@ -10956,16 +11797,16 @@ packages: purls: [] size: 383261 timestamp: 1767821977053 -- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-21.1.8-hf598326_1.conda - sha256: 3a924cbce92b0dceb5d392036e692bac1e60ae90d85c7c78264c672a205c007b - md5: cd7367d0c0f49853f8f3560bfb4456ab +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-21.1.8-h55c6f16_2.conda + sha256: 5fbeb2fc2673f0455af6079abf93faaf27f11a92574ad51565fa1ecac9a4e2aa + md5: 4cb5878bdb9ebfa65b7cdff5445087c5 depends: - __osx >=11.0 license: Apache-2.0 WITH LLVM-exception license_family: Apache purls: [] - size: 570705 - timestamp: 1769754656112 + size: 570068 + timestamp: 1770238262922 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-devel-19.1.7-h6dc3340_2.conda sha256: ec07ebaa226792f4e2bf0f5dba50325632a7474d5f04b951d8291be70af215da md5: 9f7810b7c0a731dbc84d46d6005890ef @@ -11007,6 +11848,7 @@ packages: - __osx >=11.0 license: MIT license_family: MIT + purls: [] size: 55420 timestamp: 1761980066242 - conda: https://conda.anaconda.org/conda-forge/win-64/libdeflate-1.25-h51727cc_0.conda @@ -11018,6 +11860,7 @@ packages: - vc14_runtime >=14.44.35208 license: MIT license_family: MIT + purls: [] size: 156818 timestamp: 1761979842440 - conda: https://conda.anaconda.org/conda-forge/linux-64/libedit-3.1.20250104-pl5321h7949ede_0.conda @@ -11184,6 +12027,7 @@ packages: depends: - libfreetype6 >=2.14.1 license: GPL-2.0-only OR FTL + purls: [] size: 7810 timestamp: 1757947168537 - conda: https://conda.anaconda.org/conda-forge/win-64/libfreetype-2.14.1-h57928b3_0.conda @@ -11192,6 +12036,7 @@ packages: depends: - libfreetype6 >=2.14.1 license: GPL-2.0-only OR FTL + purls: [] size: 8109 timestamp: 1757946135015 - conda: https://conda.anaconda.org/conda-forge/linux-64/libfreetype6-2.14.1-h73754d4_0.conda @@ -11218,6 +12063,7 @@ packages: constrains: - freetype >=2.14.1 license: GPL-2.0-only OR FTL + purls: [] size: 346703 timestamp: 1757947166116 - conda: https://conda.anaconda.org/conda-forge/win-64/libfreetype6-2.14.1-hdbac1cb_0.conda @@ -11232,69 +12078,81 @@ packages: constrains: - freetype >=2.14.1 license: GPL-2.0-only OR FTL + purls: [] size: 340264 timestamp: 1757946133889 -- conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.2.0-he0feb66_16.conda - sha256: 6eed58051c2e12b804d53ceff5994a350c61baf117ec83f5f10c953a3f311451 - md5: 6d0363467e6ed84f11435eb309f2ff06 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.2.0-he0feb66_17.conda + sha256: 43860222cf3abf04ded0cf24541a105aa388e0e1d4d6ca46258e186d4e87ae3e + md5: 3c281169ea25b987311400d7a7e28445 depends: - __glibc >=2.17,<3.0.a0 - _openmp_mutex >=4.5 constrains: - - libgcc-ng ==15.2.0=*_16 - - libgomp 15.2.0 he0feb66_16 + - libgcc-ng ==15.2.0=*_17 + - libgomp 15.2.0 he0feb66_17 license: GPL-3.0-only WITH GCC-exception-3.1 license_family: GPL purls: [] - size: 1042798 - timestamp: 1765256792743 -- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgcc-15.2.0-hcbb3090_16.conda - sha256: 646c91dbc422fe92a5f8a3a5409c9aac66549f4ce8f8d1cab7c2aa5db789bb69 - md5: 8b216bac0de7a9d60f3ddeba2515545c + size: 1040478 + timestamp: 1770252533873 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgcc-15.2.0-hcbb3090_17.conda + sha256: 07ba27f2ef1ce444ce5c99d0f9590772fc5b58ba73c993477bfad74b17dfaa79 + md5: 65c07cee234440ae4d5d340fc4b2e69a depends: - _openmp_mutex constrains: - - libgcc-ng ==15.2.0=*_16 - - libgomp 15.2.0 16 + - libgomp 15.2.0 17 + - libgcc-ng ==15.2.0=*_17 license: GPL-3.0-only WITH GCC-exception-3.1 license_family: GPL purls: [] - size: 402197 - timestamp: 1765258985740 -- conda: https://conda.anaconda.org/conda-forge/win-64/libgcc-15.2.0-h8ee18e1_16.conda - sha256: 24984e1e768440ba73021f08a1da0c1ec957b30d7071b9a89b877a273d17cae8 - md5: 1edb8bd8e093ebd31558008e9cb23b47 + size: 402928 + timestamp: 1770254186829 +- conda: https://conda.anaconda.org/conda-forge/win-64/libgcc-15.2.0-h8ee18e1_17.conda + sha256: c99325f7c4b851a8e2a875b178186039bd320f74bd81d93eda0bff875c6f72f3 + md5: 3b93f0d28aa246cb74ed9b65250cae70 depends: - _openmp_mutex >=4.5 - libwinpthread >=12.0.0.r4.gg4f2fc60ca constrains: - - libgomp 15.2.0 h8ee18e1_16 - - libgcc-ng ==15.2.0=*_16 + - libgcc-ng ==15.2.0=*_17 + - libgomp 15.2.0 h8ee18e1_17 - msys2-conda-epoch <0.0a0 license: GPL-3.0-only WITH GCC-exception-3.1 license_family: GPL - size: 819696 - timestamp: 1765260437409 -- conda: https://conda.anaconda.org/conda-forge/noarch/libgcc-devel_linux-64-14.3.0-hf649bbc_116.conda - sha256: 812f2b3f523fc0aabaf4e5e1b44a029c5205671179e574dd32dc57b65e072e0f - md5: 0141e19cb0cd5602c49c84f920e81921 + purls: [] + size: 821940 + timestamp: 1770256702759 +- conda: https://conda.anaconda.org/conda-forge/noarch/libgcc-devel_linux-64-14.3.0-hf649bbc_117.conda + sha256: 57ef92396b4dc06c5a34792c0f601bc49793a963712e8419d5f03cb4ff87729f + md5: 50d5470d29a25808d108d3917426d24b depends: - __unix license: GPL-3.0-only WITH GCC-exception-3.1 license_family: GPL purls: [] - size: 3082749 - timestamp: 1765255729247 -- conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.2.0-h69a702a_16.conda - sha256: 5f07f9317f596a201cc6e095e5fc92621afca64829785e483738d935f8cab361 - md5: 5a68259fac2da8f2ee6f7bfe49c9eb8b + size: 3081070 + timestamp: 1770251857403 +- conda: https://conda.anaconda.org/conda-forge/noarch/libgcc-devel_win-64-15.2.0-hbb59886_117.conda + sha256: d9f3a6a6d3606b50751fb8785dfa3fc9f3d3b27358059f1d2e622a13f7fbd0ad + md5: 0eb8da4878321ab59cabdfd70b42f171 + depends: + - m2-conda-epoch + license: GPL-3.0-only WITH GCC-exception-3.1 + license_family: GPL + purls: [] + size: 2419266 + timestamp: 1770256576441 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.2.0-h69a702a_17.conda + sha256: bdfe50501e4a2d904a5eae65a7ae26e2b7a29b473ab084ad55d96080b966502e + md5: 1478bfa85224a65ab096d69ffd2af1e5 depends: - - libgcc 15.2.0 he0feb66_16 + - libgcc 15.2.0 he0feb66_17 license: GPL-3.0-only WITH GCC-exception-3.1 license_family: GPL purls: [] - size: 27256 - timestamp: 1765256804124 + size: 27541 + timestamp: 1770252546553 - conda: https://conda.anaconda.org/conda-forge/linux-64/libgdal-core-3.12.1-hf05ffb4_1.conda sha256: 4f3c0dd876e9879c1666aec58dfc72f9824bf66a6fae019b5cdfff10b4bda0cc md5: 7a8b949fb98c73b802b5e66a67dac140 @@ -11415,42 +12273,75 @@ packages: license_family: MIT size: 9814754 timestamp: 1769724104005 -- conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.2.0-h69a702a_16.conda - sha256: 8a7b01e1ee1c462ad243524d76099e7174ebdd94ff045fe3e9b1e58db196463b - md5: 40d9b534410403c821ff64f00d0adc22 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgettextpo-0.25.1-h493aca8_0.conda + sha256: 3ba35ff26b3b9573b5df5b9bbec5c61476157ec3a9f12c698e2a9350cd4338fd + md5: 98acd9989d0d8d5914ccc86dceb6c6c2 + depends: + - __osx >=11.0 + - libiconv >=1.18,<2.0a0 + - libintl 0.25.1 h493aca8_0 + license: GPL-3.0-or-later + license_family: GPL + purls: [] + size: 183091 + timestamp: 1751558452316 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.2.0-h69a702a_17.conda + sha256: 1604c083dd65bc91e68b6cfe32c8610395088cb96af1acaf71f0dcaf83ac58f7 + md5: a6c682ac611cb1fa4d73478f9e6efb06 + depends: + - libgfortran5 15.2.0 h68bc16d_17 + constrains: + - libgfortran-ng ==15.2.0=*_17 + license: GPL-3.0-only WITH GCC-exception-3.1 + license_family: GPL + purls: [] + size: 27515 + timestamp: 1770252591906 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran-15.2.0-h07b0088_17.conda + sha256: 7b96f428cb932df8d7c1aa4e433ed29b779dd9571934afdf4f9093a85155a142 + md5: 45ba22eb5381fb602a45233d89ba27ae depends: - - libgfortran5 15.2.0 h68bc16d_16 + - libgfortran5 15.2.0 hdae7583_17 constrains: - - libgfortran-ng ==15.2.0=*_16 + - libgfortran-ng ==15.2.0=*_17 license: GPL-3.0-only WITH GCC-exception-3.1 license_family: GPL purls: [] - size: 27215 - timestamp: 1765256845586 -- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran-15.2.0-h07b0088_16.conda - sha256: 68a6c1384d209f8654112c4c57c68c540540dd8e09e17dd1facf6cf3467798b5 - md5: 11e09edf0dde4c288508501fe621bab4 + size: 139757 + timestamp: 1770254394473 +- conda: https://conda.anaconda.org/conda-forge/win-64/libgfortran-15.2.0-h719f0c7_17.conda + sha256: 64d97e29a65c29ba277090aafec6410646c31a2d30078e905262f2afa70158e3 + md5: ac59c53b2a61ec6efb4a3e122ec15230 depends: - - libgfortran5 15.2.0 hdae7583_16 + - libgfortran5 15.2.0 h44d81a7_17 constrains: - - libgfortran-ng ==15.2.0=*_16 + - libgfortran-ng ==15.2.0=*_17 license: GPL-3.0-only WITH GCC-exception-3.1 license_family: GPL purls: [] - size: 138630 - timestamp: 1765259217400 -- conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-ng-15.2.0-h69a702a_16.conda - sha256: dc13ce4ceecb5b3aaca4133731e459d1111961288a1e071cc18bd71d5a47e976 - md5: e5eb2ddedabd0063e442f230755d2062 + size: 50904 + timestamp: 1770256932582 +- conda: https://conda.anaconda.org/conda-forge/noarch/libgfortran-devel_osx-arm64-14.3.0-hc965647_1.conda + sha256: f6ecc12e02a30ab7ee7a8b7285e4ffe3c2452e43885ce324b85827b97659a8c8 + md5: c1b69e537b3031d0f5af780b432ce511 + license: GPL-3.0-only WITH GCC-exception-3.1 + license_family: GPL + purls: [] + size: 2035634 + timestamp: 1756233109102 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-ng-15.2.0-h69a702a_17.conda + sha256: 60b78fd6dbb8259b4d1c8a8a363f22a86674509e7cf42497eeb5448b4d05d03d + md5: d7954bb54fc77e7952a78e5e0d134df5 depends: - - libgfortran 15.2.0 h69a702a_16 + - libgfortran 15.2.0 h69a702a_17 license: GPL-3.0-only WITH GCC-exception-3.1 license_family: GPL - size: 27300 - timestamp: 1765257039455 -- conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.2.0-h68bc16d_16.conda - sha256: d0e974ebc937c67ae37f07a28edace978e01dc0f44ee02f29ab8a16004b8148b - md5: 39183d4e0c05609fd65f130633194e37 + purls: [] + size: 27558 + timestamp: 1770252799999 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.2.0-h68bc16d_17.conda + sha256: b1c77b85da9a3e204de986f59e262268805c6a35dffdf3953f1b98407db2aef3 + md5: 202fdf8cad9eea704c2b0d823d1732bf depends: - __glibc >=2.17,<3.0.a0 - libgcc >=15.2.0 @@ -11459,11 +12350,11 @@ packages: license: GPL-3.0-only WITH GCC-exception-3.1 license_family: GPL purls: [] - size: 2480559 - timestamp: 1765256819588 -- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran5-15.2.0-hdae7583_16.conda - sha256: 9fb7f4ff219e3fb5decbd0ee90a950f4078c90a86f5d8d61ca608c913062f9b0 - md5: 265a9d03461da24884ecc8eb58396d57 + size: 2480824 + timestamp: 1770252563579 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran5-15.2.0-hdae7583_17.conda + sha256: 9c41ff08f61c953cee13fc3df3c6245741e5a71e453b2c094a6d55b0eeda3669 + md5: c6329d871fb3207e9657c384128f5488 depends: - libgcc >=15.2.0 constrains: @@ -11471,8 +12362,21 @@ packages: license: GPL-3.0-only WITH GCC-exception-3.1 license_family: GPL purls: [] - size: 598291 - timestamp: 1765258993165 + size: 599374 + timestamp: 1770254196706 +- conda: https://conda.anaconda.org/conda-forge/win-64/libgfortran5-15.2.0-h44d81a7_17.conda + sha256: 3d93c6b5a76c5f14b572b66f993be9ba10128f2bcb6a77739c230d7c279e92cf + md5: abd7b54497aaad305a61ec8782159a24 + depends: + - libgcc >=15.2.0 + - libwinpthread >=12.0.0.r4.gg4f2fc60ca + constrains: + - libgfortran 15.2.0 + license: GPL-3.0-only WITH GCC-exception-3.1 + license_family: GPL + purls: [] + size: 2792442 + timestamp: 1770256717684 - conda: https://conda.anaconda.org/conda-forge/linux-64/libglib-2.86.3-h6548e54_0.conda sha256: 82d6c2ee9f548c84220fb30fb1b231c64a53561d6e485447394f0a0eeeffe0e6 md5: 034bea55a4feef51c98e8449938e9cee @@ -11489,27 +12393,62 @@ packages: purls: [] size: 3946542 timestamp: 1765221858705 -- conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.2.0-he0feb66_16.conda - sha256: 5b3e5e4e9270ecfcd48f47e3a68f037f5ab0f529ccb223e8e5d5ac75a58fc687 - md5: 26c46f90d0e727e95c6c9498a33a09f3 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libglib-2.86.3-hfe11c1f_0.conda + sha256: 801c1835aa35a4f6e45e2192ad668bd7238d95c90ef8f02c52ce859c20117285 + md5: 057c7247514048ebdaf89373b263ebee + depends: + - __osx >=11.0 + - libffi >=3.5.2,<3.6.0a0 + - libiconv >=1.18,<2.0a0 + - libintl >=0.25.1,<1.0a0 + - libzlib >=1.3.1,<2.0a0 + - pcre2 >=10.47,<10.48.0a0 + constrains: + - glib 2.86.3 *_0 + license: LGPL-2.1-or-later + purls: [] + size: 3670602 + timestamp: 1765223125237 +- conda: https://conda.anaconda.org/conda-forge/win-64/libglib-2.86.3-h0c9aed9_0.conda + sha256: 84b74fc81fff745f3d21a26c317ace44269a563a42ead3500034c27e407e1021 + md5: c2d5b6b790ef21abac0b5331094ccb56 + depends: + - libffi >=3.5.2,<3.6.0a0 + - libiconv >=1.18,<2.0a0 + - libintl >=0.22.5,<1.0a0 + - libzlib >=1.3.1,<2.0a0 + - pcre2 >=10.47,<10.48.0a0 + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + constrains: + - glib 2.86.3 *_0 + license: LGPL-2.1-or-later + purls: [] + size: 3818991 + timestamp: 1765222145992 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.2.0-he0feb66_17.conda + sha256: b961b5dd9761907a7179678b58a69bb4fc16b940eb477f635aea3aec0a3f17a6 + md5: 51b78c6a757575c0d12f4401ffc67029 depends: - __glibc >=2.17,<3.0.a0 license: GPL-3.0-only WITH GCC-exception-3.1 license_family: GPL purls: [] - size: 603284 - timestamp: 1765256703881 -- conda: https://conda.anaconda.org/conda-forge/win-64/libgomp-15.2.0-h8ee18e1_16.conda - sha256: 9c86aadc1bd9740f2aca291da8052152c32dd1c617d5d4fd0f334214960649bb - md5: ab8189163748f95d4cb18ea1952943c3 + size: 603334 + timestamp: 1770252441199 +- conda: https://conda.anaconda.org/conda-forge/win-64/libgomp-15.2.0-h8ee18e1_17.conda + sha256: 371514e0cee6425e85a62f92931dd2fbe04ff09cea6b3cddf4ebf1c200170e90 + md5: 18f0da832fb73029007218f0c56939f8 depends: - libwinpthread >=12.0.0.r4.gg4f2fc60ca constrains: - msys2-conda-epoch <0.0a0 license: GPL-3.0-only WITH GCC-exception-3.1 license_family: GPL - size: 663567 - timestamp: 1765260367147 + purls: [] + size: 664014 + timestamp: 1770256586208 - conda: https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-2.39.0-hdb79228_0.conda sha256: d3341cf69cb02c07bbd1837968f993da01b7bd467e816b1559a3ca26c1ff14c5 md5: a2e30ccd49f753fd30de0d30b1569789 @@ -11773,6 +12712,25 @@ packages: purls: [] size: 696926 timestamp: 1754909290005 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libintl-0.25.1-h493aca8_0.conda + sha256: 99d2cebcd8f84961b86784451b010f5f0a795ed1c08f1e7c76fbb3c22abf021a + md5: 5103f6a6b210a3912faf8d7db516918c + depends: + - __osx >=11.0 + - libiconv >=1.18,<2.0a0 + license: LGPL-2.1-or-later + purls: [] + size: 90957 + timestamp: 1751558394144 +- conda: https://conda.anaconda.org/conda-forge/win-64/libintl-0.22.5-h5728263_3.conda + sha256: c7e4600f28bcada8ea81456a6530c2329312519efcf0c886030ada38976b0511 + md5: 2cf0cf76cc15d360dfa2f17fd6cf9772 + depends: + - libiconv >=1.17,<2.0a0 + license: LGPL-2.1-or-later + purls: [] + size: 95568 + timestamp: 1723629479451 - conda: https://conda.anaconda.org/conda-forge/linux-64/libjemalloc-local-5.3.0-h5888daf_1.conda sha256: 0b0b3d00feb5783c4fd0c22e49111e18a86918ff459fdc3dd418be8119af8770 md5: 8b9fc24896d9eaafecc758e8b2e823e9 @@ -11816,6 +12774,7 @@ packages: constrains: - jpeg <0.0.0a license: IJG AND BSD-3-Clause AND Zlib + purls: [] size: 551197 timestamp: 1762095054358 - conda: https://conda.anaconda.org/conda-forge/win-64/libjpeg-turbo-3.1.2-hfd05255_0.conda @@ -11828,6 +12787,7 @@ packages: constrains: - jpeg <0.0.0a license: IJG AND BSD-3-Clause AND Zlib + purls: [] size: 841783 timestamp: 1762094814336 - conda: https://conda.anaconda.org/conda-forge/linux-64/libjxl-0.11.1-ha09017c_8.conda @@ -12405,6 +13365,7 @@ packages: - __osx >=11.0 - libzlib >=1.3.1,<2.0a0 license: zlib-acknowledgement + purls: [] size: 288910 timestamp: 1768285694469 - conda: https://conda.anaconda.org/conda-forge/win-64/libpng-1.6.54-h7351971_0.conda @@ -12416,6 +13377,7 @@ packages: - ucrt >=10.0.20348.0 - libzlib >=1.3.1,<2.0a0 license: zlib-acknowledgement + purls: [] size: 383094 timestamp: 1768285706434 - conda: https://conda.anaconda.org/conda-forge/linux-64/libprotobuf-6.31.1-h49aed37_4.conda @@ -12564,9 +13526,9 @@ packages: license_family: GPL size: 403088 timestamp: 1761671197546 -- conda: https://conda.anaconda.org/conda-forge/linux-64/libsanitizer-14.3.0-h8f1669f_16.conda - sha256: 21765d3fa780eb98055a9f40e9d4defa1eaffe254ee271a3e49555a89e37d6c9 - md5: 0617b134e4dc4474c1038707499f7eed +- conda: https://conda.anaconda.org/conda-forge/linux-64/libsanitizer-14.3.0-h8f1669f_17.conda + sha256: 48a1e008a44b7d630f1243915261628d72df1c1f477f44af2e93350937b496df + md5: 5edfb6baf1af52fa7c0a7072a42d1558 depends: - __glibc >=2.17,<3.0.a0 - libgcc >=14.3.0 @@ -12574,8 +13536,8 @@ packages: license: GPL-3.0-only WITH GCC-exception-3.1 license_family: GPL purls: [] - size: 7946383 - timestamp: 1765255939536 + size: 7237991 + timestamp: 1770252070009 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libsigtool-0.1.3-h98dc951_0.conda sha256: 421f7bd7caaa945d9cd5d374cc3f01e75637ca7372a32d5e7695c825a48a30d1 md5: c08557d00807785decafb932b5be7ef5 @@ -12682,6 +13644,17 @@ packages: license_family: MOZILLA size: 8671657 timestamp: 1761681604524 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.51.2-h0c1763c_0.conda + sha256: c1ff4589b48d32ca0a2628970d869fa9f7b2c2d00269a3761edc7e9e4c1ab7b8 + md5: f7d30045eccb83f2bb8053041f42db3c + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + - libzlib >=1.3.1,<2.0a0 + license: blessing + purls: [] + size: 939312 + timestamp: 1768147967568 - conda: https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.51.2-hf4e2dac_0.conda sha256: 04596fcee262a870e4b7c9807224680ff48d4d0cc0dac076a602503d3dc6d217 md5: da5be73701eecd0e8454423fd6ffcf30 @@ -12691,7 +13664,6 @@ packages: - libgcc >=14 - libzlib >=1.3.1,<2.0a0 license: blessing - purls: [] size: 942808 timestamp: 1768147973361 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libsqlite-3.51.2-h1ae2325_0.conda @@ -12702,9 +13674,18 @@ packages: - icu >=78.2,<79.0a0 - libzlib >=1.3.1,<2.0a0 license: blessing - purls: [] size: 909777 timestamp: 1768148320535 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libsqlite-3.51.2-h1b79a29_0.conda + sha256: f942afee5568a0bfba020e52c3f22b788e14017a8dc302652d2ca500756a8a5a + md5: faedef456ba5004af365d450eb38217d + depends: + - __osx >=11.0 + - libzlib >=1.3.1,<2.0a0 + license: blessing + purls: [] + size: 905482 + timestamp: 1768148270069 - conda: https://conda.anaconda.org/conda-forge/win-64/libsqlite-3.51.2-hf5d6505_0.conda sha256: 756478128e3e104bd7e7c3ce6c1b0efad7e08c7320c69fdc726e039323c63fbb md5: 903979414b47d777d548e5f0165e6cd8 @@ -12754,39 +13735,71 @@ packages: purls: [] size: 292785 timestamp: 1745608759342 -- conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.2.0-h934c35e_16.conda - sha256: 813427918316a00c904723f1dfc3da1bbc1974c5cfe1ed1e704c6f4e0798cbc6 - md5: 68f68355000ec3f1d6f26ea13e8f525f +- conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.2.0-h934c35e_17.conda + sha256: 50c48cd3716a2e58e8e2e02edc78fef2d08fffe1e3b1ed40eb5f87e7e2d07889 + md5: 24c2fe35fa45cd71214beba6f337c071 depends: - __glibc >=2.17,<3.0.a0 - - libgcc 15.2.0 he0feb66_16 + - libgcc 15.2.0 he0feb66_17 + constrains: + - libstdcxx-ng ==15.2.0=*_17 + license: GPL-3.0-only WITH GCC-exception-3.1 + license_family: GPL + purls: [] + size: 5852406 + timestamp: 1770252584235 +- conda: https://conda.anaconda.org/conda-forge/win-64/libstdcxx-15.2.0-hae5796f_17.conda + sha256: 1a05ce8feaba0d1dd9b029cbb1603b78d5b44d0c539d352e357805b2c43be7db + md5: fc7bf20c47192ca0553c8efd0dea134d + depends: + - libgcc 15.2.0 h8ee18e1_17 + - libwinpthread >=12.0.0.r4.gg4f2fc60ca constrains: - - libstdcxx-ng ==15.2.0=*_16 + - libstdcxx-ng ==15.2.0=*_17 license: GPL-3.0-only WITH GCC-exception-3.1 license_family: GPL purls: [] - size: 5856456 - timestamp: 1765256838573 -- conda: https://conda.anaconda.org/conda-forge/noarch/libstdcxx-devel_linux-64-14.3.0-h9f08a49_116.conda - sha256: 278a6b7ebb02f1e983db06c6091b130c9a99f967acb526eac1a67077fd863da8 - md5: badba6a9f0e90fdaff87b06b54736ea6 + size: 6460933 + timestamp: 1770256736603 +- conda: https://conda.anaconda.org/conda-forge/noarch/libstdcxx-devel_linux-64-14.3.0-h9f08a49_117.conda + sha256: ffb164d31e09b18cf95c6330bfce9268c1ce799103e56b7c004250332e7f9ede + md5: 97f8b7e451f960200c057ca83d92f9be depends: - __unix license: GPL-3.0-only WITH GCC-exception-3.1 license_family: GPL purls: [] - size: 20538116 - timestamp: 1765255773242 -- conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-15.2.0-hdf11a46_16.conda - sha256: 81f2f246c7533b41c5e0c274172d607829019621c4a0823b5c0b4a8c7028ee84 - md5: 1b3152694d236cf233b76b8c56bf0eae + size: 20497917 + timestamp: 1770251920997 +- conda: https://conda.anaconda.org/conda-forge/noarch/libstdcxx-devel_win-64-15.2.0-h0a72980_117.conda + sha256: 4e71dc880da082b358f5e50de29e7a52fc18c2773e0de425e5c797609abf53b2 + md5: 6f4c69ca2e5efa1ecced869d05fe7f55 + depends: + - m2-conda-epoch + license: GPL-3.0-only WITH GCC-exception-3.1 + license_family: GPL + purls: [] + size: 12282846 + timestamp: 1770256603402 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-15.2.0-hdf11a46_17.conda + sha256: ca3fb322dab3373946b1064da686ec076f5b1b9caf0a2823dad00d0b0f704928 + md5: ea12f5a6bf12c88c06750d9803e1a570 depends: - - libstdcxx 15.2.0 h934c35e_16 + - libstdcxx 15.2.0 h934c35e_17 license: GPL-3.0-only WITH GCC-exception-3.1 license_family: GPL purls: [] - size: 27300 - timestamp: 1765256885128 + size: 27573 + timestamp: 1770252638797 +- pypi: https://files.pythonhosted.org/packages/f9/38/e2d64862f9d7c6e761f6d61ee9cd11badc98abd484f28a5dfb0310489334/libsvmdata-0.4.1-py3-none-any.whl + name: libsvmdata + version: 0.4.1 + sha256: 203d72aece603a1fc93362adacd67c6d3543ef1a7a49c5bf08c48e1f77969dfd + requires_dist: + - download + - numpy>=1.12 + - scikit-learn + - scipy - conda: https://conda.anaconda.org/conda-forge/linux-64/libthrift-0.22.0-h454ac66_1.conda sha256: 4888b9ea2593c36ca587a5ebe38d0a56a0e6d6a9e4bb7da7d9a326aaaca7c336 md5: 8ed82d90e6b1686f5e98f8b7825a15ef @@ -12863,6 +13876,7 @@ packages: - libzlib >=1.3.1,<2.0a0 - zstd >=1.5.7,<1.6.0a0 license: HPND + purls: [] size: 373892 timestamp: 1762022345545 - conda: https://conda.anaconda.org/conda-forge/win-64/libtiff-4.7.1-h8f73337_1.conda @@ -12879,6 +13893,7 @@ packages: - vc14_runtime >=14.44.35208 - zstd >=1.5.7,<1.6.0a0 license: HPND + purls: [] size: 993166 timestamp: 1762022118895 - conda: https://conda.anaconda.org/conda-forge/linux-64/libutf8proc-2.11.3-hfe17d71_0.conda @@ -12947,6 +13962,7 @@ packages: - libwebp 1.6.0 license: BSD-3-Clause license_family: BSD + purls: [] size: 294974 timestamp: 1752159906788 - conda: https://conda.anaconda.org/conda-forge/win-64/libwebp-base-1.6.0-h4d5522a_0.conda @@ -12960,6 +13976,7 @@ packages: - libwebp 1.6.0 license: BSD-3-Clause license_family: BSD + purls: [] size: 279176 timestamp: 1752159543911 - conda: https://conda.anaconda.org/conda-forge/win-64/libwinpthread-12.0.0.r4.gg4f2fc60ca-h57928b3_10.conda @@ -12998,6 +14015,7 @@ packages: - xorg-libxdmcp license: MIT license_family: MIT + purls: [] size: 323658 timestamp: 1727278733917 - conda: https://conda.anaconda.org/conda-forge/win-64/libxcb-1.17.0-h0e4246c_0.conda @@ -13012,6 +14030,7 @@ packages: - xorg-libxdmcp license: MIT license_family: MIT + purls: [] size: 1208687 timestamp: 1727279378819 - conda: https://conda.anaconda.org/conda-forge/linux-64/libxcrypt-4.4.36-hd590300_1.conda @@ -13022,6 +14041,22 @@ packages: license: LGPL-2.1-or-later size: 100393 timestamp: 1702724383534 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.15.1-h26afc86_0.conda + sha256: ec0735ae56c3549149eebd7dc22c0bed91fd50c02eaa77ff418613ddda190aa8 + md5: e512be7dc1f84966d50959e900ca121f + depends: + - __glibc >=2.17,<3.0.a0 + - icu >=75.1,<76.0a0 + - libgcc >=14 + - libiconv >=1.18,<2.0a0 + - liblzma >=5.8.1,<6.0a0 + - libxml2-16 2.15.1 ha9997c6_0 + - libzlib >=1.3.1,<2.0a0 + license: MIT + license_family: MIT + purls: [] + size: 45283 + timestamp: 1761015644057 - conda: https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.15.1-he237659_1.conda sha256: 047be059033c394bd32ae5de66ce389824352120b3a7c0eff980195f7ed80357 md5: 417955234eccd8f252b86a265ccdab7f @@ -13035,7 +14070,6 @@ packages: - libzlib >=1.3.1,<2.0a0 license: MIT license_family: MIT - purls: [] size: 45402 timestamp: 1766327161688 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libxml2-2.15.1-h8d039ee_1.conda @@ -13050,9 +14084,23 @@ packages: - libzlib >=1.3.1,<2.0a0 license: MIT license_family: MIT - purls: [] size: 40607 timestamp: 1766327501392 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libxml2-2.15.1-h9329255_0.conda + sha256: c409e384ddf5976a42959265100d6b2c652017d250171eb10bae47ef8166193f + md5: fb5ce61da27ee937751162f86beba6d1 + depends: + - __osx >=11.0 + - icu >=75.1,<76.0a0 + - libiconv >=1.18,<2.0a0 + - liblzma >=5.8.1,<6.0a0 + - libxml2-16 2.15.1 h0ff4647_0 + - libzlib >=1.3.1,<2.0a0 + license: MIT + license_family: MIT + purls: [] + size: 40607 + timestamp: 1761016108361 - conda: https://conda.anaconda.org/conda-forge/win-64/libxml2-2.15.1-h779ef1b_1.conda sha256: 8b47d5fb00a6ccc0f495d16787ab5f37a434d51965584d6000966252efecf56d md5: 68dc154b8d415176c07b6995bd3a65d9 @@ -13067,9 +14115,42 @@ packages: - vc14_runtime >=14.44.35208 license: MIT license_family: MIT - purls: [] size: 43387 timestamp: 1766327259710 +- conda: https://conda.anaconda.org/conda-forge/win-64/libxml2-2.15.1-ha29bfb0_0.conda + sha256: fb51b91a01eac9ee5e26c67f4e081f09f970c18a3da5231b8172919a1e1b3b6b + md5: 87116b9de9c1825c3fd4ef92c984877b + depends: + - icu >=75.1,<76.0a0 + - libiconv >=1.18,<2.0a0 + - liblzma >=5.8.1,<6.0a0 + - libxml2-16 2.15.1 h06f855e_0 + - libzlib >=1.3.1,<2.0a0 + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + license: MIT + license_family: MIT + purls: [] + size: 43042 + timestamp: 1761016261024 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libxml2-16-2.15.1-ha9997c6_0.conda + sha256: 71436e72a286ef8b57d6f4287626ff91991eb03c7bdbe835280521791efd1434 + md5: e7733bc6785ec009e47a224a71917e84 + depends: + - __glibc >=2.17,<3.0.a0 + - icu >=75.1,<76.0a0 + - libgcc >=14 + - libiconv >=1.18,<2.0a0 + - liblzma >=5.8.1,<6.0a0 + - libzlib >=1.3.1,<2.0a0 + constrains: + - libxml2 2.15.1 + license: MIT + license_family: MIT + purls: [] + size: 556302 + timestamp: 1761015637262 - conda: https://conda.anaconda.org/conda-forge/linux-64/libxml2-16-2.15.1-hca6bf5a_1.conda sha256: 8331284bf9ae641b70cdc0e5866502dd80055fc3b9350979c74bb1d192e8e09e md5: 3fdd8d99683da9fe279c2f4cecd1e048 @@ -13084,9 +14165,24 @@ packages: - libxml2 2.15.1 license: MIT license_family: MIT - purls: [] size: 555747 timestamp: 1766327145986 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libxml2-16-2.15.1-h0ff4647_0.conda + sha256: ebe2dd9da94280ad43da936efa7127d329b559f510670772debc87602b49b06d + md5: 438c97d1e9648dd7342f86049dd44638 + depends: + - __osx >=11.0 + - icu >=75.1,<76.0a0 + - libiconv >=1.18,<2.0a0 + - liblzma >=5.8.1,<6.0a0 + - libzlib >=1.3.1,<2.0a0 + constrains: + - libxml2 2.15.1 + license: MIT + license_family: MIT + purls: [] + size: 464952 + timestamp: 1761016087733 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libxml2-16-2.15.1-h5ef1a60_1.conda sha256: 2d5ab15113b0ba21f4656d387d26ab59e4fbaf3027f5e58a2a4fe370821eb106 md5: 7eed1026708e26ee512f43a04d9d0027 @@ -13100,9 +14196,26 @@ packages: - libxml2 2.15.1 license: MIT license_family: MIT - purls: [] size: 464886 timestamp: 1766327479416 +- conda: https://conda.anaconda.org/conda-forge/win-64/libxml2-16-2.15.1-h06f855e_0.conda + sha256: 3f65ea0f04c7738116e74ca87d6e40f8ba55b3df31ef42b8cb4d78dd96645e90 + md5: 4a5ea6ec2055ab0dfd09fd0c498f834a + depends: + - icu >=75.1,<76.0a0 + - libiconv >=1.18,<2.0a0 + - liblzma >=5.8.1,<6.0a0 + - libzlib >=1.3.1,<2.0a0 + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + constrains: + - libxml2 2.15.1 + license: MIT + license_family: MIT + purls: [] + size: 518616 + timestamp: 1761016240185 - conda: https://conda.anaconda.org/conda-forge/win-64/libxml2-16-2.15.1-h3cfd58e_1.conda sha256: a857e941156b7f462063e34e086d212c6ccbc1521ebdf75b9ed66bd90add57dc md5: 07d73826fde28e7dbaec52a3297d7d26 @@ -13118,7 +14231,6 @@ packages: - libxml2 2.15.1 license: MIT license_family: MIT - purls: [] size: 518964 timestamp: 1766327232819 - conda: https://conda.anaconda.org/conda-forge/linux-64/libxml2-devel-2.15.1-he237659_1.conda @@ -13224,6 +14336,22 @@ packages: license_family: MIT size: 266857 timestamp: 1734571826135 +- conda: https://conda.anaconda.org/conda-forge/linux-64/line_profiler-5.0.0-py312h0a2e395_1.conda + sha256: bb1d6c12ec6b230b8897913a6176e2e0ef2ca6d2958ede0c7c94d9ba7507b02b + md5: 4a5de0db29f28e14b156d645a5f64ca1 + depends: + - python + - libstdcxx >=14 + - libgcc >=14 + - __glibc >=2.17,<3.0.a0 + - python_abi 3.12.* *_cp312 + constrains: + - ipython >=8.14.0 + - rich >=12.3.0 + license: BSD-3-Clause + license_family: BSD + size: 496156 + timestamp: 1756479495799 - conda: https://conda.anaconda.org/conda-forge/linux-64/line_profiler-5.0.0-py314hc43b2c7_1.conda sha256: 84a0ef58e5cc76c7cce5f50ce11a6806909294c4a9c40773d7aa08d312f02717 md5: d41e8a0326ae78e0d5447e17bda486cb @@ -13347,21 +14475,23 @@ packages: purls: [] size: 16376095 timestamp: 1757353442671 -- conda: https://conda.anaconda.org/conda-forge/linux-64/llvmlite-0.46.0-py314h946fb2a_0.conda - sha256: 99f15d69f059aa9c7d06cc45a6519a2375cc7a93ca85127964d6325a89a2b519 - md5: 7ee180b967506bbd108ca9d5ff45eace +- conda: https://conda.anaconda.org/conda-forge/linux-64/llvmlite-0.46.0-py313hdd307be_0.conda + sha256: 0e1bc6ee1c7885cc26c37fcd1a2095169a4e4e148860c600d3f685b6a4f32322 + md5: d99ac09b331711fd12e16323ca8d96e4 depends: - __glibc >=2.17,<3.0.a0 - libgcc >=14 - libstdcxx >=14 - libzlib >=1.3.1,<2.0a0 - - python >=3.14,<3.15.0a0 - - python_abi 3.14.* *_cp314 + - python >=3.13,<3.14.0a0 + - python_abi 3.13.* *_cp313 - zstd >=1.5.7,<1.6.0a0 license: BSD-2-Clause license_family: BSD - size: 34123266 - timestamp: 1765279959565 + purls: + - pkg:pypi/llvmlite?source=hash-mapping + size: 34130706 + timestamp: 1765280056189 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/llvmlite-0.46.0-py314ha398f32_0.conda sha256: 10ee25664d790b117d84701506b60caba147f7bf599215cbd688037aaa42ff81 md5: b9eefe6197dafc779b784731fa507f60 @@ -13375,6 +14505,8 @@ packages: - zstd >=1.5.7,<1.6.0a0 license: BSD-2-Clause license_family: BSD + purls: + - pkg:pypi/llvmlite?source=hash-mapping size: 24330524 timestamp: 1765280789928 - conda: https://conda.anaconda.org/conda-forge/win-64/llvmlite-0.46.0-py314hb492ee6_0.conda @@ -13390,6 +14522,8 @@ packages: - zstd >=1.5.7,<1.6.0a0 license: BSD-2-Clause license_family: BSD + purls: + - pkg:pypi/llvmlite?source=hash-mapping size: 22926897 timestamp: 1765280131964 - conda: https://conda.anaconda.org/conda-forge/noarch/locket-1.0.0-pyhd8ed1ab_0.tar.bz2 @@ -13401,20 +14535,20 @@ packages: license_family: BSD size: 8250 timestamp: 1650660473123 -- conda: https://conda.anaconda.org/conda-forge/linux-64/lz4-4.4.5-py314hd4c109c_1.conda - sha256: 7f3083018be486b73c82e5e2421ab882d5231fcd424843c96058b01ce5f3cbaf - md5: 2f6295571ea5e9278046efc3ef377a98 +- conda: https://conda.anaconda.org/conda-forge/linux-64/lz4-4.4.5-py313h28739b2_1.conda + sha256: cbc82f4fa7587376c038d2f0471a73efa7ade4439857b04a0cc839262f1de6e5 + md5: e69ad33075938ba81e43311da86b809c depends: - python - lz4-c - - __glibc >=2.17,<3.0.a0 - libgcc >=14 - - python_abi 3.14.* *_cp314 + - __glibc >=2.17,<3.0.a0 + - python_abi 3.13.* *_cp313 - lz4-c >=1.10.0,<1.11.0a0 license: BSD-3-Clause license_family: BSD - size: 45224 - timestamp: 1765026391393 + size: 44861 + timestamp: 1765026393230 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/lz4-4.4.5-py314h24f3bdd_1.conda sha256: fb105138b325e81f8dabc859cc47e9e29295b68cd6fd4dd333ed30e527e7c08b md5: aea17e1b366b814eff15fc3c8c4c1e3c @@ -13512,6 +14646,17 @@ packages: license_family: GPL size: 165589 timestamp: 1753889311940 +- conda: https://conda.anaconda.org/conda-forge/win-64/m2-conda-epoch-20250515-0_x86_64.conda + build_number: 0 + sha256: 51e9214548f177db9c3fe70424e3774c95bf19cd69e0e56e83abe2e393228ba1 + md5: 7d60fb16df2cd07fbc3dbff1c9df4244 + constrains: + - msys2-conda-epoch <0.0a0 + license: BSD-3-Clause + license_family: BSD + purls: [] + size: 7539 + timestamp: 1747330852019 - conda: https://conda.anaconda.org/conda-forge/win-64/m2w64-gcc-libgfortran-5.3.0-6.tar.bz2 sha256: 9de95a7996d5366ae0808eef2acbc63f9b11b874aa42375f55379e6715845dc6 md5: 066552ac6b907ec6d72c0ddab29050dc @@ -13559,6 +14704,19 @@ packages: license: MIT, BSD size: 31928 timestamp: 1608166099896 +- conda: https://conda.anaconda.org/conda-forge/noarch/m2w64-sysroot_win-64-12.0.0.r4.gg4f2fc60ca-hd8ed1ab_10.conda + sha256: fb0ffe6b3c25189038c29abbd1fac2522d87fe2775a09e5f5088e5542dc3309b + md5: 9676d2a30fa3ffa4e5350041d0993758 + depends: + - m2-conda-epoch + - mingw-w64-ucrt-x86_64-crt-git 12.0.0.r4.gg4f2fc60ca hd8ed1ab_10 + - mingw-w64-ucrt-x86_64-headers-git 12.0.0.r4.gg4f2fc60ca hd8ed1ab_10 + - mingw-w64-ucrt-x86_64-windows-default-manifest + - mingw-w64-ucrt-x86_64-winpthreads-git 12.0.0.r4.gg4f2fc60ca hd8ed1ab_10 + - ucrt + purls: [] + size: 8421 + timestamp: 1759768559974 - conda: https://conda.anaconda.org/conda-forge/linux-64/make-4.4.1-hb9d3cd8_2.conda sha256: d652c7bd4d3b6f82b0f6d063b0d8df6f54cc47531092d7ff008e780f3261bdda md5: 33405d2a66b1411db9f7242c8b97c9e7 @@ -13567,6 +14725,7 @@ packages: - libgcc >=13 license: GPL-3.0-or-later license_family: GPL + purls: [] size: 513088 timestamp: 1727801714848 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/make-4.4.1-hc9fafa5_2.conda @@ -13576,6 +14735,7 @@ packages: - __osx >=11.0 license: GPL-3.0-or-later license_family: GPL + purls: [] size: 274048 timestamp: 1727801725384 - conda: https://conda.anaconda.org/conda-forge/win-64/make-4.4.1-h0e40799_2.conda @@ -13623,10 +14783,26 @@ packages: - python >=3.10 license: MIT license_family: MIT + purls: + - pkg:pypi/markdown-it-py?source=hash-mapping size: 64736 timestamp: 1754951288511 -- conda: https://conda.anaconda.org/conda-forge/linux-64/markupsafe-3.0.3-py313h3dea7bd_0.conda - sha256: a530a411bdaaf0b1e4de8869dfaca46cb07407bc7dc0702a9e231b0e5ce7ca85 +- conda: https://conda.anaconda.org/conda-forge/linux-64/markupsafe-3.0.3-py312h8a5da7c_0.conda + sha256: f77f9f1a4da45cbc8792d16b41b6f169f649651a68afdc10b2da9da12b9aa42b + md5: f775a43412f7f3d7ed218113ad233869 + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + - python >=3.12,<3.13.0a0 + - python_abi 3.12.* *_cp312 + constrains: + - jinja2 >=3.0.0 + license: BSD-3-Clause + license_family: BSD + size: 25321 + timestamp: 1759055268795 +- conda: https://conda.anaconda.org/conda-forge/linux-64/markupsafe-3.0.3-py313h3dea7bd_0.conda + sha256: a530a411bdaaf0b1e4de8869dfaca46cb07407bc7dc0702a9e231b0e5ce7ca85 md5: c14389156310b8ed3520d84f854be1ee depends: - __glibc >=2.17,<3.0.a0 @@ -13637,6 +14813,8 @@ packages: - jinja2 >=3.0.0 license: BSD-3-Clause license_family: BSD + purls: + - pkg:pypi/markupsafe?source=hash-mapping size: 25909 timestamp: 1759055357045 - conda: https://conda.anaconda.org/conda-forge/noarch/markupsafe-3.0.3-pyh7db6752_0.conda @@ -13650,6 +14828,8 @@ packages: - markupsafe_no_compile license: BSD-3-Clause license_family: BSD + purls: + - pkg:pypi/markupsafe?source=hash-mapping size: 15499 timestamp: 1759055275624 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/markupsafe-3.0.3-py313h7d74516_0.conda @@ -13681,9 +14861,9 @@ packages: license_family: BSD size: 28959 timestamp: 1759055685616 -- conda: https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.10.8-py314h1194b4b_0.conda - sha256: ee773261fbd6c76fc8174b0e4e1ce272b0bbaa56610f130e9d3d1f575106f04f - md5: b8683e6068099b69c10dbfcf7204203f +- conda: https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.10.8-py313h683a580_0.conda + sha256: b1117aa2c1d11ca70d1704054cdc8801cbcf2dfb846c565531edd417ddd82559 + md5: ffe67570e1a9192d2f4c189b27f75f89 depends: - __glibc >=2.17,<3.0.a0 - contourpy >=1.0.1 @@ -13700,15 +14880,17 @@ packages: - packaging >=20.0 - pillow >=8 - pyparsing >=2.3.1 - - python >=3.14,<3.15.0a0 + - python >=3.13,<3.14.0a0 - python-dateutil >=2.7 - - python_abi 3.14.* *_cp314 + - python_abi 3.13.* *_cp313 - qhull >=2020.2,<2020.3.0a0 - tk >=8.6.13,<8.7.0a0 license: PSF-2.0 license_family: PSF - size: 8473358 - timestamp: 1763055439346 + purls: + - pkg:pypi/matplotlib?source=hash-mapping + size: 8405862 + timestamp: 1763055358671 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/matplotlib-base-3.10.8-py314hd63e3f0_0.conda sha256: 198dcc0ed83e78bc7bf48e6ef8d4ecd220e9cf1f07db98508251b2bc0be067f9 md5: c84152e510d41378b8758826655b6ed7 @@ -13734,6 +14916,8 @@ packages: - qhull >=2020.2,<2020.3.0a0 license: PSF-2.0 license_family: PSF + purls: + - pkg:pypi/matplotlib?source=hash-mapping size: 8286510 timestamp: 1763055937766 - conda: https://conda.anaconda.org/conda-forge/win-64/matplotlib-base-3.10.8-py314hfa45d96_0.conda @@ -13761,6 +14945,8 @@ packages: - vc14_runtime >=14.44.35208 license: PSF-2.0 license_family: PSF + purls: + - pkg:pypi/matplotlib?source=hash-mapping size: 8185296 timestamp: 1763055983613 - conda: https://conda.anaconda.org/conda-forge/noarch/matplotlib-inline-0.2.1-pyhd8ed1ab_0.conda @@ -13790,6 +14976,8 @@ packages: - python >=3.9 license: MIT license_family: MIT + purls: + - pkg:pypi/mdurl?source=hash-mapping size: 14465 timestamp: 1733255681319 - conda: https://conda.anaconda.org/conda-forge/noarch/memory_profiler-0.61.0-pyhcf101f3_1.conda @@ -13803,6 +14991,53 @@ packages: license_family: BSD size: 36168 timestamp: 1764885507963 +- conda: https://conda.anaconda.org/conda-forge/noarch/mingw-w64-ucrt-x86_64-crt-git-12.0.0.r4.gg4f2fc60ca-hd8ed1ab_10.conda + sha256: de3e42149b498c16bfb485b7729f4ca0fe392be576a2a10ff702d661799b1df3 + md5: 44ffa6d68699ec9321f6d48d75bdc726 + depends: + - m2-conda-epoch + - mingw-w64-ucrt-x86_64-headers-git 12.0.0.r4.gg4f2fc60ca hd8ed1ab_10 + constrains: + - mingw-w64-ucrt-x86_64-winpthreads-git 12.0.0.r4.gg4f2fc60ca.* + license: ZPL-2.1 + purls: [] + size: 5663635 + timestamp: 1759768458961 +- conda: https://conda.anaconda.org/conda-forge/noarch/mingw-w64-ucrt-x86_64-headers-git-12.0.0.r4.gg4f2fc60ca-hd8ed1ab_10.conda + sha256: 1add86481f35163215e7076e6f06f22aa9f1f9345a5fff5cb07bc846c13fbec7 + md5: cab7b807024204893ef5bb1860d91408 + depends: + - m2-conda-epoch + constrains: + - mingw-w64-ucrt-x86_64-crt-git 12.0.0.r4.gg4f2fc60ca.* + - mingw-w64-ucrt-x86_64-winpthreads-git 12.0.0.r4.gg4f2fc60ca.* + license: ZPL-2.1 AND LGPL-2.1-or-later + purls: [] + size: 7089846 + timestamp: 1759768412123 +- conda: https://conda.anaconda.org/conda-forge/noarch/mingw-w64-ucrt-x86_64-windows-default-manifest-6.4-he206cdd_7.conda + sha256: 5b0df4e0ba8487ffd59f60c34c5dbb9e001ecd2c5d2c66ba88eada40bfa3ecb8 + md5: 1d6b5c96d7e3cce773519d7d1a4482f0 + depends: + - __win + constrains: + - m2w64-sysroot_win-64 >=12.0.0.r0 + license: FSFAP + purls: [] + size: 7412 + timestamp: 1717486007140 +- conda: https://conda.anaconda.org/conda-forge/noarch/mingw-w64-ucrt-x86_64-winpthreads-git-12.0.0.r4.gg4f2fc60ca-hd8ed1ab_10.conda + sha256: 828abb111286940473c4c665fc8ab300d28920f5af83b32295e8bf2256a8f342 + md5: ba0eeff6a5c62b83c771bb392e22dbb4 + depends: + - m2-conda-epoch + - mingw-w64-ucrt-x86_64-headers-git 12.0.0.r4.gg4f2fc60ca hd8ed1ab_10 + constrains: + - mingw-w64-ucrt-x86_64-crt-git 12.0.0.r4.gg4f2fc60ca.* + license: MIT AND BSD-3-Clause-Clear + purls: [] + size: 123916 + timestamp: 1759768539535 - conda: https://conda.anaconda.org/conda-forge/noarch/minio-7.2.20-pyhd8ed1ab_0.conda sha256: 132cd2ac509a15cb41a1f9c55f190c2c6ab278a8ee4915b178920c3606beb9af md5: 3244fc3d4bc0be3ea995df133a4d9436 @@ -13944,19 +15179,42 @@ packages: purls: [] size: 965435 timestamp: 1767634789522 -- conda: https://conda.anaconda.org/conda-forge/linux-64/msgpack-python-1.1.2-py314h9891dd4_1.conda - sha256: d41c2734d314303e329680aeef282766fe399a0ce63297a68a2f8f9b43b1b68a - md5: c6752022dcdbf4b9ef94163de1ab7f03 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/mpc-1.3.1-h8f1351a_1.conda + sha256: 2700899ad03302a1751dbf2bca135407e470dd83ac897ab91dd8675d4300f158 + md5: a5635df796b71f6ca400fc7026f50701 + depends: + - __osx >=11.0 + - gmp >=6.3.0,<7.0a0 + - mpfr >=4.2.1,<5.0a0 + license: LGPL-3.0-or-later + license_family: LGPL + purls: [] + size: 104766 + timestamp: 1725629165420 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/mpfr-4.2.1-hb693164_3.conda + sha256: 4463e4e2aba7668e37a1b8532859191b4477a6f3602a5d6b4d64ad4c4baaeac5 + md5: 4e4ea852d54cc2b869842de5044662fb + depends: + - __osx >=11.0 + - gmp >=6.3.0,<7.0a0 + license: LGPL-3.0-only + license_family: LGPL + purls: [] + size: 345517 + timestamp: 1725746730583 +- conda: https://conda.anaconda.org/conda-forge/linux-64/msgpack-python-1.1.2-py313h7037e92_1.conda + sha256: fac37e267dd1d07527f0b078ffe000916e80e8c89cfe69d466f5775b88e93df2 + md5: cd1cfde0ea3bca6c805c73ffa988b12a depends: - __glibc >=2.17,<3.0.a0 - libgcc >=14 - libstdcxx >=14 - - python >=3.14,<3.15.0a0 - - python_abi 3.14.* *_cp314 + - python >=3.13,<3.14.0a0 + - python_abi 3.13.* *_cp313 license: Apache-2.0 license_family: Apache - size: 103380 - timestamp: 1762504077009 + size: 103129 + timestamp: 1762504205590 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/msgpack-python-1.1.2-py314h784bc60_1.conda sha256: 9dc4ebe88064cf96bb97a4de83be10fbc52a24d2ff48a4561fb0fed337b526f0 md5: 305227e4de261896033ad8081e8b52ae @@ -14005,6 +15263,8 @@ packages: - python >=3.9 license: Apache-2.0 license_family: Apache + purls: + - pkg:pypi/munkres?source=hash-mapping size: 15851 timestamp: 1749895533014 - conda: https://conda.anaconda.org/conda-forge/linux-64/muparser-2.3.5-h5888daf_0.conda @@ -14374,9 +15634,9 @@ packages: license_family: MIT size: 243121 timestamp: 1755254908603 -- conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.15.0-pyhcf101f3_0.conda - sha256: 2e64699401c6170ce9a0916461ff4686f8d10b076f6abe1d887cbcb7061c0e85 - md5: 37926bb0db8b04b8b99945076e1442d0 +- conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.16.0-pyhcf101f3_0.conda + sha256: d9d358fb992938dc4ba292c4afa6677aac2b16464c9a4f35d69a6d6a923ad8f9 + md5: 648a62e4e4cf1605abf73e7f48b87d5e depends: - python >=3.10 - python @@ -14384,8 +15644,8 @@ packages: license_family: MIT purls: - pkg:pypi/narwhals?source=compressed-mapping - size: 272452 - timestamp: 1767693390284 + size: 279863 + timestamp: 1770040381392 - conda: https://conda.anaconda.org/conda-forge/noarch/nbclassic-1.3.3-pyhcf101f3_0.conda sha256: fe85d996d2622d7f5f9738f8f2ba7bd9298675d0689393c1e5f0cfd9de7c7a32 md5: beea105461da36f4f9a204484704c69a @@ -14581,9 +15841,9 @@ packages: license_family: BSD size: 16817 timestamp: 1733408419340 -- conda: https://conda.anaconda.org/conda-forge/linux-64/numba-0.63.1-py314h8169c2f_0.conda - sha256: 6ab91790aeee336cc4526b02b477eb0f261df6bd9645f44a138b1e8a3ccc5e60 - md5: 9dfbe6bd11b1c77f618b347ec654b37b +- conda: https://conda.anaconda.org/conda-forge/linux-64/numba-0.63.1-py313h5dce7c4_0.conda + sha256: 3ceba93570814df69969edff3156097dc0e86ccefa2ea2bdfe08f84b2023cf04 + md5: dbdae1a85bb346d57fae63269def955a depends: - __glibc >=2.17,<3.0.a0 - _openmp_mutex >=4.5 @@ -14592,19 +15852,21 @@ packages: - llvmlite >=0.46.0,<0.47.0a0 - numpy >=1.22.3,<2.4 - numpy >=1.23,<3 - - python >=3.14,<3.15.0a0 - - python_abi 3.14.* *_cp314 + - python >=3.13,<3.14.0a0 + - python_abi 3.13.* *_cp313 constrains: - - tbb >=2021.6.0 - - libopenblas !=0.3.6 - - cuda-version >=11.2 - cudatoolkit >=11.2 - cuda-python >=11.6 + - libopenblas !=0.3.6 + - tbb >=2021.6.0 + - cuda-version >=11.2 - scipy >=1.0 license: BSD-2-Clause license_family: BSD - size: 5797268 - timestamp: 1765466862046 + purls: + - pkg:pypi/numba?source=hash-mapping + size: 5761715 + timestamp: 1765466811957 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/numba-0.63.1-py314h945de62_0.conda sha256: 4e6acf20fafec2b390e73c54bb348f71ef2fd0092e179e370fdf4ad4c2862baa md5: 4f9128c2986d86725aa0dd5a5dfff168 @@ -14628,6 +15890,8 @@ packages: - tbb >=2021.6.0 license: BSD-2-Clause license_family: BSD + purls: + - pkg:pypi/numba?source=hash-mapping size: 5780959 timestamp: 1765466926700 - conda: https://conda.anaconda.org/conda-forge/win-64/numba-0.63.1-py314h36f8cf2_0.conda @@ -14651,6 +15915,8 @@ packages: - cuda-python >=11.6 license: BSD-2-Clause license_family: BSD + purls: + - pkg:pypi/numba?source=hash-mapping size: 5775759 timestamp: 1765466860567 - conda: https://conda.anaconda.org/conda-forge/linux-64/numexpr-2.10.0-py39he85e4be_100.conda @@ -14683,9 +15949,9 @@ packages: license_family: MIT size: 172562 timestamp: 1732612906511 -- conda: https://conda.anaconda.org/conda-forge/linux-64/numexpr-2.14.1-mkl_py314h9a883a4_1.conda - sha256: b09d02554fe0482ec0c1ca0d896429d9bd7631c72d67ac1634de91ab45ca5eda - md5: 1dbc9d3b902c9267a85ee9c5eb4e318a +- conda: https://conda.anaconda.org/conda-forge/linux-64/numexpr-2.14.1-mkl_py313hddce2c6_1.conda + sha256: 024ac3c0b8b066ea662520a94687abe22f1dcf04bd9b7481d5cef2cda4b0da17 + md5: ecbad655bb67531c65be6f1829689c5f depends: - __glibc >=2.17,<3.0.a0 - libblas * *mkl @@ -14694,14 +15960,14 @@ packages: - mkl >=2025.3.0,<2026.0a0 - numpy >=1.23,<3 - numpy >=1.23.0 - - python >=3.14,<3.15.0a0 - - python_abi 3.14.* *_cp314 + - python >=3.13,<3.14.0a0 + - python_abi 3.13.* *_cp313 license: MIT license_family: MIT purls: - pkg:pypi/numexpr?source=hash-mapping - size: 220911 - timestamp: 1764766891230 + size: 216438 + timestamp: 1764766788416 - conda: https://conda.anaconda.org/conda-forge/linux-64/numexpr-2.14.1-py310h34a7263_101.conda sha256: 39476b57c4c286e194c7f817fa26df214e54df04bfbc1da6b0177d058f8e1bb2 md5: cb02b04ff05ba415b55995bbcc82358f @@ -15071,98 +16337,98 @@ packages: license_family: BSD size: 7893263 timestamp: 1747545075833 -- conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-2.3.5-py314h2b28147_1.conda - sha256: 81425306df4f0ddba159e80c8d91323a34df335079ca93a194201e57b337231c - md5: ab17cb5f388fa17c08937cb9cc24e7b6 +- conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-2.3.5-py313hf6604e3_1.conda + sha256: 2f8aff2a17e4d43012e9863ef4392e6d5de3ae9da0c3e322831f8c5c3d86df71 + md5: dce261869f78ba9b81b9091b084d328d depends: - python - - __glibc >=2.17,<3.0.a0 - libgcc >=14 - libstdcxx >=14 - - liblapack >=3.9.0,<4.0a0 + - __glibc >=2.17,<3.0.a0 + - python_abi 3.13.* *_cp313 - libcblas >=3.9.0,<4.0a0 + - liblapack >=3.9.0,<4.0a0 - libblas >=3.9.0,<4.0a0 - - python_abi 3.14.* *_cp314 constrains: - numpy-base <0a0 license: BSD-3-Clause license_family: BSD - size: 8983076 - timestamp: 1766383421113 -- conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-2.4.1-py311h2e04523_0.conda - sha256: 51b04fafacedb05ef2865dccce212161112f22bfedd7655f8ae04489f85ce083 - md5: 716357afd11c16214cdac522da447704 + purls: + - pkg:pypi/numpy?source=hash-mapping + size: 8919234 + timestamp: 1766383469748 +- conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-2.4.2-py311h2e04523_1.conda + sha256: 2f9971a62316b9acb6ade749cebb59ffe750d1c2d99fe7061c6440589f6d3299 + md5: a8105076864776eceae69d64d30e24d7 depends: - python - __glibc >=2.17,<3.0.a0 - - libgcc >=14 - libstdcxx >=14 + - libgcc >=14 - libblas >=3.9.0,<4.0a0 - - liblapack >=3.9.0,<4.0a0 - python_abi 3.11.* *_cp311 - libcblas >=3.9.0,<4.0a0 + - liblapack >=3.9.0,<4.0a0 constrains: - numpy-base <0a0 license: BSD-3-Clause license_family: BSD - size: 9383164 - timestamp: 1768085749932 -- conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-2.4.1-py312h33ff503_0.conda - sha256: f6c29a77aa02905c01747fc83d32148673ee2eaa34d4d5d5cb420ecdf6fb5035 - md5: ba7e6cb06c372eae6f164623e6e06db8 + size: 9385101 + timestamp: 1770098496391 +- conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-2.4.2-py312h33ff503_1.conda + sha256: fec4d37e1a7c677ddc07bb968255df74902733398b77acc1d05f9dc599e879df + md5: 3569a8fca2dd3202e4ab08f42499f6d3 depends: - python - libgcc >=14 - __glibc >=2.17,<3.0.a0 - libstdcxx >=14 + - liblapack >=3.9.0,<4.0a0 - python_abi 3.12.* *_cp312 - libcblas >=3.9.0,<4.0a0 - - liblapack >=3.9.0,<4.0a0 - libblas >=3.9.0,<4.0a0 constrains: - numpy-base <0a0 license: BSD-3-Clause license_family: BSD - size: 8757015 - timestamp: 1768085678045 -- conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-2.4.1-py313hf6604e3_0.conda - sha256: 4333872cc068f1ba559026ce805a25a91c2ae4e4f804691cf7fa0f43682e9b3a - md5: 7d51e3bef1a4b00bde1861d85ba2f874 + size: 8757566 + timestamp: 1770098484112 +- conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-2.4.2-py313hf6604e3_1.conda + sha256: 2eb8be25a7504f058a153a84be70471e0ebbf6bd0411ae2b6d34904b89d86fe3 + md5: ca9c6ba4beac38cb3d0a85afde27f94c depends: - python - libgcc >=14 - - libstdcxx >=14 - __glibc >=2.17,<3.0.a0 + - libstdcxx >=14 - liblapack >=3.9.0,<4.0a0 - - libblas >=3.9.0,<4.0a0 - - python_abi 3.13.* *_cp313 - libcblas >=3.9.0,<4.0a0 + - python_abi 3.13.* *_cp313 + - libblas >=3.9.0,<4.0a0 constrains: - numpy-base <0a0 license: BSD-3-Clause license_family: BSD - size: 8854901 - timestamp: 1768085657805 -- conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-2.4.1-py314h2b28147_0.conda - sha256: 9af4bb8fef69f8b3c254b32da93bc63b7376b60b72c6ed9104fd3ad23a70891c - md5: 9536e29f857e5d0565e92fd1b54de16a + size: 8857152 + timestamp: 1770098515258 +- conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-2.4.2-py314h2b28147_1.conda + sha256: 1d8377c8001c15ed12c2713b723213474b435706ab9d34ede69795d64af9e94d + md5: 4ea6b620fdf24a1a0bc4f1c7134dfafb depends: - python - - __glibc >=2.17,<3.0.a0 - - libgcc >=14 - libstdcxx >=14 + - libgcc >=14 + - __glibc >=2.17,<3.0.a0 - libcblas >=3.9.0,<4.0a0 - - liblapack >=3.9.0,<4.0a0 - python_abi 3.14.* *_cp314 - libblas >=3.9.0,<4.0a0 + - liblapack >=3.9.0,<4.0a0 constrains: - numpy-base <0a0 license: BSD-3-Clause license_family: BSD - purls: - - pkg:pypi/numpy?source=hash-mapping - size: 8926121 - timestamp: 1768085696128 + size: 8926994 + timestamp: 1770098474394 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/numpy-1.22.4-py39h7df2422_0.tar.bz2 sha256: 764545697b30257c7d2a38bf775b701e3dceba4a6fc644f2480c05d7409dff0a md5: 859d854797724490cd0f171c35f0c38f @@ -15232,82 +16498,82 @@ packages: - numpy-base <0a0 license: BSD-3-Clause license_family: BSD + purls: + - pkg:pypi/numpy?source=hash-mapping size: 6861028 timestamp: 1766383292611 -- conda: https://conda.anaconda.org/conda-forge/osx-arm64/numpy-2.4.1-py311had1e860_0.conda - sha256: 42a228e68c89b76e3298422a67c0a7d10e295e2b47d17c460f6371817148d736 - md5: 425cb3f562c02d5daa0907099314e399 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/numpy-2.4.2-py311had1e860_1.conda + sha256: 09a06de7adea145124618b023e5b0da2949a7211083d0805c21960ab980e053b + md5: bebff6d1b28a10a57a586cc449688324 depends: - python - - python 3.11.* *_cpython - __osx >=11.0 + - python 3.11.* *_cpython - libcxx >=19 - libblas >=3.9.0,<4.0a0 + - python_abi 3.11.* *_cp311 - libcblas >=3.9.0,<4.0a0 - liblapack >=3.9.0,<4.0a0 - - python_abi 3.11.* *_cp311 constrains: - numpy-base <0a0 license: BSD-3-Clause license_family: BSD - size: 7452776 - timestamp: 1768085572337 -- conda: https://conda.anaconda.org/conda-forge/osx-arm64/numpy-2.4.1-py312he281c53_0.conda - sha256: f28e86ce957cad03881148e81d548edcae9e093f6bab5f56d4e0fec608a0d7f7 - md5: 9f51075d9ea979c5cbca44ac34b9623f + size: 7451944 + timestamp: 1770098395802 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/numpy-2.4.2-py312he281c53_1.conda + sha256: 7fd2f1a33b244129dcc2163304d103a7062fc38f01fe13945c9ea95cef12b954 + md5: 4afbe6ffff0335d25f3c5cc78b1350a4 depends: - python - - __osx >=11.0 - libcxx >=19 + - __osx >=11.0 - python 3.12.* *_cpython - - libcblas >=3.9.0,<4.0a0 - - python_abi 3.12.* *_cp312 - libblas >=3.9.0,<4.0a0 + - python_abi 3.12.* *_cp312 - liblapack >=3.9.0,<4.0a0 + - libcblas >=3.9.0,<4.0a0 constrains: - numpy-base <0a0 license: BSD-3-Clause license_family: BSD - size: 6839209 - timestamp: 1768085582339 -- conda: https://conda.anaconda.org/conda-forge/osx-arm64/numpy-2.4.1-py313h16eae64_0.conda - sha256: 409a1f254ff025f0567d3444f2a82cd65c10d403f27a66f219f51a082b2a7699 - md5: 527abeb3c3f65345d9c337fb49e32d51 + size: 6840961 + timestamp: 1770098400654 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/numpy-2.4.2-py313h16eae64_1.conda + sha256: 3e23ed9eb63d9ce4dc585aad6b65e0197d7e9f28877acf7114cc64f33763a420 + md5: e34e9c58a0ee3eca3def3bb477797621 depends: - python - __osx >=11.0 - - libcxx >=19 - python 3.13.* *_cp313 - - libcblas >=3.9.0,<4.0a0 + - libcxx >=19 - liblapack >=3.9.0,<4.0a0 - - python_abi 3.13.* *_cp313 - libblas >=3.9.0,<4.0a0 + - libcblas >=3.9.0,<4.0a0 + - python_abi 3.13.* *_cp313 constrains: - numpy-base <0a0 license: BSD-3-Clause license_family: BSD - size: 6925404 - timestamp: 1768085588288 -- conda: https://conda.anaconda.org/conda-forge/osx-arm64/numpy-2.4.1-py314hae46ccb_0.conda - sha256: e4fa9c378869e0c7e0a33ab1546ff9974050b55ad1e48b795dce4fb812513baf - md5: a67f36be1a584c382670c98b4ffea529 + size: 6925963 + timestamp: 1770098439599 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/numpy-2.4.2-py314hae46ccb_1.conda + sha256: 43b5ed0ead36e5133ee8462916d23284f0bce0e5f266fa4bd31a020a6cc22f14 + md5: 0f0ddf0575b98d91cda9e3ca9eaeb9a2 depends: - python - __osx >=11.0 - - libcxx >=19 - python 3.14.* *_cp314 - - liblapack >=3.9.0,<4.0a0 + - libcxx >=19 - libblas >=3.9.0,<4.0a0 - - libcblas >=3.9.0,<4.0a0 + - liblapack >=3.9.0,<4.0a0 - python_abi 3.14.* *_cp314 + - libcblas >=3.9.0,<4.0a0 constrains: - numpy-base <0a0 license: BSD-3-Clause license_family: BSD - purls: - - pkg:pypi/numpy?source=compressed-mapping - size: 6991931 - timestamp: 1768085575848 + size: 6992958 + timestamp: 1770098398327 - conda: https://conda.anaconda.org/conda-forge/win-64/numpy-1.22.4-py39h0948cea_0.tar.bz2 sha256: 91e1818a00756c8943297097f4d8ff7765172ebed22a29359de2992945e4b72f md5: a3d0bb2b75ee4f923ff520d25cecbe7f @@ -15377,82 +16643,82 @@ packages: - numpy-base <0a0 license: BSD-3-Clause license_family: BSD + purls: + - pkg:pypi/numpy?source=hash-mapping size: 7584934 timestamp: 1766383321713 -- conda: https://conda.anaconda.org/conda-forge/win-64/numpy-2.4.1-py311h80b3fa1_0.conda - sha256: 51c0314092c3e1077679379703af41b0ab89b5eacfdfba142ad2670fdce3ca32 - md5: 387094bb33448f55432ea38cf9b62f1f +- conda: https://conda.anaconda.org/conda-forge/win-64/numpy-2.4.2-py311h80b3fa1_1.conda + sha256: c5cd26fb28d92d6c3843b96489f433ef87d1866d03a746f7228230b74bef431a + md5: a824c6667179120c458beb9e9394932f depends: - python - vc >=14.3,<15 - vc14_runtime >=14.44.35208 - ucrt >=10.0.20348.0 - - libblas >=3.9.0,<4.0a0 + - python_abi 3.11.* *_cp311 - libcblas >=3.9.0,<4.0a0 - liblapack >=3.9.0,<4.0a0 - - python_abi 3.11.* *_cp311 + - libblas >=3.9.0,<4.0a0 constrains: - numpy-base <0a0 license: BSD-3-Clause license_family: BSD - size: 7799698 - timestamp: 1768085583840 -- conda: https://conda.anaconda.org/conda-forge/win-64/numpy-2.4.1-py312ha72d056_0.conda - sha256: 06d2acce4c5cfe230213c4bc62823de3fa032d053f83c93a28478c7b8ee769bc - md5: e06f225f5bf5784b3412b21a2a44da72 + size: 7803678 + timestamp: 1770098404597 +- conda: https://conda.anaconda.org/conda-forge/win-64/numpy-2.4.2-py312ha72d056_1.conda + sha256: bae400995eed564cf68d3939d5b782680407b3e25dc7363687df19c6b2cf396f + md5: 52254edfb993f9e61552c63813041689 depends: - python - vc >=14.3,<15 - vc14_runtime >=14.44.35208 - ucrt >=10.0.20348.0 - - python_abi 3.12.* *_cp312 - libcblas >=3.9.0,<4.0a0 - - liblapack >=3.9.0,<4.0a0 + - python_abi 3.12.* *_cp312 - libblas >=3.9.0,<4.0a0 + - liblapack >=3.9.0,<4.0a0 constrains: - numpy-base <0a0 license: BSD-3-Clause license_family: BSD - size: 7163582 - timestamp: 1768085586766 -- conda: https://conda.anaconda.org/conda-forge/win-64/numpy-2.4.1-py313hce7ae62_0.conda - sha256: 1e28379c323859e7e83bf91b0dcbd1ddc0c13a3a6939aacab3bd7db5c2e9ccde - md5: 2490cec55c24dbf3d3be2da6b61a6646 + size: 7163949 + timestamp: 1770098408393 +- conda: https://conda.anaconda.org/conda-forge/win-64/numpy-2.4.2-py313hce7ae62_1.conda + sha256: a926b0f781c44fdd10e11ec7e7a86ac588ec40b339ac2b4a8459def6d99b613b + md5: 7db4fcf0a8a985d3f15270ddc7ac0aac depends: - python - vc >=14.3,<15 - vc14_runtime >=14.44.35208 - ucrt >=10.0.20348.0 - - python_abi 3.13.* *_cp313 - liblapack >=3.9.0,<4.0a0 + - python_abi 3.13.* *_cp313 - libblas >=3.9.0,<4.0a0 - libcblas >=3.9.0,<4.0a0 constrains: - numpy-base <0a0 license: BSD-3-Clause license_family: BSD - size: 7251637 - timestamp: 1768085589970 -- conda: https://conda.anaconda.org/conda-forge/win-64/numpy-2.4.1-py314h06c3c77_0.conda - sha256: 4bcbbe320525c49f2ddf61123e4281ff76d2ba9a737dea90e14370534c6ec1f9 - md5: 794ac87f08dcca30be8c6ebfa8a5b2d1 + size: 7251046 + timestamp: 1770098409520 +- conda: https://conda.anaconda.org/conda-forge/win-64/numpy-2.4.2-py314h06c3c77_1.conda + sha256: 34fc25b81cfa987e1825586ddb1a4ac76a246fdef343c9171109017674ad6503 + md5: 2fccd2c4e9feb4e4c2a90043015525d6 depends: - python - vc >=14.3,<15 - vc14_runtime >=14.44.35208 - ucrt >=10.0.20348.0 - python_abi 3.14.* *_cp314 - - liblapack >=3.9.0,<4.0a0 - libcblas >=3.9.0,<4.0a0 + - liblapack >=3.9.0,<4.0a0 - libblas >=3.9.0,<4.0a0 constrains: - numpy-base <0a0 license: BSD-3-Clause license_family: BSD - purls: - - pkg:pypi/numpy?source=hash-mapping - size: 7306379 - timestamp: 1768085588568 + size: 7309134 + timestamp: 1770098414535 - conda: https://conda.anaconda.org/conda-forge/linux-64/openjdk-25.0.1-h5755bd7_0.conda sha256: 19b2268bf2d1fc4b4f48a68b9bfac620370c1b7f539671279053b0d3bcc348f1 md5: a40ce38da029d1d272bfd9bd7510f901 @@ -15517,6 +16783,7 @@ packages: - libzlib >=1.3.1,<2.0a0 license: BSD-2-Clause license_family: BSD + purls: [] size: 355400 timestamp: 1758489294972 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/openjpeg-2.5.4-hbfb3c88_0.conda @@ -15530,6 +16797,7 @@ packages: - libzlib >=1.3.1,<2.0a0 license: BSD-2-Clause license_family: BSD + purls: [] size: 319967 timestamp: 1758489514651 - conda: https://conda.anaconda.org/conda-forge/win-64/openjpeg-2.5.4-h24db6dd_0.conda @@ -15544,6 +16812,7 @@ packages: - vc14_runtime >=14.44.35208 license: BSD-2-Clause license_family: BSD + purls: [] size: 244860 timestamp: 1758489556249 - conda: https://conda.anaconda.org/conda-forge/noarch/openml-0.12.2-pyhd8ed1ab_0.tar.bz2 @@ -15954,6 +17223,8 @@ packages: - zstandard >=0.23.0 license: BSD-3-Clause license_family: BSD + purls: + - pkg:pypi/pandas?source=hash-mapping size: 14952243 timestamp: 1769076307505 - conda: https://conda.anaconda.org/conda-forge/linux-64/pandas-3.0.0-py314hb4ffadd_0.conda @@ -16009,8 +17280,6 @@ packages: - zstandard >=0.23.0 license: BSD-3-Clause license_family: BSD - purls: - - pkg:pypi/pandas?source=hash-mapping size: 15253498 timestamp: 1769076318460 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pandas-1.4.4-py39he7125aa_0.tar.bz2 @@ -16691,27 +17960,27 @@ packages: - pkg:pypi/pandas?source=hash-mapping size: 14005537 timestamp: 1769076350929 -- conda: https://conda.anaconda.org/conda-forge/linux-64/pandoc-3.8.3-ha770c72_0.conda - sha256: 87ec986d1e0d16d9d2aa149653abeb73d1ac4bd9e6d7dc13ba33ec00134c8a7a - md5: 0e4aa34e44a68aeb850349fe51a6a3d0 +- conda: https://conda.anaconda.org/conda-forge/linux-64/pandoc-3.9-ha770c72_0.conda + sha256: 721487cedd6130fc35c9ed219f7952aaadb33102834f3e2dd4cadf113dd39e70 + md5: 9048399267b4e56b122081aad7fda761 license: GPL-2.0-or-later license_family: GPL - size: 22458834 - timestamp: 1764589637843 -- conda: https://conda.anaconda.org/conda-forge/osx-arm64/pandoc-3.8.3-hce30654_0.conda - sha256: 39af2080d16088c0b9c19db5d0f8b2c845e70c428126a4773d0e54b609d8af91 - md5: 68bc0f4209fe5cbb03a401177f3a36c2 + size: 22470583 + timestamp: 1770211571912 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/pandoc-3.9-hce30654_0.conda + sha256: 91469ebcc33309a5212a6c1d5a8947f4333303518e3575bc12c9ed98bee11733 + md5: e1dc425f8fd8290f2953e746e46a026d license: GPL-2.0-or-later license_family: GPL - size: 28522262 - timestamp: 1764589967786 -- conda: https://conda.anaconda.org/conda-forge/win-64/pandoc-3.8.3-h57928b3_0.conda - sha256: b3d37c502e405e7d1997a028e7eae246acd52436eacdd4f053cb345bde0da8a9 - md5: 904ca93f4f00a75ee3c49147cb00f14d + size: 28164336 + timestamp: 1770211707334 +- conda: https://conda.anaconda.org/conda-forge/win-64/pandoc-3.9-h57928b3_0.conda + sha256: e017966c0188c09dc0d5c898525966cad7f1a5b716ecd514f13d4e1dd372f929 + md5: 731af1f346cb3f8f1243790f2165d851 license: GPL-2.0-or-later license_family: GPL - size: 26699611 - timestamp: 1764589773519 + size: 26783378 + timestamp: 1770211847038 - conda: https://conda.anaconda.org/conda-forge/noarch/pandocfilters-1.5.0-pyhd8ed1ab_0.tar.bz2 sha256: 2bb9ba9857f4774b85900c2562f7e711d08dd48e2add9bee4e1612fbee27e16f md5: 457c2c8c08e54905d6954e79cb5b5db9 @@ -16721,6 +17990,47 @@ packages: license_family: BSD size: 11627 timestamp: 1631603397334 +- conda: https://conda.anaconda.org/conda-forge/linux-64/pango-1.56.4-hadf4263_0.conda + sha256: 3613774ad27e48503a3a6a9d72017087ea70f1426f6e5541dbdb59a3b626eaaf + md5: 79f71230c069a287efe3a8614069ddf1 + depends: + - __glibc >=2.17,<3.0.a0 + - cairo >=1.18.4,<2.0a0 + - fontconfig >=2.15.0,<3.0a0 + - fonts-conda-ecosystem + - fribidi >=1.0.10,<2.0a0 + - harfbuzz >=11.0.1 + - libexpat >=2.7.0,<3.0a0 + - libfreetype >=2.13.3 + - libfreetype6 >=2.13.3 + - libgcc >=13 + - libglib >=2.84.2,<3.0a0 + - libpng >=1.6.49,<1.7.0a0 + - libzlib >=1.3.1,<2.0a0 + license: LGPL-2.1-or-later + purls: [] + size: 455420 + timestamp: 1751292466873 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/pango-1.56.4-h875632e_0.conda + sha256: 705484ad60adee86cab1aad3d2d8def03a699ece438c864e8ac995f6f66401a6 + md5: 7d57f8b4b7acfc75c777bc231f0d31be + depends: + - __osx >=11.0 + - cairo >=1.18.4,<2.0a0 + - fontconfig >=2.15.0,<3.0a0 + - fonts-conda-ecosystem + - fribidi >=1.0.10,<2.0a0 + - harfbuzz >=11.0.1 + - libexpat >=2.7.0,<3.0a0 + - libfreetype >=2.13.3 + - libfreetype6 >=2.13.3 + - libglib >=2.84.2,<3.0a0 + - libpng >=1.6.49,<1.7.0a0 + - libzlib >=1.3.1,<2.0a0 + license: LGPL-2.1-or-later + purls: [] + size: 426931 + timestamp: 1751292636271 - conda: https://conda.anaconda.org/conda-forge/noarch/parso-0.8.5-pyhcf101f3_0.conda sha256: 30de7b4d15fbe53ffe052feccde31223a236dae0495bab54ab2479de30b2990f md5: a110716cdb11cf51482ff4000dc253d7 @@ -16813,6 +18123,7 @@ packages: - libzlib >=1.3.1,<2.0a0 license: BSD-3-Clause license_family: BSD + purls: [] size: 850231 timestamp: 1763655726735 - conda: https://conda.anaconda.org/conda-forge/win-64/pcre2-10.47-hd2b5f0e_0.conda @@ -16826,6 +18137,7 @@ packages: - vc14_runtime >=14.44.35208 license: BSD-3-Clause license_family: BSD + purls: [] size: 995992 timestamp: 1763655708300 - conda: https://conda.anaconda.org/conda-forge/noarch/pexpect-4.9.0-pyhd8ed1ab_1.conda @@ -16837,27 +18149,29 @@ packages: license: ISC size: 53561 timestamp: 1733302019362 -- conda: https://conda.anaconda.org/conda-forge/linux-64/pillow-12.1.0-py314h8ec4b1a_0.conda - sha256: 6d8e32dc44165cff96ec9c00383e998fd035983d971c5f35ebed6f5f51c4022a - md5: f9b6a8fbb8dcb840a0c1c052dc5092e4 +- conda: https://conda.anaconda.org/conda-forge/linux-64/pillow-12.1.0-py313h80991f8_0.conda + sha256: bdad1e21cadd64154c45fa554247dd672288ad51982ca7d54b3fab63e40938df + md5: 183fe6b9e99e5c2b464c1573ec78eac8 depends: - python - - __glibc >=2.17,<3.0.a0 - libgcc >=14 - - lcms2 >=2.17,<3.0a0 - - libfreetype >=2.14.1 - - libfreetype6 >=2.14.1 + - __glibc >=2.17,<3.0.a0 + - tk >=8.6.13,<8.7.0a0 + - python_abi 3.13.* *_cp313 + - libtiff >=4.7.1,<4.8.0a0 - libjpeg-turbo >=3.1.2,<4.0a0 - - zlib-ng >=2.3.2,<2.4.0a0 + - lcms2 >=2.17,<3.0a0 - libxcb >=1.17.0,<2.0a0 + - zlib-ng >=2.3.2,<2.4.0a0 - libwebp-base >=1.6.0,<2.0a0 - openjpeg >=2.5.4,<3.0a0 - - python_abi 3.14.* *_cp314 - - libtiff >=4.7.1,<4.8.0a0 - - tk >=8.6.13,<8.7.0a0 + - libfreetype >=2.14.1 + - libfreetype6 >=2.14.1 license: HPND - size: 1072995 - timestamp: 1767353193452 + purls: + - pkg:pypi/pillow?source=hash-mapping + size: 1043309 + timestamp: 1767353193450 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pillow-12.1.0-py314hab283cf_0.conda sha256: 3f88f2600862583c8bed3d37f4b95f0f96a459e9fdd36ca680472bc89a46e7bb md5: 1f9dae6213643ac883e300c11df611eb @@ -16877,6 +18191,8 @@ packages: - tk >=8.6.13,<8.7.0a0 - libwebp-base >=1.6.0,<2.0a0 license: HPND + purls: + - pkg:pypi/pillow?source=hash-mapping size: 995543 timestamp: 1767353279681 - conda: https://conda.anaconda.org/conda-forge/win-64/pillow-12.1.0-py314h61b30b5_0.conda @@ -16899,6 +18215,8 @@ packages: - libxcb >=1.17.0,<2.0a0 - python_abi 3.14.* *_cp314 license: HPND + purls: + - pkg:pypi/pillow?source=hash-mapping size: 973387 timestamp: 1767353195064 - conda: https://conda.anaconda.org/conda-forge/noarch/pip-25.2-pyh8b19718_0.conda @@ -16912,28 +18230,28 @@ packages: license_family: MIT size: 1177168 timestamp: 1753924973872 -- conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0-pyh145f28c_0.conda - sha256: 4349de61caaa05e19be38a20a084e001d325b9c70ac10e3c88d8743d3fc9aefb - md5: f08a17c938eb6bc1b928bd8cdd37e20f +- conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0.1-pyh145f28c_0.conda + sha256: 5f66ea31d62188c266c5a8752119b0cc90a5bf05963f665cf48a33e0ec58d39c + md5: 09a970fbf75e8ed1aa633827ded6aa4f depends: - python >=3.13.0a0 license: MIT license_family: MIT purls: - pkg:pypi/pip?source=compressed-mapping - size: 1181224 - timestamp: 1769850913286 -- conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0-pyh8b19718_0.conda - sha256: 1c54649ea52f22f0e78a83749a82bddcb1e13e8dc7164bc3f46e2c219fbb5b05 - md5: 50663f09ee2931b84e5726ba1384c87b + size: 1180743 + timestamp: 1770270312477 +- conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0.1-pyh8b19718_0.conda + sha256: 8e1497814a9997654ed7990a79c054ea5a42545679407acbc6f7e809c73c9120 + md5: 67bdec43082fd8a9cffb9484420b39a2 depends: - python >=3.10,<3.13.0a0 - setuptools - wheel license: MIT license_family: MIT - size: 1181830 - timestamp: 1769850922306 + size: 1181790 + timestamp: 1770270305795 - conda: https://conda.anaconda.org/conda-forge/linux-64/pixman-0.46.4-h54a6638_1.conda sha256: 43d37bc9ca3b257c5dd7bf76a8426addbdec381f6786ff441dc90b1a49143b6a md5: c01af13bdc553d1a8fbfff6e8db075f0 @@ -16947,6 +18265,32 @@ packages: purls: [] size: 450960 timestamp: 1754665235234 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/pixman-0.46.4-h81086ad_1.conda + sha256: 29c9b08a9b8b7810f9d4f159aecfd205fce051633169040005c0b7efad4bc718 + md5: 17c3d745db6ea72ae2fce17e7338547f + depends: + - __osx >=11.0 + - libcxx >=19 + license: MIT + license_family: MIT + purls: [] + size: 248045 + timestamp: 1754665282033 +- conda: https://conda.anaconda.org/conda-forge/win-64/pixman-0.46.4-h5112557_1.conda + sha256: 246fce4706b3f8b247a7d6142ba8d732c95263d3c96e212b9d63d6a4ab4aff35 + md5: 08c8fa3b419df480d985e304f7884d35 + depends: + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + - ucrt >=10.0.20348.0 + license: MIT + license_family: MIT + purls: [] + size: 542795 + timestamp: 1754665193489 - conda: https://conda.anaconda.org/conda-forge/noarch/platformdirs-4.5.1-pyhcf101f3_0.conda sha256: 04c64fb78c520e5c396b6e07bc9082735a5cc28175dbe23138201d0a9441800b md5: 1bd2e65c8c7ef24f4639ae6e850dacc2 @@ -16965,6 +18309,8 @@ packages: - python license: MIT license_family: MIT + purls: + - pkg:pypi/pluggy?source=compressed-mapping size: 25877 timestamp: 1764896838868 - conda: https://conda.anaconda.org/conda-forge/noarch/pre-commit-4.5.1-pyha770c72_0.conda @@ -17338,6 +18684,7 @@ packages: - __osx >=11.0 license: MIT license_family: MIT + purls: [] size: 8381 timestamp: 1726802424786 - conda: https://conda.anaconda.org/conda-forge/win-64/pthread-stubs-0.4-h0e40799_1002.conda @@ -17349,6 +18696,7 @@ packages: - ucrt >=10.0.20348.0 license: MIT license_family: MIT + purls: [] size: 9389 timestamp: 1726802555076 - conda: https://conda.anaconda.org/conda-forge/noarch/ptyprocess-0.7.0-pyhd8ed1ab_1.conda @@ -17441,6 +18789,7 @@ packages: - python_abi 3.13.* *_cp313 license: Apache-2.0 license_family: APACHE + purls: [] size: 27332 timestamp: 1769291558903 - conda: https://conda.anaconda.org/conda-forge/linux-64/pyarrow-23.0.0-py314hdafbbf9_0.conda @@ -17456,7 +18805,6 @@ packages: - python_abi 3.14.* *_cp314 license: Apache-2.0 license_family: APACHE - purls: [] size: 27335 timestamp: 1769291544343 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyarrow-21.0.0-py39hdf13c20_0.conda @@ -17734,6 +19082,8 @@ packages: - apache-arrow-proc * cpu license: Apache-2.0 license_family: APACHE + purls: + - pkg:pypi/pyarrow?source=hash-mapping size: 4751647 timestamp: 1769291378117 - conda: https://conda.anaconda.org/conda-forge/linux-64/pyarrow-core-23.0.0-py314h52d6ec5_0_cpu.conda @@ -17753,8 +19103,6 @@ packages: - numpy >=1.21,<3 license: Apache-2.0 license_family: APACHE - purls: - - pkg:pypi/pyarrow?source=compressed-mapping size: 5392232 timestamp: 1769291406319 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyarrow-core-21.0.0-py39h31423f9_0_cpu.conda @@ -18006,21 +19354,23 @@ packages: - python license: BSD-3-Clause license_family: BSD + purls: + - pkg:pypi/pycparser?source=hash-mapping size: 110100 timestamp: 1733195786147 -- conda: https://conda.anaconda.org/conda-forge/linux-64/pycryptodome-3.23.0-py314h11b9afc_2.conda - sha256: ff3359008b0c86629acba02f8943ad3bb67f9bee2d48dbdf24e5a78360c5f95a - md5: cad55c9615968a78dc1e0b5d644f30c4 +- conda: https://conda.anaconda.org/conda-forge/linux-64/pycryptodome-3.23.0-py313h6123c0d_2.conda + sha256: 8a389621b0bf6bbce6f1732c4d4da09b3dad4c413e029726465f244ff25b55ec + md5: 9515285f632cbc82b783df886713f998 depends: - __glibc >=2.17,<3.0.a0 - gmp >=6.3.0,<7.0a0 - libgcc >=14 - - python >=3.14,<3.15.0a0 - - python_abi 3.14.* *_cp314 + - python >=3.13,<3.14.0a0 + - python_abi 3.13.* *_cp313 license: BSD-2-Clause license_family: BSD - size: 1697490 - timestamp: 1768755541788 + size: 1681620 + timestamp: 1768755547718 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pycryptodome-3.23.0-py314hdf96093_2.conda sha256: 860449192cdfb5229512d062f09d3eef675dd430309b04857928c2383056acde md5: b950069c1136d796a6389d6ec4b7ad0b @@ -18047,6 +19397,76 @@ packages: license_family: BSD size: 1745064 timestamp: 1768755680835 +- conda: https://conda.anaconda.org/conda-forge/noarch/pydantic-2.12.5-pyhcf101f3_1.conda + sha256: 868569d9505b7fe246c880c11e2c44924d7613a8cdcc1f6ef85d5375e892f13d + md5: c3946ed24acdb28db1b5d63321dbca7d + depends: + - typing-inspection >=0.4.2 + - typing_extensions >=4.14.1 + - python >=3.10 + - typing-extensions >=4.6.1 + - annotated-types >=0.6.0 + - pydantic-core ==2.41.5 + - python + license: MIT + license_family: MIT + purls: + - pkg:pypi/pydantic?source=hash-mapping + size: 340482 + timestamp: 1764434463101 +- conda: https://conda.anaconda.org/conda-forge/linux-64/pydantic-core-2.41.5-py313h843e2db_1.conda + sha256: b15568ddc03bd33ea41610e5df951be4e245cd61957cbf8c2cfd12557f3d53b5 + md5: f27c39a1906771bbe56cd26a76bf0b8b + depends: + - python + - typing-extensions >=4.6.0,!=4.7.0 + - libgcc >=14 + - __glibc >=2.17,<3.0.a0 + - python_abi 3.13.* *_cp313 + constrains: + - __glibc >=2.17 + license: MIT + license_family: MIT + purls: + - pkg:pypi/pydantic-core?source=hash-mapping + size: 1940186 + timestamp: 1762989000579 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/pydantic-core-2.41.5-py314haad56a0_1.conda + sha256: dded9092d89f1d8c267d5ce8b5e21f935c51acb7a64330f507cdfb3b69a98116 + md5: 420a4b8024e9b22880f1e03b612afa7d + depends: + - python + - typing-extensions >=4.6.0,!=4.7.0 + - __osx >=11.0 + - python 3.14.* *_cp314 + - python_abi 3.14.* *_cp314 + constrains: + - __osx >=11.0 + license: MIT + license_family: MIT + purls: + - pkg:pypi/pydantic-core?source=hash-mapping + size: 1784478 + timestamp: 1762989019956 +- conda: https://conda.anaconda.org/conda-forge/win-64/pydantic-core-2.41.5-py314h9f07db2_1.conda + sha256: 51773479d973c0b0b96cf581cb8444061eaac9b6c28f1cc6d33afc39201d5f13 + md5: c1f37669ed289c378f3193b35c9df2a7 + depends: + - python + - typing-extensions >=4.6.0,!=4.7.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + - ucrt >=10.0.20348.0 + - python_abi 3.14.* *_cp314 + license: MIT + license_family: MIT + purls: + - pkg:pypi/pydantic-core?source=hash-mapping + size: 1971476 + timestamp: 1762989023313 - conda: https://conda.anaconda.org/conda-forge/noarch/pygments-2.19.2-pyhd8ed1ab_0.conda sha256: 5577623b9f6685ece2697c6eb7511b4c9ac5fb607c9babc2646c811b428fd46a md5: 6b6ece66ebcae2d5f326c77ef2c5a066 @@ -18054,6 +19474,8 @@ packages: - python >=3.9 license: BSD-2-Clause license_family: BSD + purls: + - pkg:pypi/pygments?source=hash-mapping size: 889287 timestamp: 1750615908735 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyobjc-core-12.1-py314h3a4d195_0.conda @@ -18084,9 +19506,9 @@ packages: license_family: MIT size: 374792 timestamp: 1763160601898 -- conda: https://conda.anaconda.org/conda-forge/linux-64/pyogrio-0.12.1-py314hbcf5174_0.conda - sha256: 53b72845bc9051b1b94c83ed06047788cdb07af529b9368393ac1a9eb720ac21 - md5: b6696a3d5c567d3b2015bf77f454f247 +- conda: https://conda.anaconda.org/conda-forge/linux-64/pyogrio-0.12.1-py313hae45665_0.conda + sha256: 36d91e089f7c6fa3466a07e9c2167a64b97837433c09b6f3ba632c978cce22a3 + md5: fa543477ad16de26ce5f2fd5bcd249fa depends: - __glibc >=2.17,<3.0.a0 - libgcc >=14 @@ -18094,12 +19516,12 @@ packages: - libstdcxx >=14 - numpy - packaging - - python >=3.14,<3.15.0a0 - - python_abi 3.14.