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fsspec ; extra == 'io' + - cftime ; extra == 'io' + - pooch ; extra == 'io' + - pydap ; python_full_version < '3.10' and extra == 'io' + - dask[complete] ; extra == 'parallel' + - matplotlib ; extra == 'viz' + - seaborn ; extra == 'viz' + - nc-time-axis ; extra == 'viz' + requires_python: '>=3.9' - conda: https://conda.anaconda.org/conda-forge/linux-64/xerces-c-3.3.0-hd9031aa_1.conda sha256: 605980121ad3ee9393a9b53fb0996929c9732f8fc6b9f796d25244ca6fa23032 md5: 66a1db55ecdb7377d2b91f54cd56eafa @@ -22598,8 +24644,6 @@ packages: - python license: MIT license_family: MIT - purls: - - 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 @@ -22609,6 +24653,8 @@ packages: - python >=3.9 license: MIT license_family: MIT + purls: + - pkg:pypi/zipp?source=hash-mapping size: 22963 timestamp: 1749421737203 - conda: https://conda.anaconda.org/conda-forge/linux-64/zlib-1.3.1-hb9d3cd8_2.conda diff --git a/pixi.toml b/pixi.toml index feda2c830..d736ca2f1 100644 --- a/pixi.toml +++ b/pixi.toml @@ -122,9 +122,15 @@ attrs = "*" click = "*" git_root = "*" openjdk = "*" +python = ">=3.9,<3.10" # skglm requires Python <3.10 +r-base = "*" +rpy2 = "*" [feature.benchmark.pypi-dependencies] -h2o = "*" +celer = "*" +h2o = "*" # Latest version from PyPI +skglm = "*" + [feature.benchmark.target.win-64.dependencies] blas = { build = "*mkl" } [feature.benchmark.target.linux-64.dependencies] diff --git a/src/glum_benchmarks/bench_celer.py b/src/glum_benchmarks/bench_celer.py new file mode 100644 index 000000000..0e4b40892 --- /dev/null +++ b/src/glum_benchmarks/bench_celer.py @@ -0,0 +1,88 @@ +import warnings +from typing import Any, Optional, Union + +import numpy as np +from celer import ElasticNet, ElasticNetCV, Lasso, LassoCV +from scipy import sparse as sps + +from .util import benchmark_convergence_tolerance, runtime + +# TODO: Add weights +# TODO: ADD CV +# TODO: Different results (due to objective function?) + + +def _build_and_fit(model_args, fit_args, cv: bool): + model_class = model_args.pop("_model_class") + reg = model_class(**model_args) + return reg.fit(**fit_args) + + +def celer_bench( + dat: dict[str, Union[np.ndarray, sps.spmatrix]], + distribution: str, + alpha: float, + l1_ratio: float, + iterations: int, + cv: bool, + reg_multiplier: Optional[float] = None, + **kwargs, +): + result: dict[str, Any] = {} + reg_strength = alpha if reg_multiplier is None else alpha * reg_multiplier + + if distribution not in ["gaussian"]: + warnings.warn(f"Celer doesn't support {distribution}, skipping.") + return {} + + if "offset" in dat: + warnings.warn("Celer doesn't support offsets, skipping.") + return {} + + if "sample_weight" in dat: + warnings.warn("Celer sample_weight not implemented in benchmark, skipping.") + return {} + + if distribution == "gaussian": + if l1_ratio == 0.0: + warnings.warn("Celer doesn't support Ridge (l1_ratio=0), skipping.") + return {} + elif l1_ratio < 1.0: + model_class = ElasticNetCV if cv else ElasticNet + else: + model_class = LassoCV if cv else Lasso + + model_args = { + "_model_class": model_class, + "tol": benchmark_convergence_tolerance, + "fit_intercept": True, + "max_iter": 1000, + } + + if model_class in (ElasticNet, ElasticNetCV): + model_args["l1_ratio"] = l1_ratio + + if not cv: + model_args["alpha"] = reg_strength + + fit_args = {"X": dat["X"], "y": dat["y"]} + + try: + result["runtime"], m = runtime( + _build_and_fit, iterations, model_args, fit_args, cv + ) + 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) + + if cv: + result["best_alpha"] = m.alpha_ + result["n_alphas"] = len(m.alphas_) + result["max_alpha"] = m.alphas_.max() + result["min_alpha"] = m.alphas_.min() + + return result diff --git a/src/glum_benchmarks/bench_skglm.py b/src/glum_benchmarks/bench_skglm.py new file mode 100644 index 000000000..edccbb2af --- /dev/null +++ b/src/glum_benchmarks/bench_skglm.py @@ -0,0 +1,131 @@ +import warnings +from typing import Any, Optional, Union + +import numpy as np +from scipy import sparse as sps +from skglm import GeneralizedLinearEstimator, GeneralizedLinearEstimatorCV +from skglm.datafits import Gamma, Logistic, Poisson, Quadratic, WeightedQuadratic +from skglm.penalties import L1, L1_plus_L2 +from skglm.solvers import AndersonCD, ProxNewton + +from .util import benchmark_convergence_tolerance, runtime + +# TODO: For p = 1.5 Tweedie, add custom datafit + + +def _build_and_fit(model_args, fit_args, cv: bool): + """Internal helper to switch between standard and CV estimators.""" + if cv: + return GeneralizedLinearEstimatorCV(**model_args).fit(**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, + cv: bool, + reg_multiplier: Optional[float] = None, + **kwargs, +): + result: dict[str, Any] = {} + reg_strength = alpha if reg_multiplier is None else alpha * reg_multiplier + + if "offset" in dat: + warnings.warn("skglm doesn't support offsets, skipping.") + return result + + DATAFITS = { + "gaussian": Quadratic(), + "poisson": Poisson(), + "binomial": Logistic(), + "gamma": Gamma(), + } + + if distribution not in DATAFITS: + warnings.warn(f"skglm doesn't support {distribution}, skipping.") + return {} + + # For CV, alpha is determined internally, for non-CV, use reg_strength + # We use L1_plus_L2(l1_ratio=0) for pure L2 to ensure prox_1d is available + p_alpha = 1.0 if cv else reg_strength + if l1_ratio == 1: + penalty = L1(alpha=p_alpha) + else: + penalty = L1_plus_L2(alpha=p_alpha, l1_ratio=l1_ratio) + + # ProxNewton is required for Poisson/Gamma or smooth L2 problems. + if distribution in ["poisson", "gamma"] or l1_ratio == 0: + solver = ProxNewton( + tol=benchmark_convergence_tolerance, fit_intercept=True, max_iter=1000 + ) + else: + solver = AndersonCD( + tol=benchmark_convergence_tolerance, fit_intercept=True, max_iter=1000 + ) + + # Sample Weights currently only supported for Gaussian + if "sample_weight" in dat: + if distribution == "gaussian": + weights = np.asarray(dat["sample_weight"], dtype=np.float64) + datafit = WeightedQuadratic(weights) + else: + warnings.warn(f"Weighted {distribution} not natively supported, skipping.") + return {} + else: + datafit = DATAFITS[distribution] + + model_args = { + "datafit": datafit, + "penalty": penalty, + "solver": solver, + } + + if cv: + model_args.update({"cv": 5, "n_alphas": 100, "n_jobs": -1}) + + # Data Conversion + X = ( + dat["X"] + if sps.issparse(dat["X"]) + else np.ascontiguousarray(dat["X"], dtype=np.float64) + ) + y = np.asarray(dat["y"], dtype=np.float64).ravel() + fit_args = {"X": X, "y": y} + + # Numba Warm-up: Fit on a tiny subset to trigger JIT compilation before measurement + try: + warmup_df = ( + WeightedQuadratic(weights[:10]) if "sample_weight" in dat else datafit + ) + warmup_model = GeneralizedLinearEstimator( + datafit=warmup_df, penalty=penalty, solver=solver + ) + warmup_model.fit(X[:10], y[:10]) + except Exception: + pass + + try: + result["runtime"], m = runtime( + _build_and_fit, iterations, model_args, fit_args, cv + ) + 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() + if cv: + n_iter_data = getattr(m, "n_iter_", None) + result["n_iter"] = np.sum(n_iter_data) if n_iter_data is not None else None + result["best_alpha"] = getattr(m, "alpha_", None) + if hasattr(m, "alphas_"): + result["n_alphas"] = len(m.alphas_) + result["max_alpha"] = m.alphas_.max() + result["min_alpha"] = m.alphas_.min() + else: + result["n_iter"] = getattr(m, "n_iter_", None) + + return result diff --git a/src/glum_benchmarks/bench_sklearn.py b/src/glum_benchmarks/bench_sklearn.py new file mode 100644 index 000000000..8736fa4c6 --- /dev/null +++ b/src/glum_benchmarks/bench_sklearn.py @@ -0,0 +1,124 @@ +import warnings +from typing import Any, Optional, Union + +import numpy as np +from scipy import sparse as sps +from sklearn.linear_model import ElasticNet, LogisticRegression, TweedieRegressor + +from .util import benchmark_convergence_tolerance, runtime + +# TODO: Standardize features? +# TODO: Add cv for logistic and elastic net +# TODO: Design decision: do we set precompute = True for elastic net? + + +def _build_and_fit(model_args, fit_args): + """Build and fit a sklearn regressor.""" + model_class = model_args.pop("_model_class") + reg = model_class(**model_args) + return reg.fit(**fit_args) + + +def sklearn_bench( + dat: dict[str, Union[np.ndarray, sps.spmatrix]], + distribution: str, + alpha: float, + l1_ratio: float, + iterations: int, + cv: bool, + reg_multiplier: Optional[float] = None, + **kwargs, +): + """ + Benchmark scikit-learn GLM regressors. + + Parameters + ---------- + dat + distribution + alpha + l1_ratio + iterations + cv + reg_multiplier + kwargs + + Returns + ------- + Dict of + """ + + result: dict[str, Any] = {} + reg_strength = alpha if reg_multiplier is None else alpha * reg_multiplier + + if "offset" in dat.keys(): + warnings.warn("sklearn doesn't support offset, skipping this problem.") + return result + + if distribution == "gaussian": + model_args = { + "_model_class": ElasticNet, + "alpha": reg_strength, + "l1_ratio": l1_ratio, + "fit_intercept": True, + "max_iter": 1000, + "tol": benchmark_convergence_tolerance, + } + elif distribution == "binomial": + C_value = 1.0 / reg_strength if reg_strength > 0 else 1e10 + + model_args = { + "_model_class": LogisticRegression, + "C": C_value, + "solver": "saga", + "l1_ratio": l1_ratio, + "fit_intercept": True, + "max_iter": 1000, + "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 + + if l1_ratio > 0: + warnings.warn( + f"sklearn doesn't support elastic net for {distribution}, skipping." + ) + return result + + model_args = { + "_model_class": TweedieRegressor, + "power": power, + "alpha": reg_strength, + "fit_intercept": True, + "max_iter": 1000, + "tol": benchmark_convergence_tolerance, + "solver": "newton-cholesky", + } + + fit_args = {"X": dat["X"], "y": dat["y"]} + + if "sample_weight" in dat.keys(): + fit_args["sample_weight"] = dat["sample_weight"] + + try: + result["runtime"], m = runtime(_build_and_fit, iterations, model_args, fit_args) + except ValueError as e: + warnings.warn(f"Problem failed with this error: {e}") + return result + + # Extract results (handle LogisticRegression's different output format) + if distribution == "binomial": + result["intercept"] = m.intercept_[0] if m.intercept_.ndim > 0 else m.intercept_ + result["coef"] = m.coef_.ravel() + result["n_iter"] = m.n_iter_[0] if