* *_cp314 + - python >=3.13,<3.14.0a0 + - python_abi 3.13.* *_cp313 license: MIT license_family: MIT - size: 667940 - timestamp: 1764402531595 + size: 665424 + timestamp: 1764402539337 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyogrio-0.12.1-py314h3da1bed_0.conda sha256: dcaaab4d8b539f7c4ee740e0242ae09c48f68e75949476ca36d9c67e61aafc3b md5: 9b33fa020bd4da86a2dddfd0f63a43ba @@ -18140,22 +19562,24 @@ packages: - python license: MIT license_family: MIT + purls: + - pkg:pypi/pyparsing?source=compressed-mapping size: 110893 timestamp: 1769003998136 -- conda: https://conda.anaconda.org/conda-forge/linux-64/pyproj-3.7.2-py314h24aeaa0_2.conda - sha256: 6723491904d0a705106d61a01bea68552a15dd458359edb83dc2d768346bdff1 - md5: b46a7e6a2b8c064488576c3e42d85df0 +- conda: https://conda.anaconda.org/conda-forge/linux-64/pyproj-3.7.2-py313h77f6078_2.conda + sha256: a37cabb43cf5d73bacd0c20856374561dde9f0025c4a189593d961057ba4a17d + md5: 42d11c7d1ac21ae2085f58353641e71c depends: - __glibc >=2.17,<3.0.a0 - certifi - libgcc >=14 - proj >=9.7.0,<9.8.0a0 - - python >=3.14.0rc2,<3.15.0a0 - - python_abi 3.14.* *_cp314 + - python >=3.13,<3.14.0a0 + - python_abi 3.13.* *_cp313 license: MIT license_family: MIT - size: 542455 - timestamp: 1757954860485 + size: 534602 + timestamp: 1757954997735 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyproj-3.7.2-py314h87291f3_2.conda sha256: 300120265c3d7e4ceb4a1e88a71cb8968e44bf72c78904db9da774c0a5b2112d md5: 65c2365ac6ad627d91759b9b5162c38b @@ -18241,6 +19665,8 @@ packages: - pytest-faulthandler >=2 license: MIT license_family: MIT + purls: + - pkg:pypi/pytest?source=hash-mapping size: 299581 timestamp: 1765062031645 - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.8.0-pyhd8ed1ab_0.conda @@ -18337,10 +19763,10 @@ packages: license: Python-2.0 size: 31457785 timestamp: 1769472855343 -- conda: https://conda.anaconda.org/conda-forge/linux-64/python-3.13.11-hc97d973_101_cp313.conda - build_number: 101 - sha256: c9625638f32f4ee27a506e8cefc56a78110c4c54867663f56d91dc721df9dc7f - md5: aa23b675b860f2566af2dfb3ffdf3b8c +- conda: https://conda.anaconda.org/conda-forge/linux-64/python-3.13.12-hc97d973_100_cp313.conda + build_number: 100 + sha256: 8a08fe5b7cb5a28aa44e2994d18dbf77f443956990753a4ca8173153ffb6eb56 + md5: 4c875ed0e78c2d407ec55eadffb8cf3d depends: - __glibc >=2.17,<3.0.a0 - bzip2 >=1.0.8,<2.0a0 @@ -18354,19 +19780,20 @@ packages: - libuuid >=2.41.3,<3.0a0 - libzlib >=1.3.1,<2.0a0 - ncurses >=6.5,<7.0a0 - - openssl >=3.5.4,<4.0a0 + - openssl >=3.5.5,<4.0a0 - python_abi 3.13.* *_cp313 - readline >=8.3,<9.0a0 - tk >=8.6.13,<8.7.0a0 - tzdata license: Python-2.0 - size: 37170676 - timestamp: 1769473304794 + purls: [] + size: 37364553 + timestamp: 1770272309861 python_site_packages_path: lib/python3.13/site-packages -- conda: https://conda.anaconda.org/conda-forge/linux-64/python-3.14.2-h32b2ec7_101_cp314.conda - build_number: 101 - sha256: 24719868a471dd94041aa9873c6f87adf3b86c07878ad4e242ac97228f9e6460 - md5: 051f60a9d1e3aae7160d173aeb7029f8 +- conda: https://conda.anaconda.org/conda-forge/linux-64/python-3.14.3-h32b2ec7_100_cp314.conda + build_number: 100 + sha256: ff087b19d158644d3b0708eca10a5e40d692cdc8e95f53715f4490c6959f3768 + md5: b40594d5da041824087eebe12228af42 depends: - __glibc >=2.17,<3.0.a0 - bzip2 >=1.0.8,<2.0a0 @@ -18380,16 +19807,15 @@ packages: - libuuid >=2.41.3,<3.0a0 - libzlib >=1.3.1,<2.0a0 - ncurses >=6.5,<7.0a0 - - openssl >=3.5.4,<4.0a0 + - openssl >=3.5.5,<4.0a0 - python_abi 3.14.* *_cp314 - readline >=8.3,<9.0a0 - tk >=8.6.13,<8.7.0a0 - tzdata - zstd >=1.5.7,<1.6.0a0 license: Python-2.0 - purls: [] - size: 36833080 - timestamp: 1769458770373 + size: 36529771 + timestamp: 1770271970971 python_site_packages_path: lib/python3.14/site-packages - conda: https://conda.anaconda.org/conda-forge/linux-64/python-3.9.23-hc30ae73_0_cpython.conda sha256: dcfc417424b21ffca70dddf7a86ef69270b3e8d2040c748b7356a615470d5298 @@ -18483,10 +19909,10 @@ packages: license: Python-2.0 size: 12953358 timestamp: 1769472376612 -- conda: https://conda.anaconda.org/conda-forge/osx-arm64/python-3.13.11-hfc2f54d_101_cp313.conda - build_number: 101 - sha256: 8565d451dff3cda5e55fabdbae2751033c2b08b3fd3833526f8dbf3c08bcb3cf - md5: 8f2ac152fe98c22af0f4b479cf11c845 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/python-3.13.12-h20e6be0_100_cp313.conda + build_number: 100 + sha256: 9a4f16a64def0853f0a7b6a7beb40d498fd6b09bee10b90c3d6069b664156817 + md5: 179c0f5ae4f22bc3be567298ed0b17b9 depends: - __osx >=11.0 - bzip2 >=1.0.8,<2.0a0 @@ -18497,19 +19923,19 @@ packages: - libsqlite >=3.51.2,<4.0a0 - libzlib >=1.3.1,<2.0a0 - ncurses >=6.5,<7.0a0 - - openssl >=3.5.4,<4.0a0 + - openssl >=3.5.5,<4.0a0 - python_abi 3.13.* *_cp313 - readline >=8.3,<9.0a0 - tk >=8.6.13,<8.7.0a0 - tzdata license: Python-2.0 - size: 12806076 - timestamp: 1769472806227 + size: 12770674 + timestamp: 1770272314517 python_site_packages_path: lib/python3.13/site-packages -- conda: https://conda.anaconda.org/conda-forge/osx-arm64/python-3.14.2-h40d2674_101_cp314.conda - build_number: 101 - sha256: 0b3ae49a61b8baf1af8133e8dac0d404a66b634000de0760c8fb692a5ce86e84 - md5: f0999777d0ec086b14d68ed02a143b94 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/python-3.14.3-h4c637c5_100_cp314.conda + build_number: 100 + sha256: 05f63767b548e9dd1d4d3b5978721703b376ce451c7dfaba8ba3ca020e11bc76 + md5: 97852749b58606ffe363c2cc491cfce1 depends: - __osx >=11.0 - bzip2 >=1.0.8,<2.0a0 @@ -18520,7 +19946,7 @@ packages: - libsqlite >=3.51.2,<4.0a0 - libzlib >=1.3.1,<2.0a0 - ncurses >=6.5,<7.0a0 - - openssl >=3.5.4,<4.0a0 + - openssl >=3.5.5,<4.0a0 - python_abi 3.14.* *_cp314 - readline >=8.3,<9.0a0 - tk >=8.6.13,<8.7.0a0 @@ -18528,8 +19954,8 @@ packages: - zstd >=1.5.7,<1.6.0a0 license: Python-2.0 purls: [] - size: 12570588 - timestamp: 1769459207868 + size: 13553519 + timestamp: 1770271668429 python_site_packages_path: lib/python3.14/site-packages - conda: https://conda.anaconda.org/conda-forge/osx-arm64/python-3.9.23-h7139b31_0_cpython.conda sha256: f0ef9e79987c524b25cb5245770890b568db568ae66edc7fd65ec60bccf3e3df @@ -18618,10 +20044,10 @@ packages: license: Python-2.0 size: 15829087 timestamp: 1769470991307 -- conda: https://conda.anaconda.org/conda-forge/win-64/python-3.13.11-h09917c8_101_cp313.conda - build_number: 101 - sha256: c1960ba5e6a53e18514693839a43442de9c6118180eeb1c97f91c93b8cd7e5de - md5: 8e8704ea154373b4b1837087817b9991 +- conda: https://conda.anaconda.org/conda-forge/win-64/python-3.13.12-h09917c8_100_cp313.conda + build_number: 100 + sha256: da70aec20ff5a5ae18bbba9fdd1e18190b419605cafaafb3bdad8becf11ce94d + md5: 4440c24966d0aa0c8f1e1d5006dac2d6 depends: - bzip2 >=1.0.8,<2.0a0 - libexpat >=2.7.3,<3.0a0 @@ -18630,7 +20056,7 @@ packages: - libmpdec >=4.0.0,<5.0a0 - libsqlite >=3.51.2,<4.0a0 - libzlib >=1.3.1,<2.0a0 - - openssl >=3.5.4,<4.0a0 + - openssl >=3.5.5,<4.0a0 - python_abi 3.13.* *_cp313 - tk >=8.6.13,<8.7.0a0 - tzdata @@ -18638,13 +20064,13 @@ packages: - vc >=14.3,<15 - vc14_runtime >=14.44.35208 license: Python-2.0 - size: 16456375 - timestamp: 1769471259247 + size: 16535316 + timestamp: 1770270322707 python_site_packages_path: Lib/site-packages -- conda: https://conda.anaconda.org/conda-forge/win-64/python-3.14.2-h4b44e0e_101_cp314.conda - build_number: 101 - sha256: e9f1aad2a859cc10e58e1c21e379250064bc8703f07464ccfa6cdd942a29a045 - md5: 9ac6a99d9cf8a463df54747fd08feedc +- conda: https://conda.anaconda.org/conda-forge/win-64/python-3.14.3-h4b44e0e_100_cp314.conda + build_number: 100 + sha256: 048634e52c9c04e60c541e85518ff1cc1f8d0047f6a457186ac023b008cf1374 + md5: c5d8e616403f111858e66540d8348150 depends: - bzip2 >=1.0.8,<2.0a0 - libexpat >=2.7.3,<3.0a0 @@ -18653,7 +20079,7 @@ packages: - libmpdec >=4.0.0,<5.0a0 - libsqlite >=3.51.2,<4.0a0 - libzlib >=1.3.1,<2.0a0 - - openssl >=3.5.4,<4.0a0 + - openssl >=3.5.5,<4.0a0 - python_abi 3.14.* *_cp314 - tk >=8.6.13,<8.7.0a0 - tzdata @@ -18663,8 +20089,8 @@ packages: - zstd >=1.5.7,<1.6.0a0 license: Python-2.0 purls: [] - size: 16629248 - timestamp: 1769457277306 + size: 17810776 + timestamp: 1770272059899 python_site_packages_path: Lib/site-packages - conda: https://conda.anaconda.org/conda-forge/win-64/python-3.9.23-h8c5b53a_0_cpython.conda sha256: 07b9b6dd5e0acee4d967e5263e01b76fae48596b6e0e6fb3733a587b5d0bcea5 @@ -18710,15 +20136,33 @@ packages: license_family: BSD size: 244628 timestamp: 1755304154927 -- conda: https://conda.anaconda.org/conda-forge/noarch/python-gil-3.14.2-h4df99d1_101.conda - sha256: ef9d512824e3d6e1d8d07236795b60b61f13f6f3dafcc93c4d9a87ed058f8928 - md5: 90fd30fc6dd044c0f79c7ef4e7e9fb16 +- conda: https://conda.anaconda.org/conda-forge/noarch/python-gil-3.12.12-hd8ed1ab_2.conda + sha256: 3307c01627ae45524dfbdb149f7801818608c9c49d88ac89632dff32e149057f + md5: d41b6b394546ee6e1c423e28a581fc71 depends: - - cpython 3.14.2.* + - cpython 3.12.12.* + - python_abi * *_cp312 + license: Python-2.0 + size: 46618 + timestamp: 1769471082980 +- conda: https://conda.anaconda.org/conda-forge/noarch/python-gil-3.13.12-h4df99d1_100.conda + sha256: f306304235197434494355351ac56020a65b7c5c56ff10ca1ed53356d575557a + md5: 3d92938d5b83c49162ade038aab58a59 + depends: + - cpython 3.13.12.* + - python_abi * *_cp313 + license: Python-2.0 + size: 48618 + timestamp: 1770270436560 +- conda: https://conda.anaconda.org/conda-forge/noarch/python-gil-3.14.3-h4df99d1_100.conda + sha256: 864362bab43e68c88e60d1efeea1cd6d3874bce3df0da87eb3772492d8acc088 + md5: 90c63d1e08a8bce206e66e95ef249f48 + depends: + - cpython 3.14.3.* - python_abi * *_cp314 license: Python-2.0 - size: 49598 - timestamp: 1769457423645 + size: 49417 + timestamp: 1770271873546 - conda: https://conda.anaconda.org/conda-forge/noarch/python-json-logger-2.0.7-pyhd8ed1ab_0.conda sha256: 4790787fe1f4e8da616edca4acf6a4f8ed4e7c6967aa31b920208fc8f95efcca md5: a61bf9ec79426938ff785eb69dbb1960 @@ -18971,6 +20415,7 @@ packages: - python 3.13.* *_cp313 license: BSD-3-Clause license_family: BSD + purls: [] size: 7002 timestamp: 1752805902938 - conda: https://conda.anaconda.org/conda-forge/noarch/python_abi-3.14-8_cp314.conda @@ -19033,18 +20478,59 @@ packages: license_family: MIT size: 216325 timestamp: 1759557436167 -- conda: https://conda.anaconda.org/conda-forge/noarch/pyyaml-6.0.3-pyh7db6752_0.conda - sha256: 828af2fd7bb66afc9ab1c564c2046be391aaf66c0215f05afaf6d7a9a270fe2a - md5: b12f41c0d7fb5ab81709fcc86579688f +- conda: https://conda.anaconda.org/conda-forge/linux-64/pyyaml-6.0.3-py313h3dea7bd_1.conda + sha256: ef7df29b38ef04ec67a8888a4aa039973eaa377e8c4b59a7be0a1c50cd7e4ac6 + md5: f256753e840c3cd3766488c9437a8f8b depends: - - python >=3.10.* - - yaml - track_features: - - pyyaml_no_compile + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + - python >=3.13,<3.14.0a0 + - python_abi 3.13.* *_cp313 + - yaml >=0.2.5,<0.3.0a0 + license: MIT + license_family: MIT + size: 201616 + timestamp: 1770223543730 +- conda: https://conda.anaconda.org/conda-forge/linux-64/pyyaml-6.0.3-py314h67df5f8_1.conda + sha256: b318fb070c7a1f89980ef124b80a0b5ccf3928143708a85e0053cde0169c699d + md5: 2035f68f96be30dc60a5dfd7452c7941 + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + - python >=3.14,<3.15.0a0 + - python_abi 3.14.* *_cp314 + - yaml >=0.2.5,<0.3.0a0 + license: MIT + license_family: MIT + size: 202391 + timestamp: 1770223462836 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyyaml-6.0.3-py314h6e9b3f0_1.conda + sha256: 95f385f9606e30137cf0b5295f63855fd22223a4cf024d306cf9098ea1c4a252 + md5: dcf51e564317816cb8d546891019b3ab + depends: + - __osx >=11.0 + - python >=3.14,<3.15.0a0 + - python >=3.14,<3.15.0a0 *_cp314 + - python_abi 3.14.* *_cp314 + - yaml >=0.2.5,<0.3.0a0 + license: MIT + license_family: MIT + size: 189475 + timestamp: 1770223788648 +- conda: https://conda.anaconda.org/conda-forge/win-64/pyyaml-6.0.3-py314h2359020_1.conda + sha256: a2aff34027aa810ff36a190b75002d2ff6f9fbef71ec66e567616ac3a679d997 + md5: 0cd9b88826d0f8db142071eb830bce56 + depends: + - python >=3.14,<3.15.0a0 + - python_abi 3.14.* *_cp314 + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + - yaml >=0.2.5,<0.3.0a0 license: MIT license_family: MIT - size: 45223 - timestamp: 1758891992558 + size: 181257 + timestamp: 1770223460931 - conda: https://conda.anaconda.org/conda-forge/linux-64/pyzmq-27.1.0-py312hfb55c3c_0.conda noarch: python sha256: a00a41b66c12d9c60e66b391e9a4832b7e28743348cf4b48b410b91927cd7819 @@ -19066,64 +20552,571 @@ packages: sha256: ef33812c71eccf62ea171906c3e7fc1c8921f31e9cc1fbc3f079f3f074702061 md5: bbd22b0f0454a5972f68a5f200643050 depends: - - python + - python + - __osx >=11.0 + - libcxx >=19 + - _python_abi3_support 1.* + - cpython >=3.12 + - zeromq >=4.3.5,<4.4.0a0 + license: BSD-3-Clause + license_family: BSD + size: 191115 + timestamp: 1757387128258 +- conda: https://conda.anaconda.org/conda-forge/win-64/pyzmq-27.1.0-py312hbb5da91_0.conda + noarch: python + sha256: fd46b30e6a1e4c129045e3174446de3ca90da917a595037d28595532ab915c5d + md5: 808d263ec97bbd93b41ca01552b5fbd4 + depends: + - python + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + - ucrt >=10.0.20348.0 + - zeromq >=4.3.5,<4.3.6.0a0 + - _python_abi3_support 1.* + - cpython >=3.12 + license: BSD-3-Clause + license_family: BSD + size: 185711 + timestamp: 1757387025899 +- conda: https://conda.anaconda.org/conda-forge/linux-64/qhull-2020.2-h434a139_5.conda + sha256: 776363493bad83308ba30bcb88c2552632581b143e8ee25b1982c8c743e73abc + md5: 353823361b1d27eb3960efb076dfcaf6 + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc-ng >=12 + - libstdcxx-ng >=12 + license: LicenseRef-Qhull + purls: [] + size: 552937 + timestamp: 1720813982144 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/qhull-2020.2-h420ef59_5.conda + sha256: 873ac689484262a51fd79bc6103c1a1bedbf524924d7f0088fb80703042805e4 + md5: 6483b1f59526e05d7d894e466b5b6924 + depends: + - __osx >=11.0 + - libcxx >=16 + license: LicenseRef-Qhull + purls: [] + size: 516376 + timestamp: 1720814307311 +- conda: https://conda.anaconda.org/conda-forge/win-64/qhull-2020.2-hc790b64_5.conda + sha256: 887d53486a37bd870da62b8fa2ebe3993f912ad04bd755e7ed7c47ced97cbaa8 + md5: 854fbdff64b572b5c0b470f334d34c11 + depends: + - ucrt >=10.0.20348.0 + - vc >=14.2,<15 + - vc14_runtime >=14.29.30139 + license: LicenseRef-Qhull + purls: [] + size: 1377020 + timestamp: 1720814433486 +- conda: https://conda.anaconda.org/conda-forge/linux-64/r-base-4.5.2-h835929b_3.conda + sha256: f962637b3c9f4ca6d92491518c2e9f8f3189e7217e33787f86402ec91cc73de9 + md5: 788ae9c821c368f7356d91298d486d2f + depends: + - __glibc >=2.17,<3.0.a0 + - _openmp_mutex >=4.5 + - _r-mutex 1.* anacondar_1 + - bwidget + - bzip2 >=1.0.8,<2.0a0 + - cairo >=1.18.4,<2.0a0 + - curl + - gcc_impl_linux-64 >=10 + - gfortran_impl_linux-64 + - gsl >=2.7,<2.8.0a0 + - gxx_impl_linux-64 >=10 + - icu >=75.1,<76.0a0 + - libblas >=3.9.0,<4.0a0 + - libcurl >=8.17.0,<9.0a0 + - libdeflate >=1.25,<1.26.0a0 + - libexpat >=2.7.3,<3.0a0 + - libgcc + - libgcc-ng >=12 + - libgfortran + - libgfortran-ng + - libgfortran5 >=10.4.0 + - libglib >=2.86.3,<3.0a0 + - libiconv >=1.18,<2.0a0 + - libjpeg-turbo >=3.1.2,<4.0a0 + - liblapack >=3.9.0,<4.0a0 + - liblzma >=5.8.1,<6.0a0 + - libpng >=1.6.53,<1.7.0a0 + - libstdcxx + - libstdcxx-ng >=12 + - libtiff >=4.7.1,<4.8.0a0 + - libuuid >=2.41.3,<3.0a0 + - libzlib >=1.3.1,<2.0a0 + - make + - pango >=1.56.4,<2.0a0 + - pcre2 >=10.47,<10.48.0a0 + - readline >=8.3,<9.0a0 + - sed + - tk >=8.6.13,<8.7.0a0 + - tktable + - tzdata >=2024a + - xorg-libxt + license: GPL-2.0-or-later + license_family: GPL + purls: [] + size: 27346624 + timestamp: 1766427117838 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/r-base-4.5.2-hb2e0d2d_3.conda + sha256: ca5ab00f68d37c6ff73a47f85b0ec18a4ca6738b619bda070a408a979c4ac578 + md5: 65c53fabca7c0000d7798704c0c2f391 + depends: + - __osx >=11.0 + - _r-mutex 1.* anacondar_1 + - bwidget + - bzip2 >=1.0.8,<2.0a0 + - cairo >=1.18.4,<2.0a0 + - clang_osx-arm64 >=19 + - clangxx_osx-arm64 >=19 + - curl + - fontconfig >=2.15.0,<3.0a0 + - fonts-conda-ecosystem + - gfortran_osx-arm64 14.* + - gsl >=2.7,<2.8.0a0 + - icu >=75.1,<76.0a0 + - libasprintf >=0.25.1,<1.0a0 + - libblas >=3.9.0,<4.0a0 + - libcurl >=8.17.0,<9.0a0 + - libcxx >=19 + - libdeflate >=1.25,<1.26.0a0 + - libexpat >=2.7.3,<3.0a0 + - libgettextpo >=0.25.1,<1.0a0 + - libgfortran + - libgfortran5 >=14.3.0 + - libglib >=2.86.3,<3.0a0 + - libiconv >=1.18,<2.0a0 + - libintl >=0.25.1,<1.0a0 + - libjpeg-turbo >=3.1.2,<4.0a0 + - liblapack >=3.9.0,<4.0a0 + - liblzma >=5.8.1,<6.0a0 + - libpng >=1.6.53,<1.7.0a0 + - libtiff >=4.7.1,<4.8.0a0 + - libzlib >=1.3.1,<2.0a0 + - llvm-openmp >=19.1.7 + - make + - pango >=1.56.4,<2.0a0 + - pcre2 >=10.47,<10.48.0a0 + - readline >=8.3,<9.0a0 + - tk >=8.6.13,<8.7.0a0 + - tktable + - tzdata >=2024a + license: GPL-2.0-or-later + license_family: GPL + purls: [] + size: 27869158 + timestamp: 1766428131524 +- conda: https://conda.anaconda.org/conda-forge/win-64/r-base-4.4.3-h347933c_7.conda + sha256: bc0bdee60ffb9db55459c34f349079819807b5e30fe124f1cbf94e28d59adbb8 + md5: ca2e71a5361ca7073b7275fe51d0f4a3 + depends: + - _r-mutex 1.* anacondar_1 + - bwidget + - bzip2 >=1.0.8,<2.0a0 + - cairo >=1.18.4,<2.0a0 + - curl + - fontconfig >=2.15.0,<3.0a0 + - fonts-conda-ecosystem + - gcc_impl_win-64 >=13 + - gfortran_impl_win-64 + - gsl >=2.7,<2.8.0a0 + - gxx_impl_win-64 >=13 + - icu >=75.1,<76.0a0 + - libblas >=3.9.0,<4.0a0 + - libcurl >=8.17.0,<9.0a0 + - libdeflate >=1.25,<1.26.0a0 + - libgcc >=13 + - libgfortran + - libgfortran5 >=13.4.0 + - libiconv >=1.18,<2.0a0 + - libjpeg-turbo >=3.1.2,<4.0a0 + - liblapack >=3.9.0,<4.0a0 + - liblzma >=5.8.1,<6.0a0 + - libpng >=1.6.53,<1.7.0a0 + - libstdcxx >=13 + - libtiff >=4.7.1,<4.8.0a0 + - libwinpthread >=12.0.0.r4.gg4f2fc60ca + - libzlib >=1.3.1,<2.0a0 + - pcre2 >=10.47,<10.48.0a0 + - tk >=8.6.13,<8.7.0a0 + - tktable + - tzdata >=2024a + - ucrt >=10.0.20348.0 + license: GPL-2.0-or-later + license_family: GPL + purls: [] + size: 39321417 + timestamp: 1766427185682 +- conda: https://conda.anaconda.org/conda-forge/noarch/r-codetools-0.2_20-r44hc72bb7e_2.conda + sha256: 3cf1c320913ddae7bd91ba770535a9d4765862a9fb1bac419a4ea41b8c39d693 + md5: d81c629f78e571b14f9a617253c44552 + depends: + - r-base >=4.4,<4.5.0a0 + license: GPL-2.0-or-later + license_family: GPL + purls: [] + size: 109324 + timestamp: 1757452124857 +- conda: https://conda.anaconda.org/conda-forge/noarch/r-codetools-0.2_20-r45hc72bb7e_2.conda + sha256: aec327dd836824278a2adf006f426a58d834828de74de7f8348f2f5f068de702 + md5: 8e9e5b14f74a6040c77e0b9c8bfa84ca + depends: + - r-base >=4.5,<4.6.0a0 + license: GPL-2.0-or-later + license_family: GPL + purls: [] + size: 109200 + timestamp: 1757452164030 +- conda: https://conda.anaconda.org/conda-forge/noarch/r-foreach-1.5.2-r44hc72bb7e_4.conda + sha256: 125ba846cceaf85ff9a50ac41cb89d78fd1680dc4dde84812667c7f26eec2ff8 + md5: 0740e45e44a5a36ef5c453249820bd94 + depends: + - r-base >=4.4,<4.5.0a0 + - r-codetools + - r-iterators + license: Apache-2.0 + license_family: APACHE + purls: [] + size: 140625 + timestamp: 1757490405216 +- conda: https://conda.anaconda.org/conda-forge/noarch/r-foreach-1.5.2-r45hc72bb7e_4.conda + sha256: c95d1e61946bf81128be213dea7a07b5196c6e13caf9c6452c38145da8d2dfb1 + md5: 5abe392c8f8c5b954ebdc5fe46fcc709 + depends: + - r-base >=4.5,<4.6.0a0 + - r-codetools + - r-iterators + license: Apache-2.0 + license_family: APACHE + purls: [] + size: 140909 + timestamp: 1757490449004 +- conda: https://conda.anaconda.org/conda-forge/linux-64/r-glmnet-4.1_10-r45ha36cffa_1.conda + sha256: 4971136fbcec60676d1671d513e88dffe495ff473868afd65611ad52c30f2dbf + md5: 3b87325916cb2f097eae73f531ff0ec1 + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + - libgfortran + - libgfortran5 >=14.3.0 + - libstdcxx >=14 + - r-base >=4.5,<4.6.0a0 + - r-foreach + - r-matrix >=1.0_6 + - r-rcpp + - r-rcppeigen + - r-shape + - r-survival + license: GPL-2.0-only + license_family: GPL2 + purls: [] + size: 1981308 + timestamp: 1757514921805 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/r-glmnet-4.1_10-r45hbf3f414_1.conda + sha256: 01b74bd8245b0dd082c316c86fd8f22705b173a27eac6974b1fbf37b8fde7c27 + md5: cf53395edeba492b33d56e9f7cb018fd + depends: + - __osx >=11.0 + - libcxx >=19 + - libgfortran + - libgfortran5 >=14.3.0 + - libgfortran5 >=15.1.0 + - r-base >=4.5,<4.6.0a0 + - r-foreach + - r-matrix >=1.0_6 + - r-rcpp + - r-rcppeigen + - r-shape + - r-survival + license: GPL-2.0-only + license_family: GPL2 + purls: [] + size: 1905001 + timestamp: 1757515577856 +- conda: https://conda.anaconda.org/conda-forge/win-64/r-glmnet-4.1_10-r44hf64fc22_1.conda + sha256: a2ac95e66ad523b8106a0e6b40e97ac455b82e2753368d22a49d78c854b2b70b + md5: 893e6f05dc18ccd96a22a27ee574b810 + depends: + - libgcc >=14 + - libgfortran + - libgfortran5 >=14.3.0 + - libstdcxx >=14 + - libwinpthread >=12.0.0.r4.gg4f2fc60ca + - r-base >=4.4,<4.5.0a0 + - r-foreach + - r-matrix >=1.0_6 + - r-rcpp + - r-rcppeigen + - r-shape + - r-survival + - ucrt >=10.0.20348.0 + license: GPL-2.0-only + license_family: GPL2 + purls: [] + size: 1922420 + timestamp: 1757515196130 +- conda: https://conda.anaconda.org/conda-forge/noarch/r-iterators-1.0.14-r44hc72bb7e_4.conda + sha256: a02d27a592863a3bf3229696a8bef2dae2caa862a901f820d994ee4675b8bc88 + md5: 28b5c21aae2a093ffbf601f40e7cfc0e + depends: + - r-base >=4.4,<4.5.0a0 + license: Apache-2.0 + license_family: APACHE + purls: [] + size: 350472 + timestamp: 1757459827397 +- conda: https://conda.anaconda.org/conda-forge/noarch/r-iterators-1.0.14-r45hc72bb7e_4.conda + sha256: c90d0faa668d2753db9da9458ca085891a3030c6537f5675fe0c1a1b5af2103c + md5: 7746a41a4cb97cec59db2d5a2cac0701 + depends: + - r-base >=4.5,<4.6.0a0 + license: Apache-2.0 + license_family: APACHE + purls: [] + size: 350171 + timestamp: 1757459846270 +- conda: https://conda.anaconda.org/conda-forge/linux-64/r-lattice-0.22_7-r45h54b55ab_1.conda + sha256: a3aa7269a4c992756550ba931f21af1c83ffa8b4a3eeb56c02613b04918f270a + md5: ecad721ffa28700cb4318bcffc13b812 + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + - r-base >=4.5,<4.6.0a0 + license: GPL-2.0-or-later + license_family: GPL3 + purls: [] + size: 1382158 + timestamp: 1757424554794 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/r-lattice-0.22_7-r45h6168396_1.conda + sha256: 7a8d08d153c2f701f587b74167d96cb8c7117b50e66e0ad4f68cb28b004836a0 + md5: 6d1c518304c069c4180946b0a65cceab + depends: + - __osx >=11.0 + - r-base >=4.5,<4.6.0a0 + license: GPL-2.0-or-later + license_family: GPL3 + purls: [] + size: 1381592 + timestamp: 1757424990314 +- conda: https://conda.anaconda.org/conda-forge/win-64/r-lattice-0.22_7-r44heceb674_1.conda + sha256: 52aaacb15251892296d03e1266266cec476aa12caf90c143babe992845c67c11 + md5: 0ba8e127f4bf53dfc155543b580a89e4 + depends: + - libgcc >=14 + - libwinpthread >=12.0.0.r4.gg4f2fc60ca + - r-base >=4.4,<4.5.0a0 + - ucrt >=10.0.20348.0 + license: GPL-2.0-or-later + license_family: GPL3 + purls: [] + size: 1383657 + timestamp: 1757424731019 +- conda: https://conda.anaconda.org/conda-forge/linux-64/r-matrix-1.7_4-r45h0e4624f_1.conda + sha256: b15710324f7e365ac662fc0017b743d6f22b7a2ba989e7c1c83760cd3b603525 + md5: c6bafb33c4ace44ef33c18e543befe41 + depends: + - __glibc >=2.17,<3.0.a0 + - libblas >=3.9.0,<4.0a0 + - libgcc >=14 + - liblapack >=3.9.0,<4.0a0 + - r-base >=4.5,<4.6.0a0 + - r-lattice + license: GPL-2.0-or-later + license_family: GPL3 + purls: [] + size: 4259495 + timestamp: 1757441312520 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/r-matrix-1.7_4-r45hb2d3ebe_1.conda + sha256: 7dbf4c4a8a0e21a2fdd70dbaad9d637c80405557cdfafcd1761fcee7ca0a5ee6 + md5: 539e3ece3686e72355cc5642cd352974 + depends: + - __osx >=11.0 + - libblas >=3.9.0,<4.0a0 + - libgfortran + - libgfortran5 >=14.3.0 + - libgfortran5 >=15.1.0 + - liblapack >=3.9.0,<4.0a0 + - r-base >=4.5,<4.6.0a0 + - r-lattice + license: GPL-2.0-or-later + license_family: GPL3 + purls: [] + size: 4085491 + timestamp: 1757442359230 +- conda: https://conda.anaconda.org/conda-forge/win-64/r-matrix-1.7_4-r44h5ea86f4_1.conda + sha256: 9c2850843bd2993f510f90c06d41b6096119e59b2afa92518d62f75c844d14e2 + md5: 2f93038af39ffed26986a82dc794a958 + depends: + - libblas >=3.9.0,<4.0a0 + - libgcc >=14 + - liblapack >=3.9.0,<4.0a0 + - libwinpthread >=12.0.0.r4.gg4f2fc60ca + - r-base >=4.4,<4.5.0a0 + - r-lattice + - ucrt >=10.0.20348.0 + license: GPL-2.0-or-later + license_family: GPL3 + purls: [] + size: 4193190 + timestamp: 1757441780809 +- conda: https://conda.anaconda.org/conda-forge/linux-64/r-rcpp-1.1.1-r45h3697838_0.conda + sha256: a92cb7db892c5c195c039d478d66912528d575ba2e742ba9de9ed6242d3891ee + md5: 6903480a85621c864d8d448c283b5294 + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + - libstdcxx >=14 + - r-base >=4.5,<4.6.0a0 + license: GPL-2.0-or-later + license_family: GPL2 + purls: [] + size: 2110489 + timestamp: 1768070822548 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/r-rcpp-1.1.1-r45h1380947_0.conda + sha256: 2cdb03c696bbd2df85d198b56cb446af5922fd8e7a759d71e59e314683120603 + md5: e721f64ff162a3cc4f8b6d279047d4a4 + depends: + - __osx >=11.0 + - libcxx >=19 + - r-base >=4.5,<4.6.0a0 + license: GPL-2.0-or-later + license_family: GPL2 + purls: [] + size: 2122327 + timestamp: 1768071215181 +- conda: https://conda.anaconda.org/conda-forge/win-64/r-rcpp-1.1.1-r44hd8a2815_0.conda + sha256: fd63c53d4a427542d026ef3ef22bceea818d953d2ea4ef6f39d68559fa856064 + md5: b670e30794a7017f0450b154a34fa205 + depends: + - libgcc >=14 + - libstdcxx >=14 + - libwinpthread >=12.0.0.r4.gg4f2fc60ca + - r-base >=4.4,<4.5.0a0 + - ucrt >=10.0.20348.0 + license: GPL-2.0-or-later + license_family: GPL2 + purls: [] + size: 2106262 + timestamp: 1768070990886 +- conda: https://conda.anaconda.org/conda-forge/linux-64/r-rcppeigen-0.3.4.0.2-r45h3704496_1.conda + sha256: fd1cc8ec804fede96bbd07867c58fd15c9fdb3fbae800ba0eb7b2779910734dc + md5: 743927aa75562d9a53b8fda4777bdf93 + depends: + - __glibc >=2.17,<3.0.a0 + - libblas >=3.9.0,<4.0a0 + - libgcc >=14 + - liblapack >=3.9.0,<4.0a0 + - libstdcxx >=14 + - r-base >=4.5,<4.6.0a0 + - r-matrix >=1.1_0 + - r-rcpp >=0.11.0 + license: GPL-2.0-or-later + license_family: GPL2 + purls: [] + size: 1496128 + timestamp: 1757496030112 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/r-rcppeigen-0.3.4.0.2-r45hd057375_1.conda + sha256: 7277d7adc0e17e3d5d823cacabf3f754b5787309284f31add665384be19f6233 + md5: e75aff545233472b7e1f1249f952b664 + depends: - __osx >=11.0 + - libblas >=3.9.0,<4.0a0 - libcxx >=19 - - _python_abi3_support 1.* - - cpython >=3.12 - - zeromq >=4.3.5,<4.4.0a0 - license: BSD-3-Clause - license_family: BSD - size: 191115 - timestamp: 1757387128258 -- conda: https://conda.anaconda.org/conda-forge/win-64/pyzmq-27.1.0-py312hbb5da91_0.conda - noarch: python - sha256: fd46b30e6a1e4c129045e3174446de3ca90da917a595037d28595532ab915c5d - md5: 808d263ec97bbd93b41ca01552b5fbd4 + - libgfortran + - libgfortran5 >=14.3.0 + - libgfortran5 >=15.1.0 + - liblapack >=3.9.0,<4.0a0 + - r-base >=4.5,<4.6.0a0 + - r-matrix >=1.1_0 + - r-rcpp >=0.11.0 + license: GPL-2.0-or-later + license_family: GPL2 + purls: [] + size: 1476319 + timestamp: 1757496293279 +- conda: https://conda.anaconda.org/conda-forge/win-64/r-rcppeigen-0.3.4.0.2-r44hac2c72c_1.conda + sha256: c61ff37f34ae8ab6313d9cb48dace213a61beeb2fcff899fe250ca95aaba8532 + md5: 35e97a2c8dab048e201021c6ad37d547 depends: - - python - - vc >=14.3,<15 - - vc14_runtime >=14.44.35208 - - ucrt >=10.0.20348.0 - - vc >=14.3,<15 - - vc14_runtime >=14.44.35208 + - libblas >=3.9.0,<4.0a0 + - libgcc >=14 + - liblapack >=3.9.0,<4.0a0 + - libstdcxx >=14 + - libwinpthread >=12.0.0.r4.gg4f2fc60ca + - r-base >=4.4,<4.5.0a0 + - r-matrix >=1.1_0 + - r-rcpp >=0.11.0 - ucrt >=10.0.20348.0 - - zeromq >=4.3.5,<4.3.6.0a0 - - _python_abi3_support 1.* - - cpython >=3.12 - license: BSD-3-Clause - license_family: BSD - size: 185711 - timestamp: 1757387025899 -- conda: https://conda.anaconda.org/conda-forge/linux-64/qhull-2020.2-h434a139_5.conda - sha256: 776363493bad83308ba30bcb88c2552632581b143e8ee25b1982c8c743e73abc - md5: 353823361b1d27eb3960efb076dfcaf6 + license: GPL-2.0-or-later + license_family: GPL2 + purls: [] + size: 1488051 + timestamp: 1757496391587 +- conda: https://conda.anaconda.org/conda-forge/noarch/r-shape-1.4.6.1-r44ha770c72_2.conda + sha256: 582816eb5c8035ce708a075e66bb083550964beee12acaab85b84cfd69e66173 + md5: 053e7028323d6662b1e2c8ab23aa3ea2 + depends: + - r-base >=4.4,<4.5.0a0 + license: GPL (>= 3) + license_family: GPL3 + purls: [] + size: 766125 + timestamp: 1757463761090 +- conda: https://conda.anaconda.org/conda-forge/noarch/r-shape-1.4.6.1-r45ha770c72_2.conda + sha256: 57516dfc8e57d4cc41d97ee56f92c022120508896228c65f75f1c2cb00a22568 + md5: 9f062131995e818b7bcf8fe65d3f4424 + depends: + - r-base >=4.5,<4.6.0a0 + license: GPL (>= 3) + license_family: GPL3 + purls: [] + size: 765739 + timestamp: 1757463722297 +- conda: https://conda.anaconda.org/conda-forge/linux-64/r-survival-3.8_6-r45h54b55ab_0.conda + sha256: 66b6d9694f54e8af86b394dbdd65a7164af13869be05babb5e4e22d44eb04106 + md5: 55cc543da938b44c6b2106516815ac35 depends: - __glibc >=2.17,<3.0.a0 - - libgcc-ng >=12 - - libstdcxx-ng >=12 - license: LicenseRef-Qhull - size: 552937 - timestamp: 1720813982144 -- conda: https://conda.anaconda.org/conda-forge/osx-arm64/qhull-2020.2-h420ef59_5.conda - sha256: 873ac689484262a51fd79bc6103c1a1bedbf524924d7f0088fb80703042805e4 - md5: 6483b1f59526e05d7d894e466b5b6924 + - libgcc >=14 + - r-base >=4.5,<4.6.0a0 + - r-matrix + license: LGPL-2.0-or-later + license_family: LGPL + purls: [] + size: 8329223 + timestamp: 1768637162736 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/r-survival-3.8_6-r45hbe92478_0.conda + sha256: 561275d89c93cbe6ef21dc7e624ba7a67381fd72f5c4dea05e9f18f7a9e14663 + md5: 223c8c34cf4302952807be4018c3de8c depends: - __osx >=11.0 - - libcxx >=16 - license: LicenseRef-Qhull - size: 516376 - timestamp: 1720814307311 -- conda: https://conda.anaconda.org/conda-forge/win-64/qhull-2020.2-hc790b64_5.conda - sha256: 887d53486a37bd870da62b8fa2ebe3993f912ad04bd755e7ed7c47ced97cbaa8 - md5: 854fbdff64b572b5c0b470f334d34c11 + - r-base >=4.5,<4.6.0a0 + - r-matrix + license: LGPL-2.0-or-later + license_family: LGPL + purls: [] + size: 8306992 + timestamp: 1768637610295 +- conda: https://conda.anaconda.org/conda-forge/win-64/r-survival-3.8_6-r44h2a2a84f_0.conda + sha256: 24a469a8a2c2c94fc0150d7654b79e1ebd939e9d9787694f68ae6a69415dce4c + md5: 28ee677e26d72634610d6917285f49a2 depends: + - libgcc >=14 + - r-base >=4.4,<4.5.0a0 + - r-matrix - ucrt >=10.0.20348.0 - - vc >=14.2,<15 - - vc14_runtime >=14.29.30139 - license: LicenseRef-Qhull - size: 1377020 - timestamp: 1720814433486 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + license: LGPL-2.0-or-later + license_family: LGPL + purls: [] + size: 8316428 + timestamp: 1768637338029 - conda: https://conda.anaconda.org/conda-forge/linux-64/re2-2025.11.05-h5301d42_0.conda sha256: 2f225ddf4a274743045aded48053af65c31721e797a45beed6774fdc783febfb md5: 0227d04521bc3d28c7995c7e1f99a721 @@ -19248,6 +21241,21 @@ packages: license_family: MIT size: 22913 timestamp: 1752876729969 +- conda: https://conda.anaconda.org/conda-forge/noarch/rich-14.3.2-pyhcf101f3_0.conda + sha256: ed17985cec5a0540002c6cabe67848f7cc17e5f4019c0e2a40534e9b7c0b38de + md5: 33950a076fd589a7655c6888cc3d2b34 + depends: + - markdown-it-py >=2.2.0 + - pygments >=2.13.0,<3.0.0 + - python >=3.10 + - typing_extensions >=4.0.0,<5.0.0 + - python + license: MIT + license_family: MIT + purls: + - pkg:pypi/rich?source=compressed-mapping + size: 208269 + timestamp: 1769971520792 - conda: https://conda.anaconda.org/conda-forge/noarch/roman-numerals-4.1.0-pyhd8ed1ab_0.conda sha256: 30f3c04fcfb64c44d821d392a4a0b8915650dbd900c8befc20ade8fde8ec6aa2 md5: 0dc48b4b570931adc8641e55c6c17fe4 @@ -19265,20 +21273,20 @@ packages: license: 0BSD OR CC0-1.0 size: 11074 timestamp: 1766025162370 -- conda: https://conda.anaconda.org/conda-forge/linux-64/rpds-py-0.30.0-py314h2e6c369_0.conda - sha256: e53b0cbf3b324eaa03ca1fe1a688fdf4ab42cea9c25270b0a7307d8aaaa4f446 - md5: c1c368b5437b0d1a68f372ccf01cb133 +- conda: https://conda.anaconda.org/conda-forge/linux-64/rpds-py-0.30.0-py313h843e2db_0.conda + sha256: 076d26e51c62c8ecfca6eb19e3c1febdd7632df1990a7aa53da5df5e54482b1c + md5: 779e3307a0299518713765b83a36f4b1 depends: - python - libgcc >=14 - __glibc >=2.17,<3.0.a0 - - python_abi 3.14.* *_cp314 + - python_abi 3.13.* *_cp313 constrains: - __glibc >=2.17 license: MIT license_family: MIT - size: 376121 - timestamp: 1764543122774 + size: 383230 + timestamp: 1764543223529 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/rpds-py-0.30.0-py314haad56a0_0.conda sha256: e161dd97403b8b8a083d047369a5cf854557dba1204d29e2f0250f5ac4403925 md5: 76a4f88d1b7748c477abf3c341edc64c @@ -19306,6 +21314,74 @@ packages: license_family: MIT size: 235780 timestamp: 1764543046065 +- conda: https://conda.anaconda.org/conda-forge/linux-64/rpy2-3.6.4-py313r45h67dc6d7_1.conda + sha256: 71005bb466f785d018c70626f18da20eb2c9e2a07c17437e58b98fc89da6144c + md5: fbe04f46e657418b827228ce8453ef58 + depends: + - __glibc >=2.17,<3.0.a0 + - cffi >=1.15.1 + - jinja2 + - libgcc >=14 + - liblzma >=5.8.1,<6.0a0 + - libzlib >=1.3.1,<2.0a0 + - packaging + - python >=3.13,<3.14.0a0 + - python_abi 3.13.* *_cp313 + - r-base >=4.5,<4.6.0a0 + - tzlocal + license: GPL-2.0-or-later + license_family: GPL + purls: + - pkg:pypi/rpy2?source=hash-mapping + - pkg:pypi/rpy2-rinterface?source=hash-mapping + - pkg:pypi/rpy2-robjects?source=hash-mapping + size: 1852475 + timestamp: 1763422365515 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/rpy2-3.6.4-py314r45h77b1809_1.conda + sha256: 6a131ae2864569e532fd5ad805b2adfca6d93f292b7150b3c513e0593d4fb33c + md5: 9d813e559dd7d4e1f1061ba8bd8d4181 + depends: + - __osx >=11.0 + - cffi >=1.15.1 + - jinja2 + - liblzma >=5.8.1,<6.0a0 + - libzlib >=1.3.1,<2.0a0 + - packaging + - python >=3.14,<3.15.0a0 + - python >=3.14,<3.15.0a0 *_cp314 + - python_abi 3.14.* *_cp314 + - r-base >=4.5,<4.6.0a0 + - tzlocal + license: GPL-2.0-or-later + license_family: GPL + purls: + - pkg:pypi/rpy2?source=hash-mapping + - pkg:pypi/rpy2-rinterface?source=hash-mapping + - pkg:pypi/rpy2-robjects?source=hash-mapping + size: 1892834 + timestamp: 1763422871997 +- conda: https://conda.anaconda.org/conda-forge/win-64/rpy2-3.6.4-py314r44h83472a1_1.conda + sha256: 2590c4babfc50ba04d0837749e3805afeebd047176f8e0ee6d646c74299caf2f + md5: 4528e3ff68056d2329062f73ee36be3b + depends: + - cffi >=1.15.1 + - jinja2 + - libgcc >=15 + - libwinpthread >=12.0.0.r4.gg4f2fc60ca + - packaging + - python >=3.14.0rc3,<3.15.0a0 + - python_abi 3.14.* *_cp314 + - r-base >=4.4,<4.5.0a0 + - tzlocal + - ucrt >=10.0.20348.0 + license: GPL-2.0-or-later + license_family: GPL + purls: + - pkg:pypi/rpy2?source=hash-mapping + - pkg:pypi/rpy2-rinterface?source=hash-mapping + - pkg:pypi/rpy2-robjects?source=hash-mapping + size: 1907399 + timestamp: 1759324987295 - conda: https://conda.anaconda.org/conda-forge/noarch/ruamel.yaml-0.19.1-pyhcf101f3_0.conda sha256: b48bebe297a63ae60f52e50be328262e880702db4d9b4e86731473ada459c2a1 md5: 06ad944772941d5dae1e0d09848d8e49 @@ -19315,8 +21391,24 @@ packages: - python license: MIT license_family: MIT + purls: + - pkg:pypi/ruamel-yaml?source=hash-mapping size: 98448 timestamp: 1767538149184 +- conda: https://conda.anaconda.org/conda-forge/linux-64/ruamel.yaml.clib-0.2.15-py313h54dd161_1.conda + sha256: e7655f12e29add10ef6842ca7e06167fc326903f32b0a9e62f464afda4e0d3d1 + md5: ef8c7c9f4ea478806d9056bbc9c9c093 + depends: + - python + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + - python_abi 3.13.* *_cp313 + license: MIT + license_family: MIT + purls: + - pkg:pypi/ruamel-yaml-clib?source=hash-mapping + size: 149946 + timestamp: 1766159512977 - conda: https://conda.anaconda.org/conda-forge/linux-64/ruamel.yaml.clib-0.2.15-py314h0f05182_1.conda sha256: 3bd8db7556e87c98933a47ff9f962af7b8e0dc3757a72180b27cbfcb1f98d2d9 md5: 4f35ae1228a6c5d9df593367ffe8dda1 @@ -19339,6 +21431,8 @@ packages: - python_abi 3.14.* *_cp314 license: MIT license_family: MIT + purls: + - pkg:pypi/ruamel-yaml-clib?source=hash-mapping size: 133016 timestamp: 1766159585543 - conda: https://conda.anaconda.org/conda-forge/win-64/ruamel.yaml.clib-0.2.15-py314hc5dbbe4_1.conda @@ -19352,12 +21446,14 @@ packages: - python_abi 3.14.* *_cp314 license: MIT license_family: MIT + purls: + - pkg:pypi/ruamel-yaml-clib?source=hash-mapping size: 105668 timestamp: 1766159584330 -- conda: https://conda.anaconda.org/conda-forge/linux-64/ruff-0.14.14-h40fa522_1.conda +- conda: https://conda.anaconda.org/conda-forge/linux-64/ruff-0.15.0-h40fa522_0.conda noarch: python - sha256: 0c6c9825ff88195fd13936d63872213d6c88c1fe795d136881c0753c3037c5ff - md5: d3e1d08b141529c7fce6a13b4d670605 + sha256: fc456645570586c798d2da12fe723b38ea0d0901373fd9959cab914cbb19518b + md5: fe90be2abf12b301dde984719a02ca0b depends: - python - __glibc >=2.17,<3.0.a0 @@ -19366,12 +21462,12 @@ packages: - __glibc >=2.17 license: MIT license_family: MIT - size: 9131490 - timestamp: 1769520999080 -- conda: https://conda.anaconda.org/conda-forge/osx-arm64/ruff-0.14.14-h279115b_1.conda + size: 9103793 + timestamp: 1770153712370 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/ruff-0.15.0-h279115b_0.conda noarch: python - sha256: 20f93b70375e6ad43ec507611cf28814277be17a2794a2a94e2df13a0b34f8d3 - md5: bcc5ef166c4de9a225c9ca9cb4fa631e + sha256: d0d55cd450f7e66b98aec49bd76e7476badeed78563988003766d4dd5c4850fa + md5: 67e036614accdbee477daac1ba2441b9 depends: - python - __osx >=11.0 @@ -19379,12 +21475,12 @@ packages: - __osx >=11.0 license: MIT license_family: MIT - size: 8337249 - timestamp: 1769521105071 -- conda: https://conda.anaconda.org/conda-forge/win-64/ruff-0.14.14-h213852a_1.conda + size: 8383076 + timestamp: 1770153856208 +- conda: https://conda.anaconda.org/conda-forge/win-64/ruff-0.15.0-h213852a_0.conda noarch: python - sha256: d9b08d86503e400b7ad52f806e410ce8b52b4f2c0835b9d61a4515515544fe83 - md5: 5a805587f5194e8f3ef78941e5a71732 + sha256: 2a35ebac465ee4d278cb7ef9dd45672927652d64924bf59dc6044e98951ac3b5 + md5: 5a017ed8ef2bfb6e69cbf5a3e7eba820 depends: - python - vc >=14.3,<15 @@ -19392,8 +21488,8 @@ packages: - ucrt >=10.0.20348.0 license: MIT license_family: MIT - size: 9568276 - timestamp: 1769521017574 + size: 9623640 + timestamp: 1770153731442 - conda: https://conda.anaconda.org/conda-forge/linux-64/s2n-1.6.2-he8a4886_1.conda sha256: dec76e9faa3173579d34d226dbc91892417a80784911daf8e3f0eb9bad19d7a6 md5: bade189a194e66b93c03021bd36c337b @@ -19515,6 +21611,8 @@ packages: - numpy >=1.23,<3 license: BSD-3-Clause license_family: BSD + purls: + - pkg:pypi/scikit-learn?source=hash-mapping size: 9897583 timestamp: 1765801239271 - conda: https://conda.anaconda.org/conda-forge/linux-64/scikit-learn-1.8.0-np2py314hf09ca88_1.conda @@ -19534,8 +21632,6 @@ packages: - numpy >=1.23,<3 license: BSD-3-Clause license_family: BSD - purls: - - pkg:pypi/scikit-learn?source=hash-mapping size: 9992698 timestamp: 1765801260253 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/scikit-learn-0.24.2-py39h12ba089_1.tar.bz2 @@ -19898,6 +21994,8 @@ packages: - python_abi 3.13.* *_cp313 license: BSD-3-Clause license_family: BSD + purls: + - pkg:pypi/scipy?source=hash-mapping size: 16857028 timestamp: 1768801011489 - conda: https://conda.anaconda.org/conda-forge/linux-64/scipy-1.17.0-py314hf07bd8e_1.conda @@ -19919,8 +22017,6 @@ packages: - python_abi 3.14.* *_cp314 license: BSD-3-Clause license_family: BSD - purls: - - pkg:pypi/scipy?source=hash-mapping size: 17048277 timestamp: 1768800950735 - conda: https://conda.anaconda.org/conda-forge/linux-64/scipy-1.7.3-py39hddc5342_1.tar.bz2 @@ -20237,6 +22333,45 @@ packages: purls: [] size: 4931 timestamp: 1768922945029 +- pypi: https://files.pythonhosted.org/packages/83/11/00d3c3dfc25ad54e731d91449895a79e4bf2384dc3ac01809010ba88f6d5/seaborn-0.13.2-py3-none-any.whl + name: seaborn + version: 0.13.2 + sha256: 636f8336facf092165e27924f223d3c62ca560b1f2bb5dff7ab7fad265361987 + requires_dist: + - numpy>=1.20,!=1.24.0 + - pandas>=1.2 + - matplotlib>=3.4,!=3.6.1 + - pytest ; extra == 'dev' + - pytest-cov ; extra == 'dev' + - pytest-xdist ; extra == 'dev' + - flake8 ; extra == 'dev' + - mypy ; extra == 'dev' + - pandas-stubs ; extra == 'dev' + - pre-commit ; extra == 'dev' + - flit ; extra == 'dev' + - numpydoc ; extra == 'docs' + - nbconvert ; extra == 'docs' + - ipykernel ; extra == 'docs' + - sphinx<6.0.0 ; extra == 'docs' + - sphinx-copybutton ; extra == 'docs' + - sphinx-issues ; extra == 'docs' + - sphinx-design ; extra == 'docs' + - pyyaml ; extra == 'docs' + - pydata-sphinx-theme==0.10.0rc2 ; extra == 'docs' + - scipy>=1.7 ; extra == 'stats' + - statsmodels>=0.12 ; extra == 'stats' + requires_python: '>=3.8' +- conda: https://conda.anaconda.org/conda-forge/linux-64/sed-4.9-h6688a6e_0.conda + sha256: ee826aa0c6157d4a947722b1205964482ff8e88136bd3161864f8cefdca85b5b + md5: 171afc5f7ca0408bbccbcb69ade85f92 + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=13 + license: GPL-3.0-only + license_family: GPL + purls: [] + size: 228948 + timestamp: 1746562045847 - conda: https://conda.anaconda.org/conda-forge/noarch/send2trash-2.1.0-pyh5552912_0.conda sha256: 6b1a863b2a3e106e573a6efce2303963c3adc2764dfdbf08c4a35dbe62604988 md5: 297e2901b530c5d321c563e66a65db99 @@ -20322,20 +22457,20 @@ packages: - pkg:pypi/setuptools-scm?source=hash-mapping size: 52539 timestamp: 1760965125925 -- conda: https://conda.anaconda.org/conda-forge/linux-64/shapely-2.1.2-py314hbe3edd8_2.conda - sha256: 17cb5cec9283f993072e8b6f5e1417d8d892cc5efa27029eae954ab06b33c7e2 - md5: 5963e6ee81772d450a35e6bc95522761 +- conda: https://conda.anaconda.org/conda-forge/linux-64/shapely-2.1.2-py313had47c43_2.conda + sha256: 0bf96349dd2cccba4faf6b98f2f3e02767cdc8b78a6bc1a0ee4f88bddee84917 + md5: 6e550dd748e9ac9b2925411684e076a1 depends: - __glibc >=2.17,<3.0.a0 - geos >=3.14.1,<3.14.2.0a0 - libgcc >=14 - numpy >=1.23,<3 - - python >=3.14,<3.15.0a0 - - python_abi 3.14.* *_cp314 + - python >=3.13,<3.14.0a0 + - python_abi 3.13.* *_cp313 license: BSD-3-Clause license_family: BSD - size: 652785 - timestamp: 1762523657698 + size: 648024 + timestamp: 1762523698473 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/shapely-2.1.2-py314h277790e_2.conda sha256: 3d6f64391563dbe47f2e795ce99b2389c84c695df12170e0a1743b10963ebce7 md5: 947d1f4e3160c83140a9c8bcb046fdac @@ -20389,6 +22524,21 @@ packages: - pkg:pypi/six?source=hash-mapping size: 18455 timestamp: 1753199211006 +- conda: https://conda.anaconda.org/conda-forge/noarch/skglm-0.5-pyhd8ed1ab_0.conda + sha256: a12dfb292dbd58262a92b943792c5754f942274748bdcfd85115a096e362d4ae + md5: 4ad02f88ba0e50c823da71a49d608b0d + depends: + - numba + - numpy >=1.12 + - python >=3.9 + - scikit-learn >=1.0 + - scipy >=0.18.0 + license: BSD-3-Clause + license_family: BSD + purls: + - pkg:pypi/skglm?source=hash-mapping + size: 81241 + timestamp: 1753876402061 - conda: https://conda.anaconda.org/conda-forge/linux-64/snappy-1.2.2-h03e3b7b_1.conda sha256: 48f3f6a76c34b2cfe80de9ce7f2283ecb55d5ed47367ba91e8bb8104e12b8f11 md5: 98b6c9dc80eb87b2519b97bcf7e578dd @@ -21099,9 +23249,9 @@ packages: license_family: BSD size: 579898 timestamp: 1752784371236 -- conda: https://conda.anaconda.org/conda-forge/linux-64/tabmat-4.2.0-py310h0158d43_0.conda - sha256: 1dedc771485cd7350a01da1af4433fd83e17529b983511ceaaf0f3ecb1328344 - md5: 7e1a4930e8c55c72620807e948a1e587 +- conda: https://conda.anaconda.org/conda-forge/linux-64/tabmat-4.2.1-py310h0158d43_0.conda + sha256: f199a82e3351553bdbcc9af8dd893dba7f5b5e2333c74eb0cf4a0d19374a46c7 + md5: 5ccf1da09c5edde68146c0faf4612333 depends: - __glibc >=2.17,<3.0.a0 - _openmp_mutex >=4.5 @@ -21117,11 +23267,11 @@ packages: - scipy license: BSD-3-Clause license_family: BSD - size: 536809 - timestamp: 1768399854063 -- conda: https://conda.anaconda.org/conda-forge/linux-64/tabmat-4.2.0-py311hed34c8f_0.conda - sha256: fda94c4ac84ce7b2ef9ebb725764e75fad892f0d74f0c27e5ec842e82ad19bb1 - md5: 6fbcc82e5574ea3227ceb2b3ceb337cd + size: 534482 + timestamp: 1770305526590 +- conda: https://conda.anaconda.org/conda-forge/linux-64/tabmat-4.2.1-py311hed34c8f_0.conda + sha256: bcac8c3e255cd5d59815ec007b5ce1deaadebba8d7f5b258b5804cf16cc17676 + md5: 4ad9d1931b2e69de9f13d6b63e7cc791 depends: - __glibc >=2.17,<3.0.a0 - _openmp_mutex >=4.5 @@ -21137,11 +23287,11 @@ packages: - scipy license: BSD-3-Clause license_family: BSD - size: 562282 - timestamp: 1768399845418 -- conda: https://conda.anaconda.org/conda-forge/linux-64/tabmat-4.2.0-py312hf79963d_0.conda - sha256: 4ab1eb56bf13c7a635c5a65ec628bbe8580481fafa0566d9afaaffb0eee9d75b - md5: 8f9279327f84a3fbe60519d7364dd26f + size: 560592 + timestamp: 1770305536708 +- conda: https://conda.anaconda.org/conda-forge/linux-64/tabmat-4.2.1-py312hf79963d_0.conda + sha256: c294a2452b15fff4b73d6fe29504f0aad9a49605b5ffc185298ec5e5833e6282 + md5: 2a3e9e48b0cd1ecb1e2cfa8661960218 depends: - __glibc >=2.17,<3.0.a0 - _openmp_mutex >=4.5 @@ -21157,11 +23307,11 @@ packages: - scipy license: BSD-3-Clause license_family: BSD - size: 545987 - timestamp: 1768399863605 -- conda: https://conda.anaconda.org/conda-forge/linux-64/tabmat-4.2.0-py313h08cd8bf_0.conda - sha256: 00f23c47ed839fbc122ac03e6ee0dbc87a13e7fdbddfaeb0e360ec9c47c62ba4 - md5: 5d7869685ba137bbc8767d9b28d8fd08 + size: 543899 + timestamp: 1770305530186 +- conda: https://conda.anaconda.org/conda-forge/linux-64/tabmat-4.2.1-py313h08cd8bf_0.conda + sha256: d6256ff8810035693f07056d2c9f2d05587b28135223b82618ded12e58b62ed1 + md5: 8f7a8b15e57062772aacd55ecbca0141 depends: - __glibc >=2.17,<3.0.a0 - _openmp_mutex >=4.5 @@ -21177,11 +23327,13 @@ packages: - scipy license: BSD-3-Clause license_family: BSD - size: 539011 - timestamp: 1768399861179 -- conda: https://conda.anaconda.org/conda-forge/linux-64/tabmat-4.2.0-py314ha0b5721_0.conda - sha256: 369cbd30dfa46fb5a1996e0eeaf5680d104e288ad9ea959f91c1cdad1ffe1883 - md5: 9bd0379ffe41501cf52fee573189b815 + purls: + - pkg:pypi/tabmat?source=hash-mapping + size: 538580 + timestamp: 1770305539242 +- conda: https://conda.anaconda.org/conda-forge/linux-64/tabmat-4.2.1-py314ha0b5721_0.conda + sha256: 7202b4c77b96ba51a0ce8d1c84ed1f4a19242d5a2716e43f305aa925984b3f02 + md5: 91355e55d73babf2fcbdb25810c0d613 depends: - __glibc >=2.17,<3.0.a0 - _openmp_mutex >=4.5 @@ -21197,10 +23349,8 @@ packages: - scipy license: BSD-3-Clause license_family: BSD - purls: - - pkg:pypi/tabmat?source=hash-mapping - size: 549467 - timestamp: 1768399854852 + size: 549401 + timestamp: 1770305534820 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tabmat-4.0.1-py39he9a93b6_2.conda sha256: 6f971d7818b5bf0349ef03fc341f02af3ee5b01bc320d1c75633e4595e489c3f md5: 77eb6f890e2b07fc243df30079d87323 @@ -21240,9 +23390,9 @@ packages: license_family: BSD size: 472895 timestamp: 1752784638410 -- conda: https://conda.anaconda.org/conda-forge/osx-arm64/tabmat-4.2.0-py310h01cd53d_0.conda - sha256: 318a7e75a156f2b4b90c662f7be7d3a8755bc1868cc53a1af63d9d2f1fcf87f4 - md5: d95a3aa34d6f2d0e411e112547091eca +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/tabmat-4.2.1-py310h01cd53d_0.conda + sha256: 4ec796047d15f39714bb09c4d6801ad9453c6500f442f189f35f23432c0c8ba9 + md5: 8781b0368ef870b1aac1d2d45b40ddb5 depends: - __osx >=11.0 - formulaic >=1.2 @@ -21258,11 +23408,11 @@ packages: - scipy license: BSD-3-Clause license_family: BSD - size: 430699 - timestamp: 1768400365478 -- conda: https://conda.anaconda.org/conda-forge/osx-arm64/tabmat-4.2.0-py311hf45e3a0_0.conda - sha256: fe9b66fb0c9f9156d924cb9b5771e4db6870d1eccdd3c31daa391830910a5e59 - md5: 16c23fc74b079b91670611ca28e5b550 + size: 429705 + timestamp: 1770305804338 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/tabmat-4.2.1-py311hf45e3a0_0.conda + sha256: a17ae7fbe1766eb4adaa22378e6e1db93b8c87bcdbc3e4d86c2ff82241423c19 + md5: cd2114f9ed6a8761d5e1cfb7f7a5993a depends: - __osx >=11.0 - formulaic >=1.2 @@ -21278,11 +23428,11 @@ packages: - scipy license: BSD-3-Clause license_family: BSD - size: 452895 - timestamp: 1768400148294 -- conda: https://conda.anaconda.org/conda-forge/osx-arm64/tabmat-4.2.0-py312h31e0735_0.conda - sha256: dcf3ce4cacaf65b5260758a4157d09dc0caee4edd3d7df012c335582d416315a - md5: f7a55f880286ca37517b5030438dfd6c + size: 454345 + timestamp: 1770306192466 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/tabmat-4.2.1-py312h31e0735_0.conda + sha256: 63f01f9f529f90b10e261bc94347459877ef24e102e4d94bfb7c763d5d779e82 + md5: 1f71a8e94fbf2702cdc144a21f8e7bc7 depends: - __osx >=11.0 - formulaic >=1.2 @@ -21298,11 +23448,11 @@ packages: - scipy license: BSD-3-Clause license_family: BSD - size: 462933 - timestamp: 1768400534028 -- conda: https://conda.anaconda.org/conda-forge/osx-arm64/tabmat-4.2.0-py313h5e3876c_0.conda - sha256: fc142e6fafb2dbb975a59824a895e303afce2f5f1848a7f03091a7b181889540 - md5: ba6617eb5fb7cced80e7f715ee4a1c1d + size: 462456 + timestamp: 1770305763108 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/tabmat-4.2.1-py313h5e3876c_0.conda + sha256: b6074df769337471700b47473200f826eadecf8066ec23b12ad76454755da2d6 + md5: 8bd41fa0e0cd0c933aa50f6cbb4002eb depends: - __osx >=11.0 - formulaic >=1.2 @@ -21318,11 +23468,11 @@ packages: - scipy license: BSD-3-Clause license_family: BSD - size: 466990 - timestamp: 1768400094556 -- conda: https://conda.anaconda.org/conda-forge/osx-arm64/tabmat-4.2.0-py314h759a76e_0.conda - sha256: 7bc228178d754d4c0b2d959bd75e4fb191e032246b683af4d649a19d49122dfa - md5: e89e24fbce3aa4cf8f4b34637bb23627 + size: 466226 + timestamp: 1770306018029 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/tabmat-4.2.1-py314h759a76e_0.conda + sha256: a39852a31063370e06fa542320094ec069ab8fe7f94f2a37055e6086eb0a47a1 + md5: 82d0aed3a3c3641d55f49f727a67af18 depends: - __osx >=11.0 - formulaic >=1.2 @@ -21340,8 +23490,8 @@ packages: license_family: BSD purls: - pkg:pypi/tabmat?source=hash-mapping - size: 469097 - timestamp: 1768400327441 + size: 468530 + timestamp: 1770306175306 - conda: https://conda.anaconda.org/conda-forge/win-64/tabmat-4.0.1-py39h2366fc2_2.conda sha256: b4c5d235f0d184809f4479a0c45e257d35d2ef8564c00d8d3c5205eba03e2aaa md5: 942d17032472d85f24810cef012e0374 @@ -21377,9 +23527,9 @@ packages: license_family: BSD size: 406523 timestamp: 1752784656092 -- conda: https://conda.anaconda.org/conda-forge/win-64/tabmat-4.2.0-py310hed136d8_0.conda - sha256: 5f2cf5d9908f76449b07c337a3671492b1051eb46f2199f449109bb9251ed879 - md5: d44a287935c96a9eb35ab63b80e0ab9c +- conda: https://conda.anaconda.org/conda-forge/win-64/tabmat-4.2.1-py310hed136d8_0.conda + sha256: 7cec22635ba79393536ec935316b7e5ee931f35f68005ad6786ff2bb4966722f + md5: 4f30fbcc174a94d91e162bcdbf0716ea depends: - formulaic >=1.2 - narwhals >=2.0 @@ -21393,11 +23543,11 @@ packages: - vc14_runtime >=14.44.35208 license: BSD-3-Clause license_family: BSD - size: 388260 - timestamp: 1768399970754 -- conda: https://conda.anaconda.org/conda-forge/win-64/tabmat-4.2.0-py311h11fd7f3_0.conda - sha256: 8f21794e75153ed345bf2a78b02dd8be781d78a6b76d90703e24af276774ad90 - md5: cdcbbdb914d231a4b03570d0a88638cd + size: 386669 + timestamp: 1770305597058 +- conda: https://conda.anaconda.org/conda-forge/win-64/tabmat-4.2.1-py311h11fd7f3_0.conda + sha256: d07daba159b0197cbc39f84c12213a9d67966e66a16f52a80d91e523f6bddd3c + md5: c76e4b42dfcab18048811c6ea6d5e966 depends: - formulaic >=1.2 - narwhals >=2.0 @@ -21411,11 +23561,11 @@ packages: - vc14_runtime >=14.44.35208 license: BSD-3-Clause license_family: BSD - size: 416389 - timestamp: 1768399987116 -- conda: https://conda.anaconda.org/conda-forge/win-64/tabmat-4.2.0-py312hc128f0a_0.conda - sha256: f6331587a5bf7d4a02074f9f942c7c79d800698db79b718309c559f9eac4d59d - md5: 049fe16beb127d38adb0d3e1a0134c04 + size: 415087 + timestamp: 1770305601832 +- conda: https://conda.anaconda.org/conda-forge/win-64/tabmat-4.2.1-py312hc128f0a_0.conda + sha256: 6e4ee7f2e0557e12313faa3224025c3bb2d7cbf3d03c8adc401738d2b39ef237 + md5: a4e7603427423847ad389d9eb64f96c5 depends: - formulaic >=1.2 - narwhals >=2.0 @@ -21429,11 +23579,11 @@ packages: - vc14_runtime >=14.44.35208 license: BSD-3-Clause license_family: BSD - size: 403995 - timestamp: 1768400225430 -- conda: https://conda.anaconda.org/conda-forge/win-64/tabmat-4.2.0-py313hc90dcd4_0.conda - sha256: a2dd24069d112de0441ea5da4ed8aa5ff4744caf2da6883026c42692aa6132a6 - md5: 941122fa60e4993d3a317cd38e000d36 + size: 402990 + timestamp: 1770305628338 +- conda: https://conda.anaconda.org/conda-forge/win-64/tabmat-4.2.1-py313hc90dcd4_0.conda + sha256: 4df2e531fd7ffc0f500beda31e22d64f886c3aaa0c99fa58ed71a1a0e25af221 + md5: cfcb43c0c63caca720fa52a26e4cd9bc depends: - formulaic >=1.2 - narwhals >=2.0 @@ -21447,11 +23597,11 @@ packages: - vc14_runtime >=14.44.35208 license: BSD-3-Clause license_family: BSD - size: 404748 - timestamp: 1768399985491 -- conda: https://conda.anaconda.org/conda-forge/win-64/tabmat-4.2.0-py314hd8fd7ce_0.conda - sha256: a6534de4b987848fb1f762d83b33803e7994ef2bdca69d741ccbecdbb1218717 - md5: 8d49168c84709aca93c20aba9195d872 + size: 406749 + timestamp: 1770305666596 +- conda: https://conda.anaconda.org/conda-forge/win-64/tabmat-4.2.1-py314hd8fd7ce_0.conda + sha256: 4f23d434ff5cd7b44e2fa34e2ebeca5a11f07bcefbe8e92ec575489822ebc0f3 + md5: 67f3540cca536f7f48a206cbbe0c2011 depends: - formulaic >=1.2 - narwhals >=2.0 @@ -21467,8 +23617,8 @@ packages: license_family: BSD purls: - pkg:pypi/tabmat?source=hash-mapping - size: 410783 - timestamp: 1768399982903 + size: 410264 + timestamp: 1770305614910 - pypi: https://files.pythonhosted.org/packages/40/44/4a5f08c96eb108af5cb50b41f76142f0afa346dfa99d5296fe7202a11854/tabulate-0.9.0-py3-none-any.whl name: tabulate version: 0.9.0 @@ -21608,6 +23758,39 @@ packages: purls: [] size: 3526350 timestamp: 1769460339384 +- conda: https://conda.anaconda.org/conda-forge/linux-64/tktable-2.10-h8d826fa_7.conda + sha256: dd5d8aa7f434acbe4ef5a54f5f2c221650f6c25a4c5ad62c46b36267e1b8fb9b + md5: 3ac51142c19ba95ae0fadefa333c9afb + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=13 + - tk >=8.6.13,<8.7.0a0 + license: TCL + purls: [] + size: 92307 + timestamp: 1750266495866 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/tktable-2.10-h3c7de25_7.conda + sha256: af0be0e8a391a4c2269e8a2eeee711955368d01f53b69a4b05c9ea4094c31dd5 + md5: 7c2e2e25a80f1538b0dcee34026bec42 + depends: + - __osx >=11.0 + - tk >=8.6.13,<8.7.0a0 + license: TCL + purls: [] + size: 79744 + timestamp: 1750266680133 +- conda: https://conda.anaconda.org/conda-forge/win-64/tktable-2.10-h7e9e0db_7.conda + sha256: 740134653bd3af7b1c0a9919333d248d773b49fd64613ba4397e6be042afd1bd + md5: 01ed7f7b4fbffe402c11823e3572c0a9 + depends: + - tk >=8.6.13,<8.7.0a0 + - ucrt >=10.0.20348.0 + - vc >=14.2,<15 + - vc14_runtime >=14.29.30139 + license: TCL + purls: [] + size: 133920 + timestamp: 1750266780179 - conda: https://conda.anaconda.org/conda-forge/noarch/tokenize-rt-6.2.0-pyhd8ed1ab_0.conda sha256: b8da0c728e1313e116a06084ea770c6ad752b9cd086d52b20fcd464bdce52e4b md5: 0a42378794e0425eb5defc9d63e60607 @@ -21658,6 +23841,30 @@ packages: license_family: BSD size: 53978 timestamp: 1760707830681 +- conda: https://conda.anaconda.org/conda-forge/linux-64/tornado-6.5.3-py312h4c3975b_0.conda + sha256: bed440cad040f0fe76266f9a527feecbaf00385b68a96532aa69614fe5153f8e + md5: e03a4bf52d2170d64c816b2a52972097 + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + - python >=3.12,<3.13.0a0 + - python_abi 3.12.* *_cp312 + license: Apache-2.0 + license_family: Apache + size: 850918 + timestamp: 1765458857375 +- conda: https://conda.anaconda.org/conda-forge/linux-64/tornado-6.5.3-py313h07c4f96_0.conda + sha256: 6006d4e5a6ff99be052c939e43adee844a38f2dc148f44a7c11aa0011fd3d811 + md5: 82da2dcf1ea3e298f2557b50459809e0 + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + - python >=3.13,<3.14.0a0 + - python_abi 3.13.* *_cp313 + license: Apache-2.0 + license_family: Apache + size: 878109 + timestamp: 1765458900582 - conda: https://conda.anaconda.org/conda-forge/linux-64/tornado-6.5.3-py314h5bd0f2a_0.conda sha256: b8f9f9ae508d79c9c697eb01b6a8d2ed4bc1899370f44aa6497c8abbd15988ea md5: e35f08043f54d26a1be93fdbf90d30c3 @@ -21704,9 +23911,9 @@ packages: license: MPL-2.0 or MIT size: 89498 timestamp: 1735661472632 -- conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.2-pyh8f84b5b_2.conda - sha256: 7de2b8490734090551313735acf79a9c5eb2cf6b2ed1909aacd1dc45f487b19e - md5: ae1639958d43389d07a307fbc23537c7 +- conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.3-pyh8f84b5b_0.conda + sha256: 9ef8e47cf00e4d6dcc114eb32a1504cc18206300572ef14d76634ba29dfe1eb6 + md5: e5ce43272193b38c2e9037446c1d9206 depends: - python >=3.10 - __unix @@ -21714,11 +23921,11 @@ packages: license: MPL-2.0 and MIT purls: - pkg:pypi/tqdm?source=compressed-mapping - size: 94104 - timestamp: 1769864658869 -- conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.2-pyha7b4d00_2.conda - sha256: c9f430c96a08d4b0389e136e0519b1cd7c23cb40af5b68f6f8602e1d44a45801 - md5: 4b4d4e4d2a72a4ad967397342b1a723f + size: 94132 + timestamp: 1770153424136 +- conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.3-pyha7b4d00_0.conda + sha256: 63cc2def6e168622728c7800ed6b3c1761ceecb18b354c81cee1a0a94c09900a + md5: af77160f8428924c17db94e04aa69409 depends: - python >=3.10 - colorama @@ -21726,9 +23933,9 @@ packages: - python license: MPL-2.0 and MIT purls: - - pkg:pypi/tqdm?source=hash-mapping - size: 93361 - timestamp: 1769864679553 + - pkg:pypi/tqdm?source=compressed-mapping + size: 93399 + timestamp: 1770153445242 - conda: https://conda.anaconda.org/conda-forge/noarch/traitlets-5.14.3-pyhd8ed1ab_1.conda sha256: f39a5620c6e8e9e98357507262a7869de2ae8cc07da8b7f84e517c9fd6c2b959 md5: 019a7385be9af33791c989871317e1ed @@ -21757,6 +23964,18 @@ packages: purls: [] size: 91383 timestamp: 1756220668932 +- conda: https://conda.anaconda.org/conda-forge/noarch/typing-inspection-0.4.2-pyhd8ed1ab_1.conda + sha256: 70db27de58a97aeb7ba7448366c9853f91b21137492e0b4430251a1870aa8ff4 + md5: a0a4a3035667fc34f29bfbd5c190baa6 + depends: + - python >=3.10 + - typing_extensions >=4.12.0 + license: MIT + license_family: MIT + purls: + - pkg:pypi/typing-inspection?source=hash-mapping + size: 18923 + timestamp: 1764158430324 - conda: https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.14.1-pyhe01879c_0.conda sha256: 4f52390e331ea8b9019b87effaebc4f80c6466d09f68453f52d5cdc2a3e1194f md5: e523f4f1e980ed7a4240d7e27e9ec81f @@ -21795,6 +24014,33 @@ packages: purls: [] size: 119135 timestamp: 1767016325805 +- conda: https://conda.anaconda.org/conda-forge/noarch/tzlocal-5.3.1-pyh8f84b5b_0.conda + sha256: 6447388bd870ab0a2b38af5aa64185cd71028a2a702f0935e636a01d81fba7fc + md5: 369f3170d6f727d3102d83274e403b66 + depends: + - python >=3.10 + - __unix + - python + license: MIT + license_family: MIT + purls: + - pkg:pypi/tzlocal?source=hash-mapping + size: 23880 + timestamp: 1756227235167 +- conda: https://conda.anaconda.org/conda-forge/noarch/tzlocal-5.3.1-pyha7b4d00_0.conda + sha256: f52fb21263b56951406ac6ab5006eefdb85efbfe7a30e925f7b03e424651cb9c + md5: bd2369032c6ed9f0b5fa269a63093f7c + depends: + - python >=3.10 + - python-tzdata + - __win + - python + license: MIT + license_family: MIT + purls: + - pkg:pypi/tzlocal?source=hash-mapping + size: 22970 + timestamp: 1756227213967 - conda: https://conda.anaconda.org/conda-forge/win-64/ucrt-10.0.26100.0-h57928b3_0.conda sha256: 3005729dce6f3d3f5ec91dfc49fc75a0095f9cd23bab49efb899657297ac91a5 md5: 71b24316859acd00bdb8b38f5e2ce328 @@ -21847,18 +24093,6 @@ packages: license_family: MIT size: 18504 timestamp: 1769438844417 -- conda: https://conda.anaconda.org/conda-forge/linux-64/unicodedata2-17.0.0-py314h5bd0f2a_1.conda - sha256: d1dafc15fc5d2b1dd5b0a525e8a815028de20dd53b2c775a1b56e8e4839fb736 - md5: 58e2ee530005067c5db23f33c6ab43d2 - depends: - - __glibc >=2.17,<3.0.a0 - - libgcc >=14 - - python >=3.14,<3.15.0a0 - - python_abi 3.14.* *_cp314 - license: Apache-2.0 - license_family: Apache - size: 409745 - timestamp: 1763055060898 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/unicodedata2-17.0.0-py314h0612a62_1.conda sha256: 48c51dd2ef696f7a1a3635716585a8e383a8c00e719305cfda2b480c36ee1283 md5: c673decfe1f120b0717d0aa193b10060 @@ -21869,6 +24103,8 @@ packages: - python_abi 3.14.* *_cp314 license: Apache-2.0 license_family: Apache + purls: + - pkg:pypi/unicodedata2?source=hash-mapping size: 416770 timestamp: 1763055099322 - conda: https://conda.anaconda.org/conda-forge/win-64/unicodedata2-17.0.0-py314h5a2d7ad_1.conda @@ -21882,6 +24118,8 @@ packages: - vc14_runtime >=14.44.35208 license: Apache-2.0 license_family: Apache + purls: + - pkg:pypi/unicodedata2?source=hash-mapping size: 405783 timestamp: 1763054877424 - conda: https://conda.anaconda.org/conda-forge/noarch/uri-template-1.3.0-pyhd8ed1ab_1.conda @@ -22116,63 +24354,68 @@ packages: license_family: BSD size: 56536 timestamp: 1755007159292 -- conda: https://conda.anaconda.org/conda-forge/linux-64/wrapt-2.1.0-py310h7c4b9e2_0.conda - sha256: a08a6dcd3e7a781eafb19e458204805516285d7fb8169612e829464419aa5b70 - md5: 9aaa7e989334af57eea770808194dbf6 +- conda: https://conda.anaconda.org/conda-forge/linux-64/wrapt-2.1.1-py310h7c4b9e2_0.conda + sha256: cf8dcf7871218571e2cbfc7e1e40eee1bc27e7996a2dcd8d941679838e7a08b8 + md5: f741b997d2f04545cd40c8f87f66f7bb depends: - __glibc >=2.17,<3.0.a0 - libgcc >=14 - python >=3.10,<3.11.0a0 - python_abi 3.10.* *_cp310 license: BSD-2-Clause - size: 78186 - timestamp: 1769985915929 -- conda: https://conda.anaconda.org/conda-forge/linux-64/wrapt-2.1.0-py311h49ec1c0_0.conda - sha256: d52f79724d29d005832df9f4baca60767bf3550471b5ac4d98f06e663ea74c92 - md5: ddbef76cdab78a6b35a2907c94b82619 + license_family: BSD + size: 78969 + timestamp: 1770112044318 +- conda: https://conda.anaconda.org/conda-forge/linux-64/wrapt-2.1.1-py311h49ec1c0_0.conda + sha256: 2208c3a7a36e2c36e028ac5494d4b4812f3c6034bfe98ef1bea5ccaac0c81122 + md5: 248f851a54a5bb314ff5693663a75e64 depends: - __glibc >=2.17,<3.0.a0 - libgcc >=14 - python >=3.11,<3.12.0a0 - python_abi 3.11.* *_cp311 license: BSD-2-Clause - size: 89028 - timestamp: 1769985902272 -- conda: https://conda.anaconda.org/conda-forge/linux-64/wrapt-2.1.0-py312h4c3975b_0.conda - sha256: 4325a5427210cb47aee87397afeeaa248c765ec586b149500e32bdc72bc70ab7 - md5: 48866a003206e931ae841281389e8a33 + license_family: BSD + size: 88691 + timestamp: 1770112032657 +- conda: https://conda.anaconda.org/conda-forge/linux-64/wrapt-2.1.1-py312h4c3975b_0.conda + sha256: 450743011cc1a3a557c3f7c0b65e0de8e3a5474261b05a2209273455f392fff1 + md5: 8d156d9c38ef7af6eded19dddb71b543 depends: - __glibc >=2.17,<3.0.a0 - libgcc >=14 - python >=3.12,<3.13.0a0 - python_abi 3.12.* *_cp312 license: BSD-2-Clause - size: 87945 - timestamp: 1769985925610 -- conda: https://conda.anaconda.org/conda-forge/linux-64/wrapt-2.1.0-py313h07c4f96_0.conda - sha256: 38aca5ae6fc57076e6da5db1fe945bd57b09ca6dc3335e0d4f8419da28aa1560 - md5: bf6a5ae9b5ccaaf8af1c3b98270fae29 + license_family: BSD + size: 87294 + timestamp: 1770112026776 +- conda: https://conda.anaconda.org/conda-forge/linux-64/wrapt-2.1.1-py313h07c4f96_0.conda + sha256: 7b2790c5cc8ea4acf9bfd9e53aab839f017c3b17110bd55c9fa417d1e30bcd91 + md5: 3408e776e4e43f04b24dcf74fe8d89a9 depends: - __glibc >=2.17,<3.0.a0 - libgcc >=14 - python >=3.13,<3.14.0a0 - python_abi 3.13.* *_cp313 license: BSD-2-Clause - size: 87989 - timestamp: 1769985905774 -- conda: https://conda.anaconda.org/conda-forge/linux-64/wrapt-2.1.0-py314h5bd0f2a_0.conda - sha256: f071fd0d34e420002a80ffabf0b6a553777be508889fe253637ec7b3272daa18 - md5: 91dc91631ff83fdc75529f3f82cfd810 + license_family: BSD + purls: + - pkg:pypi/wrapt?source=hash-mapping + size: 88204 + timestamp: 1770112039341 +- conda: https://conda.anaconda.org/conda-forge/linux-64/wrapt-2.1.1-py314h5bd0f2a_0.conda + sha256: c95bd78d4cdd1457e03342e8e72e62226225c9b9172bdfe6f7d66578787ffcf2 + md5: 283c1cbf4998f556c2638b72923e52e9 depends: - __glibc >=2.17,<3.0.a0 - libgcc >=14 - python >=3.14,<3.15.0a0 - python_abi 3.14.* *_cp314 license: BSD-2-Clause - purls: - - pkg:pypi/wrapt?source=hash-mapping - size: 88736 - timestamp: 1769985909144 + license_family: BSD + size: 89116 + timestamp: 1770112037261 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/wrapt-1.17.3-py39he7485ab_0.conda sha256: 724edf7df6d966e84893fb458af84e35d5e73f1445ea3385cf495911eedead13 md5: 1585bf53fcb4c0a9cdf6d12020af3ac1 @@ -22185,63 +24428,68 @@ packages: license_family: BSD size: 53643 timestamp: 1755006587733 -- conda: https://conda.anaconda.org/conda-forge/osx-arm64/wrapt-2.1.0-py310h72544b6_0.conda - sha256: 619095878308cbc30f2f600c8a38e014d2d9d4660cbc12d23d4844762bb84b1c - md5: d719eb369af6e3d967d47c93827288c7 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/wrapt-2.1.1-py310h72544b6_0.conda + sha256: b47f097287f26b3703ab94caec88d4de70a48524632d48206e971118fd6e59b4 + md5: c92f3073ae2a0b70d8672db189f10457 depends: - __osx >=11.0 - python >=3.10,<3.11.0a0 - python >=3.10,<3.11.0a0 *_cpython - python_abi 3.10.* *_cp310 license: BSD-2-Clause - size: 75061 - timestamp: 1769986197757 -- conda: https://conda.anaconda.org/conda-forge/osx-arm64/wrapt-2.1.0-py311hc949640_0.conda - sha256: c02037b9331bff5600bf4ba8080e7f57f433d501218c286e018b854b0c97772d - md5: 86b62d437e1d36a1e02589b150c1a80a + license_family: BSD + size: 75255 + timestamp: 1770112291786 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/wrapt-2.1.1-py311hc949640_0.conda + sha256: b08c5daa1357a4f4703f3d8479f126528cd6e77c0f4d5ba41f5c15d0e657c8cf + md5: ebce628e4f0fa95d05fe5069e96f5791 depends: - __osx >=11.0 - python >=3.11,<3.12.0a0 - python >=3.11,<3.12.0a0 *_cpython - python_abi 3.11.* *_cp311 license: BSD-2-Clause - size: 85647 - timestamp: 1769986137462 -- conda: https://conda.anaconda.org/conda-forge/osx-arm64/wrapt-2.1.0-py312h2bbb03f_0.conda - sha256: e2b769a151854c16a8e31d5d422d893040feda5dbf4a5f5e7a0b8aedeb3dce2d - md5: f30305e2ce4b7c247b57e48e4df654a5 + license_family: BSD + size: 85075 + timestamp: 1770112352866 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/wrapt-2.1.1-py312h2bbb03f_0.conda + sha256: 370b4f392cb58b16b356c767d2ac29a04ccb5b9621eb2113b7e383e1a1e5a5da + md5: c381a55d808d45013bd765a9bc062f54 depends: - __osx >=11.0 - python >=3.12,<3.13.0a0 - python >=3.12,<3.13.0a0 *_cpython - python_abi 3.12.* *_cp312 license: BSD-2-Clause - size: 83523 - timestamp: 1769986217946 -- conda: https://conda.anaconda.org/conda-forge/osx-arm64/wrapt-2.1.0-py313h0997733_0.conda - sha256: 1a45bdafb4c5ec1e6114c2ef5049fac1fcb698dc9a868a76a42c5e70a043b329 - md5: 5133ce5e187099850e987090c26a091a + license_family: BSD + size: 83439 + timestamp: 1770112345278 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/wrapt-2.1.1-py313h0997733_0.conda + sha256: cbfb2d8bfa6073727f6681cda3240a09711ae659bd2f213e0d1f258fc334ee8f + md5: faecb00eed320fb11124939fcc89a797 depends: - __osx >=11.0 - python >=3.13,<3.14.0a0 - python >=3.13,<3.14.0a0 *_cp313 - python_abi 3.13.* *_cp313 license: BSD-2-Clause - size: 84655 - timestamp: 1769986527386 -- conda: https://conda.anaconda.org/conda-forge/osx-arm64/wrapt-2.1.0-py314h6c2aa35_0.conda - sha256: 693b13b8ab6cf9f35b033c31a48c57be42ea8b086d129dc6a961153b96aa8ffe - md5: f5169e24cdfb7ce8d65e071c064e1a5a + license_family: BSD + size: 85438 + timestamp: 1770112236135 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/wrapt-2.1.1-py314h6c2aa35_0.conda + sha256: 045f1c83c2f5a1bbcef995c2fdc4e0c365d6c035b428ee0c438ce313de3823a1 + md5: ff7d1485acebdcf037ac956e918bcf9e depends: - __osx >=11.0 - python >=3.14,<3.15.0a0 - python >=3.14,<3.15.0a0 *_cp314 - python_abi 3.14.* *_cp314 license: BSD-2-Clause + license_family: BSD purls: - pkg:pypi/wrapt?source=hash-mapping - size: 85401 - timestamp: 1769986136014 + size: 85765 + timestamp: 1770112491012 - conda: https://conda.anaconda.org/conda-forge/win-64/wrapt-1.17.3-py39h0802e32_0.conda sha256: fddc1388e904bbab37111ccaec89454c9434909d515c91d222a7cc7c392993f6 md5: 94fa83514062e368fa94bec54c5b50d0 @@ -22255,9 +24503,9 @@ packages: license_family: BSD size: 55538 timestamp: 1755007511517 -- conda: https://conda.anaconda.org/conda-forge/win-64/wrapt-2.1.0-py310h29418f3_0.conda - sha256: 7c79ec552ca5052b0c2f796119494fa4fb7426b02e4e71006f27e5061f65c325 - md5: 14c797656db0ae2bf991595a41ab54b4 +- conda: https://conda.anaconda.org/conda-forge/win-64/wrapt-2.1.1-py310h29418f3_0.conda + sha256: 22a205bbc8494fa9b8b4fb3380afccc1f0955dae27a377599b51a304ed6efc0d + md5: 0aebbebf710cd733440c6a996c1edfce depends: - python >=3.10,<3.11.0a0 - python_abi 3.10.* *_cp310 @@ -22265,11 +24513,12 @@ packages: - vc >=14.3,<15 - vc14_runtime >=14.44.35208 license: BSD-2-Clause - size: 76417 - timestamp: 1769985967625 -- conda: https://conda.anaconda.org/conda-forge/win-64/wrapt-2.1.0-py311h3485c13_0.conda - sha256: 4e668bbf5f3d94130b170505d8206437f3c0b54d6e735a02432283a30b4cd0c0 - md5: 664b7b1d01bfd5379d38041fb647d717 + license_family: BSD + size: 76056 + timestamp: 1770112133775 +- conda: https://conda.anaconda.org/conda-forge/win-64/wrapt-2.1.1-py311h3485c13_0.conda + sha256: c7042eec2064b3806ea52497dca5e35ab8393af7160012be2a054b9684c19453 + md5: d36a4dbd346d2daf28b822abb6a3312c depends: - python >=3.11,<3.12.0a0 - python_abi 3.11.* *_cp311 @@ -22277,11 +24526,12 @@ packages: - vc >=14.3,<15 - vc14_runtime >=14.44.35208 license: BSD-2-Clause - size: 86201 - timestamp: 1769985973495 -- conda: https://conda.anaconda.org/conda-forge/win-64/wrapt-2.1.0-py312he06e257_0.conda - sha256: 73aa1efc55b6709e8db47d2eb0b6388315e41cf000892ee17fdca84332bb4cc9 - md5: b8ece2c7ad1200eb4ab60826d9fb72ef + license_family: BSD + size: 86734 + timestamp: 1770112102604 +- conda: https://conda.anaconda.org/conda-forge/win-64/wrapt-2.1.1-py312he06e257_0.conda + sha256: 4b35f4d2730df16e8e5d67c4a5ed8c2c7bb2a9eb2b5576f3fc56ec75a85e646c + md5: 0bce572a8f9d1e7b7c4124111747ab10 depends: - python >=3.12,<3.13.0a0 - python_abi 3.12.* *_cp312 @@ -22289,11 +24539,12 @@ packages: - vc >=14.3,<15 - vc14_runtime >=14.44.35208 license: BSD-2-Clause - size: 85110 - timestamp: 1769985965372 -- conda: https://conda.anaconda.org/conda-forge/win-64/wrapt-2.1.0-py313h5ea7bf4_0.conda - sha256: 9a93adc75e4c14eb32d0e835426b479d72d3f36d21d5cc4bb483f10b11a3e890 - md5: 655da646effc84f514c87e89052c574f + license_family: BSD + size: 85114 + timestamp: 1770112116808 +- conda: https://conda.anaconda.org/conda-forge/win-64/wrapt-2.1.1-py313h5ea7bf4_0.conda + sha256: 1b67e62db527976a6194aab8cbedc8c6e3674f01545e622259daabbcb0f32eae + md5: 793dd7317d8017677dd1b9ae7d8a5179 depends: - python >=3.13,<3.14.0a0 - python_abi 3.13.* *_cp313 @@ -22301,11 +24552,12 @@ packages: - vc >=14.3,<15 - vc14_runtime >=14.44.35208 license: BSD-2-Clause - size: 84984 - timestamp: 1769985970175 -- conda: https://conda.anaconda.org/conda-forge/win-64/wrapt-2.1.0-py314h5a2d7ad_0.conda - sha256: 6e6a7ced71c2118bae978bb0935c1ccc40a644c81807ba1b6c49876f4e1a9325 - md5: 5e4c3003603c6f7ba9ad379547efb45b + license_family: BSD + size: 85796 + timestamp: 1770112119498 +- conda: https://conda.anaconda.org/conda-forge/win-64/wrapt-2.1.1-py314h5a2d7ad_0.conda + sha256: 82dc56df4ee2ac82ae54fa88c129cec773e752945805f2e86a0addd1f2f9aa29 + md5: 330fceeb8f04b03bb40637952a5eb0c8 depends: - python >=3.14,<3.15.0a0 - python_abi 3.14.* *_cp314 @@ -22313,10 +24565,58 @@ packages: - vc >=14.3,<15 - vc14_runtime >=14.44.35208 license: BSD-2-Clause + license_family: BSD purls: - pkg:pypi/wrapt?source=hash-mapping - size: 86088 - timestamp: 1769985998683 + size: 86137 + timestamp: 1770112141331 +- pypi: https://files.pythonhosted.org/packages/7d/8e/952a351c10df395d9bab850f611f4368834ae9104d6449049f5a49e00925/xarray-2026.1.0-py3-none-any.whl + name: xarray + version: 2026.1.0 + sha256: 5fcc03d3ed8dfb662aa254efe6cd65efc70014182bbc2126e4b90d291d970d41 + requires_dist: + - numpy>=1.26 + - packaging>=24.1 + - pandas>=2.2 + - scipy>=1.13 ; extra == 'accel' + - bottleneck ; extra == 'accel' + - numbagg>=0.8 ; extra == 'accel' + - numba>=0.62 ; extra == 'accel' + - flox>=0.9 ; extra == 'accel' + - opt-einsum ; extra == 'accel' + - xarray[accel,etc,io,parallel,viz] ; extra == 'complete' + - netcdf4>=1.6.0 ; extra == 'io' + - h5netcdf>=1.4.0 ; extra == 'io' + - pydap ; extra == 'io' + - scipy>=1.13 ; extra == 'io' + - zarr>=2.18 ; extra == 'io' + - fsspec ; extra == 'io' + - cftime ; extra == 'io' + - pooch ; extra == 'io' + - sparse>=0.15 ; extra == 'etc' + - dask[complete] ; extra == 'parallel' + - cartopy>=0.23 ; extra == 'viz' + - matplotlib>=3.8 ; extra == 'viz' + - nc-time-axis ; extra == 'viz' + - seaborn ; extra == 'viz' + - pandas-stubs ; extra == 'types' + - scipy-stubs ; extra == 'types' + - types-pyyaml ; extra == 'types' + - types-pygments ; extra == 'types' + - types-colorama ; extra == 'types' + - types-decorator ; extra == 'types' + - types-defusedxml ; extra == 'types' + - types-docutils ; extra == 'types' + - types-networkx ; extra == 'types' + - types-pexpect ; extra == 'types' + - types-psutil ; extra == 'types' + - types-pycurl ; extra == 'types' + - types-openpyxl ; extra == 'types' + - types-python-dateutil ; extra == 'types' + - types-pytz ; extra == 'types' + - types-requests ; extra == 'types' + - types-setuptools ; extra == 'types' + requires_python: '>=3.11' - conda: https://conda.anaconda.org/conda-forge/linux-64/xerces-c-3.3.0-hd9031aa_1.conda sha256: 605980121ad3ee9393a9b53fb0996929c9732f8fc6b9f796d25244ca6fa23032 md5: 66a1db55ecdb7377d2b91f54cd56eafa @@ -22416,6 +24716,7 @@ packages: - __osx >=11.0 license: MIT license_family: MIT + purls: [] size: 14105 timestamp: 1762976976084 - conda: https://conda.anaconda.org/conda-forge/win-64/xorg-libxau-1.0.12-hba3369d_1.conda @@ -22427,6 +24728,7 @@ packages: - ucrt >=10.0.20348.0 license: MIT license_family: MIT + purls: [] size: 109246 timestamp: 1762977105140 - conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxdmcp-1.1.5-hb03c661_1.conda @@ -22447,6 +24749,7 @@ packages: - __osx >=11.0 license: MIT license_family: MIT + purls: [] size: 19156 timestamp: 1762977035194 - conda: https://conda.anaconda.org/conda-forge/win-64/xorg-libxdmcp-1.1.5-hba3369d_1.conda @@ -22458,6 +24761,7 @@ packages: - ucrt >=10.0.20348.0 license: MIT license_family: MIT + purls: [] size: 70691 timestamp: 1762977015220 - conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxext-1.3.7-hb03c661_0.conda @@ -22686,7 +24990,7 @@ packages: license: MIT license_family: MIT purls: - - pkg:pypi/zipp?source=compressed-mapping + - pkg:pypi/zipp?source=hash-mapping size: 24194 timestamp: 1764460141901 - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.23.0-pyhd8ed1ab_0.conda @@ -22733,38 +25037,41 @@ packages: license_family: Other size: 107439 timestamp: 1727963788936 -- conda: https://conda.anaconda.org/conda-forge/linux-64/zlib-ng-2.3.2-hceb46e0_1.conda - sha256: f2b6a175677701a0b6ce556b3bd362dc94a4e36ffcd10e3860e52ca036b4ad96 - md5: 40feea2979654ed579f1cda7c63ccb94 +- conda: https://conda.anaconda.org/conda-forge/linux-64/zlib-ng-2.3.3-hceb46e0_1.conda + sha256: ea4e50c465d70236408cb0bfe0115609fd14db1adcd8bd30d8918e0291f8a75f + md5: 2aadb0d17215603a82a2a6b0afd9a4cb depends: - __glibc >=2.17,<3.0.a0 - libgcc >=14 - libstdcxx >=14 license: Zlib license_family: Other - size: 122303 - timestamp: 1766076745735 -- conda: https://conda.anaconda.org/conda-forge/osx-arm64/zlib-ng-2.3.2-hed4e4f5_1.conda - sha256: ab481487381a6a6213d667e883252e52b8ca867b3b466c31a058126f964efffe - md5: 75f39a44c08cb5dc4ea847698de34ba3 + purls: [] + size: 122618 + timestamp: 1770167931827 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/zlib-ng-2.3.3-hed4e4f5_1.conda + sha256: a339606a6b224bb230ff3d711e801934f3b3844271df9720165e0353716580d4 + md5: d99c2a23a31b0172e90f456f580b695e depends: - __osx >=11.0 - libcxx >=19 license: Zlib license_family: Other - size: 94882 - timestamp: 1766076931977 -- conda: https://conda.anaconda.org/conda-forge/win-64/zlib-ng-2.3.2-h0261ad2_1.conda - sha256: e058e925bed8d9e5227cecc098e02992813046fd89206194435e975a9f6eff56 - md5: bc2fba648e1e784c549e20bbe1a8af40 + purls: [] + size: 94375 + timestamp: 1770168363685 +- conda: https://conda.anaconda.org/conda-forge/win-64/zlib-ng-2.3.3-h0261ad2_1.conda + sha256: 71332532332d13b5dbe57074ddcf82ae711bdc132affa5a2982a29ffa06dc234 + md5: 46a21c0a4e65f1a135251fc7c8663f83 depends: - ucrt >=10.0.20348.0 - vc >=14.3,<15 - vc14_runtime >=14.44.35208 license: Zlib license_family: Other - size: 123890 - timestamp: 1766076739436 + purls: [] + size: 124542 + timestamp: 1770167984883 - conda: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.7-hb78ec9c_6.conda sha256: 68f0206ca6e98fea941e5717cec780ed2873ffabc0e1ed34428c061e2c6268c7 md5: 4a13eeac0b5c8e5b8ab496e6c4ddd829 diff --git a/pixi.toml b/pixi.toml index feda2c830..29d6f9581 100644 --- a/pixi.toml +++ b/pixi.toml @@ -7,9 +7,9 @@ platforms = ["linux-64", "osx-arm64", "win-64"] postinstall = "pip install --no-build-isolation --no-deps --disable-pip-version-check -e ." [feature.test.tasks] -store-benchmark-golden-master = "python tests/glm/test_benchmark_golden_master.py" -store-golden-master = "python tests/glm/test_golden_master.py" -test = "pytest tests/glm --doctest-modules src/glum" +store-benchmark-golden-master = { cmd = "python tests/glm/test_benchmark_golden_master.py", env = { PYTHONPATH = "." } } +store-golden-master = { cmd = "python tests/glm/test_golden_master.py", env = { PYTHONPATH = "." } } +test = { cmd = "pytest tests/glm --doctest-modules src/glum", env = { PYTHONPATH = "." } } [feature.docs.tasks] make-docs = "cd docs && make html" @@ -21,8 +21,8 @@ serve-docs = { cmd = "python -m http.server --directory docs/_build/html", depen ] } [feature.benchmark.tasks] -glm-benchmarks-analyze = "glm_benchmarks_analyze" -glm-benchmarks-run = "glm_benchmarks_run" +run-benchmarks = { cmd = "python glum_benchmarks/run_benchmarks.py", env = { PYTHONPATH = "." } } +test-benchmarks = { cmd = "pytest glum_benchmarks/tests", env = { PYTHONPATH = "." } } [feature.lint.tasks] pre-commit-install = "pre-commit install" @@ -119,12 +119,22 @@ shapely = "*" # used in docs/tutorials/regularization_housing_data [feature.benchmark.dependencies] attrs = "*" -click = "*" git_root = "*" +matplotlib-base = "*" openjdk = "*" +pydantic = "*" +pytest = "*" +r-base = "*" +r-glmnet = "*" +rich = "*" +rpy2 = "*" +"ruamel.yaml" = "*" +skglm = "*" [feature.benchmark.pypi-dependencies] -h2o = "*" +celer = "*" +h2o = "*" # Latest version from PyPI + [feature.benchmark.target.win-64.dependencies] blas = { build = "*mkl" } [feature.benchmark.target.linux-64.dependencies] diff --git a/pyproject.toml b/pyproject.toml index 11f90e81d..d3894c691 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -11,7 +11,6 @@ requires = [ [tool.ruff] line-length = 88 target-version = "py39" -exclude = ["src/glum_benchmarks/orig_sklearn_fork/"] [tool.ruff.lint] ignore = ["E731", "N802", "N803", "N806"] @@ -32,10 +31,10 @@ select = [ known-first-party = ["glum", "glum_benchmarks"] [tool.mypy] -python_version = '3.9' +python_version = "3.9" exclude = [ - "tests/", - "src/glum_benchmarks/orig_sklearn_fork/", + "^tests/", + "^\\.pixi/", ] no_implicit_optional = false check_untyped_defs = true @@ -47,6 +46,11 @@ ignore_missing_imports = true module = ["sklearn.*"] ignore_missing_imports = true +[[tool.mypy.overrides]] +# pytest source uses Python 3.10+ syntax, but we target 3.9 +module = ["pytest.*", "_pytest.*"] +follow_imports = "skip" + [tool.cibuildwheel] skip = [ "*-win32", diff --git a/setup.py b/setup.py index 46b0b2485..d177b5b19 100644 --- a/setup.py +++ b/setup.py @@ -70,12 +70,7 @@ "Programming Language :: Python :: 3.13", ], package_dir={"": "src"}, - packages=find_packages( - where="src", - include=( - ["glum"] if os.environ.get("CONDA_BUILD") else ["glum", "glum_benchmarks"] - ), - ), + packages=find_packages(where="src", include=["glum"]), python_requires=">=3.9", install_requires=[ "joblib", @@ -87,15 +82,7 @@ "formulaic>=0.6", "tabmat>=4.0.0", ], - entry_points=( - None - if os.environ.get("CONDA_BUILD") - else """ - [console_scripts] - glm_benchmarks_run = glum_benchmarks.cli_run:cli_run - glm_benchmarks_analyze = glum_benchmarks.cli_analyze:cli_analyze - """ - ), + entry_points=None, ext_modules=cythonize( ext_modules, annotate=False, diff --git a/src/glum/_distribution.py b/src/glum/_distribution.py index a2551ae10..94696f881 100644 --- a/src/glum/_distribution.py +++ b/src/glum/_distribution.py @@ -1574,7 +1574,7 @@ def guess_intercept( if (not isinstance(link, IdentityLink)) and (len(np.unique(y)) == 1): raise ValueError("No variation in `y`. Coefficients can't be estimated.") - avg_y = np.average(y, weights=sample_weight) + avg_y: float = np.average(y, weights=sample_weight) if isinstance(link, IdentityLink): # This is only correct for the normal. For other distributions, the diff --git a/src/glum/_glm.py b/src/glum/_glm.py index eb9e3afdc..c01f1ba6a 100644 --- a/src/glum/_glm.py +++ b/src/glum/_glm.py @@ -1668,7 +1668,7 @@ def score( sample_weight = check_weights(sample_weight, y.shape[0], y.dtype) mu = self.predict(X, offset=offset, context=context) - y_mean = np.average(y, weights=sample_weight) + y_mean: float = np.average(y, weights=sample_weight) dev = self.family_instance.deviance(y, mu, sample_weight=sample_weight) dev_null = self.family_instance.deviance(y, y_mean, sample_weight=sample_weight) @@ -2926,10 +2926,10 @@ def _get_info_criteria( ): skl.utils.validation.check_is_fitted(self, "coef_") - context = capture_context(context) + context_: Optional[Mapping[str, Any]] = capture_context(context) if not hasattr(self, "_info_criteria"): - self._compute_information_criteria(X, y, sample_weight, context=context) + self._compute_information_criteria(X, y, sample_weight, context=context_) if ( self.alpha is None or (self.alpha is not None and self.alpha > 0) diff --git a/src/glum_benchmarks/README.md b/src/glum_benchmarks/README.md deleted file mode 100644 index 51ad118aa..000000000 --- a/src/glum_benchmarks/README.md +++ /dev/null @@ -1,67 +0,0 @@ -# glm_benchmarks - -![CI](https://github.com/Quantco/glm_benchmarks/workflows/CI/badge.svg) - -Python package to benchmark GLM implementations. - -## Running the benchmarks - -After installing the package, you should have two CLI tools: `glm_benchmarks_run` and `glm_benchmarks_analyze`. Use the `--help` flag for full details. Look in `src/glum/problems.py` to see the list of problems that will be run through each library. - -To run the full benchmarking suite, just run `glm_benchmarks_run` with no flags. This will probably take a very long time. - -For a more advanced example: `glm_benchmarks_run --problem_name narrow-insurance-no-weights-l2-poisson --library_name glum --storage dense --num_rows 1000 --output_dir mydatadirname` will run just the first 1000 rows of the `narrow-insurance-no-weights-l2-poisson` problem through the `glum` library and save the output to `mydatadirname`. This demonstrates several capabilities that will speed development when you just want to run a subset of either data or problems or libraries. - -Demonstrating the command above: -``` -(glum) ➜ glum git:(master) ✗ glm_benchmarks_run --problem_name narrow-insuranc -e-no-weights-l2-poisson --library_name glum --storage dense --num_rows 1000 --output_dir - mydatadirname -running problem=narrow-insurance-no-weights-l2-poisson library=glum -Diagnostics: - convergence n_cycles iteration_runtime intercept -n_iter -0 1.444101e+00 0 0.001196 -1.843114 -1 5.008199e-01 1 0.009937 -1.843114 -2 8.087132e-02 2 0.001981 -2.311497 -3 1.143680e-02 3 0.001860 -2.563429 -4 3.526882e-04 4 0.001864 -2.607574 -5 3.658644e-07 5 0.002538 -2.608770 -ran problem narrow-insurance-no-weights-l2-poisson with library glum -ran in 0.045558929443359375 -``` - -The `--problem_name` and `--library_name` flags take comma separated lists. This mean that if you want to run both `glum` and `r-glmnet`, you could run `glm_benchmarks_run --library_name glum,r-glmnet`. - -The `glm_benchmarks_analyze` tool produces a dataframe comparing the correct and runtime of several runs/libraries. `glm_benchmarks_analyze` accepts an almost identical range of command line parameters as `glm_benchmarks_run`. You can use these CLI parameters to filter which problems and runs you would like to compare. - -For example: -``` -(glum) ➜ glum git:(master) ✗ glm_benchmarks_analyze --problem_name narrow-insurance-no-weights-l2-poisson --library_name glum --storage dense --num_rows 1000 --output_dir mydatadirname --cols intercept,runtime,n_iter - library_name intercept runtime n_iter -problem_name num_rows regularization_strength offset -narrow-insurance-no-weights-l2-poisson 1000 0.001 False glum -3.3194 0.0456 5 -``` - -Benchmarks can be sped up by enabling caching of generated data. If you don't do this, you will spend a lot of time repeatedly generating the same data set. To enable caching, set the GLM_BENCHMARKS_CACHE environment variable to the directory you would like to write to. - -We support several types of matrix storage, passed with the argument "--storage". The default is "auto" which splits the matrix into dense, sparse, and categorical subcomponents using `tabmat`. "dense" stores the data as a numpy array. "sparse" stores data as a CSC sparse matrix. "cat" splits the matrix into a dense component and categorical components. "split0.1" splits the matrix into sparse and dense parts, where any column with more than 10% nonzero elements is put into the dense part, and the rest is put into the sparse part. - -## Profiling - -For line-by-line profiling, mark any functions that you'd like to profile with the `@profile` decorator and then launch using line_profiler with `kernprof -lbv src/glum_benchmarks/cli_run.py --problem_name narrow-insurance-no-weights-l2-poisson --library_name glum` - -For stack sampling profiling, use py-spy: `py-spy top -- python src/glum_benchmarks/cli_run.py --problem_name narrow-insurance-no-weights-l2-poisson --library_name glum` - -## Memory profiling - -To create a graph of memory usage: -``` -mprof run --python -o mprofresults.dat --interval 0.01 src/glum_benchmarks/cli_run.py --problem_name narrow-insurance-no-weights-l2-poisson --library_name glum --num_rows 100000 -mprof plot mprofresults.dat -o prof2.png -``` - -To do line-by-line memory profiling, add a `@profile` decorator to the functions you care about and then run: -``` -python -m memory_profiler src/glum_benchmarks/cli_run.py --problem_name narrow-insurance-no-weights-l2-poisson --library_name glum --num_rows 100000 -``` diff --git a/src/glum_benchmarks/__init__.py b/src/glum_benchmarks/__init__.py deleted file mode 100644 index 7d4f27b79..000000000 --- a/src/glum_benchmarks/__init__.py +++ /dev/null @@ -1,9 +0,0 @@ -import importlib.metadata - -try: - __version__ = importlib.metadata.distribution(__name__).version -except Exception: - __version__ = "unknown" - -if "profile" not in __builtins__: # type: ignore - __builtins__["profile"] = lambda x: x # type: ignore diff --git a/src/glum_benchmarks/bench_liblinear.py b/src/glum_benchmarks/bench_liblinear.py deleted file mode 100644 index 1cb14366e..000000000 --- a/src/glum_benchmarks/bench_liblinear.py +++ /dev/null @@ -1,107 +0,0 @@ -import warnings -from typing import Any, Optional, Union - -import numpy as np -import pandas as pd -from scipy import sparse as sps -from sklearn.linear_model import LogisticRegression - -from .util import benchmark_convergence_tolerance, runtime - - -def _build_and_fit(model_args, train_args): - return LogisticRegression(**model_args).fit(**train_args) - - -def liblinear_bench( - dat: dict[str, Union[sps.spmatrix, np.ndarray]], - distribution: str, - alpha: float, - l1_ratio: float, - iterations: int, - cv: bool, - reg_multiplier: Optional[float] = None, - **kwargs, -) -> dict[str, Any]: - """ - Run the benchmark for sklearn.linear_model.LogisticRegression. - - Parameters - ---------- - dat - distribution - alpha - l1_ratio - iterations - cv - reg_multiplier - kwargs - - Returns - ------- - dict - - """ - result: dict = {} - - X = dat["X"] - if not isinstance(X, (np.ndarray, sps.spmatrix, pd.DataFrame)): - warnings.warn( - "liblinear requires data as scipy.sparse matrix, pandas dataframe, or " - "numpy array. Skipping." - ) - return result - - if distribution != "binomial": - warnings.warn("liblinear only supports binomial") - return result - - if l1_ratio == 1 and alpha > 0: - sklearn_l1_ratio = 1.0 - elif l1_ratio == 0 and alpha > 0: - sklearn_l1_ratio = 0.0 - else: - warnings.warn( - "liblinear only supports lasso and ridge regression with positive alpha" - ) - return result - - if "offset" in dat.keys(): - warnings.warn("liblinear does not support offsets") - return result - - if cv: - warnings.warn("liblinear does not yet support CV") - return result - - model_args = dict( - l1_ratio=sklearn_l1_ratio, - tol=benchmark_convergence_tolerance, - C=( - 1 / (X.shape[0] * alpha) - if reg_multiplier is None - else 1 / (X.shape[0] * alpha * reg_multiplier) - ), - # Note that when an intercept is fitted, it is subject to regularization, unlike - # other solvers. intercept_scaling helps combat this by inflating the intercept - # column, though too low of a value leaves too much regularization and too high - # of a value results in poor matrix properties. - # See https://scikit-learn.org/stable/modules/generated/ - # sklearn.linear_model.LogisticRegression.html - intercept_scaling=1e3, - solver="liblinear", - max_iter=1000, - ) - - fit_args = dict( # type: ignore - X=X, - y=dat["y"].astype(np.int64).copy(), - sample_weight=dat.get("sample_weight"), - ) - - result["runtime"], m = runtime(_build_and_fit, iterations, model_args, fit_args) - result["intercept"] = m.intercept_[0] - result["coef"] = np.squeeze(m.coef_) - result["n_iter"] = m.n_iter_[0] - - return result diff --git a/src/glum_benchmarks/benchmark_dense_sandwich.py b/src/glum_benchmarks/benchmark_dense_sandwich.py deleted file mode 100644 index 91add9e74..000000000 --- a/src/glum_benchmarks/benchmark_dense_sandwich.py +++ /dev/null @@ -1,146 +0,0 @@ -import time -from typing import Any, Callable - -import numpy as np -import pandas as pd -from tabmat.ext.dense import dense_sandwich - - -def _numpy_mklC(X, d): - sqrtD = np.sqrt(d)[:, np.newaxis] - x_d = X[0] * sqrtD - return x_d.T @ x_d - - -def _numpy_mklF(X, d): - sqrtD = np.sqrt(d)[:, np.newaxis] - x_d = X[1] * sqrtD - return x_d.T @ x_d - - -def _bench(f: Callable, iter: int) -> tuple[list[float], Any]: - ts = [] - for _ in range(iter): - start = time.time() - out: Any = f() - ts.append(time.time() - start) - return ts, out - - -def _dense_sandwichC(X, d): - return dense_sandwich(X[0], d) - - -def _dense_sandwichF(X, d): - return dense_sandwich(X[1], d) - - -def _mn_run(m, n, iter, dtype): - precision = dtype().itemsize * 8 - X = [np.random.rand(n, m).astype(dtype=dtype)] - d = np.random.rand(n).astype(dtype=dtype) - - X.append(np.asfortranarray(X[0])) - - out: dict[str, Any] = {"name": [], "runtime": []} - to_run = [ - "numpy_mklC", - # "numpy_mklF", - "_dense_sandwichC", - "_dense_sandwichF", - ] - for name in to_run: - ts, result = _bench(lambda: globals()[name](X, d), iter) # noqa B023 - if name == "numpy_mklC": - true = result - elif "numpy_mklC" in to_run: - err = np.abs((true - result) / true) - np.testing.assert_almost_equal(err, 0, 4 if precision == 32 else 7) - runtime = np.min(ts) # type: ignore - out["name"].append(name) - out["runtime"].append(runtime) - print(name, runtime) - out_df = pd.DataFrame(out) - out_df["m"] = m - out_df["n"] = n - out_df["precision"] = precision - return out_df - - -def main(): - """Run some kind of benchmark.""" - iter = 20 - Rs = [] - for m, n in [ - (20, 1000000), - # (50, 500000), - # (150, 200000), - # (300, 100000), - # (2048, 2048), - # (1500, 1500), - (500, 500), - ]: - for dt in [np.float64]: - Rs.append(_mn_run(m, n, iter, dt)) - df = pd.concat(Rs) - df.set_index(["m", "n", "name", "precision"], inplace=True) - df.sort_index(inplace=True) - print(df) - - -def main2(): - """Run some kind of benchmark.""" - n = 500 - m = 500 - dtype = np.float64 - X = np.asfortranarray(np.random.rand(n, m).astype(dtype=dtype)) - d = np.random.rand(n).astype(dtype=dtype) - t1d = [] - pls = [] - krs = [] - ibs = [] - results = [] # type: ignore - # for thresh1d in [16, 32, 64, 128]: - # for parlevel in [5, 7, 10, 13]: - # for kratio in [1, 10, 20, 80]: - for thresh1d in [32, 64]: - for parlevel in [9]: - for kratio in [8, 16]: - for innerblock in [32, 64, 128, 256]: - t1d.append(thresh1d) - pls.append(parlevel) - krs.append(kratio) - ibs.append(innerblock) - # results.append(np.min(bench(lambda: X.T @ X, 1)[0])) - results.append( - np.min( - _bench( - lambda: dense_sandwich( - X, # noqa B023 - d, # noqa B023 - thresh1d, # noqa B023 - parlevel, # noqa B023 - kratio, # noqa B023 - innerblock, # noqa B023 - ), - 50, - )[0] - ) - ) - print(results[-1]) - df = pd.DataFrame( - dict(thresh1d=t1d, parlevel=pls, kratio=krs, innerblock=ibs, results=results) - ) - df.set_index(["thresh1d", "parlevel", "kratio", "innerblock"], inplace=True) - df.sort_index(inplace=True) - with pd.option_context("display.max_rows", None, "display.max_columns", None): - print(df) - - -# 841650213 L1-dcache-load-misses # 12.01% of all L1-dcache hits (71.87%) -# 7006517280 L1-dcache-loads (71.53%) -# 1016757397 L1-dcache-stores (69.82%) - - -if __name__ == "__main__": - main() diff --git a/src/glum_benchmarks/benchmark_sparse_sandwich.py b/src/glum_benchmarks/benchmark_sparse_sandwich.py deleted file mode 100644 index 18663036c..000000000 --- a/src/glum_benchmarks/benchmark_sparse_sandwich.py +++ /dev/null @@ -1,115 +0,0 @@ -import time -from typing import Any, Callable - -import numpy as np -import pandas as pd -import tabmat as tm -from scipy import sparse as sps -from tabmat.ext.dense import dense_sandwich -from tabmat.ext.sparse import sparse_sandwich - -from .problems import ( - generate_narrow_insurance_dataset, - generate_wide_insurance_dataset, - load_data, -) - - -def _load(which: str, n_rows: int) -> tuple[Any, np.ndarray]: - if which == "narrow": - x = sps.csc_matrix(load_data(generate_narrow_insurance_dataset, n_rows)["X"]) - else: - x = sps.csc_matrix(load_data(generate_wide_insurance_dataset, n_rows)["X"]) - np.random.seed(0) - d = np.random.uniform(0, 1, n_rows) - return x, d - - -def _naive_sandwich(x, d): - return (x.T @ (x.multiply(d[:, None]))).toarray() - - -def _fast_sandwich(X, d): - return sparse_sandwich(X, X.XT, d) - - -def _split_sandwich(X, threshold): - Xsplit = tm.from_split(X, threshold) - return lambda _, d: Xsplit.sandwich(d) - - -def _dense_sandwich(X, d): - return dense_sandwich(X.X_dense, d) - - -def _run_one_problem_all_methods(x, d, include_naive, dtype) -> pd.DataFrame: - x = x.astype(dtype) - d = d.astype(dtype) - x.XT = x.T.tocsc() - x.X_dense = np.asfortranarray(x.toarray()) - funcs: dict[str, Callable[[Any, np.ndarray], Any]] = { - "sparse_sandwich": _fast_sandwich, - "dense_sandwich": _dense_sandwich, - } - funcs["split_sandwich_0.05"] = _split_sandwich(x, 0.05) - funcs["split_sandwich_0.1"] = _split_sandwich(x, 0.1) - # for threshold in [0.0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3,0.4,0.5,0.7,0.9,1.0]: - # for threshold in [0.01, 0.02, 0.03, 0.04, 0.05]: - # funcs[f"split_sandwich_{threshold}"] = split_sandwich(x, threshold) - - if include_naive: - funcs["naive"] = _naive_sandwich - - info: dict[str, Any] = {} - for name, func in funcs.items(): - ts = [] - for _ in range(7): - start = time.perf_counter() - res = func(x, d) - ts.append(time.perf_counter() - start) - elapsed = np.min(ts) # type: ignore - - info[name] = {} - info[name]["res"] = res - info[name]["time"] = elapsed - - if include_naive: - naive_result = info["naive"]["res"] - for k in info: - np.testing.assert_allclose(naive_result, info[k]["res"], 4) - - return pd.DataFrame( - {"method": list(info.keys()), "time": [elt["time"] for elt in info.values()]} - ) - - -def main() -> None: - """Run sparse sandwich benchmarks.""" - # "killed" with 1e7 and 4 * 1e6 - row_counts = [ - int(1e4), - # int(1e5), - # int(3e5), - int(1e6), - # int(2e6), - ] # , int(2e6), int(4e6), int(10e6)] - benchmarks = [] - - x, d = _load("narrow", row_counts[-1]) - - for i, n_rows in enumerate(row_counts): - for dtype in [np.float32, np.float64]: - benchmarks.append( - _run_one_problem_all_methods( - x[:n_rows, :].copy(), d[:n_rows].copy(), i == 0, dtype - ) - ) - benchmarks[-1]["dtype"] = str(dtype) - benchmarks[-1]["n_rows"] = n_rows - - result_df = pd.concat(benchmarks).sort_values(["n_rows", "method"]) - print(result_df.set_index(["n_rows", "method"])) - - -if __name__ == "__main__": - main() diff --git a/src/glum_benchmarks/cli_analyze.py b/src/glum_benchmarks/cli_analyze.py deleted file mode 100644 index 12ad11a57..000000000 --- a/src/glum_benchmarks/cli_analyze.py +++ /dev/null @@ -1,221 +0,0 @@ -import os -import pickle -from typing import Any, Optional - -import click -import numpy as np -import pandas as pd - -from glum_benchmarks.problems import get_all_problems -from glum_benchmarks.util import ( - BenchmarkParams, - benchmark_params_cli, - clear_cache, - get_params_from_fname, -) - - -def _get_comma_sep_names(xs: str) -> list[str]: - return [x.strip() for x in xs.split(",")] - - -@click.command() -@click.option( - "--output_dir", - default="benchmark_output", - help="The directory where we load benchmarking output.", -) -@click.option( - "--export", - default=None, - type=str, - help="File name or path to export the results to CSV or Pickle.", -) -@click.option( - "--cols", default=None, type=str, help="Which output analysis columns to display?" -) -@benchmark_params_cli -def cli_analyze( - params: BenchmarkParams, output_dir: str, export: Optional[str], cols: str -): - """ - Describe runtime, objective function values, and other statistics on the \ - already-run problems specified by the command line options. - - Parameters - ---------- - params: BenchmarkParams - output_dir: str - export: string or None - cols: str - """ - clear_cache() - display_precision = 4 - np.set_printoptions(precision=display_precision, suppress=True) - pd.set_option("display.precision", display_precision) - - file_names = _identify_parameter_fnames(output_dir, params) - - raw_results = { - fname: _load_benchmark_results(output_dir, fname) for fname in file_names - } - formatted_results = [ - _extract_dict_results_to_pd_series(name, res) - for name, res in raw_results.items() - if len(res) > 0 - ] - - if not formatted_results: - return - - res_df = pd.DataFrame.from_records(formatted_results) - res_df["offset"] = res_df["problem_name"].apply(lambda x: "offset" in x) - res_df["problem_name"] = [ - "weights".join(x.split("offset")) for x in res_df["problem_name"] - ] - problem_id_cols = ["problem_name", "num_rows", "regularization_strength", "offset"] - res_df = res_df.set_index(problem_id_cols).sort_values("library_name").sort_index() - if params.cv: - for col in ["max_alpha", "min_alpha"]: - res_df[col] = res_df[col].astype(float) - - res_df["rel_obj_val"] = ( - res_df[["obj_val"]] - res_df.groupby(level=[0, 1, 2, 3])[["obj_val"]].min() - ) - - with pd.option_context( - "display.expand_frame_repr", - False, - "display.max_columns", - None, - "display.max_rows", - None, - ): - if cols is not None: - cols_to_show = _get_comma_sep_names(cols) - else: - cols_to_show = [ - "library_name", - "storage", - "threads", - "single_precision", - "n_iter", - "runtime", - ] - if res_df["cv"].any(): - cols_to_show += ["n_alphas", "max_alpha", "min_alpha", "best_alpha"] - else: - cols_to_show += [ - "intercept", - "num_nonzero_coef", - "obj_val", - "rel_obj_val", - ] - if "library_name" not in cols_to_show: - cols_to_show.insert(0, "library_name") - print(res_df[cols_to_show]) - - if export: - if export.endswith(".pkl"): - res_df.to_pickle(export) - else: - res_df.to_csv(export) - - return res_df - - -def _extract_dict_results_to_pd_series( - fname: str, - results: dict[str, Any], -) -> dict: - assert "coef" in results.keys() - params = get_params_from_fname(fname) - assert params.problem_name is not None - - coefs = results["coef"] - if results["n_iter"] is not None: - runtime_per_iter = results["runtime"] / results["n_iter"] - else: - runtime_per_iter = None - l1_norm = np.sum(np.abs(coefs)) # type: ignore - l2_norm = np.sum(coefs**2) # type: ignore - num_nonzero_coef = np.sum(np.abs(coefs) > 1e-8) # type: ignore - - # weights and offsets are solving the same problem, but the objective is set up to - # deal with weights, so load the data for the weights problem rather than the - # offset problem - if "housing" not in params.problem_name: - prob_name_weights = "weights".join(params.problem_name.split("offset")) - else: - prob_name_weights = params.problem_name - problem = get_all_problems()[prob_name_weights] - - formatted: dict[str, Any] = params.__dict__ - items_to_use_from_results = ["n_iter", "runtime", "intercept"] - if params.cv: - items_to_use_from_results += [ - "n_alphas", - "max_alpha", - "min_alpha", - "best_alpha", - ] - formatted.update( - {k: v for k, v in results.items() if k in items_to_use_from_results} - ) - - formatted.update( - { - "num_rows": results["num_rows"], - "regularization_strength": ( - problem.regularization_strength - if params.regularization_strength is None - else params.regularization_strength - ), - "runtime per iter": runtime_per_iter, - "l1": l1_norm, - "l2": l2_norm, - "num_nonzero_coef": num_nonzero_coef, - "obj_val": results["obj_val"], - "offset": "offset" in params.problem_name, - } - ) - - return formatted - - -def _identify_parameter_fnames( - root_dir: str, constraint_params: BenchmarkParams -) -> list[str]: - def _satisfies_constraint(params: BenchmarkParams, k: str) -> bool: - constraint = getattr(constraint_params, k) - param = getattr(params, k) - return ( - constraint is None - or param == constraint - # e.g. this_file_params['library_name'] is 'sklearn-fork' - # and constraint_params.library_name is 'sklearn-fork,h2o' - or (isinstance(constraint, str) and param in constraint.split(",")) - ) - - results_to_keep = [] - for fname in os.listdir(root_dir): - this_file_params = get_params_from_fname(fname) - - keep_this_problem = { - k: _satisfies_constraint(this_file_params, k) - for k in constraint_params.param_names - } - - if all(keep_this_problem.values()): - results_to_keep.append(fname) - return results_to_keep - - -def _load_benchmark_results(output_dir: str, fname: str): - results_path = os.path.join(output_dir, fname) - with open(results_path, "rb") as f: - return pickle.load(f) - - -if __name__ == "__main__": - cli_analyze() diff --git a/src/glum_benchmarks/cli_run.py b/src/glum_benchmarks/cli_run.py deleted file mode 100644 index 17ba403a7..000000000 --- a/src/glum_benchmarks/cli_run.py +++ /dev/null @@ -1,259 +0,0 @@ -import os -import pickle -from typing import Any, Optional - -import click - -from glum_benchmarks.bench_glum import glum_bench -from glum_benchmarks.problems import Problem, get_all_problems -from glum_benchmarks.util import ( - BenchmarkParams, - benchmark_params_cli, - clear_cache, - defaults, - get_obj_val, -) -from glum_benchmarks.zeros_benchmark import zeros_bench - -try: - from .bench_h2o import h2o_bench # isort:skip - - H20_INSTALLED = True -except ImportError: - H20_INSTALLED = False - -try: - from .bench_liblinear import liblinear_bench # isort:skip - - LIBLINEAR_INSTALLED = True -except ImportError: - LIBLINEAR_INSTALLED = False - - -@click.command() -@click.option( - "--output_dir", - default="benchmark_output", - help="The directory to store benchmarking output.", -) -@click.option( - "--iterations", - default=1, - type=int, - help="Number of times to re-run the benchmark. This can be useful for avoid " - "performance noise.", -) -@benchmark_params_cli -def cli_run( - params: BenchmarkParams, - output_dir: str, - iterations: int, -): - """ - Run benchmark problems through the command line. - - See the README for more info. - - Parameters - ---------- - params: BenchmarkParams, parsed by benchmark_params_cli decorator - output_dir - iterations - - Returns - ------- - None - """ - clear_cache() - problems, libraries = get_limited_problems_libraries( - params.problem_name, params.library_name - ) - - for Pn in problems.keys(): - for Ln in libraries.keys(): - click.echo(f"running problem={Pn} library={Ln}") - new_params = params.update_params(problem_name=Pn, library_name=Ln) - result, _ = execute_problem_library( - new_params, - iterations, - ( - defaults["diagnostics_level"] # type: ignore - if params.diagnostics_level is None # type: ignore - else params.diagnostics_level # type: ignore - ), - ) - _save_benchmark_results( - output_dir, - new_params, - result, - ) - if len(result) > 0: - click.echo(f"ran problem {Pn} with library {Ln}") - click.echo(f"ran in {result.get('runtime')}") - - -def execute_problem_library( - params: BenchmarkParams, - iterations: int = 1, - diagnostics_level: str = "basic", - **kwargs, -): - """ - Run the benchmark problem specified by 'params', 'iterations' times. - - Parameters - ---------- - params - iterations - diagnostics_level - kwargs - - Returns - ------- - Tuple: Result data on this run, and the regularization applied - """ - assert params.problem_name is not None - assert params.library_name is not None - P = get_all_problems()[params.problem_name] - L = get_all_libraries()[params.library_name] - - for k in params.param_names: - if getattr(params, k) is None: - params.update_params(**{k: defaults[k]}) - - dat = P.data_loader( - num_rows=params.num_rows, - storage=params.storage, - single_precision=params.single_precision, - ) - os.environ["OMP_NUM_THREADS"] = str(params.threads) - - if params.regularization_strength is None: - params.regularization_strength = P.regularization_strength - # Weights have been multiplied by exposure. The new sum of weights - # should influence the objective function (in order to keep everything comparable - # to the "weights instead of offset" setup), but this will get undone by weight - # normalization. So instead divide the penalty by the new weight sum divided by - # the old weight sum - reg_multiplier = ( - 1 / dat["sample_weight"].mean() if "sample_weight" in dat.keys() else None - ) - result = L( - dat, - distribution=P.distribution, - alpha=params.regularization_strength, - l1_ratio=P.l1_ratio, - iterations=iterations, - cv=params.cv, - diagnostics_level=diagnostics_level, - reg_multiplier=reg_multiplier, - hessian_approx=params.hessian_approx, - **kwargs, - ) - if len(result) > 0: - result["num_rows"] = dat["y"].shape[0] - # Use best_alpha from CV if available, otherwise use regularization_strength - alpha_for_obj = result.get("best_alpha", P.regularization_strength) - obj_val = get_obj_val( - dat, - P.distribution, - alpha_for_obj, - P.l1_ratio, - result["intercept"], - result["coef"], - ) - - result["obj_val"] = obj_val - result["num_rows"] = dat["y"].shape[0] - - return result, params.regularization_strength - - -def get_all_libraries() -> dict[str, Any]: - """ - Get the names of all available libraries and the functions to benchmark them. - - Returns - ------- - dict - """ - all_libraries = { - "glum": glum_bench, - "zeros": zeros_bench, - } - - if H20_INSTALLED: - all_libraries["h2o"] = h2o_bench - - if LIBLINEAR_INSTALLED: - all_libraries["liblinear"] = liblinear_bench - - return all_libraries - - -def get_limited_problems_libraries( - problem_names: Optional[str], library_names: Optional[str] -) -> tuple[dict, dict]: - """ - Get only the problems and libraries specified by problem_names and library_names. - - Parameters - ---------- - problem_names - library_names - - Returns - ------- - tuple: dict of problems, dict of libraries - """ - all_libraries = get_all_libraries() - - if library_names is not None: - library_names_split = _get_comma_sep_names(library_names) - libraries = {k: all_libraries[k] for k in library_names_split} - else: - libraries = all_libraries - return get_limited_problems(problem_names), libraries - - -def get_limited_problems(problem_names: Optional[str]) -> dict[str, Problem]: - """ - Get the names of problems and problems specified by problem_names. - - Parameters - ---------- - problem_names - - Returns - ------- - dict mapping problem name to Problem - """ - all_problems = get_all_problems() - - if problem_names is not None: - problem_names_split = _get_comma_sep_names(problem_names) - problems = {k: all_problems[k] for k in problem_names_split} - else: - problems = all_problems - return problems - - -def _get_comma_sep_names(xs: str) -> list[str]: - return [x.strip() for x in xs.split(",")] - - -def _save_benchmark_results( - output_dir: str, - params: BenchmarkParams, - result, -) -> None: - results_path = output_dir + "/" + params.get_result_fname() - - if not os.path.exists(output_dir): - os.makedirs(output_dir) - with open(results_path + ".pkl", "wb") as f: - pickle.dump(result, f) - - -if __name__ == "__main__": - cli_run() diff --git a/src/glum_benchmarks/data/simulated_glm.py b/src/glum_benchmarks/data/simulated_glm.py deleted file mode 100644 index 137b99f58..000000000 --- a/src/glum_benchmarks/data/simulated_glm.py +++ /dev/null @@ -1,151 +0,0 @@ -from functools import partial - -import numpy as np -import pandas as pd -import scipy.sparse as sps - -from glum._glm import get_family, get_link - - -def tweedie_rv(mu, sigma2=1, p=1.5): - """Generate draws from a tweedie distribution with power p. - - mu is the location parameter and sigma2 is the dispersion coefficient. - """ - n = len(mu) - rand = np.random.default_rng(1) - - # transform tweedie parameters into poisson and gamma - lambda_ = (mu ** (2 - p)) / ((2 - p) * sigma2) - alpha_ = (2 - p) / (p - 1) - beta_ = (mu ** (1 - p)) / ((p - 1) * sigma2) - - arr_N = rand.poisson(lambda_) - out: np.ndarray = np.empty(n, dtype=np.float64) - for i, N in enumerate(arr_N): # type: ignore - out[i] = np.sum(rand.gamma(alpha_, 1 / beta_[i], size=N)) - - return out - - -def _get_family_rv(family, rand: np.random._generator.Generator): - family_rv = { - "poisson": rand.poisson, - "gamma": rand.gamma, - "normal": rand.normal, - "binomial": partial(rand.binomial, 1), - } - - if family in family_rv.keys(): - return family_rv[family] - elif "tweedie" in family: - p = float(family.split("=")[1]) - return partial(tweedie_rv, p=p) - else: - raise ValueError( - 'family must take the value "poisson", "gamma", "normal", "binomial", or ' - '"tweedie_p=XX". ' - f"Currently {family}." - ) - - -def simulate_glm_data( - family: str = "poisson", - link: str = "auto", - n_rows: int = 5000, - dense_features: int = 10, - sparse_features: int = 0, - sparse_density: float = 0.05, - categorical_features: int = 2, - categorical_levels: int = 10, - ohe_categorical: bool = True, - intercept: float = 0.2, - drop_first: bool = False, - random_seed: int = 1, -): - """ - Simulate the data we will use for benchmarks. - - Parameters - ---------- - family - link - n_rows - dense_features - sparse_features - sparse_density - categorical_features - categorical_levels - ohe_categorical - intercept - drop_first - random_seed - - Returns - ------- - dict - """ - rand = np.random.default_rng(random_seed) - - # Creating dense component - if dense_features > 0: - dense_feature_names = [f"dense{i}" for i in range(dense_features)] - X_dense = rand.normal( - rand.integers(-2, 2, size=dense_features), size=(n_rows, dense_features) - ) - X_dense = pd.DataFrame(data=X_dense, columns=dense_feature_names) - coefs_dense = np.concatenate( - [ - [1, 0.5, 0.1, -0.1, -0.5, -1, 0, 0, 0, 0], - rand.choice([0, 1, -1], size=dense_features), - ] - )[:dense_features] - coefs_dense = pd.Series(data=coefs_dense, index=dense_feature_names) - - # Creating sparse component - sparse_feature_names = [f"sparse{i}" for i in range(sparse_features)] - X_sparse = sps.random(n_rows, sparse_features, density=sparse_density).toarray() - X_sparse = pd.DataFrame(data=X_sparse, columns=sparse_feature_names) - coefs_sparse = rand.choice([0, 1, -1], size=sparse_features) - coefs_sparse = pd.Series(data=coefs_sparse, index=sparse_feature_names) - - # Creating categorical component - cat_feature_names = [f"cat{i}" for i in range(categorical_features)] - fixed_effects = rand.choice( - np.arange(categorical_levels), size=(n_rows, categorical_features) - ) - X_cat = pd.DataFrame(data=fixed_effects, columns=cat_feature_names) - X_cat_ohe = pd.get_dummies( - X_cat, columns=cat_feature_names, drop_first=drop_first, dtype=float - ) - - coefs_cat = pd.Series( - data=rand.uniform(size=len(X_cat_ohe.columns)), index=X_cat_ohe.columns - ) - - # Merging - X = pd.concat([X_dense, X_sparse, X_cat_ohe], axis=1) - coefs = pd.concat([coefs_dense, coefs_sparse, coefs_cat]) - - intercept = intercept - - link_inst = get_link(link=link, family=get_family("poisson")) - family_rv = _get_family_rv(family, rand) - - y = family_rv(link_inst.inverse(intercept + X.to_numpy() @ coefs.to_numpy())) - - weights = rand.uniform(size=n_rows) - offset = np.log(rand.uniform(size=n_rows)) - - if not ohe_categorical: - X = pd.concat([X_dense, X_sparse, X_cat], axis=1) - - data = { - "X": X, - "y": y, - "sample_weight": weights, - "offset": offset, - "intercept": intercept, - "coefs": coefs, - } - return data diff --git a/src/glum_benchmarks/scripts/run_intermediate_benchmarks.sh b/src/glum_benchmarks/scripts/run_intermediate_benchmarks.sh deleted file mode 100755 index 1cfd92d3b..000000000 --- a/src/glum_benchmarks/scripts/run_intermediate_benchmarks.sh +++ /dev/null @@ -1,40 +0,0 @@ -#!/bin/bash - -set -uo pipefail - -OUTPUT_DIR=$(git rev-parse HEAD) -PROBLEM_NAMES="intermediate-insurance-weights-net-poisson,intermediate-insurance-weights-l2-poisson,intermediate-insurance-weights-net-gamma,intermediate-insurance-weights-l2-gamma" -LIBRARY_NAMES="sklearn-fork,orig-sklearn-fork,glmnet-python,h2o" -THREADS=8 - -export GLM_BENCHMARKS_CACHE_SIZE_LIMIT=20737418240 # 20 GB -export GLM_BENCHMARKS_CACHE=.cache - -for NUM_ROWS in 100000 1000000 10000000 -do - for REG_STRENGTH in 0.1 0.001 0.00001 - do - for STORAGE in "dense" "sparse" "split0.1" - do - echo "---------------------------------" - echo "NUM_ROWS = ${NUM_ROWS}" - echo "REG_STRENGTH = ${REG_STRENGTH}" - echo "STORAGE = ${STORAGE}" - glm_benchmarks_run \ - --problem_name "${PROBLEM_NAMES}" \ - --library_name "${LIBRARY_NAMES}" \ - --num_rows ${NUM_ROWS} \ - --threads ${THREADS} \ - --storage ${STORAGE} \ - --regularization_strength ${REG_STRENGTH} \ - --output_dir ${OUTPUT_DIR} - - done - done -done - -glm_benchmarks_analyze \ - --problem_name "${PROBLEM_NAMES}" \ - --library_name "${LIBRARY_NAMES}" \ - --output_dir ${OUTPUT_DIR} \ - --export "intermediate_results.csv" diff --git a/src/glum_benchmarks/scripts/run_narrow_benchmarks.sh b/src/glum_benchmarks/scripts/run_narrow_benchmarks.sh deleted file mode 100755 index 32a52653e..000000000 --- a/src/glum_benchmarks/scripts/run_narrow_benchmarks.sh +++ /dev/null @@ -1,40 +0,0 @@ -#!/bin/bash - -set -uo pipefail - -OUTPUT_DIR=$(git rev-parse HEAD) -PROBLEM_NAMES="narrow-insurance-weights-net-poisson,narrow-insurance-weights-l2-poisson,narrow-insurance-weights-net-gamma,narrow-insurance-weights-l2-gamma" -LIBRARY_NAMES="sklearn-fork,orig-sklearn-fork,glmnet-python,h2o" -THREADS=8 - -export GLM_BENCHMARKS_CACHE_SIZE_LIMIT=20737418240 # 20 GB -export GLM_BENCHMARKS_CACHE=.cache - -for NUM_ROWS in 100000 1000000 10000000 -do - for REG_STRENGTH in 0.1 0.001 0.00001 - do - for STORAGE in "dense" "sparse" "split0.1" - do - echo "---------------------------------" - echo "NUM_ROWS = ${NUM_ROWS}" - echo "REG_STRENGTH = ${REG_STRENGTH}" - echo "STORAGE = ${STORAGE}" - glm_benchmarks_run \ - --problem_name "${PROBLEM_NAMES}" \ - --library_name "${LIBRARY_NAMES}" \ - --num_rows ${NUM_ROWS} \ - --threads ${THREADS} \ - --storage ${STORAGE} \ - --regularization_strength ${REG_STRENGTH} \ - --output_dir ${OUTPUT_DIR} - - done - done -done - -glm_benchmarks_analyze \ - --problem_name "${PROBLEM_NAMES}" \ - --library_name "${LIBRARY_NAMES}" \ - --output_dir ${OUTPUT_DIR} \ - --export "narrow_results.csv" diff --git a/src/glum_benchmarks/scripts/run_real_benchmarks.sh b/src/glum_benchmarks/scripts/run_real_benchmarks.sh deleted file mode 100755 index c16935ca5..000000000 --- a/src/glum_benchmarks/scripts/run_real_benchmarks.sh +++ /dev/null @@ -1,40 +0,0 @@ -#!/bin/bash - -set -uo pipefail - -OUTPUT_DIR=$(git rev-parse HEAD) -PROBLEM_NAMES="real-insurance-weights-net-poisson,real-insurance-weights-l2-poisson,real-insurance-weights-net-gamma,real-insurance-weights-l2-gamma" -LIBRARY_NAMES="sklearn-fork,orig-sklearn-fork,glmnet-python,h2o" -THREADS=8 - -export GLM_BENCHMARKS_CACHE_SIZE_LIMIT=20737418240 # 20 GB -export GLM_BENCHMARKS_CACHE=.cache - -for NUM_ROWS in 100000 1000000 10000000 -do - for REG_STRENGTH in 0.1 0.001 0.00001 - do - for STORAGE in "dense" "sparse" "split0.1" - do - echo "---------------------------------" - echo "NUM_ROWS = ${NUM_ROWS}" - echo "REG_STRENGTH = ${REG_STRENGTH}" - echo "STORAGE = ${STORAGE}" - glm_benchmarks_run \ - --problem_name "${PROBLEM_NAMES}" \ - --library_name "${LIBRARY_NAMES}" \ - --num_rows ${NUM_ROWS} \ - --threads ${THREADS} \ - --storage ${STORAGE} \ - --regularization_strength ${REG_STRENGTH} \ - --output_dir ${OUTPUT_DIR} - - done - done -done - -glm_benchmarks_analyze \ - --problem_name "${PROBLEM_NAMES}" \ - --library_name "${LIBRARY_NAMES}" \ - --output_dir ${OUTPUT_DIR} \ - --export "real_results.csv" diff --git a/src/glum_benchmarks/scripts/run_wide_benchmarks.sh b/src/glum_benchmarks/scripts/run_wide_benchmarks.sh deleted file mode 100755 index 07b8cecb3..000000000 --- a/src/glum_benchmarks/scripts/run_wide_benchmarks.sh +++ /dev/null @@ -1,40 +0,0 @@ -#!/bin/bash - -set -uo pipefail - -OUTPUT_DIR=$(git rev-parse HEAD) -PROBLEM_NAMES="wide-insurance-weights-net-poisson,wide-insurance-weights-l2-poisson,wide-insurance-weights-net-gamma,wide-insurance-weights-l2-gamma" -LIBRARY_NAMES="sklearn-fork,h2o" # don't run these for glmnet and orig-sklearn-fork -> these take forever -THREADS=8 - -export GLM_BENCHMARKS_CACHE_SIZE_LIMIT=20737418240 # 20 GB -export GLM_BENCHMARKS_CACHE=.cache - -for NUM_ROWS in 100000 1000000 10000000 -do - for REG_STRENGTH in 0.1 0.001 0.00001 - do - for STORAGE in "dense" "sparse" "split0.1" - do - echo "---------------------------------" - echo "NUM_ROWS = ${NUM_ROWS}" - echo "REG_STRENGTH = ${REG_STRENGTH}" - echo "STORAGE = ${STORAGE}" - glm_benchmarks_run \ - --problem_name "${PROBLEM_NAMES}" \ - --library_name "${LIBRARY_NAMES}" \ - --num_rows ${NUM_ROWS} \ - --threads ${THREADS} \ - --storage ${STORAGE} \ - --regularization_strength ${REG_STRENGTH} \ - --output_dir ${OUTPUT_DIR} - - done - done -done - -glm_benchmarks_analyze \ - --problem_name "${PROBLEM_NAMES}" \ - --library_name "${LIBRARY_NAMES}" \ - --output_dir ${OUTPUT_DIR} \ - --export "wide_results.csv" diff --git a/tests/benchmark/test_cli.py b/tests/benchmark/test_cli.py deleted file mode 100644 index b9672b0e3..000000000 --- a/tests/benchmark/test_cli.py +++ /dev/null @@ -1,140 +0,0 @@ -import os -import tempfile -import warnings -from typing import Any - -import click -import pytest -from click.testing import CliRunner - -from glum_benchmarks.cli_analyze import _identify_parameter_fnames -from glum_benchmarks.cli_run import cli_run -from glum_benchmarks.util import BenchmarkParams, benchmark_params_cli, defaults - - -@pytest.mark.parametrize( - "cli_options, expected_params", - [ - ([], {}), - (["--num_rows", "1000", "--cv", "True"], {"num_rows": 1000, "cv": True}), - ], -) -def test_make_params(cli_options: list[str], expected_params: dict[str, Any]): - """ - Test that the basic command line interface runs and that the benchmark_params_cli \ - decorator works. - - Parameters - ---------- - cli_options: List of strings - expected_params - """ - - @click.command() - @benchmark_params_cli - def _params_test(params: BenchmarkParams): - for k in params.param_names: - as_expected = getattr(params, k) == expected_params.get(k) - if not as_expected: - click.echo( - f""" - For parameter {k} expected {expected_params.get(k)} but - got {getattr(params, k)}.""" - ) - - runner = CliRunner() - result = runner.invoke(_params_test, cli_options) - if not result.exit_code == 0: - raise ValueError(result.output) - - -def test_correct_problems_run(): - """Test that the correct problems are run given certain command-line inputs.""" - output_dir = "test_output_tmp" - - problem_names = [ - "narrow-insurance-weights-l2-gamma", - "wide-insurance-no-weights-net-poisson", - ] - library_names = ["zeros", "glum"] - num_rows = 20 - regularization_strength = 1000.0 - - assert output_dir not in os.listdir() - with tempfile.TemporaryDirectory() as d: - args = [ - "--problem_name", - ",".join(problem_names), - "--library_name", - ",".join(library_names), - "--num_rows", - str(num_rows), - "--regularization_strength", - str(regularization_strength), - "--output_dir", - d, - ] - runner = CliRunner() - result = runner.invoke(cli_run, args) - if not result.exit_code == 0: - problem_name_str = " ".join(args) - raise ValueError( - f"Failed on problem {problem_name_str} with output: \n {result.output}" - ) - problems_run = os.listdir(d) - - expected_problems_run = [ - BenchmarkParams( - pn, - ln, - num_rows=num_rows, - regularization_strength=regularization_strength, - **{ - k: defaults[k] - for k in [ - "storage", - "threads", - "single_precision", - "cv", - "hessian_approx", - "diagnostics_level", - ] - }, - ).get_result_fname() - + ".pkl" - for pn in problem_names - for ln in library_names - ] - - n_threads = os.environ.get("OMP_NUM_THREADS", os.cpu_count()) - - expected_problems_run_2 = [ - f"narrow-insurance-weights-l2-gamma_zeros_20_dense_{n_threads}_False_1000.0_Fals" - "e_0.0_basic.pkl", - f"narrow-insurance-weights-l2-gamma_glum_20_dense_{n_threads}_False_10" - "00.0_False_0.0_basic.pkl", - f"wide-insurance-no-weights-net-poisson_zeros_20_dense_{n_threads}_False_1000.0" - "_False_0.0_basic.pkl", - f"wide-insurance-no-weights-net-poisson_glum_20_dense_{n_threads}_False" - "_1000.0_False_0.0_basic.pkl", - ] - - assert sorted(problems_run) == sorted(expected_problems_run) - assert sorted(problems_run) == sorted(expected_problems_run_2) - - -def test_correct_problems_analyzed(): - """ - Test that cli_analyze runs on the correct problems. - - Everything should be analyzed when cli_analyze is not given any parameters. - This test checks that everything in benchmark_output/ is run, so if that directory - is empty, the test will not be meaningful. - """ - output_dir = "benchmark_output" - if output_dir not in os.listdir(): - warnings.warn("Output directory not found") - return - - to_analyze = _identify_parameter_fnames(output_dir, BenchmarkParams()) - assert sorted(to_analyze) == sorted(os.listdir(output_dir)) diff --git a/tests/glm/golden_master/benchmark_gm.json b/tests/glm/golden_master/benchmark_gm.json index 547d46a91..f9ee5dc33 100644 --- a/tests/glm/golden_master/benchmark_gm.json +++ b/tests/glm/golden_master/benchmark_gm.json @@ -1,6049 +1,5941 @@ { "intermediate-insurance-weights-l2-gaussian": { "coef": [ - -111.20521894742464, - -152.65280716354238, - 54.52853585769478, - 243.4456132893867, - -67.71472140609603, - 33.59859836995376, - -1.1679631500339738, - 43.74622721378188, - -42.578264063764244, - 76.28986368392576, - -49.518060923792156, - -122.11637892393885, - -77.11494418031695, - -110.42615751547648, - 42.765098668183434, - 240.12057919139036, - 4.692227041337642, - -15.771647834404611, - 187.66023249727115, - 19.82238045910794, - 166.6250198373313, - -78.88500483320156, - -19.151064084616298, - -141.37777066636573, - -57.012639409473756, - 26.317711562834255, - -14.367548521971559, - -73.85966900653575, - -37.97662951486118, - 37.976629514836134, - -14.252086451784956, - -133.17294231994947, - 79.71147210500044, - -33.51643011097684, - -31.327155590426965, - 4.605772854960708, - 272.8363154575449, - 5.062777172715366, - -31.05080603315581, - -74.75923638864245, - -30.091849136156178, - -3.55852289603077, - -148.51253354559506, - -145.51041200420434, - -36.27982141224948, - 71.40307973571032, - -10.589180138680096, - -48.973770951734224, - -81.93844866239544, - -14.661755781770388, - 228.98490967934734, - 139.01047971973, - 22.3280582469388, - -32.55954344381378, - 9.556666742458134, - 14.47150994335041, - -6.713409480634468, - 15.932161664701427, - 31.268084012805538, - -2.0399165424193777, - -19.22861180165514, - 4.605544915949731, - -16.81307768796686, - 10.756031046408225, - 23.579965288416485, - -13.441143480777711, - -17.24007080972586, - 60.43392893431373, - -51.51533865696488, - -5.171850295968909, - -16.085445737154274, - -20.14029069059139, - -55.768691235656455, - -14.525575116719152, - -1.635427357974667, - -23.914296288422868, - 123.72800012149473, - -18.796101870445636, - -10.646858493002874, - 45.4544381327198, - 140.92293255411553, - -32.38473075988196, - 0.009354275199407958, - -0.774320919431513, - -97.57826458333489, - 6.555118785386792, - -11.606277887708664, - -15.23938776641439, - -9.569881979774285, - -53.699717158600784, - -2.993464068597337, - -1.1279842535355622, - -14.473708174297633, - -3.4887723567136697, - 72.16590946306442, - -4.959948619358001, - -5.975358210513289, - 12.893669064936823, - -2.625342766082833, - -7.717454389706286, - -5.3703142303266205, - 40.3487241858284, - -0.9862280654610684, - 13.052805548383889, - -61.48758294374067 + -128.5966412621167, + 76.78121470762069, + 270.1175741090245, + -56.12455423543155, + 55.63964625086347, + 44.50049683889798, + -40.62280188254876, + -52.33571220628373, + -128.21294858843353, + -79.98594980306889, + -112.96453467252206, + 41.675789308503745, + 238.98881756876168, + 4.5826373474698325, + 192.1890241425983, + 25.099403225193914, + 159.13828263498874, + -76.10779321823837, + -20.78052152562215, + -137.27325735889798, + -50.43552028142142, + 28.211710437726293, + -9.914056453351483, + -73.58440620779189, + 54.943929296420805, + -22.750573313726537, + 79.83188487379455, + -31.86044688096922, + -29.82930838826024, + 13.960987687707055, + 275.85093556916485, + 6.373760695202618, + -26.051855950656574, + -70.15763905017309, + -28.67223549989036, + -3.388391984692691, + -140.38917688138557, + -137.5562837542761, + -32.04760444473408, + 76.46056294223628, + -7.724226239928681, + -48.24605334017213, + -64.97000833093946, + -13.900477557469388, + 234.36800974331675, + 153.67431977131318, + 23.044032248298535, + -30.643347170168845, + 13.072420812856796, + -6.826238881341179, + 14.82737878107952, + 28.744984093318635, + -2.996983634896787, + -19.25092334450624, + 4.1186026805279985, + -18.12745593982211, + 10.100978386894022, + 22.965829761083434, + -13.793677749847125, + -18.987961914813045, + 60.021048527060856, + -52.318596600199704, + -5.5754037157230485, + -16.172052172596604, + -20.193372093430856, + -55.944942242893, + -14.832494756848238, + -1.893538771192311, + -23.753802652141953, + 123.03535397068276, + -18.92096261474263, + -10.597754524854954, + 45.92847204934916, + 138.3770637572935, + -32.18411332622428, + -0.17355320733049992, + -0.5860866368885655, + -98.72656476554506, + 6.562521806483979, + -11.571951463376362, + -15.85847112774425, + -9.27993081199345, + -55.51647718943306, + -3.166311051191031, + -1.0735744844228645, + -14.850259731268697, + -3.701624787243918, + 70.8434496375683, + -4.858202647481315, + -6.085396474174578, + 12.876852853674698, + -2.614758648923359, + -6.153559548474939, + -5.331279324006948, + 40.21633829473862, + -1.0838509466864483, + 12.977411570028728, + -55.85654716846747 ], - "intercept": 1752.2457774384839 + "intercept": 1722.177471343845 }, "intermediate-insurance-no-weights-l2-gaussian": { "coef": [ - -122.14098309542278, - -134.31134117376624, - 43.89992834591836, - 212.93993328682095, - -60.28657293868708, - 59.89903557510967, - 3.1989854970766944, - 52.4871790151698, - -55.686164512253924, - 71.35658318579831, - -51.740271216738385, - -111.58301183617252, - -74.85658697795557, - -89.12789478107132, - 25.79779241803267, - 230.1533892080847, - 4.772027157917781, - -33.50606950484751, - 188.91911934807712, - 30.195474323416054, - 87.74820187466881, - -66.65091901141717, - -14.682888716272442, - -110.1032328733811, - -66.22554234401065, - 39.25273952201362, - 3.0084168430412555, - -57.95529946130891, - -19.81381006630076, - 19.813810066291904, - -16.732773764783076, - -151.20335985717082, - 33.56115130640763, - -29.782366786602985, - -24.82629604956311, - 14.269089876831911, - 284.2627457938937, - 15.779576409554506, - -21.710815995231126, - -67.28824813122012, - -24.923263691212945, - -3.038931970031732, - -155.34111624278611, - -123.79041855966182, - -19.28617726943309, - 60.40767616962494, - -8.251512568191357, - -43.03151395612291, - -83.32553949692895, - -12.049014138148728, - 208.74586260252724, - 147.9762436780568, - 2.8462288753923732, - -16.811713151690928, - -20.98402528341879, - 13.828690468622412, - -2.870808019280999, - 18.515292958508876, - 37.42733133447843, - -7.162968654627547, - -15.776657372249401, - 3.841155421210984, - -7.656034243221042, - 11.22244019808997, - -29.887209496419803, - -12.325043061572824, - -2.6522846154440516, - 59.872785462484764, - -45.05574229612207, - -7.341510886581639, - -15.19055456465556, - -19.84519433722049, - -52.82614675188764, - -10.110448431297813, - 1.3801894368118175, - -21.96981251794114, - 114.65040163933857, - -15.564497403323784, - -9.090428414501853, - 49.2724464060313, - 135.29555636879513, - -28.693544210010987, - 0.47341311234303707, - -0.7113036073671328, - -85.51867545752592, - 6.9552046295832834, - -9.770760693811765, - -13.740028950947085, - -9.66988052597207, - -45.19675503987653, - -4.185661645962484, - -1.1462513683058617, - -14.46049130879286, - -3.2796705473090837, - 81.73859732054119, - -5.2549177973707115, - -5.085306667456962, - 18.436205353413854, - -2.6049757856159257, - -5.088503802965824, - -2.250441566029599, - 18.372647095138497, - -0.8850059859076953, - 13.052863646772586, - -50.48367954118778 + -108.79468914765327, + 67.0422866464174, + 240.41294959414978, + -47.15850225219875, + 83.35723316419563, + 52.84493302844697, + -55.055056421217145, + -54.891137141050024, + -115.61153152811171, + -74.4519233569173, + -92.31426118121114, + 23.402237013943765, + 228.47309053267583, + 5.195816027162087, + 195.6746878225027, + 36.94444782616531, + 83.28364606922717, + -62.98162336437164, + -16.03978407633725, + -100.71772083612699, + -55.49255609385598, + 43.512747228319014, + 10.28840016019357, + -55.633903707501865, + 21.82463518736934, + -25.91833027246508, + 33.8190539498564, + -27.83239193344906, + -23.187488363980624, + 23.477346433439934, + 287.05395739169523, + 16.846790028375587, + -16.46418570171754, + -62.451549619624835, + -23.819364041017504, + -2.814431658874885, + -147.1745467662508, + -115.32410206394863, + -14.821439284637657, + 65.85320523515348, + -5.0898614335600305, + -42.220678621779314, + -65.2157252805768, + -10.938984943282902, + 214.78393018190695, + 163.97554118934664, + 3.689061055533057, + -14.630970789946579, + 15.557858210777157, + -1.2300421993386728, + 18.683199171133936, + 37.95393786388804, + -6.027167479896391, + -14.44782677087454, + 5.82286597329901, + -5.8732891983616025, + 11.546328334754541, + -27.839553702752283, + -12.411972821376276, + -0.047943679700416166, + 60.798029570856954, + -43.845623493381254, + -7.3139360081065306, + -14.815201822616713, + -19.072518828040007, + -50.46968400168037, + -10.206998012028905, + 1.4529989977239877, + -21.073947817155503, + 115.87152032248615, + -15.380570638239092, + -8.795454223323938, + 50.03440937184804, + 133.99674752589326, + -27.966661961495838, + 0.42348567610066357, + -0.49600297597692256, + -85.96795143466258, + 7.059810341081723, + -9.625407879485364, + -14.22122965237128, + -9.284220704675143, + -46.53512100940624, + -4.246798284442683, + -1.0833555169046352, + -14.888683841560214, + -3.493054214963915, + 80.8229349855815, + -5.147192368922594, + -5.181755249092181, + 18.34656364843471, + -2.5733166450751765, + -4.305637928574972, + -2.2203547941752597, + 18.322133943930165, + -0.9979098778639581, + 12.970355125077397, + -51.28436863015381 ], - "intercept": 1695.1030924502904 + "intercept": 1625.0686737861586 }, "intermediate-insurance-offset-l2-gaussian": { "coef": [ - -111.08871598336638, - -156.03902678679012, - 52.679429525214914, - 256.223882182802, - -71.07640652372304, - 29.300837585820098, - -8.776014070243063, - 58.741307131115214, - -49.965293060880256, - 72.43642004305782, - -40.139557424795285, - -132.25282573206573, - -95.07301467585181, - -118.94817046799187, - 56.17911583489175, - 257.79803242272146, - 6.709866434668122, - -28.289076203522335, - 177.32718625956744, - 14.3908284877711, - 222.6341503412276, - -67.1593055221106, - -17.61822146062909, - -153.19350761642298, - -69.13638751029148, - 15.746825463544296, - -18.761806307907705, - -75.94068593125978, - -35.53905325673367, - 35.539053256723285, - -13.302944132543558, - -125.00990225503169, - 85.12752599919617, - -43.76415085012595, - -36.07751721114133, - -10.656180080489289, - 274.8871440808685, - 5.241977265184491, - -28.133266802170873, - -85.09917175462188, - -22.06090850398952, - -4.629811101367585, - -155.4757648486792, - -146.05472119037665, - -45.03364516121169, - 76.63705184534021, - -16.0502797371168, - -49.5134537859456, - -84.43944283146949, - -18.722654422181513, - 234.16109528569902, - 162.93809679274548, - 31.727979266857503, - -29.992739474516313, - 11.019322286698836, - 12.409181332654775, - -4.451541055252671, - 23.879987600460733, - 33.73367818742275, - -1.7582327936875684, - -20.04989671829264, - 5.477490564834413, - -11.349339775986508, - 12.616532263356323, - 21.255026585269203, - -16.357809247103493, - -7.559298421778137, - 59.6877009266992, - -53.714619919992245, - -0.28991784090344136, - -17.409693024990904, - -22.788484519224596, - -65.33610530912175, - -10.711078324332124, - -0.6525932151873852, - -28.817496374886808, - 107.43215978490984, - -13.376010416452802, - -11.15363142655749, - 46.47453791857368, - 140.53555500597935, - -22.809816700704154, - -2.4009137091932713, - -1.845479170581106, - -99.62535816910707, - 6.528739919879721, - -11.935491319445978, - -14.700762517768592, - -10.866419029096184, - -66.66286718708753, - -2.680106007330884, - -1.3192670778786042, - -15.029784134882481, - -4.209601557945848, - 58.38314832807913, - -4.265147737221525, - -4.818736672193541, - 20.296085604454788, - -3.0533867674404314, - -11.811378647516543, - 1.5440875022940195, - 43.62387352995569, - -1.2319579098546676, - 12.76972945242305, - -52.624614094971115 + -133.1286461663034, + 72.91812556596727, + 281.405067898197, + -59.822959748227504, + 50.88369625511952, + 60.92489092932182, + -47.79696538451361, + -42.81447559100911, + -138.0569761927028, + -97.77321736832825, + -121.07171693310752, + 55.371303572375915, + 257.09819299806605, + 6.564820660140271, + 182.7199990179157, + 20.340221993819576, + 218.67065625220513, + -63.77064130948716, + -18.780721336421166, + -145.35335998470669, + -60.072487641036965, + 18.91670688314323, + -12.811637457389375, + -73.95438255737676, + 50.1165397994097, + -21.107000114609356, + 85.20008721613435, + -42.329216191123955, + -34.8688081989454, + -2.3094659833575437, + 277.3750216374209, + 6.26334201309926, + -24.12217602759118, + -80.7816057937179, + -21.01486723249782, + -4.464798803495786, + -148.44444583009803, + -139.10464875770467, + -41.224744208733156, + 81.04147035060069, + -13.488512775049122, + -48.8240664344946, + -69.70831658126534, + -17.954149139319266, + 238.96213053420652, + 176.11450088685444, + 32.39375106053048, + -28.066784828258154, + 10.983452007708452, + -4.49042192872081, + 22.992862131181376, + 31.643210007488275, + -2.67978507862212, + -20.17752942581421, + 4.580915570685904, + -12.587124208990037, + 11.897553167569889, + 20.52110817761151, + -16.771477817383815, + -9.649896059768926, + 59.23519980783472, + -54.59759564217622, + -0.8078085616123288, + -17.501561323192412, + -22.93396836267986, + -65.51167497148927, + -11.094712494141477, + -0.8064239975861713, + -28.71748261354709, + 106.72066329845508, + -13.50133508909776, + -11.0619150410595, + 46.85732282419134, + 138.00007793518435, + -22.60060597131495, + -2.6462701322001365, + -1.7058469075671503, + -100.98383272648516, + 6.541656273881687, + -11.909559757922915, + -15.217482090431456, + -10.566131964809076, + -68.1349784113862, + -2.7523727593212297, + -1.2636646331732422, + -15.441534776757974, + -4.419065952623164, + 57.93049497159326, + -4.170568113250889, + -4.915295015187043, + 20.252901320900495, + -3.043973619603685, + -10.131212939669, + 1.5551962949565874, + 43.58872332139981, + -1.3211380702854936, + 12.701886885650582, + -47.614436204567475 ], - "intercept": 1707.3949168218355 + "intercept": 1674.5925938060475 }, "narrow-insurance-weights-l2-gaussian": { "coef": [ - -110.21574838874518, - -152.55289456994035, - 53.52845826747633, - 244.76937943358678, - -69.22517610470588, - 33.69598136230699, - -2.579978559033479, - 44.405789915531415, - -41.82581135651333, - 74.88058762912512, - -47.94707189915141, - -119.3248482182066, - -75.57821176839325, - -111.90977417404076, - 41.46272191526365, - 238.41659651538473, - 3.8837719545644065, - -15.464469810591133, - 188.04446917797725, - 19.732098075323115, - 165.97246056115503, - -78.27968424445426, - -18.853844279605077, - -142.0151690346241, - -57.610213402048636, - 26.80956847664126, - -14.102473556267876, - -74.232741963524, - -37.72503471203383, - 37.72503471201525, - -13.794756937772854, - -131.66867595925217, - 79.80991534695711, - -32.70855086644031, - -31.925343591958498, - 2.535372796420943, - 271.8196821187047, - 5.230477399005955, - -30.78329131886789, - -74.741707627047, - -30.19751057197051, - -3.507236269596473, - -149.84967982986515, - -146.9818724489029, - -35.952283702913036, - 71.66670178355764, - -9.888879619474237, - -49.299865403813556, - -81.73801899519303, - -14.620158304702084, - 230.63846914866954, - 139.51441070032524, - 22.64804521634225, - -33.40584632897258 + -128.7134212866144, + 75.67935202196132, + 271.1511432355359, + -57.674322789847906, + 55.50872574763626, + 45.399003528347, + -39.55357744470037, + -50.92884552303039, + -124.95617195380345, + -77.75202473717411, + -114.65576539595175, + 39.95251534762472, + 237.15926681691863, + 3.987866720332384, + 192.54764148478577, + 24.933468883220947, + 158.37188569693097, + -75.6051438369235, + -20.499041195570545, + -138.03553289185933, + -51.10181574532778, + 28.7674801093553, + -9.714166828926928, + -74.00196027510843, + 54.52924373184466, + -22.111529414012953, + 79.97830994430964, + -31.038349394464312, + -30.431306033089925, + 11.727214160963516, + 274.78631195249017, + 6.466495181600085, + -25.80292308889872, + -70.21534753097507, + -28.83058897688521, + -3.3357792418090195, + -141.87127190547406, + -139.04408202062382, + -31.770980408858005, + 76.61315048511814, + -7.0590445139222915, + -48.588930272303934, + -64.86338437279417, + -13.853851985865827, + 235.9882191689853, + 154.0222974600432, + 23.349533873142224, + -31.51358825953298 ], - "intercept": 1805.567181564571 + "intercept": 1756.9802697945054 }, "narrow-insurance-no-weights-l2-gaussian": { "coef": [ - -121.35738046821804, - -134.32148283431, - 43.302959712680966, - 214.00675782452203, - -61.321886002473285, - 59.69103176777411, - 1.6851948688133924, - 52.79030302808924, - -54.47549789690687, - 69.45116006482911, - -50.731161233760766, - -107.63117916822516, - -71.2086284389138, - -91.45199013728974, - 23.303268403170723, - 228.26853051017025, - 4.641628421831379, - -33.161314088556104, - 189.11655612446899, - 30.0661556594211, - 86.58788415946599, - -66.13211920601879, - -14.591948487111912, - -110.61820189398257, - -66.69958964115891, - 40.21235200236587, - 3.423573591981843, - -58.203348220893815, - -19.525843718009046, - 19.525843718002886, - -15.99166150639686, - -149.8507749679993, - 33.55979068174464, - -28.97447622322047, - -25.672823109033125, - 12.41887228173995, - 283.24287009812383, - 15.86865996003646, - -21.23172306752609, - -67.59227521893874, - -24.969155970000415, - -2.9801163637686394, - -156.74631254385537, - -125.20558388103258, - -18.80709296327133, - 60.671376947504214, - -7.826866102025393, - -43.27468975609284, - -82.74149278388441, - -12.030780466709805, - 210.3315865033898, - 148.58947378207438, - 3.221533162731291, - -17.70920221679922 + -108.91821175630464, + 66.2755982225235, + 241.43256625266184, + -48.42627534779092, + 83.05684147488665, + 53.35135325594508, + -53.902360160868284, + -53.375122932600085, + -113.09613515991062, + -72.56912791893593, + -93.85810900104975, + 22.134440377342994, + 227.065440995342, + 4.706123785041367, + 195.75405577109817, + 36.89154452805517, + 82.28666824246909, + -62.36300409186936, + -15.900355781841741, + -101.21931010089759, + -56.14084892390406, + 44.11869393094295, + 10.534221450070811, + -55.95287499355697, + 21.65354171354028, + -25.36052933976864, + 33.742720073380404, + -27.11662484004017, + -24.079489689495976, + 21.891678332832395, + 286.10351726895, + 17.03360416358702, + -16.126826354388836, + -62.67364297210302, + -23.817139419585864, + -2.7632544420106524, + -148.48180226782156, + -116.88800735579828, + -14.352372288679641, + 66.11184348518621, + -4.696508912890745, + -42.46106955963109, + -64.94742038982892, + -10.9796897397404, + 216.21133284047343, + 164.4084557261684, + 4.043583305992484, + -15.506330761628337 ], - "intercept": 1690.909362113078 + "intercept": 1655.2413164984828 }, "narrow-insurance-offset-l2-gaussian": { "coef": [ - -110.31972617028191, - -155.94097658439398, - 52.03720325973498, - 257.3968720071419, - -72.3550193861293, - 29.181646873890955, - -9.900456789814044, - 59.11073517530734, - -49.21027838550401, - 71.54520537496288, - -38.854766082800055, - -131.19664655870238, - -94.00819880659094, - -119.83882401236079, - 55.578037636035724, - 256.77519244942545, - 5.926997017584868, - -27.881328723691347, - 177.63443068500536, - 14.415599365627026, - 221.99193702991323, - -66.3966668041759, - -17.369850821251465, - -153.61604524373578, - -69.79272253332886, - 16.117781203706624, - -18.661051591564238, - -76.4420825665348, - -35.391012235235436, - 35.39101223522245, - -12.996021145553412, - -124.08362032405813, - 85.15477803956311, - -43.03324832337104, - -36.78239763876639, - -12.191781670900797, - 274.02815157354553, - 5.436788281568393, - -27.97107302826517, - -84.98217748475214, - -22.066010046199718, - -4.571025434158433, - -156.81830269511525, - -147.52843632101565, - -44.55963412428321, - 76.98279017771651, - -15.565418679719679, - -49.81739942971244, - -83.82592634952492, - -18.743863147295325, - 235.5030396341309, - 163.38129001835958, - 32.053476972229355, - -30.815518461103345 + -133.20755723102943, + 72.2142367291071, + 282.3328442285382, + -61.10080653150354, + 50.57123070997536, + 61.49596111043097, + -46.74130438732179, + -41.73659219999051, + -136.44871608134733, + -95.89572658977461, + -122.23270423476758, + 54.26393638884636, + 255.90037493504678, + 6.017539095129124, + 183.00648550468887, + 20.28156407506463, + 217.85642684514323, + -63.110752546199876, + -18.56313180274058, + -145.95135909465492, + -60.800859143439, + 19.368884385533885, + -12.746524907130842, + -74.52277680522886, + 49.84958090424143, + -20.650921401855534, + 85.27046933245288, + -41.577473191923076, + -35.56106779188514, + -4.018629110585168, + 276.4850671826412, + 6.393015280473019, + -23.941336778432582, + -80.74075640376184, + -21.055069082547675, + -4.4032657429376165, + -149.87740520163482, + -140.55042466886638, + -40.79187680151358, + 81.32009489026699, + -13.032219280224549, + -49.139048480547736, + -69.10025499893736, + -17.96068413558807, + 240.30718920826286, + 176.4783946763287, + 32.71244865955648, + -28.898165065994743 ], - "intercept": 1760.396641154273 + "intercept": 1706.9723879594192 }, "wide-insurance-weights-l2-gaussian": { "coef": [ - -709.1634688293617, - -3.696545138036503, - 0.0023035083779233438, - 0.017192055593998935, - 0.7926398886805918, - -709.163468829366, - -30.45175506566459, - -12.076243379657372, - 51.90718391676323, - -24.979309130579498, - 12.46518116335075, - 3.1349424955104697, - -55.713745635532455, - 8.99189024347091, - -18.04941600711418, - -21.223075235880867, - -0.3147067313643201, - 35.84984395821327, - 5.4036357187123745, - 1.1948434668842853, - 9.980754944882758, - 26.076113811418157, - 10.809764374696268, - -3.005902908666987, - 9.815466861747463, - -9.034130317743154, - -26.45538670055677, - 40.11047366244844, - 32.21429094594428, - 1.8002013559317334, - 4.52656162917412, - 19.73873889429865, - 14.197869932239605, - 1.656377966914405, - 6.571021821756786, - 19.994122672259657, - -58.21469643810967, - -22.94867138141954, - 6.042340876921753, - -6.862009220923304, - -72.02813612877122, - 11.701210280683751, - 26.440697846724582, - -0.281391157663505, - 1.2440461197036214, - -0.3158993525852999, - -0.7314108552176269, - -0.25412912716752395, - -0.7505317416687106, - 2.7348396649754085, - -0.19403706126509726, - -0.5208462827033596, - -0.24682697436233866, - -0.01880091789667462, - -0.2698466750583635, - 0.3300801588800766, - -0.05743073839868038, - -0.11485247130257874, - 0.35853089575301356, - -0.17783804382368035, - 5.407941678574752, - 12.542748967917612, - -7.645248801501278, - 7.391904505944257, - -42.28416504177746, - 14.82857558181594, - 10.158257994161108, - -1.8124245166294009, - 1.2348610847360717, - -9.979665400238714, - -0.30774730621144447, - 2.9449819310704966, - 10.5242081037758, - 12.924007340550487, - -21.274301621281936, - 17.660989896603642, - 18.16049376022296, - -2.2053491841252795, - -53.866161474832474, - -2.892040273752098, - -6.665408258214577, - 5.0443914382332, - 15.046237973334858, - 9.274561912426487, - 31.750839848774053, - -1.438078040229476, - 18.662508315249646, - 5.373163225035141, - 22.57843352664601, - 22.511288090471773, - -35.624590434758744, - -2.126769233731063, - 3.357817761623517, - 11.220584347341399, - -68.48143342131938, - -19.61063705548088, - -14.761644350857612, - 1.873916577495683, - 27.05086424915207, - -2.7035389206710594, - -8.883201506649637, - 0.38542976076412855, - -99.93828775707864, - 6.687998569000773, - 5.589818250654184, - 0.4861202222463691, - 38.68850172539377, - 9.973502975859786, - 22.687268011330595, - 26.705101248638314, - 0.9379852299958404, - -13.369190188317342, - -23.23490454524074, - -2.027390521214576, - 3.6331450435247086, - 17.702158222609345, - 13.448119301902828, - 1.6344945857491189, - 29.29625778683074, - -49.108223062569714, - 20.037030650578117, - 3.461481767605201, - 1.3224744533717396, - 1.067590916686411, - 0.5022518224995234, - 0.03293703140694397, - 0.7952113995796092, - -4.855713238068204, - 1.5710633366282807, - 0.24554738290374178, - -0.0619499562155017, - -0.9788731389501036, - -0.16613212128233948, - 1.9988636779037858, - -0.11086211390722107, - -71.06046640717805, - -2.336486393713184, - 4.99843006807534, - 5.7771324398695345, - 15.271308254428968, - -6.525886163902411, - 2.0119639411850816, - -2.218764647736992, - 8.627014091837301, - 3.3241402097136454, - -81.74886232628549, - -0.544171092652214, - 19.738844375775308, - -1.1019319694399983, - 1.5422862629286962, - -6.629800808994635, - -2.4174899895162856, - -1.9987199313343924, - -3.1507647073693907, - 5.08800189094943, - 4.024331239656428, - -0.9964675057879884, - 9.960843061586152, - 0.8387404403368955, - 0.39495716082027127, - 14.091867222233674, - 15.26296055860535, - -0.28021498586046784, - 1.8232368533815946, - 0.3271554401870975, - 2.089486317770977, - 2.0483935790064947, - 0.6799122263660449, - 0.14318095094411204, - -1.469104561075672, - 9.020663944714828, - -2.3466755079285235, - -0.13213931202597293, - -2.3001079829252675, - -0.03698124316493644, - 37.14157952237965, - -1.6596508358528275, - 0.2037309619226218, - 10.6248018508529, - -0.4399852124866515, - 8.713842036954906, - 4.606204591455416, - -28.447764372518208, - 0.026965687630505553, - 2.4417453235075635, - 16.024879470545283, - 10.915075587143942, - 1.6400480291901212, - -0.16714622222142425, - -0.3692583836142929, - 0.876161104626087, - 7.23470136040634, - 0.1968981454106897, - 1.3557396340022183, - -0.28300510014380276, - 12.883110410011112, - 1.201518217476373, - -0.06873515969570475, - -0.19232293404061332, - -0.11671743941936218, - 16.613385073066667, - 1.0710561105488594, - 0.570634536539815, - 2.2812123192897107, - -0.4818239271918735, - -0.016870511891136416, - -4.483054543625162, - -0.21117827517022716, - -0.08058680773853996, - -0.6505913571246753, - 3.1370020559808256, - -1.1721583952803472, - -0.14060297372383604, - -42.13927826537033, - 0.10513709678822127, - -0.04305841069494289, - 0.6804354913771378, - 0.8281095169178373, - -46.02499168239974, - 36.23734454303963, - 13.532337409088479, - -103.99404098743207, - 6.472979466944495, - 7.868647798734494, - 32.52562414011674, - -19.154957026052674, - 28.440618541475086, - 26.87888658966138, - 17.217551206546112, - 27.67658044429794, - -27.676580444603893, - -37.524398858755674, - -69.56796721542742, - 2.761923521647077, - 6.556679229992765, - 19.98075835235115, - 67.31509893977271, - 18.002972856698094, - 15.282100640951485, - 2.3603992653377093, - 2.0383993891501895, - 0.331754793884071, - -28.82908211058691, - 11.934019710001897, - 27.300640695051392, - -2.2739641800031096, - 4.633837863582426, - 0.793545826237287, - -7.314757725440034, - 3.1389738730310057, - -41.6122400062687, - 32.79804551087025, - -28.10674037235493 + -0.02176058498924486, + -22.20774237677405, + -80.99511641655779, + -198.5567947129949, + 105.52614004181358, + -28.696088807989547, + -140.50537330663096, + 77.11683832936939, + 256.36244262532557, + -64.59635711695378, + 43.82160972750756, + -41.95096417812329, + -28.229945551098098, + 31.94008839072705, + 116.97455212073511, + 22.5308775779578, + -33.247410674372844, + 14.409229596474754, + 25.15306800285657, + 153.09608685118272, + 225.72985048615254, + 10.743636151245779, + -50.44697745269097, + 38.229322484558054, + -79.86816041740624, + -93.79183946710663, + -116.16845028676723, + -47.1812088527681, + -44.76522831543658, + 43.65791761187878, + -108.24860773237414, + -111.86067088422801, + 155.23414182532304, + -7.54840716067434, + 92.57798954539409, + -22.373823268133524, + -17.137434645520546, + -15.119709213250557, + -7.211929810756743, + -0.4613885293655074, + -4.6530191620952035, + -10.172227974404, + -0.9951026373383883, + -3.1980832738196288, + -0.8989286694475811, + -1.2061145362610515, + -4.172535708864433, + 0.32962220377643636, + -0.520662796815861, + -1.3112166420650617, + 2.1006972046956416, + -1.2249922799691093, + 61.93452252412838, + 59.00261296345624, + 38.914454762849864, + 26.515791557742467, + 1.0469900196955615, + 27.12195645688526, + -82.86349830320997, + -28.434520881266902, + -34.536102782248385, + -4.993842572232279, + -47.91188545694528, + -27.768794720088472, + 68.07271456948504, + -14.601344611212417, + 18.35787171393789, + -3.781493309738707, + -38.93792716843971, + 66.78343932097557, + -152.23736949392386, + -29.586938903623928, + -93.38680172731907, + 18.91597008275526, + -99.79480337797757, + 52.777658150244804, + -70.81169318697638, + -39.25831399306616, + -59.76912283357316, + 97.63693945209228, + -42.79619352700257, + 14.44762271327469, + -114.0018729982055, + -55.37751526966203, + -8.451080717125082, + 84.67138454701295, + -63.24870647899515, + -12.402637346143381, + -82.7680941864277, + -29.772222647911427, + -79.39829243946988, + -42.12860611940431, + -37.187546931238614, + 124.90598946796813, + 75.48758269276374, + -43.2136655805164, + -3.290561109448098, + 145.87198301255586, + -10.867392058070001, + 32.906646945514154, + 128.48921707312127, + -31.347340658171277, + -34.0842142547481, + 27.229351025281346, + -37.32036330232173, + 83.3432311060433, + 18.3118095597669, + 47.15597174506133, + 10.086928510495516, + 120.52461585492826, + 80.58629869338901, + 95.08728194833104, + -5.179597560091591, + -14.958056617354544, + 0.6236519808728381, + -11.904673943022175, + -12.446934586579268, + -8.800062628120001, + 4.9788896240341485, + 1.4532999160037992, + 0.6341906393996081, + -2.482558688789661, + -7.852846284650672, + -1.7404747321462606, + -5.3022520825847765, + -0.5727184821109645, + 5.025171653749701, + -12.590932765971893, + 14.560667785025021, + 18.79372593383429, + -11.038498272523366, + -25.04424584779048, + -2.4323268478681332, + -18.25943949935961, + 6.3852149037151555, + 14.721547291394105, + -13.685201021978193, + -14.490848623079264, + 54.504854105394266, + -59.60186348878437, + -3.851417468730092, + -15.589474434745078, + -19.88831396202521, + -59.195547265172834, + -12.661688435964994, + -1.439538508912485, + -23.675811110949905, + 127.03686599785996, + -15.04889129681842, + -9.728568098286544, + 48.668366409162616, + 144.1840546794116, + -25.5004007473232, + 0.9827863560629039, + -1.0417811948568787, + -90.287683344843, + 7.941969294745151, + -9.844010366009815, + -14.614161864471697, + -9.523392404226087, + -44.71911775412441, + -1.3119270728835215, + -0.887815865728536, + -13.133972886201283, + -2.9568909581899034, + 88.57877806541204, + -3.1439402409692883, + -5.62066157170908, + 14.73186172904888, + -2.246143600417063, + 23.532190504780385, + -2.8696686610579323, + 42.46023052027408, + -0.9768071848649158, + 13.201497628361867, + -15.478806400093475, + -10.521465471365055, + -2.6899958228402077, + -0.5450726757110395, + -2.121710291465887, + -1.0317073228290867, + -9.262418356983613, + -1.6868963487080226, + -1.2312828474122435, + -1.9096075183736412, + 34.663083144597444, + -2.0518529054910393, + 0.30705816391275725, + -0.5584473319095778, + -0.3513773086926537, + 28.703127850821602, + 5.8974010395487175, + -0.7190475870226565, + -24.846952128581062, + -2.592650726031045, + -0.7562765372779087, + 7.261066413131902, + -0.9870170243510449, + -0.3687308340752874, + -3.185016350873887, + -3.8529288511565145, + 4.516613837797752, + -4.385080644337923, + 66.19512563589498, + -0.5878736279399217, + -0.7692056016774543, + 4.008879440436109, + -1.8233194048578796, + 187.44265621186972, + 22.838610388495177, + 156.77924209447215, + -74.94349466715242, + -19.68437879370103, + -128.85461513749374, + -55.59860092035474, + 24.623959298795008, + -4.569909809270789, + -66.41957120563258, + 60.945558533237794, + 79.24303287564976, + -31.78411025929554, + -35.65236088743924, + 35.002659323340126, + 276.24081236153086, + 5.242307356898204, + -24.652809031588447, + -59.504347585523654, + -31.301075107064573, + -3.7411603368117263, + -146.2801480482895, + -133.23259096088398, + -20.931292419274357, + 69.64698467927367, + -12.704212517206575, + -45.62720413512325, + -77.33351217620282, + -14.721722055412728, + 233.32403127462965, + 119.98508634037617, + 27.189638316264627 ], - "intercept": 1748.4426270328668 + "intercept": 1862.2517330912453 }, "wide-insurance-no-weights-l2-gaussian": { "coef": [ - -574.7873059726361, - -8.85126716047138, - 0.0005334901306444611, - 0.01064532916511159, - 0.8752325440008164, - -574.7873059726365, - -19.66288827104498, - -9.123180054016304, - 27.064800667580503, - -7.564528218878776, - 5.415435475234749, - 3.8703604008284005, - -24.72855784857285, - 3.26691111868731, - -10.92267381033696, - -19.108977259372423, - -0.43099521109864675, - 21.22574815412229, - 2.40378439547914, - 0.626975155391338, - 7.14943499150229, - 16.1861118906565, - 5.050342305312896, - -0.7181038820648433, - 5.612686829441203, - -8.889824978173992, - -26.227024529922765, - 24.68954172279502, - 15.094194390851342, - -0.3628053547003114, - 8.417350251360027, - 9.15662674765761, - 9.4966165926435, - -2.579556482349909, - 4.553783850892844, - 13.18280928599084, - -12.518525409076256, - -12.662353004318872, - 2.1588927680053294, - -4.302845348139868, - -46.81763107107167, - 6.6115889450359955, - 15.424387842294577, - 0.4107024749361621, - 0.781694814096406, - -0.9445989032544649, - -0.07722762762206997, - -0.02060488007719555, - -0.29705749854945795, - 0.1849950909209364, - -0.01680301143624719, - -0.2372151243921665, - -0.11291157519686748, - 0.011821352231258853, - -0.013961597332523168, - 0.18664079445724885, - -0.0062526104076176, - -0.040799290754273694, - 0.2380984660263411, - -0.0844339230953454, - 2.8483452170663153, - 6.1900141906046535, - -4.3079914664125125, - 4.110245450355449, - -31.180831075133515, - 5.915178803835733, - 5.256987435559099, - -1.0040038031829206, - 2.237362248355117, - -6.609724539699227, - -0.3754645070159813, - 2.4085121389420117, - 5.404400538206907, - 7.950111603556632, - -11.773660278187629, - 13.002004690727297, - 11.947001847661197, - -3.2431837895647058, - -29.06508039572055, - -1.368477443871058, - -3.135952068953971, - 2.693737218457178, - 9.811752866497034, - 6.401659248327998, - 20.249807674738616, - -2.5789518857808518, - 11.812298138427378, - -1.9584348187958018, - 14.526170586165902, - 13.034987769344847, - -21.43091269206946, - -0.3585909746457588, - 0.9763505914155335, - 6.2531370614652335, - -11.326719085833549, - -6.549685058091298, - -10.488694779804439, - 2.658157685293541, - 10.104853426355417, - -2.359546559079581, - -5.695778368053061, - 0.1363323071821782, - -65.26468589775952, - 3.497031636931648, - 3.306606058499983, - 0.6180960526801277, - 23.980180166573856, - 6.360671071189356, - 11.021782984465252, - 13.499221417923344, - -0.44531989190418425, - -7.430752396962815, - -15.422642004054445, - -0.11019235499009652, - 2.3635225790547647, - 9.040717686354649, - 7.590558929173009, - -1.199208958564452, - 16.670479207013337, - -34.6245966641869, - 12.257010444736856, - 2.600029139704421, - 0.9146278051850983, - 1.0807071436064637, - -0.0427210505559077, - 0.42723065154259465, - 0.24689725127055454, - -3.351528272559368, - 0.8747335481531523, - 0.13510347627785338, - -0.0016410148156073628, - -0.41038243042341893, - -0.09425347114937335, - 0.8429522023909318, - -0.047960193741934616, - -29.212683404743686, - 0.7436904639234844, - 3.0468294540610517, - 2.418197432405682, - 8.759195402188599, - -4.013013420471811, - 1.8208208086694742, - -0.5157415008795372, - 5.777214000117844, - 1.8319517775488576, - -49.334665573899066, - 0.4938254747537102, - 11.056099424378456, - -0.08925985264334772, - 2.1712729585531636, - -4.364966782415144, - -0.8794025462392839, - -0.31026242753314504, - 0.8351552699019685, - 2.224220114965413, - 2.3628653624459313, - 0.7868177795593139, - 9.301691697453453, - -0.4077867336703154, - 0.31279527475814767, - 8.494349331196972, - 8.840110249745383, - -2.1190967445120843, - 1.5139622656025742, - 0.4677289832972033, - 1.6691369538038592, - 1.2276876749248034, - 0.453251014631736, - -0.05333520968280726, - -0.561007878700283, - 6.737930324760092, - -1.4910078694002242, - -0.039553403174400564, - -1.304549928223168, - 0.04209910102129147, - 21.946995330356668, - -1.236813409290211, - -0.026508481465112678, - 4.4671020952884835, - -0.15027251121222554, - 3.1409952772002643, - 1.1919800938155587, - -18.651405037684203, - 0.06467697960095656, - 1.521617561789336, - 2.7143633675000327, - 2.9266678758796014, - 0.43083491430830984, - -0.07461642424070669, - -0.14715574100500267, - 0.18068746004441652, - 4.447812213289549, - 0.192752674122962, - 0.5379750850029269, - -0.1265111008969367, - 8.579031553791895, - 0.3028666147105897, - 0.0177926670150375, - -0.07738040779705543, - -0.06352484147093078, - 9.45050937809372, - 0.6727200182842177, - 0.023932242764804375, - 1.7472403529354612, - -0.21663991603474758, - 0.03597639883475642, - -3.524999634980562, - -0.06692975661971974, - -0.026760578620483384, - -0.2626439943746884, - 0.7423384565531093, - -1.579386781461835, - 0.03950615726550525, - -28.536099397671137, - 0.07465024278768774, - -0.008665781229239857, - 0.4325110388426138, - 0.2002124272216881, - -17.131611842916527, - 24.877278610630547, - 9.548777013408342, - -76.40934294593052, - 1.4895810403947598, - 2.6245687921204555, - 20.385761390069348, - -11.320099225731614, - 19.662125439419963, - 17.234446305927158, - 9.038515422330178, - 13.210461224051667, - -13.210461224294075, - -28.906806654542805, - -41.91921977117667, - 3.5964381677435537, - 4.844619120601427, - 11.727362782669978, - 40.03148366715742, - 10.353862338223443, - 6.11614078262081, - 2.7807264460858376, - -0.489463714635745, - 0.47529681716551697, - -19.692374723222642, - 5.629542924808966, - 15.908282224375855, - -4.745876185133582, - 4.110706264429317, - 0.37912211348750685, - -9.451801752854475, - 2.632120213697018, - 0.4279387893208524, - 16.441046692471243, - -20.24914654356564 + -0.019422709050008504, + -25.94512910090344, + -40.34560000416062, + -182.1389718674843, + 111.4954688872194, + -25.814238158164947, + -122.08026781502917, + 62.60497173161834, + 225.1398279746468, + -57.777669654502105, + 60.38847874081522, + -32.429387618069576, + -26.81055013410527, + 33.7962754751916, + 119.14325925388391, + 26.57919283647349, + -31.227046036577267, + 6.027887248407939, + 2.5056996115057637, + 162.00411162028126, + 222.12443311549225, + 7.595647191338185, + -39.78053603554418, + 51.91943241131885, + -65.17801019976748, + -86.42392870102317, + -103.53363664821154, + -32.244786885637694, + -53.59836208516247, + 23.777750201849813, + -94.37467970121912, + -105.53976013360986, + 92.19750508479616, + -0.8534317326690687, + 99.69037168543177, + -20.97388641380362, + -15.150787066848151, + -12.875881752309798, + -6.824313404508695, + -0.4832200346448139, + -4.5568828883000805, + -8.593904026172906, + -0.9474953283032268, + -3.1376569524513145, + -0.9315413420641145, + -1.1124316069394846, + -3.859323699062355, + 0.428683875385499, + -0.4850447251588748, + -1.2776943282422402, + 2.0656995413647805, + -1.166309271601839, + 62.37515134720349, + 48.567940970276005, + 40.10429921624562, + -0.4534353809837534, + 9.212174173156754, + 33.708082007843046, + -73.67182328999114, + -23.465464053966926, + -33.71532777877898, + -2.090974830292157, + -37.52940130845264, + -17.459071609224615, + 71.1867926761324, + -24.221246903137608, + 26.649338615124854, + 9.691456374289832, + -36.875874077822914, + 29.5916591329716, + -137.45541234836185, + -31.26570789736093, + -81.48146936545014, + 30.848979864317663, + -85.45611713253703, + 63.4441770112196, + -63.51713695620745, + -25.26749232160531, + -54.119384067624814, + 101.9385990677613, + -27.310523488028984, + -12.322358735871449, + -106.11127190546829, + -50.15474901626826, + -5.080323703949309, + 63.61597922877145, + -63.085400425235214, + -23.14588342620031, + -75.36274812966496, + -14.410555460607165, + -71.62331125398524, + -44.00143373389065, + -34.28032711815561, + 48.421759003689935, + 70.12484966436035, + -38.12292277263301, + -4.4879749914801295, + 151.31033224692348, + -4.383077671960145, + 39.83806013851982, + 130.50261776809808, + -28.543831301658127, + -39.30775058171377, + 9.248155230147265, + -35.41534787130778, + 75.15044898935062, + 24.683034918431897, + 49.312422075773824, + 11.302327547160889, + 124.61855494307945, + 41.19724971965003, + 97.15202864561456, + -3.4153809475021975, + -12.959332902111642, + 1.2455938055369107, + -10.831237819954456, + -11.473468586950894, + -7.6759900036314495, + 1.3631575810899113, + 2.2195511339842247, + 0.6453243092752943, + -2.3476868350829223, + -7.653089967220263, + -1.730576265172019, + -3.5658114959466025, + -0.5747142127404381, + 7.5222075371107255, + -7.13447312670925, + 17.68746546929281, + 27.897268055075084, + -13.10542552231341, + -19.871540478806185, + -1.2482176902363231, + -6.423548535470474, + 8.418049114854334, + -35.48223146442139, + -12.21360209867827, + 2.6809269568595533, + 55.535543608255054, + -48.96287804193606, + -6.258304419208782, + -14.547576553341125, + -18.150535586044384, + -52.16228266318022, + -8.221658336571922, + 1.655387283788632, + -20.298955056899, + 121.74563203077236, + -11.629631672955462, + -8.29824802743714, + 52.35987423708277, + 141.04910762599127, + -22.19531220444416, + 2.2324964406418166, + -0.626007357567771, + -75.59033684771767, + 8.516678088888733, + -7.826217043662895, + -12.46294788237195, + -9.129414989558697, + -31.143907319549008, + -2.5940444216350853, + -0.8466723968230906, + -12.910827633012204, + -2.6836178940539637, + 102.28969477162961, + -3.6616188624269164, + -4.589280819121383, + 19.390602915637313, + -2.1503912547952875, + 29.53184116098697, + -0.1623137656261079, + 20.373777243587405, + -0.86308167387964, + 13.184246556232637, + -0.30620644948101833, + -5.320225783309735, + -1.5150947198693856, + -0.5488726292920258, + -2.056449482721929, + -0.5181803321435273, + -3.8559378715190147, + -1.4230930325972262, + -0.6440274206414239, + -1.8256924356110436, + 40.028169000708495, + -0.9072399796106735, + 0.27595914143476846, + -0.5717089484037374, + -0.36243578596904286, + 37.30610292783774, + 5.896631504080562, + -0.37387894076228845, + -19.37620077250944, + -2.551901072054134, + -0.6779030127642586, + 3.499850549708208, + -0.9403547137353883, + -0.36560066375994116, + -3.154804095860928, + -1.2824362637301965, + 3.078197726946268, + -4.135853802558928, + 32.627307739136285, + -0.4893144736124595, + -0.7081103603314924, + 3.9872005903085697, + -0.9413843224093168, + 191.96829297781755, + 35.61535522368014, + 87.47042510452313, + -62.190045723442104, + -14.642723106507642, + -95.55154037110177, + -58.60682193814874, + 41.1084318112043, + 14.518533560245993, + -50.42279252448726, + 28.363102361448227, + 33.41770601029143, + -26.909538430954154, + -28.469292569556487, + 39.261078004812084, + 286.2494613944767, + 14.936662640423652, + -13.593010557421891, + -53.16989850718822, + -26.22272764912663, + -3.110122018936175, + -152.50658425624783, + -114.9596809862195, + -4.935602052352657, + 60.39676234681705, + -9.263900653907239, + -41.29379173325365, + -75.8704787174106, + -11.443054576160286, + 211.0744266162747, + 134.92173567201255, + 6.934806403432216 ], - "intercept": 1327.415236994778 + "intercept": 1823.3026414385706 }, "wide-insurance-offset-l2-gaussian": { "coef": [ - -0.17842186411401204, - -0.0016828931074200032, - -6.825011245797861e-07, - 5.204820864904499e-07, - 0.9999973421990139, - -0.1784218641141617, - 0.0006559708695223315, - 0.00019351941129933402, - -0.0014828830478361085, - -0.0006533152413278663, - 0.0007651877267248207, - 0.0005215200718651406, - -0.0005162456138518295, - -0.0018538910309670229, - 0.0006161741119183156, - -0.0006451062779585238, - 5.651393872598295e-05, - 0.0007613721337563787, - -0.0007102649913859752, - 0.0011694727896514165, - 0.0008734287709224847, - 0.0007303694012626095, - -0.0011578404373040786, - 0.0006760170170597232, - 0.0015151477632772577, - -4.345710174823488e-05, - -0.003750603198467383, - 0.0014430846762570862, - -0.0004049212059652025, - -0.0003617436309019378, - 0.0006407758279082438, - -0.002184674358075835, - 0.0009640225986272549, - -0.001644368765785769, - 0.0005581392192701353, - 0.0008167592283435218, - 0.000601847243717099, - 0.0020752816497805948, - -0.0010216002328029743, - 0.00018505753922330213, - 1.0195079040083292e-05, - 0.00016388822973396468, - -0.00023344735333941775, - 0.0007050234209376642, - 0.00013142237343546196, - -0.0007785115471745969, - 0.00027303664171977676, - 0.00011453339599213852, - 0.00010301830838900853, - -0.00020718418726870357, - 7.405897366065108e-05, - 4.4108483733645796e-05, - 1.9385146182043194e-05, - 1.2573944362285865e-05, - 7.824063084230885e-05, - 1.8486199727614034e-05, - 1.7287738852505662e-05, - 2.3706407738295455e-05, - 2.6271566696711143e-05, - 1.5159344575733757e-05, - -0.0005404749591180608, - -0.0007402675788931721, - 0.0005613300429750743, - -0.00019741585888178665, - -0.0029599982426333315, - -0.0005885406058569849, - -0.0008565715456950555, - 0.0003973201734228329, - 0.0010205811414987925, - -0.000324554423485223, - -0.00047641664887135765, - -0.00024970066823885726, - -0.0004403564818999958, - 0.0013148091934376686, - 0.0012523887734584458, - 0.0018560848734776555, - 0.0005946418671519799, - -0.0008581481867168856, - 0.0020983787377413473, - -0.0002923225174769753, - 0.0009282046340106181, - -0.0005341455032755476, - 0.0004588620760027898, - 0.000499916627047794, - 0.0016620757355356418, - -0.0011964972882654234, - 0.00020861587236870228, - -0.0017932591688952703, - 0.0010460312401426276, - 0.0007439375901024451, - 0.001359064350718881, - 0.0006841012208266124, - 0.00035900577867998976, - 0.00018869028066481765, - 0.0008570464928934464, - -4.344723053009526e-05, - 0.00017617943438083848, - 0.0013142542848616774, - -0.002026902027327201, - -0.0008030253110250331, - -0.000375674359670472, - 6.558460031350634e-05, - -0.0011448686345775676, - 0.00019764088068255643, - -0.00017782354951880255, - 0.0005698977027376044, - 0.000785967097982894, - 0.00027090485131604764, - -0.0009464835767915218, - -0.0016933876530219642, - -0.0006779964246426872, - 0.0007103197114524025, - -0.0002981740513762438, - 0.0008957772022155714, - 0.0009245453865623793, - -0.000891922841561966, - -0.00019015135174918642, - -0.0015048220306022235, - -0.0004631567900991139, - -0.0015597774599577494, - 0.00043995315850501617, - 0.00044902534980782293, - 0.0001501366798803575, - 0.0003407163418057559, - -0.0003609986754483513, - 0.0003180502499256858, - -0.00016692738211773476, - -0.0001824520834657876, - -7.922715558135076e-05, - 9.368221815624073e-06, - 3.118391032928965e-05, - 0.00015166209130430767, - 4.7782506964299314e-05, - -0.00031750785446361544, - 1.335955573372458e-05, - 0.0055491155688220635, - 0.0014000447093183953, - -0.000146656527480347, - -0.0010505138961427695, - 0.00033927111175505154, - 0.0002569878411504504, - 0.00041279489887074424, - 0.0007466266464069126, - -0.00021618355976028305, - -4.139273442669714e-05, - 0.0011378371340598837, - 0.0005874848027004111, - -0.0013591953921371902, - 0.00036535109901333115, - 0.0005746515316174843, - -0.00018693599097615874, - 0.0003065337996986668, - 0.0003859837174813756, - 0.0015536455513960343, - -0.000790800738394876, - -0.0001287067915327116, - 0.00090809518372025, - 0.0031110884152349924, - -0.0009703641449408827, - 1.6406181456651183e-05, - -3.882755703664751e-05, - 0.0004632655399652035, - -0.0018726434575067573, - 0.0003530168454569168, - 0.00017259989636693018, - -0.00048073546750069217, - -1.4791298015603912e-05, - 3.644965903327884e-05, - -0.00025472268400330596, - 0.00016151364166337083, - 0.0017013522149830174, - -0.00014492543782891582, - 2.253403042878148e-05, - -2.67439262484253e-05, - 8.552033282559953e-05, - 0.0015053180447948946, - -0.00015845129017263396, - -0.00022690253393520035, - -0.0008560445677926179, - 5.0434014988917265e-05, - -0.0010850404835548511, - -0.0014513600829625405, - -0.0003963856549933361, - 2.585086051723146e-05, - 6.624819031146019e-05, - -0.005434304032466343, - -0.0005221032985476043, - -0.0005162833309503851, - 1.1760920505428127e-05, - 3.2559344459712176e-05, - -0.0003142070586043435, - 0.0008346877187714782, - 4.4654234859986964e-05, - -0.00029456715622164594, - 2.0057611931979374e-05, - 0.0002664701266118724, - -0.00030539640940425473, - 3.832782473994426e-05, - 2.018992773364527e-05, - 1.8687548047817515e-05, - -0.0004802577484643571, - 3.134159131226469e-05, - -0.0003149221242477635, - 6.140273787105902e-05, - 3.597638015566947e-05, - 2.6676490410833514e-05, - -0.0005611586710333537, - 3.116758310172694e-05, - 1.8500554781768596e-05, - 8.995252378785453e-05, - -0.0010737595904023601, - -0.000741136540782338, - 0.00015999884127823507, - -0.0013363608908582138, - 1.839955375269605e-05, - 1.915434631723653e-05, - 3.276083135278694e-05, - -0.0003159671543741123, - 0.0023655792847582994, - 0.0024530876525197787, - 0.0006535641874713577, - -0.006447771582662142, - -0.0024307504819395882, - -0.00020128389967173006, - 0.0002804398647099127, - 0.0013286301028860603, - 0.0018284779095272823, - 0.0002045445417802812, - -3.451771821666764e-05, - -0.0008591782687342637, - 0.0008591783553241504, - -0.001377095327584273, - -0.0007459665517439515, - 0.0014199840371567109, - 0.0006920029476566605, - 0.0019915614628304977, - 0.00032814403685063083, - -0.000226322371906324, - -0.0005501209315858733, - 0.0015212950115235965, - -0.001460303135495587, - 0.00017775093668520012, - -4.748884452175625e-05, - -0.0008372302497873707, - 0.0016101382165576482, - -0.0007077169529987401, - 0.0008829362993413875, - 1.7739388644759317e-05, - -0.0004578737722315684, - 0.0005635108529156114, - 0.0008791839628470158, - -0.002196002197077491, - -0.0014781269331703539 + -0.01929464868376598, + -23.717605807809324, + -75.01544338247221, + -197.8159858363348, + 115.33870397947568, + -32.78875375012906, + -142.01856276869842, + 75.10157061851955, + 269.4146648327231, + -68.41941336753374, + 38.317365969938024, + -39.896485416181484, + -37.25043283932909, + 26.002633754179264, + 114.0079080960048, + 28.462607219733222, + -37.79328974228096, + 18.464768246680983, + 51.41054487421983, + 148.1074188732748, + 236.69782982469727, + 9.068884717103534, + -53.99461850459482, + 52.3087005458691, + -88.2127292499863, + -87.66159816820725, + -125.19426663192507, + -54.76520071231087, + -44.00717912991601, + 33.532246347365884, + -106.23646470868373, + -115.97712266509676, + 158.10258569132833, + -8.957767282285516, + 94.45967696401257, + -27.092008773572502, + -18.497618869676767, + -10.60500386750527, + -8.793049678186414, + -1.0126963817830696, + -5.256384564756405, + -8.01759289719937, + -1.3921371149667472, + -3.4795415809953667, + -1.0146121410303022, + -1.3137409145518988, + -4.669707050768169, + 0.19023435409808945, + -0.6210341213624224, + -1.4940414444889114, + 1.9787969088046384, + -1.316949571828134, + 66.12870631642261, + 57.1798865118883, + 43.22333426712554, + 48.51454341728686, + 7.778316647938904, + 34.853822324554244, + -83.95480492916896, + -33.868919240460656, + -30.771843314388004, + -0.7819995491097496, + -45.00819975928327, + -23.961203425649643, + 66.20281720351386, + -19.59211130115218, + 7.48024730147173, + -5.837815860252049, + -34.33681730856795, + 55.11088690369829, + -154.47547406512246, + -36.15943248658953, + -93.98416155225253, + 13.93261627612243, + -106.35514033731788, + 42.89019999433522, + -64.84016181411698, + -43.13781315696032, + -50.61322567266525, + 91.45410111900149, + -47.974560080744844, + 5.296026263657261, + -121.49416104830756, + -58.45389610735746, + -13.294591593605066, + 86.21938565341068, + -61.6431070207343, + -14.05773827946308, + -93.36302330628374, + -17.092325390665035, + -74.84411329641182, + -41.366830435825236, + -39.27635092722523, + 134.01028253036148, + 72.85755953798923, + -46.13584347537232, + -7.856168130674345, + 141.86911891407786, + -13.132903970801012, + 37.653211848096255, + 139.0657326317001, + -29.114928789784802, + -38.970269341626505, + 29.15485572406834, + -43.400379442605285, + 79.01165858908001, + 21.812270740285964, + 49.503465427518854, + 20.04157812652175, + 124.6644653637114, + 93.65291008060511, + 92.43585522696146, + -8.566645660412338, + -16.306325178090038, + -1.2242314974290383, + -10.79321904866156, + -14.64811609604076, + -8.340558836449055, + 6.122471481339611, + 1.8412047622929415, + 0.5618899266051028, + -2.7683899483491388, + -8.793523786883451, + -2.042025610508497, + -3.7585971992794946, + -0.6448113073434149, + -0.6490121307116171, + -11.479889687232253, + 21.217052638446894, + 18.39099171285604, + -11.93977498019343, + -26.807727629196854, + -5.082796802542726, + -13.815982186019111, + 7.4161931888786805, + 10.111342830146215, + -16.953313832301085, + -5.47707433562495, + 52.888718451288476, + -61.67754397261773, + 0.46039635479309454, + -16.89399856720412, + -22.17895109968349, + -67.93185574473864, + -8.83769943586096, + -0.4383926740177723, + -28.08892952433183, + 113.64034600238074, + -9.097664875828725, + -10.086556766883179, + 49.75348139613475, + 147.46428703894983, + -15.940547155198615, + -0.7528867249168627, + -2.0353656232386856, + -87.73666762832727, + 8.235370774672218, + -9.70771509628343, + -13.095570290488917, + -10.497315255675007, + -49.11567061159645, + -0.7632344672933588, + -1.030089292848085, + -13.04141253308692, + -3.4615085989812453, + 86.43207625682767, + -2.2572804876619066, + -4.300514411898509, + 21.42889380281262, + -2.536958350821167, + 33.06577116307317, + 4.013111923623569, + 45.86582280725225, + -1.1284131789354204, + 13.035846043691558, + 17.636507754577188, + -5.156300068586528, + -0.13548314425062682, + -0.6040101144122391, + -2.3460925073688923, + 0.4082321886418903, + -12.206945774801827, + -1.9661600805464918, + -0.20514754145622746, + -1.9966425982814804, + 34.35789507322617, + -0.1632339345684062, + 0.10609961548206066, + -0.6641380264647166, + -0.4382265561828817, + 33.36668966579999, + 5.8097268331601795, + 0.7558370279009882, + -24.796300480597452, + -2.8081758813648743, + -0.9133190763890892, + 10.174246143077033, + -1.1353903661978018, + -0.44927838631149697, + -3.645534277552436, + 1.7637583769767546, + 8.181622568446405, + -5.268755222140483, + 74.70712636586595, + -0.6933730624841237, + -0.8811537944448289, + 3.8990198481601195, + -0.08405082741739764, + 176.35188911071694, + 17.57290569440439, + 212.8725282274415, + -64.14415346511653, + -17.731303341983253, + -135.45037584848643, + -63.11255810382299, + 15.814812455501496, + -6.534356179801677, + -66.99453402120209, + 58.417357557405055, + 84.78700453225531, + -41.14653811533762, + -41.32128327873227, + 19.60709466921075, + 278.2066775916072, + 6.031576470025038, + -20.57981348540535, + -70.68180853001024, + -23.855733483205125, + -4.8875046684467796, + -153.03037203628878, + -134.34279313546836, + -29.384323306710165, + 76.20686099415765, + -18.8779474384306, + -47.467727833474406, + -81.30707358863015, + -18.683169133711395, + 237.4301776077878, + 141.3160830337959, + 36.28321172083321 ], - "intercept": 0.3433324481040927 + "intercept": 1953.580733831093 }, "intermediate-insurance-no-weights-l2-poisson": { "coef": [ - -0.12099098082354058, - 0.045405514837403335, - 0.09670837907878212, - 0.1149427581888698, - -0.08242507508517755, - -0.05364059619633753, - 0.38003120512203564, - -0.1768025789408174, - -0.20322862618121795, - 0.0013597186808617038, - 0.012408689137524239, - -0.031079051821617146, - 0.013641894875142604, - -0.09213174185810204, - 0.136750940645666, - -0.04095044965947552, - 0.0097553091804098, - -0.05716745610674949, - -0.021385567431797394, - -0.0041232217579253655, - 0.2428578167120018, - -0.019626749244720583, - 0.0007198780015432777, - -0.03467012309036777, - -0.08818998561178694, - -0.017399199086190902, - -0.0422182898480025, - 0.04120289746399597, - -0.1745836250080995, - 0.17458362500809949, - -0.018440371724843016, - 0.013164656588566479, - 0.0028920396943976022, - -0.009488515678039022, - -0.018663603633103428, - -0.13738915054855383, - -0.004389892824498112, - 0.036829549614448405, - 0.005161017248936508, - -0.02068631643396341, - -0.0070527064786285705, - -0.003610020495665427, - 0.02423227997147517, - 0.08664345807650886, - -0.02526855265898976, - -0.04880527589428691, - 0.08556351258090844, - -0.0006777668253455516, - 0.06676767526455768, - -0.016677588952853966, - -0.006521172179096868, - -0.00957652192562405, - -0.012447104511150327, - 0.001025820623414704, - 0.012880032411171468, - -0.028474236199717905, - -0.010671917310163105, - -0.001666689835204374, - -0.014199638779791435, - -0.0136476912947697, - 0.005732290491660413, - -0.020094868600974255, - 0.0007062581831667692, - -0.00688330491064092, - 0.11418864483542078, - -0.00036851324254484304, - 0.013043958991428132, - 0.007845724809972265, - -0.037869651911863776, - -0.002106257365463816, - -0.0036903389766120434, - -0.002275959891873693, - -0.020014586196069804, - 0.006820076417650333, - -0.004078775911850604, - -0.006230384446495487, - 0.03441606065029278, - -0.0021055380933512825, - -0.0011105687036077239, - 0.0030674648044092616, - 0.0061143989232948495, - -0.00360563572463216, - 0.0005413863345667254, - -0.00017570240881118273, - 0.01696353962873743, - -0.001929633011235366, - -0.0014954860371528221, - 0.0005242159118196847, - -0.0006516482136324048, - -0.02306524175227705, - -0.001475900139795596, - -0.002366497569192472, - -0.0007424934459462604, - -0.014372866247537981, - -0.0007493142096002051, - 0.0005050356329337264, - -0.0005315587622233638, - -0.022658454946839632, - 0.001977909241820083, - -0.0008643044904203929, - -0.0005418541401351165, - 0.025388515502081956 + 0.053851496146216614, + 0.09288686319558663, + 0.1201402324133018, + -0.07986875964582478, + -0.0416994973670312, + -0.23257413476028116, + -0.23103840673419243, + 0.011560597726967551, + -0.03147304277355049, + 0.00916691424027309, + -0.09320184209375892, + 0.14137396566460425, + -0.03861120990292949, + 0.009169848173537616, + -0.02232306606691541, + -0.0028023685366082456, + 0.27518268116588557, + -0.019440545812559214, + 0.0007117572098551293, + -0.03931841517003503, + -0.08605289974205044, + -0.0180487274288674, + -0.04195028237765103, + 0.03986398756353841, + 0.22512617969193666, + -0.013628157480688663, + 0.0030427810771796317, + -0.010044301564857744, + -0.018718377793744023, + -0.14501037666600972, + -0.004942087534689763, + 0.0371637146533088, + 0.006008174916075592, + -0.019445600330068744, + -0.0068212517508265446, + -0.003361595535270172, + 0.021451748821729157, + 0.08440951401656104, + -0.025696186327556055, + -0.05009203578275645, + 0.08648617291981094, + -0.0010498386113090483, + 0.06520032962249867, + -0.016675350821120354, + -0.005947094112092331, + -0.005724942363954191, + -0.012285933975432215, + 0.00385155471520278, + -0.02950075409150949, + -0.010826649855397693, + -0.001744369263137094, + -0.015048335769801871, + -0.013491092139944254, + 0.005486607566747415, + -0.020348349742385183, + 0.0002621450316013555, + -0.0066457421027412, + 0.1136968844750543, + -0.00039120778794275154, + 0.012534496576409981, + 0.007845042481789927, + -0.03922445094404692, + -0.0021794665713754527, + -0.003862770409883528, + -0.002626339807758695, + -0.020715808695973198, + 0.0071568448941042, + -0.004163492004815433, + -0.006266975019065174, + 0.03394290955848881, + -0.0018440582790747173, + -0.0010685616213855222, + 0.0031766972132471114, + 0.004713518433977924, + -0.003248362867144937, + 0.0005835987830595721, + -0.00018346463212491932, + 0.016162405771523263, + -0.0019045701341240575, + -0.0015030145643113446, + 0.00046256232036405833, + -0.0006401627406537449, + -0.022921687688395854, + -0.0015390671950197949, + -0.0020910737534826414, + -0.0005287270006942144, + -0.013547232841027525, + -0.000773624229023316, + 0.0004986232917222954, + -0.0005452429394016158, + -0.023211351323791825, + 0.0019587451110458253, + -0.0009328588047216558, + -0.0005155321403904046, + 0.026802797917159865 ], - "intercept": -1.9480186212943043 + "intercept": -1.9975893690202935 }, "intermediate-insurance-offset-l2-poisson": { "coef": [ - -0.01707510781202881, - -0.011271905838913637, - 0.0099104070136615, - 0.010868892212624926, - -0.004932320468036763, - 0.012500034892692774, - 0.058324692736029554, - -0.017498979425244202, - -0.0408257133107854, - -0.003107712812079971, - 0.0019144296054411307, - -0.013365503033566354, - -0.012021477784418528, - 0.007511246767601413, - 0.02297095881971704, - -0.0039019415626947366, - 0.020607569633451218, - 0.00610503572602699, - -0.0023506917571947425, - 0.0012606527277215479, - 0.03402698667305615, - -0.0042117294919023635, - 0.0021356189840603564, - -0.01641467761279335, - -0.015353352135410104, - -0.009207404469976746, - -0.00012941575348337072, - 0.004138977109895643, - -0.002180220875130877, - 0.0021802208751308797, - 0.046919916867165144, - 0.008715411906016695, - 0.002206039203695012, - -0.0028129724920329595, - -0.0024268739978365976, - -0.011449131475687234, - -0.0016566255188344879, - 0.0041672324104087685, - 0.0011428314030494096, - -0.006572386490428494, - -0.0010440067260932705, - -0.00047685782571840366, - -0.0033433405202405835, - 0.006132659451290599, - -0.004397714619624091, - -0.009323054178262357, - -0.0022642099025200688, - 0.0019031933453593866, - 0.007851341028969713, - -0.00200619871881047, - -0.0030063113423663807, - 0.0185468536184877, - 0.00011412144117810979, - 0.023507676917780197, - -0.011683430116793953, - -0.0038591273181657806, - 0.0003520869503017086, - 0.001036473939052418, - -0.00019712189629104933, - -0.0011170467522312983, - 0.003209668284408791, - 0.0002950797846649387, - 0.004870155564298215, - 0.0005281284716142341, - 0.0032976180003061665, - 8.089619159129397e-05, - 0.012981159180592932, - 0.0012981507922238459, - -0.003646505885682922, - 0.0007916523080138416, - -0.0010943482620494284, - 0.0006785459552605013, - -0.005146677018905757, - 0.0046008683897878185, - -0.0011365313987768033, - -0.000616043709688008, - -0.0032838676377235257, - -0.0007940179412086074, - -9.102480526289562e-05, - 0.002757928898180831, - -0.0007877141938478921, - -0.00014869981051338814, - 0.0019088769114735424, - -2.22108476178035e-05, - 0.0009666143296130504, - -0.0003700022086788276, - -0.0003599474529828417, - 0.0016152064001824793, - -3.888451470823439e-05, - -0.006166870306681, - -0.00037899453181333904, - -0.0004476428061416924, - -3.059512684674799e-05, - -0.0026034005347869847, - -0.00018550549579032905, - 0.0004106348809313714, - -0.0002308007291573528, - -0.007984091382848378, - 0.002329036023588654, - -0.00011908477940809042, - -4.312540365410379e-05, - 0.00857453161217041 + -0.01100818850877987, + 0.010163680017326567, + 0.011145863416642724, + -0.004806815938986811, + 0.012795996012470468, + -0.018503172433126172, + -0.041367033045909406, + 0.001846930842982152, + -0.013210236889180745, + -0.011864492121683074, + 0.007496886436100603, + 0.02287515189253749, + -0.003985773161462195, + 0.020798835782001642, + -0.002361164917936537, + 0.0012723540068446577, + 0.03446220381622805, + -0.004223338072008866, + 0.002129267388417305, + -0.016906676127930023, + -0.015343370506728172, + -0.009307859677116923, + -0.00020712944238640077, + 0.004019647906257578, + 0.00199541918203904, + 0.04742512558632791, + 0.0022057309867778217, + -0.0028152401447378013, + -0.0024304520182687546, + -0.011651147823718462, + -0.0016663665907505477, + 0.004174508123660997, + 0.0011302145495915253, + -0.0065915265245166926, + -0.0010533271498273254, + -0.00047619178111706577, + -0.003386112855672984, + 0.006051098879171446, + -0.004427625243148538, + -0.009382898170587402, + -0.0022601394179611227, + 0.0019078442015588732, + 0.007825584965493577, + -0.001994933593041902, + -0.0030185411738593328, + 0.018558052479835586, + 9.977652895757155e-05, + 0.02380379489508592, + -0.0037809952360007937, + 0.0003764067867432375, + 0.0010660281898097234, + -0.0001347787378540293, + -0.0010826196609614953, + 0.0032189711478353266, + 0.0004015935948643132, + 0.004892131025314672, + 0.0005452219964941357, + 0.0033920769859138926, + 8.976297348196183e-05, + 0.013022284721059643, + 0.0013140399160326826, + -0.0035754709320854885, + 0.0007904965355095788, + -0.0010895199983695903, + 0.0006875454238751711, + -0.005053165384375391, + 0.0046319981858709445, + -0.0011247353509536131, + -0.000606523733772127, + -0.003202563552959186, + -0.0007768534721430959, + -9.098922329122806e-05, + 0.002766283000492085, + -0.0007435284901458934, + -0.0001397682737040051, + 0.001912841669529531, + -2.2188428121046516e-05, + 0.0009969456312467313, + -0.0003698601573061661, + -0.00035892214024827666, + 0.0016164899494226501, + -3.8939487488171095e-05, + -0.0061608937649456205, + -0.0003786780076928655, + -0.0004444994365459732, + -2.895978981684826e-05, + -0.0025966650797438185, + -0.0001861786467178437, + 0.00041059845430823174, + -0.0002304328484131002, + -0.008029828519584968, + 0.002326128562258692, + -0.00012124059064418364, + -4.2884575264295805e-05, + 0.008416772719703427 ], - "intercept": -3.814196509367211 + "intercept": -3.8339290342013608 }, "narrow-insurance-no-weights-l2-poisson": { "coef": [ - -0.12114291768120367, - 0.04542375336122067, - 0.09673341536016924, - 0.11474143519097732, - -0.08233926394933849, - -0.05341642228182519, - 0.3801240295376259, - -0.17710307405383086, - -0.20302095548379548, - 0.0015596347358889917, - 0.011975233525616674, - -0.03150104730940787, - 0.013635166580395071, - -0.09204126027608356, - 0.13724123983056757, - -0.04086896708697703, - 0.009616289108779656, - -0.05715055623157514, - -0.021347726877370358, - -0.004189720423468242, - 0.24293240508988204, - -0.019719971902210847, - 0.0007172040262384434, - -0.03448026925788265, - -0.08807273315270674, - -0.017521038436546812, - -0.04232194109316463, - 0.04115434825880495, - -0.17474082816565337, - 0.1747408281656532, - -0.018417093347160653, - 0.012882049164812991, - 0.0029165580105231133, - -0.00953944822205262, - -0.018632023507906437, - -0.13744976559653696, - -0.0044271328062692825, - 0.036865418704128114, - 0.00552435023277081, - -0.020624328412583198, - -0.007021738830528163, - -0.0036435095670589997, - 0.024243098614545818, - 0.0868463147560493, - -0.025220675152523082, - -0.048964482222210254, - 0.08556020681739109, - -0.0006601522937961244, - 0.06670069073355919, - -0.016714290363497773, - -0.006563295170189944, - -0.009600995877102744, - -0.012476849011524984, - 0.0010206136346631786 + 0.05388308607579253, + 0.09300576196107342, + 0.11993721279103624, + -0.07978625972418633, + -0.04152167620081181, + -0.2328823272956903, + -0.23078237759441342, + 0.011119699804332578, + -0.03153498465661023, + 0.009724346438047097, + -0.09330589849123061, + 0.14133755493280376, + -0.038662895891777786, + 0.009296907019963559, + -0.022275445756351896, + -0.0028457216890995533, + 0.27507646231552896, + -0.019513084138580366, + 0.0007077363198325564, + -0.03910747342492707, + -0.08587458565569053, + -0.018140981967280402, + -0.042030504607229416, + 0.03980666084175243, + 0.22524670381663187, + -0.013561787059533271, + 0.0030742244569834563, + -0.010092836238185073, + -0.018678165183051067, + -0.14514309077416812, + -0.0049756854741346375, + 0.0372264173870564, + 0.006401100908839918, + -0.01944376171644767, + -0.006798812018233508, + -0.003394088181816369, + 0.0214785183864922, + 0.08458763206856047, + -0.025659385401543413, + -0.05021577397556097, + 0.08647399565471257, + -0.0010401337183613116, + 0.06520041276715047, + -0.01671316628028309, + -0.0059336581170718015, + -0.0057097308420974, + -0.012325499703230304, + 0.003921058979394998 ], - "intercept": -1.9311032810088813 + "intercept": -2.004594296058199 }, "narrow-insurance-offset-l2-poisson": { "coef": [ - -0.017076139394364066, - -0.011263670931487738, - 0.00990377529522711, - 0.010851876386365863, - -0.004912453884471531, - 0.012496612528730364, - 0.05832959975891602, - -0.01751071232939096, - -0.04081888742952506, - -0.0031083240013817493, - 0.0018832465126164327, - -0.01330748934617037, - -0.011885425951706594, - 0.007483082979060443, - 0.02287820414031339, - -0.0039432943327315255, - 0.020853845152530127, - 0.0060977092619938856, - -0.0023399240987143238, - 0.0012654340101332946, - 0.034005872271063796, - -0.004209433220613481, - 0.00213360357675337, - -0.01642895012400273, - -0.015341410820949482, - -0.009197807836468707, - -0.00011337734246257584, - 0.004128284323266956, - -0.002157839908715875, - 0.002157839908715911, - 0.0469587339411302, - 0.008718694697974958, - 0.002205863806874254, - -0.002809622556884692, - -0.002426288626285627, - -0.011470452030037466, - -0.0016583984856032776, - 0.004172331178602782, - 0.001151128709053008, - -0.006580857725672885, - -0.0010481130127426564, - -0.00047949528830486184, - -0.0033518711213370127, - 0.006130940155382689, - -0.0044017539361374935, - -0.009331459022985877, - -0.002264897448561193, - 0.0019018694728274775, - 0.007882833037794626, - -0.0020068178287707163, - -0.003002204706054386, - 0.018553497769567374, - 0.0001150729613009736, - 0.023538296246868713 + -0.011011282401664038, + 0.010148064169053802, + 0.01115356821753964, + -0.004790944277773678, + 0.012799421971724532, + -0.018492507221550908, + -0.041380199966649885, + 0.0018075735948580017, + -0.013297491094843183, + -0.011873201051658592, + 0.007539096162933599, + 0.0229393903379277, + -0.003967106480783348, + 0.020829002491816843, + -0.002346999093265472, + 0.0012734308359545208, + 0.034458800676127624, + -0.004225534300760387, + 0.0021280916671513966, + -0.01690376531627688, + -0.01534210121336051, + -0.009309290116248037, + -0.00020431100405397706, + 0.004015738911910774, + 0.001993464973189715, + 0.04741294603441194, + 0.002204689802021929, + -0.0028182355675487295, + -0.002432322039833914, + -0.011652750578505558, + -0.0016650513909381643, + 0.004176303442900928, + 0.0011334976229379066, + -0.006590400911717707, + -0.0010544806433323147, + -0.00047849781569958013, + -0.003392209448302726, + 0.006059066481643334, + -0.004434368227854672, + -0.009390339726624656, + -0.0022616991963132424, + 0.001907778266819392, + 0.007825121071648953, + -0.001997061864015382, + -0.003015130070000933, + 0.01856291066553122, + 0.00010279858368418707, + 0.023793973846376073 ], - "intercept": -3.836105351310394 + "intercept": -3.8358140835026573 }, "wide-insurance-no-weights-l2-poisson": { "coef": [ - 1.5340982412329585, - -0.5124856864682698, - 7.997065919313554e-06, - 2.0783389078766012e-05, - 2.0783389079817753e-05, - 0.31409929318261776, - -0.04166699411461313, - 0.06119810495185602, - 0.09880100806515425, - -0.05022889967945503, - -0.08327250271306975, - 0.015169283490127786, - -0.0921226997973782, - 0.05898792287672411, - 0.08839016618903285, - 0.10936596955883442, - -0.0815967043634139, - -0.010599549222756447, - -0.03166326573502048, - -0.03646906831855416, - 0.016331522654170778, - -0.006529665864688879, - -0.005161300023666493, - -0.008933327953283606, - 0.3532929918444233, - -0.08252999506941346, - -0.03210077517719862, - 0.02017302989933634, - -0.053803034237530245, - -0.024059868469729725, - -0.038140407060367885, - -0.028303696542799968, - -0.005031889706285233, - -0.03582008901423944, - 0.10264674445770904, - -0.03594044787632086, - 0.004648895737190699, - -0.037341002597366835, - -0.024186495013180213, - -0.0076969251028378444, - -0.012793968246261781, - -0.01882159044179766, - -0.01110433491011885, - -0.012954849803295594, - -0.005872512130773154, - -0.005450454132773472, - -0.0012799206444625677, - -0.001342036908597344, - -0.0011799582851826054, - -0.0009014450483042457, - -0.0005395779834072294, - 0.0009583220440470007, - -0.0004853961052484153, - -0.00033241880798860594, - -1.1391128862060259e-05, - -0.001057209377823617, - -0.00046505284880788745, - -0.00022157638857240602, - -0.00028030654527511035, - -0.00041582485995523655, - -0.00013423495508264717, - -0.00044435747029930925, - -0.0001687699508809815, - -0.0001540627207737349, - -0.00016178862914920743, - -0.00019231979174238112, - -0.0001637222491803793, - 0.003577619101308802, - -0.0011958617406145693, - 0.003922398238460115, - 0.016749857669720588, - 0.0008052438689986182, - 0.0019147734221392919, - -0.009541153568570169, - -0.007446524381189839, - -0.010389736631475828, - 0.035951198848757034, - -0.007917645587824786, - -0.02359744316331213, - -0.00994758100487796, - 0.025367469804417057, - 0.11370593660904166, - -0.027779647785427155, - -0.018165973055380938, - -0.001423414444222014, - -0.007356641572594215, - -0.024530291607699498, - -0.008530011188715107, - -0.0211614685408577, - -0.04158333511921033, - 0.029603956717789856, - -0.007211315096128431, - -0.022432709161800492, - -0.03026172891886051, - -0.018824003592385702, - -0.023202980913359317, - -0.008588069091282813, - -0.00908958667889808, - -0.022984582622940522, - -0.005939555974276501, - 0.06652954542345972, - 0.028997683454236256, - 0.0059453500732047655, - -0.01917952906695914, - 0.005591541809013588, - -0.013605509363628976, - 0.002653294671220065, - 0.002814259796027182, - -0.013069526997441905, - -0.016757720404728026, - -0.014172705732621776, - -0.018168148230542844, - 0.016331752267045477, - 0.009258746185735538, - -0.016600623545121207, - -0.012315179002867301, - 0.0722412858226324, - 0.010875804132042304, - 0.0850346536269191, - -0.010223940181595116, - -0.010193295419846097, - -0.008209994980391136, - -0.00853978967590174, - 0.00417319601679818, - 0.004186838405511392, - -0.003581067106657449, - 0.0011153210659154906, - -0.003082083043885925, - -0.006034637216086051, - -0.003288775046806188, - -0.002287027441465734, - -0.0021736885709431235, - -0.0009279396079132887, - -2.2658718731616265e-05, - -0.0005172639856340942, - 0.006924630900803117, - -0.0003010229225316978, - -0.0008352048191864975, - -0.00011705981054473613, - -0.00013335569784922772, - -0.00022038796818134326, - -0.00011313716932316216, - -0.00019717812937638656, - -0.00013892438335077085, - 0.011288359682670435, - -0.025922500961891734, - -0.010833724573825731, - -0.00204245741308035, - -0.01391314001222724, - -0.014568960467631547, - -0.0022682789771240617, - -0.020221180338146905, - -0.001422566310247353, - -0.005763453856513968, - 0.1022153794351409, - -0.00023641786750658206, - -0.0009420061046414592, - 0.007628875230757953, - -0.036980124936713446, - -0.0018821989452229369, - -0.002504116797030086, - -0.0019153619155274023, - -0.012433781745249084, - 0.0035789427670893273, - -0.002745188486355734, - -0.004720477836672887, - 0.04143886654215852, - -0.0011564147650164874, - -0.0009453940645582676, - 0.0008530048277218397, - 0.009920928801981603, - -0.002403227338955952, - -0.0007569988424362255, - -0.00011462918925977624, - 0.0224008296727807, - -0.0014015576013499053, - -0.0008887061688101245, - -0.003133819442134989, - -0.0004442804623587826, - -0.008129044119477957, - -0.0008213801434607981, - -0.0012884069960897666, - -0.0003907853314960745, - -0.010914643954150406, - -0.00036943056392519063, - 6.558114068373894e-05, - -0.0003008936993891214, - -0.007055948694784851, - -0.004520513712364609, - -0.00046131950795030087, - -0.00025988176304737637, - -0.008683291752957751, - 0.0007649204582645262, - -0.00011666917765043789, - -0.003108501591188853, - -0.00017994940198494579, - 0.006869523461641003, - -0.0001105737087224357, - 0.01114205567263173, - 0.0021102195752866577, - -0.00024242306910356547, - 0.00018574863114640062, - -0.0004326628856001479, - -0.00026107016353692745, - -0.00025488024258495107, - -0.07250828365589626, - -0.023060258339691105, - -0.008487613229452245, - 0.22298456322823076, - -0.012895596260042079, - -0.00025896033888255616, - -0.01591642279924129, - -0.07014158903391252, - -0.0062828464803604414, - -0.04307147688941195, - 0.029638483798659524, - -0.17978527696596555, - 0.1797852769659656, - -0.003697935543931447, - 0.0037061466184482063, - -0.005940022650995221, - -0.014968846587138188, - -0.11633649055399402, - -0.0001989804226269707, - 0.029703149310787137, - -0.0014065807237683007, - -0.012812466632198661, - -0.005600569877215222, - -0.002585780451050365, - 0.021396169667551677, - 0.06583420791012713, - -0.017654325713685467, - -0.03851145516139698, - 0.09175443126328249, - -0.0076320419038216954, - 0.07105129382641666, - -0.0121904450562172, - -0.0009333463668544494, - -0.034104544597121006, - -0.008871566354598183 + 1.8431600623045515e-05, + 0.053412702110198315, + 0.10406811712771018, + -0.043316947539229864, + -0.06061571114681992, + 0.011871820310529036, + 0.058641487989892854, + 0.09174248844742469, + 0.12271899942384977, + -0.07899150718251276, + 0.00021720000971940325, + -0.019693040325979187, + -0.03473436951081643, + 0.016434439964721815, + -0.00810184296513845, + -0.006136199648951022, + -0.007938758686837775, + -0.10040246968665444, + -0.04752121738390727, + 0.003201379496229708, + -0.050686363816626646, + -0.0365165308336465, + -0.04885704048318991, + -0.03401776556348443, + -0.013836270834235359, + -0.0526914962793013, + 0.10690228124028268, + -0.04375634410100298, + -0.001378108851880017, + -0.04903428427179197, + -0.019758793542675755, + -0.013631208655640875, + -0.015729694666646567, + -0.02621766044571649, + -0.013882694169097884, + -0.0166893673078815, + -0.008777243267325872, + -0.007921619213627912, + -0.0018669712569839624, + -0.001786008658558237, + -0.0016980691865837976, + -0.0011116430307134603, + -0.0009015462554469917, + 0.0014554999325082382, + -0.0007281828890401492, + -0.0004323939963237184, + 0.0002636701273656607, + -0.0013225395416707347, + -0.0006585498986488216, + -0.00037765903537908304, + -0.0004662521277625543, + -0.0005971552624449602, + -0.00025105958433062266, + -0.0006928460748371775, + -0.00023023576719464862, + -0.0002073436692101615, + -0.0002389638532499668, + -0.00021308721383479968, + 0.0027288559023922964, + 0.00509819114826005, + 0.0031009372094297515, + 0.015448370067505055, + 0.008261584552383825, + 0.016207776460595878, + -0.005279194120347016, + -0.010992686405373516, + -0.005906669122486418, + 0.030375141789909264, + 0.0038419760397665472, + -0.029950529618184025, + -0.005133193830632328, + 0.02261715495561964, + 0.1252044774944274, + -0.036188967901696366, + -0.017915324423507777, + -0.0011884392926954652, + -0.015777576234913736, + -0.033329246596228115, + -0.008976075863841712, + -0.022884167067020296, + -0.04258293551674894, + 0.03610972561237058, + -0.0030076168225199843, + -0.027286648544013383, + -0.039867971221377654, + -0.018720542368464033, + -0.029177132758024936, + -0.00842488318243052, + -0.011113030006930069, + -0.021972771256640548, + -0.0037015047409862173, + 0.06627874885145849, + 0.03798196529222541, + 0.0032363020379447857, + -0.0107695451604649, + 0.006665737067169232, + -0.01993683110643503, + -0.0005610380728740601, + 0.010320110784817076, + -0.01590987905379404, + -0.019755445481538885, + -0.01940604730578857, + -0.01091561467689176, + 0.014358371391366568, + 0.010421091569055197, + -0.019143125542089766, + -0.015945870156046416, + 0.07394558912938339, + 0.009087208247730752, + 0.08302342595755312, + -0.012592456894160858, + -0.013338575853692637, + -0.011591431905128331, + -0.011822916441711235, + 0.003772065123641043, + 0.003385692166422696, + -0.005237320382211591, + 0.008540697820170806, + -0.004565361728015535, + -0.0018809792047418115, + -0.004994352776404528, + -0.0036655683182650503, + -0.0021648051770333278, + -0.0014003645906534665, + 7.014610963564217e-05, + -0.000816917924114999, + 0.008487492838820129, + -0.00045606187950226584, + -0.001108583610891696, + -0.0001905787611105729, + -0.0002123230865504732, + -0.0004095791250341589, + -0.00019210930396541676, + -0.00024562786459505576, + -0.00019871092557396716, + -0.034013272437056555, + -0.012142027892923804, + -0.0020998772278527167, + -0.01956444812510498, + -0.014091264822094291, + 0.004952895893769874, + -0.023437672273125643, + -0.0003579039104511523, + -0.006521300436874999, + 0.11184998023717573, + -0.0003010708755203609, + 0.013434004985791358, + 0.007959819701927363, + -0.03946054706997264, + -0.0024016440792967715, + -0.003725816298318004, + -0.0023123097405562437, + -0.01781057581146706, + 0.007771239855604079, + -0.003840410166664179, + -0.005871375830464764, + 0.038739162607239405, + -0.0015807761785311984, + -0.0011971263717896123, + 0.003723950752993814, + 0.01084404196562586, + -0.0026217192758938254, + 0.0010057451891037291, + -0.0001450000032046158, + 0.02455488931388499, + -0.0017003904023613122, + -0.0012429610720698474, + 0.0007277215837593633, + -0.0005155709117166522, + -0.011579294686142109, + -0.0011622889245144498, + -0.0015254566178521277, + -0.00034260844870406625, + 0.0004454461934844636, + -0.0005491133393055514, + 0.0006220735494920274, + -0.0004480654409888641, + -0.006387821530476859, + 0.002198367305498095, + -0.0006081999849168364, + -0.00030272687735460814, + 0.03837406875020494, + 0.0012852276944607236, + -0.0001970057008738034, + -0.00326951354483143, + -0.00025205903963694965, + 0.01017831407048282, + -0.00016139084965258172, + 0.019461269852833165, + 0.0027113965070382313, + -0.0004388749444757049, + 0.000535736669847077, + -0.0006765822460280971, + -0.00028444340267458356, + -0.00031966600008749216, + -0.022960567473530022, + -0.0037147766050396303, + 0.2833380473900891, + -0.019461831867573907, + 0.0003938549258384232, + -0.03957355599412494, + -0.08598140883910796, + -0.01839036670307766, + -0.04148853917308147, + 0.04069867942349152, + 0.22066733417505993, + 0.0034840789436448253, + -0.009272316528294713, + -0.01881198178411248, + -0.144803153415425, + -0.004834819873334286, + 0.038579712734678376, + 0.01066647693738964, + -0.018849588028675875, + -0.006703136607380041, + -0.002991436343368823, + 0.019649355026083098, + 0.08114492939982208, + -0.025130632107242014, + -0.04813019762895605, + 0.08758587760026755, + -0.0009037707379945619, + 0.06794490030111719, + -0.015747295916598306, + -0.00476800175315428, + 0.0014982654792900678, + -0.010235216962654193 ], - "intercept": -1.6901474567204635 + "intercept": -1.7270429893141566 }, "wide-insurance-offset-l2-poisson": { "coef": [ - 0.5140906887881211, - -0.0759657475992516, - 3.4011590812399624e-05, - 4.437520769383449e-05, - 4.4375207693842215e-05, - 0.3533175668930003, - -0.00915825788697564, - -0.013338804149846204, - 0.001299669317229711, - 0.008140992637852383, - 0.01650437539131584, - -0.0034479753095760855, - -0.0109454464905945, - -0.0051711044674350995, - 0.012026642616809403, - 0.003296028102725457, - -0.006467757830187587, - 0.0041529997489064075, - 0.0019663203625452127, - 0.0012243008836517277, - 0.0022957778316998936, - -0.0012641272484216336, - -0.0008758877866517621, - -0.0002377457230475195, - 0.05675971850846098, - -0.015005378245519311, - -0.013083506363473262, - 0.0015970094039773725, - 0.002225117924458878, - -0.001330829912847815, - -0.002234984478260172, - 0.0008705297013952992, - 0.00213509831686636, - -0.007862233580048672, - 0.008743914120155872, - -0.0038407684544961183, - 0.002320421594355021, - -0.009171068573819078, - -0.0022576957574198483, - -0.005137927122687593, - -0.00302829840163015, - -0.004478803842073639, - 0.00029746698158377794, - -0.0016883209834764837, - -0.0023979416512631834, - -0.0018982728003017913, - -0.00046436683809717734, - 0.00011248399131556233, - -0.00040846969136902096, - -0.00010316833677865988, - -0.00029488609305457285, - 0.0005530780431633217, - -0.00018271565959295725, - -5.5130181380112646e-05, - 0.00046885161955750426, - -0.0001961318412934002, - -0.00013800754842499796, - -0.00016878844571344972, - -0.00017306744778993247, - -0.00010952843101497992, - -8.319592908144624e-05, - -0.00017248162410242986, - -4.5160101951404494e-05, - -4.2234048968098886e-05, - -2.8565981772009063e-05, - -1.7618375881545441e-06, - 0.000672394927738837, - -9.040167484587381e-05, - -0.009772195626987957, - 0.0002857399012733497, - 0.0011626348014516156, - 0.003728678030444281, - 0.007662325213928733, - 0.002639360661188022, - -0.0011306887321457864, - 0.003102658284928741, - -0.002206505552980322, - 0.0052029153291062525, - -0.002987114885043847, - 0.001144674379581075, - 0.0025144990133793275, - 0.00032616053235897925, - -0.003761086695556741, - -0.0014681516134374664, - 0.0026622679039070883, - -0.004459112418150408, - -0.004449589159327713, - 0.0003183536184301682, - -8.131834576616064e-05, - -0.0049021390224632784, - 0.006161456235500199, - 0.0014863009824729804, - -0.0033752815394864147, - -0.0056635983346996245, - 0.0033427702445982606, - -0.003936741999524711, - 0.0014311254573602125, - 0.0005248904509513574, - 0.005374018725982002, - -0.0005620914283382706, - 0.0029297254009755477, - 0.006678970570229143, - 0.0014823017940240703, - 0.004873928301193919, - 0.0013653713795153013, - -0.00291500558063469, - 0.0013649686330637213, - -0.00015039840898827014, - -0.0011557721827293063, - -0.001146456480933351, - -0.002886200191326497, - 0.0003803181903791244, - 0.0005133016833088213, - 9.507957209883302e-05, - -0.000594264359126474, - -0.0024863870123203787, - 0.00038620547506604367, - -6.895183771717926e-05, - -0.001553862567380851, - -0.0008363277689000394, - -0.002031016350260547, - -0.0021394484750155464, - -0.0021389068352308488, - 0.0007438450240215469, - -0.00018405934648979888, - -0.0007578839647103143, - 0.0009571918330690775, - -0.0009236906131269325, - 0.0004289897900201746, - -0.0013050680688676598, - -0.0011642129866932848, - 2.9167156173157547e-05, - -0.0004210918224985484, - 0.000687285711884795, - -0.00028897629795337934, - 0.002199910703439498, - -0.00011187462681142407, - -0.00026686515253096765, - -7.994953362769996e-05, - -8.686815491027597e-05, - -0.00016293915651265615, - -7.980764444840334e-05, - -2.2524068297420287e-05, - -5.495939624696213e-05, - -0.052885060748305006, - -0.005499225833155809, - -0.0005164635083835333, - 0.0006985831284127478, - -0.0014449026888089902, - -0.0017748077572290598, - 0.002741963434064846, - -0.0009715509231718485, - 0.003711141891233674, - 0.00038001401038828384, - 0.0011823250629044812, - 0.00014385856639201658, - 0.009274662089553954, - 0.0013471418106347098, - -0.0037493557285004815, - 0.0007625915509356921, - -0.0008774335477823003, - 0.000664491912663738, - -0.0023856165830569813, - 0.004471754702953571, - -0.0008387176117371644, - -0.000370459257130151, - 0.0007709464234230139, - -0.00039490886786349886, - -6.272542140515301e-05, - 0.0027787332901907705, - 0.0036993088340171337, - 6.576377181885355e-05, - 0.001890409650157194, - -1.532044276350945e-05, - 0.006395096822465341, - -0.00021034304069670595, - -0.00020321203793813907, - 0.0011048524676486387, - -2.1098813820884204e-05, - 0.0007411815152451408, - -0.0001955843000691957, - -0.00022855743322862092, - -1.4631560990478346e-05, - 0.005394408341006542, - -8.346559274293366e-05, - 0.0005931224279438513, - -0.00010565390846176426, - 0.002301511695358209, - 0.0020478095880224357, - -5.140255630304065e-05, - -1.649490748881452e-05, - 0.00670903753333174, - 0.0008470209463275525, - -7.423427155542498e-05, - 0.0006062310372184178, - -6.361977316457935e-05, - 0.004294004668707317, - -2.3555692367218975e-05, - 0.006765819541928678, - 0.00045932917032785644, - -0.00019525997716424566, - 0.0006216023933260354, - -0.00015786959942538478, - -3.988755796302588e-06, - -2.919713809523403e-05, - -0.0035541736325135054, - -0.0017367435380898112, - 0.001716940209079799, - 0.032353983533421594, - -0.0033115736077748855, - 0.0020353750427425355, - -0.015080702074116291, - -0.011854133740279047, - -0.007775624683750668, - 0.001349393467459786, - 0.005857259023820474, - -0.0012769889816963923, - 0.0012769889816963804, - 0.005232108733069121, - 0.002086948229373991, - -0.0017430555206023124, - -0.0023261747328872703, - -0.02272591761139548, - -0.001205210185397206, - 0.0037201574012836183, - 0.0031688031441783732, - -0.005360059910856328, - -0.0007256353352178549, - -0.0003057236388331681, - -0.0014913916312888579, - 0.006044094379132115, - -0.003099537092134979, - -0.007117757595809169, - -0.0013824419972825394, - 0.001621131850821979, - 0.010816408973655852, - -0.0017348539182369894, - -0.0010969254087249342, - 0.016972266768959802, - 0.0006527650981922174 + 3.662947201758984e-05, + -0.0070572170078768755, + -0.003925524642576752, + 0.007413983151939755, + 0.020585693543324374, + -0.004016153491094997, + -0.008340138156638895, + 0.009903717591643647, + 0.011853465972263937, + -0.006041394433072192, + 0.0030796734121335823, + 0.003377633734030454, + 0.0014441639117614314, + 0.0023176545357513007, + -0.0014295002647435843, + -0.0010616218648924092, + -0.0003031105426086336, + -0.006478444843999095, + -0.013615538012298936, + 0.00048082734496995573, + 0.0026365108294612483, + -0.002461567241143091, + -0.0026174452313825615, + 0.000235202041987995, + 0.0015897624754058223, + -0.00960347288227182, + 0.008247222745165417, + -0.004539029851436841, + 0.001383908388341551, + -0.00947907331619057, + -0.0030091031247467827, + -0.0051409054814082235, + -0.0035363086611495138, + -0.005202973110640526, + 3.621825642058306e-05, + -0.001941646195365535, + -0.0027886961896520907, + -0.0022010510612536782, + -0.000532451623508857, + 0.00016166473022676448, + -0.0004671380753740291, + -0.0001146642931124939, + -0.0003476852170404175, + 0.0005250540485523308, + -0.00021585649079889477, + -5.979469912485873e-05, + 0.000538532890256293, + -0.00022070332058034948, + -0.00016131222732407825, + -0.0001961125971906907, + -0.00019515798339474948, + -0.00012259579124783337, + -9.947488345868386e-05, + -0.00020137927279007147, + -5.159446963502743e-05, + -4.8263797158395766e-05, + -3.106148132424255e-05, + -1.987233147466094e-06, + -0.00018894553807093374, + 2.050584679694412e-05, + 0.0003447166218451178, + 0.0009986619532265802, + 0.00414736217338916, + 0.008593839167068754, + 0.002813637118836289, + -0.0014724105924110275, + 0.0033886485763053504, + -0.0026680600852046054, + 0.005579492555697458, + -0.003438605094422414, + 0.0007753705822965875, + 0.0023192078578357076, + 0.003537012607697121, + -0.004401972133965562, + -0.0012484111835437577, + 0.0026539568254140052, + -0.0050387999936606425, + -0.0055622600764607824, + 6.503377291520657e-05, + 0.00013783235844066322, + -0.005679770373014747, + 0.0060117241937853215, + 0.0010831247925483965, + -0.004315399886176643, + -0.006819312277963521, + 0.0037622364614958827, + -0.005416946389589488, + 0.0007923833496825371, + -0.0002151709255728884, + 0.005664505549217626, + -0.000951123547354231, + 0.0027876225634649175, + 0.007414279636521745, + 0.0007265651690855532, + 0.005256524943262219, + 0.0014676604919443494, + -0.0036673690516455314, + 0.0008212265627952209, + -0.0005621750135647927, + -0.0013641882569020434, + -0.001362388037096256, + -0.0034603245577423695, + 0.00037539659207084305, + 1.3466206206864143e-05, + 0.0014835677953928907, + -0.000658438292951928, + -0.003111824854490786, + 6.143083612579233e-06, + -0.0004324470193377779, + -0.0020034165017255093, + -0.001190321510479399, + -0.0026588731509076444, + -0.002529505403088884, + -0.0025126007243157435, + 0.0006083732965491639, + -0.0004846113189979251, + -0.0009320574183676152, + 0.0021255152349118917, + -0.0011687916958550396, + 0.0008034401561527104, + -0.0015400863410846316, + -0.001373074491868653, + 2.3990997170337397e-05, + -0.0004958558053554635, + 0.0006900223385563581, + -0.00034594973112966583, + 0.0022970251569022515, + -0.00013196205850055483, + -0.00030699772698372234, + -9.531720761031118e-05, + -9.691606735699611e-05, + -0.0001957995493886469, + -9.62520272586776e-05, + -2.400038796490355e-05, + -6.0668836768387676e-05, + -0.0059950270980116014, + -0.00025378821100977586, + 0.000820793092591003, + -0.0017296238481048303, + -0.001597136143231157, + 0.0030462731501196623, + -0.0010673503139101144, + 0.004723002321311277, + 0.0005249732417188731, + 0.0033198799361312975, + 9.890517893152058e-05, + 0.0133447018635824, + 0.0013715291434069517, + -0.00252149155825716, + 0.0008650891842309096, + -0.0009728094909140464, + 0.0008602889725116991, + -0.0030319538067816185, + 0.004853843547572775, + -0.0009331049899009371, + -0.0003591912039015158, + 0.0006284037748330958, + -0.0005168142363553269, + -6.738792161791441e-05, + 0.0030763143774821654, + 0.004007380116536199, + 0.00018214162575652307, + 0.002238141274122672, + -1.662314618613085e-05, + 0.006761938652353949, + -0.00023164025932299816, + -0.00022282062711647793, + 0.0017554643188534991, + -2.2605818247272328e-05, + 0.0006939694861789118, + -0.00021548480121064018, + -0.00024600560309450266, + -1.490677031401106e-05, + 0.006526798484176582, + -9.187915127091418e-05, + 0.0007058920588345768, + -0.00011608494071191709, + 0.00318179195752759, + 0.002687722899650077, + -5.60323765204781e-05, + -1.728975955650945e-05, + 0.01865482381269222, + 0.0009265053675021153, + -8.360975510522918e-05, + 0.000801556245562301, + -6.914851821503235e-05, + 0.004830964464311655, + -2.535932311728818e-05, + 0.00806015434036345, + 0.0004093489037977551, + -0.00022623386426958082, + 0.0007918735730655997, + -0.000174146430954747, + -4.2006444172568955e-06, + -3.115876533800837e-05, + -0.00227990284141128, + 0.0013494262296160405, + 0.03432482209495164, + -0.00417150057593013, + 0.002077537220629246, + -0.017900517426459255, + -0.014126254861465019, + -0.009366882086878981, + 0.0008554806123434553, + 0.005330526948347288, + 0.002734764885542733, + 0.0021230612145529625, + -0.0022617373448238346, + -0.0027784470636097814, + -0.01933142591965252, + -0.0018002024654353408, + 0.004169394586193622, + 0.003287194720145626, + -0.0065402644386677485, + -0.0008729412168035146, + -0.00033989402186918505, + -0.003520549282003536, + 0.005852311934050858, + -0.004303892209050418, + -0.008321796650179524, + -0.0022034243303804004, + 0.001545331690283274, + 0.011056932065873768, + -0.001916393717030303, + -0.0017314335502731247, + 0.02155625851427218, + 0.0005939228879654476 ], - "intercept": -2.3436216377188743 + "intercept": -2.304057637311608 }, "intermediate-insurance-no-weights-l2-gamma": { "coef": [ - -0.06117061082316096, - -0.07856802365036249, - 0.028072964672047493, - 0.10920334495165916, - -0.03273650363631794, - 0.03519882848613479, - 0.0077187769367807095, - 0.030755153625684522, - -0.0384739305624651, - 0.03696960782480964, - -0.02785360179437727, - -0.062410157382807536, - -0.03938552408460079, - -0.039243886410092764, - 0.014765289412021064, - 0.1171582724350476, - 0.00292089131348924, - -0.005103955098751234, - 0.08598849563642759, - 0.02235004469776944, - 0.04462498579181675, - -0.03477208872972823, - -0.007655710350740117, - -0.05762195773408928, - -0.03726231856605633, - 0.011925699316898538, - 0.0030065370605070656, - -0.02547973202405406, - -0.006239117489604888, - 0.006239117489604983, - -0.010168274875043714, - -0.07987464292463385, - 0.016047575070287146, - -0.012696875827641679, - -0.011801900207023165, - 0.01656258047205601, - 0.1342465792799468, - 0.016664409640102587, - -0.008440263881412603, - -0.03192361441212608, - -0.013811610080972368, - -0.0017771222107803028, - -0.0928942626216902, - -0.06922949162417696, - -0.006164602509392152, - 0.04032161018056899, - -0.0023051396534722415, - -0.02233191722302318, - -0.04541432027583557, - -0.005424374344978517, - 0.09502076762541094, - 0.08335226630848873, - 0.0018743492202978327, - -0.003364134403834577, - -0.014173709692873853, - 0.006548598591154376, - 0.0004212468858524637, - 0.009321055392777512, - 0.020228816090867126, - -0.0033214873385870943, - -0.007896209077230697, - -0.0038947349651511157, - -0.00019840057498524073, - 0.010453911760004982, - -0.016951774169143775, - -0.0063460971328778305, - 0.0026302905415967804, - 0.031289480958179884, - -0.019648723811640383, - -0.003475893045230235, - -0.008126433962913002, - -0.007645296818230555, - -0.029597383422921928, - -0.005514575992900445, - 0.0013973271419437695, - -0.014316143975316562, - 0.04420320034118481, - -0.00676558730657715, - -0.0042948593333186885, - 0.021377711982708353, - 0.07453451829492996, - -0.015856242902587966, - 0.0014043670943371808, - -0.00043519327079318454, - -0.04275656628031755, - 0.0039751396603603184, - -0.005037046893457147, - -0.007278921243020712, - -0.004836455911195919, - -0.024021978228468133, - -0.0017850864789602178, - -0.0004936796288340484, - -0.007739475574322446, - -0.001667550860311826, - 0.04495249227483759, - -0.0029028103323250148, - -0.0025070943001499505, - 0.008555610603232086, - -0.0012674742758198223, - 0.0007213396156720929, - -0.000690170814638845, - 0.008716953551054481, - -0.0004205958032456804, - 0.007135266385860448, - -0.026003673748207126 + -0.06624343131385657, + 0.039538537692595424, + 0.12320673274258508, + -0.026976570096320616, + 0.046636363810355116, + 0.02999775449094616, + -0.03836470446964431, + -0.029893129488637257, + -0.06443493155627479, + -0.040003508439481215, + -0.042164876412727356, + 0.014335839149063506, + 0.11666881653720441, + 0.0032236901071305444, + 0.0894348762018115, + 0.024378667196207227, + 0.04039641392799995, + -0.03360929750935805, + -0.00854910544333243, + -0.057182220071860225, + -0.03343677307070735, + 0.013383907506773839, + 0.006396139439646451, + -0.026294299758177767, + 0.004693191131352426, + -0.014254777579185762, + 0.016221062840190727, + -0.011878893394930952, + -0.011081628509533413, + 0.021067650424677727, + 0.13739310400335414, + 0.016583267157327084, + -0.005520038701890525, + -0.03000199645993251, + -0.01347419519923186, + -0.0016611374669100402, + -0.08965519311353054, + -0.06513514081210807, + -0.004602692785878704, + 0.04403801001252902, + -0.0012562182571319081, + -0.02202785436981033, + -0.035822845828857884, + -0.004909810502568576, + 0.09898009178502717, + 0.09066045590076438, + 0.0018117316028592673, + -0.00336369205079681, + 0.007883894969792925, + 0.001602123640293585, + 0.010121087698286176, + 0.02140360698605034, + -0.002790098541997823, + -0.006930705236368041, + -0.0017352731024772327, + 0.0006371049966822944, + 0.010915775701632697, + -0.015939845122829293, + -0.0065607372692327946, + 0.0050538449124099445, + 0.032840766941564695, + -0.019950197323509007, + -0.0033900716689341847, + -0.007981658851845184, + -0.007039093569325295, + -0.02840524628716654, + -0.005688713130835992, + 0.001579298164952133, + -0.01369535816995739, + 0.044567477175521425, + -0.006622981598786669, + -0.00419235569880389, + 0.021826931591487794, + 0.07450673672793705, + -0.015552262585687135, + 0.0012588360008076477, + -0.00030584313289575216, + -0.04245594054234652, + 0.004116595012294897, + -0.005013645855073431, + -0.007359446619514126, + -0.0047458628744390195, + -0.024344142349751078, + -0.001828663529057171, + -0.00047981125044178717, + -0.007821562277452608, + -0.0017418476813434202, + 0.04438500751175221, + -0.0028861408135054225, + -0.0025407565720911465, + 0.008611406039460645, + -0.001260584760086137, + 8.020375002605615e-05, + -0.0006095449758343444, + 0.008280071913656251, + -0.0004569094646359118, + 0.007014917423906694, + -0.026419708512810107 ], - "intercept": 7.376328401761933 + "intercept": 7.344478586810852 }, "intermediate-insurance-offset-l2-gamma": { "coef": [ - -0.061170610823160936, - -0.07856802365036249, - 0.02807296467204754, - 0.10920334495165918, - -0.032736503636317936, - 0.03519882848613477, - 0.0077187769367807295, - 0.03075515362568457, - -0.038473930562465036, - 0.03696960782480961, - -0.027853601794377285, - -0.062410157382807536, - -0.0393855240846008, - -0.03924388641009276, - 0.014765289412021118, - 0.11715827243504764, - 0.002920891313489245, - -0.005103955098751231, - 0.08598849563642758, - 0.02235004469776945, - 0.04462498579181672, - -0.03477208872972822, - -0.0076557103507401145, - -0.057621957734089325, - -0.03726231856605633, - 0.011925699316898554, - 0.0030065370605070626, - -0.025479732024054055, - -0.006239117489604819, - 0.006239117489605019, - -0.010168274875043699, - -0.07987464292463382, - 0.01604757507028715, - -0.012696875827641669, - -0.011801900207023175, - 0.016562580472056, - 0.13424657927994674, - 0.016664409640102615, - -0.008440263881412615, - -0.03192361441212607, - -0.013811610080972368, - -0.0017771222107803048, - -0.09289426262169023, - -0.06922949162417699, - -0.006164602509392088, - 0.040321610180568956, - -0.0023051396534722423, - -0.02233191722302317, - -0.04541432027583559, - -0.005424374344978523, - 0.09502076762541094, - 0.08335226630848876, - 0.0018743492202978253, - -0.003364134403834612, - -0.014173709692873803, - 0.006548598591154369, - 0.00042124688585247505, - 0.009321055392777527, - 0.02022881609086718, - -0.003321487338587076, - -0.007896209077230685, - -0.0038947349651510857, - -0.00019840057498520853, - 0.010453911760004963, - -0.016951774169143775, - -0.0063460971328778235, - 0.002630290541596785, - 0.0312894809581799, - -0.019648723811640394, - -0.003475893045230247, - -0.008126433962913005, - -0.007645296818230555, - -0.029597383422921956, - -0.005514575992900449, - 0.0013973271419437628, - -0.014316143975316557, - 0.044203200341184794, - -0.006765587306577155, - -0.004294859333318692, - 0.021377711982708356, - 0.07453451829492999, - -0.01585624290258798, - 0.0014043670943371734, - -0.00043519327079318665, - -0.04275656628031758, - 0.003975139660360317, - -0.005037046893457145, - -0.007278921243020712, - -0.004836455911195921, - -0.024021978228468164, - -0.001785086478960216, - -0.0004936796288340486, - -0.007739475574322451, - -0.0016675508603118266, - 0.044952492274837594, - -0.002902810332325016, - -0.0025070943001499527, - 0.00855561060323208, - -0.001267474275819823, - 0.000721339615672063, - -0.0006901708146388465, - 0.00871695355105448, - -0.0004205958032456812, - 0.007135266385860445, - -0.026003673748207196 + -0.06624343131385657, + 0.03953853769259537, + 0.123206732742585, + -0.02697657009632063, + 0.046636363810355075, + 0.029997754490946188, + -0.03836470446964432, + -0.029893129488637222, + -0.06443493155627479, + -0.04000350843948123, + -0.04216487641272732, + 0.014335839149063547, + 0.11666881653720437, + 0.0032236901071305436, + 0.08943487620181155, + 0.024378667196207224, + 0.04039641392799994, + -0.033609297509358024, + -0.008549105443332428, + -0.057182220071860246, + -0.033436773070707346, + 0.013383907506773887, + 0.006396139439646418, + -0.026294299758177753, + 0.004693191131352419, + -0.014254777579185838, + 0.016221062840190762, + -0.011878893394930942, + -0.011081628509533406, + 0.021067650424677644, + 0.13739310400335414, + 0.016583267157327074, + -0.00552003870189049, + -0.03000199645993251, + -0.01347419519923186, + -0.0016611374669100415, + -0.08965519311353053, + -0.06513514081210808, + -0.004602692785878706, + 0.044038010012529015, + -0.0012562182571319192, + -0.022027854369810335, + -0.03582284582885785, + -0.004909810502568573, + 0.09898009178502722, + 0.09066045590076438, + 0.0018117316028592788, + -0.0033636920507967454, + 0.007883894969792875, + 0.001602123640293585, + 0.0101210876982862, + 0.02140360698605034, + -0.002790098541997829, + -0.006930705236368048, + -0.0017352731024772637, + 0.0006371049966822837, + 0.010915775701632699, + -0.0159398451228293, + -0.006560737269232791, + 0.005053844912409958, + 0.032840766941564674, + -0.019950197323509004, + -0.003390071668934183, + -0.007981658851845182, + -0.007039093569325289, + -0.028405246287166532, + -0.00568871313083599, + 0.001579298164952127, + -0.013695358169957378, + 0.04456747717552141, + -0.006622981598786662, + -0.004192355698803887, + 0.02182693159148778, + 0.07450673672793705, + -0.015552262585687135, + 0.0012588360008076482, + -0.00030584313289575037, + -0.042455940542346526, + 0.004116595012294903, + -0.00501364585507343, + -0.007359446619514125, + -0.004745862874439021, + -0.024344142349751085, + -0.0018286635290571717, + -0.00047981125044178744, + -0.007821562277452608, + -0.0017418476813434202, + 0.04438500751175224, + -0.002886140813505421, + -0.0025407565720911448, + 0.008611406039460645, + -0.001260584760086136, + 8.020375002603583e-05, + -0.0006095449758343428, + 0.008280071913656262, + -0.0004569094646359116, + 0.007014917423906701, + -0.026419708512810076 ], - "intercept": 7.376328401761933 + "intercept": 7.344478586810852 }, "narrow-insurance-no-weights-l2-gamma": { "coef": [ - -0.06020964491008935, - -0.07925220680485431, - 0.026602600565880215, - 0.11070590221585375, - -0.032664811658871196, - 0.034818160592080837, - 0.007046392241125862, - 0.031361783584571076, - -0.03840817582569692, - 0.03551847680489446, - -0.026296837843993847, - -0.06128865618975414, - -0.03580401287968206, - -0.041723774186329166, - 0.01451833382140992, - 0.11507647047345478, - 0.002971840463113346, - -0.006893484560188362, - 0.08677833464215604, - 0.023823021206403866, - 0.043756458166052344, - -0.034878027000757705, - -0.007824700615266546, - -0.05781903754711668, - -0.037251225572632635, - 0.012345276876713076, - 0.003984091311185248, - -0.026020706906548733, - -0.00581439453639699, - 0.00581439453639731, - -0.008915994599773675, - -0.07963009044889492, - 0.015724178313904217, - -0.012564094786101872, - -0.01226561979394692, - 0.015369090572082317, - 0.1325078431685091, - 0.016756392925138072, - -0.007993586165284018, - -0.03220026847412813, - -0.013916442029630164, - -0.0017814115020394294, - -0.09427560735334647, - -0.06904499474166265, - -0.006587263495251524, - 0.041758677955175655, - -0.002629851726821573, - -0.022369354928651608, - -0.04553543997176089, - -0.005284134515489595, - 0.09754117030785875, - 0.08457617579286267, - 0.00184463089747872, - -0.00437352254419158 + -0.06727185243868852, + 0.03806167675057497, + 0.12456124434630976, + -0.02711700430277377, + 0.046002480420617796, + 0.030652200206605005, + -0.03821449684820079, + -0.027757047208403083, + -0.06415881110276654, + -0.03780535021676745, + -0.04388630813835433, + 0.014454853774130354, + 0.1151548008906592, + 0.003030479847623291, + 0.09032835089435368, + 0.025950336912525462, + 0.040072061607007825, + -0.03355838085048294, + -0.008642313089620655, + -0.05694967156007144, + -0.03337977696610251, + 0.013867160066018474, + 0.007525883554213245, + -0.026601302174542353, + 0.004248845811222704, + -0.013279408929332711, + 0.01599980528292383, + -0.011772306789534163, + -0.011512835996129277, + 0.019988049665552936, + 0.13621834184509504, + 0.016649277243589496, + -0.005202182339476996, + -0.03014141657681939, + -0.013561002589444529, + -0.0016744144862934616, + -0.09093624055737946, + -0.06512690364771632, + -0.004960719143919811, + 0.04543119756641308, + -0.0014853028549785135, + -0.022122375695937238, + -0.03629443447073496, + -0.00477239034593755, + 0.10123233652973064, + 0.09184118810732209, + 0.0018135459051246158, + -0.0042370290873054725 ], - "intercept": 7.363707446301816 + "intercept": 7.355292258021936 }, "narrow-insurance-offset-l2-gamma": { "coef": [ - -0.06020964491008933, - -0.07925220680485431, - 0.026602600565880256, - 0.11070590221585373, - -0.032664811658871216, - 0.03481816059208088, - 0.007046392241125845, - 0.03136178358457101, - -0.03840817582569697, - 0.03551847680489444, - -0.026296837843993854, - -0.06128865618975416, - -0.03580401287968203, - -0.041723774186329096, - 0.01451833382140997, - 0.11507647047345483, - 0.0029718404631133497, - -0.006893484560188375, - 0.08677833464215601, - 0.023823021206403845, - 0.043756458166052316, - -0.03487802700075773, - -0.007824700615266545, - -0.0578190375471167, - -0.037251225572632655, - 0.012345276876713078, - 0.003984091311185257, - -0.026020706906548733, - -0.005814394536397082, - 0.005814394536397228, - -0.008915994599773724, - -0.07963009044889492, - 0.0157241783139042, - -0.012564094786101872, - -0.012265619793946915, - 0.01536909057208236, - 0.13250784316850917, - 0.016756392925138065, - -0.007993586165284018, - -0.032200268474128116, - -0.01391644202963016, - -0.0017814115020394299, - -0.09427560735334647, - -0.06904499474166263, - -0.0065872634952515, - 0.04175867795517563, - -0.0026298517268215703, - -0.0223693549286516, - -0.04553543997176092, - -0.0052841345154896, - 0.09754117030785872, - 0.08457617579286267, - 0.0018446308974787219, - -0.0043735225441914926 + -0.06727185243868851, + 0.038061676750575035, + 0.12456124434630986, + -0.02711700430277375, + 0.04600248042061781, + 0.03065220020660499, + -0.038214496848200795, + -0.027757047208403097, + -0.06415881110276662, + -0.03780535021676741, + -0.04388630813835427, + 0.014454853774130336, + 0.11515480089065917, + 0.003030479847623296, + 0.09032835089435365, + 0.025950336912525452, + 0.04007206160700783, + -0.03355838085048294, + -0.00864231308962065, + -0.05694967156007147, + -0.03337977696610252, + 0.013867160066018479, + 0.007525883554213224, + -0.026601302174542374, + 0.004248845811222703, + -0.01327940892933254, + 0.015999805282923843, + -0.011772306789534163, + -0.011512835996129261, + 0.019988049665552992, + 0.13621834184509504, + 0.016649277243589507, + -0.005202182339477025, + -0.0301414165768194, + -0.013561002589444529, + -0.0016744144862934612, + -0.09093624055737945, + -0.06512690364771621, + -0.00496071914391982, + 0.04543119756641306, + -0.0014853028549785012, + -0.02212237569593725, + -0.03629443447073494, + -0.004772390345937545, + 0.10123233652973063, + 0.09184118810732209, + 0.0018135459051246197, + -0.004237029087305682 ], - "intercept": 7.363707446301815 + "intercept": 7.355292258021934 }, "wide-insurance-no-weights-l2-gamma": { "coef": [ - -0.08218458336512706, - -0.05733898810461679, - -3.193337900542819e-06, - -2.6234473556684273e-05, - 0.00024080826789374675, - -0.08218458336512706, - -0.0003563109538987326, - -0.009950512867444165, - 0.010282022378688851, - -0.0008488660586231761, - 0.007413381496852848, - -0.00653971399557567, - 0.0026842075474118195, - -0.001961360208177611, - -0.010873509545779142, - -0.006166403811131922, - 0.007821399330236008, - 0.006675712608286491, - 0.004163448985876006, - 0.0009850538794011284, - -0.0007962347534113073, - 0.0016058022519264816, - 0.0009642129802786527, - -0.005102329264916665, - 0.016828208126751103, - -0.003698774521050092, - -0.0017629782395417477, - 0.006234147327224453, - 0.010752392047524408, - 0.00492778530694123, - 0.014216334729572278, - 0.013225070709309898, - -0.0008496971516448103, - -0.0037210399421553596, - -0.013246808317981609, - -0.015456791289692585, - 0.0033850921270787136, - -0.013134682645931739, - -0.01214345541572974, - -0.005941870190051394, - -0.0010868602911832527, - -0.0017794201965210543, - -0.0018855964329524098, - 0.002250699751231199, - -0.0007563325180342071, - -0.001832878481048179, - 0.0007856750661310766, - 0.0011586250883423416, - -1.6356918125576983e-05, - 0.0011079131515677099, - 0.0011253493061276641, - -0.00040951743847279825, - 0.00011690903434982332, - 0.00015558935592307265, - -0.0005136614996421189, - 0.0010877661355504138, - 0.000120300940106631, - 0.0003790637690334884, - 0.0009071058010052784, - -0.0005273062840121171, - 0.004879268654929928, - -0.004977133031012567, - 0.005747223831072773, - 0.006275568226399729, - -0.009524337124398373, - -0.0052911596854374405, - -0.008406566793350452, - -0.003552099283645758, - -0.002168089251901313, - -0.008495951161612603, - -0.0025605496827096202, - -0.006516210238765695, - -0.0005371424493704635, - -0.003369182933391334, - -0.005413275128606559, - -0.005418558994417265, - -0.001857230332285467, - -0.02237161922677842, - -0.003671063788292791, - -0.016522875327750236, - -0.00858947492608417, - -0.007784618465682019, - 0.005713953046273465, - 6.753412817031397e-05, - 0.011241560810643429, - -0.0038666542228221654, - 0.005433837396084952, - -0.000779349357625198, - 0.008367444101666297, - 0.002840967157215848, - 0.010365072408431365, - -0.01572073297778972, - 0.01029503093800601, - 0.0030395140449332404, - -0.003509419773822332, - -0.006551772181663498, - 0.0014470858673504225, - 0.0022078120034484117, - -0.003817581155639647, - 0.00524154137492483, - -0.0016629470989898219, - 0.009212157626612075, - 0.0019501035991874604, - 0.00042124191716832077, - 0.006752821737190947, - 0.0008321041856508986, - 0.005115511818953608, - 0.009799073613712269, - -0.0017392696745981556, - -0.008811676939948753, - 0.004173084809136253, - 1.8714808384714004e-05, - 0.005365874031697979, - -0.0007879301808981752, - 0.009617776559206172, - 0.008140019784413546, - 0.0016939501378982135, - 0.005010901734196988, - 0.001662168459105318, - 0.0014629832118027881, - -0.003025719955248017, - 0.004790411269039293, - 0.004561933352761188, - 0.004227249999757755, - 0.002431528420666999, - 0.003247355343613682, - 9.001522442867393e-05, - 0.001254266271283904, - 0.003015295875468908, - 0.0007608963579573006, - 0.00045372604259151663, - -0.0005459151915010194, - 0.0002893411003404545, - -0.0017102020503565307, - 4.238730461731412e-05, - -0.030080959232107598, - -0.028782990606231792, - 0.0012560395691678758, - -0.0015982023170862103, - -0.016189455966304545, - 0.007373829337630398, - -0.004806751677188604, - -0.015132782107002559, - 0.005363189979652788, - 0.004536997311687944, - -0.01800917604656581, - 0.0026989040381190585, - 0.024819200988384877, - 0.0038111111105842478, - -0.010226176006993283, - 0.005292013280510762, - 0.0036735369086466083, - 0.003848348556591649, - -0.009729185037002036, - -0.000327235285276384, - -0.0007011774971956703, - 0.0028141296902581747, - -0.006397563840806818, - 0.003898202128317264, - 0.0002579878292771769, - 0.0010807464643520811, - 0.009040978313603993, - 0.0008953086949706816, - 0.008148384959175392, - 0.002913439940338131, - -0.014638705931040562, - 0.003533983246587033, - 0.001957198767056534, - 0.0001156705367431727, - -0.001330741041622006, - 0.005083480131532394, - 0.0008988491714390134, - 9.544191716100115e-05, - -0.0004776693594382425, - -6.1675415891089875e-06, - 0.0039108130862421575, - 0.00026696269413712916, - 0.0004674859442604199, - -0.002032036201518655, - 0.00018713034893588938, - 0.01002102973229635, - 0.0011185200419466918, - -0.0014825640544619454, - 9.253130046141634e-05, - 0.0011669741442244892, - 0.008269134412015862, - -0.00022479681678721014, - 0.0012996707109457642, - 2.2003322555488185e-05, - 0.0001127022309165667, - -6.47301255934227e-05, - 0.004417838023663473, - 0.0001013560236260907, - -0.0003056615782789538, - -0.00020462975601136796, - 0.006533780273771368, - -9.200849444694848e-05, - 0.000980302913608202, - 3.9822852785119646e-05, - 4.968854357195492e-05, - 0.008552150771518052, - 0.0007400328298945341, - 2.2302225357820506e-05, - 0.00853212789991777, - -0.0006897904729641921, - 4.472345562761915e-05, - -0.00019201000571881458, - -8.68403937577216e-05, - 7.438022086463787e-05, - 0.0004231335635283775, - 0.0014318315155275945, - 0.0013793547389520315, - 0.00047566333164083765, - -0.0014811586497310853, - 0.0001146325166618741, - 0.00013837792774340222, - 0.0010064402359534622, - -0.00010870466221920963, - -0.006036586314332285, - -0.00013157150234034324, - -0.010413071208548237, - 0.03151269043378126, - 0.009818222173915245, - 0.005382917478693723, - 0.0021421780588567804, - -0.00832339031019803, - 0.007362571762386297, - -0.01660369058159242, - -0.014710269990622037, - 0.0032515555915707656, - -0.003251555591570841, - 0.008671004147748542, - -0.003229202759778102, - 0.006988196575759833, - -4.81267665565685e-05, - -0.012409122441399842, - -0.0038441014141085755, - 0.0030305957836269794, - -0.010629318439912857, - -0.003622871707940916, - -0.0051993139390301485, - 0.0010490207900909871, - 0.004045008305795241, - 0.012469123341848536, - 0.002811817701944047, - 0.009343328162093885, - 0.0037165869119792456, - -0.00025835883862227523, - -0.0211937274421002, - 0.0003449317139456662, - 0.00580184808319713, - 0.0018279529273379776, - 0.00033472930408137567 + -1.1365569369101637e-05, + -0.008169304449216416, + -0.026978300064783637, + -0.09244254191416469, + 0.06672297065936231, + -0.012691848608782143, + -0.06475100820539961, + 0.035266401279758566, + 0.11182832706971925, + -0.03057343811180754, + 0.03757354247235544, + -0.01254934185277395, + -0.012713326725327103, + 0.0179154766609782, + 0.05097855904407157, + 0.021555996878595496, + -0.016595562680923843, + 0.008549771124543066, + -0.005947526359993026, + 0.06995649972172581, + 0.1173904100488592, + 0.01067502704620932, + -0.011573695828658815, + 0.029406701486719106, + -0.03174509324438114, + -0.041744432723767384, + -0.05684518352624433, + -0.020737341890126867, + -0.01920870519566547, + 0.012727215603246175, + -0.05356084828732919, + -0.05349617847947984, + 0.04283162605271213, + -0.002409226089011421, + 0.038478220018723405, + -0.009431095477657124, + -0.007398472066275836, + -0.006707051818588165, + -0.0036824946919706857, + -0.0003526525243371103, + -0.0025530562291389515, + -0.004387095928542415, + -0.0003576315101874417, + -0.0015769929086173731, + -0.00042224550126932506, + -0.0004605029648795104, + -0.00184220481189265, + 0.0002307879190598837, + -0.00027795088418297364, + -0.0006368642708073342, + 0.0010261262810623696, + -0.0007249606670862656, + 0.03562775547057632, + 0.027886895054950806, + 0.02195727704784371, + -3.929441904835755e-05, + 0.00616620767242291, + 0.018960418899725953, + -0.04069768783596387, + -0.013153244058501132, + -0.018341386677645236, + -0.00525826930058856, + -0.01869759387560462, + -0.004609057462412287, + 0.03924550870767013, + -0.006603590101075794, + 0.015438083516069227, + 0.004634527785027511, + -0.020412630674957213, + 0.01294946838904752, + -0.0753224452850959, + -0.014053482573754666, + -0.042079580252664894, + 0.01813681800393646, + -0.04762001202904294, + 0.03259255388081402, + -0.031084775018229902, + -0.00950608583288646, + -0.024826136316658545, + 0.05022983212443651, + -0.017490684629085113, + -0.004841749455535058, + -0.05452782646692558, + -0.0279773308089723, + -0.0026411537568907643, + 0.037307585284990376, + -0.027792800516627504, + -0.005779128335613242, + -0.03503358616968421, + -0.004945361273188558, + -0.038634996417558486, + -0.021439470325993464, + -0.015450261772337773, + 0.02191144727718692, + 0.04069969955337546, + -0.01884396504553214, + 0.0016207078565042962, + 0.062352263182604076, + 0.0023211570593651984, + 0.018106578180176143, + 0.04342377320339884, + -0.014649722598671725, + -0.021765816236869438, + 0.007041247433602618, + -0.018919918895528902, + 0.039362518927777604, + 0.01257588329155328, + 0.026676212514738427, + 0.00757723778446822, + 0.06635474419178952, + 0.022488309954083706, + 0.0381601243331189, + -0.001497775203392498, + -0.006872966016042364, + 0.0011195418823358358, + -0.00591440364792565, + -0.005677911580928359, + -0.00400094952597595, + 0.0011049639221865594, + 0.0014751396933683446, + 0.00041753762349524106, + -0.001135277489759015, + -0.004075777546403503, + -0.0009196749328066346, + -0.0018814740978891462, + -0.0003258672014106182, + -0.0008350620754452147, + -0.0012017954922456996, + 0.010215188907993273, + 0.014122428731848793, + -0.006363889795766022, + -0.007936763887484398, + -0.0037455286541630425, + 0.0027794770169710884, + 0.00933424935490734, + -0.017147814229606245, + -0.006379037094404001, + 0.007326946921394649, + 0.027445700882829372, + -0.019730598204794596, + -0.0028811497363088346, + -0.007392439944294197, + -0.007385854080004402, + -0.0264302029151127, + -0.004140241166385021, + 0.0019189416438071634, + -0.01310902795685984, + 0.04656719027881093, + -0.004914230347326219, + -0.0035854676307605924, + 0.024458597654337322, + 0.07552559772915171, + -0.012623939143821454, + 0.0029026744744357893, + -0.00025414185608846855, + -0.033127907577567756, + 0.0054821012800292295, + -0.004248557265727881, + -0.0064692872736894945, + -0.004706166812563416, + -0.013972626797462607, + -0.0009526981317178319, + -0.000391180128657492, + -0.00713649211328495, + -0.0014433295365133662, + 0.06025697136397784, + -0.0022414886219249835, + -0.0022547306605083133, + 0.010913021230968212, + -0.00107700631262259, + 0.017902125438644275, + 0.0006207287710589328, + 0.01088078782247301, + -0.0003942659223651137, + 0.007640313857536753, + 0.0021105419555160505, + -0.00267201367171293, + -0.0006685105905213091, + -0.00031753870228035704, + -0.0009421549645193708, + -0.000344090185983398, + 0.0007305950157891804, + -0.0007410323897996293, + -0.0005428619434860178, + -0.0010040765466175561, + 0.024562742293015614, + -0.00045731025207243546, + 0.00024413333127209813, + -0.00033436890073258756, + -0.00024624513742241093, + 0.01908003529897682, + 0.0031667852940277263, + -0.00023229939857736655, + -0.009337947025022908, + -0.0013414588739354498, + -0.0003255730669067662, + 0.0020923803131595964, + -0.0004011461735147823, + -0.000248987410125753, + -0.0015933603629629779, + -0.0010657395045675102, + 0.0020841520166361665, + -0.002134150677722507, + 0.018123866488571998, + -0.00029509227834383133, + -0.0003623743098469084, + 0.002729663438278915, + -0.0004700532626069008, + 0.08404834023724511, + 0.018151265784223714, + 0.048237882702574474, + -0.032544611211293685, + -0.00649598215666985, + -0.04780860713775424, + -0.03065275595556263, + 0.010739381008581583, + 0.011478595885144709, + -0.02195013147499483, + 0.005488563774945845, + 0.014638889380856585, + -0.01282045245509872, + -0.013259159220390285, + 0.02207913906912537, + 0.12231556330759377, + 0.01310803044986552, + -0.007914045863553163, + -0.02748946808192071, + -0.013650894547045206, + -0.0016107533408731205, + -0.08552143599623617, + -0.05826263884803137, + 0.00031147771330268997, + 0.03976984874069635, + -0.002431020886296618, + -0.022475744450163135, + -0.033639962106926255, + -0.004599981291484161, + 0.09759686783164746, + 0.07288855373234956, + 0.004590003952764683 ], - "intercept": 6.933968417984374 + "intercept": 7.481281239528637 }, "wide-insurance-offset-l2-gamma": { "coef": [ - -0.08218458336512714, - -0.05733898810461684, - -3.193337900542818e-06, - -2.6234473556684284e-05, - 0.00024080826789374683, - -0.08218458336512713, - -0.000356310953898754, - -0.009950512867444156, - 0.010282022378688856, - -0.0008488660586231649, - 0.0074133814968528845, - -0.006539713995575668, - 0.0026842075474118126, - -0.0019613602081776133, - -0.010873509545779146, - -0.006166403811131898, - 0.007821399330236024, - 0.006675712608286478, - 0.00416344898587601, - 0.0009850538794011592, - -0.0007962347534113205, - 0.0016058022519264707, - 0.0009642129802786536, - -0.005102329264916644, - 0.016828208126751128, - -0.0036987745210500817, - -0.001762978239541752, - 0.006234147327224466, - 0.010752392047524414, - 0.004927785306941198, - 0.0142163347295723, - 0.01322507070930994, - -0.000849697151644816, - -0.0037210399421553396, - -0.01324680831798162, - -0.0154567912896926, - 0.0033850921270787188, - -0.013134682645931746, - -0.01214345541572978, - -0.0059418701900514105, - -0.0010868602911832399, - -0.0017794201965210595, - -0.0018855964329524215, - 0.002250699751231193, - -0.000756332518034213, - -0.001832878481048177, - 0.0007856750661310739, - 0.0011586250883423375, - -1.635691812557723e-05, - 0.0011079131515677138, - 0.0011253493061276674, - -0.00040951743847279765, - 0.00011690903434982279, - 0.000155589355923073, - -0.0005136614996421231, - 0.0010877661355504127, - 0.00012030094010663404, - 0.00037906376903348773, - 0.0009071058010052763, - -0.0005273062840121173, - 0.004879268654929933, - -0.004977133031012567, - 0.005747223831072777, - 0.006275568226399727, - -0.00952433712439837, - -0.005291159685437438, - -0.008406566793350462, - -0.003552099283645741, - -0.0021680892519013446, - -0.008495951161612583, - -0.0025605496827096237, - -0.0065162102387656875, - -0.0005371424493704504, - -0.003369182933391348, - -0.0054132751286065876, - -0.005418558994417271, - -0.0018572303322854846, - -0.02237161922677837, - -0.00367106378829279, - -0.016522875327750222, - -0.008589474926084166, - -0.007784618465682024, - 0.005713953046273466, - 6.753412817030357e-05, - 0.011241560810643444, - -0.0038666542228221897, - 0.00543383739608495, - -0.000779349357625175, - 0.008367444101666292, - 0.002840967157215841, - 0.010365072408431344, - -0.01572073297778972, - 0.010295030938006012, - 0.003039514044933239, - -0.003509419773822331, - -0.006551772181663519, - 0.0014470858673503886, - 0.00220781200344845, - -0.003817581155639671, - 0.005241541374924835, - -0.0016629470989898147, - 0.009212157626612073, - 0.00195010359918747, - 0.0004212419171683125, - 0.006752821737190968, - 0.0008321041856508926, - 0.005115511818953608, - 0.009799073613712276, - -0.001739269674598145, - -0.008811676939948733, - 0.004173084809136251, - 1.8714808384706926e-05, - 0.005365874031697989, - -0.0007879301808981743, - 0.009617776559206188, - 0.008140019784413564, - 0.0016939501378982274, - 0.005010901734196996, - 0.0016621684591053222, - 0.0014629832118027823, - -0.0030257199552480227, - 0.0047904112690392905, - 0.004561933352761192, - 0.004227249999757765, - 0.0024315284206669926, - 0.003247355343613673, - 9.001522442867012e-05, - 0.0012542662712839053, - 0.003015295875468909, - 0.0007608963579573023, - 0.0004537260425915189, - -0.0005459151915010193, - 0.0002893411003404579, - -0.0017102020503565304, - 4.238730461731447e-05, - -0.030080959232107584, - -0.028782990606231792, - 0.0012560395691678752, - -0.0015982023170862036, - -0.016189455966304563, - 0.00737382933763041, - -0.004806751677188609, - -0.015132782107002548, - 0.005363189979652797, - 0.004536997311687953, - -0.0180091760465658, - 0.0026989040381190576, - 0.02481920098838488, - 0.003811111110584266, - -0.01022617600699328, - 0.0052920132805107795, - 0.0036735369086466235, - 0.003848348556591644, - -0.00972918503700201, - -0.00032723528527638833, - -0.0007011774971956794, - 0.002814129690258181, - -0.006397563840806807, - 0.0038982021283172678, - 0.0002579878292771757, - 0.0010807464643520874, - 0.009040978313603978, - 0.0008953086949706694, - 0.008148384959175387, - 0.0029134399403381284, - -0.014638705931040531, - 0.0035339832465870365, - 0.0019571987670565344, - 0.00011567053674316982, - -0.001330741041622005, - 0.005083480131532403, - 0.0008988491714390157, - 9.544191716100269e-05, - -0.00047766935943824535, - -6.1675415891057985e-06, - 0.003910813086242148, - 0.00026696269413712867, - 0.00046748594426042225, - -0.0020320362015186543, - 0.00018713034893589153, - 0.010021029732296334, - 0.0011185200419466903, - -0.001482564054461942, - 9.253130046141634e-05, - 0.001166974144224483, - 0.00826913441201582, - -0.00022479681678721505, - 0.0012996707109457595, - 2.2003322555488947e-05, - 0.00011270223091656861, - -6.473012559342121e-05, - 0.004417838023663476, - 0.00010135602362609011, - -0.00030566157827895466, - -0.0002046297560113713, - 0.006533780273771369, - -9.200849444694753e-05, - 0.000980302913608202, - 3.9822852785121666e-05, - 4.9688543571956065e-05, - 0.008552150771518038, - 0.0007400328298945386, - 2.2302225357820134e-05, - 0.008532127899917759, - -0.0006897904729641932, - 4.4723455627618376e-05, - -0.00019201000571881737, - -8.684039375772283e-05, - 7.438022086463838e-05, - 0.00042313356352837817, - 0.0014318315155275984, - 0.0013793547389520256, - 0.000475663331640837, - -0.0014811586497310847, - 0.00011463251666187517, - 0.00013837792774340195, - 0.0010064402359534614, - -0.00010870466221920931, - -0.006036586314332268, - -0.00013157150234036123, - -0.010413071208548246, - 0.03151269043378127, - 0.009818222173915231, - 0.005382917478693718, - 0.0021421780588568294, - -0.008323390310198062, - 0.007362571762386282, - -0.016603690581592435, - -0.014710269990621985, - 0.003251555591570793, - -0.0032515555915708172, - 0.008671004147748563, - -0.0032292027597780955, - 0.006988196575759849, - -4.812676655658215e-05, - -0.01240912244139983, - -0.0038441014141085746, - 0.0030305957836269685, - -0.010629318439912869, - -0.0036228717079409022, - -0.005199313939030154, - 0.0010490207900909867, - 0.00404500830579524, - 0.012469123341848572, - 0.002811817701944062, - 0.0093433281620939, - 0.0037165869119792404, - -0.0002583588386222785, - -0.021193727442100248, - 0.00034493171394565973, - 0.005801848083197132, - 0.0018279529273379752, - 0.00033472930408137117 + -1.1365569369101636e-05, + -0.008169304449216422, + -0.026978300064783682, + -0.09244254191416472, + 0.0667229706593623, + -0.01269184860878212, + -0.06475100820539959, + 0.03526640127975855, + 0.1118283270697192, + -0.030573438111807554, + 0.03757354247235543, + -0.012549341852773961, + -0.0127133267253271, + 0.01791547666097818, + 0.05097855904407157, + 0.021555996878595527, + -0.01659556268092384, + 0.008549771124543056, + -0.005947526359993097, + 0.06995649972172575, + 0.1173904100488592, + 0.010675027046209333, + -0.011573695828658758, + 0.029406701486719086, + -0.03174509324438116, + -0.04174443272376743, + -0.0568451835262443, + -0.02073734189012689, + -0.01920870519566546, + 0.012727215603246191, + -0.05356084828732923, + -0.053496178479479885, + 0.04283162605271215, + -0.0024092260890114466, + 0.038478220018723426, + -0.00943109547765713, + -0.007398472066275839, + -0.00670705181858816, + -0.003682494691970688, + -0.0003526525243371084, + -0.0025530562291389523, + -0.0043870959285424145, + -0.0003576315101874413, + -0.001576992908617373, + -0.00042224550126932506, + -0.00046050296487951093, + -0.001842204811892649, + 0.0002307879190598823, + -0.0002779508841829736, + -0.0006368642708073342, + 0.0010261262810623696, + -0.0007249606670862656, + 0.03562775547057629, + 0.02788689505495081, + 0.021957277047843737, + -3.929441904835698e-05, + 0.006166207672422941, + 0.018960418899725925, + -0.04069768783596385, + -0.013153244058501145, + -0.018341386677645257, + -0.005258269300588572, + -0.018697593875604608, + -0.004609057462412299, + 0.03924550870767017, + -0.006603590101075773, + 0.015438083516069229, + 0.004634527785027522, + -0.020412630674957202, + 0.012949468389047529, + -0.0753224452850959, + -0.014053482573754654, + -0.042079580252664874, + 0.018136818003936486, + -0.04762001202904293, + 0.03259255388081401, + -0.031084775018229906, + -0.009506085832886452, + -0.02482613631665854, + 0.05022983212443651, + -0.017490684629085134, + -0.004841749455535038, + -0.05452782646692558, + -0.0279773308089723, + -0.0026411537568907456, + 0.037307585284990306, + -0.02779280051662751, + -0.005779128335613241, + -0.03503358616968422, + -0.004945361273188562, + -0.038634996417558506, + -0.02143947032599347, + -0.015450261772337761, + 0.02191144727718691, + 0.04069969955337551, + -0.018843965045532155, + 0.0016207078565042984, + 0.0623522631826041, + 0.002321157059365193, + 0.018106578180176122, + 0.04342377320339877, + -0.01464972259867172, + -0.021765816236869438, + 0.007041247433602623, + -0.018919918895528902, + 0.03936251892777761, + 0.01257588329155329, + 0.02667621251473843, + 0.007577237784468217, + 0.06635474419178956, + 0.022488309954083643, + 0.0381601243331189, + -0.0014977752033924988, + -0.006872966016042366, + 0.0011195418823358345, + -0.0059144036479256545, + -0.005677911580928363, + -0.004000949525975951, + 0.0011049639221865626, + 0.001475139693368342, + 0.00041753762349524106, + -0.001135277489759015, + -0.004075777546403503, + -0.0009196749328066352, + -0.0018814740978891462, + -0.00032586720141061825, + -0.0008350620754451852, + -0.001201795492245704, + 0.010215188907993276, + 0.014122428731848841, + -0.006363889795766025, + -0.007936763887484403, + -0.0037455286541630524, + 0.002779477016971108, + 0.009334249354907339, + -0.017147814229606283, + -0.006379037094404001, + 0.007326946921394677, + 0.027445700882829386, + -0.019730598204794576, + -0.0028811497363088415, + -0.007392439944294199, + -0.007385854080004405, + -0.026430202915112694, + -0.004140241166385029, + 0.0019189416438071657, + -0.013109027956859854, + 0.046567190278810865, + -0.00491423034732622, + -0.0035854676307605976, + 0.024458597654337312, + 0.07552559772915175, + -0.012623939143821445, + 0.0029026744744357832, + -0.00025414185608846953, + -0.03312790757756777, + 0.005482101280029233, + -0.0042485572657278824, + -0.006469287273689494, + -0.004706166812563416, + -0.013972626797462594, + -0.0009526981317178321, + -0.000391180128657492, + -0.00713649211328495, + -0.0014433295365133664, + 0.060256971363977896, + -0.0022414886219249844, + -0.0022547306605083116, + 0.010913021230968212, + -0.0010770063126225903, + 0.017902125438644233, + 0.0006207287710589298, + 0.01088078782247301, + -0.00039426592236511417, + 0.007640313857536752, + 0.002110541955516039, + -0.002672013671712929, + -0.0006685105905213101, + -0.00031753870228035715, + -0.0009421549645193708, + -0.00034409018598339814, + 0.0007305950157891761, + -0.0007410323897996293, + -0.0005428619434860181, + -0.0010040765466175564, + 0.024562742293015614, + -0.0004573102520724356, + 0.00024413333127209973, + -0.0003343689007325876, + -0.0002462451374224105, + 0.01908003529897679, + 0.0031667852940277298, + -0.00023229939857736655, + -0.009337947025022922, + -0.0013414588739354505, + -0.00032557306690676627, + 0.0020923803131595994, + -0.00040114617351478355, + -0.00024898741012575393, + -0.0015933603629629792, + -0.0010657395045675113, + 0.0020841520166361652, + -0.0021341506777225053, + 0.018123866488571998, + -0.00029509227834383203, + -0.0003623743098469084, + 0.002729663438278919, + -0.0004700532626069008, + 0.08404834023724515, + 0.018151265784223717, + 0.04823788270257442, + -0.0325446112112937, + -0.006495982156669848, + -0.04780860713775425, + -0.030652755955562612, + 0.010739381008581562, + 0.01147859588514469, + -0.021950131474994795, + 0.005488563774945854, + 0.01463888938085659, + -0.012820452455098713, + -0.013259159220390306, + 0.022079139069125373, + 0.12231556330759377, + 0.01310803044986553, + -0.007914045863553149, + -0.027489468081920706, + -0.01365089454704521, + -0.0016107533408731215, + -0.08552143599623618, + -0.058262638848031394, + 0.00031147771330272743, + 0.03976984874069637, + -0.0024310208862966293, + -0.02247574445016314, + -0.03363996210692634, + -0.0045999812914841644, + 0.09759686783164737, + 0.07288855373234955, + 0.00459000395276469 ], - "intercept": 6.933968417984374 + "intercept": 7.481281239528637 }, "intermediate-insurance-no-weights-l2-tweedie-p=1.5": { "coef": [ - -0.6445432930126285, - 0.21908355617451084, - -0.035381420360628094, - 0.279511651696271, - 0.01571495426796372, - 0.16561455123451044, - -0.20492017764815912, - 0.06477715422938912, - 0.14014302341876647, - 0.9486199678866027, - -0.21656753198266435, - -0.9863518178869164, - -0.15620148852572627, - 0.23287276148139116, - 0.2793380240141261, - -0.10170991498680582, - 0.014734629829418389, - 0.30584845591470017, - -0.22488344223042733, - -0.2788906033837647, - 0.22602626639272655, - -0.46878123503141106, - 0.4015270085706094, - 0.18976185332287945, - -0.49613768409850667, - 0.2132622633900296, - -0.3122037289214089, - 0.4444708460745815, - 0.1486840599694229, - -0.14868405996942755, - 0.13679909199594437, - 0.08323413866257123, - -0.03780306129046433, - 0.14232079324567912, - -0.6440061536956017, - -0.17643357152332562, - 0.46797669556478805, - -0.43194970625142964, - 0.1783652957426971, - 0.2153327768111299, - -0.23210954244253312, - -0.113543020898077, - 0.17750338475972693, - 0.537452895358027, - -0.1682859549685144, - 0.12031099840100255, - -0.009148131722383507, - 0.10518589361323685, - 0.5112532446416966, - -0.5070827728938655, - -0.5503648208491626, - 0.49206081352778647, - -0.16027019379298968, - -0.02514327796137161, - -0.5457587612500858, - -0.6141989201606226, - 0.29821258381937227, - -0.27650405519900606, - -0.23206759386311843, - -0.5126106011307204, - 0.27118324852448855, - 0.009364799214726468, - 0.3363263851070704, - -0.10345614797398552, - 0.4483389725431507, - 0.017839457676860147, - 1.2621828079280937, - -0.32647338600216674, - 0.2812547821932947, - -0.019404336192229248, - -0.194545055045439, - -0.057308464647608075, - -0.6581623819957234, - 0.4865464535942961, - -0.21339110656186902, - -0.25382209883287127, - 0.9563406206594658, - -0.0929180358090927, - -0.06153032891638153, - 0.15654784789307302, - -0.278106396525952, - -0.1853386705095305, - 0.05922045774942007, - -0.0058976413578009914, - 0.380706167620783, - -0.10919813709135584, - -0.08086970785764235, - -0.024055674039766727, - -0.031606508775827144, - -0.5435512153160598, - -0.08078679345754194, - -0.07108234500510696, - -0.015496649112804649, - -0.4005393295978989, - -0.04178889895216099, - 0.04403177353718022, - -0.03567895324716683, - 0.4165976840703047, - 0.0980699359138957, - -0.02099462341990708, - -0.0179669293430661, - 0.5823457691450294 + 0.6513337940985096, + 0.43496089838071894, + 0.7683755217832818, + 0.41725423397654837, + 0.5863478999447324, + 0.2455928796575939, + 0.2847940512616333, + -0.5533503014785601, + -1.2485124036300366, + -0.41397604114054, + -0.03989433592038011, + -0.015274114041473072, + -0.34988454305835387, + 0.029692576523252988, + -0.370122735712934, + -0.4466824715455553, + -0.028150826817120896, + -0.5995609337927142, + 0.2599127248161134, + -0.06167775522798902, + -0.6877777728892999, + 0.016828167340601545, + -0.4926005972877852, + 0.21695126229807934, + -0.28515419501916023, + 0.11644614832066918, + -0.03932042160335541, + 0.1176171308792397, + -0.6411377799666595, + -0.14786721994138677, + 0.4613540663078068, + -0.43696293502070105, + 0.13237346086696353, + 0.17321850781242798, + -0.24748642732264076, + -0.11918314184233973, + 0.15058855484278472, + 0.5231392273516968, + -0.21729006665338096, + 0.10646959449131758, + -0.06966608600392539, + 0.06502263048585852, + 0.505375208746846, + -0.5139025376910721, + -0.5917395820812321, + 0.4429211546450475, + -0.17788334469946465, + -0.003413049610526845, + -0.35326133950047284, + 0.5483158909560493, + -0.18653582549178138, + 0.04363457850004219, + -0.3317290744542926, + 0.4375559436712449, + 0.2850074904872649, + 0.5339398127806183, + -0.0009814305612638457, + 0.7362719314753332, + 0.09308638165352207, + 1.5065098525170404, + -0.2518436422432861, + 0.4784718368611832, + 0.0136270845665894, + -0.17397489153756066, + -0.003368204815962187, + -0.5240618028726652, + 0.5289964626445968, + -0.19727459327757405, + -0.22567174336101128, + 1.0987357374074624, + -0.06974342955395939, + -0.05757430285902911, + 0.18604745534585407, + -0.230612058203026, + -0.17249272474478428, + 0.07683383050573267, + -0.006152401022859348, + 0.4383993655333465, + -0.10964360350677893, + -0.07789520578334705, + -0.018907433462225207, + -0.03178293198481997, + -0.5779937432437321, + -0.08053302753073084, + -0.07547256996319462, + -0.015920518320478385, + -0.48852678353672263, + -0.044033063134718345, + 0.040998533105937145, + -0.03878218517670537, + 0.34121433208068846, + 0.07225443238020511, + -0.025582268209133147, + -0.01963015970247848, + 0.48496711865509795 ], - "intercept": 3.0675316122509146 + "intercept": 1.9297484657598094 }, "intermediate-insurance-offset-l2-tweedie-p=1.5": { "coef": [ - -0.4666763212112879, - 0.06306840701817097, - -0.09999288151031532, - 0.18728119905050666, - 0.1277056088172062, - 0.18861398783571723, - 0.12152641175313494, - -0.033668840915799414, - -0.08785757083734073, - 0.8924766997978093, - -0.1887058303070504, - -0.9209210974783526, - -0.1283062554278948, - 0.20788161359362703, - 0.20233693929051907, - -0.06476206946865412, - 0.018191291060587922, - 0.4012660884908979, - -0.2642854185566587, - -0.1768326493403666, - 0.3115405130907912, - -0.3911302801043934, - 0.40946359127566745, - 0.19450736388081352, - -0.3617384595411138, - -0.1018992130529739, - -0.24127422629603418, - 0.22038269015337203, - 0.1443734691042791, - -0.14437346910427684, - 0.09238661803289705, - 0.03164109294717734, - 0.049174916841808615, - -0.12346419481416598, - -0.32219971018288207, - -0.1119408095819439, - 0.3663883098294925, - -0.18442812259744068, - 0.14012494671832051, - -0.008582333547102496, - -0.1449969359142485, - -0.05937583566738633, - -0.041320981263686364, - 0.44919499908099103, - -0.22208006680931175, - 0.06960988209447365, - -0.15364423655607437, - 0.06989533828837818, - 0.33904027111710916, - -0.29566554699056036, - -0.2156105328438563, - 0.4743176999340804, - -0.10607815008316702, - 0.04612989693396429, - -0.6337676083757365, - -0.5421535771533746, - 0.3418165160709712, - -0.17579515413312524, - -0.18534261549505207, - -0.3561959822171297, - 0.20573160913571711, - -0.17182794904416004, - 0.40905193308888027, - 0.04188455950057107, - 0.418657406647395, - -0.014490657710255952, - 1.219914846954977, - -0.16788661391883067, - 0.34535584382032897, - 0.0234645281345456, - -0.13578494665388052, - -0.015266907313395764, - -0.6022614554073281, - 0.28628919880351617, - -0.1426860562000553, - -0.1316293802531973, - 0.31312102468079045, - -0.0862369908001176, - -0.02228756843409225, - 0.13767213215119803, - -0.20704621491916092, - -0.11011551496762889, - 0.08385711555568609, - -0.002766654702769874, - 0.07362525563014563, - -0.05136966743676791, - -0.05002598408846815, - 0.02370370592961553, - -0.009836375630045805, - -0.43417337414222296, - -0.05501720664833699, - -0.049790956780137265, - -0.00734618919310739, - -0.3424653657357155, - -0.02738811762163594, - 0.035357704492038185, - -0.024098583947431213, - 0.216874838032925, - 0.1084560494140338, - -0.01308810038639824, - -0.007753659274154705, - 0.4910611605403784 + 0.33864188828913416, + 0.2175772977576907, + 0.5239013124461047, + 0.36087683786669744, + 0.43785586626588785, + -0.09003397715177218, + -0.15838051525106397, + -0.4221553780160837, + -1.065738408992573, + -0.2858515340143595, + 0.03322659040923148, + -0.005362886737122063, + -0.23229803611868702, + 0.03479446541601124, + -0.41497891451391344, + -0.33821423653579025, + 0.02505401954303501, + -0.5217880440311446, + 0.27405182689107604, + -0.11206473408683851, + -0.5923945533552447, + -0.30194027913627425, + -0.4457821711151572, + -0.03069763574289147, + -0.2721318149027276, + 0.0671715699017815, + 0.052763607082148646, + -0.1310199352383699, + -0.3053504562359601, + -0.06278962298286479, + 0.3807598901153021, + -0.17445197756357447, + 0.12359684397957456, + -0.018961093044455025, + -0.1581981261763708, + -0.06174322786805876, + -0.05657968028556494, + 0.4554882259695147, + -0.2763080592883879, + 0.06040041613422554, + -0.1906408139950164, + 0.04327760593297827, + 0.3604774939074042, + -0.2955811382269542, + -0.2421650807390298, + 0.45818929085974, + -0.11584405284041192, + 0.06992078659446462, + -0.2845754505563912, + 0.5864064867488574, + -0.09719939823528301, + 0.06959009527187761, + -0.1850005082172865, + 0.3327999646254314, + 0.07648666774085562, + 0.5961788802256794, + 0.12903945886979512, + 0.7002923168949204, + 0.030978972813177887, + 1.4777519096952672, + -0.1021709787848039, + 0.5437368932312431, + 0.05133970205354417, + -0.11939650194861078, + 0.0359808536912128, + -0.4786939248651589, + 0.3336933600180428, + -0.12926614569420797, + -0.10730190356321216, + 0.44667822961783826, + -0.06969489988778611, + -0.020557480554192157, + 0.16583486904639336, + -0.15659833465377296, + -0.09409633315375225, + 0.09786842133113134, + -0.0029268237249819805, + 0.1202643725341927, + -0.05197603875810904, + -0.04828760568110613, + 0.027970646519113292, + -0.010020642455197489, + -0.4687833687796617, + -0.05513516796600764, + -0.052742344493338526, + -0.0073528323119765124, + -0.4215492049204773, + -0.0294523911160294, + 0.02890130787315693, + -0.026194384715327562, + 0.13718226669532627, + 0.08046338115110749, + -0.015922421956454224, + -0.0087516677797044, + 0.37698390387847774 ], - "intercept": 3.075889390500747 + "intercept": 2.1384452089713073 }, "narrow-insurance-no-weights-l2-tweedie-p=1.5": { "coef": [ - -0.7154216860948341, - 0.20660543064932235, - -0.019867311301373838, - 0.3002627609494653, - 0.03524398792938268, - 0.19317681786803467, - -0.3049258141150357, - 0.08619964868537063, - 0.21872616542965923, - 0.9482728491549878, - -0.302575455629968, - -0.997635638131047, - -0.07758573591371302, - 0.2363740828510801, - 0.3485058522774804, - -0.1553559546088358, - 0.03349501858004821, - 0.3145495654533618, - -0.38657298404365914, - -0.20534628049779566, - 0.22489808560439725, - -0.44537503661797134, - 0.37170074550983606, - 0.29234163872718316, - -0.4277159122903291, - 0.15004281090737767, - -0.2498793026350795, - 0.361356669882673, - 0.1734945571094497, - -0.17349455710944522, - 0.054509454917458594, - 0.10434503220121921, - -0.06843734727792526, - 0.14110892797490002, - -0.5662980555592608, - -0.15297965782510756, - 0.3985009681623123, - -0.26581541536720044, - 0.13242977697715527, - 0.19586874997931508, - -0.25897915130985527, - -0.11749053820888192, - 0.15740597883298188, - 0.5636852010718748, - -0.26279185064874744, - 0.06578439439040458, - -0.023735585036894637, - 0.04705852304797412, - 0.6510846980392891, - -0.5253713202595343, - -0.5590855356222595, - 0.5285420436117023, - -0.18482983717346801, - 0.09591701570792323 + 0.7006663165464931, + 0.4979279994404208, + 0.8431661195337313, + 0.5010658493538763, + 0.6746261621756839, + 0.32660106060276756, + 0.4155215724612098, + -0.6622435618265494, + -1.3298357023862826, + -0.38565088050351853, + -0.04903229616786955, + 0.04946872660810077, + -0.3837111665353607, + 0.03635454741408315, + -0.5537355804077021, + -0.3827312740045102, + -0.03026723497771886, + -0.5776394650188111, + 0.23558236478024608, + 0.025487313912120342, + -0.6465440876057812, + -0.049164747591539326, + -0.46311150694834713, + 0.1317942581822426, + -0.3354035705463592, + 0.042020009887458715, + -0.06490579423574983, + 0.1035148312152434, + -0.5814454612508144, + -0.12936116592332378, + 0.3891203029894777, + -0.3018138745535499, + 0.07695752734455567, + 0.15052452003272263, + -0.2719459396685358, + -0.12391078706173994, + 0.13938341087617792, + 0.5359402949197868, + -0.2843764385037419, + 0.05393053939497815, + -0.07205096242163828, + 0.01995691027091033, + 0.6133829130208585, + -0.5351983722051067, + -0.60702237419442, + 0.4726794665342628, + -0.2067459971284424, + 0.10906067607008758 ], - "intercept": 1.7428304091202178 + "intercept": 1.6093670897047079 }, "narrow-insurance-offset-l2-tweedie-p=1.5": { "coef": [ - -0.5194676257219082, - 0.04710023774126406, - -0.10281532773540099, - 0.18390179027864798, - 0.15779233910464346, - 0.23348858633274613, - 0.038832210890379144, - 0.0032594784762837894, - -0.04209168936666218, - 0.9002313285486534, - -0.26186055961453375, - -0.9078724783277181, - -0.02159409779232097, - 0.2087423435635199, - 0.23233202198149258, - -0.1499785583590911, - 0.03535596248147891, - 0.4062780213781663, - -0.3621968916519924, - -0.13933649480147198, - 0.28135054236441526, - -0.37484923139901316, - 0.3627200760482194, - 0.26417748972957805, - -0.3193308478343998, - -0.10721802387503755, - -0.15911511236509404, - 0.1475204724066323, - 0.1566941153143607, - -0.15669411531435537, - 0.04008794680679651, - 0.025311425749468087, - 0.023384687074193652, - -0.12921811142992837, - -0.2903864058903891, - -0.045790427883702293, - 0.3098174415741462, - -0.015578092202887724, - 0.11146473453277148, - -0.014960757542513843, - -0.16340695374593106, - -0.06317516372401308, - -0.05932825567887183, - 0.4617214485211112, - -0.30390508734024335, - 0.003183736677349915, - -0.1526272764683408, - 0.025859096829518712, - 0.3921861442182276, - -0.30927182803646336, - -0.24335595965007356, - 0.5449006022884788, - -0.10682499787190564, - 0.12475029489640925 + 0.36868424484538287, + 0.24168673188321424, + 0.5574014495511227, + 0.4340212544297031, + 0.5288039228358522, + -0.017951096667585882, + -0.09085032101325297, + -0.5141502065310288, + -1.1380268262793498, + -0.236020699832683, + 0.03152231722837415, + 0.03358463781430284, + -0.2765727751068692, + 0.03828507884290203, + -0.53026825264093, + -0.3074812988818197, + -0.004756154973647775, + -0.5049360369617427, + 0.23937857673991028, + -0.05330757462236757, + -0.5664615959098361, + -0.3309199642086078, + -0.40604822824630976, + -0.09768554044571363, + -0.2995717603383649, + 0.025740431793287607, + 0.0353139419565572, + -0.14477392150324772, + -0.28733240542424876, + 0.006833173528190082, + 0.3320215477411536, + -0.029688705100995708, + 0.08793009113788834, + -0.018393211873895646, + -0.17033247110237962, + -0.06483296325816454, + -0.05406936126489647, + 0.4671677717757796, + -0.3211772755628241, + 0.0099150981998907, + -0.17131415103951236, + 0.016212072672005362, + 0.3902374952571472, + -0.3140067077250751, + -0.2587563520256852, + 0.5280067149150951, + -0.11661843248933984, + 0.13415255694166872 ], - "intercept": 1.7480436559045995 + "intercept": 1.9676839820996457 }, "wide-insurance-no-weights-l2-tweedie-p=1.5": { "coef": [ - 2.57299660824223, - -1.2892716728569589, - 3.475889683623802e-05, - 7.414524357058619e-05, - 7.414524357071121e-05, - 3.412911947939066, - -0.1726488369243278, - -0.02322642255944228, - -0.11185258796401035, - 0.5425587782923321, - -0.31803393470200525, - 0.08320300385745291, - -0.08370501040268633, - 0.11987255343242799, - 0.10988241860181276, - 0.06018106189258259, - -0.410581600748519, - 0.022483916849487792, - -0.43285935329457104, - 0.12475777459873734, - 0.5344679662043837, - -0.06359364608931231, - -0.007515118801103086, - 0.026609037756761114, - -0.48551165438988453, - -0.29200532357412695, - -0.3284898071454073, - 0.4875049769557421, - 0.009292147626535076, - -0.29004942033551273, - 0.3108975341322463, - -0.20895540953145225, - 0.2762248595164083, - 0.15444463446267637, - -0.04235056322255145, - -0.2816557124631115, - 0.5993413398038218, - 0.0645022472353636, - -0.07406942770687147, - 0.293327288132807, - -0.3522623210530784, - 0.06013553460355886, - 0.16700715310327302, - -0.028947853705458507, - -0.0490996849957939, - -0.04518061126587741, - -0.012932240251766862, - 0.05948487530685015, - -0.009947506859747095, - -0.005878487550979257, - -0.003546296479306133, - 0.049222465545079135, - -0.004538400823931545, - -0.003616777117252867, - 0.016769669980618452, - -0.00836117271898578, - -0.002896665634017213, - -0.0013332053673101993, - -0.0019909964055634184, - -0.0038563661196292748, - -0.0006817335270320391, - -0.003618639449257936, - -0.0013526100429711763, - -0.001209581405983958, - -0.0023467919006041065, - -0.0014694653615158744, - 0.14470341944775142, - 0.08194283829381335, - -0.3818150495136664, - -0.0506761268668398, - -0.06291750756582719, - -0.15550591813030976, - -0.33830750303436885, - -0.17331573258057117, - 0.09299132381549972, - -0.41306551596069807, - -0.022073507852456365, - -0.4633247822174313, - -0.012385070749201144, - -0.1935746799873778, - 0.44365824764974343, - -0.3227383356030471, - 0.06501441566751252, - 0.0613948244886228, - 0.016134759064911144, - 0.11132406531696883, - 0.05049363513082555, - 0.08373345723066113, - 0.5853974589413236, - -0.43185514682964754, - 0.32702403080219306, - 0.4235161957969, - -0.25298125120900655, - 0.055605731250559606, - 0.20262271452424707, - 0.06144726331978704, - -0.0031023064721012526, - 0.18450588837534584, - 0.34002961468304144, - -0.35442543239744845, - 0.23534468405441666, - -0.004209089600080834, - 0.28146429765871267, - -0.1727928074101256, - 0.1496903921936768, - 0.2277987437200989, - 0.09882833492337101, - -0.2940025245157163, - 0.42531149118998846, - 0.023937044040937543, - 0.022609188082224424, - -0.4912333164913397, - 0.39602221243293384, - 0.0576992323688541, - 0.05013086435516556, - -0.0966733862419997, - 0.06921249780925345, - 0.034703631004213586, - -0.10763331700940063, - 0.007153961341414121, - 0.02303008968585606, - -0.03606720756614931, - 0.19436436774102955, - 0.052466198813297706, - 0.09805257183335261, - 0.02867563644002144, - -0.5905975107166533, - 0.028654922976981155, - -0.30938583872634173, - -0.027778016714743954, - -0.017489549293274738, - 0.004158091208083916, - -0.007202140704134802, - -0.007231800404399585, - -0.006010795192964626, - -0.024070545950733786, - -0.0029601662007544523, - -0.006534068316857691, - -0.0009124806741953771, - -0.0009420261271401823, - -0.0014916343982047464, - -0.0009901877732498908, - -0.0011137248503765027, - -0.0014623358247519847, - -0.06516543838387175, - -0.059253652879905014, - 0.17014891814908586, - 0.029617986067303762, - 3.729189282130731e-05, - -0.28667217209951623, - -0.23715610853522037, - 0.10203638216480067, - 0.24888583958153446, - 0.007421429408554645, - -0.18693775370360532, - 0.10290311911852655, - 0.13379261650872684, - -0.03153842501577737, - -0.08503793805329887, - 0.08147157245944904, - -0.021829386998626247, - 0.05647866523053549, - 0.04805296510757864, - 0.2756803709394185, - -0.022289082675689117, - -0.03669958187857004, - 0.4168876549859402, - 0.045017038746138806, - -0.005874687305513471, - 0.12716039343299912, - 0.017886615749682127, - -0.0009191471242273804, - 0.07909241516251198, - -0.0008224317839011369, - 0.49996293247216095, - -0.012928270663256653, - -0.008422535797252157, - -0.3180675934763252, - -0.0029125856741836456, - 0.023285639827271644, - -0.00866828077597839, - -0.006897165477638212, - -0.000743135491482559, - -0.37993175739065604, - -0.002393061174817158, - 0.04425513807911147, - -0.0042010176984761055, - -0.005493803549191791, - -0.2229544028780015, - -0.002198127733043192, - -0.0018957068502992783, - -0.8651019869587002, - 0.014467323005036614, - -0.00107656406262826, - 0.0564246263658473, - -0.0012659580872861034, - 0.17866182370625286, - -0.002315892210219773, - 0.11830914325508014, - -0.020064659224668463, - -0.0021618938109419705, - 0.03930317383744012, - -0.003102443316426218, - -0.0013777165058172563, - -0.0028707100087942217, - -0.032472979322486474, - -0.07349653178173898, - -0.24661839730109272, - -0.1454693492978319, - 0.07976925108183809, - 0.18001355696019447, - 0.21849391617716332, - -0.10578943705599375, - 0.20062008879681253, - -0.36876445054665613, - 0.2937143322897896, - -0.07182185105541741, - 0.07182185105542276, - -0.059568461880101814, - 0.09318306738004846, - 0.16997280748917046, - -0.0440899309398057, - -0.07876253063886092, - 0.3618540936769404, - -0.3319779460690281, - -0.5517151750392116, - 0.26081818590996064, - -0.043988491163900245, - -0.014300534752573368, - 0.08775196919442399, - -0.4617496243182021, - 0.168038581918362, - -0.027373950722392324, - 0.1332971047379058, - -0.19981211052461073, - 0.6448743761613788, - -0.06307813946409382, - -0.188596820327108, - 0.042417623808188386, - 0.1028059055635084 + 4.63669416607214e-05, + 0.3470459491678997, + -0.031066321433471936, + 0.6903146368307822, + 0.12465739982690963, + 0.18057937557596118, + 0.4354696250997257, + 0.26763625835936805, + 0.5106798480096915, + 0.23290860762411345, + 0.32791764154297, + 0.13395677945048726, + 0.4017116008107385, + 0.9052411316739125, + -0.28528455599956637, + -0.08383736040708858, + -0.04172266575182067, + 0.00991878295971806, + 0.083091719745628, + 0.6749638941836867, + 0.6636696030485096, + -0.2280197976656066, + 0.46370684943213736, + -0.2469872727636252, + 0.26784163822847934, + -0.20300602715956506, + -0.0783058063558148, + -0.1563448469783483, + 1.0858727555705823, + 0.1397649837875705, + 0.10643755120875896, + 0.4377930658378821, + -0.4396254327358313, + -0.27608106013987377, + 0.20556583920267518, + -0.29316402764304167, + -0.31605007876107605, + -0.29685257361826783, + -0.08503083234688628, + 0.0022867817558547003, + -0.07479937573629707, + -0.04026990870289845, + -0.0343202483675905, + 0.3012585572644166, + -0.029762371615009154, + -0.02204648033356318, + 0.0379764466693625, + -0.04893977965123161, + -0.019158558837744758, + -0.011185926253947103, + -0.012579872081283021, + -0.021568655605810474, + -0.007520955398853346, + -0.02550357410519687, + -0.006232749472258859, + -0.007989697581223046, + -0.018226301891337015, + -0.007325143156152994, + -0.20024063574129056, + 1.4354152254121353, + -0.4188821525482463, + -0.4420996021103072, + 0.12230468885331028, + 0.647599297369397, + 0.1384045402825854, + -0.484450234259735, + -0.37176250540419536, + -0.4363377680614956, + -0.3840224400017502, + -0.44015942911003825, + -0.1745964108431901, + 0.36630176934638603, + 0.5106294445400557, + -0.5064523982584006, + 0.04589777183388864, + -0.3520732990645363, + -0.3741385188768223, + -0.43834015064181997, + 0.27084677726965706, + 0.5807369616149067, + -0.9550184149541039, + 0.11594522425143042, + 0.9770033931178318, + -0.37196610060292723, + -0.3423147307601241, + 0.8940398292224084, + -0.502675739508667, + 0.06130598715768042, + -0.16305845719579792, + 0.5887344355197995, + -0.08172535322575782, + 0.18997588679661429, + 0.268688063193981, + 0.3273729336593601, + -0.12698368226797976, + 0.35656796193729756, + 0.2509490823145441, + -0.14399615989187736, + -0.21280325525719176, + -0.009917215334327757, + -0.08239979730849417, + -0.2507762441845787, + 0.2557520073932709, + 1.3694196633437938, + 0.9840988273249138, + -0.2635593703293214, + -0.578051801101364, + 0.11464882010934942, + -0.3115334347858814, + -0.4635126423035536, + -0.22175845914510492, + -0.3137630909270902, + -0.3406027470048116, + 0.4947258966742941, + -0.06153101130046197, + 0.12864431144497238, + -0.1663290094776769, + 0.3343017932035036, + -0.09207675682836296, + 0.12210694202947174, + -0.2010019435015016, + -0.13411194689727138, + -0.054415011621535184, + -0.057293332976809934, + -0.03520862496740838, + -0.04380055713758233, + -0.05800763837940694, + -0.02086327088079583, + -0.051814173668490233, + -0.0058029474558968135, + -0.009263948019344293, + -0.010516291184017075, + -0.007289526790200934, + -0.00992841250320414, + -0.011471015360321099, + -0.46402675053595416, + 0.4262809235681098, + -0.1262942162330128, + -0.08547325034019648, + -0.2346435215041854, + 0.4625481289151363, + 0.12462599627012015, + 0.5267092270550197, + -0.09513837339135228, + 0.7031617252260239, + 0.23548262108249013, + 1.5642815982072198, + -0.17948546264346196, + 0.5104539424934444, + 0.07022960783435263, + -0.14060307928963545, + 0.04990966356874814, + -0.3043432573665755, + 0.905580840681465, + -0.1454370422300252, + -0.13978799708308604, + 0.7700553392044702, + 0.12109020346167322, + -0.03971933783336448, + 0.41572809386860216, + 0.05855204891878697, + -0.08605411229611827, + 0.1899644288634543, + -0.004936750798071264, + 0.8404700523608393, + -0.07022051968004289, + -0.05183358050779002, + 0.11418559498443663, + -0.020225971218004794, + -0.09748336925181228, + -0.05884691387294007, + -0.04333046193944573, + -0.009642416535170437, + 0.24514773390852646, + -0.024451810937903606, + 0.11920790826514996, + -0.02299491150171148, + 0.6689868834421435, + 0.29301784816724075, + -0.011163284791544695, + -0.009481541594345685, + 1.4838535865003486, + 0.05217369968190964, + -0.005931414371630313, + 0.04620308151146632, + -0.005575908410495332, + 0.4141604872013159, + -0.012605780001397181, + 0.9646711536016132, + -0.11097741499433326, + -0.01689136838319655, + 0.17002229372299565, + -0.025422142853744414, + -0.008677489280540119, + -0.014870147064101256, + -0.25504368468557925, + -0.35297521361676376, + -0.157282561106536, + -0.5416408028433359, + 0.3110799212251369, + -0.07096846726615523, + -0.5556672574709798, + -0.20405105787822492, + -0.4619232571676713, + 0.4668690274805863, + -0.18342052360351307, + 0.017934317242216523, + 0.155847018435759, + -0.44246562524549354, + -0.14274143205405412, + 0.39844504893840965, + -0.31787515014376877, + 0.18778771203032857, + 0.04502938606642312, + -0.21432896730644227, + -0.10791921574604768, + 0.1557729506307858, + 0.4054154856727456, + -0.22967515627801602, + -0.12922193420490313, + -0.08459956221056664, + 0.1473071087883613, + 0.539837156944107, + -0.46013063488130546, + -0.43206911155537736, + 0.48972427514575884, + -0.04034874509850344 ], - "intercept": 0.9516962091512506 + "intercept": 3.758190234093063 }, "wide-insurance-offset-l2-tweedie-p=1.5": { "coef": [ - 2.4510003799979425, - -1.1953006368895003, - 2.700560216808665e-05, - 7.417228437997964e-05, - 7.417228438021818e-05, - 3.016148113062454, - -0.13579621287942722, - -0.10579942498942407, - -0.08107318185967752, - 0.4751979639683699, - -0.19233383588649094, - 0.03980469164665167, - -0.07865182519528502, - 0.060848410754117364, - 0.07458056175129973, - 0.0037553053667275494, - -0.29573996843668815, - 0.03568856429426113, - -0.24257566073138656, - 0.16715509445372417, - 0.3006758360051564, - -0.05295617274470965, - -0.00448727709942121, - 0.03170713158220614, - -0.17218715410646235, - -0.20848637201764345, - -0.3390431622015989, - 0.43154949696328304, - -0.027919836890316452, - -0.13593775796414498, - 0.29674485356992236, - -0.15958385786791712, - 0.21815968104213732, - 0.06533812237350127, - 0.006655055957145329, - -0.2526907735183714, - 0.37197752709759907, - 0.028055437685176075, - -0.07181753042453584, - 0.1196650993284673, - -0.29995622289365154, - 0.036639093994615984, - 0.12456017798774735, - -0.0029025802778857965, - -0.04119791071349188, - -0.03601984309449932, - -0.010557797262664979, - 0.04752576668003147, - -0.007852242864367793, - -0.003630774806126697, - -0.0034987553732929765, - 0.03649427260406693, - -0.0035782744686143533, - -0.0021471646784552026, - 0.01807066417172421, - -0.005177725841646899, - -0.002331041249963303, - -0.0018559229675182889, - -0.002497440613178352, - -0.0026417432405080672, - -0.0009635595951255358, - -0.0034368259951787503, - -0.001106503008146561, - -0.001039616780207042, - -0.0011299215245745451, - -0.0002469372153271219, - 0.0801196760293777, - 0.025978973907890668, - -0.30866284511266606, - -0.01950541372989903, - -0.034535564373974606, - -0.04002945119928528, - -0.2280130483438731, - -0.07454049857183986, - 0.05428304146978381, - -0.19943990107299253, - -0.027976050943236495, - -0.34964836125610244, - -0.07184622437481832, - -0.07675131133660294, - 0.24955440734033926, - -0.2514267942886058, - 0.0654120274049892, - 0.0028022646037540256, - 0.03806753031671694, - 0.1272330840259783, - 0.011747431720957715, - 0.00046660990551340817, - 0.4778187453996168, - -0.33971338577758853, - 0.2964240321715933, - 0.30484193758388634, - -0.2438144904039292, - 0.035409659313460226, - 0.213475178915827, - -0.02308226620472307, - 0.03451381364584346, - 0.12503886896396343, - 0.29493667331371287, - -0.29625264228369846, - 0.22695420659726587, - 0.036569061299419926, - 0.21989673846332053, - -0.1261040203643314, - 0.021376514280134623, - 0.12426274233546704, - 0.09399647020579713, - -0.23246402482929243, - 0.21391614303162637, - 0.046179868468648445, - 0.035493655995925424, - -0.24666574443317513, - 0.19455653667163486, - 0.0028259245747554954, - 0.0919359697129354, - -0.08991920300445011, - 0.0709400018942152, - 0.03541923401933767, - -0.07678755518083287, - 0.011955206412027951, - -0.01321052326490538, - -0.023101712630705866, - 0.14781657663706937, - 0.06544845728744704, - 0.019235296823569815, - 0.026872403425158894, - -0.4270462473633449, - 0.01787364199394025, - -0.2382775880600327, - -0.022013141717499164, - -0.01644741839268668, - 0.012021551486703, - -0.00614734388928084, - -0.006845857884670292, - -0.004802177348644964, - -0.02311431683108208, - -0.0024595670635637825, - -0.005597244872131552, - -0.0010889543497443095, - -0.001237079518987865, - -0.0018806401709973986, - -0.001129559032267006, - -0.0007965418608285343, - -0.0012954463123129845, - -0.17226907399621927, - -0.06270792625188251, - 0.1461517431495558, - 0.019847814907022615, - -0.0017039712457476072, - -0.24096202188699603, - -0.11738902010593981, - 0.03096034798735166, - 0.23589738369194418, - 0.028579976635173896, - -0.16806225203792963, - 0.046932940263630166, - 0.15621187959738653, - -0.00807439722441035, - 0.04363690741312989, - 0.08324266164428648, - -0.017203535106060742, - 0.0555224424101109, - -0.004908963608657902, - 0.19124379182394688, - -0.014559688563397259, - -0.012970618376206078, - 0.2701079430499887, - 0.03212242678797673, - -0.0021256666630116807, - 0.10707259855000345, - 0.010749149873593844, - 0.006038125063977501, - 0.07698790574608098, - -0.0004912719816041223, - 0.2897871395928417, - -0.006247509508053058, - -0.005151538819611162, - -0.24296811097831103, - -0.0010500072469884587, - 0.021268117674025835, - -0.005242470732464302, - -0.00520614060164207, - -0.00044992617753479065, - -0.2629053703973993, - -0.0018323122842226753, - 0.03409357757391588, - -0.003143220372842507, - 0.03321046797783878, - -0.17433510396930513, - -0.0013009099913321854, - -0.0009402146831266665, - -0.7347262389067108, - 0.008456551279223937, - -0.0012195179005674353, - 0.06776170933777495, - -0.0012906255630805166, - 0.14312809107047672, - -0.0013150064050766474, - 0.10483532441039557, - -0.012318227517697419, - -0.002185880309610822, - 0.04800150922613022, - -0.0025974553610051466, - -0.0003698500692193042, - -0.0016244818939194889, - 0.044435564458909185, - -0.03429476147987754, - -0.12938363759741658, - -0.16053187291365253, - 0.036596650598560904, - 0.1719507394169584, - 0.1809653504222439, - -0.04286158359077435, - 0.08754283116790541, - -0.2053622592587599, - 0.05094297877590397, - -0.05460607672105015, - 0.05460607672105303, - -0.07068911599179167, - 0.08946920920817605, - 0.05269349256244624, - -0.0053730634056627985, - -0.08429252433125435, - 0.2806044200192966, - -0.22914689371124425, - -0.3556777496847168, - 0.13617526284440945, - -0.025042281138400443, - -0.007593050483610923, - -0.04775650263793672, - -0.2827547669924051, - 0.08096737531681768, - -0.019138676649878318, - 0.10346839289332405, - -0.08913186980203287, - 0.5512425639242127, - -0.027156362217226646, - -0.06142659350412049, - -0.05280140660229363, - 0.06336014038389269 + 3.821897320284291e-05, + 0.19556458474055605, + -0.04090127442806765, + 0.6197144901450531, + 0.2527426482108897, + 0.10119780729037049, + 0.21281862331671067, + 0.11949294674778703, + 0.3991751467765161, + 0.2208428033526063, + 0.12750191250915494, + 0.19230204534786055, + 0.3980105673471575, + 0.481069769450344, + -0.20476505240979365, + -0.06932904571805926, + 0.008751416528463464, + 0.01625749065481836, + -0.042635111636092356, + 0.42211578969584307, + 0.4614382848944076, + -0.270441029414522, + 0.3424778946528306, + -0.29967671569819954, + 0.17352017287287932, + -0.3170310502690093, + -0.16655619971424032, + -0.16966683926633477, + 0.6782079434087394, + 0.021629824980909013, + 0.02774583767877988, + 0.16162933845355393, + -0.4033025810610617, + -0.24691128916879768, + 0.1010390768802745, + -0.16229810224448507, + -0.26000383749370565, + -0.22913016663716637, + -0.058596229221096974, + 0.013893755488055265, + -0.05406485964267428, + -0.020223932008907985, + -0.032222132514534194, + 0.17774650004899595, + -0.02256670541611714, + -0.011714778453796008, + 0.03446858488424353, + -0.030478038057434157, + -0.015426402230118836, + -0.012733839687132957, + -0.014237891338326277, + -0.01243276720722166, + -0.008337874035604499, + -0.021697796534237007, + -0.004714135520050722, + -0.005932765354291636, + -0.007494416791591101, + -0.0011118358506946742, + -0.1389378529672739, + 1.3319406833386485, + -0.20120916196222963, + -0.27288412569075193, + 0.1658920972004591, + 0.5648314386251458, + 0.12512968922450982, + -0.3468704505635451, + -0.14108093567804028, + -0.3549844226775832, + -0.2787469528160184, + -0.4496966478485146, + -0.11927684250485737, + 0.2699911235188294, + 0.6094337365137447, + -0.34663786994145257, + -0.061435070042400175, + -0.21883905878301366, + -0.24697612969986824, + -0.3566592811326564, + 0.0823468603024155, + 0.5598990433815558, + -0.6886107863582844, + 0.16672150390916024, + 0.7365129190090561, + -0.3542169023627746, + -0.3588600310030097, + 0.8043606920909462, + -0.5721636601607641, + 0.015127761949116416, + -0.21718064489967118, + 0.47203081613292286, + -0.04534061091498122, + 0.1993461908313944, + 0.24204315206477545, + 0.16494908488336946, + -0.02931602077847561, + 0.1326768150724605, + 0.005233653320106545, + -0.11996927971444955, + -0.2067684242248939, + -0.11105545679208755, + -0.010152503812703223, + -0.2076138128989234, + 0.26490050231290607, + 0.6526016688971763, + 0.7661059042069391, + -0.11755940651464289, + -0.49461190970978336, + 0.1282430423613169, + -0.23292274319112044, + -0.33778531203230605, + -0.1348418813144954, + -0.3191926188337655, + -0.24813408423475353, + 0.3716729571980969, + 0.008108622521333457, + -0.08815883714228002, + -0.14110765952215493, + 0.33716643285024767, + -0.11995235781848904, + 0.11815296566132874, + -0.1516648667358194, + -0.11800226329637158, + -0.029652338655742017, + -0.04382581435138583, + -0.030453600753255185, + -0.034334014027437604, + -0.050924310676048405, + -0.017206191687418156, + -0.039569245711079185, + -0.006153819580384819, + -0.008068521214778742, + -0.011965732369341574, + -0.007529681745357189, + -0.005220569962264288, + -0.007468556317212586, + -0.3975267240755598, + 0.4638432691169107, + -0.07374377992029903, + -0.0826027059039842, + -0.1308327892086457, + 0.34332556952397364, + -0.050260895280289784, + 0.5647218293934196, + 0.03337865704645364, + 0.601727382758051, + 0.08551515273586151, + 1.494700183136205, + -0.05098303727592521, + 0.5475411388450465, + 0.1096873394486952, + -0.09917693056996582, + 0.09212040117148249, + -0.2828184598498217, + 0.5946107232136595, + -0.09747066068827609, + -0.05204934642410007, + 0.4708330930226829, + 0.06426164689508215, + -0.011754738451224026, + 0.32214093723078047, + 0.11643602267781282, + -0.02053637084469013, + 0.17093266810569552, + -0.002519514767539298, + 0.5727154978785645, + -0.028346665779210065, + -0.029215115103253298, + 0.13674029421198752, + -0.006717170219161342, + -0.05318596408558338, + -0.035449752712287556, + -0.028525392911518715, + -0.004260308046479164, + 0.25533430392194334, + -0.016071809179776752, + 0.09330784991422894, + -0.014498207013209412, + 0.6037876116130015, + 0.3009272355107596, + -0.006344375688733952, + -0.0038589689294160783, + 1.4363526869356242, + 0.044726080303259766, + -0.00596158798797396, + 0.10639577003742547, + -0.004891440054345124, + 0.4023774518008327, + -0.006745687892247612, + 0.8239405528268887, + -0.06194084714002446, + -0.01458086846923517, + 0.1881555983442009, + -0.018649347482871035, + -0.0019657819647742824, + -0.007210801530288033, + -0.2763193314304639, + -0.2827189089576603, + -0.05263161725291153, + -0.4895979083163108, + 0.3204809285554278, + -0.11363900200510375, + -0.4992081759214453, + -0.382864298610961, + -0.4155641256348792, + 0.18035775637973642, + -0.19717654156196854, + 0.05138485356542736, + -0.10662126298863495, + -0.20907338444109821, + -0.14232740746877331, + 0.30859882415088674, + -0.09483264188719977, + 0.18178787400946594, + -0.13068252610384387, + -0.15190372525160653, + -0.052397136653006035, + -0.08058620971098311, + 0.3543263966395429, + -0.2761483814181947, + -0.09132900676995889, + -0.1651899523051153, + 0.07036520346751367, + 0.41806711362713145, + -0.2650857928911622, + -0.20785841987755915, + 0.4595444847067055, + -0.05971621982659227 ], - "intercept": 1.4249090657605907 + "intercept": 4.125815500716222 }, "intermediate-insurance-weights-l2-binomial": { "coef": [ - -0.007988089586318291, - -0.009409708100422753, - 0.005342915862423973, - 0.009287761858476882, - -0.003679259698165102, - 0.0064463796640053354, - 0.013929215499815559, - 0.008752788707365856, - -0.0226820042071814, - -0.003013331093062823, - -0.003087306735216301, - -0.010514521514566801, - -0.0066468830890089505, - 0.005375199111925438, - 0.017843398186286306, - 4.3445133643175324e-05, - 0.020548233971419798, - 0.014737308029971363, - -0.00022630938691720547, - -0.00019143433182112771, - -0.0011039233757079835, - -0.0025763587596365463, - 0.001226076862801878, - -0.0032634214588347686, - -0.004851076875680287, - -0.005596320084741563, - 0.0036302506402225046, - -0.001784791259656222, - -0.0018780904459561455, - 0.0018780904459561637, - 0.029966274528585923, - 0.0015555184016315144, - 0.0014334719278255583, - -0.002394639968715849, - -0.0017249294575489222, - 0.004964593923921146, - -0.0011489351660606288, - 0.0017606953656519291, - -0.0030939467937756626, - -0.004206988982722885, - -0.0004648642082121284, - -0.000273413395123363, - -0.004039145588771166, - 0.0032789490906185523, - -0.0030977312444033615, - -0.005344370807959485, - -0.0031854952270044586, - 0.0002856901607235233, - 0.011578011743278499, - -0.0007467154036172956, - -0.00292386512243653, - 0.007557302422479757, - 0.00023080833022128786, - 0.0145364566581556, - -0.01051102076148002, - -0.0021987735686805196, - 0.0011985310238031908, - 0.0009630949443292274, - -0.0003146070001742215, - -0.0004976569443458782, - 0.001300534516347588, - 0.0006125139162123041, - 0.005595431645046313, - 0.0008840218812506273, - -0.0004533723007901981, - -0.0003528111224023072, - 0.010682469906039617, - 0.0005519697810547237, - -0.0004834287351320913, - 0.0007930214808192916, - -0.0006411580623732865, - 0.001033276715351064, - -0.004148135435085529, - 0.002818987743811618, - -0.0006309555590067021, - 7.352172613561772e-05, - -0.0028947466985008792, - -0.0006895591477607534, - -5.129706753078297e-05, - 0.0020555799342361317, - -0.000994831456425774, - 0.0003345865740278903, - 0.0019742577674408826, - -1.3981628140072815e-05, - -0.0003550704721677586, - -0.00020192016039283385, - -0.00019942144314592705, - 0.0007904103806800798, - -2.0217926914067907e-05, - -0.0030759097999042634, - -0.00021027084549399334, - -0.00023594812572682154, - -1.428132271582773e-05, - -0.002320806893965307, - -9.323586012227885e-05, - 0.00048182415730240954, - -0.00012735893602049166, - -0.00427221316677849, - 0.0016283000715494452, - -6.60974858085323e-05, - -2.183664953953595e-05, - 0.0023185904110871697 + -0.00937378967644213, + 0.005489145960170666, + 0.009400214644426632, + -0.0036813812439902777, + 0.006455868577289029, + 0.008572455613981216, + -0.022660825772892836, + -0.003105666174767854, + -0.010442241796825106, + -0.006562272643531505, + 0.005347469915667315, + 0.01777183544910685, + 1.925246987155004e-05, + 0.020735426614556084, + -0.0002537488899325981, + -0.00020464864645604748, + -0.00124868025431586, + -0.0025928636548505015, + 0.001219763389246026, + -0.003608202455091615, + -0.004925174915512978, + -0.0056498541696111725, + 0.003565612036260965, + -0.0018483563112139693, + 0.0019124579401756424, + 0.030020105098744827, + 0.0014303569561886062, + -0.0023950485445544904, + -0.0017232147986704647, + 0.005011180418530039, + -0.0011403625076898708, + 0.0017639689732646346, + -0.0031028370588199964, + -0.004221282788509361, + -0.00046805536927481433, + -0.00027324240182351384, + -0.004018905368059588, + 0.0032743332993986213, + -0.0031071969390303817, + -0.0053590054387038615, + -0.00319651249042399, + 0.0002852638921558881, + 0.011597226869567939, + -0.0007496973566221105, + -0.0029346751612477195, + 0.007534774558179375, + 0.00022428125191071636, + 0.01456633362684917, + -0.0021493212830439603, + 0.0012134988794706791, + 0.0009715664836925611, + -0.000277813878987707, + -0.0004797005637780355, + 0.0013050125167989104, + 0.0006616772311781395, + 0.005607316852333194, + 0.0008900861553263008, + -0.0004060328101322889, + -0.00034872732105805754, + 0.010705729228551175, + 0.0005589495263066777, + -0.0004424992603451754, + 0.0007936671476475581, + -0.0006380780603441502, + 0.0010399338874068208, + -0.004113451475886578, + 0.002825862489901727, + -0.0006285195819914721, + 7.663633012566475e-05, + -0.002861136954138487, + -0.0006861476253973631, + -5.124805427846884e-05, + 0.002060646778271927, + -0.0009725853387507174, + 0.00033732436445395514, + 0.0019765504070153126, + -1.4004994304336096e-05, + -0.00034459904876441147, + -0.00020181611766421068, + -0.00019940123800365068, + 0.0007905549322776577, + -2.0239466524219857e-05, + -0.0030693648737733902, + -0.000210672962487274, + -0.00023563705533894737, + -1.4134073426706678e-05, + -0.00232243250889158, + -9.348966983266553e-05, + 0.0004808162211495533, + -0.0001265684092810493, + -0.004284207308033841, + 0.0016273010238064963, + -6.674154121783088e-05, + -2.1877774950414397e-05, + 0.0022461608946354565 ], - "intercept": -4.168447155523067 + "intercept": -4.183502045034046 }, "intermediate-insurance-no-weights-l2-binomial": { "coef": [ - -0.01918261845310707, - -0.00406407579262125, - 0.015397490037892885, - 0.011357903825352034, - -0.009282311456753059, - 0.005773611839236423, - 0.034920864380300634, - -0.007499193660193838, - -0.02742167072010686, - -0.00374909471484868, - -0.0001646670519945093, - -0.016102439690055696, - -0.012610887199715731, - 0.005217221045863097, - 0.026443670723424205, - 0.0009661968873272627, - 0.01351847040006955, - 0.015711251329803555, - -0.0010083908170101428, - -0.0004139380851229565, - 0.0038704250292944356, - -0.0031614936681629816, - 0.0024937970649043777, - -0.004185848453623961, - -0.0114470257512286, - -0.006863083369060369, - 0.0016626295541599364, - 0.0033416771660466447, - -0.012079042053911629, - 0.0120790420539116, - 0.033614447054567516, - 0.004492940392944888, - 0.0018341513352796005, - -0.002270985298633668, - -0.0037370768337514405, - 0.0058206383173421425, - 0.00036150278740846404, - 0.0014258486841370265, - -0.005323403071294479, - -0.0036275303357345577, - -0.0008027166594621377, - -0.0006906874965695786, - 0.0005383891847734152, - 0.007397802975720402, - -0.0024798552192585787, - -0.011113384444483839, - -0.0035586835177015604, - 0.000922676525957424, - 0.013536576640757624, - -0.0026193976231576, - -0.008507552427379453, - 0.009000857040083004, - -0.0006001109569771457, - 0.011161850735291858, - -0.005248402964170565, - -0.0033112211428451607, - 0.00032588986587874175, - 0.001187041080696008, - -0.0019173461911472958, - -0.001433694340785866, - 0.001962362709675567, - 0.0002152878922175327, - 0.00493021256323387, - -4.344964495367643e-05, - 0.0017609152896218133, - -0.00030652746952674535, - 0.013051148403360963, - 0.0012434050135440467, - -0.003978013460469864, - 0.0007247480952718923, - -0.000990246166735098, - 0.0011487765528673533, - -0.006407879976776646, - 0.004967054323111442, - -0.0010173786654154382, - -0.0006084772743791732, - -0.0018672660088101807, - -0.00025109559072890097, - -0.0002756055754489435, - 0.0030754134274833373, - 0.0003127718067971687, - -4.624149557930927e-05, - 0.0022537552590302513, - -5.768524631057985e-05, - 0.0006803575617555308, - -0.0005508403308691611, - -0.00034336429390043483, - 0.0005749519581323961, - -0.00015043589021314534, - -0.00595762737111864, - -0.000360240482569971, - -0.00044402555550619846, - -8.504787398062623e-05, - -0.0036736389387844862, - -0.00016633463136826948, - 0.0007666180328547312, - -0.00016678638999771884, - -0.006084803226866727, - 0.0016757138846126372, - -0.00015808389119739265, - -9.029307711546413e-05, - 0.005135629447426369 + -0.003869631965764067, + 0.015590389177920857, + 0.011734859225979884, + -0.009245556690479264, + 0.006033517210340866, + -0.008520682281052246, + -0.027620848228426004, + -0.00022391208104457689, + -0.01603631917932896, + -0.01257747512107677, + 0.005178344266985153, + 0.026462286082954544, + 0.0009898500979774662, + 0.013579297056047547, + -0.0010723597958041343, + -0.00041985315898209174, + 0.003968506566376503, + -0.003190046831953606, + 0.0024831675769941774, + -0.004861486505497503, + -0.011565177925091185, + -0.006979023900169205, + 0.0015476800494466237, + 0.0031991071488817195, + 0.013011937487823361, + 0.03398000265964068, + 0.0018258575159171644, + -0.0022855328099292004, + -0.003738313480608809, + 0.005793918418317049, + 0.00037375253025318694, + 0.0014303814644687216, + -0.005346387679098226, + -0.0036344327162246316, + -0.0008027918593407592, + -0.0006891618755546079, + 0.0005214470270454992, + 0.007375233944557057, + -0.0025004136894776594, + -0.011161984570723373, + -0.003575297553957367, + 0.0009188584067297877, + 0.013518026147669312, + -0.0026280101728971823, + -0.008529016925256377, + 0.008997532776282263, + -0.0006117927887249152, + 0.011389526370134455, + -0.0032789413224395105, + 0.0003373770109127657, + 0.0011912713484768642, + -0.0018859113497102027, + -0.0014148032456763032, + 0.0019604344529715895, + 0.0002607116942684077, + 0.004930597157600413, + -3.355839972856401e-05, + 0.0017989217501577096, + -0.0003001642340953141, + 0.01306545308352527, + 0.0012531647330452924, + -0.0039617260053054805, + 0.0007224055609051336, + -0.0009912722613463478, + 0.0011483793302959758, + -0.006382649689390879, + 0.0049746736798606484, + -0.0010173921224946461, + -0.0006052324854629112, + -0.0018397632015931414, + -0.0002447531855247404, + -0.0002737373285306015, + 0.003081449749510735, + 0.000301842064171857, + -3.869159724745898e-05, + 0.0022570189607356793, + -5.8041875160613495e-05, + 0.0006849298084797876, + -0.0005484999327074056, + -0.00034223528020471927, + 0.0005754392011293457, + -0.000150241561383491, + -0.0059518608521192005, + -0.0003618728908109719, + -0.00044104432389182664, + -8.234526498407301e-05, + -0.0036570243629493048, + -0.00016697330405862436, + 0.0007659333283509509, + -0.0001662951898740586, + -0.006111239797584317, + 0.0016744586002125231, + -0.00016045670132571656, + -8.978877063032487e-05, + 0.0050837165774634836 ], - "intercept": -3.968878987510292 + "intercept": -3.9801004784496943 }, "intermediate-insurance-offset-l2-binomial": { "coef": [ - -0.008026936969530921, - -0.009299280950820929, - 0.005436163383062704, - 0.009299850752283923, - -0.003735312093501475, - 0.006325515878506732, - 0.013709389583837496, - 0.008765362070509632, - -0.022474751654347102, - -0.003050802352468344, - -0.0031177442166546924, - -0.010551212139620179, - -0.006634865301477818, - 0.005323431489133697, - 0.017901766604191592, - 0.00012942591689579054, - 0.019639593759394714, - 0.014997739410690696, - -0.00020257159260765847, - -0.00022756776972772207, - -0.0016563786406907331, - -0.002571940113542556, - 0.0012315804209886152, - -0.0030737946603219307, - -0.004829783363205316, - -0.0055532437943263185, - 0.0036591743762576074, - -0.0017732142735146757, - -0.0019544798811878053, - 0.0019544798811878443, - 0.029272026456816386, - 0.0014195825858612685, - 0.0014256278367488955, - -0.00240520379091642, - -0.0017359513840913107, - 0.005216112676521095, - -0.0011238085780000014, - 0.0017513883723340494, - -0.0031785258614797146, - -0.004185149314624435, - -0.00045847241445738483, - -0.00027634576334875107, - -0.003943517414049424, - 0.003362635280159369, - -0.003071667536584511, - -0.005359800254225843, - -0.003220743913482778, - 0.00027656761500818655, - 0.011642052921046829, - -0.0007648077893758174, - -0.002986079260297319, - 0.007407222439653825, - 0.00020888354760019169, - 0.014045374237800065, - -0.010611844772693705, - -0.002213668856692372, - 0.0011968801674337643, - 0.0009646250918476422, - -0.0003435610191095114, - -0.0004865329606404484, - 0.0012993619246471303, - 0.0006082515622365309, - 0.005606593758487912, - 0.0008763420843434003, - -0.00043348547498850697, - -0.00035920819037234784, - 0.010695719665973099, - 0.0005531911504712619, - -0.0004515905705595267, - 0.0007933382198113769, - -0.000639361501909464, - 0.0010402088858042474, - -0.0041455788299323984, - 0.0028233669804580913, - -0.0006237137530970406, - 7.8683447097261e-05, - -0.002836023274711293, - -0.0006772518299049899, - -5.277622464594273e-05, - 0.002062884148109666, - -0.0009314377098482277, - 0.0003437093707404372, - 0.0019840045884308376, - -1.45196843696418e-05, - -0.00032127797097033107, - -0.00020361272439849108, - -0.00019900536656878947, - 0.0007907465059173543, - -2.1416561326900468e-05, - -0.0030629325628252738, - -0.00020970774730665233, - -0.00024103592097700878, - -1.5207838914857824e-05, - -0.0022753531853267057, - -9.586708351451674e-05, - 0.0004935302746828474, - -0.00012746122666562227, - -0.004311056108571949, - 0.001636908305729026, - -6.82187920511201e-05, - -2.3363193718324913e-05, - 0.0021477248043901176 + -0.009263044325954954, + 0.005587046085804121, + 0.009416861874767618, + -0.003738278444545398, + 0.0063350611562846965, + 0.008578265720528104, + -0.02245061083878747, + -0.0031361495286421634, + -0.010476852602685587, + -0.006546768919149287, + 0.00529415219248033, + 0.01782761345994, + 0.00010525778407223867, + 0.019825158132842958, + -0.00023114296184245577, + -0.00024144689884596172, + -0.0018112770302831992, + -0.00258893292074616, + 0.0012251274221773115, + -0.003427264848954524, + -0.004906871391401709, + -0.005608172720461733, + 0.0035926271238470074, + -0.0018386072198513703, + 0.001994817667876973, + 0.029322674687770552, + 0.0014223344788092702, + -0.00240593629699388, + -0.0017341451114988831, + 0.005266173016901761, + -0.0011145396362666736, + 0.00175486354617288, + -0.0031879239312923744, + -0.004199573072576049, + -0.0004615890602184733, + -0.00027619357024830554, + -0.003922799871835437, + 0.0033584851888201887, + -0.003081319090354799, + -0.00537448153997832, + -0.003232231028418079, + 0.0002761125145687656, + 0.011662123720866429, + -0.0007680423708313983, + -0.0029973212978718963, + 0.007384505531975677, + 0.0002020625314933467, + 0.014073734655062854, + -0.002163161396159843, + 0.0012121959888101103, + 0.0009731446429648462, + -0.00030540932784289127, + -0.00046827589421456555, + 0.0013039136697341066, + 0.0006585686529188402, + 0.005618602588954527, + 0.0008826287947641888, + -0.00038488220380615155, + -0.00035493215160767023, + 0.010719702425377852, + 0.0005604284187065041, + -0.0004094715134403902, + 0.0007940044552222023, + -0.0006362039832523067, + 0.0010470190898136567, + -0.004109367139020632, + 0.0028302793895429237, + -0.0006212428390894086, + 8.184450114054464e-05, + -0.0028007288403749057, + -0.0006737365055768396, + -5.2714209034123524e-05, + 0.0020680248710651314, + -0.0009082025199042926, + 0.0003465144310511345, + 0.001986369912170051, + -1.4545641228737494e-05, + -0.000309557442109392, + -0.00020345671557381653, + -0.00019895818167007615, + 0.0007909358588419622, + -2.1439439872158265e-05, + -0.0030554637964315744, + -0.00021011617654011016, + -0.0002406962825397612, + -1.503424373506809e-05, + -0.002276017213402969, + -9.614205726008909e-05, + 0.0004925621615449159, + -0.00012665443545918493, + -0.004323484851648153, + 0.001635977364818571, + -6.893572248123347e-05, + -2.3412884328984744e-05, + 0.002071470595240017 ], - "intercept": -4.127258416688175 + "intercept": -4.142255897500308 }, "narrow-insurance-weights-l2-binomial": { "coef": [ - -0.007988590559957785, - -0.009404117905530224, - 0.005344247660411858, - 0.00927705824127337, - -0.003672745455967756, - 0.006444148019770559, - 0.01392945346457004, - 0.008751625356050325, - -0.02268107882062031, - -0.003017697698207066, - -0.003097173066393676, - -0.01046715973847675, - -0.006574470552526564, - 0.005353764950785403, - 0.017784525657106105, - 1.821044771259071e-05, - 0.020742701731833145, - 0.014736592527765763, - -0.00022302757362795817, - -0.0001894725505805548, - -0.001115847559877436, - -0.0025738099712927422, - 0.001225266114718465, - -0.003275557554748648, - -0.004844450311763803, - -0.005590661072503457, - 0.00363899061876213, - -0.001788022666851722, - -0.0018633204108532916, - 0.0018633204108533456, - 0.029993376440771023, - 0.0015571971441620425, - 0.0014335155377319894, - -0.0023926788181596395, - -0.0017246087436506956, - 0.004952456065337209, - -0.0011505587005657878, - 0.0017629782711153952, - -0.003092591219623077, - -0.004211431625714503, - -0.00046663229923737725, - -0.00027410725103221866, - -0.004040108539528768, - 0.0032764397672725713, - -0.003098854900122134, - -0.005347536750567937, - -0.0031856027399726375, - 0.000284653926831144, - 0.011596711139007992, - -0.0007467531570313474, - -0.0029233029079837837, - 0.0075606105643283225, - 0.00023020523740328123, - 0.014558974036675562 + -0.009375535475132436, + 0.005486113586920518, + 0.009401291246043277, + -0.0036766604035049126, + 0.00645696579399282, + 0.00857858619614294, + -0.022666747629287137, + -0.0031152602564047055, + -0.010465633034628054, + -0.0065674669533686125, + 0.005360459974040909, + 0.017791958923748616, + 2.2470787834632388e-05, + 0.0207209564419218, + -0.00025020628285716, + -0.00020439153414123903, + -0.001252375623635599, + -0.0025927535112816, + 0.0012194356944112443, + -0.003609045910812796, + -0.00492433290382952, + -0.005649223930851828, + 0.003567094901907578, + -0.0018491058582796843, + 0.0019102626594976885, + 0.030010252695345015, + 0.0014300118159813117, + -0.0023960208163602333, + -0.0017240978885673449, + 0.005011306804667752, + -0.0011404524567788692, + 0.0017646855777548678, + -0.0031034300700592534, + -0.004220950733329102, + -0.0004682904647973778, + -0.00027369367899828634, + -0.004019440250551955, + 0.003275038427840579, + -0.0031095023325250627, + -0.005361053262372658, + -0.003196701294963971, + 0.0002849591721732216, + 0.011599586089431672, + -0.0007503831616371497, + -0.0029342114808662717, + 0.007537295929813511, + 0.00022490434947754039, + 0.01456003651950311 ], - "intercept": -4.1865630467625365 + "intercept": -4.182790251971983 }, "narrow-insurance-no-weights-l2-binomial": { "coef": [ - -0.01918614209107905, - -0.004061354451536444, - 0.015397578632614038, - 0.011349513972461702, - -0.00927194071754743, - 0.005772344655087125, - 0.034918756015292984, - -0.0075007799843511466, - -0.027417976030941794, - -0.0037441811543960264, - -0.00018698963725223515, - -0.016112930051763005, - -0.012557401778623337, - 0.005218771012813614, - 0.026426732137657574, - 0.0009559994715633606, - 0.013621116541397636, - 0.01571002531527578, - -0.0010040297177306633, - -0.00041008706395403977, - 0.0038552851985762756, - -0.0031613075568466013, - 0.002493611036405689, - -0.004183556578914375, - -0.01144307056537662, - -0.006859345854118061, - 0.0016649926465556713, - 0.0033374831401269027, - -0.012070797230042578, - 0.012070797230042594, - 0.033629483257415556, - 0.004494641149872479, - 0.001832520110483835, - -0.002271844301736503, - -0.003738759009669629, - 0.005811667183902893, - 0.00035981679809060386, - 0.0014266721450213132, - -0.005316367201506285, - -0.0036300199474924907, - -0.0008038528976142997, - -0.0006919918662798552, - 0.0005388362377042285, - 0.007397745278719263, - -0.0024815488615728227, - -0.011117931467111888, - -0.003558573118683036, - 0.0009227827458903572, - 0.01355036797515816, - -0.002620666889306378, - -0.008504778719960707, - 0.0090034355647904, - -0.0006021509086996729, - 0.011165052630828952 + -0.0038695853911270657, + 0.015582456443332407, + 0.011730883737317888, + -0.009234117807722235, + 0.006037029561159938, + -0.008522687975141049, + -0.027621492297890574, + -0.0002431949155030523, + -0.016095924941123427, + -0.01259658745762022, + 0.005208599164111136, + 0.026510007085253876, + 0.0009977445638377955, + 0.013578449024893947, + -0.0010671058361865417, + -0.0004177353683435115, + 0.003969822110923954, + -0.003191732588627666, + 0.0024832363923669534, + -0.004857992882805205, + -0.011566025016633074, + -0.006979480237345671, + 0.001547153958805142, + 0.0031948281635700757, + 0.013014628199323187, + 0.03396879194871139, + 0.0018243060950831368, + -0.002287294722313583, + -0.003741837528012861, + 0.005794394466918182, + 0.0003719204957618093, + 0.0014303079645018543, + -0.005339946633497461, + -0.0036334343724805304, + -0.0008030385738581641, + -0.0006902825899692298, + 0.000520569308310343, + 0.007377610464410894, + -0.002502133304439048, + -0.011168346409114903, + -0.003575770924971139, + 0.0009197631749752907, + 0.013522653888562407, + -0.0026294523218232143, + -0.008527796386058795, + 0.008998585809675939, + -0.0006129280852788567, + 0.011371548557948832 ], - "intercept": -3.9784288506068988 + "intercept": -3.98029468563706 }, "narrow-insurance-offset-l2-binomial": { "coef": [ - -0.00802735146791777, - -0.00929388618245076, - 0.005437725513610625, - 0.009289301176733723, - -0.0037286636851544723, - 0.006322874645178652, - 0.01370954118859604, - 0.008764013484765782, - -0.022473554673361793, - -0.0030554556005902714, - -0.0031274426204313268, - -0.010502140502298996, - -0.006559634811352042, - 0.005300764456398252, - 0.017840349867126657, - 0.00010355921114771826, - 0.01983300730062763, - 0.01499737179752296, - -0.00019939629990102, - -0.00022546594897666165, - -0.0016691723574258333, - -0.0025693207218555974, - 0.001230819641591594, - -0.003085644190754453, - -0.004823311880904164, - -0.005547440815990734, - 0.0036680094415177745, - -0.001776448664823855, - -0.001939322746314056, - 0.0019393227463140623, - 0.029299794837992187, - 0.0014211000248803625, - 0.0014256550306837833, - -0.0024032393574866546, - -0.00173565011570729, - 0.005203707761082757, - -0.0011253916075052036, - 0.0017536332276958725, - -0.003176989043944803, - -0.004189698319346952, - -0.00046020702828868066, - -0.00027705754213758156, - -0.003944400308671404, - 0.00335977277594358, - -0.003072745053457306, - -0.005362803418021206, - -0.0032208171549437817, - 0.0002755242315732852, - 0.01166061853187173, - -0.0007648728888916035, - -0.0029853472175635663, - 0.007410968815672557, - 0.00020823865656212408, - 0.014068721200999217 + -0.0092646047291303, + 0.005584094945284108, + 0.00941790704816613, + -0.0037333697457637015, + 0.006336029295113662, + 0.008583944034034695, + -0.022456085799154663, + -0.0031460714547910614, + -0.010500107129757989, + -0.006552109982474077, + 0.005307392747303926, + 0.017847982667362643, + 0.00010838303631918871, + 0.019810471789004767, + -0.00022755370460997732, + -0.00024113778387640166, + -0.0018148256126373815, + -0.002588899215476945, + 0.0012248322768733293, + -0.0034280576073225867, + -0.0049059731033651065, + -0.005607520111034166, + 0.0035940992332227084, + -0.0018396283104956373, + 0.001992650076187562, + 0.029312865597464082, + 0.0014219707585668075, + -0.0024068918804631347, + -0.001735077697633112, + 0.005266461813106803, + -0.00111473467446525, + 0.0017555640934232644, + -0.0031883495744308436, + -0.004199289742427454, + -0.00046178870173312563, + -0.0002766701377315954, + -0.003923288677572983, + 0.003359186361210829, + -0.0030835766207820397, + -0.005376634044818348, + -0.0032324539179642304, + 0.00027583653130767164, + 0.011664479946207573, + -0.0007687201062770784, + -0.0029967719974211783, + 0.007386869313738898, + 0.00020265008892949392, + 0.014067195662169393 ], - "intercept": -4.145377342363562 + "intercept": -4.141531382531452 }, "wide-insurance-weights-l2-binomial": { "coef": [ - 0.06995461458506161, - -0.0006015587195713083, - -2.246359185069218e-06, - 0.056414380112907635, - 0.056414380112907676, - 0.002062522040932357, - 0.06339135173475983, - 0.0033931999327403103, - 0.0037568848550788474, - -0.002334896445693776, - -0.003320642722466864, - -0.0025857152570606737, - 0.0010911696374021555, - -0.0026740586392946467, - 0.0026970104507309718, - 0.0017688430586885145, - -0.0002858720418495606, - -0.0019491651941221546, - 0.001073699433946194, - 0.001667542459663382, - -0.0011968349763860349, - -0.0004047860820383764, - -0.0002458456881862939, - -0.0002301187682737888, - -0.00022041401287819883, - 0.013042018259218505, - -0.0006781352150608233, - -0.0012343983284264133, - 0.00031911869814961355, - -0.00032245322483062357, - -0.0006562796791581896, - -0.0014706624859930194, - 0.0006348567242437808, - -1.4233920444389153e-05, - -0.0013844777703805745, - 0.0005826955826431145, - -0.0011576966788746058, - 0.0023228755633800877, - -0.0013706086837375771, - -0.002468462645146155, - -0.002074759236699105, - -0.0012751119043381903, - -0.0012117840616271947, - 0.00018965430619223274, - -0.0005832100158983576, - -0.00034891055108004934, - -0.00028151552503207004, - -6.838365499694613e-05, - -9.305617840297204e-05, - -6.066278412910442e-05, - -1.5216162703935748e-05, - -3.920248142963085e-05, - -5.674358039036524e-05, - -2.875027888651852e-05, - -7.324277628517527e-06, - -2.4271003121990004e-05, - -2.735839574788205e-05, - -2.1076049260205215e-05, - -2.627209765105392e-05, - -2.5345301357837558e-05, - -8.842005079033463e-06, - -1.3228082775400918e-05, - -2.6592648538932016e-05, - -6.855074091517437e-06, - -6.444413350576201e-06, - -2.6300347128871793e-06, - -2.6470284468849017e-07, - -3.593665141203198e-05, - -0.00014647282373140769, - -0.00015438654309920968, - -0.0002296764169236898, - 1.9424929733529062e-05, - -0.0003364826089248995, - -0.00014006133583483897, - -0.0006887033917032497, - -0.0007910652227537008, - 0.00039323827296263775, - -0.00107019599366101, - 4.965184720860183e-05, - 7.176432652070534e-05, - 0.00016273685477015713, - 0.0002405875611210778, - -0.0007610020057135052, - -0.0010521199374292928, - -0.0012954381474235225, - 0.0013770270375155595, - -0.0015808169020907325, - -0.0010634547155375863, - -0.00032692204106344875, - 0.0006604297373151351, - -0.00023593733691868322, - -0.00017468837331026965, - -0.0011085277711985213, - 0.0007633604366566087, - -0.0005243186677980153, - 0.0019152762159496152, - 0.0003842750034897973, - 0.0008769681531580782, - 0.0003110056826048167, - 0.0026960991740927116, - -0.0006267564900395651, - -0.00040212439933414324, - 2.390891722070936e-05, - -0.0011982399566044112, - 0.000767691438740054, - -0.0005842317176602333, - -0.00021199919843966828, - 0.002487126631803729, - -0.00046921038677223703, - -0.001044645527660725, - -0.00011300610052849321, - -0.0007282500016252006, - 0.00032781853900375803, - 0.0013622213234794713, - 0.0006322103763550384, - -0.0006267889358081162, - 0.00026174998494392295, - -0.00048576912805012204, - -0.000565275033306362, - -0.0005981062115435365, - 0.0012239838525946838, - 0.0010361712593760296, - -0.00043351524657380644, - -0.0005378615314951155, - 0.000596347566059422, - 0.00043904536312347605, - 0.0006673558004778676, - 0.00019287428330164215, - -0.0002631745430022852, - -0.00013437427637072683, - -0.00018811469368844614, - -0.00016852092167429658, - -0.00011257289369168295, - -6.317564411993712e-05, - 0.0009577725234890034, - -4.260434153460317e-05, - 0.0005473955459632662, - -1.6122622504185124e-05, - -3.7740689778016926e-05, - -1.3275837641069724e-05, - -1.3115629872892746e-05, - -2.6205991058868063e-05, - -1.3224674836783841e-05, - -3.121778541772033e-06, - -8.187348746197985e-06, - 0.006981720866962389, - -0.0013909828686412033, - -0.00047635139451772195, - 1.686282499117266e-05, - -0.001199849332140716, - -0.0006314054977391931, - -0.00020659966447854835, - 0.0003555889703911556, - -0.0004257691675127747, - -0.00015914743496039916, - -0.0003210074721575101, - -0.00010874504953196085, - 0.00026412918946747973, - 0.0001293324201634775, - -0.0013456461044045712, - -0.00011279848147302654, - -0.00011777045032734118, - -0.0002089750585323174, - -0.00021013129794536793, - 0.0006459761116763981, - -0.00010350958774641282, - -0.00014095981329648606, - -0.0009777956619667718, - -0.00015158480277434165, - -7.6085278669502415e-06, - 0.0003021820557439929, - -0.0006765981082419405, - -8.66613784719439e-05, - -5.179003269720828e-05, - -2.205018292317326e-06, - -2.866111513410819e-05, - -2.590322878058309e-05, - -2.7339078737799447e-05, - -1.3517677918147441e-05, - -2.4611347694344917e-06, - -0.00020708498537117264, - -2.6438672938515035e-05, - -2.8386966153191812e-05, - -1.5755361737567784e-06, - 0.00037127775329678936, - -9.833749216134766e-06, - -2.573048618453657e-05, - -1.3618819391002404e-05, - -0.00010510100822674655, - -1.3198421716944358e-05, - -7.2089750530684445e-06, - -2.091322606726033e-06, - 0.0007021834421822944, - -1.4343702726048632e-06, - -1.1327527481570574e-05, - -0.00010300249475689987, - -9.104374965543324e-06, - 1.8783593568794454e-05, - -3.0647678806999086e-06, - -6.204386279359102e-05, - 0.00010993026280423108, - -1.739542415552179e-05, - -1.3066841091264572e-05, - -2.1323089824192374e-05, - -5.227397156455166e-07, - -3.638614221747378e-06, - -8.503184232983531e-05, - -0.0017063655364599874, - -0.0010920665019650084, - 0.007577164446304268, - -0.0005462703249460369, - -8.282639897653399e-05, - 0.0005982282469167595, - -0.0022163993383253404, - -0.0011314518715396511, - -0.00016997814632295065, - -0.0011450027323556865, - -0.005940575562593932, - 0.00594057556259393, - 0.0003351381852911608, - 0.0008857141040042846, - -0.0007516764552477677, - -0.00039663933783015595, - 0.006823151418796197, - -0.0003951497278923835, - 0.0007394994320348889, - -0.0013311039781327514, - -0.0013874104048624421, - -0.00022389226120424785, - -4.335710800130887e-05, - -0.0006248466876541233, - 0.004189701931808538, - -0.0010761472596597516, - -0.0022541559498798942, - -0.001166355369669497, - 0.00018456600814679145, - -0.0049439327213408645, - -0.0003551399185838552, - -0.0002946744904421371, - 0.0016351110715873162, - 0.00045159951873199216 + 3.661012320478394e-05, + -0.0019825117396381126, + -0.009604359761720268, + 8.305606865076553e-05, + 0.01437172097458809, + -0.0027388447300527113, + -0.007944504451785551, + 0.005558667292392344, + 0.009780387018813818, + -0.004371880126888333, + -0.0007760500544278181, + 0.0021966507831131968, + 0.0021916185232510894, + 0.0008598000186849924, + -0.0003955588022833517, + -0.0006459965378838599, + 5.2995499250548424e-05, + -0.000858783154897106, + -0.00824421543460758, + 0.003032488667925317, + 0.00023653133763242907, + 0.0010629093142765916, + 0.0033337875415990936, + 0.0032197214334529975, + 0.003894901050782448, + -0.00364054074372092, + 0.005235937201737469, + -0.0017399599561674917, + 0.0026202189183354417, + -0.005867841840421985, + -0.003109210165848451, + -0.004773655295511054, + -0.0037005356342190788, + -0.00222276053663804, + 0.0011950922126089814, + 9.55284240328636e-05, + -0.0016448663639480453, + -0.0012944343965467814, + -0.0003157470879046104, + 0.0005107893774115218, + -0.00027521280997403044, + -6.762865483981948e-05, + -0.00019768798002148355, + 6.22609535107363e-05, + -0.00012867968291576447, + -3.449127332733159e-05, + 0.0006817041085509997, + -0.00012940126331214264, + -9.456671423862896e-05, + -0.00011661899720322635, + -0.00011481041204452238, + -6.783732674155164e-05, + -5.8158311018560964e-05, + -0.00011916217790476764, + -3.055037120074981e-05, + -2.8653397770074188e-05, + -1.7385669071583253e-05, + -1.184047679310257e-06, + -0.0005802466471425595, + -0.0006130851632414553, + 0.0005217999304278104, + -0.0008061354852174385, + 0.001934457933662187, + 0.0023768823306965585, + 0.000688620605812807, + -0.00156124790859592, + 0.0021234006311516094, + -0.0023817452569366003, + 0.0002444229251726562, + -0.0011302735488876714, + -0.0011712570599815546, + 0.000496697408733179, + 0.00046263555255662457, + -0.0017367907043794918, + -0.0002247828039450522, + 0.0014178269470326585, + -0.00213593917487779, + -0.0028704154021673665, + -0.00033560235252581003, + 0.0022863677743407886, + -0.003225642996540807, + 0.002432278966566328, + -0.0012459407766440647, + -0.0032462167853926707, + -0.0028122184018961353, + 0.00502071788805353, + -0.0049144649622339664, + 0.0009900275631528677, + -0.0003372497019139018, + 0.00602502470811938, + -0.0020860716285189614, + 0.002771788539979659, + 0.004960277847679276, + 0.0004748048749072007, + 0.0036326896580393827, + 0.0009555190133963023, + -0.0009291731965377339, + 0.0011663122099134114, + -0.0017051464034156544, + 0.0008791207272801974, + 0.0006925605240535926, + -0.0019433796987068882, + -0.0004049467025632969, + 0.0006439839510967776, + 0.00018619878943370053, + 0.0012823058606156621, + -0.0015526878236496026, + -0.001378761086721499, + 0.0004159031116172629, + -0.0009266480857397983, + -2.0672927769898926e-05, + -0.001125947754618245, + -0.001063250530689297, + -0.0012465602570413653, + 0.0010142451150202158, + -0.0006218392534414985, + 0.00028881573983094194, + 0.0007068140625437565, + -0.0007781280860849355, + 0.0002979921079790199, + -0.0008976471132521951, + -0.0008056127923538656, + 0.00042532424480142976, + -0.0002934417750265016, + 0.0008171543843052812, + -0.00020446934356508975, + 0.0012086699115779977, + -7.651228780572255e-05, + -0.00017945159839622346, + -5.794168720355142e-05, + -5.846761105924248e-05, + -0.00011638360740525751, + -5.869752196685526e-05, + -1.4121181954587525e-05, + -3.6303192625720036e-05, + -0.003297749942895899, + 0.0008919287534651555, + 0.0008383348012102069, + -0.0010795011180052255, + -0.0007531762999549489, + 0.0012264729831703578, + -6.584562369517777e-05, + 0.0055587455508699585, + 0.0008924937036091157, + -0.0003720625311972725, + -0.0003341131663957069, + 0.010928850720169352, + 0.0005947506157961009, + 0.0002258926388750178, + 0.0008444257073405283, + -0.000565806568506304, + 0.0011458381831785653, + -0.002969230094444023, + 0.0029531484236711386, + -0.0005329007116170814, + 0.0002205702191670795, + -0.0007807882704457738, + -0.0005498067779941613, + -3.878056634494843e-05, + 0.0022482235928482624, + 0.0016321693722389581, + 0.0005198626771936341, + 0.0021419355501697238, + -1.0009061396755622e-05, + 0.0027043788569859483, + -0.00013492172737717803, + -0.0001265866283649552, + 0.0008584367222430531, + -1.2790605792488907e-05, + 0.0006073915807644902, + -0.00012401842507210085, + -0.0001391550199183349, + -8.177685283053097e-06, + 0.0024231740542767707, + -5.0619022916260474e-05, + 0.0006370229384834496, + -6.81926356272399e-05, + 0.0014218319536468667, + 0.0018164279052357163, + -3.187749635825568e-05, + -9.637723053628218e-06, + 0.0032292870939346395, + 0.0005469795580788766, + -5.015184375188758e-05, + 0.001252966365569117, + -4.063087667856061e-05, + 0.0034935829925895096, + -1.4787822357338834e-05, + 0.005395730849539642, + -0.0001653197856562745, + -0.00012776681514002966, + 0.000879261888094227, + -0.00010052457803694761, + -2.3499656393059516e-06, + -1.7426696851596645e-05, + -0.0003388627926337222, + -0.00020956608814002287, + -0.001939417168492356, + -0.0025698700840516495, + 0.0011874210468675532, + -0.0036699916782798785, + -0.0043582203319935085, + -0.005600775125887971, + 0.004180356052994995, + -0.00127254788243816, + 0.001616069484588631, + 0.0014086979812403064, + -0.0020839038313044647, + -0.0019006568044697128, + 0.002570467354745571, + -0.0011518527244126944, + 0.0018606577279566016, + -0.002072556028538074, + -0.004211366005562792, + -0.00037511155994931486, + -0.0001944909999723405, + -0.004063581412772406, + 0.003324739749527783, + -0.002861759901177081, + -0.004741174837295768, + -0.003104316677649513, + 0.0001581052628653117, + 0.012889957404391323, + -0.0006752778237345894, + -0.002164309622567058, + 0.008636510575105236, + 0.0005119626947570066 ], - "intercept": -4.332950996797808 + "intercept": -2.7805683742802296 }, "wide-insurance-no-weights-l2-binomial": { "coef": [ - 0.1505466996364924, - -0.008537917177413552, - 2.3021443849284327e-05, - 0.059071533005785486, - 0.05907153300578559, - 0.002718627291583162, - 0.1397909117056109, - 0.0014815545568421164, - 0.003695695238683257, - 0.004370255974041876, - -0.005475825030328193, - -0.005827413443026228, - 0.0017557327037870532, - -0.008739167894114865, - 0.008468505141448163, - 0.005369224765126216, - 0.00029907320553784213, - -0.004662424988760486, - 0.0021584351596973094, - 0.002438032081594401, - -0.0030170647694974613, - -0.0005008110473449528, - -0.0005936347231849847, - -0.0005432302271968467, - -0.0006769367033044484, - 0.0461256746561295, - -0.006774278732320169, - -0.005590905493303565, - -0.0013617918280399634, - -0.003374889778016219, - -7.332121387367347e-07, - -0.003945237337144702, - -0.0010094578105668795, - -0.001177333498663531, - -0.003025862509423032, - 0.0006292568717552201, - -0.00288715594811398, - 0.0014527383751139966, - -0.00288045784219255, - -0.003926539895911778, - -0.00368185711425992, - -0.002061745375642277, - -0.0022944415414705276, - -0.000658806101660899, - -0.001259337135234176, - -0.0006223945737755138, - -0.000555837539930134, - -0.00014406459083379382, - -0.0001727153735903966, - -0.00011416586421405662, - -7.151136280987004e-05, - -5.89665596977726e-05, - -8.553744758567954e-05, - -5.7787715313755766e-05, - -2.937077925953521e-05, - -4.054237676028927e-05, - -8.738473401765601e-05, - -4.2591671383025786e-05, - -2.833003171671894e-05, - -2.8335124175374494e-05, - -4.065370646023442e-05, - -1.441159663227054e-05, - -4.2235328473628056e-05, - -1.4489358751620451e-05, - -1.4381897791427427e-05, - -1.673786403441433e-05, - -1.4393251688863133e-05, - -0.0002307886596353075, - -0.00039987330734760356, - 0.00025713969953400855, - 0.00013741576057793307, - 0.0019609879265671012, - -0.00014583760071120244, - 0.0005309384198329175, - -0.0017667067042237937, - -0.0012783965408655776, - 0.0006286849520063497, - -0.0017100010783417919, - 0.0010523559509082977, - -0.0013941226004846627, - 0.0008347187941018324, - 0.0007510298123187654, - 0.0005991931311099698, - -0.0022003215127277846, - -0.0023339250732190018, - 0.0015367580636485806, - -0.002532299789469222, - -0.001748802845764858, - -0.0006130370565716469, - 0.0004377171647684711, - -0.0015501501625044395, - 0.002156006871672197, - -0.0024837039698850003, - 0.0005355610587935724, - -0.0022563958811460372, - 0.0014369236851812304, - 0.0005854586145109848, - 0.0006334605799154827, - 0.0008338502867697794, - 0.0015470118650058175, - 0.0019657400721562633, - 0.0003068286302953897, - 0.0013312384357455696, - -0.0012843656007913594, - 0.000803172272161295, - -0.0005360423649280412, - -0.0015643533650394276, - 0.002578479294133041, - 0.0001236958375986603, - -0.0021718527882883076, - -0.0008071571347977435, - -0.0016605631507827495, - -0.0003951783060279452, - 0.0016902414499030695, - 0.0016166707368201827, - -0.001522013459148372, - -0.00022925687629706516, - 0.0007211231738429207, - 0.000743404301093767, - -8.789292449604797e-05, - 0.0009515958219791366, - 0.0008177613232754564, - -0.0007887091805909452, - -0.0009536121769541057, - 0.0012536570880655273, - 0.0003382649444714893, - 0.0005302658146956622, - 0.0013502482245345473, - -0.00039851537321624215, - -0.0002485815731573258, - -0.00032348472407895, - -0.0002476067483674732, - -0.00018963401779685418, - -8.851489749687006e-05, - 0.0009369448976492432, - -5.8217351877776234e-05, - 0.0018882826073133728, - -2.8536432427678947e-05, - -7.46713139086368e-05, - -1.4366408450451502e-05, - -1.4414259024491078e-05, - -2.8255995445063028e-05, - -1.4186040858584009e-05, - -1.4193022400110483e-05, - -1.4289293411439754e-05, - 0.010761062807319786, - -0.0028802892501504514, - -0.0010908580653995318, - 0.00047853776509539737, - -0.001856549884867598, - -0.0012821121562478616, - -0.0005521628134976664, - -0.000703745014580548, - -0.0010470596043705607, - -0.0005477851270523789, - 0.001095616400734928, - -0.00035033177974629217, - 0.0006328849085366679, - 0.0012520471544052948, - -0.002930004258576554, - -0.00032845342973186563, - -0.0002589849873234751, - -0.00043984033961214005, - 4.7577670399376775e-05, - 0.0008028535413200181, - -0.00025378291404982414, - -0.00040260141625764233, - -0.0009624689812840203, - -0.00030527237914516013, - -7.708297230137554e-05, - 0.0008201617065245319, - 0.00024807266755999893, - -0.0002312510715943355, - -0.0001221221922316113, - -1.3637982946068573e-05, - -0.0008045016963936908, - -0.00011908659932367163, - -7.506215358717108e-05, - -7.172067683406605e-05, - -3.130917467359826e-05, - -0.00042417652187060404, - -7.158233211701563e-05, - -9.119790918685967e-05, - -1.6586056502798795e-05, - 0.00047983096189708736, - -3.0302882347168717e-05, - -2.7441905739040403e-05, - -3.100787442371236e-05, - -0.0005066432403225291, - -2.9209149545947058e-05, - -2.8984091688399412e-05, - -1.4913574133663357e-05, - 0.0007766228478754849, - -1.4442089148946726e-05, - -1.424484035435538e-05, - -0.0003387325800155833, - -1.4234330454625938e-05, - 0.0007329343487545552, - -1.3837315174971752e-05, - 0.0006232418081150148, - 0.0007748747422362962, - -3.395167855056633e-05, - -1.4475030199672323e-05, - -4.2503244610793144e-05, - -1.4716313253397391e-05, - -1.505944935472523e-05, - -0.004505490183397545, - -0.0034866170095124655, - -0.001996688503423564, - 0.028772685517343913, - -0.0004856461772550641, - 0.00032574035483720057, - -0.006609588466324275, - -0.007340155185998108, - -0.0014756438126247364, - -0.002592211342194548, - -0.0006063851914509069, - -0.018362684612421413, - 0.018362684612421302, - 0.004153063917841851, - 0.0013742456211957293, - -0.0016593878189062757, - -0.0006488046932994465, - 0.004503364322314647, - -0.00011414578486455539, - 0.001912428276902734, - -0.0023735665907283644, - -0.000970166262774482, - -0.0004820563412691604, - -0.0001729662533091372, - -8.153749974085939e-05, - 0.0061688228027773784, - -0.000997214676249495, - -0.004346196059298996, - 0.00040435760617760994, - 0.00011870943727092558, - -0.008985067480108128, - -0.0010138546452905825, - 0.00010737725546443735, - 0.0030219504783900483, - 8.064438750400623e-05 + 3.1482978283945444e-05, + -0.002651766911427092, + -0.0044729908169108, + 0.0007735548591416164, + 0.010013487156280549, + -0.0019304177763339782, + -0.002553374912247589, + 0.01590208291114593, + 0.01223796344488758, + -0.010267242055589534, + 0.0017351454149126823, + 0.0006732835188984163, + 0.0010892285933415977, + 0.001393855022463362, + -0.0011909715833261748, + -0.000992042810324257, + -0.0004929925290474602, + -0.0132837779503926, + -0.01534328653877041, + 0.0027829935705653637, + 0.0020155950577717394, + 0.00035788145214218355, + 0.0017316292603233032, + 0.0016721470955654003, + 0.005606323689399756, + -0.00449846989596423, + 0.008050994276806448, + -0.0020004320976495615, + 0.00645771549614382, + -0.006422422360590052, + -0.0024306546380578886, + -0.0052431712992137, + -0.0051533417523477905, + -0.003432705976436218, + 0.0008352328748081577, + -0.001597211908007712, + -0.0022170398611128053, + -0.0019568360723636402, + -0.0005132694605608401, + 0.0003306638165572858, + -0.00040418792303707415, + -0.0002465652764501875, + -0.00021050624582797517, + 0.0006495461351997162, + -0.0002037042053830859, + -0.0001055621843553953, + 0.0007880850973791603, + -0.000308304841728878, + -0.00014788916891287284, + -9.818820813718116e-05, + -9.867606102486569e-05, + -0.00016151352129559253, + -4.92946800669445e-05, + -0.0001483528986966061, + -5.018052966155454e-05, + -4.9493798409730184e-05, + -6.69142338547591e-05, + -4.996478689075911e-05, + -0.00047746618294563435, + -0.0007123585189684178, + -0.0001440434873282272, + 0.0003212682722201724, + 0.0026293489271722124, + 0.005295576359784502, + 0.0012584400280233829, + -0.0023720466597175415, + 0.0012397771906742798, + -0.003044182075657362, + 0.001096275918982516, + -0.0034026508115661, + -0.0015188753278319608, + 0.0007000472279800932, + 0.0013127740009444594, + -0.004612700003341923, + -0.0015144572944637048, + 0.0010169177066842623, + -0.003983647850540581, + -0.0046035970315750335, + 0.00044126583179257583, + 0.0009531280132428889, + -0.006712989647216799, + 0.005735889331808123, + 0.0009629127871570173, + -0.004311304832040241, + -0.005650342800883239, + 0.004815470754443575, + -0.0050382317579171195, + 0.0006925417525504836, + 0.0004944764375517202, + 0.006100469566805814, + 0.00024863374131185907, + 0.0042637238928496, + 0.0074500381861859945, + 0.0028016746496007977, + 0.004068866484039072, + 0.001621319978012571, + -0.0023938805544857045, + 0.0013547266536958471, + -0.0007472850523502982, + -0.0001366143957640147, + 0.00029232486015851115, + -0.0030303556074238484, + -0.0010270691785130134, + 0.0013169526376881153, + 0.0019835122355516127, + -0.0005921226833707961, + -0.0023298709712247095, + -0.0006238515479784226, + 0.001392726887922085, + -0.0008808479024288192, + -0.0006695436878612659, + -0.0011425198469003307, + -0.0017900701263118476, + -0.0014520067923859885, + 0.0013159858611194706, + -0.00036506298973718726, + 0.00029079665182072994, + 0.0016243576917043007, + -0.00044266711567516194, + 7.968222832498647e-05, + -0.0011460914331790223, + -0.0008769129906197305, + 0.0002928757321845595, + -0.000312117926656298, + 0.0007940942970112572, + -0.0002087731407814775, + 0.0025798396376605533, + -0.00010129013954547714, + -0.00026256239116422805, + -4.902534488299097e-05, + -4.975480341336638e-05, + -9.825654746649544e-05, + -4.951194740110123e-05, + -4.9776673339301416e-05, + -4.951195584058583e-05, + -0.004611812062682026, + -0.00010055413626869373, + 0.0010525615706074465, + -0.0032157767619369454, + -0.0017279004769874913, + 0.0018210720106764027, + -0.0008077967639358417, + 0.004811790667097054, + -3.3371845910384406e-05, + 0.0015528458225477623, + -0.00029680779858838813, + 0.013190971855063504, + 0.0012474508679671068, + -0.0035692591714844507, + 0.0007122380522104515, + -0.0009351032350685323, + 0.0012354911644240566, + -0.005248974287601843, + 0.005074941283260489, + -0.0009048334706041426, + -0.0004967815113956607, + 0.0001945237423862526, + -0.000158035444440007, + -0.0002893239092507201, + 0.0032274228438661776, + 0.0028883549986355632, + 0.0001087998570024924, + 0.002393045762619985, + -4.835332065537349e-05, + 0.004363534465596995, + -0.0004310888653747106, + -0.0002669964323625727, + 0.0006811020895741922, + -0.00011089809300706172, + -0.0015600816701495288, + -0.0002547645209220185, + -0.00031704357457985237, + -5.6372777404566965e-05, + 0.0018321397447732556, + -0.00010989328318696045, + 0.0008444949761993787, + -0.00011690252882535928, + 0.0007367896743615394, + 0.0017944119012485776, + -9.699337751844782e-05, + -5.126238207594321e-05, + 0.005993593615587122, + 0.0008964606446291224, + -4.927920113954249e-05, + 0.0006648503476326216, + -4.9242867870965144e-05, + 0.004812181986919186, + -5.10759026754992e-05, + 0.007225882934818102, + 0.00020652988295726804, + -0.00013057215935286315, + 0.0008979467625131149, + -0.00015180336541694185, + -4.9032519026940886e-05, + -5.242934308997654e-05, + -0.0011809561949544644, + -0.0002867916076581377, + 0.00043244666800725293, + -0.003093957818100759, + 0.002418043528579513, + -0.0037645421615083567, + -0.010697947650238232, + -0.006793467773996961, + 0.0026006401941119283, + 0.003927085066442614, + 0.013549774786316174, + 0.001769503291108565, + -0.0020109404861805504, + -0.003811016403066206, + 0.004292335365627665, + 0.00031814429484186736, + 0.0015829232745176217, + -0.003528154331603045, + -0.003535872580204541, + -0.0007330518307589412, + -0.0005862343537552115, + 0.0004440759143134912, + 0.007313304953506633, + -0.0024511116918279046, + -0.010573262916324842, + -0.0036012924188829755, + 0.0007941777460898548, + 0.01505620212737511, + -0.0025311717567040727, + -0.007559169042555203, + 0.010764242633031447, + -0.00022143873518980117 ], - "intercept": -4.255548737334165 + "intercept": -2.97460894891179 }, "wide-insurance-offset-l2-binomial": { "coef": [ - 0.21939935285045112, - 0.018688679050272668, - 2.938025833319957e-05, - 0.0006636460989974418, - 0.0006636460989975035, - 0.15516679500440125, - 0.21102077084724796, - 0.005085803310282839, - 0.0015990630915354012, - -0.006496587139775243, - -0.006037810022672894, - 0.007383646920903379, - -0.0015341161602734881, - -0.0031687906477762783, - -0.006565212156019511, - 0.00584238264404478, - 0.005518027567495152, - -0.00418523015819148, - 0.00021004100262442257, - 0.0017401359572509067, - 0.0005275972005312747, - 0.0004759587439212543, - -1.3940473581986832e-06, - -0.0004290482368342766, - 3.553213031193881e-05, - 0.011271806894260347, - -0.0006651702769427555, - -0.007206726138940661, - 0.00014349515352294657, - -0.0017561497123907632, - 0.0019736328817306207, - 0.00034813311234637145, - 0.004145296323299983, - 0.003232535629699348, - -0.0016170380148560239, - 0.006029475040796727, - -0.001431188185799686, - 0.0026243259513300888, - -0.00448664338082654, - -0.003713058622650443, - -0.0041218172124493115, - -0.0024821438773108798, - -0.0017510905293231988, - 0.001296688428944728, - 0.0004589974654803739, - -0.0011478518399501494, - -0.0009079190047050183, - -0.00022260224072565352, - 0.00040464958273442455, - -0.0001950011795003332, - -4.828571020205646e-05, - -0.00013434535360135267, - 6.319041897134347e-05, - -9.100278535283944e-05, - -2.427799739603561e-05, - 0.0005568955221510154, - -9.140981777020906e-05, - -6.607710025802982e-05, - -8.272446186988064e-05, - -8.150337538169899e-05, - -4.524484966749014e-05, - -4.090495957094314e-05, - -8.409896622305563e-05, - -2.1810934419381507e-05, - -2.0422634295618977e-05, - -1.1759298438104233e-05, - -8.539444502097494e-07, - 5.926068841365874e-05, - -0.0003992615867848618, - -0.0012531681195508888, - 0.0005103604286433159, - -0.00046436545227061387, - 0.0011204722905429798, - 0.0009398116063914853, - 0.0004923948353655966, - -0.0007608916870956849, - 0.001710451697143883, - -0.0013333780631338294, - 0.0005308390098061512, - 0.00046851470565941535, - -0.0003372398666152528, - -0.0007491903405825478, - -0.0005675353778410579, - -0.0008583390414964255, - -0.0004557753771684389, - 0.0010704585393794591, - -0.001990958257363557, - -0.0014271671049115372, - -0.0002738147089221216, - -0.000624020978978382, - -0.001748676265512192, - 0.0011928259220023345, - -0.002704445483277438, - -0.001911629517413586, - -0.001526985088668985, - 0.003700789500168638, - -0.0027675454705029558, - 0.0014302075061703814, - 0.00046188466366749045, - 0.004150864651472117, - -0.0019092074337589647, - 0.00215799224081783, - 0.003111755890661567, - 0.000983962697660272, - 0.0026892065277113798, - 0.0006033049201274771, - -0.00015119993236635532, - 0.00146841289649577, - -0.001169822588472444, - 0.0011379982059463493, - 0.00032740765880522906, - -0.0013470154039705313, - -0.001762752779619191, - 0.0002829148289965856, - -0.00021597680372774315, - 0.0012765795117904116, - -0.0005326692609674, - -0.001679195183661139, - 0.0007804298862715566, - -0.00029660284533168635, - 2.964001862525973e-05, - -0.0002965434689909686, - -0.000543026087633835, - -0.0016585631902521867, - 0.0009479789079939708, - -0.00020258068112455934, - 0.00016792986949891274, - 0.0002060678546097437, - -0.0005259193608999273, - -0.000178856735873206, - -0.0006170860288909953, - -0.00055580774189648, - 0.0004331976483799659, - -0.0002048809764243658, - 0.0008520473151169458, - -0.00014092525523106718, - 0.0012666199529072488, - -5.257472157482499e-05, - -0.00012513848219527376, - -4.076414444389716e-05, - -4.190753501838076e-05, - -8.147936162975236e-05, - -4.1009057449710956e-05, - -1.0153894815007028e-05, - -2.6536132933145023e-05, - -0.02387205925417972, - -0.0021657885887059, - -2.4446242183059765e-07, - 0.0009052000763929935, - -0.0005464919349962961, - -0.0007836081091421493, - 0.0008908249503024628, - 0.0008712429063500635, - 0.003716772518597352, - 0.0005084725312629165, - -0.002167334231341355, - -0.0002388287120397379, - 0.005264071141895276, - 0.0006451910499649117, - -0.0013961907060770047, - 0.0006896772056971236, - -0.00039918588240431556, - 0.0008801936262915481, - -0.0017370346204098716, - 0.0024393508159500253, - -0.00036947766102458446, - 0.00021925998255998235, - -0.0019496373831173798, - -0.0003630904657287561, - -2.732453917964953e-05, - 0.0017357131482265004, - 0.0019131042596006193, - 0.00038384134270699885, - 0.0016364009946782268, - -7.251576968339598e-06, - 0.001896497301054064, - -9.446167176710662e-05, - -8.978201386590115e-05, - 0.00030391733712130836, - -9.033660017945708e-06, - 0.0008388820411402715, - -8.719139520669258e-05, - -9.8683909410903e-05, - -5.808236468484451e-06, - 0.001576038975407207, - -3.529293059652563e-05, - 0.00048269012934698064, - -4.837963444439442e-05, - 0.0006320775571663132, - 0.0009845680716206384, - -2.2980697984547262e-05, - -6.907464529556466e-06, - 0.00017087895782055384, - 0.0004893751460957888, - -3.6488949372113015e-05, - 0.000842443018754235, - -2.91907149497551e-05, - 0.002519159502131934, - -1.0372327495232877e-05, - 0.0031817333779242574, - -6.407175067950302e-05, - -8.300128195496533e-05, - 0.0002122182183053661, - -7.05865208895893e-05, - -1.6922118604334533e-06, - -1.232268513534104e-05, - 0.008154658923544495, - 0.0003797405904161733, - 0.00017696514512090107, - -0.0034513326593864045, - -0.0014753278331642514, - 0.0004854590689546006, - -0.0028612596683249415, - -0.0020581560589191463, - -0.0037680517796761116, - 0.005719852569924535, - -0.0013025482984898667, - -0.005859775071620743, - 0.0058597750716207755, - -0.0018885538181749406, - 0.0013670071974161712, - -0.0012320585449018635, - -0.0017283063428098835, - 0.00878302154298226, - -0.0015801729634324108, - 0.0013124030574528055, - -0.0027877117548751923, - -0.004128170440375395, - -0.0001205044032081861, - -0.0001379071913624606, - -0.00244443969262448, - 0.00204853960024512, - -0.0012107548217924415, - -0.003710237068405114, - -0.0025684421160488647, - 0.00027547733975265856, - 0.00615286890974564, - -0.00039146358262671856, - -0.0020015323085506485, - 0.005343767377332333, - 0.0006471700242616141 + 3.528062402637088e-05, + -0.0019622189794549663, + -0.009607811153379463, + 5.696318467091183e-06, + 0.014300308809904122, + -0.0027163211465522107, + -0.007866110018055957, + 0.00566053515798028, + 0.009804281390054808, + -0.004437259220515785, + -0.0007658021459423366, + 0.0021613737677776967, + 0.002171934974430335, + 0.000826497334650663, + -0.00039174797914861415, + -0.0006486255159185701, + 5.067219784879771e-05, + -0.0010147665912080457, + -0.008256851975883828, + 0.0030252718045021654, + 0.0002455871043366567, + 0.001084588004005508, + 0.0033766725082180764, + 0.0032814570553239275, + 0.003929026520628736, + -0.0035930650472224693, + 0.005224405435180318, + -0.0016982846483793988, + 0.0026709498908568576, + -0.005826066774547078, + -0.0030762603963061705, + -0.004784652017196017, + -0.0037064097065713313, + -0.0022082788009356205, + 0.001203373073429884, + 9.815945493629182e-05, + -0.0016401712356673506, + -0.001293180523027896, + -0.00031717645855987146, + 0.0005129327837143375, + -0.0002749479836194066, + -6.968605309676025e-05, + -0.00019458911697337454, + 6.41134342624193e-05, + -0.00012912558722999175, + -3.5237019996082415e-05, + 0.0006886446385461016, + -0.00013100318721292786, + -9.457499642308062e-05, + -0.00011519850971854765, + -0.00011350063362918633, + -7.105071432142522e-05, + -5.744252373452617e-05, + -0.00011932926517690945, + -3.10415201310253e-05, + -2.9160593750374275e-05, + -1.8138985252204774e-05, + -1.2407996354005323e-06, + -0.0005866860096677838, + -0.0006295199249656945, + 0.0005125242266358492, + -0.0008205586187129517, + 0.0019241590872438727, + 0.002349944078297126, + 0.0006615556385259835, + -0.0015796492314805257, + 0.0021058051666676047, + -0.0023919345009268296, + 0.00022161976909363247, + -0.0011572453774341919, + -0.001204781484822367, + 0.00046729956397641094, + 0.0004349488555278963, + -0.0017516594372714819, + -0.00022163873307161813, + 0.0013927730512847518, + -0.0021340429471605102, + -0.0028739982791721017, + -0.00033603391915208454, + 0.0022774548048782525, + -0.003231826734277472, + 0.0024267658697666424, + -0.0012626139273414824, + -0.00325467659189981, + -0.0028058091955909504, + 0.005036566287831452, + -0.004907793799615352, + 0.0010002890840376615, + -0.0003316538376189188, + 0.006027843375659905, + -0.0020569094670775945, + 0.0027804315065858344, + 0.004985480092742967, + 0.0004895901506443297, + 0.003632763976292598, + 0.0009654621687076924, + -0.0009255874564784009, + 0.001181120391597655, + -0.0016931536779352986, + 0.0008790723187779388, + 0.0007134043114878668, + -0.0019460629833060045, + -0.00039633919292408627, + 0.0006620754040771572, + 0.00018323636617102298, + 0.0012816915093824045, + -0.001535401909879406, + -0.0013812952753000842, + 0.0004445591657453425, + -0.000915310793383266, + -1.6921700220995203e-05, + -0.0011032857722134556, + -0.0010562400945090152, + -0.0012289964555821417, + 0.0010178400338671702, + -0.0006236602532284592, + 0.0003089643796814394, + 0.0007158325752030399, + -0.0007719815670035922, + 0.0003026974677140889, + -0.000892250261051653, + -0.0007971818936926743, + 0.00042914925698663687, + -0.0002905621976935893, + 0.0008191425463588341, + -0.00020224151110785564, + 0.0012131839166067047, + -7.606698992470991e-05, + -0.00017923327768479394, + -5.725245544945564e-05, + -5.774702626663618e-05, + -0.0001149550890269697, + -5.7994407963703415e-05, + -1.458898141382972e-05, + -3.666742334709785e-05, + -0.0032983170244392647, + 0.0008910363624713943, + 0.0008417027639739534, + -0.001111907742390278, + -0.0007433934832477658, + 0.0012239920767723675, + -7.225067811988785e-05, + 0.0055643426981143865, + 0.000883166235394268, + -0.00038277296202484984, + -0.0003418573239862812, + 0.010924131391931926, + 0.0005901833282945515, + 0.00021319068207858457, + 0.0008403894994500336, + -0.0005674602121841265, + 0.0011478272467493368, + -0.003017327981097284, + 0.002952058424921246, + -0.0005303511088482166, + 0.00021806803008315794, + -0.000803246884633053, + -0.0005461098075978351, + -4.0050651517322274e-05, + 0.0022476722357828256, + 0.0016059569963424204, + 0.000520846105910092, + 0.0021436835845924343, + -1.0386087360057563e-05, + 0.0026714788349623464, + -0.00013694505911754002, + -0.00012772993436045314, + 0.0008581528284007967, + -1.3363037359501196e-05, + 0.0005589720372929027, + -0.00012481594848624266, + -0.00014216707298869125, + -8.536114217099064e-06, + 0.002385321229552865, + -5.175886476950124e-05, + 0.0006387532007570151, + -6.901430198315045e-05, + 0.0013545028488382229, + 0.0018169114391317453, + -3.265238332573556e-05, + -1.0004114619594107e-05, + 0.0031298260699779827, + 0.000546267969746797, + -4.994734452230383e-05, + 0.00124471761683611, + -4.0827475559883276e-05, + 0.003490628601803446, + -1.5285542559782214e-05, + 0.005394536792180794, + -0.0001718487847357964, + -0.0001249927647691305, + 0.0008808876680496267, + -0.0001007951168104256, + -2.4538761717470055e-06, + -1.7958592558040203e-05, + -0.00031546552265957986, + -0.00022896244314017257, + -0.002543512729841962, + -0.0025726889911184495, + 0.0011901328917982741, + -0.0034453623788225822, + -0.00432265716334198, + -0.005561280150437677, + 0.004209580736480187, + -0.0012807643862601864, + 0.0017469210081202176, + 0.0013990100659920348, + -0.002095975721402371, + -0.0019066167439844577, + 0.002872969339223211, + -0.0011361521611941525, + 0.0018537803019624492, + -0.002132991379717686, + -0.004186716299974795, + -0.000374753912888609, + -0.00019661236389127476, + -0.003994269068209212, + 0.003398724571898329, + -0.0028575254052497744, + -0.0047735197861260344, + -0.0031414583690965604, + 0.0001512139824725116, + 0.012934190887525983, + -0.0006943456620067494, + -0.002234080645057085, + 0.008482133481380442, + 0.0004952751895037395 ], - "intercept": -3.1621804018543456 + "intercept": -2.793953953439047 }, "intermediate-insurance-weights-net-gaussian": { "coef": [ - -135.97225351273457, - -170.7278294258302, - 76.08330388783897, - 288.1488763686928, - -96.9707424557564, - 39.43864459966348, - -5.68860869471813, - 48.50065420782869, - -41.81204675282258, - 124.44450466592401, - -83.11882413497419, - -179.11164034412178, - -105.2657995638292, - -136.6828423185384, - 38.11416203355816, - 342.6204427033026, - 5.730628219750534, - -25.373702180682233, - 302.00146421041984, - 25.948253528627806, - 201.85571399995285, - -135.4389835882114, - -33.789344269358786, - -169.163728252986, - -73.51258615969122, - 38.64509917364712, - -17.411386106229312, - -110.76080292016519, - -39.83117598211267, - 39.83117898488584, - -5.569083272519722, - -192.26024456743147, - 152.31190962348828, - -63.003405869468615, - -57.472773409615435, - 1.7426387290259027, - 468.94879030963665, - 7.361843993990561, - -52.390266315255225, - -133.66379515722554, - -57.43575698077455, - -6.261483887318252, - -214.74866789680453, - -202.53749287043507, - -62.061259782593616, - 104.51290186577836, - -20.517787432571648, - -87.37867094885542, - -108.8590598132569, - -28.872839064222347, - 350.6869589835612, - 170.00148818758396, - 37.896970381546694, - -42.383804992820856, - 13.412337778617367, - 28.42810162431156, - -11.145666215769712, - 28.083319996529443, - 58.11278791487451, - -1.9014993932258273, - -35.0755535094701, - 11.42867708771167, - -26.920306175964786, - 20.922083106175663, - 41.89813578063905, - -23.999754443989115, - -24.969461135433864, - 110.93719020748243, - -83.06314465053431, - -7.620741262136709, - -30.10969154009323, - -35.83312002335239, - -90.94526167834961, - -28.83800353270629, - -2.959831346985292, - -41.57541417190977, - 205.62358084736854, - -37.240966776177544, - -21.86048142796414, - 85.80947117886444, - 232.48760702944543, - -61.54141085173834, - 0.05232487039550681, - -1.1323417733545194, - -156.62504395401962, - 12.285412524924618, - -22.97434299138792, - -28.23828352670649, - -18.827235345287658, - -84.30817621157713, - -5.270386003672566, - -1.587214775433057, - -27.17285871968248, - -6.510266089725779, - 111.7305117848694, - -8.395355914358813, - -11.06045392754654, - 24.236699280534438, - -4.405866311804625, - -16.145736396638302, - -9.425271760229501, - 77.76655802396219, - -1.0676345564089629, - 25.066979273148316, - -102.53500274205761 + -123.080205062531, + 121.42664082205751, + 339.13464162464186, + -67.44947251593935, + 84.22940723343115, + 51.461583395447825, + -37.2476837825143, + -91.98445353821664, + -193.24996782316, + -113.8816472472203, + -144.51506707944114, + 32.12951751095123, + 337.4091701705283, + 5.567211111285439, + 312.66411003791205, + 37.96256070639717, + 193.07668711368683, + -128.93276751602585, + -36.948542192071, + -158.43946766680529, + -60.18631648526357, + 44.36603075581241, + -7.072503413769209, + -107.08825511002544, + 60.667225528972196, + -19.98963534109678, + 153.59766995660547, + -57.091594017138334, + -51.441386089227535, + 23.524022288712054, + 478.27950232839345, + 12.394430315178296, + -36.6750083052355, + -120.76577947044245, + -53.15177498974355, + -5.617908252048629, + -193.56828554753025, + -181.9012057482713, + -49.06589944226546, + 120.27687801103586, + -11.218213101110676, + -84.13754944798835, + -72.85402159630297, + -25.755844484136293, + 366.54192285036186, + 203.44117496022085, + 41.1994787692365, + -36.98144567907778, + 25.120978951553106, + -11.548781425558374, + 25.13118925204561, + 52.29803187535566, + -4.204071933588149, + -35.41345845307423, + 10.078605765744323, + -30.76585714191125, + 19.469431222712554, + 40.661178441680235, + -25.14059457929025, + -28.96798111847623, + 110.09171588947603, + -84.8244654747476, + -8.795141127377308, + -30.36410491495948, + -35.869457652069364, + -91.29491915679128, + -29.659170059205398, + -3.889169350436428, + -41.23188538827445, + 203.9221368614235, + -37.77745901628874, + -21.866992222874337, + 86.75872026169023, + 227.19797047014768, + -61.27819716097426, + 0.0, + -0.653438456914951, + -158.96189314100033, + 12.276921219385844, + -22.81410904348941, + -29.74944530989824, + -18.150696745880175, + -88.1343441748489, + -5.729892984775219, + -1.4611037434386611, + -28.11741391075422, + -6.846855176313247, + 109.6361158893839, + -8.194992401574874, + -11.390944756938977, + 24.08207798370489, + -4.393663648055847, + -11.184208126469825, + -9.211883439077658, + 77.56026594261719, + -1.3292335185581243, + 24.927744185018955, + -90.70465642515067 ], - "intercept": 1683.2549465957368 + "intercept": 1637.3990639941153 }, "intermediate-insurance-no-weights-net-gaussian": { "coef": [ - -150.34338334398421, - -152.23362622574015, - 62.26726830378132, - 252.8839766296377, - -87.15581437694357, - 74.5815783124338, - 1.8155804770229134, - 57.6082595635532, - -60.4238412257001, - 116.64036329929144, - -86.87792770218638, - -165.60776039184225, - -103.30699364774395, - -111.69017041095191, - 19.467403951338103, - 332.3750875872391, - 5.461125733914454, - -48.168365631584194, - 301.98498219051146, - 41.293951262018446, - 104.12134611824045, - -117.85353887382179, - -25.626632155127638, - -136.15000281621488, - -89.3333219492871, - 56.887917966539646, - 3.6843199267787683, - -89.84065849732254, - -20.54644141881017, - 20.546444273948513, - -10.34974557021739, - -215.7262818665339, - 64.02815214091946, - -55.90107722464566, - -44.58873608097826, - 13.008105246146336, - 491.8300160260735, - 27.221245454019773, - -37.2217165071331, - -121.00973536631396, - -47.64768235274898, - -5.089253942038541, - -226.09251550450455, - -174.54399703022, - -35.003055540006095, - 89.19852180670833, - -15.581820207348153, - -76.66791952870909, - -111.08121903263195, - -23.530827324418, - 323.2114930912838, - 185.09004960562177, - 2.0982523188535502, - -22.3105587484115, - -27.46347190249148, - 25.23636283809239, - -5.24976056947283, - 32.69586436960769, - 67.1127349920838, - -12.932945841772359, - -29.776189505593294, - 8.768018279603467, - -12.596250019371842, - 20.789412538173163, - -48.99399391889602, - -21.890148821374776, - -4.9859143172361815, - 109.89965167606556, - -73.54941660244721, - -12.577592788423578, - -28.7490966636249, - -35.90408849332127, - -87.5937992607071, - -20.380679172225918, - 0.7644190351897389, - -38.58393609958255, - 190.3255098007647, - -30.96467843387611, - -18.759403304778726, - 93.52519131545219, - 225.91566296821173, - -54.354295125275925, - 0.7599571460989856, - -0.9854584371506118, - -137.10871911830333, - 12.966889051667778, - -18.971684173153545, - -25.357785692263544, - -19.132773615665766, - -69.5675561161415, - -7.7618920216149885, - -1.6536435496663227, - -27.10513569285146, - -5.992240450797755, - 129.82447859894978, - -8.89227025588034, - -9.230391331153154, - 35.71739446074217, - -4.350108785460884, - -9.239137310725932, - -3.6776622318456487, - 34.79099409150891, - -0.804276509845653, - 25.11373611506914, - -80.06988185633911 + -101.59148129778997, + 109.60473876585509, + 306.4358609536371, + -54.5056010716305, + 122.9682082566443, + 58.67077602276055, + -58.525959317831415, + -95.70808111566112, + -176.06179848564426, + -106.8625557327675, + -119.98237577792152, + 12.098380610665169, + 327.16437077521294, + 6.072318845873149, + 317.54337231631166, + 57.00656629007028, + 101.62488499670235, + -108.77175628314735, + -27.98249423993762, + -115.85414797088859, + -67.65186607630945, + 68.2148572669623, + 20.298306546347128, + -81.20770860381498, + 22.530445913410617, + -25.676171131203223, + 65.51139098538317, + -49.27259222657532, + -38.363901131960674, + 35.220811705069444, + 500.8909186922089, + 31.808726765995516, + -20.93347219262683, + -107.42494731138747, + -44.211546212140625, + -4.317519899520307, + -204.51597653400484, + -152.73602041025134, + -21.37704045719086, + 105.6995012093353, + -5.584320998248612, + -73.24603008880969, + -72.76095785549957, + -19.672853623797177, + 340.50872457077725, + 221.2156067746529, + 5.551437554043075, + -16.37260889157728, + 29.312276493111103, + -1.1861327634988832, + 33.08370777768311, + 68.57412484548883, + -9.892965947217402, + -26.644789829292797, + 13.279701901788432, + -9.002940311462972, + 21.765038683239958, + -44.1218230125461, + -22.23119240988915, + 0.0, + 112.24383687557516, + -70.67047035924939, + -12.565969874251193, + -27.790583859082446, + -33.94174665648145, + -82.36805973176044, + -20.628361140611872, + 0.7587814754602336, + -36.421937556529755, + 192.72652130240112, + -30.620907702443226, + -18.178055335626176, + 95.1992582053896, + 223.193778714299, + -52.77886324175332, + 0.6089941110902204, + -0.42929379291972575, + -138.2016624022761, + 13.155914806400839, + -18.55156117811022, + -26.554774834437495, + -18.219244085323425, + -72.88980064169647, + -7.951173776916229, + -1.5068134674544789, + -28.230294822499733, + -6.337589719884079, + 128.18130444749588, + -8.683193948897515, + -9.543447795539013, + 35.39722717391011, + -4.3114167799787735, + -6.809947063624344, + -3.5427690654310156, + 34.70680939654032, + -1.1145509056917393, + 24.93825188459099, + -81.11712297538412 ], - "intercept": 1655.8085233086113 + "intercept": 1546.2065311695255 }, "intermediate-insurance-offset-net-gaussian": { "coef": [ - -137.1821478478351, - -174.56845148023103, - 75.80214391189452, - 306.44925821510697, - -104.38288370853286, - 33.88208018388397, - -19.233422758520515, - 66.26500870784677, - -46.03158717639712, - 118.49955933250081, - -69.83634670860718, - -193.9709591815697, - -128.1696649599903, - -148.35259819912233, - 51.78587135895313, - 371.044141138227, - 7.803307814821048, - -40.145963627790856, - 286.8625533260651, - 17.445262955952032, - 275.9531094268179, - -116.83832612215333, - -30.812972180242465, - -183.854333171746, - -89.90035644205382, - 23.1475821125515, - -23.933358388435472, - -114.92320043580521, - -36.73864827327773, - 36.73865122982746, - -4.796796454083095, - -183.54313947393837, - 163.47703934990085, - -82.0560269493453, - -66.3391607573972, - -15.282705880407235, - 475.324692080338, - 7.428090622194371, - -47.8160734693195, - -152.08047052091547, - -41.729214071088826, - -8.54664933411879, - -225.7100664528533, - -204.6990608858722, - -76.68933024527549, - 113.19386365253585, - -30.695790706029523, - -88.9421266458812, - -111.74292999556242, - -36.977641882266504, - 361.78082479824013, - 204.05239186993123, - 55.59348301386223, - -40.271350904204596, - 13.754054208519337, - 24.799416933805748, - -7.411598806076237, - 43.420341859466916, - 61.9859014895183, - -2.0037385009394004, - -36.64804970578474, - 13.637377178522724, - -17.60971612279041, - 24.625296444990912, - 38.4333535542083, - -29.7145706947571, - -10.539622343659339, - 110.61593835276017, - -87.26314083065627, - 0.0, - -32.555858844186304, - -41.28799623156915, - -107.14380403382813, - -21.787472600234867, - -1.2833606866395408, - -50.662722328287636, - 178.69448991856487, - -26.845425746823654, - -23.065550494642856, - 87.94008071312408, - 233.94791302142764, - -43.360100923564815, - -2.7149509943979253, - -3.25468010361007, - -161.45981949355286, - 12.17410550622098, - -23.569018073517, - -27.29956724953177, - -21.420874834170423, - -106.24066827541051, - -4.696806906316266, - -1.9950058393830872, - -28.176209011291824, - -8.019513360923087, - 87.81278512736586, - -6.908252206450579, - -8.72307364560473, - 39.24314624165113, - -5.214415216567047, - -23.28945728427551, - 1.843127086155177, - 84.55586555622841, - -1.6040553004816323, - 24.513944373475738, - -92.9900323095439 + -128.59163725730647, + 118.554217054612, + 355.5902203199646, + -75.60989029800666, + 78.1811961424713, + 73.08744869754594, + -39.22906520028145, + -78.08305545375612, + -207.2478731938784, + -135.9500846258528, + -155.21711504807115, + 46.556664990577815, + 367.1405300011205, + 7.629290706036934, + 299.65407890055667, + 31.240510500674517, + 272.5868520580382, + -108.67505533836697, + -32.925662305036305, + -166.9025066116144, + -71.71110335904412, + 31.799333123828355, + -10.31974985625727, + -107.70229845579311, + 54.03345915867786, + -18.29286403073084, + 164.44879247622205, + -76.79820683304541, + -61.27685305186428, + 4.071599130547026, + 483.2996873389801, + 11.524253948365006, + -34.392241791380116, + -140.0506540439253, + -38.536297474419804, + -7.9372924683415045, + -206.88126608777299, + -186.29370610844697, + -64.9026764272881, + 127.08751564584742, + -22.367140527594515, + -85.99561825650657, + -79.57225890886362, + -34.02098214148289, + 375.9792606477069, + 234.48983699505118, + 58.51352930746197, + -35.04565276561139, + 21.680004774651554, + -7.525967226647919, + 41.12587217513542, + 57.34843103715832, + -3.922106301587722, + -37.03254071633042, + 11.712506156293534, + -20.92948187789772, + 23.070591830518342, + 37.10949081000856, + -30.95642113218227, + -14.665363093361659, + 109.73105096959837, + -89.06349287838894, + 0.0, + -32.7727113397386, + -41.45400021885184, + -107.33592681133356, + -22.756147978448507, + -1.8841783426855723, + -50.34034732994757, + 177.11393032343634, + -27.291282226017213, + -22.97208505125922, + 88.73816647897976, + 228.7624761579619, + -42.998915743863066, + -3.304011767285421, + -2.8653549569363475, + -164.23268970365382, + 12.165400007074956, + -23.449481344750673, + -28.536173970047827, + -20.707854953801935, + -109.55157482996354, + -4.906970718172997, + -1.8649569271342337, + -29.200070429524004, + -8.361875997594439, + 87.39742397552253, + -6.713301937063144, + -9.014903935513672, + 39.0511858935287, + -5.2194784794562885, + -18.35763080924869, + 1.938450428331219, + 84.48319100768363, + -1.850579908898042, + 24.386741026130025, + -82.58606190920607 ], - "intercept": 1636.1494454814326 + "intercept": 1581.7923604798389 }, "narrow-insurance-weights-net-gaussian": { "coef": [ - -133.79072061649694, - -170.48766983914223, - 74.25288719057816, - 290.8522908462669, - -100.46107040571306, - 39.63428228504178, - -9.154678472710922, - 50.04115134451183, - -39.88647407966074, - 120.36531877530757, - -79.37303021334233, - -172.03247106393516, - -101.36314068520325, - -139.91938921490313, - 35.06750711371721, - 338.2552080676649, - 4.379897045954267, - -25.023403038194637, - 303.0317310081288, - 25.559738107756772, - 200.30246562711773, - -133.7823897210709, - -32.82618511161289, - -170.53227091817118, - -74.92894867414957, - 39.82634732883282, - -16.857370848659226, - -111.76971635446066, - -39.29463538485066, - 39.29463839338194, - -4.454343892210496, - -188.6570348342755, - 152.6738020274605, - -60.53090767771483, - -59.245840822072935, - -2.0180190200239485, - 465.94432908704664, - 7.769950359605259, - -51.66738538851087, - -133.63557845129, - -57.793254127965454, - -6.126940802280332, - -218.0104017088739, - -205.77770652059166, - -61.25894749884111, - 105.13001614510402, - -18.47018994731428, - -88.44785284212404, - -108.29676624366932, - -28.679972897427355, - 354.9319389651268, - 171.20456621760675, - 38.96219403754899, - -44.321142436040994 + -123.5890998349285, + 119.0579785000537, + 340.9317585537849, + -71.2560847011558, + 83.67019887010065, + 54.00740432764358, + -34.2188230960167, + -88.30736410348445, + -185.42587831913576, + -108.94877833879909, + -147.90975725216902, + 28.613851231988242, + 332.9256948904478, + 4.562321139095993, + 313.59548423382466, + 37.420205755280854, + 191.3373311501735, + -127.52130401004474, + -36.02004420370663, + -159.98942992018212, + -61.78438932107559, + 45.620466818916306, + -6.675323751396005, + -108.25055371449297, + 59.796852797072354, + -18.539915317660057, + 154.06398032185288, + -54.6903978265678, + -53.31034884411226, + 18.771759872928676, + 475.0557319338218, + 12.524994712678945, + -36.26031665881518, + -121.04837677749397, + -53.68446717057274, + -5.488361144911165, + -197.4107079653837, + -185.5111200444501, + -48.575236612870334, + 120.4191746968854, + -9.39508873326512, + -85.33003847569996, + -72.92209808801321, + -25.606514361526866, + 370.45471018349434, + 203.95942377226376, + 42.15807851662899, + -39.024505581207606 ], - "intercept": 1770.9016493292672 + "intercept": 1696.2900886101559 }, "narrow-insurance-no-weights-net-gaussian": { "coef": [ - -148.63474283637083, - -152.22034455353, - 61.16959213510756, - 255.21443923891078, - -89.66851635921954, - 74.13957165514702, - -0.4490928603632888, - 58.87072045371574, - -57.42162876120172, - 111.8619535982607, - -83.81060597546899, - -156.932953453188, - -96.60006799217031, - -116.12888543736814, - 14.86269558490329, - 327.74786613160745, - 5.1351311116205585, - -47.70570301806477, - 302.44880666968197, - 40.98147013233087, - 101.63621228805138, - -116.35877566866087, - -25.336345926794184, - -137.14774198017304, - -90.50876753167766, - 59.00386306607335, - 4.575027982367633, - -90.58804853500675, - -20.058086394498932, - 20.058089293140302, - -8.854895598211392, - -212.51971584717685, - 63.94985090526844, - -53.49431214390207, - -47.21946935594943, - 9.355097581562141, - 488.8917557009861, - 27.494252133543224, - -36.134833752690604, - -121.88013506099743, - -47.76052378116896, - -4.933825311598898, - -229.5426840313486, - -177.9173647344643, - -33.78586472080929, - 89.8550240224562, - -14.354807046145277, - -77.53283129332301, - -109.94018206494799, - -23.484023810382208, - 327.1961886874674, - 186.47832192231024, - 3.280080138047533, - -24.40949203564939 + -102.04208222701529, + 107.94174005957655, + 308.32320355659857, + -57.644516691106936, + 122.10688147188115, + 60.381346118179046, + -55.492414533244016, + -91.66303787947263, + -169.77455664665632, + -102.60737202233422, + -123.11347295928942, + 9.410211811604725, + 323.5187424798854, + 5.271435325522805, + 317.72320032020747, + 56.8047399788384, + 99.44989785702457, + -107.07111068081244, + -27.55367141993032, + -116.92225552927404, + -69.18215800369458, + 69.66236280205224, + 20.826939286507343, + -82.15264691098321, + 22.098465998090642, + -24.35279842424975, + 65.25457753090116, + -47.13459938403499, + -41.10692081218702, + 31.76852727482959, + 498.0279570249034, + 32.2617233195227, + -20.251300836227244, + -108.19093727325954, + -44.23418226760711, + -4.185015572891784, + -207.9202698933595, + -156.51952266259522, + -20.293592195386086, + 106.21068034791558, + -4.4999103928922315, + -74.12092361855072, + -72.39897071970047, + -19.79739296319423, + 344.0466947296409, + 222.03170686418488, + 6.644564135122923, + -18.40235151585297 ], - "intercept": 1659.7183661178624 + "intercept": 1595.6786520933629 }, "narrow-insurance-offset-net-gaussian": { "coef": [ - -135.4188891243343, - -174.30478443568893, - 74.61968502081201, - 308.84000952239734, - -107.34920904735084, - 33.6131873246625, - -22.109310852359652, - 67.28061935391341, - -44.171309701077426, - 115.40492111851185, - -66.91298731397958, - -190.28197704010088, - -124.81577088587862, - -150.49879334150674, - 49.94576005052426, - 368.15884993558757, - 6.477347730606978, - -39.58592619838926, - 287.7198545629841, - 17.3518037752938, - 274.3304377476719, - -114.74870797622584, - -29.982526399650823, - -184.8342868763282, - -91.40684783016304, - 24.17605020957734, - -23.720365219992633, - -116.29948838505, - -36.32965102101205, - 36.3296539982303, - -3.9058394811722588, - -181.21081326532737, - 163.60822602445057, - -79.81778010509036, - -68.42684665851445, - -18.62425349425852, - 472.67499279667317, - 7.880729001576767, - -47.45995664459596, - -151.8299459533434, - -41.80083290489659, - -8.375716188308752, - -229.15765382066306, - -208.11341528983002, - -75.52799428918738, - 113.98957337807964, - -29.315732241049826, - -89.98435162731705, - -110.42982391251938, - -36.98950026023936, - 365.190586250304, - 205.06558255350598, - 56.654924736744235, - -42.162115978578484 + -128.94246315060946, + 116.95737976274019, + 357.2358445259446, + -78.78619282268804, + 77.28000807154125, + 74.96260521768846, + -36.41269631443085, + -75.294000062849, + -202.8225521277765, + -131.53554401264267, + -157.5820070102236, + 44.18545618575878, + 364.03976255010593, + 6.644897273351623, + 300.41109867252305, + 30.96267373439537, + 270.7056668054007, + -106.82373242130947, + -32.17944807792853, + -168.18511311961572, + -73.42886775492342, + 32.883899660469545, + -10.241088617588723, + -109.27781441123014, + 53.377990344980496, + -17.15479322626791, + 164.6760801556781, + -74.56584428180938, + -63.37289872775457, + 0.37525424015078335, + 480.57413315673693, + 11.816879574789294, + -34.12784298652963, + -140.00986901536066, + -38.707166203565286, + -7.763170478204278, + -210.6015889803534, + -189.78569877706929, + -63.90512233200025, + 127.65069791891419, + -21.117756166279005, + -87.09132344626657, + -78.50386480376363, + -34.025109794942594, + 379.2752015291284, + 235.1762102034343, + 59.5271523822756, + -36.97695044567512 ], - "intercept": 1722.9069655718445 + "intercept": 1639.750760668854 }, "wide-insurance-weights-net-gaussian": { "coef": [ - -826.529674619129, - -5.561578957108002, - 0.0038159988602916406, - 0.016320700054382208, - 0.796920959294311, - -826.5296807920851, - -39.4476837772851, - -17.73312560711094, - 62.14942836974627, - -25.830053216612285, - 15.746435704569176, - 5.115014226027927, - -70.01260831435172, - 3.95789036763555, - -28.880645569838535, - -30.605797906369204, - -3.3384571790696533, - 48.42594372381803, - 6.176521488864412, - 0.0, - 14.32146850511075, - 45.306137746664206, - 21.131849926595983, - -7.432605461165329, - 15.068059111792433, - -4.900065529849091, - -22.077426693294033, - 56.00445564089858, - 46.75757451696896, - 1.3548387385022376, - 5.952385885077449, - 30.5969185344609, - 16.677581296971706, - 4.451946881496434, - 6.4516682457045595, - 24.83927972753853, - -83.0920560935007, - -42.16591140929914, - 9.324531023834448, - -12.931291911890758, - -123.24183731202852, - 17.957220368356243, - 47.41181610770383, - -1.7909227235482827, - 0.9700544434484826, - 0.0, - -1.4387406317817597, - 0.0, - -0.941031263605205, - 6.151324594158031, - 0.0, - -0.27340836770817156, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 10.52976467435477, - 22.1192238719626, - -17.900182798656285, - 13.42647123009753, - -68.41264048050367, - 27.430418068012028, - 15.358373680464672, - -4.68152431764313, - -1.835547053055332, - -18.450401578325163, - -1.2504958919231246, - 2.538378998180281, - 17.244754907625786, - 18.804342579972626, - -40.28429822628064, - 23.352191826569456, - 27.55155084623568, - -0.7232055465508912, - -94.59055127823684, - -4.745341057976063, - -14.268303780296293, - 7.203237838000427, - 22.583753790834542, - 14.05436463547427, - 49.53125827506581, - 0.24921438752509864, - 27.94193020201948, - 13.94780231887568, - 34.16646128053561, - 34.53608573534509, - -61.96495078568693, - -2.3932237428204757, - 4.877567971116236, - 16.544646996394597, - -108.35812585276564, - -29.704570086387, - -23.889769563136554, - 0.6255739056904867, - 51.88599533102997, - -1.071626509321652, - -13.95135453691235, - 0.0, - -176.41571510028635, - 10.37512676947708, - 9.901349777582109, - 0.0, - 67.79995951350969, - 16.186282482841296, - 44.44232779645546, - 51.7706657255245, - 2.8542880896147698, - -26.00027484264288, - -41.30098022842082, - -5.046582511481724, - 3.6202582685941462, - 35.04821417653976, - 26.93021278255715, - 6.412703366450095, - 55.493812131136224, - -87.24826414747501, - 36.536580055708235, - 4.181575737667525, - 1.2324821181922099, - 0.28833417158752567, - 0.7431474997439041, - 0.0, - 1.161539033860921, - -7.939056023298702, - 2.0015608149709294, - 0.0, - 0.0, - -1.4091784018198872, - 0.0, - 3.8742145721603887, - 0.0, - -89.96549632022295, - -8.630464184376052, - 8.064911194741024, - 11.18548773319993, - 24.58762551945977, - -12.365318730154764, - 0.5628627832327958, - -7.286631697080732, - 12.833664055556705, - 5.43371554102215, - -143.59870212286097, - -1.510268852445143, - 28.147311659234926, - -2.914338955606766, - 0.0, - -9.356357008776117, - -5.568398577304635, - -3.8569552753094474, - -10.906515561245929, - 10.944053215823569, - 6.522180868542402, - -4.146228611096823, - 6.80722702515524, - 3.04294160456917, - 0.0, - 25.621783939563716, - 21.106636221824314, - 2.905946593337263, - 1.309867489376451, - 0.0, - 0.7642531624475088, - 2.9071525511182794, - 0.28248581808249157, - 0.08386104392195151, - -2.696504367113524, - 11.116528541039726, - -3.801754529777893, - 0.0, - -3.7041711182172152, - 0.0, - 58.17707565198709, - -2.147167255481328, - 0.0, - 23.91791580842603, - -0.13955016787261906, - 13.652272453881, - 11.464017208238749, - -54.32309202763787, - 0.0, - 3.6739535359978865, - 34.04640622832466, - 25.225585616034657, - 3.7854872653645386, - 0.0, - 0.0, - 1.5495853712292422, - 11.424007802967974, - 0.0, - 2.3363664363078045, - 0.0, - 20.52278205288814, - 2.2818757495619564, - 0.0, - 0.0, - 0.0, - 30.52739605645704, - 1.0112674418912255, - 0.8934387765143533, - 2.157804596358057, - -0.13314243139551843, - 0.0, - -6.544412847554214, - 0.0, - 0.0, - -0.6894367094580618, - 7.484112463891942, - 0.0, - 0.0, - -78.90778781249071, - 0.0, - 0.0, - 0.21287962708566033, - 1.1855324805072558, - -59.93309400731324, - 46.4689658859131, - 14.280704938501895, - -120.98986453527081, - 12.15605939608908, - 16.385103301658454, - 31.808168342221048, - -32.971239666452426, - 34.621366224856665, - 31.54510424531187, - 21.628726952696073, - 31.095526672158382, - -31.09509180481619, - -47.83548804044321, - -131.44031134036868, - 0.6920340449949023, - 10.667891570910951, - 20.807859334279296, - 114.32209458549038, - 31.86721547098127, - 26.115841412596623, - 0.0, - 6.194789065638971, - 0.0, - -39.871668794219026, - 16.940361631169555, - 41.470990965990424, - -1.5697955131229133, - 7.865905225425052, - 1.5154327870742437, - -8.884619069365648, - 3.829982185384888, - -61.26477044458186, - 49.535464982859004, - -48.50399497713871 + -0.02341505693625767, + -48.395440605303065, + -120.669982387682, + -257.7932709180844, + 102.02340567287548, + -45.318691717250154, + -150.79573229915778, + 116.59895603021924, + 315.1391076984239, + -86.90267149322662, + 80.09314012928594, + -59.796382318615386, + -44.142867145937096, + 53.098525308479395, + 221.62190415323954, + 47.69377083644262, + -65.50244171677004, + 14.882652607029552, + 34.74967981470959, + 214.51032795748895, + 322.7601474332636, + 17.21888853113757, + -72.95019243787814, + 58.06263502597274, + -113.74824206604853, + -128.7527765493108, + -162.7360123214556, + -64.33918649851822, + -61.538001748336555, + 68.29385690543042, + -167.3271514337519, + -178.37053958645694, + 266.05456883411796, + -12.811288501963707, + 169.5779069920882, + -42.46173078494631, + -32.75925181883484, + -30.476540526517894, + -13.297741327379356, + 0.0, + -7.526872519104776, + -18.6948004221151, + -1.2178368283330727, + -5.730235645952306, + -1.0339259189300665, + -2.1225438735696964, + -8.249503355881663, + 0.0, + 0.0, + -1.7135631420907307, + 3.0610868928642385, + -1.4274347204642812, + 115.64476429595788, + 107.44260728601691, + 74.28975376482884, + 45.8276801048008, + 0.9257844690324629, + 43.83846793471031, + -147.9177575380652, + -51.79619050379019, + -60.968878925514794, + -10.775784045377666, + -83.97558779093943, + -50.769711269822835, + 115.13915958173128, + -30.73983688610268, + 28.089530165096836, + -12.51903203408893, + -66.82371198867853, + 110.66459568267398, + -255.65945563074865, + -51.17891616266688, + -158.73435072691862, + 28.882089854158355, + -168.1161896527039, + 83.61942536110675, + -119.70571823729021, + -68.72362258133884, + -99.17075073112217, + 159.19439603594466, + -71.07247285681134, + 21.138966717114627, + -189.6271465455767, + -92.87381296275808, + -15.13940971833098, + 134.84788623506068, + -112.92969369797275, + -27.089790508270287, + -144.18392418392, + -56.725585979575186, + -139.01851406321506, + -76.8554654569642, + -70.33101920103852, + 217.62813752581397, + 130.01838228133943, + -79.7090812584194, + -6.125214899100778, + 267.01498849323497, + -20.530603832006236, + 60.0094380819271, + 235.6072577744996, + -55.672636089556526, + -62.826948454590216, + 48.592567501053395, + -69.9335781339745, + 154.4217620003308, + 31.22816276294313, + 91.53780605027981, + 15.713482020354785, + 223.64114964659476, + 149.37004817042006, + 180.22655684521493, + -9.811598786546377, + -28.924827512365308, + 0.0, + -22.401311030117927, + -24.229800331162696, + -16.452448033003307, + 9.17457548607969, + 2.2023185447986244, + 0.3341535491268876, + -4.325765788491535, + -14.166404373778093, + -2.349107023227726, + -9.837765142957277, + -0.031176297167116503, + 10.248953458693357, + -25.095380860111646, + 27.508939084469667, + 33.23343087612249, + -22.51373564601464, + -48.402902957642446, + -1.1622658919507645, + -28.727873966972233, + 10.948766724813751, + 27.32020317220983, + -25.221090283589184, + -19.075181828562236, + 98.55197852560707, + -99.12174579290944, + -4.699005993375226, + -27.781437870764645, + -35.51313961849686, + -96.04174713486229, + -23.936894993232173, + -2.5713916405548574, + -39.881092544655885, + 212.71982731125127, + -29.23300794774289, + -19.52388725343688, + 93.99531551614625, + 239.8035777491858, + -45.596960217239335, + 3.064112207148885, + -1.7985436637368277, + -141.81889290290692, + 15.912216023218795, + -18.20805472044534, + -26.865626644405744, + -17.941457493134973, + -66.01173252188339, + -1.2693837918406332, + -0.8562834643243679, + -23.912942051095254, + -4.829484435966462, + 144.7014337799262, + -3.907225906870665, + -10.93689301034383, + 28.613472789310755, + -3.4830340964928395, + 44.386770244336, + -2.21579555085951, + 82.87139523646336, + -1.098606987624881, + 25.429482686151115, + -19.593167279511807, + -20.542217632615053, + -4.859948643497586, + 0.0, + -3.85631393670968, + -0.8255825856434814, + -16.078067842090995, + -2.395743898213862, + -1.05755339880662, + -2.625510144642637, + 63.11872909554892, + -3.2264662674137115, + 0.0, + -0.07464118045762932, + 0.0, + 51.30932473920387, + 10.69538636261749, + 0.0, + -42.03620653297266, + -4.207405896859818, + -0.5376500283023949, + 13.39144630090409, + -1.1559840511980577, + 0.0, + -5.242051960596025, + -4.276585655958405, + 7.957900106395317, + -6.978084404566817, + 130.43807166827312, + -0.19913419564798096, + -0.547793470543266, + 7.234008995608904, + -3.2768490252029987, + 305.94499473695913, + 34.626418701971645, + 187.61521997019162, + -125.46362686326134, + -33.37143610847035, + -149.52733886374756, + -71.24634798088071, + 37.03678222365849, + 4.24572705729689, + -92.84618656002377, + 69.74513322479145, + 152.19608685453758, + -56.59241602341717, + -63.490010900482176, + 47.00272577456118, + 476.5269616330123, + 7.9116084238232975, + -32.83359406880597, + -95.57395202281486, + -59.371455071536595, + -6.969965954759735, + -205.17865162876177, + -175.73925652434184, + -28.75785739612888, + 106.64707211830766, + -20.682527291585473, + -79.80037427117985, + -86.72441086986025, + -27.663118862318242, + 363.8789556215396, + 156.37434458176787, + 49.91178202981881 ], - "intercept": 2019.3011933553903 + "intercept": 1866.0826731744523 }, "wide-insurance-no-weights-net-gaussian": { "coef": [ - -743.6315771132564, - -11.49747994302718, - 0.0017564654335501374, - 0.010030068094564389, - 0.878305218410883, - -743.6315787846164, - -25.583066335109333, - -12.958105583547736, - 32.989894624087775, - -7.159005404852511, - 6.419534880361406, - 6.290760786542861, - -32.03880185262002, - 0.3692816208620686, - -18.04919360412513, - -26.037336609380805, - -1.9914291060204194, - 28.487732057637526, - 3.0498232661852107, - 0.0, - 10.358168009993502, - 27.561305907709087, - 10.213503852527019, - -2.1468190120214485, - 8.027220731107315, - -7.246227033935995, - -26.456994963115033, - 34.30320320409682, - 22.453926603031938, - 0.0, - 11.01133784066639, - 15.318874221314212, - 11.35021274390664, - -1.2542690636918374, - 4.538862045533124, - 16.536845233655388, - -18.42673049548476, - -23.858917168851004, - 3.5757320940211743, - -7.949159862700295, - -79.961829564175, - 9.748101751183032, - 27.568341808799858, - -0.056025039751405575, - 0.1628565714599461, - 0.0, - 0.0, - 0.0, - 0.0, - 0.1562259833160297, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 5.571594979354684, - 10.962719436840164, - -9.63358590193274, - 6.998652806818928, - -50.909739071136734, - 10.446683608803493, - 8.38932033682395, - -2.5167797252260824, - 0.28214881073280285, - -11.422254595306766, - -0.0714847510879393, - 3.106361808449412, - 8.930246033273527, - 10.478228746724497, - -22.458572763978964, - 17.529411661924993, - 18.2981870278917, - -2.8511371496587525, - -51.7387124128766, - -1.671400993141137, - -6.899908250168078, - 4.107156588648671, - 14.801038233937453, - 9.728887352712622, - 30.73129527499063, - -0.9592202446582692, - 17.828635177768138, - 0.0, - 21.744447547440135, - 19.282134960595364, - -37.28545222991012, - -0.1434640342120114, - 0.6147775714398999, - 9.108835543221902, - -17.82685835311042, - -9.383817269429231, - -16.941181687955847, - 2.507384232576348, - 20.783490168781164, - -1.5491472697417314, - -8.527904344946771, - 0.0, - -114.74636424719657, - 5.124554980762603, - 5.962997442822572, - 0.0, - 41.621677307050724, - 10.10199192467454, - 21.860863374006207, - 27.059425054670424, - 0.0, - -14.28281159422343, - -26.944957117976912, - -0.8403544082262083, - 1.825675333781663, - 18.183614483589373, - 14.665414161211688, - 0.19390371022472622, - 31.731966797666388, - -61.50339465524825, - 21.849453682873794, - 3.0648563600161625, - 0.5589000038135247, - 0.5375942842433331, - 0.0, - 0.0, - 0.0, - -5.083991704471627, - 0.7639626500514987, - 0.0, + -0.020643208270415794, + -48.107348319970654, + -66.99355309564561, + -232.62916902293716, + 116.2768478345515, + -39.026580806462576, + -132.60829891179148, + 96.65159811272754, + 278.8501450802123, + -77.70604041109848, + 104.8885672053931, + -45.18386977952988, + -40.29507112546727, + 57.32913445641567, + 225.5988679594379, + 54.302329518375515, + -61.47170297796883, + 7.176023961510216, + 5.656409374851567, + 230.61551483881567, + 321.6170053481942, + 11.84933653488103, + -59.130740685540346, + 77.55296650506402, + -93.82243469431825, + -120.41986401471195, + -146.81832727616455, + -44.45516417877038, + -76.1632917323716, + 37.15103644428583, + -147.2111228474638, + -169.99234269023117, + 159.93564635991984, + -1.6125140067918873, + 183.12780990557695, + -39.951298765277556, + -29.1864515870233, + -25.484856766632245, + -12.591378406182782, + 0.0, + -7.466324472876761, + -16.127064694180312, + -1.2015334954263723, + -5.607489451018027, + -1.1669967982695808, + -1.8252176698117921, + -7.508350040173297, + 0.0, + 0.0, + -1.676653267452862, + 2.922219808291324, + -1.282850480980129, + 115.85252945020011, + 87.5386872804409, + 75.84622565238018, + -1.7365602907862314, + 14.744557721617978, + 55.048126281723874, + -133.1217274292025, + -43.739610007521264, + -59.31058896150438, + -5.943475767323522, + -66.11145219312682, + -33.39080453499787, + 120.24194367633639, + -46.66700829167224, + 41.90128924938626, + 10.098208475923323, + -63.45856918692684, + 48.63839186008405, + -232.7293082996356, + -54.26540901016604, + -139.55964881527265, + 49.55153843550148, + -144.3399805390103, + 102.55097788235086, + -106.1096618570412, + -45.854214083035245, + -89.99861584495694, + 167.51325264924108, + -45.692880893896955, + -21.35051489358039, + -176.80988000295574, + -84.06876895061522, + -10.016390342144202, + 102.2101009176081, + -112.21562687218187, + -44.05069198473255, + -130.8499796496041, + -30.092356077158275, + -125.12656821971594, + -80.62706440045375, + -65.10208442348535, + 83.06185365000023, + 120.96974883288513, + -70.9261218238668, + -8.413850606407783, + 277.2374987409627, + -8.557489299730461, + 72.75185318463988, + 240.97547256530615, + -50.75611102515912, + -72.36865783837729, + 16.105507926285014, + -66.15120293400369, + 139.65608172492142, + 43.6069055116536, + 95.5296160553969, + 19.725440300648327, + 233.24163145094067, + 76.63532348346732, + 184.3753321066529, + -6.4828145992344, + -24.939056624874734, + 1.0038584141630655, + -20.46006578853066, + -22.16809875930907, + -14.284902013627736, + 2.1830505608839124, + 3.5958104163218865, + 0.3467467597370641, + -4.030607398187124, + -13.823267746561678, + -2.367679109414726, + -6.334367151287693, + -0.04810076226669864, + 14.978484682196248, + -14.32557067040231, + 33.213927819573485, + 48.93034643169029, + -25.33998588980714, + -38.42478548611111, + 0.0, + -7.351037823125137, + 15.14545412299545, + -57.11646297196039, + -22.05475047487875, + 6.7500364719275785, + 101.2249963358133, + -81.08698944924394, + -9.93389203858988, + -26.235823932375137, + -31.541858397583464, + -84.94101570527624, + -15.44230231785628, + 1.3474811577488235, + -33.78465061292442, + 204.53376786661076, + -21.80724982577995, + -16.93723798098785, + 100.94391369849482, + 237.03004834811793, + -38.915456659656435, + 5.3227190021943995, + -0.8964192966338232, + -119.35154873919365, + 16.84908605798634, + -13.960089505010071, + -22.398823656651935, + -17.25229733686872, + -43.6765689089147, + -3.982698626463659, + -0.7913426044127891, + -23.67201537368422, + -4.268056334708386, + 168.7043553997363, + -5.101845519919988, + -8.513849423076362, + 37.78571112686558, + -3.295677684805069, + 54.563190587379786, + 0.04906159024311634, + 39.763722889627175, + -0.8463628535630606, + 25.38571287065205, + 2.0175250393816926, + -10.375854233346722, + -2.2280531335231877, + 0.0, + -3.737537167056313, + 0.0, + -5.617953872011368, + -1.7992657230787898, + -0.10107286013508854, + -2.4531337301605123, + 73.04402359408533, + -0.8411306940034659, + 0.0, + -0.1119133735516515, + 0.0, + 68.56952231656905, + 10.679448383931952, + 0.0, + -32.39965729656578, + -4.148505035650649, + -0.3803587300452121, + 6.046927220378251, + -1.068247397161677, + 0.0, + -5.258669455034104, + 0.0, + 4.888263430868653, + -6.5028215427472436, + 64.31834391491958, + 0.0, + -0.40478564257843386, + 7.161354275978451, + -1.2477197535737174, + 313.1333260137873, + 55.099425979413375, + 106.57286010033944, + -105.66910506626367, + -23.881661624623085, + -110.20314210648418, + -75.98999092692844, + 62.81111161429979, + 30.855481930040675, + -70.95805674678532, + 31.767627179833152, + 64.54336198164125, + -47.35750421894875, + -49.37488445707045, + 54.05769768309951, + 496.99425272290813, + 25.52957156222592, + -15.157340408907253, + -84.91564133095454, + -50.07894834137201, + -5.2972932406382895, + -214.42696685369827, + -152.39659117359574, + -3.226907990542584, + 94.27216149826606, + -13.360427969460378, + -71.68898129473611, + -85.6308639488921, + -21.119071443210142, + 332.71226632208885, + 180.37459197916985, + 12.29866754930584 + ], + "intercept": 1816.492495081855 + }, + "wide-insurance-offset-net-gaussian": { + "coef": [ + -0.021142920358700353, + -48.705318098639474, + -113.7353484992917, + -257.1778897374064, + 114.50901418566102, + -52.38963871987222, + -153.07807138059314, + 116.24031629639538, + 333.68988607955725, + -94.17755082147657, + 72.4737420287763, + -58.625114467185504, + -59.89487560072143, + 41.66428646646748, + 216.54442732236163, + 58.99004540383407, + -74.49492768548875, + 18.825238773985852, + 69.24510737690565, + 209.29727492051813, + 342.7070740635246, + 14.898176506688644, + -77.94665913820674, + 80.57003785669733, + -125.59851807324937, + -119.55463236916917, + -175.7629450227671, + -74.9812780860508, + -59.202378977338185, + 54.45614529486424, + -164.52848160524582, + -184.71240841377357, + 273.9631106560206, + -14.755255293978871, + 174.2666303727023, + -51.29890176627448, + -35.287014239592885, + -20.79243112564392, + -16.386858951170048, + -0.5937554658396467, + -8.593901986315878, + -14.689585268647678, + -1.9849879093257323, + -6.286872849895394, + -1.2534983064054812, + -2.3509134550229884, + -9.30702122387234, + 0.0, + -0.08943059442720648, + -2.0487851042174325, + 2.8156968369997024, + -1.6192596620313042, + 122.66356506911941, + 103.50045081857009, + 82.17224410242662, + 86.64755753123568, + 12.85249831653218, + 57.49523993570294, + -151.17740291068583, + -61.845870912238, + -53.61923152811078, + -3.539670069543883, + -78.96892607630663, + -44.67397384700823, + 112.09069817479123, + -38.935790117678515, + 9.173986787244038, + -15.777683668767374, + -59.67496173186146, + 90.99579902206098, + -261.6891876132357, + -62.57665851312523, + -160.75633328265266, + 20.67467516551891, + -180.78896437645125, + 67.00172946327876, + -109.77922471831802, + -75.52319425506153, + -84.98599014679856, + 149.8309863944661, + -79.60188396155745, + 6.0472463079136185, + -203.36300602017556, + -98.27302921178848, + -23.933858519707172, + 138.9443472983866, + -111.16874612064815, + -29.540370991387988, + -163.49716854984484, + -35.58187897289468, + -130.90370103091226, + -76.7411614393983, + -74.91025501693976, + 236.1498580721918, + 124.79653478158991, + -86.7516037707514, + -15.385773399015726, + 259.7122686581393, + -24.767613970338356, + 68.26300472133141, + 256.7789984605572, + -51.744061024692385, + -71.99399734545929, + 52.22881358824682, + -81.66523211492945, + 146.73719425012828, + 37.59316754369019, + 95.83622166263947, + 35.43880081438144, + 232.58282992038824, + 176.3531413738607, + 174.8475033852912, + -16.485509517764314, + -31.586815170021744, + -2.1262577488938965, + -20.37165246103522, + -28.702971696449005, + -15.737888079355692, + 11.44140321671539, + 2.921937360664216, + 0.20665983379262864, + -4.89857268921203, + -16.01226355068507, + -2.97481599779446, + -6.751531825423892, + -0.16096975576696945, + 1.2740904106357755, + -22.965307502536803, + 40.0543993144202, + 32.123451697671406, + -23.932543532977352, + -51.25642689079025, + -5.120739865985543, + -20.527978854795606, + 13.063208563738275, + 19.3010609313268, + -31.575457256991303, + -4.025993037537834, + 96.71058009069996, + -103.09804862421329, + 1.7605536302380134, + -30.203045084696736, + -39.67415988748199, + -110.98254800883913, + -16.86443573895175, + -0.8289590688997142, + -47.902641611202775, + 191.3294162993697, + -17.197654251323726, + -20.54587542974576, + 95.86071299559744, + 248.0804122589311, + -27.381544254722908, + 0.0, + -3.848049999456145, + -139.15050328639435, + 16.426296028278223, + -17.87721930537637, + -23.68466900369571, + -19.833689743025648, + -73.21926468347397, + -0.09337613802544496, + -1.152835392588329, + -23.72443328082492, + -5.879185545084135, + 141.51015653081714, + -2.1877667540184134, + -8.086855740152393, + 41.73765216354705, + -4.0213074597185665, + 60.52213260068615, + 8.463304646741868, + 89.94885917107848, + -1.4321822862522247, + 25.11966406751234, + 28.19316935039462, + -10.240531283533988, + 0.0, + -0.08317164899399897, + -4.331088211505262, + 0.0, + -21.90254685142285, + -2.988404011528942, + 0.0, + -2.7019507190500094, + 62.53810979286614, + 0.0, + 0.0, + -0.2779578964200368, + 0.0, + 60.660948906606635, + 10.497635484684313, + 0.8133504445082432, + -42.17224484937526, + -4.633672916484093, + -0.8692162609387621, + 19.198163989621857, + -1.4636885869565772, + 0.0, + -6.167253548889172, + 4.163658634484175, + 14.869060519776353, + -8.71492631621116, + 147.58170521169518, + -0.4232599343111888, + -0.7774822365032483, + 7.002031110385464, + 0.0, + 290.83960076264145, + 26.857760774385753, + 262.7949506329608, + -108.07821265918791, + -29.482657980165673, + -156.29484505956418, + -81.28983334358206, + 24.503902793619, + 2.7665589423497474, + -94.17736371107435, + 66.58200928038933, + 163.68182274402346, + -74.46977821836222, + -74.7124403734756, + 29.772526512400823, + 482.23457343149926, + 8.77696759973531, + -27.127460287420856, + -115.53200572077698, + -45.45174793215237, + -9.513911696118175, + -216.17383529493537, + -178.98972741965096, + -43.13165685643093, + 116.74525616020104, + -32.62019137502371, + -83.4725847138484, + -92.94901435805741, + -35.802089925448726, + 372.35127775252687, + 184.975660323374, + 66.89927705509729 + ], + "intercept": 1953.5306708515093 + }, + "intermediate-insurance-no-weights-net-poisson": { + "coef": [ 0.0, - -0.15450723202388816, 0.0, - 1.3361759475856037, 0.0, - -39.89955958492826, - -1.4166872395167225, - 4.918682413063746, - 5.248652466571887, - 13.812694467395465, - -7.249191900287899, - 1.0782550521608147, - -2.8715648619741287, - 9.220349207045112, - 2.6991547305508985, - -86.44662720616357, 0.0, - 17.002358514101083, - -0.37270914997444543, - 1.3161613937242378, - -6.732029824509657, - -1.8772849886421052, - -0.15771111980002228, - -1.8720708557820496, - 4.939036825560448, - 3.5951068149366767, - 0.0, - 8.41662066952367, - 0.23256152682956638, - 0.0, - 15.265727951682118, - 12.150470564549316, - 0.0, - 1.0772824053340362, - 0.0, - 1.6764325057574165, - 1.365837352212856, - 0.0, - 0.0, - -0.6047484218242954, - 7.858294462848472, - -1.8892216909979738, - 0.0, - -1.6651435174220923, 0.0, - 33.26089047269608, - -1.277150606174814, 0.0, - 10.193898556427715, 0.0, - 5.4605676441587025, - 4.158828418348079, - -35.107508702662805, 0.0, - 1.8949709101886156, - 11.966724095332067, - 7.020047731950549, - 1.004827836638375, 0.0, 0.0, 0.0, - 6.283136764565994, 0.0, - 0.6454401728373902, 0.0, - 13.93460873827147, - 0.26756062191535707, + 0.007568570828587876, 0.0, 0.0, 0.0, - 17.643756394951964, - 0.25906493638608025, 0.0, - 1.8741809986554676, 0.0, 0.0, - -4.816653204248038, 0.0, 0.0, 0.0, - 2.5249632582543553, - -0.6975169288169004, 0.0, - -52.895658802885976, 0.0, 0.0, 0.0, 0.0, - -24.81159764020873, - 32.12093114613066, - 10.812955375309032, - -88.92498220204634, - 4.245045501926276, - 5.025645221193456, - 20.199242612735013, - -19.857438129203427, - 24.491485402025873, - 20.87839591236966, - 10.820318538663061, - 15.187920452741343, - -15.187566652897345, - -37.53168274714055, - -78.72935842729903, - 3.2227280551392803, - 7.618414534758406, - 11.225436798688712, - 67.96740432992709, - 18.199818493187014, - 10.222773302266525, - 1.3916234028125483, - 0.714296095426254, 0.0, - -27.857157745241736, - 7.878448583962834, - 22.722284888903147, - -5.494064619247661, - 6.316307488363265, - 0.23265521912207585, - -12.010455567616944, - 3.210085529463375, 0.0, - 25.681928535758782, - -34.50096258717286 - ], - "intercept": 1690.4249783547652 - }, - "wide-insurance-offset-net-gaussian": { - "coef": [ 0.0, 0.0, - -1.7566539076511085e-06, - 1.2010150499709031e-06, - 0.9999940256080957, 0.0, 0.0, 0.0, @@ -6106,6 +5998,12 @@ 0.0, 0.0, 0.0, + 0.0 + ], + "intercept": -1.94629044914188 + }, + "intermediate-insurance-offset-net-poisson": { + "coef": [ 0.0, 0.0, 0.0, @@ -6119,6 +6017,7 @@ 0.0, 0.0, 0.0, + 0.019176638932166475, 0.0, 0.0, 0.0, @@ -6199,6 +6098,12 @@ 0.0, 0.0, 0.0, + 0.0 + ], + "intercept": -3.380840257130897 + }, + "narrow-insurance-no-weights-net-poisson": { + "coef": [ 0.0, 0.0, 0.0, @@ -6212,6 +6117,7 @@ 0.0, 0.0, 0.0, + 0.007568570828587847, 0.0, 0.0, 0.0, @@ -6245,6 +6151,12 @@ 0.0, 0.0, 0.0, + 0.0 + ], + "intercept": -1.9462904491418782 + }, + "narrow-insurance-offset-net-poisson": { + "coef": [ 0.0, 0.0, 0.0, @@ -6258,6 +6170,7 @@ 0.0, 0.0, 0.0, + 0.019176638932166454, 0.0, 0.0, 0.0, @@ -6293,27 +6206,23 @@ 0.0, 0.0 ], - "intercept": -0.02632365602221398 + "intercept": -3.3808402571308953 }, - "intermediate-insurance-no-weights-net-poisson": { + "wide-insurance-no-weights-net-poisson": { "coef": [ + 2.283396845556978e-05, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, - 0.05568228752756176, - 0.0, - 0.0, - 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, - 0.007603547852105811, 0.0, 0.0, 0.0, @@ -6394,16 +6303,6 @@ 0.0, 0.0, 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0 - ], - "intercept": -1.9533929100698604 - }, - "intermediate-insurance-offset-net-poisson": { - "coef": [ 0.0, 0.0, 0.0, @@ -6420,7 +6319,6 @@ 0.0, 0.0, 0.0, - 0.019176638932166454, 0.0, 0.0, 0.0, @@ -6505,19 +6403,12 @@ 0.0, 0.0, 0.0, - 0.0 - ], - "intercept": -3.3808402571308953 - }, - "narrow-insurance-no-weights-net-poisson": { - "coef": [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, - 0.055682287527561075, 0.0, 0.0, 0.0, @@ -6527,7 +6418,6 @@ 0.0, 0.0, 0.0, - 0.0076035478521057735, 0.0, 0.0, 0.0, @@ -6546,6 +6436,13 @@ 0.0, 0.0, 0.0, + 0.0 + ], + "intercept": -1.5329029963032796 + }, + "wide-insurance-offset-net-poisson": { + "coef": [ + 3.777279380314614e-05, 0.0, 0.0, 0.0, @@ -6564,12 +6461,6 @@ 0.0, 0.0, 0.0, - 0.0 - ], - "intercept": -1.9533929100698577 - }, - "narrow-insurance-offset-net-poisson": { - "coef": [ 0.0, 0.0, 0.0, @@ -6586,7 +6477,6 @@ 0.0, 0.0, 0.0, - 0.01917663893216645, 0.0, 0.0, 0.0, @@ -6623,17 +6513,6 @@ 0.0, 0.0, 0.0, - 0.0 - ], - "intercept": -3.380840257130895 - }, - "wide-insurance-no-weights-net-poisson": { - "coef": [ - 1.86852390042062, - -0.2479952429153137, - 1.5001147140916258e-05, - 3.744433890365774e-05, - 1.2871202986876457e-10, 0.0, 0.0, 0.0, @@ -6789,6 +6668,12 @@ 0.0, 0.0, 0.0, + 0.0 + ], + "intercept": -2.3017156242460635 + }, + "intermediate-insurance-no-weights-net-gamma": { + "coef": [ 0.0, 0.0, 0.0, @@ -6802,6 +6687,7 @@ 0.0, 0.0, 0.0, + 0.0013528205761349998, 0.0, 0.0, 0.0, @@ -6813,6 +6699,7 @@ 0.0, 0.0, 0.0, + -0.0017841862781379663, 0.0, 0.0, 0.0, @@ -6868,17 +6755,7 @@ 0.0, 0.0, 0.0, - 0.0 - ], - "intercept": -1.7628172532552664 - }, - "wide-insurance-offset-net-poisson": { - "coef": [ - 0.13882341300926518, 0.0, - 3.9204821897616114e-05, - 0.00010558239322679975, - 5.746494953744223e-10, 0.0, 0.0, 0.0, @@ -6894,6 +6771,12 @@ 0.0, 0.0, 0.0, + 0.0 + ], + "intercept": 7.418129091275571 + }, + "intermediate-insurance-offset-net-gamma": { + "coef": [ 0.0, 0.0, 0.0, @@ -6907,6 +6790,7 @@ 0.0, 0.0, 0.0, + 0.001352820576135001, 0.0, 0.0, 0.0, @@ -6918,6 +6802,7 @@ 0.0, 0.0, 0.0, + -0.001784186278137965, 0.0, 0.0, 0.0, @@ -6989,6 +6874,12 @@ 0.0, 0.0, 0.0, + 0.0 + ], + "intercept": 7.418129091275571 + }, + "narrow-insurance-no-weights-net-gamma": { + "coef": [ 0.0, 0.0, 0.0, @@ -7002,6 +6893,7 @@ 0.0, 0.0, 0.0, + 0.001352820576135002, 0.0, 0.0, 0.0, @@ -7013,6 +6905,7 @@ 0.0, 0.0, 0.0, + -0.0017841862781379622, 0.0, 0.0, 0.0, @@ -7034,6 +6927,12 @@ 0.0, 0.0, 0.0, + 0.0 + ], + "intercept": 7.418129091275571 + }, + "narrow-insurance-offset-net-gamma": { + "coef": [ 0.0, 0.0, 0.0, @@ -7047,6 +6946,7 @@ 0.0, 0.0, 0.0, + 0.0013528205761350013, 0.0, 0.0, 0.0, @@ -7058,6 +6958,7 @@ 0.0, 0.0, 0.0, + -0.0017841862781379561, 0.0, 0.0, 0.0, @@ -7079,6 +6980,13 @@ 0.0, 0.0, 0.0, + 0.0 + ], + "intercept": 7.4181290912755715 + }, + "wide-insurance-no-weights-net-gamma": { + "coef": [ + -7.63297747001027e-06, 0.0, 0.0, 0.0, @@ -7113,12 +7021,6 @@ 0.0, 0.0, 0.0, - 0.0 - ], - "intercept": -2.35617270043091 - }, - "intermediate-insurance-no-weights-net-gamma": { - "coef": [ 0.0, 0.0, 0.0, @@ -7135,7 +7037,6 @@ 0.0, 0.0, 0.0, - 0.001352820576135, 0.0, 0.0, 0.0, @@ -7149,7 +7050,6 @@ 0.0, 0.0, 0.0, - -0.0017841862781379785, 0.0, 0.0, 0.0, @@ -7223,12 +7123,6 @@ 0.0, 0.0, 0.0, - 0.0 - ], - "intercept": 7.418129091275571 - }, - "intermediate-insurance-offset-net-gamma": { - "coef": [ 0.0, 0.0, 0.0, @@ -7245,7 +7139,6 @@ 0.0, 0.0, 0.0, - 0.001352820576135001, 0.0, 0.0, 0.0, @@ -7259,7 +7152,6 @@ 0.0, 0.0, 0.0, - -0.001784186278138011, 0.0, 0.0, 0.0, @@ -7335,10 +7227,11 @@ 0.0, 0.0 ], - "intercept": 7.418129091275571 + "intercept": 7.509962544667503 }, - "narrow-insurance-no-weights-net-gamma": { + "wide-insurance-offset-net-gamma": { "coef": [ + -7.632977470010278e-06, 0.0, 0.0, 0.0, @@ -7355,10 +7248,6 @@ 0.0, 0.0, 0.0, - 0.0013528205761350013, - 0.0, - 0.0, - 0.0, 0.0, 0.0, 0.0, @@ -7369,7 +7258,6 @@ 0.0, 0.0, 0.0, - -0.0017841862781379626, 0.0, 0.0, 0.0, @@ -7392,12 +7280,6 @@ 0.0, 0.0, 0.0, - 0.0 - ], - "intercept": 7.418129091275571 - }, - "narrow-insurance-offset-net-gamma": { - "coef": [ 0.0, 0.0, 0.0, @@ -7414,7 +7296,6 @@ 0.0, 0.0, 0.0, - 0.0013528205761350017, 0.0, 0.0, 0.0, @@ -7428,7 +7309,6 @@ 0.0, 0.0, 0.0, - -0.001784186278137966, 0.0, 0.0, 0.0, @@ -7451,17 +7331,8 @@ 0.0, 0.0, 0.0, - 0.0 - ], - "intercept": 7.418129091275571 - }, - "wide-insurance-no-weights-net-gamma": { - "coef": [ 0.0, 0.0, - -2.499788489807341e-06, - -2.605030175147048e-05, - 0.00023939311300965154, 0.0, 0.0, 0.0, @@ -7599,85 +7470,186 @@ 0.0, 0.0, 0.0, + 0.0 + ], + "intercept": 7.509962544667503 + }, + "intermediate-insurance-no-weights-net-tweedie-p=1.5": { + "coef": [ + 0.5912017317782841, + 0.37598895925466175, + 0.6961906263560983, + 0.3460286617007586, + 0.4626998292992289, + 0.16216269153967516, + 0.1914819731545811, + -0.4956478977265533, + -1.2489679799141056, + -0.37994736965231907, 0.0, 0.0, + -0.2503474721289984, + 0.03112412082437058, + -0.22629290261084964, + -0.30613473648748873, 0.0, + -0.4704696723087974, 0.0, + -0.018223581643753962, + -0.6210398146410836, 0.0, + -0.41072495903968886, + 0.1666037198299883, + -0.2672304931528181, + 0.11248774644163774, 0.0, 0.0, + -0.4977835575955465, + -0.158436665290147, + 0.30924498302482517, + -0.2551460624097875, 0.0, 0.0, 0.0, 0.0, + 0.0643955268165037, + 0.49753156222216866, + -0.10568449288599496, + 0.0019652690864583747, 0.0, 0.0, + 0.4734517513304336, + -0.24525757609001067, + -0.5743208104503882, + 0.3835467270916377, 0.0, 0.0, + -0.2061192035564236, + 0.4197275163961812, 0.0, 0.0, + -0.03724489872816644, + 0.1994203762386245, + 0.17218592396739507, + 0.4036843549687117, 0.0, + 0.6959841886063725, 0.0, + 1.5552969806106678, 0.0, + 0.3895249219760724, 0.0, 0.0, 0.0, + -0.4365918658303166, + 0.2700914508124727, 0.0, 0.0, + 1.0846213648360001, 0.0, 0.0, 0.0, + -0.14406938069008635, 0.0, 0.0, 0.0, + 0.3449548220485073, 0.0, 0.0, 0.0, 0.0, + -0.5354148255356641, 0.0, 0.0, 0.0, + -0.4790492821806637, 0.0, 0.0, 0.0, + 0.2165645427441265, 0.0, 0.0, 0.0, + 0.4007215959470885 + ], + "intercept": 1.9823520625412736 + }, + "intermediate-insurance-offset-net-tweedie-p=1.5": { + "coef": [ + 0.20005039438486094, + 0.08117662637708602, + 0.3943177316099815, + 0.22394449045312687, + 0.25568454984036937, 0.0, + -0.051242978617664955, + -0.3530344079839473, + -1.0786047739123743, + -0.24373398498869722, + 0.04396701683477774, 0.0, + -0.09332325480120318, + 0.03637923894559472, + -0.22426299087605872, + -0.11440698964863867, + 0.05497934240117592, + -0.31847653919690483, 0.0, + -0.06094918473144264, + -0.5011292318156345, + -0.1426624640467986, + -0.31900517393133493, 0.0, + -0.2634003976250141, + 0.06866304572806707, 0.0, 0.0, 0.0, + -0.0195192554989121, + 0.20600942531149938, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.45718917520667823, + -0.09070069390774668, 0.0, 0.0, 0.0, + 0.36219005511601715, 0.0, + -0.11569824108670157, + 0.43097030477502524, 0.0, + 0.07047848018347769, + -0.05447808312357991, + 0.4530142786866848, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.4291459804596105, 0.0, + 0.6473970398622833, 0.0, + 1.527558839859661, 0.0, + 0.4220030333413867, 0.0, 0.0, 0.0, + -0.34267793278876235, 0.0, 0.0, 0.0, + 0.3240699607987593, 0.0, 0.0, 0.0, + -0.038492784179591066, 0.0, 0.0, 0.0, @@ -7686,9 +7658,11 @@ 0.0, 0.0, 0.0, + -0.3834625311085829, 0.0, 0.0, 0.0, + -0.365881046843763, 0.0, 0.0, 0.0, @@ -7696,62 +7670,152 @@ 0.0, 0.0, 0.0, + 0.2486243647631018 + ], + "intercept": 1.9978545687012783 + }, + "narrow-insurance-no-weights-net-tweedie-p=1.5": { + "coef": [ + 0.6346200341572986, + 0.43288608080075575, + 0.7788952845568482, + 0.42652460200900505, + 0.5715929051514738, + 0.24197091795338602, + 0.33567226593550353, + -0.6043183336902769, + -1.3229204350699828, + -0.34499585001619215, 0.0, + 0.0727258598495284, + -0.2772198779151608, + 0.036529818365018334, + -0.43710317885356026, + -0.2460862508698631, + -0.0075921531503506225, + -0.4538848255373119, 0.0, + 0.023523879181150516, + -0.5994599230241552, 0.0, + -0.3972344595898378, + 0.07026428553287428, + -0.33722617752734657, + 0.04871713617687333, 0.0, 0.0, + -0.4133401006464048, + -0.10304773809764749, + 0.24907998464678643, + -0.09466156630657256, 0.0, 0.0, 0.0, 0.0, + 0.07379090464093237, + 0.5074930192317565, + -0.15979578542411188, 0.0, 0.0, 0.0, + 0.6038012171279155, + -0.2694038967851822, + -0.5794315416569014, + 0.4407566439592326, 0.0, - 0.0 + 0.10103232631350913 ], - "intercept": 6.709879831337602 + "intercept": 1.682387792378914 }, - "wide-insurance-offset-net-gamma": { + "narrow-insurance-offset-net-tweedie-p=1.5": { "coef": [ + 0.22877222004290648, + 0.10600660733030877, + 0.4334411152861392, + 0.2969666249946838, + 0.37766297956447886, 0.0, + -0.048295201999834586, + -0.47579521255442675, + -1.174164293254311, + -0.22898209535274502, + 0.0007585914160931875, 0.0, - -2.4997884898073383e-06, - -2.605030175147046e-05, - 0.00023939311300965149, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, + -0.1641105734539216, + 0.037995111289890186, + -0.3542943117615369, + -0.07776079470833501, + 0.0004193859100225464, + -0.30421405783712346, 0.0, 0.0, + -0.48056878096960065, + -0.1781062465667459, + -0.29734059888845765, 0.0, + -0.2966583108841432, + 0.025537751241558243, 0.0, 0.0, 0.0, 0.0, + 0.14894069527133422, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.4319169399007408, + -0.164753750860469, 0.0, 0.0, 0.0, + 0.3679955801850757, 0.0, + -0.1515705801898865, + 0.4928167170816349, 0.0, + 0.133235063607289 + ], + "intercept": 2.053504594092499 + }, + "wide-insurance-no-weights-net-tweedie-p=1.5": { + "coef": [ + 5.496110304810839e-05, + 0.24438136302412805, + -0.04333021063777568, + 0.6703396710726421, + 0.046085954947029516, 0.0, + 0.3380511568770244, + 0.19780843330054015, + 0.4037014394279892, + 0.08898993931742927, + 0.13537533623767195, 0.0, + 0.1995531058525481, + 0.69862177801542, 0.0, 0.0, 0.0, 0.0, + 0.017994670748750028, + 0.6310950941129893, + 0.6476261880475465, + -0.11889948450225706, + 0.4256635484147355, + -0.1361383611398656, + 0.2174452144673326, + -0.08590544997886265, 0.0, + -0.03864100731687262, + 1.105253787088369, 0.0, + 0.025864394963135515, + 0.4460993182071556, + -0.2933196624755746, + -0.05767446151997184, 0.0, 0.0, 0.0, @@ -7777,26 +7841,65 @@ 0.0, 0.0, 0.0, + 1.6139673742778, + -0.05689004415141505, + -0.14584853861469685, 0.0, + 0.6143637173589458, 0.0, + -0.28655069734603617, + -0.16016528402744906, + -0.26530673237782415, + -0.22197260810028263, + -0.2501934730794513, + -0.019411770481434113, + 0.3086745640749522, + 0.49795686230592245, + -0.4003217901225523, 0.0, + -0.19178598417890247, + -0.1536179855327765, + -0.32432475411669803, + 0.20183756654527168, + 0.5706624159855338, + -0.8747806903473428, + 0.04444626372546142, + 0.9928550902999611, + -0.20061196614920818, + -0.21142864345068957, + 0.957583135223019, + -0.34854203736479866, 0.0, 0.0, + 0.5699327382076662, 0.0, + 0.05663014550863446, + 0.18510712961207024, + 0.2600800783914133, 0.0, + 0.28762656613724863, + 0.07142331884563742, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.09615287402986515, + 1.4927163049528074, + 1.0250643853307286, 0.0, + -0.32160743779990236, 0.0, + -0.008245813134188124, + -0.14910880884456276, 0.0, 0.0, 0.0, + 0.4198073705500742, 0.0, 0.0, 0.0, + 0.04867921213032528, 0.0, 0.0, 0.0, @@ -7814,18 +7917,28 @@ 0.0, 0.0, 0.0, + -0.35299375176660397, + 0.18623823512833665, 0.0, 0.0, 0.0, + 0.18067784954409943, 0.0, + 0.39364119651781226, 0.0, + 0.6634406117603232, 0.0, + 1.6706858359392536, 0.0, + 0.4398469688264971, 0.0, 0.0, 0.0, + -0.19656983693536553, + 0.857239496849291, 0.0, 0.0, + 0.6729854833224397, 0.0, 0.0, 0.0, @@ -7833,6 +7946,7 @@ 0.0, 0.0, 0.0, + 0.8131397124467287, 0.0, 0.0, 0.0, @@ -7841,43 +7955,98 @@ 0.0, 0.0, 0.0, + 0.08032082814872116, 0.0, 0.0, 0.0, + 0.5604840027027862, 0.0, 0.0, 0.0, + 1.4894721262395691, 0.0, 0.0, 0.0, 0.0, + 0.08935909945449602, 0.0, + 0.9609701151936308, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, + -0.018081658220516175, + -0.17594144957436184, + -0.031785805695133174, + -0.3366014090736293, + 0.06841922898636198, 0.0, + -0.45600867286689034, 0.0, + -0.33356923829646296, + 0.4634166026708563, + -0.19054872822602917, 0.0, 0.0, + -0.18129172138399494, + -0.10726556754975816, + 0.3505626876318792, + -0.07752631872698049, + 0.05765830368963847, 0.0, 0.0, 0.0, + 0.09473900395571247, + 0.44781259666327905, + -0.05212298690415671, + -0.001904713910970333, 0.0, 0.0, + 0.546565691557806, + -0.139779734934517, + -0.3701112510015432, + 0.4901376630358666, + 0.0 + ], + "intercept": 3.762633627917702 + }, + "wide-insurance-offset-net-tweedie-p=1.5": { + "coef": [ + 4.436614889305929e-05, + 0.06252211697570131, + -0.038908904212526615, + 0.5940452832816253, + 0.196194041641377, 0.0, + 0.058300720065319264, 0.0, + 0.2426783925680549, + 0.04616440658543907, 0.0, 0.0, + 0.1495578556697616, + 0.10368619459910196, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.38671341559642, + 0.43843778251393944, + -0.12788993866566176, + 0.26581190248455444, + -0.14023859179270257, + 0.10157311938858539, + -0.13640706326731164, + -0.006192283326279186, + -0.039906730291949466, + 0.6935339996867343, 0.0, 0.0, + 0.10747581319648973, + -0.2072871340240621, 0.0, 0.0, 0.0, @@ -7904,18 +8073,40 @@ 0.0, 0.0, 0.0, + 1.5204123029917767, 0.0, 0.0, 0.0, + 0.4599921866621021, 0.0, + -0.05193323629125901, 0.0, + -0.10343657539422058, + -0.040373988077362394, + -0.24004608892602214, 0.0, + 0.1080273925510183, + 0.6134160775560306, + -0.18234356307737384, 0.0, 0.0, 0.0, + -0.18517893881321532, 0.0, + 0.5448626107055934, + -0.5010497593847314, + 0.030116856379955786, + 0.7107188173284917, + -0.16304813635019774, + -0.2102296514999457, + 0.8484672213998907, + -0.3932382209055175, 0.0, + -0.002109464745212441, + 0.4096559007842802, 0.0, + 0.02943178975905245, + 0.103945950941043, 0.0, 0.0, 0.0, @@ -7925,16 +8116,22 @@ 0.0, 0.0, 0.0, + 0.04328336441863019, + 0.5946994413496997, + 0.6951389395577484, 0.0, + -0.1756728416776102, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.18600542691733016, 0.0, 0.0, 0.0, + 0.03990993872099556, 0.0, 0.0, 0.0, @@ -7952,215 +8149,142 @@ 0.0, 0.0, 0.0, + -0.24789910329292925, + 0.18483821516202711, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.436459316196224, 0.0, + 0.5072871649465018, 0.0, + 1.6108674782419203, 0.0, + 0.4678431666436311, 0.0, 0.0, 0.0, + -0.09119849749171419, + 0.3712169055584842, 0.0, 0.0, + 0.3534949479293033, 0.0, 0.0, - 0.0 - ], - "intercept": 6.709879831337602 - }, - "intermediate-insurance-no-weights-net-tweedie-p=1.5": { - "coef": [ - -0.7081565230643326, - 0.13325320596379572, - -0.07600737682918302, - 0.18450300112003346, 0.0, - 0.014308443675054159, - -0.19744170965911023, 0.0, - 0.058011739695550416, - 1.0247456556662589, - -0.0959259201473701, - -0.9368018524053002, - -0.0789851237591659, - 0.2981025931737737, - 0.3264541830686392, 0.0, - 0.017332198917401825, - 0.1793340419465533, - -0.17770258289466725, - -0.22430216604236405, - 0.07950092100083228, - -0.4193746215737773, - 0.10158316455532143, - 0.06381843867444591, - -0.5687396921606179, - 0.03424954050395273, - -0.33778473006576676, - 0.2694171650960271, - 0.13412313111758403, - -0.13318622631168983, - 0.12533929078077785, 0.0, 0.0, + 0.4448650361106876, 0.0, - -0.5225063205372122, - -0.22645284774018742, - 0.2850059072755779, - -0.259559013195509, 0.0, - 0.001009765522235864, 0.0, 0.0, - 0.056551692483447887, - 0.4673420880505547, - -0.09643360836237705, 0.0, 0.0, 0.0, - 0.43360377066053746, - -0.23777580394318198, - -0.5613177854896549, - 0.3931604091876813, 0.0, 0.0, - -0.48246248410438397, - -0.4987782254599315, - 0.11814536969806567, 0.0, - -0.07485139967421045, - -0.27236128958183947, 0.0, 0.0, - 0.19298091604029197, + 0.4807919264963892, 0.0, - 0.40355383405228223, 0.0, - 1.312122566384707, 0.0, - 0.19112407336092513, + 1.4598618291791063, 0.0, 0.0, 0.0, - -0.5832583479441095, - 0.22200112684040668, 0.0, + 0.029685394336720867, 0.0, - 0.9640182513211806, + 0.7065650926325286, 0.0, 0.0, 0.0, - -0.1811390659099606, 0.0, 0.0, 0.0, - 0.28519849373487954, + -0.04283120075197899, + -0.08300318576590573, 0.0, + -0.251591452475086, + 0.031434626504655505, + -0.056107528453615586, + -0.42402374952725497, + -0.19889926817008446, + -0.2869828646293571, + 0.09725489239818289, + -0.21309224649869835, 0.0, 0.0, 0.0, - -0.49338762754961074, + -0.08591831924113753, + 0.2134111770790366, 0.0, + 0.002181666743647506, 0.0, 0.0, - -0.38466082664144957, 0.0, 0.0, + 0.3930139693344341, + -0.05572516142154788, 0.0, - 0.2850282423187872, 0.0, 0.0, + 0.4504215455476914, 0.0, - 0.4703951921014977 + -0.05114893197264272, + 0.4638900402211218, + 0.0 ], - "intercept": 3.144705333847643 + "intercept": 4.175006673758393 }, - "intermediate-insurance-offset-net-tweedie-p=1.5": { + "intermediate-insurance-weights-net-binomial": { "coef": [ - -0.5307156993846597, 0.0, - -0.14946529868912026, - 0.07620697195862242, 0.0, - 0.008684456272350143, - 0.0723023206967811, 0.0, - -0.004730474218865743, - 0.9508442533097397, - -0.06727209948593131, - -0.9049059422783905, - -0.06870034557633492, - 0.23156390020964635, - 0.22104498782174956, 0.0, - 0.02209241848683024, - 0.38168772085731206, - -0.051430329980868064, 0.0, - 0.32071299716087165, - -0.1672564903523828, - 0.14722900307297535, - 0.15109957692362877, - -0.3214126083336642, 0.0, - -0.14016884745010796, - 0.0925543478857995, - 0.13490100185081255, - -0.13443817767099622, - 0.09158194513524855, 0.0, 0.0, 0.0, 0.0, - -0.07519814656571598, - 0.18019297899397543, 0.0, 0.0, 0.0, + 0.01579013183789617, 0.0, 0.0, 0.0, - 0.4432044031039984, - -0.033878436854658515, 0.0, 0.0, 0.0, - 0.32807825239388344, 0.0, - -0.09258727344890114, - 0.4545355964888385, 0.0, - 0.054551923639732415, - -0.5239829746014741, - -0.34257875830403395, - 0.1701660835308518, 0.0, 0.0, 0.0, 0.0, 0.0, - 0.23338662398789148, 0.0, - 0.37281340623591924, 0.0, - 1.295890528868916, 0.0, - 0.2333448733974391, 0.0, 0.0, 0.0, - -0.4722413557539961, 0.0, 0.0, 0.0, - 0.21503006303722821, 0.0, 0.0, 0.0, - -0.068227239511726, 0.0, 0.0, 0.0, @@ -8169,11 +8293,9 @@ 0.0, 0.0, 0.0, - -0.32595661387691166, 0.0, 0.0, 0.0, - -0.27578479228311703, 0.0, 0.0, 0.0, @@ -8181,171 +8303,69 @@ 0.0, 0.0, 0.0, - 0.3222854082974611 - ], - "intercept": 2.7735924966626726 - }, - "narrow-insurance-no-weights-net-tweedie-p=1.5": { - "coef": [ - -0.7850511244887864, - 0.10266801175778481, - -0.08621094893998643, - 0.1984960797150802, 0.0, - 0.04531669319199921, - -0.30472690302773375, 0.0, - 0.11797493198053262, - 1.0048846682910535, - -0.1867480256006293, - -0.9353081678995023, 0.0, - 0.299377311342219, - 0.41216343352874785, - -0.0037605266084718837, - 0.033339721969626576, - 0.20488550467924493, - -0.33841248671529617, - -0.129167189280779, - 0.0985081761739133, - -0.37236140037070775, - 0.058071629554553826, - 0.19031716115621508, - -0.4810982470342566, 0.0, - -0.26710424880516276, - 0.2029945403105367, - 0.1637578857738574, - -0.16295910227494398, - 0.06860496756383025, 0.0, 0.0, 0.0, - -0.4246852544081278, - -0.18213536639160355, - 0.21089867674988466, - -0.0828024013142534, 0.0, 0.0, 0.0, 0.0, - 0.03498073001514754, - 0.4795884778533537, - -0.18496362938590016, 0.0, 0.0, 0.0, - 0.5834121874265273, - -0.26645134614960403, - -0.5650615770027922, - 0.44278722190129643, 0.0, - 0.08748440954762954 - ], - "intercept": 1.9640584992029382 - }, - "narrow-insurance-offset-net-tweedie-p=1.5": { - "coef": [ - -0.5510444607650209, 0.0, - -0.13806707594019654, - 0.09688430075177736, - 0.04941799457981185, - 0.10270036821915421, 0.0, 0.0, - -4.421051196654368e-05, - 0.9122305898326706, - -0.15875553537103082, - -0.8870640049394625, 0.0, - 0.2049120636617672, - 0.23702136253363879, - -0.023704967240876926, - 0.034807606661082804, - 0.3827148347831163, - -0.16089500302975215, 0.0, - 0.2808026339571988, - -0.15570813260446653, - 0.05863674553924475, - 0.22541964154457986, - -0.2714677949626281, 0.0, - -0.07405966623732144, - 0.023740912550083725, - 0.14739141345139337, - -0.1470743886748214, - 0.037856134791048726, 0.0, 0.0, 0.0, 0.0, 0.0, - 0.11360065470256013, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, - 0.4296487537501752, - -0.11787689725496794, 0.0, 0.0, 0.0, - 0.382814699685094, 0.0, - -0.11705281922603479, - 0.5288202773568872, 0.0, - 0.13275614129073246 + 0.0 ], - "intercept": 1.8031004856622426 + "intercept": -3.645573951928438 }, - "wide-insurance-no-weights-net-tweedie-p=1.5": { + "intermediate-insurance-no-weights-net-binomial": { "coef": [ - 2.236338979080552, - -1.4895300502856796, - 4.434237268834607e-05, - 0.00014541856875741383, - 9.980961098770063e-12, - 3.7220880978353854, 0.0, 0.0, 0.0, - 0.6766471277634128, - -0.2085503616170281, 0.0, 0.0, 0.0, - 0.01890531202304153, 0.0, - -0.4023169064065399, 0.0, - -0.31738746092430337, 0.0, - 0.22655882120451273, 0.0, 0.0, 0.0, - -0.3334682995784029, - -0.1828453211761103, - -0.25190367882770104, - 0.4224865589685124, 0.0, - -0.07192419182426264, - 0.22991570726526372, + 0.010666528976005047, + 0.0, 0.0, - 0.12495406206472912, 0.0, 0.0, - -0.13699810935562765, - 0.6421811143389902, 0.0, 0.0, - 0.14683982508013688, - -0.026410864543311725, 0.0, 0.0, 0.0, @@ -8377,35 +8397,23 @@ 0.0, 0.0, 0.0, - -0.12001425104233, 0.0, 0.0, - -0.13437025019111862, 0.0, - -0.29566978775975467, 0.0, 0.0, - 0.23899576070939327, - -0.1234525540257854, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, - 0.4005703380066747, - -0.07917910346536737, - 0.09326122344164706, - 0.19046181178849153, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, - 0.08539682159155132, - -0.18099896277564656, - 0.007203609041484494, 0.0, 0.0, 0.0, @@ -8413,11 +8421,8 @@ 0.0, 0.0, 0.0, - 0.15507095835866266, 0.0, 0.0, - -0.27264098920159746, - 0.13069488472382068, 0.0, 0.0, 0.0, @@ -8431,11 +8436,16 @@ 0.0, 0.0, 0.0, - -0.3079056707008321, 0.0, 0.0, 0.0, 0.0, + 0.0 + ], + "intercept": -3.5472589055737163 + }, + "intermediate-insurance-offset-net-binomial": { + "coef": [ 0.0, 0.0, 0.0, @@ -8449,6 +8459,7 @@ 0.0, 0.0, 0.0, + 0.015093393794309358, 0.0, 0.0, 0.0, @@ -8471,7 +8482,6 @@ 0.0, 0.0, 0.0, - 0.2791819057686054, 0.0, 0.0, 0.0, @@ -8479,7 +8489,6 @@ 0.0, 0.0, 0.0, - 0.2850247116812913, 0.0, 0.0, 0.0, @@ -8488,7 +8497,6 @@ 0.0, 0.0, 0.0, - -0.281372311959518, 0.0, 0.0, 0.0, @@ -8496,7 +8504,6 @@ 0.0, 0.0, 0.0, - -1.0013602850046082, 0.0, 0.0, 0.0, @@ -8513,80 +8520,53 @@ 0.0, 0.0, 0.0, - -0.14938775787812789, 0.0, 0.0, - 0.16872218501439876, 0.0, - 0.029873234568831286, - -0.18523766890059448, - 0.09851546743707001, - -0.03543039413679224, - 0.03543038344630576, 0.0, 0.0, 0.0, 0.0, 0.0, - 0.07325987564450777, 0.0, - -0.47409361866399846, 0.0, 0.0, 0.0, 0.0, - -0.3797331267199636, 0.0, 0.0, 0.0, 0.0, - 0.6725671413114391, 0.0, 0.0, 0.0, 0.0 ], - "intercept": 0.9986144654908955 + "intercept": -3.621483628062589 }, - "wide-insurance-offset-net-tweedie-p=1.5": { + "narrow-insurance-weights-net-binomial": { "coef": [ - 2.1914263956709807, - -1.304145121358758, - 3.123960307477809e-05, - 0.00014383658827431942, - 1.0391388460540954e-11, - 3.236375466085687, - 0.0, 0.0, 0.0, - 0.5900676526205596, - -0.05899423907538806, 0.0, 0.0, 0.0, 0.0, 0.0, - -0.23307755501855149, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.01579013183789617, 0.0, 0.0, - -0.032585135291801764, - -0.22931339691087482, - 0.3399119251526905, 0.0, 0.0, - 0.20790468778413487, 0.0, - 0.0463319254945283, 0.0, 0.0, - -0.029861355468492758, - 0.2920677284760607, 0.0, 0.0, 0.0, @@ -8613,6 +8593,12 @@ 0.0, 0.0, 0.0, + 0.0 + ], + "intercept": -3.645573951928438 + }, + "narrow-insurance-no-weights-net-binomial": { + "coef": [ 0.0, 0.0, 0.0, @@ -8626,8 +8612,8 @@ 0.0, 0.0, 0.0, + 0.010666528976005014, 0.0, - -0.05434977146906226, 0.0, 0.0, 0.0, @@ -8638,9 +8624,7 @@ 0.0, 0.0, 0.0, - 0.2535988579737078, 0.0, - 0.022441686197482113, 0.0, 0.0, 0.0, @@ -8649,7 +8633,6 @@ 0.0, 0.0, 0.0, - -0.046263550587015825, 0.0, 0.0, 0.0, @@ -8663,6 +8646,12 @@ 0.0, 0.0, 0.0, + 0.0 + ], + "intercept": -3.5472589055737145 + }, + "narrow-insurance-offset-net-binomial": { + "coef": [ 0.0, 0.0, 0.0, @@ -8676,7 +8665,7 @@ 0.0, 0.0, 0.0, - -0.05779467114526761, + 0.01509339379430936, 0.0, 0.0, 0.0, @@ -8694,7 +8683,6 @@ 0.0, 0.0, 0.0, - -0.08560041815822819, 0.0, 0.0, 0.0, @@ -8711,12 +8699,18 @@ 0.0, 0.0, 0.0, + 0.0 + ], + "intercept": -3.621483628062589 + }, + "wide-insurance-weights-net-binomial": { + "coef": [ + 3.686842962005865e-05, 0.0, 0.0, 0.0, 0.0, 0.0, - 0.009815817309817794, 0.0, 0.0, 0.0, @@ -8733,7 +8727,6 @@ 0.0, 0.0, 0.0, - -0.017637131151686112, 0.0, 0.0, 0.0, @@ -8741,7 +8734,6 @@ 0.0, 0.0, 0.0, - -0.7889289860852348, 0.0, 0.0, 0.0, @@ -8758,16 +8750,12 @@ 0.0, 0.0, 0.0, - -0.1935441910495725, 0.0, 0.0, - 0.07957693962934584, 0.0, 0.0, 0.0, 0.0, - -0.006786876602715692, - 0.006786942545579651, 0.0, 0.0, 0.0, @@ -8775,26 +8763,17 @@ 0.0, 0.0, 0.0, - -0.13426171326972647, 0.0, 0.0, 0.0, 0.0, - -0.0916422901975575, 0.0, 0.0, 0.0, 0.0, - 0.5865830253373281, 0.0, 0.0, 0.0, - 0.0 - ], - "intercept": 1.3954142056137357 - }, - "intermediate-insurance-weights-net-binomial": { - "coef": [ 0.0, 0.0, 0.0, @@ -8811,7 +8790,6 @@ 0.0, 0.0, 0.0, - 0.01579013183789621, 0.0, 0.0, 0.0, @@ -8896,12 +8874,6 @@ 0.0, 0.0, 0.0, - 0.0 - ], - "intercept": -3.64557395192844 - }, - "intermediate-insurance-no-weights-net-binomial": { - "coef": [ 0.0, 0.0, 0.0, @@ -8918,7 +8890,6 @@ 0.0, 0.0, 0.0, - 0.010666528976005018, 0.0, 0.0, 0.0, @@ -8960,6 +8931,13 @@ 0.0, 0.0, 0.0, + 0.0 + ], + "intercept": -2.7778830443206566 + }, + "wide-insurance-no-weights-net-binomial": { + "coef": [ + 3.161022466845739e-05, 0.0, 0.0, 0.0, @@ -9003,12 +8981,6 @@ 0.0, 0.0, 0.0, - 0.0 - ], - "intercept": -3.547258905573715 - }, - "intermediate-insurance-offset-net-binomial": { - "coef": [ 0.0, 0.0, 0.0, @@ -9025,7 +8997,6 @@ 0.0, 0.0, 0.0, - 0.015093393794309424, 0.0, 0.0, 0.0, @@ -9110,12 +9081,6 @@ 0.0, 0.0, 0.0, - 0.0 - ], - "intercept": -3.6214836280625935 - }, - "narrow-insurance-weights-net-binomial": { - "coef": [ 0.0, 0.0, 0.0, @@ -9132,7 +9097,6 @@ 0.0, 0.0, 0.0, - 0.01579013183789614, 0.0, 0.0, 0.0, @@ -9169,12 +9133,6 @@ 0.0, 0.0, 0.0, - 0.0 - ], - "intercept": -3.6455739519284363 - }, - "narrow-insurance-no-weights-net-binomial": { - "coef": [ 0.0, 0.0, 0.0, @@ -9191,1882 +9149,6 @@ 0.0, 0.0, 0.0, - 0.010666528976005042, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0 - ], - "intercept": -3.5472589055737167 - }, - "narrow-insurance-offset-net-binomial": { - "coef": [ - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.015093393794309419, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0 - ], - "intercept": -3.6214836280625935 - }, - "wide-insurance-weights-net-binomial": { - "coef": [ - 0.0, - 0.0, - 1.2862730992807592e-05, - 0.03251448032700773, - 0.028911838801085, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0 - ], - "intercept": -4.2521012254363235 - }, - "wide-insurance-no-weights-net-binomial": { - "coef": [ - 0.0, - 0.0, - 2.4978310164880074e-05, - 0.03613704001481592, - 0.030281261789258686, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0 - ], - "intercept": -4.197930263894253 - }, - "wide-insurance-offset-net-binomial": { - "coef": [ - 0.0, - 0.0, - 3.056749134803382e-05, - 0.0016852442187581024, - 6.890400794617137e-08, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0 - ], - "intercept": -3.128548197920388 - }, - "intermediate-insurance-weights-lasso-gaussian": { - "coef": [ - -232.36028606542035, - -226.74626295840963, - 85.09676857807783, - 281.0825517361355, - -212.1691182506496, - 0.01909407526537654, - 0.0, - 72.3882161891375, - -27.628236430421016, - 573.6514661689214, - -38.024034751101425, - -191.8777629859848, - -3.36299017792173, - 0.0, - 181.96178062973112, - 790.3311286561241, - 16.52101993266951, - 0.0, - 851.734394625562, - 104.48552636647092, - 283.0441567817194, - -428.7068714718941, - -413.00701383335576, - -165.1165121076449, - -50.92494768505806, - 135.3311951827545, - 16.639331243217764, - -158.81582555373717, - -84.049965486007, - 0.0, - 12.939502059393545, - -168.57121276462615, - 2921.8972346776736, - -232.92426714936633, - -176.64925143930836, - 165.99253130220873, - 1894.7326332404293, - 215.0867078680652, - 24.277250803088357, - -350.2268884025763, - -693.0367168474448, - -66.10679245321519, - -209.73145798505882, - -166.42553224350308, - -12.870578758722006, - 357.3842160111198, - 46.06352144757246, - -311.1888919154201, - 0.009750958042364654, - -152.4067809833437, - 943.6710406849941, - 377.0856347282709, - 211.31617475644427, - -51.67127258103148, - 420.62525946126925, - 501.2943505499117, - 297.0740885603476, - 552.9746512374375, - 631.6762589220411, - 325.7792337169249, - 41.92206265073669, - 403.99831451162976, - 236.7557660887636, - 547.2185700333927, - 438.4422974740385, - -39.241369759919074, - 259.4332070199217, - 906.2827253962074, - 39.43011970367049, - 174.10547870261286, - -210.13426594256973, - -61.93079709949274, - -20.58509618027574, - -356.74853099316874, - 91.28241967880757, - 0.0, - 848.1192079844178, - -489.34098771261364, - -528.0620490665071, - 1224.5884659144324, - 868.4447598306233, - -432.8152813990616, - 197.62798518930668, - 0.0, - -236.8632866670186, - 548.6575172636582, - -642.299759490625, - -441.21461658600833, - -993.5586096857515, - -78.92277301151479, - -108.00955376418044, - -636.96187885664, - -620.382119401316, - -704.9429080120632, - 340.4983578392416, - -251.52640984199599, - -649.6273069975673, - 1197.9770860773945, - -553.4130562858812, - -32.548349735144726, - -262.25564720912564, - 4328.641390817087, - -37.92203216889108, - 6045.779898744444, - -333.6991012868638 - ], - "intercept": 361.6908292207215 - }, - "intermediate-insurance-no-weights-lasso-gaussian": { - "coef": [ - -291.4626608376146, - -255.69586780516647, - 12.760410146163887, - 189.23922632843508, - -255.0875579421845, - 0.1321041162381766, - 0.0, - 63.00047845210107, - -77.57998572272527, - 486.46367634778767, - -107.5798890102399, - -228.39205682233128, - -37.957863238245054, - 0.0, - 132.0183179285389, - 769.9697307599744, - 10.175449324746713, - 0.0, - 887.1732242345649, - 174.67886006712243, - 212.89541199734953, - -387.03331404735326, - -244.97981217529494, - -102.30378248208402, - -48.79779072739917, - 202.62435881775178, - 71.06831904032983, - -100.52739804923486, - -41.52769169813677, - 1.982280845891808e-08, - 5.512664396659098, - -240.83173981361426, - 1494.0684268442762, - -218.6448052795535, - -109.77339036622458, - 151.22006787592946, - 2028.073301529143, - 291.6724614962953, - 36.28977270071098, - -331.08600502773885, - -695.0453114754285, - -9.40677297294303, - -259.1375999756895, - -148.3832436807212, - 17.11607343249239, - 302.5595812162198, - 33.80293247489802, - -286.2123178322499, - -29.40146730641555, - -116.29872436049612, - 880.1896243208985, - 374.3595783412313, - 0.0, - -27.738871983146545, - 206.10757858280365, - 348.5678270478249, - 183.5270655190087, - 468.63949869919463, - 535.4348916385615, - 117.4664588042932, - -34.05979764012091, - 288.13907331717616, - 170.70874864553988, - 430.75655601118825, - 55.388335185509, - -89.8426783281692, - 205.09013662194954, - 878.9396127037624, - 0.0, - 1.3284443275033264, - -280.99737218495846, - -110.14989455606498, - -69.20885766203517, - -330.8943669644791, - 134.55240814181477, - -14.576946324299008, - 782.020788508838, - -428.3784182144926, - -494.01693615565443, - 1422.9965087292765, - 882.0746495396559, - -424.83951374749876, - 206.8105548916186, - 0.0, - -169.24225354648732, - 623.3738315351643, - -456.9200652509145, - -391.1620816010564, - -991.7203166012771, - 8.108767276540005, - -243.84277933093585, - -657.3141736450001, - -584.9613768623914, - -607.4442494231903, - 490.90831776349575, - -216.42176348322258, - -506.5640438792777, - 2803.767927750961, - -452.8580380354554, - 105.48448363202132, - -49.51026168731465, - 2574.968832771703, - 0.0, - 6213.318157926296, - -91.38231260856918 - ], - "intercept": 883.003026270445 - }, - "intermediate-insurance-offset-lasso-gaussian": { - "coef": [ - -225.57911478794065, - -222.25284450311048, - 95.61554173859915, - 320.5303937264592, - -226.93807253114494, - 0.0006075128459977645, - -25.467882192119458, - 114.09998602070034, - 0.0, - 592.7889900722015, - 0.0, - -208.89648687211545, - -21.92892595079059, - -6.217503101977889, - 199.3474820189603, - 861.1510812438811, - 21.585677210614943, - 0.0, - 848.54072262986, - 106.88614931388344, - 415.7385853511316, - -404.8896867597108, - -450.83903251542057, - -163.48664169986748, - -53.58497630372603, - 128.12914244532502, - 23.24948373060714, - -149.79168471840782, - -70.92386826244672, - 3.961111390465478e-08, - 16.710437185171962, - -160.78330477360697, - 3589.0283394122007, - -339.6104348989207, - -229.77882848659488, - 147.78407359671505, - 1993.2580536577339, - 214.51399969648836, - 48.53192331967177, - -420.0105969330887, - -557.19771868675, - -245.60388659842357, - -225.50891969639628, - -170.6559180446821, - -52.02073297866186, - 376.84284264432335, - 0.0, - -334.4496567367679, - 0.9039719620279049, - -241.51563536892428, - 1008.4397287149134, - 437.96169767037776, - 502.0053847644196, - -53.0408729082183, - 447.3995570450632, - 501.84159111142986, - 332.533048069225, - 743.2808214036539, - 667.1898859988262, - 328.1305319341969, - 25.54624698129673, - 420.60457991427035, - 267.8119665344679, - 588.7653881081742, - 445.97873005935213, - -144.46513379198726, - 286.28900580944963, - 942.7133993985829, - 0.0, - 259.5120364512863, - -279.5668359006214, - -162.30531946033855, - -90.87618347216821, - -345.51656685807984, - 54.62053774286518, - -89.77552494783629, - 739.7196915440638, - -482.7598642167312, - -660.6080882317245, - 1257.3694017923135, - 850.9591150624329, - -352.30865989309154, - 79.89802878182743, - -168.9669299126292, - -330.8479626468396, - 498.624250214722, - -796.6048088099425, - -538.5777099363585, - -1211.9597493895276, - -218.79237440384077, - -160.79409368115356, - -947.4548269642426, - -756.9980018443182, - -974.9906364325235, - 172.87306805490516, - -272.853222494625, - -640.5463472235715, - 2838.295719808878, - -760.2102394014632, - -160.29183916946135, - 0.0, - 5881.252548044479, - -465.0136695103024, - 5796.393779724736, - -480.6104747504865 - ], - "intercept": 56.273203905204355 - }, - "narrow-insurance-weights-lasso-gaussian": { - "coef": [ - -212.8255221318107, - -220.716197907368, - 77.13160693174655, - 304.4389010627475, - -231.1106719892047, - 0.03492390659040064, - 0.0, - 89.66211688201908, - -6.299473418072054, - 496.3965369347419, - -34.273327698859624, - -169.45329021852956, - 0.0, - -38.99756656296876, - 150.4755550012623, - 737.3327285116634, - 5.44020180734561, - 0.0, - 858.6432275006857, - 88.72760157989426, - 280.3780608918419, - -386.65069905074546, - -229.4169279585995, - -169.7833932937787, - -56.71842263029184, - 132.04558051302132, - 25.86874506744182, - -172.20075027170503, - -85.82800796226053, - 0.0, - 17.690948990053318, - -150.98194688731434, - 2907.396735008268, - -187.3986690874049, - -203.79321877674752, - 151.4024358801334, - 1859.0570063201346, - 211.2651927562683, - 26.579023092168836, - -361.8399796891185, - -676.4640328436788, - -152.49230872090857, - -232.5308830774557, - -180.46585016486918, - -7.711356982084635, - 381.03362297692587, - 73.41260948771055, - -356.67581984345145, - 0.005122227288229182, - -174.30734739609764, - 960.5256193906407, - 380.5528305360196, - 519.5739786244516, - -62.39483560358512 - ], - "intercept": 1362.6285394884103 - }, - "narrow-insurance-no-weights-lasso-gaussian": { - "coef": [ - -275.65514932582795, - -248.026240153373, - 13.214827470141683, - 216.19145098415697, - -266.79206693726775, - 0.11170162501689335, - 0.0, - 84.22052780530444, - -45.58250915715625, - 469.50570439527945, - -54.310274398226994, - -154.32085403241248, - 0.0, - -16.533666305107324, - 120.21755668529215, - 731.6498499927252, - 6.197903405309777, - 0.0, - 886.2058797432126, - 163.46022274495996, - 208.2558318605161, - -341.94945899592756, - -140.49858837945638, - -105.72824625339453, - -52.24190667434888, - 204.43298956602268, - 87.51770051478724, - -113.42920881129302, - -41.655076113515065, - 2.937745369542974e-09, - 13.345561537818243, - -248.55340404517827, - 1417.4268730606393, - -200.81361850842472, - -186.70717407659677, - 114.13400733638771, - 1964.1927837530104, - 258.87706627755404, - 8.479467584828571, - -383.34520188796466, - -703.2459313910091, - -125.4507154417672, - -313.1011749793166, - -194.7054167596024, - 0.0, - 299.40332178188356, - 36.4006501518609, - -360.51034321933685, - -55.16201546388161, - -177.95702260019655, - 869.8439304081064, - 354.2468942449613, - 106.16588574479692, - -40.835830134426786 - ], - "intercept": 1314.805172319574 - }, - "narrow-insurance-offset-lasso-gaussian": { - "coef": [ - -210.60465715780717, - -212.2998280293069, - 92.41391285957457, - 344.75509765298784, - -243.98851306636797, - 0.0, - -33.65341423057724, - 110.56253844467253, - 0.0, - 502.7018275571454, - 0.0, - -190.40865667696858, - -15.418485398948818, - -32.70970988193745, - 181.0901584405041, - 824.1519604920915, - 7.665092094576497, - 0.0, - 855.5395026758279, - 87.49846117250824, - 408.6830950365582, - -337.48424709939763, - -192.14963555306568, - -167.1230889690846, - -61.93226721282613, - 123.0266081457739, - 31.065782771844834, - -169.1765923254327, - -74.44010507341949, - 4.8949129933190985e-09, - 22.289334008426863, - -147.57731753912742, - 3544.2390318738503, - -302.11436437007285, - -264.5875556376039, - 133.1804708819154, - 1956.0877777573921, - 208.4987230157547, - 34.87907643385668, - -436.55135405486095, - -539.0269610798492, - -306.24072602086005, - -257.049702516105, - -188.4970852342654, - -49.243351170177235, - 408.3609899695041, - 23.011264904020223, - -388.76493405608744, - 4.415806938820934e-05, - -279.9097041599639, - 1011.2291397908735, - 437.74137337009194, - 766.573612341302, - -66.3140663211927 - ], - "intercept": 1242.4102467465682 - }, - "wide-insurance-weights-lasso-gaussian": { - "coef": [ - -0.04178556324233749, - -38.97527311287221, - 0.0038922115353091915, - 0.015590753809534294, - 0.8055504907463141, - -1997.2948970952532, - -72.53916361331564, - -43.94242586029572, - 60.25635362092436, - -47.20824970458766, - 8.80293747532909, - 46.407787400519474, - -88.6048653633953, - -0.15870549153973643, - -51.02499634583787, - -42.85649388612374, - -12.17447970944527, - 81.85861885946302, - 2.530483212866261, - 31.620997766072406, - 54.49291655283946, - 410.7598534405523, - 260.7444585012792, - -61.21937971618851, - 20.54975994340834, - 0.0, - 11.223184247695201, - 113.11802384918025, - 70.43731057802007, - -13.792913583850428, - 0.0, - 62.03681990973658, - 27.353794407227838, - 8.923519489570348, - 12.96605982457367, - 31.365528147341706, - -179.1295795378683, - -108.82311670231547, - 45.91343899587753, - -45.93106946168872, - -402.49007485811916, - 63.89080774027264, - 276.7869639701537, - -66.98312079796985, - 0.0, - 0.0, - -72.4196898871751, - 0.0, - -73.11196513213339, - 252.96938448597672, - 0.0, - 0.0, - 0.0, - 0.0, - -55.65695344855691, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 193.26552900887265, - 128.32933587688015, - -196.05296442474577, - 9.593113357140423, - -248.35982156855317, - 86.05156851589273, - -9.859249594659929, - -112.77367248211104, - -113.79902970302675, - -133.44965198465889, - -26.96059414913464, - -79.31894744217503, - 23.29597169495222, - 2.78082293213841, - -206.04061906506706, - 9.157822361225655, - 40.25318473244267, - -5.922783161836476, - -322.99691848606574, - -44.671996837715845, - -94.67625891953223, - -23.88482341368489, - 25.206453687065366, - 23.56048107945115, - 117.56333693954656, - 34.50551424614866, - 30.794709335457544, - 23.66559224596959, - 62.33045182123782, - 54.71727465147669, - -217.20194353617438, - -36.01998042483348, - 0.0, - 0.0, - -319.934299572066, - -97.68779535851026, - -121.91151696844835, - 10.215857394053739, - 132.65700175419286, - -2.378180210210249, - -94.77749892827596, - -50.71315015432138, - -867.3529708011706, - 0.6287457358509128, - 35.40847489206853, - -53.334383591599924, - 369.5062430587436, - 58.62667342664464, - 268.95493641993204, - 314.89977190281877, - 22.013291945136817, - -221.30130516663965, - -178.91744147476996, - -103.8059120994055, - -1.3331150572244364, - 294.9866202589024, - 332.84735628587936, - 223.40056107396242, - 486.66324524199166, - -820.5739958681373, - 477.8398167493015, - 0.0, - 0.0, - 0.0, - 0.0, - -15.093233752006917, - 23.89202529070516, - -301.5031653589308, - 0.0, - 0.0, - 0.0, - -55.697217392297105, - 0.0, - 696.3485929006168, - 0.0, - -159.18543286506807, - -69.29998858870542, - 35.71683914734774, - 42.84162460315863, - 47.17531220187365, - -113.66528321949447, - -37.94744100553666, - -66.25310426480245, - 10.406712722091658, - 55.82698595756486, - -490.31805282131666, - -42.82809980970011, - 1.563151127116793, - -21.128262022938493, - -32.88404192265008, - -82.98024079964578, - -150.068437994594, - 0.0, - -98.51129879684875, - 227.56994561855984, - 46.21918008168038, - -58.68130631120067, - -21.400647812377024, - 229.99413292402812, - -6.742070214969517, - 328.7305070820581, - 31.209155999274337, - 60.015796883332854, - 0.0, - -9.667694683980914, - -27.320257839252488, - 67.70039773200735, - 63.43688085199097, - 37.63186513463184, - -173.6246850116705, - 0.0, - -182.6394791303474, - 0.0, - -76.82941412617267, - -0.07380102470754814, - 131.9043054615234, - -96.62651902059925, - 0.0, - 1322.4928699962238, - 0.0, - 18.691094327350495, - 502.0466566577123, - -2980.924719468345, - 0.0, - 646.3942395483194, - 77.12106032942418, - 1026.4286196735407, - 590.5415291984086, - 0.0, - 0.0, - 509.9480312088703, - 49.142111600372644, - 0.0, - 545.9358022970937, - 0.0, - 81.37879659089889, - 632.7287488245988, - 0.0, - 0.0, - 0.0, - 194.96764823144827, - 0.0, - 255.76822283477043, - -4.793929478571659, - 0.0, - 0.0, - -226.44001047124195, - 0.0, - 0.0, - -29.395273371315525, - 216.07038617189266, - 0.0, - 0.0, - -2789.798289003607, - 0.0, - 0.0, - 0.0, - 0.0, - -99.38478558810648, - 35.38840114123526, - -50.2395518027603, - -136.25723947753806, - 16.307572637018392, - 314.25855062322364, - 1.1036125924455362, - -77.52971769958903, - 51.04700275712665, - 0.0, - -1.8300579458222688, - 80.09549459281916, - -5.806322897043662e-08, - -114.18540577397873, - -2428.586111636943, - -46.24817895567681, - 40.50732529845832, - -5.655946688028663, - 386.0080285808877, - 153.3281153250071, - 35.57210485151393, - -25.488489789539905, - 105.85595683569859, - 8.051080087489488, - -40.19520999654332, - 0.0, - 57.05186025049631, - -36.42638913619206, - 4.655209554866159, - 0.0, - -39.14837428761322, - 0.0, - -159.02921421210092, - 54.27684062109895, - -297.2341152570549 - ], - "intercept": 2512.967310379697 - }, - "wide-insurance-no-weights-lasso-gaussian": { - "coef": [ - -0.0433689766248036, - -31.00571675627949, - 0.00239145141672889, - 0.009131747430810869, - 0.8845359462724305, - -2099.2431485621205, - -45.32820773920006, - -30.659631253205443, - 33.650537727556255, - -16.27491081319454, - 2.3287882613185618, - 48.28523386674969, - -46.07046971313927, - -2.59118435206307, - -36.17718519478628, - -40.47467965030737, - -12.437979752982539, - 42.32377842923175, - 0.0, - 15.687774962420756, - 27.823097388683255, - 233.83736329995654, - 150.5217571375219, - 0.0, - 3.65269135247719, - -7.47511985821049, - -16.195431971558477, - 60.71132155099234, - 30.751722842708276, - -10.026272323200297, - 7.448130688263462, - 37.32403631211729, - 16.99533073029767, - 0.0, - 4.787313817563923, - 17.410860895329407, - -51.810677355273775, - -74.21287005539924, - 20.79998063060184, - -33.68349664197669, - -272.81573220687704, - 32.43688132804827, - 173.8481453782261, - -49.301331010724, - 0.0, - 0.0, - -9.578665762625414, - 0.0, - 0.0, - 2.768530330352513, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 131.99802426265254, - 62.51724830433287, - -116.31575842319563, - 0.0, - -207.6531274225491, - 38.04819583287822, - 0.0, - -68.76902195655038, - -57.15617935600235, - -78.61529489147128, - -5.98367980416933, - -30.621938019181478, - 16.991131835767565, - 0.0, - -122.96222245755106, - 16.72353510471641, - 32.5241435119523, - 0.0, - -187.20074737744244, - -19.638583631708592, - -53.979309031781575, - -3.3901469582087134, - 22.718121624204137, - 17.370285407297114, - 72.5132961174443, - 10.596498948089225, - 25.560274014563344, - 0.0, - 41.76809274703031, - 30.866829534316658, - -131.3946428907276, - -19.073884895272567, - -3.144430896097979, - 2.9876730982146813, - -65.41704644164683, - -28.998472112955913, - -72.2635433145383, - 17.202653347611548, - 71.2643803447576, - 0.0, - -54.221644555516264, - -19.805818129161935, - -582.0964590552386, - 0.0, - 29.341361706988852, - -23.234497868459993, - 226.88300945094903, - 38.98849847936051, - 151.19780952551648, - 195.5863836933887, - 7.218207634089044, - -126.48787010277331, - -146.1265114443023, - -36.81350566762313, - -0.9135403266184315, - 176.9472779541055, - 191.74724681568114, - 173.4087137434848, - 309.357082797453, - -622.4694530034775, - 288.75772975269655, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - -172.6528193418041, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 380.2101958570919, - 0.0, - -98.30533179969012, - -43.19776662276518, - 16.681544904421482, - 23.46719112340401, - 10.430035049511234, - -88.74374469062316, - -25.45914911309587, - -55.19179819852638, - 4.428505226556499, - 19.01124109003858, - -328.4040626492923, - -17.67606312570296, - 0.0, - -28.13115894147266, - -26.12100814500724, - -97.21833469344789, - -84.20643619242082, - 0.0, - -65.1418127837664, - 116.42403098582155, - 5.118138714489759, - -35.564564701844915, - -24.130596614774035, - 138.5914549596138, - -17.1301102954731, - 198.76757813625306, - 2.3999094163738066, - 26.88476063038202, - 0.0, - 0.0, - -22.683829001538893, - 0.0, - 15.028385943178128, - 0.0, - -44.56732525626167, - -11.821458542810637, - -64.66397624897995, - 0.0, - -40.42905510390804, - 0.0, - 58.27853602704687, - -44.716428465586084, - 0.0, - 931.2880716674014, - 0.0, - 0.0, - 389.07246511005076, - -2403.4990839975653, - 0.0, - 176.66755708054322, - 34.48922457720899, - 430.96029034140037, - 284.4392551620183, - 0.0, - 0.0, - 85.81900285125361, - 2.827395819989371, - 0.0, - 302.22982253624895, - 0.0, - 53.312107755952844, - 201.51028810292598, - 0.0, - 0.0, - 0.0, - 116.74600105616551, - 0.0, - 0.0, - -5.596408682999366, - 0.0, - 0.0, - -150.2036283399316, - 0.0, - 0.0, - 0.0, - 153.98930212791134, - 0.0, - 0.0, - -2730.366296752021, - 0.0, - 0.0, - 0.0, - 0.0, - -51.07958539291975, - 25.151689843282913, - -25.59112073502406, - -98.09129486122224, - 14.41342023412495, - 160.0812324244905, - 0.0, - -51.9956268917845, - 35.94758012341604, - 0.15208575615896758, - -8.707532234139764, - 40.36154917520632, - -4.979136801449472e-08, - -82.35849816863774, - -1628.8220434856873, - -14.33723766367482, - 32.27507661947853, - -5.422304451692429, - 237.36913413204016, - 109.9833237790653, - 10.169886318378822, - -12.616048439526354, - 59.796609232938515, - 0.0, - -36.2821739198936, - 0.850436020279658, - 25.435898683215573, - -23.60280902407642, - 4.228754068754589, - 0.0, - -30.395396676956985, - 0.0, - -19.06862641680566, - 32.44395985235282, - -272.22924297362135 - ], - "intercept": 2423.30883167592 - }, - "wide-insurance-offset-lasso-gaussian": { - "coef": [ - 0.0, - 0.0, - -1.7519835869500939e-06, - 1.163481068122706e-06, - 0.999994087848399, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, 0.0, 0.0, 0.0, @@ -11081,6 +9163,13 @@ 0.0, 0.0, 0.0, + 0.0 + ], + "intercept": -2.962544724854638 + }, + "wide-insurance-offset-net-binomial": { + "coef": [ + 3.5511417102803386e-05, 0.0, 0.0, 0.0, @@ -11308,116 +9397,1212 @@ 0.0, 0.0 ], - "intercept": -0.02637337865007794 + "intercept": -2.7911142776723867 }, - "intermediate-insurance-no-weights-lasso-poisson": { + "intermediate-insurance-weights-lasso-gaussian": { "coef": [ - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.006804949442766916, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0 + 2.043870106899162, + 313.84643315976666, + 510.7548313654924, + 12.850733705501533, + 229.37832181320837, + 72.26969535605726, + -28.427388619110502, + -593.3917185452443, + -748.8393679907728, + -557.6016650198413, + -551.0021246718765, + -367.95188033523476, + 239.4638559594822, + 14.818480617920516, + 852.5607212511801, + 105.53411274499126, + 283.3395723883905, + -429.03850417075455, + -412.737460926977, + -164.7806169255793, + -50.386037850496876, + 135.78308304744755, + 16.45633605291358, + -158.67408532346764, + 83.10824628889596, + 11.783725115964641, + 3086.090436709828, + -69.56131537924722, + -9.922773744906662, + 330.70038159207945, + 2060.341815779492, + 379.9791712747717, + 188.7090442275973, + -184.20115644999555, + -521.1958203321674, + 0.0, + -43.79107740398343, + -1.586514185987863, + 145.40952215678408, + 522.1982278209082, + 210.21987150655718, + -145.89821054378393, + 164.77028558454342, + 0.0, + 1108.25596492783, + 541.58150528294, + 373.74685496135544, + -51.09410605769936, + 88.56061234485574, + -89.1380052145567, + 141.74201961032009, + 223.84205109023745, + -64.54754577885524, + -337.5122297183901, + 1.8295365293325554, + -156.2093593077271, + 147.76442636902857, + 41.417932816672, + -434.24807055596574, + -131.62221015067522, + 514.1784176515309, + -338.8588281389154, + -172.46162610395012, + -598.3820070222466, + -447.42747217103744, + -404.0757278414992, + -740.7225173722373, + -217.45132997392741, + -367.5736822129375, + 472.6889877560905, + -866.3035932449737, + -903.6019087583297, + 853.9594406422245, + 499.3519556343484, + -799.7231994071826, + -113.50169043998577, + -342.98340497454814, + -598.5220802802205, + 186.87286390942688, + -1002.9275356732844, + -800.6588840816374, + -1351.0100126697869, + -432.40082777206777, + -460.20682071085156, + -984.289183671195, + -971.6974159896691, + -1051.3908266229987, + 0.0, + -595.503204798736, + -995.2991437231184, + 858.3256656200746, + -891.9831911087565, + -365.9577157537016, + -600.2521325916815, + 3991.1254660813916, + -373.6941781077075, + 5715.277636801959, + -642.5512338437741 ], - "intercept": -1.8992525759458232 + "intercept": 941.5715417034249 }, - "intermediate-insurance-offset-lasso-poisson": { + "intermediate-insurance-no-weights-lasso-gaussian": { + "coef": [ + 31.89306862445124, + 300.507231674374, + 477.76553419141015, + 28.32431010547447, + 288.10093618209504, + 62.60077567421404, + -78.43466982556615, + -573.4301871869382, + -696.1383063542596, + -501.8148433016824, + -460.78891834065416, + -327.92163935396763, + 308.90356022585496, + 8.289689257037379, + 888.0625783712427, + 176.09944885338726, + 212.0813384346397, + -387.33605632459034, + -245.95970136307125, + -101.73781586061476, + -48.42023991755994, + 202.73630389827372, + 70.65489826428866, + -100.81540890785057, + 40.2885053656546, + 3.3207906477152918, + 1719.9336107412357, + 0.0, + 97.3481204271177, + 377.555802067971, + 2255.584075580665, + 517.3886836973949, + 263.44821866917533, + -102.7019230283023, + -461.8969825250047, + 0.0, + -31.2683813149008, + 75.99495036255637, + 244.02061549582692, + 528.9899261634, + 260.63359078841296, + -60.20646306287122, + 197.32575817587045, + 67.09894225796177, + 1106.9739977993343, + 602.5354715303172, + 226.54476963567095, + -26.65445976839155, + 148.8021421552884, + 0.0, + 270.9453446205645, + 341.5067073467976, + -57.537683045148164, + -224.13806935267692, + 100.85870087375845, + -7.564755976805556, + 246.05755794326433, + -115.49672154277172, + -270.1353738581024, + 24.73399325483548, + 702.2356428330894, + -170.42473210150385, + -124.62400361657095, + -452.9678025722503, + -278.47341337158986, + -235.16765090355761, + -498.89206134722457, + 0.0, + -173.68774085009508, + 624.721565617872, + -588.5673646829118, + -652.4106147430352, + 1270.3344908059519, + 730.5160869982296, + -568.4637234148856, + 57.99625755171666, + -100.66737059621994, + -312.0685797349043, + 480.2893702336095, + -598.5057701040178, + -531.9991936273909, + -1128.2806076082072, + -118.6266983628295, + -375.12825056973946, + -783.2210761109357, + -717.3760153446722, + -716.8040063906537, + 367.3964262327026, + -337.88769248117893, + -632.4873337337871, + 2684.682365870311, + -572.2884669548694, + 0.0, + -164.24847925493088, + 2459.208572593361, + 0.0, + 6105.388461193816, + -175.69245067564674 + ], + "intercept": 1094.8691993837901 + }, + "intermediate-insurance-offset-lasso-gaussian": { + "coef": [ + 1.1386770450343622, + 319.15477638974306, + 545.1538112530701, + -4.542000575552067, + 224.59090181824652, + 137.70217428139023, + 22.18431246631575, + -569.8215098145777, + -781.9056304150431, + -592.4504236893118, + -573.9473152535444, + -366.63926327818433, + 293.7211551225547, + 19.060197317432227, + 848.3153277065887, + 106.05746509283405, + 415.18075986190155, + -407.35900599923974, + -449.8359136363656, + -163.14927103579592, + -54.20485485020536, + 128.68419431468794, + 22.22702843759689, + -150.6444161533595, + 70.40505192330625, + 15.591199798302327, + 3747.3602739920225, + -182.76055007150575, + -69.22529218583163, + 305.9159898393987, + 2151.772924724716, + 373.40117363198954, + 206.14891007017965, + -260.5106801049888, + -393.36433553748253, + -88.38388161459474, + -66.4781506320782, + -12.352701128803442, + 99.71068256766692, + 534.2672647109733, + 157.54890559047806, + -175.69524107566198, + 159.2814292139222, + -85.99883099051388, + 1165.902584672898, + 595.8450665032315, + 657.5314355159425, + -52.43867899026704, + 63.84636705378781, + -77.98765355266603, + 308.4688981783727, + 236.61594946285302, + -83.41196116989917, + -374.4654209627878, + 0.0, + -144.03691789105582, + 170.57515081313156, + 30.925101177430843, + -557.2228960178771, + -120.7170741862139, + 535.2934187284493, + -394.4166556149487, + -96.67317446535, + -680.8590125457652, + -560.0947783973403, + -485.95591320935574, + -740.3712143570218, + -258.4527636922531, + -476.1706879898521, + 355.4729014295311, + -866.6361221796486, + -1041.9871074260861, + 880.6397322584236, + 476.9771137789306, + -726.8723171844782, + -232.8702772030187, + -536.1830727499749, + -694.4196535730465, + 136.11396504015545, + -1156.7523457266134, + -896.9636620476123, + -1567.1792307469932, + -569.7766180859902, + -510.4971225710012, + -1293.9974443814842, + -1103.3246208264127, + -1318.2996295580708, + -156.84449185209766, + -609.7131464501047, + -977.3686313386236, + 2508.2606444554813, + -1089.8931067605763, + -482.8533279745662, + -192.55934509104006, + 5556.628203339794, + -786.1729496204506, + 5482.0832684818, + -767.1389599699014 + ], + "intercept": 721.2207544006685 + }, + "narrow-insurance-weights-lasso-gaussian": { + "coef": [ + -7.803034299584369, + 289.97299848337406, + 517.386321486646, + -18.38502756006647, + 212.9201810247434, + 89.74942861352321, + -5.891160238476633, + -517.8871751011696, + -652.6098606757936, + -481.99198836741397, + -521.0971729713009, + -330.8812684440839, + 255.32103836466132, + 5.482848815276272, + 858.6522003961461, + 89.04984644445634, + 279.81308350116245, + -387.0001337539819, + -230.15465513104692, + -169.640212454991, + -56.50262800230261, + 132.01584885472673, + 25.853078882633497, + -171.77121003380572, + 85.83384304991519, + 17.355537971121063, + 3055.994398593501, + -38.91262680057567, + -54.86859704268451, + 299.5314891368257, + 2007.2083827120866, + 359.24671998616157, + 175.10281840424665, + -213.51146676137787, + -526.9050820164293, + -4.1162696656137525, + -84.21065176074985, + -32.169761221801636, + 134.32382806894856, + 529.522340735089, + 221.77883596390186, + -208.6206556751888, + 148.4926560761335, + -25.764809550085264, + 1108.899260834444, + 529.1615582898476, + 668.039594097048, + -62.30897787158227 + ], + "intercept": 1396.7258999437445 + }, + "narrow-insurance-no-weights-lasso-gaussian": { + "coef": [ + 23.35324018316299, + 285.7391906770955, + 488.8520431294109, + 1.717287424724001, + 272.3453681487357, + 84.00745261499904, + -45.554664401243, + -509.46447675033204, + -610.1216204391696, + -453.9133062534272, + -470.6056039545668, + -333.19701859578015, + 277.2759231139385, + 6.240540915473899, + 887.8384744112656, + 164.7989582575602, + 207.2781556357362, + -341.9732167666226, + -143.78082850154408, + -105.23357265183567, + -51.35543863333557, + 204.35471034502297, + 87.86333043479502, + -113.27875337422343, + 40.61269799878829, + 11.410728508092042, + 1651.1319767568125, + 14.013051042632513, + 26.894491553878026, + 347.3726942410778, + 2197.9954232773084, + 491.6398118767131, + 243.09666860509694, + -148.6495967125728, + -467.16425075898576, + 0.0, + -78.87705343056393, + 36.51591958037348, + 233.9258032437565, + 533.4793246573693, + 270.5362586607041, + -127.64616261767297, + 178.67509018396598, + 15.023153884583667, + 1103.8714853094857, + 589.3836971531625, + 340.21097908549353, + -39.62403467561026 + ], + "intercept": 1226.1919510081527 + }, + "narrow-insurance-offset-lasso-gaussian": { + "coef": [ + -1.6568753791524378, + 303.080355269122, + 555.5121369527508, + -33.51962185295026, + 210.74664228043866, + 141.79576369568218, + 31.114751696535425, + -487.4565013365738, + -679.1167801483034, + -503.52370917616565, + -520.51395694845, + -305.9657945415135, + 336.4091165412331, + 7.702020851862285, + 855.5275654654854, + 87.80445285630643, + 407.758199608446, + -337.73633275964767, + -193.08804113230832, + -167.00608030429126, + -61.84558785880059, + 122.9402444312309, + 30.997135916811953, + -168.74475745095924, + 74.49831288663697, + 21.955941350796742, + 3689.479918632337, + -157.3815631117204, + -119.4595092383008, + 277.8149849236372, + 2100.731933498317, + 352.82902054064397, + 179.89971047874337, + -291.76266982516313, + -393.38511794868253, + -161.60940210953444, + -112.18234237884015, + -43.755915764797116, + 89.07098646080996, + 553.2688861046855, + 167.81174465617693, + -244.14000434765634, + 144.96762139557234, + -135.14976145874834, + 1155.9250035247194, + 582.7966529877535, + 911.8185017058555, + -66.23710619636408 + ], + "intercept": 1268.6244228712296 + }, + "wide-insurance-weights-lasso-gaussian": { + "coef": [ + -0.007013633646061236, + -271.99103261063715, + -297.21943974267197, + -480.5486147960784, + -45.76498693098229, + -344.6974037743958, + -59.1750818175708, + 310.1118544742819, + 521.8245699004782, + 15.466477501270887, + 412.44639507011976, + 39.41948186173723, + 0.0, + 362.5824121336442, + 2662.0111677367177, + 970.7394881643073, + -1225.0494588215392, + 77.69738546628739, + 153.74618883567032, + 429.7393576190684, + 662.9733328301315, + 97.86294411858204, + -102.55592218175094, + 186.77828200979542, + -128.1737185826235, + -126.7911586155378, + -192.95871296198487, + -5.287505048219065, + -39.37158894439441, + 120.34255602648712, + -360.746850685122, + -377.40066493672254, + 967.3549088814973, + 5.017835458245539, + 995.5973160546505, + -386.2926756998894, + -468.0337369959068, + -1324.2762601324275, + -418.22253421840276, + 0.0, + -231.68695010120115, + -411.07340120513766, + -438.2389435266, + -1003.831676261575, + -624.3082054816991, + -4231.718852615065, + -1720.0085666739728, + -183.83692063456402, + 0.0, + -935.6975231944705, + 976.8379435596781, + -300.5122601370261, + 777.2234707123763, + 680.4481077510083, + 633.5925309289124, + 107.09827353165471, + -3.0322675838987396, + 146.46133300586212, + -568.6902329229943, + -232.83348473496477, + -152.66112326049424, + 10.198319866984537, + -219.6335543029776, + -124.66939001220919, + 504.11201460800106, + -48.62992280955429, + 198.96464755607806, + 18.736344371658085, + -48.88010810763824, + 511.83268920900997, + -606.437569461997, + 35.81704570121833, + -346.2417290416948, + 279.861322943753, + -312.6906526086107, + 421.15435776426733, + -189.8343741413164, + -2.163739749631654, + -63.80037064362597, + 722.6570466081621, + 9.778597399255412, + 302.88884891679817, + -297.1641444818201, + -82.48192893523019, + 153.8617315993935, + 567.1492542518795, + -200.83762871140195, + 0.0, + -325.12013603877244, + -23.482856451660933, + -328.805866699466, + -172.4329536235254, + -214.79963484823728, + 1247.0949612668187, + 786.2722259204728, + -162.80813087953885, + 173.10957997378023, + 1923.6639351880558, + 44.064440698669664, + 650.1029100056326, + 1783.6326642242973, + -124.41243101806198, + -158.62134725297628, + 348.1691924960927, + -314.42496362318803, + 1461.7133530462997, + 524.6715455166212, + 1639.42233876205, + 256.03050218081023, + 2259.9696865922247, + 1727.3961045867793, + 2442.239416316814, + 161.20849166411912, + -283.7415083048668, + 271.4049372010791, + -343.16676226853883, + -286.0575194624996, + -263.16037244732615, + 582.0496218466134, + 239.934566817738, + 136.25825449674014, + -720.6059651294198, + -304.44049344471375, + 0.0, + -1357.1792089440435, + 0.0, + 141.47629985397495, + -242.41705344816702, + 349.1333688694293, + 283.3170748332518, + -322.16182980227916, + -407.5951044007261, + 101.31694234070854, + -26.669848049778366, + 168.1808322878868, + 222.61270670183012, + -224.0242366416385, + -10.098064444670126, + 669.5369694452096, + -170.97146463316864, + 151.79072737069492, + -256.8195539331206, + -358.16008242126316, + -153.43381316681968, + -366.4547550280836, + -101.52575987193936, + -67.84532036393757, + 841.9632601090085, + -715.7528815523473, + -693.9551152139074, + 1318.0319315760512, + 928.6549603034446, + -338.9108643094995, + 170.10468192849595, + -197.2396768208975, + -153.22499124733505, + 782.9035227860466, + -471.89511361536216, + -379.7592842158986, + -648.9831652101363, + 61.50143606647506, + 0.0, + -57.12958052401125, + -322.0397867878049, + -495.3598135882549, + 607.1870063627628, + 4.252187727694856, + -1097.733455921286, + 1563.2850163193261, + -303.07354785902777, + 314.35146014810783, + 155.3287140742438, + 4990.985933097723, + 0.0, + 6437.002593648241, + 121.761445385328, + -542.851361273323, + -565.628565668379, + 0.0, + -1205.2100089622588, + 0.0, + 17.332939489460152, + -188.780303127393, + 0.0, + -117.76366363354583, + 538.8249070759888, + -274.2012374605015, + 111.7447419887588, + 0.0, + 0.0, + 423.0136212414192, + 5009.459223575257, + 0.0, + -73.77036423863383, + -495.95100937196725, + 0.0, + 1123.547723336854, + 0.0, + 0.0, + -199.33000137469153, + 356.51597156151183, + 432.47450051624037, + 0.0, + 5061.065648859614, + -661.3194553001099, + 0.0, + 3523.4718138546377, + -1520.6601703774752, + 771.5163910453895, + -2.9320597371164596, + 256.5622525573261, + -462.8060992713961, + -240.74546097531444, + -183.6884484865644, + -102.11262441621302, + 100.9563123976728, + 28.724418772042323, + -105.08995557341628, + 33.674302673022986, + 3045.191580603854, + 0.0, + 0.0, + 337.19165089754614, + 2072.94865029321, + 291.96011300275836, + 268.90771347628186, + -7.316217917756371, + -588.324193860112, + -198.74354219507094, + -93.0779759893328, + 1.0991924964723214, + 221.55982957335914, + 521.7438125657799, + 226.56768506959796, + -178.1374271673474, + 182.7004920967871, + 0.0, + 1146.111197227965, + 498.36358247839354, + 522.0531922455689 + ], + "intercept": 1256.8099006604957 + }, + "wide-insurance-no-weights-lasso-gaussian": { + "coef": [ + -0.005956704817421112, + -224.25096022171996, + -210.10034723966308, + -418.64141292108104, + 9.07747455455771, + -240.45926101091786, + -25.66675541574223, + 297.0167287138371, + 500.0849706376916, + 35.73734878468237, + 467.8838233428209, + 87.59891354338811, + 57.79568493830724, + 409.3384466655526, + 2723.791832411834, + 1091.7477557492778, + -1108.0683687301891, + 59.228167636182256, + 105.76578742860332, + 462.1446809912564, + 661.2798090605928, + 55.609867176232996, + -97.16017923812292, + 200.2724225284388, + -104.66273319975375, + -138.7940720658578, + -185.14366153896782, + 0.0, + -102.45720683237329, + 52.80756888051553, + -310.7803706044712, + -398.5653871676074, + 624.1351767839076, + 30.72094719332183, + 1106.5270450521834, + -395.9182735650869, + -477.38981441378627, + -1353.7308955884712, + -416.21595455175475, + 0.0, + -294.820413940049, + -510.4241688495168, + -494.07014204276845, + -1000.4193085878181, + -847.4389779650572, + -3643.224450861645, + -1705.9118698420148, + -213.94901102157, + 0.0, + -966.3185489799092, + 777.8132006582782, + -225.50705861168814, + 822.3967622725372, + 506.12977575398094, + 607.1081155755911, + -101.83825952336541, + 0.0, + 116.38392642149677, + -615.1174752785793, + -292.41587115206016, + -214.82832099650648, + -17.90501851208234, + -242.87629450893158, + -135.87291916839675, + 458.9171276025985, + -178.22988470890107, + 193.27720648850234, + 41.534881627914366, + -81.7465220579293, + 286.57979102535074, + -617.4972103736997, + -33.91601959557028, + -330.65204374722657, + 310.5295683621564, + -281.0205111926819, + 442.7908909941588, + -152.75899518015618, + 0.0, + -87.7619921348325, + 724.7966266963343, + 35.89757849125376, + 142.5492062366624, + -315.60749640496323, + -91.14209043787778, + 121.65155355291155, + 456.83756527249847, + -242.99139834216348, + -76.80192699556667, + -298.13938701612364, + 3.709292344410753, + -327.1486614899233, + -245.90087981003524, + -247.2103547848383, + 604.2349966464861, + 716.498470342855, + -164.62991503577175, + 98.74897981937252, + 1988.4133088709902, + 80.20170986064694, + 715.9544662045085, + 1893.36741459714, + -147.67254254278097, + -269.987880079426, + 239.88254616085194, + -308.1734205910832, + 1334.7986810668508, + 615.2962553657023, + 1744.8740756846785, + 377.1669034488897, + 2488.5333903729456, + 1084.6709081590234, + 2545.8159248378174, + 154.80763509209538, + -268.79496774377424, + 241.21198569270825, + -378.51147987677297, + -272.2813282883861, + -285.45871122458954, + 315.1961219620739, + 237.89750798748273, + 148.47750357498904, + -715.5429924081344, + -333.30787578617276, + 0.0, + -1259.910287987032, + 0.0, + 204.99741808259563, + -122.56998925852444, + 452.37267057580436, + 358.96914911325075, + -331.3945094511675, + -319.4941593498244, + 133.9854992668075, + 58.15807757766823, + 277.2817034068101, + -8.985899213964428, + -121.28355596985205, + 70.54290436892819, + 769.6292680689257, + -94.13823405773351, + 62.057276713726836, + -255.0001447000676, + -235.65000661785777, + -116.38705348807399, + -240.41877716819351, + -21.084133696986495, + -8.145987633350387, + 847.5321386980256, + -499.92577156520264, + -687.7104926311353, + 1514.3801437513173, + 983.9309980523349, + -279.5862485014183, + 224.7737573493682, + -125.93997369410923, + -87.10167988455738, + 874.5978555195628, + -289.7746045674241, + -238.1896280515717, + -620.4291467314798, + 153.33570466283373, + 0.0, + -86.69801140617447, + -382.8241736282284, + -505.3681086666988, + 710.4085831537203, + 0.0, + -786.1656877033378, + 2906.51724566274, + -266.44556254616305, + 377.3479726240725, + 224.93228462117852, + 3295.915497963692, + 0.0, + 6519.609369987311, + 192.73387660944977, + -244.71805381323304, + -19.690851356327034, + 0.0, + -1173.4323328293183, + 0.0, + 118.94038286185624, + -8.508638032700855, + 0.0, + -43.1921829042979, + 645.5306942540145, + 0.0, + 17.677841718462282, + 0.0, + 0.0, + 590.9964556970958, + 4997.805445447853, + 0.0, + -4.453194448854209, + -461.45423076276336, + 0.0, + 900.6769118107184, + 0.0, + 0.0, + -190.00372913924932, + 359.549681122895, + 352.11918056230724, + 0.0, + 3808.5620735891976, + 0.0, + 0.0, + 3461.3030005206538, + -1201.3046596576337, + 808.0838990576095, + 31.453907549718796, + 197.177444528225, + -429.9630829024156, + -74.83527412999746, + -122.38483106939756, + -89.28997609647858, + 182.8981869935008, + 86.19080022983262, + -60.59096071860656, + -3.426044117756457, + 1639.9788796585933, + 46.86790839846504, + 112.68509826358849, + 381.2268559103829, + 2223.221362696329, + 408.50342636081626, + 337.9831827563886, + 44.22868347092625, + -562.7512665901683, + 0.0, + -44.668200150671865, + 68.99673460492345, + 292.5238880038404, + 515.1186881660559, + 280.7055025110245, + -101.61530809129432, + 203.02151181478678, + 57.60268444375887, + 1120.3690344070217, + 566.1752601492713, + 349.85038912577033 + ], + "intercept": 1145.0039877796908 + }, + "wide-insurance-offset-lasso-gaussian": { + "coef": [ + -0.002310730402401163, + -271.98873702168186, + -305.04320797094994, + -486.6013784885298, + -56.052268236319776, + -444.89212671896615, + -76.8253138030351, + 303.6511848936528, + 548.9660489640204, + -20.488300202441433, + 407.9633027112826, + 31.043561275506626, + -65.92308800075152, + 290.3754670479583, + 2652.591179246097, + 1196.0443278008765, + -1475.0680862877227, + 120.13706752651348, + 251.72276327302166, + 471.8303289647742, + 773.2988578877815, + 132.3762171191321, + -61.35683835845257, + 271.4377733069848, + -102.83452849663846, + -63.12740232163062, + -168.5111743462152, + 18.413678515482147, + 7.428583903006408, + 143.68141600139683, + -309.8761156942445, + -355.0112680359864, + 1086.8547694580834, + 41.98095206370172, + 1100.7665640212717, + -430.8474192825494, + -467.0440865360962, + -1101.7216301527478, + -469.94395583955327, + 0.0, + -232.09365869027522, + -292.1802946396041, + -537.4834444312336, + -1064.6037615877228, + -726.4590239174222, + -4486.946871672157, + -1962.121867388431, + -126.84477548116698, + 0.0, + -922.6896071599082, + 909.2581747515007, + -542.5374550741951, + 869.219621227301, + 654.0036931083685, + 716.9799804303789, + 353.7693302671464, + -29.601818949493705, + 178.6232766572776, + -622.9847775213108, + -307.92743631871014, + -131.63371582125134, + 16.148406917805772, + -228.0870309440835, + -148.0666041453983, + 490.46420585708483, + -84.21599110498171, + 134.10714315577178, + 3.6573377531648217, + -41.05666524565662, + 456.5750594926139, + -652.7315244643977, + 0.0, + -340.9517247962381, + 270.7361268146477, + -363.9469920744348, + 356.84077025181233, + -145.91402171224857, + -23.677185236740986, + -29.407735011619145, + 721.3621553571419, + -8.489430736904312, + 277.03846569523654, + -342.69270078414183, + -101.82965174456197, + 124.41276231436467, + 609.6039067595821, + -217.11353273302578, + 1.7881878745481696, + -421.14486853808523, + 26.976320650446905, + -303.8863775960384, + -178.6363853982475, + -269.2861577889222, + 1419.592000324234, + 775.5370315081336, + -226.5504746091303, + 104.61697439385743, + 1931.1268634074402, + 19.570834408458584, + 737.8810417964283, + 2075.6636832059103, + -110.93319897500915, + -205.21202940144016, + 455.2580041219484, + -432.2837532570046, + 1424.4243765518409, + 608.8866120467524, + 1819.5030669747384, + 455.5183132518038, + 2532.5172725626194, + 2261.430131838938, + 2390.861415196166, + 101.56642365490727, + -328.0215473989615, + 166.4114597773488, + -348.9320754222189, + -385.7679490937788, + -280.9637265245521, + 697.3901050037368, + 208.01994967253978, + 128.6507374674688, + -850.3638746574609, + -387.15317249780605, + -69.76892529965457, + -1339.3187772871315, + 0.0, + 144.63017406688655, + -213.18880286392783, + 547.8495591940235, + 302.0364064637956, + -338.98929569719957, + -425.4059158893362, + 115.79099027283897, + 0.0, + 218.97308913429387, + 237.30107092866743, + -298.95266993919967, + 52.5130372227336, + 741.510394774836, + -172.48939450076736, + 286.253924205649, + -289.11512992475247, + -380.6726412175119, + -177.2613091914545, + -272.47318364412496, + -115.18553840181505, + -110.79782372716372, + 814.5433683012884, + -599.8065496240355, + -784.264638359081, + 1402.6295688949508, + 1017.1977341056867, + -176.81476663662326, + 165.6004179509569, + -332.0863529973311, + -138.67924588819213, + 861.7193678715684, + -507.1740142512791, + -310.59239512133246, + -697.3992254463546, + 76.94619194495982, + 92.02983701036322, + -137.68885517059496, + -405.7798536836322, + -686.0409664481721, + 618.759427672198, + 120.2495743524823, + -828.7587712849236, + 3209.7185357611233, + -303.6918404228361, + 384.111002152229, + 700.8210922596863, + 6882.012524637103, + -7.105558142888173, + 6420.51845232058, + 226.9838147849297, + -278.72462341211985, + -135.50912024450125, + 0.0, + -1348.1350047139865, + 0.0, + -0.8146901651113836, + -346.23479424161314, + 0.0, + -16.66422494605574, + 554.2088229858034, + 0.0, + 102.5222761335648, + 0.0, + 0.0, + 490.4909187992201, + 4890.787842695081, + 567.9493304428896, + -67.25776433818109, + -560.7942193519839, + 0.0, + 2827.398319124717, + 0.0, + 0.0, + -284.78791578518656, + 637.4817801232953, + 765.5232442050659, + 0.0, + 8527.894567864549, + -1239.775802350143, + 0.0, + 3400.5960354969175, + -176.63680576571952, + 780.4607661642764, + -17.5378299154922, + 387.1521861472892, + -459.1158724598519, + -255.8954942827216, + -183.63596674844123, + -110.55971557799944, + 79.76331669076406, + 43.8071089842326, + -91.95121210067144, + 16.320854606310885, + 3729.4423745372283, + -107.36733150138156, + -67.23744579934272, + 310.26460182490115, + 2125.0092903170353, + 228.0609095894029, + 290.87502492633394, + -94.85434667830842, + -512.8727345289989, + -460.0013366104802, + -112.50234228994375, + -7.418423638061335, + 159.7233300753888, + 530.8556434251963, + 160.00209033232898, + -213.4621795971902, + 167.61098512127128, + -82.43240308301345, + 1195.2287511673112, + 546.598919525955, + 818.8022141419302 + ], + "intercept": 1291.7294253583182 + }, + "intermediate-insurance-no-weights-lasso-poisson": { "coef": [ 0.0, 0.0, @@ -11432,66 +10617,7 @@ 0.0, 0.0, 0.0, - 0.0, - 0.0, - 0.0, - 0.016512871099053337, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, + 0.006804949442766962, 0.0, 0.0, 0.0, @@ -11520,12 +10646,6 @@ 0.0, 0.0, 0.0, - 0.0 - ], - "intercept": -3.21453788109357 - }, - "narrow-insurance-no-weights-lasso-poisson": { - "coef": [ 0.0, 0.0, 0.0, @@ -11542,7 +10662,6 @@ 0.0, 0.0, 0.0, - 0.006804949442766828, 0.0, 0.0, 0.0, @@ -11581,9 +10700,9 @@ 0.0, 0.0 ], - "intercept": -1.8992525759458176 + "intercept": -1.8992525759458259 }, - "narrow-insurance-offset-lasso-poisson": { + "intermediate-insurance-offset-lasso-poisson": { "coef": [ 0.0, 0.0, @@ -11598,10 +10717,10 @@ 0.0, 0.0, 0.0, + 0.01651287109905336, 0.0, 0.0, 0.0, - 0.016512871099053354, 0.0, 0.0, 0.0, @@ -11638,17 +10757,7 @@ 0.0, 0.0, 0.0, - 0.0 - ], - "intercept": -3.214537881093571 - }, - "wide-insurance-no-weights-lasso-poisson": { - "coef": [ - 2.240810625088553, 0.0, - 1.3502582088562383e-05, - 7.675431901591506e-06, - 2.6128750663479487e-21, 0.0, 0.0, 0.0, @@ -11689,6 +10798,12 @@ 0.0, 0.0, 0.0, + 0.0 + ], + "intercept": -3.214537881093572 + }, + "narrow-insurance-no-weights-lasso-poisson": { + "coef": [ 0.0, 0.0, 0.0, @@ -11702,6 +10817,7 @@ 0.0, 0.0, 0.0, + 0.006804949442766884, 0.0, 0.0, 0.0, @@ -11735,6 +10851,12 @@ 0.0, 0.0, 0.0, + 0.0 + ], + "intercept": -1.899252575945821 + }, + "narrow-insurance-offset-lasso-poisson": { + "coef": [ 0.0, 0.0, 0.0, @@ -11748,6 +10870,7 @@ 0.0, 0.0, 0.0, + 0.016512871099053365, 0.0, 0.0, 0.0, @@ -11781,6 +10904,13 @@ 0.0, 0.0, 0.0, + 0.0 + ], + "intercept": -3.214537881093572 + }, + "wide-insurance-no-weights-lasso-poisson": { + "coef": [ + 2.2825116163993788e-05, 0.0, 0.0, 0.0, @@ -11883,17 +11013,8 @@ 0.0, 0.0, 0.0, - 0.0 - ], - "intercept": -2.0759662777621872 - }, - "wide-insurance-offset-lasso-poisson": { - "coef": [ 0.0, 0.0, - 3.956048917759844e-05, - 0.00010847869157265461, - 1.2282697498257696e-21, 0.0, 0.0, 0.0, @@ -12015,6 +11136,14 @@ 0.0, 0.0, 0.0, + 0.0 + ], + "intercept": -1.5328829252597858 + }, + "wide-insurance-offset-lasso-poisson": { + "coef": [ + 3.7741821170749864e-05, + 0.0, 0.0, 0.0, 0.0, @@ -12128,12 +11257,6 @@ 0.0, 0.0, 0.0, - 0.0 - ], - "intercept": -2.3454898357694467 - }, - "intermediate-insurance-no-weights-lasso-gamma": { - "coef": [ 0.0, 0.0, 0.0, @@ -12150,7 +11273,6 @@ 0.0, 0.0, 0.0, - 0.0012110195230119545, 0.0, 0.0, 0.0, @@ -12238,12 +11360,6 @@ 0.0, 0.0, 0.0, - 0.0 - ], - "intercept": 7.416391716142663 - }, - "intermediate-insurance-offset-lasso-gamma": { - "coef": [ 0.0, 0.0, 0.0, @@ -12252,6 +11368,12 @@ 0.0, 0.0, 0.0, + 0.0 + ], + "intercept": -2.3016419497961746 + }, + "intermediate-insurance-no-weights-lasso-gamma": { + "coef": [ 0.0, 0.0, 0.0, @@ -12260,12 +11382,13 @@ 0.0, 0.0, 0.0, - 0.0012110195230119443, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.0012110195230119528, + 0.0, 0.0, 0.0, 0.0, @@ -12350,9 +11473,9 @@ 0.0, 0.0 ], - "intercept": 7.416391716142665 + "intercept": 7.416391716142663 }, - "narrow-insurance-no-weights-lasso-gamma": { + "intermediate-insurance-offset-lasso-gamma": { "coef": [ 0.0, 0.0, @@ -12367,10 +11490,11 @@ 0.0, 0.0, 0.0, + 0.0012110195230119443, + 0.0, 0.0, 0.0, 0.0, - 0.0012110195230119545, 0.0, 0.0, 0.0, @@ -12407,12 +11531,6 @@ 0.0, 0.0, 0.0, - 0.0 - ], - "intercept": 7.416391716142663 - }, - "narrow-insurance-offset-lasso-gamma": { - "coef": [ 0.0, 0.0, 0.0, @@ -12429,7 +11547,6 @@ 0.0, 0.0, 0.0, - 0.0012110195230119424, 0.0, 0.0, 0.0, @@ -12457,6 +11574,12 @@ 0.0, 0.0, 0.0, + 0.0 + ], + "intercept": 7.416391716142664 + }, + "narrow-insurance-no-weights-lasso-gamma": { + "coef": [ 0.0, 0.0, 0.0, @@ -12466,19 +11589,11 @@ 0.0, 0.0, 0.0, - 0.0 - ], - "intercept": 7.416391716142665 - }, - "wide-insurance-no-weights-lasso-gamma": { - "coef": [ 0.0, 0.0, - -2.496869549838821e-06, - -2.6046931481576907e-05, - 0.0002393715321065132, 0.0, 0.0, + 0.001211019523011951, 0.0, 0.0, 0.0, @@ -12512,6 +11627,12 @@ 0.0, 0.0, 0.0, + 0.0 + ], + "intercept": 7.416391716142664 + }, + "narrow-insurance-offset-lasso-gamma": { + "coef": [ 0.0, 0.0, 0.0, @@ -12525,6 +11646,7 @@ 0.0, 0.0, 0.0, + 0.0012110195230119476, 0.0, 0.0, 0.0, @@ -12558,6 +11680,13 @@ 0.0, 0.0, 0.0, + 0.0 + ], + "intercept": 7.416391716142664 + }, + "wide-insurance-no-weights-lasso-gamma": { + "coef": [ + -7.629595980490412e-06, 0.0, 0.0, 0.0, @@ -12724,17 +11853,8 @@ 0.0, 0.0, 0.0, - 0.0 - ], - "intercept": 6.709908633972339 - }, - "wide-insurance-offset-lasso-gamma": { - "coef": [ 0.0, 0.0, - -2.496869549838838e-06, - -2.604693148157686e-05, - 0.000239371532106513, 0.0, 0.0, 0.0, @@ -12805,6 +11925,13 @@ 0.0, 0.0, 0.0, + 0.0 + ], + "intercept": 7.509955846941981 + }, + "wide-insurance-offset-lasso-gamma": { + "coef": [ + -7.629595980490417e-06, 0.0, 0.0, 0.0, @@ -12982,68 +12109,31 @@ 0.0, 0.0, 0.0, - 0.0 - ], - "intercept": 6.70990863397234 - }, - "intermediate-insurance-no-weights-lasso-tweedie-p=1.5": { - "coef": [ - -0.693734192649815, - 0.12993944912217645, - -0.04150351048498497, - 0.1601741184775718, 0.0, 0.0, - -0.14307622173356635, 0.0, - 0.04203713068236271, - 1.1401272299670886, 0.0, - -0.9089565336539595, - -0.027692282263748702, - 0.3206269470493256, - 0.3463022949284172, 0.0, - 0.023898790272962562, - 0.12676029915059273, - -0.08346657627992872, - -0.08871165418317313, 0.0, - -0.2809558259771789, 0.0, 0.0, - -0.5792810434863533, 0.0, - -0.31242904508451774, - 0.14089080681567406, - 0.24323573794406228, - -2.0558213938071994e-13, - 0.12702672328570014, 0.0, 0.0, 0.0, - -0.13528622011217167, - -0.20984219493029413, - 0.11757279460586698, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, - 0.46238254307533566, 0.0, 0.0, 0.0, 0.0, - 0.4329848454225673, 0.0, - -0.5103986759936615, - 0.37954004381865303, 0.0, 0.0, - -0.38302124479170113, - -0.2759467419683112, 0.0, 0.0, 0.0, @@ -13052,36 +12142,27 @@ 0.0, 0.0, 0.0, - 0.3498945753469739, 0.0, - 1.3512970986115247, 0.0, - 0.058795769975734796, 0.0, 0.0, 0.0, - -0.5616884191704287, 0.0, 0.0, 0.0, - 0.9214553655206265, 0.0, 0.0, 0.0, - -0.13808493663705448, 0.0, 0.0, 0.0, - 0.07532746726544941, 0.0, 0.0, 0.0, 0.0, - -0.530128360139938, 0.0, 0.0, 0.0, - -0.4902674140207448, 0.0, 0.0, 0.0, @@ -13089,93 +12170,99 @@ 0.0, 0.0, 0.0, - 0.13243868938709596 + 0.0 ], - "intercept": 2.6178143439434756 + "intercept": 7.509955846941981 }, - "intermediate-insurance-offset-lasso-tweedie-p=1.5": { + "intermediate-insurance-no-weights-lasso-tweedie-p=1.5": { "coef": [ - -0.506637328629486, - 0.0, - -0.11227050544902677, - 0.07264201045357778, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.9961039685924649, + 0.5013694388431055, + 0.2906848559239075, + 0.5890734916055138, + 0.23377995324647716, + 0.30542623566697275, + 0.05842076487655857, + 0.06837640172704947, + -0.4747908005694326, + -1.2796193223281387, + -0.3696628569793284, 0.0, - -0.9175931176377933, - -0.044313137213069405, - 0.2006899125532592, - 0.19291861316928818, 0.0, - 0.022228850224504694, - 0.3303451430005853, + -0.1453361398998658, + 0.03463544196913227, + -0.049207269205290394, + -0.09751638954613054, 0.0, + -0.2460044062720026, 0.0, - 0.27076253545039813, 0.0, + -0.5506860493017481, 0.0, - 0.0881299439832092, - -0.28018641261125327, + -0.31078775749948806, + 0.10302069047155618, + -0.24691735348184612, + 0.11211685969563605, 0.0, - -0.03444358545088715, 0.0, - 0.24276091165284736, + -0.11352836846738765, + -0.13910420232629922, + 0.10057309250672103, 0.0, - 0.07437977802425437, 0.0, 0.0, 0.0, 0.0, - -0.05980633864619509, + 0.010058467348266924, + 0.5114860796850409, 0.0, 0.0, 0.0, 0.0, + 0.4782071744485416, 0.0, + -0.5205476045771279, + 0.3556313812088064, 0.0, 0.0, - 0.3950419486561884, 0.0, + 0.1867849595240809, 0.0, 0.0, 0.0, - 0.31121537308868424, 0.0, + 0.01302483600466736, + 0.16408854574640042, 0.0, - 0.42751117695053886, + 0.6423318690044753, 0.0, - 0.07903860210931044, - -0.4829369713306901, - -0.01838179749410493, + 1.5915294620793268, 0.0, + 0.2492192979947469, 0.0, 0.0, 0.0, + -0.38859831245089604, 0.0, 0.0, 0.0, + 1.0248122414068306, 0.0, - 0.26060304975300064, 0.0, - 1.3736023478947221, 0.0, - 0.051515199460171295, + -0.09120815940922354, 0.0, 0.0, 0.0, - -0.2718601035379641, + 0.18043688329516164, 0.0, 0.0, 0.0, - 0.10917975195943715, 0.0, + -0.5433078899251416, 0.0, 0.0, 0.0, + -0.553618523814469, 0.0, 0.0, 0.0, @@ -13183,115 +12270,85 @@ 0.0, 0.0, 0.0, + 0.17849100217702665 + ], + "intercept": 1.968148964808154 + }, + "intermediate-insurance-offset-lasso-tweedie-p=1.5": { + "coef": [ + 0.06065463050660829, 0.0, - -0.09644275010431153, + 0.27496196133759376, + 0.054718752028280276, + 0.05333413796397054, 0.0, + -0.0228968856665085, + -0.29982440002006405, + -1.1129428250676314, + -0.2114350200165838, + 0.04775896644782023, 0.0, 0.0, - -0.08738331038960588, + 0.03597230927990533, 0.0, 0.0, + 0.08962372610002793, 0.0, 0.0, 0.0, + -0.3794683023650429, 0.0, + -0.14526054693363624, 0.0, - 0.22812651358849945 - ], - "intercept": 2.696165012617445 - }, - "narrow-insurance-no-weights-lasso-tweedie-p=1.5": { - "coef": [ - -0.7998824923635337, - 0.05711844203667741, - -0.09751229436467858, - 0.1508185864812073, + -0.24600530489585437, + 0.053292322461846425, 0.0, 0.0, - -0.24101561323052356, 0.0, - 0.10118106437623871, - 1.0071174594104753, - -0.1332239198758025, - -0.9388612379008661, 0.0, - 0.2833259469378664, - 0.4001201771722685, 0.0, - 0.03297052887691294, - 0.18085617986069236, - -0.22017487277824352, 0.0, - 0.04938816675016082, - -0.18813716181123824, 0.0, - 0.16654778701436035, - -0.4677288997314617, 0.0, - -0.21946462941891, - 0.105951205407592, - 0.3079942659025673, - -4.585371492761573e-13, - 0.07635451253105666, 0.0, 0.0, 0.0, - -0.0022190652428631355, - -0.1497963233825524, - 0.03710463901721521, + 0.4326116255107886, 0.0, 0.0, 0.0, 0.0, + 0.35416602080110365, 0.0, 0.0, - 0.46491357278261686, - -0.0225978359715524, + 0.38261713717694196, 0.0, + 0.090714250625111, 0.0, + 0.18061196794595807, 0.0, - 0.5906708987426261, 0.0, - -0.5050937303731704, - 0.4360279847991301, 0.0, - 0.08025111548770669 - ], - "intercept": 1.8573417103329946 - }, - "narrow-insurance-offset-lasso-tweedie-p=1.5": { - "coef": [ - -0.5639432980092569, 0.0, - -0.14320928661921017, - 0.05001397326960753, 0.0, - 0.007886546332397002, + 0.11657487755407672, 0.0, + 0.597398954898362, 0.0, + 1.635641600237162, 0.0, - 0.9069123441084125, - -0.058779013171658186, - -0.8905024304550175, + 0.2748400936114556, 0.0, - 0.17351473070446205, - 0.214491543117745, 0.0, - 0.03393935365288584, - 0.3557152590177933, 0.0, + -0.09832499719165917, 0.0, - 0.25017282377476496, 0.0, 0.0, - 0.19431420661247178, - -0.22211994726322534, + 0.19711148733975603, 0.0, 0.0, 0.0, - 0.2633022612422935, - -1.1567185051525643e-14, - 0.03470954034038956, 0.0, 0.0, 0.0, @@ -13301,72 +12358,103 @@ 0.0, 0.0, 0.0, + -0.16711865502583817, 0.0, 0.0, 0.0, - 0.3639822212169686, + -0.21394282891001676, 0.0, 0.0, 0.0, 0.0, - 0.35251952052364777, 0.0, 0.0, - 0.4906649456589475, 0.0, - 0.13699478960658323 + 0.19294986543860923 ], - "intercept": 1.7945224403071474 + "intercept": 2.0890629232248465 }, - "wide-insurance-no-weights-lasso-tweedie-p=1.5": { + "narrow-insurance-no-weights-lasso-tweedie-p=1.5": { "coef": [ + 0.5418137459775059, + 0.3342642402763651, + 0.6818868487993645, + 0.3113342280911693, + 0.43213264801776785, + 0.14948866702057997, + 0.2396359934443445, + -0.5804706433610334, + -1.3477518622839986, + -0.3317497884434171, 0.0, - -1.7106138169204836, - 4.5113974278916536e-05, - 0.00014236303665435146, - 0.0, - 5.9813032888991575, - 0.0, + 0.06943454398757695, + -0.17449067007126084, + 0.0365131822971506, + -0.28353381138777134, + -0.04108657013718008, 0.0, + -0.2386958179590794, 0.0, - 0.6851680068656077, - -0.15061303822649372, + 0.01905968825131565, + -0.5530990735354196, 0.0, + -0.32148916477939493, 0.0, + -0.34125402929484816, + 0.05464675429758986, 0.0, 0.0, + -0.005590283287615984, + -0.06649236655730761, + 0.04280706996883978, 0.0, - -0.32985194620205044, 0.0, - -0.10194129587447183, 0.0, 0.0, 0.0, + 0.0173244192438433, + 0.4999541624963635, 0.0, 0.0, - -0.10353693809532111, 0.0, - -0.11724007725007407, - 0.3332881138785838, 0.0, + 0.6094327119468465, 0.0, - 0.14815865857703808, + -0.5328843727238299, + 0.4259208187864867, 0.0, + 0.09448712766668198 + ], + "intercept": 1.8231900293392358 + }, + "narrow-insurance-offset-lasso-tweedie-p=1.5": { + "coef": [ + 0.07581908396779534, 0.0, + 0.3050935500238575, + 0.12078689325556742, + 0.19452675019000523, 0.0, + -0.00408606005890453, + -0.4196275127636025, + -1.1905053533942078, + -0.1869784157978087, + 0.003946599469573101, 0.0, 0.0, - 0.6729345266378687, - 0.0, - 0.0, - 0.0, - 0.0, + 0.037804770174578645, + -0.0838974592761582, 0.0, + 0.009381749859928307, 0.0, 0.0, 0.0, + -0.38763321171156123, 0.0, + -0.17005262795521697, 0.0, + -0.2925645335994756, + 0.01984084292827874, 0.0, 0.0, 0.0, @@ -13378,28 +12466,55 @@ 0.0, 0.0, 0.0, + 0.3786770038688263, 0.0, 0.0, 0.0, 0.0, + 0.33658824159654877, 0.0, + -0.014110520578196612, + 0.4438798232980878, 0.0, + 0.13838915078905348 + ], + "intercept": 2.149878258316429 + }, + "wide-insurance-no-weights-lasso-tweedie-p=1.5": { + "coef": [ + 5.616665646472832e-05, + 0.14562920258300718, + -0.059837097582000634, + 0.6574125890402197, + 0.003095902651170782, 0.0, + 0.2528251632693164, + 0.11168453775796909, + 0.299187066218822, 0.0, 0.0, 0.0, 0.0, + 0.42092990241619066, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.6126448506935795, + 0.6565120792273673, + -0.004480386797291995, + 0.3974605593385022, 0.0, + 0.16991273889557484, 0.0, 0.0, 0.0, + 1.1366067486964118, 0.0, 0.0, + 0.46590562979725947, + -0.05005248713362053, 0.0, 0.0, 0.0, @@ -13408,7 +12523,6 @@ 0.0, 0.0, 0.0, - 0.09433801013362123, 0.0, 0.0, 0.0, @@ -13427,25 +12541,43 @@ 0.0, 0.0, 0.0, + 1.87283823336867, 0.0, 0.0, 0.0, + 0.5313600057083326, 0.0, 0.0, 0.0, + -0.04243340150354697, 0.0, + -0.028084555451556328, 0.0, + 0.22214693910480932, + 0.5004192000587198, + -0.2524890520692829, 0.0, 0.0, 0.0, + -0.13505421210932528, + 0.06602201755948488, + 0.5507569508379255, + -0.776679903304665, 0.0, + 1.0022752691504528, 0.0, 0.0, + 1.039661988258523, + -0.10923354605688186, 0.0, 0.0, + 0.5495610411399386, 0.0, 0.0, + 0.05599395407071691, + 0.1400282534590595, 0.0, + 0.15741685668141187, 0.0, 0.0, 0.0, @@ -13453,6 +12585,8 @@ 0.0, 0.0, 0.0, + 1.6879761572844565, + 0.9996819869850251, 0.0, 0.0, 0.0, @@ -13461,6 +12595,7 @@ 0.0, 0.0, 0.0, + 0.25854465931650633, 0.0, 0.0, 0.0, @@ -13482,126 +12617,115 @@ 0.0, 0.0, 0.0, + -0.16991716117361264, 0.0, 0.0, 0.0, 0.0, - 0.013725697351746233, 0.0, 0.0, + 0.2147762358274018, 0.0, + 0.609815103178744, 0.0, + 1.7616307392966324, 0.0, + 0.34649669156537255, 0.0, 0.0, 0.0, + -0.05343633553609041, + 0.6812651464923576, 0.0, 0.0, + 0.5272047846612667, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, - -0.0847224355581617, 0.0, + 0.76096399057154, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, - -1.1050875988183042, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.39959791353285207, 0.0, 0.0, 0.0, + 1.4460172641660738, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.8478683084017126, 0.0, - -0.16773300354918688, 0.0, 0.0, - 0.1514040998915283, 0.0, 0.0, 0.0, 0.0, - -0.05464051384111259, - 2.606585844811548e-12, 0.0, 0.0, + -0.02071840493497409, 0.0, 0.0, + -0.3830938492254789, 0.0, + -0.22062377340324235, + 0.4446065348697627, + -0.19499421501855588, 0.0, 0.0, - -0.3511442133839086, 0.0, + -0.1140195469381468, + 0.24762984365469473, 0.0, 0.0, 0.0, - -0.2992759863827312, 0.0, 0.0, + 0.006445367832921502, + 0.42411752131922165, 0.0, 0.0, - 0.668852730482051, 0.0, 0.0, + 0.5397004446748866, 0.0, + -0.30740633416900226, + 0.46565566118553, 0.0 ], - "intercept": 1.1135284702402024 + "intercept": 3.8375224708349682 }, "wide-insurance-offset-lasso-tweedie-p=1.5": { "coef": [ + 4.431047815363907e-05, 0.0, - -1.4034947863856497, - 3.0679686435220957e-05, - 0.0001409350020948575, - 6.261205004793176e-19, - 5.48713193378542, - 0.0, - 0.0, - 0.0, - 0.6024190119794569, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - -0.09040879978216985, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - -0.1032505987400393, - 0.12266056925705672, - 0.0, - 0.0, + -0.02017730473375002, + 0.6024741730941284, + 0.1713070704661592, 0.0, 0.0, 0.0, + 0.1735635417203534, 0.0, 0.0, 0.0, - 0.04231231615474641, 0.0, 0.0, 0.0, @@ -13609,12 +12733,16 @@ 0.0, 0.0, 0.0, + 0.31590391537614954, + 0.36058015866622695, 0.0, + 0.16527135865462303, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.6975228967357416, 0.0, 0.0, 0.0, @@ -13645,9 +12773,11 @@ 0.0, 0.0, 0.0, + 1.7596716515000943, 0.0, 0.0, 0.0, + 0.15432562053115448, 0.0, 0.0, 0.0, @@ -13656,17 +12786,24 @@ 0.0, 0.0, 0.0, + 0.6201853672756769, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.46351382659306783, + -0.19995866191966868, 0.0, + 0.6536592589757924, 0.0, 0.0, + 0.902310879916255, + -0.10181719699948404, 0.0, 0.0, + 0.32268706090204036, 0.0, 0.0, 0.0, @@ -13680,6 +12817,8 @@ 0.0, 0.0, 0.0, + 0.4627884264936266, + 0.4653434425666198, 0.0, 0.0, 0.0, @@ -13717,9 +12856,13 @@ 0.0, 0.0, 0.0, + 0.15354590146120953, 0.0, + 0.40905888779265265, 0.0, + 1.751805284054229, 0.0, + 0.3680521022125689, 0.0, 0.0, 0.0, @@ -13727,6 +12870,7 @@ 0.0, 0.0, 0.0, + 0.23494387281958834, 0.0, 0.0, 0.0, @@ -13734,6 +12878,7 @@ 0.0, 0.0, 0.0, + 0.2516751001758155, 0.0, 0.0, 0.0, @@ -13746,17 +12891,19 @@ 0.0, 0.0, 0.0, + 0.3468599092028931, 0.0, 0.0, 0.0, + 1.4560951694939055, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, + 0.4605232097262032, 0.0, - -0.7563124276774372, 0.0, 0.0, 0.0, @@ -13768,45 +12915,34 @@ 0.0, 0.0, 0.0, + -0.3225563819695458, 0.0, + -0.13893464720176593, 0.0, + -0.21134503149009493, 0.0, 0.0, 0.0, - -0.1670295610064742, + -0.10811975671493956, + 0.024442750158479604, 0.0, 0.0, - 0.009915669641784674, 0.0, 0.0, 0.0, 0.0, + 0.3002326275479781, 0.0, 0.0, 0.0, 0.0, + 0.43394134414059937, 0.0, 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.5379274639025537, - 0.0, - 0.0, - 0.0, + 0.4095856150877719, 0.0 ], - "intercept": 1.4033754288345277 + "intercept": 4.230014250995753 }, "intermediate-insurance-weights-lasso-binomial": { "coef": [ @@ -13823,14 +12959,7 @@ 0.0, 0.0, 0.0, - 0.0, - 0.0, - 0.0, - 0.009823958823844382, - 0.0, - 0.0, - 0.0, - 0.0, + 0.009823958823844342, 0.0, 0.0, 0.0, @@ -13913,7 +13042,7 @@ 0.0, 0.0 ], - "intercept": -3.2850887536502746 + "intercept": -3.285088753650273 }, "intermediate-insurance-no-weights-lasso-binomial": { "coef": [ @@ -13930,14 +13059,7 @@ 0.0, 0.0, 0.0, - 0.0, - 0.0, - 0.0, - 0.007236906693861937, - 0.0, - 0.0, - 0.0, - 0.0, + 0.007236906693861902, 0.0, 0.0, 0.0, @@ -14020,7 +13142,7 @@ 0.0, 0.0 ], - "intercept": -3.335354305576348 + "intercept": -3.3353543055763453 }, "intermediate-insurance-offset-lasso-binomial": { "coef": [ @@ -14037,14 +13159,7 @@ 0.0, 0.0, 0.0, - 0.0, - 0.0, - 0.0, - 0.009293237394470428, - 0.0, - 0.0, - 0.0, - 0.0, + 0.009293237394470426, 0.0, 0.0, 0.0, @@ -14144,13 +13259,7 @@ 0.0, 0.0, 0.0, - 0.0, - 0.0, - 0.0, - 0.009823958823844412, - 0.0, - 0.0, - 0.0, + 0.009823958823844398, 0.0, 0.0, 0.0, @@ -14186,7 +13295,7 @@ 0.0, 0.0 ], - "intercept": -3.2850887536502764 + "intercept": -3.2850887536502755 }, "narrow-insurance-no-weights-lasso-binomial": { "coef": [ @@ -14203,13 +13312,7 @@ 0.0, 0.0, 0.0, - 0.0, - 0.0, - 0.0, - 0.0072369066938619015, - 0.0, - 0.0, - 0.0, + 0.007236906693862005, 0.0, 0.0, 0.0, @@ -14245,7 +13348,7 @@ 0.0, 0.0 ], - "intercept": -3.3353543055763453 + "intercept": -3.335354305576352 }, "narrow-insurance-offset-lasso-binomial": { "coef": [ @@ -14262,13 +13365,7 @@ 0.0, 0.0, 0.0, - 0.0, - 0.0, - 0.0, - 0.00929323739447045, - 0.0, - 0.0, - 0.0, + 0.009293237394470433, 0.0, 0.0, 0.0, @@ -14304,25 +13401,11 @@ 0.0, 0.0 ], - "intercept": -3.2702950465346423 + "intercept": -3.2702950465346414 }, "wide-insurance-weights-lasso-binomial": { "coef": [ - 0.0, - 0.0, - 2.1961959229850212e-05, - 0.04425964067401379, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, + 3.68033061408944e-05, 0.0, 0.0, 0.0, @@ -14550,25 +13633,11 @@ 0.0, 0.0 ], - "intercept": -4.1900236186679995 + "intercept": -2.7777395645188974 }, "wide-insurance-no-weights-lasso-binomial": { "coef": [ - 0.0, - 0.0, - 2.6684333116529556e-05, - 0.05249742009277703, - 1.9902134692534983e-17, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, + 3.1572777637232055e-05, 0.0, 0.0, 0.0, @@ -14796,25 +13865,11 @@ 0.0, 0.0 ], - "intercept": -4.160846599342084 + "intercept": -2.9624557599273156 }, "wide-insurance-offset-lasso-binomial": { "coef": [ - 0.0, - 0.0, - 3.052240878164809e-05, - 0.0016798688811464843, - 1.368706668144578e-19, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, + 3.5449191449198846e-05, 0.0, 0.0, 0.0, @@ -15042,276 +14097,276 @@ 0.0, 0.0 ], - "intercept": -3.127108407607002 + "intercept": -2.7909747928424578 }, "intermediate-housing-no-weights-l2-gaussian": { "coef": [ - -34900.264057582455, - 35706.16591519635, - 188.91085653564014, - 29706.659921321934, - 28632.67894196987, - 58179.03313483703, - 12579.97855307969, - 106593.36134444115, - -3498.903606643634, - 2.5323445299950396 + -34900.26405759195, + 35706.165915266145, + 188.9108565356291, + 29706.65992136593, + 28632.678941968326, + 58179.03313481798, + 12579.978553046634, + 106593.36134446345, + -3498.9036066486997, + 2.5323445299125047 ], - "intercept": 6167911.73097537 + "intercept": 6167911.730985148 }, "intermediate-housing-offset-l2-gaussian": { "coef": [ - -34900.26405759277, - 35706.1659152846, - 188.91085653560458, - 29706.659921366412, - 28632.678941968476, - 58179.03313481301, - 12579.978553029827, - 106593.36134449074, - -3498.903606649883, - 2.532344529893477 + -34900.264057583285, + 35706.16591520025, + 188.9108565356301, + 29706.659921312832, + 28632.678941969873, + 58179.03313483376, + 12579.978553083905, + 106593.36134445705, + -3498.9036066439976, + 2.532344529991285 ], - "intercept": 6167911.730987346 + "intercept": 6167911.73097598 }, "intermediate-housing-no-weights-l2-gamma": { "coef": [ - -0.026057143557409634, - 0.06089886392927654, + -0.026057143557409543, + 0.06089886392927613, 0.00021660604175974707, - 0.06514059223041092, - 0.02368106712446843, - 0.06139456329032187, - 0.02621290382428229, - 0.19113686136471733, - -0.005304112098156193, - -4.203834665098301e-06 + 0.06514059223041065, + 0.023681067124468495, + 0.061394563290321995, + 0.026212903824282572, + 0.19113686136471728, + -0.005304112098156164, + -4.203834665097875e-06 ], - "intercept": 21.396279851925065 + "intercept": 21.39627985192501 }, "intermediate-housing-offset-l2-gamma": { "coef": [ - -0.02605714355740957, - 0.0608988639292764, - 0.00021660604175974694, - 0.06514059223041084, - 0.023681067124468533, - 0.061394563290322016, - 0.026212903824282416, - 0.19113686136471733, - -0.005304112098156182, - -4.203834665098073e-06 + -0.02605714355740962, + 0.06089886392927654, + 0.00021660604175974702, + 0.06514059223041095, + 0.023681067124468467, + 0.061394563290321946, + 0.026212903824282312, + 0.19113686136471747, + -0.005304112098156196, + -4.203834665098312e-06 ], - "intercept": 21.39627985192504 + "intercept": 21.396279851925073 }, "intermediate-housing-no-weights-l2-binomial": { "coef": [ - 0.14688489619668837, - -0.11795339515805803, - -0.0015450125052554461, - -0.16436617746306748, - -0.010386595117581936, - -0.12708185376786282, - -0.04670159336780462, - -0.533364151080909, - 0.02219245874934546, - 8.718727165553902e-05 + 0.14688489619668801, + -0.11795339515805554, + -0.0015450125052554426, + -0.16436617746306548, + -0.010386595117581972, + -0.12708185376786355, + -0.04670159336780622, + -0.5333641510809065, + 0.022192458749345056, + 8.718727165553317e-05 ], - "intercept": -36.514516745896536 + "intercept": -36.51451674589576 }, "intermediate-housing-offset-l2-binomial": { "coef": [ - 0.14688489619668743, - -0.11795339515805207, - -0.0015450125052554361, - -0.16436617746306287, - -0.010386595117581988, - -0.12708185376786432, - -0.04670159336780944, - -0.5333641510809045, - 0.022192458749344533, - 8.718727165552548e-05 + 0.146884896196688, + -0.11795339515805571, + -0.0015450125052554416, + -0.16436617746306542, + -0.01038659511758198, + -0.12708185376786363, + -0.046701593367806216, + -0.5333641510809073, + 0.022192458749345074, + 8.718727165553332e-05 ], - "intercept": -36.514516745894745 + "intercept": -36.51451674589579 }, "intermediate-housing-no-weights-net-gaussian": { "coef": [ - -36842.73000758104, - 41661.85782539061, - 178.3606016074342, - 32457.533534410835, - 51269.22769271997, - 61099.39284909209, - 14192.22081193384, - 114771.20257756117, - -3622.1939903436914, - 0.12322481014220352 + -36842.73000761543, + 41661.85782549124, + 178.36060160742352, + 32457.533534473736, + 51269.22769271699, + 61099.39284906292, + 14192.220811863726, + 114771.2025775973, + -3622.1939903504936, + 0.12322481002812684 ], - "intercept": 6353907.595228084 + "intercept": 6353907.595241301 }, "intermediate-housing-offset-net-gaussian": { "coef": [ - -36842.730007601414, - 41661.85782539459, - 178.36060160745416, - 32457.53353441183, - 51269.22769271861, - 61099.39284909133, - 14192.220811945963, - 114771.20257754783, - -3622.193990343393, - 0.12322481014634808 + -36842.73000760385, + 41661.85782539764, + 178.3606016074658, + 32457.533534424983, + 51269.227692718254, + 61099.39284909402, + 14192.2208119519, + 114771.20257752825, + -3622.1939903430443, + 0.12322481015034346 ], - "intercept": 6353907.595227574 + "intercept": 6353907.595226972 }, "intermediate-housing-no-weights-net-gamma": { "coef": [ 0.0, 0.0, - 0.0003036765789852555, + 0.0003036765789852487, 0.0, 0.0, 0.0, 0.0, - 0.1399903845264566, - -0.004161331457058833, - 2.388838117638057e-05 + 0.13999038452646193, + -0.004161331457058893, + 2.3888381176380115e-05 ], - "intercept": 19.600631216397154 + "intercept": 19.600631216397247 }, "intermediate-housing-offset-net-gamma": { "coef": [ 0.0, 0.0, - 0.0003036765789852415, + 0.00030367657898523715, 0.0, 0.0, 0.0, 0.0, - 0.1399903845264684, - -0.0041613314570589305, - 2.388838117638e-05 + 0.13999038452647158, + -0.004161331457058951, + 2.3888381176380054e-05 ], - "intercept": 19.600631216397282 + "intercept": 19.600631216397307 }, "intermediate-housing-no-weights-net-binomial": { "coef": [ 0.0, 0.0, - -0.0016158788809640787, + -0.0016158788809640826, 0.0, 0.0, 0.0, 0.0, - -0.37970269431878334, - 0.018068854334044517, - 2.7881052462757147e-05 + -0.3797026943187861, + 0.018068854334044877, + 2.7881052462762236e-05 ], - "intercept": -29.56851251949115 + "intercept": -29.568512519491826 }, "intermediate-housing-offset-net-binomial": { "coef": [ 0.0, 0.0, - -0.0016158788809640817, + -0.0016158788809640774, 0.0, 0.0, 0.0, 0.0, - -0.37970269431878634, - 0.018068854334044863, - 2.7881052462761945e-05 + -0.37970269431878195, + 0.018068854334044426, + 2.7881052462755927e-05 ], - "intercept": -29.5685125194918 + "intercept": -29.568512519490984 }, "intermediate-housing-no-weights-lasso-gaussian": { "coef": [ - -37371.72870452731, - 50777.58719523621, - 164.67774094279542, - 33490.56195790451, - 400911.7164429708, - 47728.39371447193, - 15275.266512818116, - 125172.78259610344, - -3775.7369251554987, - -6.46951746345171 + -37371.72870451251, + 50777.58719523503, + 164.677740942748, + 33490.56195790177, + 400911.71644298243, + 47728.393714467136, + 15275.266512813932, + 125172.78259615097, + -3775.7369251563305, + -6.469517463460993 ], - "intercept": 6583346.599033009 + "intercept": 6583346.599034356 }, "intermediate-housing-offset-lasso-gaussian": { "coef": [ - -37371.728704517205, - 50777.587195225104, - 164.67774094277814, - 33490.56195790543, - 400911.716442979, - 47728.39371446861, - 15275.266512814425, - 125172.78259612751, - -3775.7369251559285, - -6.469517463456473 + -37371.72870452876, + 50777.58719535245, + 164.6777409427136, + 33490.56195796844, + 400911.71644298005, + 47728.39371443962, + 15275.266512724798, + 125172.7825961866, + -3775.736925163222, + -6.469517463577697 ], - "intercept": 6583346.59903371 + "intercept": 6583346.599047765 }, "intermediate-housing-no-weights-lasso-gamma": { "coef": [ 0.0, 0.0, - 0.0003766353662303452, + 0.00037663536623034586, 0.0, 0.0, 0.0, 0.0, 0.05737074806563918, - -0.00331998336894884, - 3.245211416693942e-05 + -0.0033199833689488427, + 3.245211416693934e-05 ], - "intercept": 18.428891832457342 + "intercept": 18.428891832457346 }, "intermediate-housing-offset-lasso-gamma": { "coef": [ 0.0, 0.0, - 0.00037663536623034385, + 0.0003766353662303408, 0.0, 0.0, 0.0, 0.0, - 0.05737074806564091, - -0.00331998336894886, - 3.245211416693916e-05 + 0.05737074806564525, + -0.003319983368948902, + 3.2452114166938714e-05 ], - "intercept": 18.42889183245737 + "intercept": 18.428891832457424 }, "intermediate-housing-no-weights-lasso-binomial": { "coef": [ 0.0, 0.0, - -0.0019077378849164965, + -0.001907737884916493, 0.0, 0.0, 0.0, 0.0, 0.0, - 0.014035070881509204, + 0.014035070881509033, 0.0 ], - "intercept": -23.899507643941647 + "intercept": -23.899507643941316 }, "intermediate-housing-offset-lasso-binomial": { "coef": [ 0.0, 0.0, - -0.0019077378849164915, + -0.0019077378849164932, 0.0, 0.0, 0.0, 0.0, 0.0, - 0.01403507088150894, + 0.014035070881509027, 0.0 ], - "intercept": -23.899507643941142 + "intercept": -23.89950764394131 } } diff --git a/tests/glm/performance/test_performance.py b/tests/glm/performance/test_performance.py index 5293a1bf5..0844a25fa 100644 --- a/tests/glm/performance/test_performance.py +++ b/tests/glm/performance/test_performance.py @@ -12,7 +12,7 @@ import tabmat as tm from glum import GeneralizedLinearRegressor -from glum_benchmarks.cli_run import get_all_problems +from glum_benchmarks.problems import get_all_problems from glum_benchmarks.util import get_sklearn_family, runtime diff --git a/tests/glm/test_benchmark_golden_master.py b/tests/glm/test_benchmark_golden_master.py index 2a7e435c8..7577e0c6a 100644 --- a/tests/glm/test_benchmark_golden_master.py +++ b/tests/glm/test_benchmark_golden_master.py @@ -6,11 +6,10 @@ import pytest from git_root import git_root -from glum_benchmarks.cli_run import execute_problem_library from glum_benchmarks.problems import Problem, get_all_problems -from glum_benchmarks.util import BenchmarkParams, get_obj_val +from glum_benchmarks.util import BenchmarkParams, execute_problem_library, get_obj_val -bench_cfg = dict(num_rows=10000, regularization_strength=0.1, diagnostics_level="none") +bench_cfg = dict(num_rows=10000, alpha=0.1, diagnostics_level="none") all_test_problems = get_all_problems() @@ -33,13 +32,17 @@ def expected_all(): return json.load(fh) +# Filter out simulated datasets which are for benchmarking only +_gm_test_problems = {k: v for k, v in all_test_problems.items() if "simulated" not in k} + + @pytest.mark.parametrize( ["Pn", "P"], [ x if "wide" not in x[0] else pytest.param(x[0], x[1], marks=pytest.mark.slow) - for x in all_test_problems.items() + for x in _gm_test_problems.items() ], # mark the "wide" problems as "slow" so that we can call pytest -m "not slow" - ids=all_test_problems.keys(), + ids=_gm_test_problems.keys(), ) def test_gm_benchmarks(Pn: str, P: Problem, expected_all: dict): result, params = exec(Pn) @@ -61,7 +64,7 @@ def test_gm_benchmarks(Pn: str, P: Problem, expected_all: dict): obj_result = get_obj_val( dat, P.distribution, - P.regularization_strength, + P.alpha, P.l1_ratio, all_result[0], all_result[1:], @@ -69,7 +72,7 @@ def test_gm_benchmarks(Pn: str, P: Problem, expected_all: dict): expected_result = get_obj_val( dat, P.distribution, - P.regularization_strength, + P.alpha, P.l1_ratio, all_expected[0], all_expected[1:], @@ -92,7 +95,9 @@ def run_and_store_golden_master(overwrite, problem_name): except FileNotFoundError: gm_dict = {} - for Pn in get_all_problems().keys(): + # Keep generation aligned with test coverage: benchmark golden master excludes + # benchmark-only simulated datasets (including categorical-simulated). + for Pn in _gm_test_problems.keys(): if is_weights_problem_with_offset_match(Pn): continue if problem_name is not None: @@ -128,7 +133,9 @@ def exec(Pn): print("Running", Pn) result, _ = execute_problem_library( - params, **{k: v for k, v in bench_cfg.items() if k in execute_args} + params, + standardize=False, # Don't standardize for golden master tests + **{k: v for k, v in bench_cfg.items() if k in execute_args}, ) return result, params diff --git a/tests/glm/test_golden_master.py b/tests/glm/test_golden_master.py index a30bfcd33..ab84d5eb4 100644 --- a/tests/glm/test_golden_master.py +++ b/tests/glm/test_golden_master.py @@ -12,7 +12,7 @@ from glum._distribution import TweedieDistribution from glum._glm import GeneralizedLinearRegressor from glum._glm_cv import GeneralizedLinearRegressorCV -from glum_benchmarks.data import simulate_glm_data +from glum_benchmarks.data import simulate_mixed_data distributions_to_test = ["normal", "poisson", "gamma", "tweedie_p=1.5", "binomial"] custom_family_link = [("normal", "log")] @@ -37,7 +37,7 @@ def _make_P2(): @pytest.fixture(scope="module") def data_all(): return { - dist: simulate_glm_data( + dist: simulate_mixed_data( family=dist, link=link_map[dist], n_rows=5000, @@ -69,21 +69,21 @@ def data_all_storage(request): } if request.param == "dense": - data_dist = simulate_glm_data(**data_config, ohe_categorical=True) + data_dist = simulate_mixed_data(**data_config, ohe_categorical=True) data_dist["X"] = mx.DenseMatrix(data_dist["X"]) elif request.param == "scipy-sparse": - data_dist = simulate_glm_data(**data_config, ohe_categorical=True) + data_dist = simulate_mixed_data(**data_config, ohe_categorical=True) data_dist["X"] = sparse.csc_matrix(data_dist["X"]) elif request.param == "mkl-sparse": - data_dist = simulate_glm_data(**data_config, ohe_categorical=True) + data_dist = simulate_mixed_data(**data_config, ohe_categorical=True) data_dist["X"] = mx.SparseMatrix(sparse.csc_matrix(data_dist["X"])) elif request.param == "split": - data_dist = simulate_glm_data(**data_config, ohe_categorical=True) + data_dist = simulate_mixed_data(**data_config, ohe_categorical=True) data_dist["X"] = mx.csc_to_split( sparse.csc_matrix(data_dist["X"]), threshold=0.1 ) elif request.param == "categorical": - data_dist = simulate_glm_data(**data_config, ohe_categorical=False) + data_dist = simulate_mixed_data(**data_config, ohe_categorical=False) dense_X = mx.DenseMatrix(np.ascontiguousarray(data_dist["X"].iloc[:, :10])) cat0 = mx.CategoricalMatrix(data_dist["X"]["cat0"]) cat1 = mx.CategoricalMatrix(data_dist["X"]["cat1"]) @@ -412,7 +412,7 @@ def run_and_store_golden_master( gm_dict = {} for dist in distributions_to_test: - data = simulate_glm_data(family=dist, link=link_map[dist]) + data = simulate_mixed_data(family=dist, link=link_map[dist]) for mdl_param in gm_model_parameters.items(): for use_weights in [True, False]: for use_offset in [True, False]: @@ -429,7 +429,7 @@ def run_and_store_golden_master( ) for dist in distributions_to_test: - data = simulate_glm_data(family=dist, link=link_map[dist]) + data = simulate_mixed_data(family=dist, link=link_map[dist]) gm_dict = run_and_store_golden_master( distribution=dist, model_parameters={ @@ -447,7 +447,7 @@ def run_and_store_golden_master( ) for dist, link in custom_family_link: - data = simulate_glm_data(family=dist, link=link_map[dist]) + data = simulate_mixed_data(family=dist, link=link_map[dist]) for use_weights in [True, False]: for use_offset in [True, False]: gm_dict = run_and_store_golden_master( diff --git a/tests/glm/test_offset_weight_obj_equivalent.py b/tests/glm/test_offset_weight_obj_equivalent.py index 791a82792..138173bbb 100644 --- a/tests/glm/test_offset_weight_obj_equivalent.py +++ b/tests/glm/test_offset_weight_obj_equivalent.py @@ -1,8 +1,7 @@ import numpy as np import pytest -from glum_benchmarks.cli_run import get_all_problems -from glum_benchmarks.problems import Problem +from glum_benchmarks.problems import Problem, get_all_problems from glum_benchmarks.util import ( BenchmarkParams, exposure_and_offset_to_weights, @@ -18,7 +17,7 @@ bench_cfg = dict( num_rows=10000, - regularization_strength=0.1, + alpha=0.1, storage="dense", ) @@ -37,14 +36,14 @@ def test_offset_solution_matches_weights_solution( ): params = BenchmarkParams( problem_name=Pn, - library_name="sklearn-fork", + library_name="glum", # storage=storage, **bench_cfg, ) tweedie_p = get_tweedie_p(P.distribution) - dat = P.data_loader(num_rows=params.num_rows) + dat = P.data_loader(num_rows=params.num_rows, standardize=False) weights_dat = {"X": dat["X"]} weights_dat["y"], weights_dat["weights"] = exposure_and_offset_to_weights( tweedie_p, dat["y"], offset=dat["offset"] @@ -61,20 +60,20 @@ def test_offset_solution_matches_weights_solution( def get_obj_val_(dat, coefs): if "weights" in dat.keys(): - reg_multiplier = dat["weights"].mean() + weight_multiplier = dat["weights"].mean() else: - reg_multiplier = 1 + weight_multiplier = 1 res = get_obj_val( dat, P.distribution, - P.regularization_strength / reg_multiplier, + P.alpha / weight_multiplier, P.l1_ratio, coefs[0], coefs[1:], ) if "weights" in dat.keys(): - res *= reg_multiplier + res *= weight_multiplier return res offset_result = get_obj_val_(dat, coefs)