hasattr(m.n_iter_, "__len__") else m.n_iter_ + else: + result["intercept"] = m.intercept_ + result["coef"] = m.coef_ + result["n_iter"] = m.n_iter_ if hasattr(m, "n_iter_") else None + + return result diff --git a/src/glum_benchmarks/bench_statsmodels.py b/src/glum_benchmarks/bench_statsmodels.py new file mode 100644 index 000000000..f642d49f9 --- /dev/null +++ b/src/glum_benchmarks/bench_statsmodels.py @@ -0,0 +1,206 @@ +import warnings +from typing import Any, Optional, Union + +import numpy as np +import statsmodels.api as sm +from scipy import sparse as sps + +from .util import benchmark_convergence_tolerance, runtime + +# TODO: Add scaling/centering to prevent numerical overflow + + +def _fit_statsmodels(model, alpha, l1_ratio, fit_args): + """ + Use fit_regularized if alpha is provided, else standard fit. + + alpha can be a scalar or vector. When vector, position 0 (intercept) should be 0. + """ + if alpha is not None: + return model.fit_regularized(alpha=alpha, L1_wt=l1_ratio, **fit_args) + return model.fit(**fit_args) + + +def statsmodels_bench( + dat: dict[str, Union[np.ndarray, sps.spmatrix]], + distribution: str, + alpha: float, + l1_ratio: float, + iterations: int, + cv: bool, + reg_multiplier: Optional[float] = None, + **kwargs, +): + """ + Benchmark statsmodels GLM implementation. + + Parameters + ---------- + dat + distribution + alpha + l1_ratio + iterations + cv + reg_multiplier + kwargs + + Returns + ------- + dict + + """ + if cv: + warnings.warn( + "statsmodels benchmarks do not support cross-validation, skipping." + ) + return {} + + result: dict[str, Any] = {} + X = dat["X"] + reg_strength = alpha if reg_multiplier is None else alpha * reg_multiplier + + # Skip regularized problems for distributions where statsmodels.fit_regularized() + # produces poor results despite our fixes (alpha vector + target scaling) + if reg_strength > 0: + if distribution == "gaussian": + warnings.warn( + "statsmodels.fit_regularized() produces poor results for Gaussian " + "distributions (objective values thousands of times worse). Skipping." + ) + return {} + if distribution == "gamma": + warnings.warn( + "statsmodels.fit_regularized() is unreliable for Gamma distributions " + "(numerical issues, convergence failures). Skipping." + ) + return {} + + # Map family distributions with explicit canonical links for stability + fam_map = { + "gaussian": sm.families.Gaussian(link=sm.families.links.Identity()), + "poisson": sm.families.Poisson(link=sm.families.links.Log()), + "gamma": sm.families.Gamma(link=sm.families.links.Log()), + "binomial": sm.families.Binomial(link=sm.families.links.Logit()), + } + + if "tweedie" in distribution: + p = float(distribution.split("-p=")[1]) + family = sm.families.Tweedie(var_power=p, link=sm.families.links.Log()) + else: + family = fam_map.get(distribution) + + if family is None: + warnings.warn( + f"Distribution {distribution} not supported by statsmodels, skipping." + ) + return {} + + # Convert to numpy/sparse and add intercept manually + if sps.issparse(X): + # Handle sparse matrices by adding intercept column manually + n_samples = X.shape[0] + intercept_col = sps.csr_matrix(np.ones((n_samples, 1))) + X = sps.hstack([intercept_col, X], format="csr") + else: + if hasattr(X, "values"): + X = X.values + elif not isinstance(X, np.ndarray): + X = np.asarray(X) + + # Ensure proper dtype and add intercept + X = np.asarray(X, dtype=np.float64) + n_samples = X.shape[0] + intercept_col = np.ones((n_samples, 1)) + X = np.hstack([intercept_col, X]) + + # Scale target for numerical stability (but not for binomial) + y = dat["y"] + y_mean = np.mean(y) + + # Only scale for continuous distributions, not binomial + if distribution == "binomial": + y_scaled = y + scale_factor = 1.0 + else: + y_scaled = y / y_mean if y_mean > 0 else y + scale_factor = float(y_mean) + + model = sm.GLM( + y_scaled, # Use scaled y for better numerical stability + X, + family=family, + offset=dat.get("offset"), + freq_weights=dat.get("sample_weight"), + ) + + # Initialize start_params to help with convergence + # Initialize start_params at 0 + start_params = np.zeros(X.shape[1]) + + # Set the intercept (index 0) to a reasonable guess based on scaled y + if distribution in ["poisson", "gamma"] or "tweedie" in distribution: + # For Log link with scaled y, intercept near 0 (y_scaled mean ~1) + start_params[0] = 0.0 + elif distribution == "binomial": + # For Logit link, intercept approx logit(mean(y)) + p_mean = np.clip(y_mean, 1e-6, 1 - 1e-6) + start_params[0] = np.log(p_mean / (1 - p_mean)) + else: + # For Gaussian / Identity link with scaled y, intercept should be near 1 + start_params[0] = 1.0 + + # fit_regularized has different arg names than fit + if reg_strength > 0: + # Create alpha vector: 0 for intercept (position 0), reg_strength for others + # This prevents statsmodels from shrinking the intercept + alpha_vector = np.full(X.shape[1], reg_strength) + alpha_vector[0] = 0.0 + fit_args = {"maxiter": 10000, "start_params": start_params} + alpha_param = alpha_vector + else: + fit_args = { + "maxiter": 10000, + "tol": benchmark_convergence_tolerance, + "start_params": start_params, + } + alpha_param = None + + try: + result["runtime"], m = runtime( + _fit_statsmodels, iterations, model, alpha_param, l1_ratio, fit_args + ) + except Exception as e: + warnings.warn(f"statsmodels failed with error: {e}") + return {} + + # Adjust coefficients back to original scale + if distribution in ["poisson", "gamma"] or "tweedie" in distribution: + # For Log link: log(y/c) = X*beta', so log(y) = X*beta' + log(c) + # Therefore: intercept_true = intercept_scaled + log(scale_factor) + result["intercept"] = m.params[0] + np.log(scale_factor) + result["coef"] = m.params[1:] # Other coefficients unchanged + elif distribution == "binomial": + # Binomial y is not scaled, so no adjustment needed + result["intercept"] = m.params[0] + result["coef"] = m.params[1:] + else: + # For Gaussian / Identity link: y/c = X*beta', so y = X*(c*beta') + # Therefore: all coefficients scale by scale_factor + result["intercept"] = m.params[0] * scale_factor + result["coef"] = m.params[1:] * scale_factor + + # Check for non-finite values (NaN or Inf) which can occur with numerical issues + if not np.isfinite(result["intercept"]) or not np.all(np.isfinite(result["coef"])): + warnings.warn( + "statsmodels returned non-finite values (NaN/Inf), skipping this problem." + ) + return {} + + # fit_regularized doesn't expose iteration count, use None for regularized + if reg_strength > 0: + result["n_iter"] = None + else: + result["n_iter"] = getattr(m, "iteration", None) + + return result diff --git a/src/glum_benchmarks/cli_run.py b/src/glum_benchmarks/cli_run.py index 17ba403a7..f21501f8a 100644 --- a/src/glum_benchmarks/cli_run.py +++ b/src/glum_benchmarks/cli_run.py @@ -29,6 +29,41 @@ except ImportError: LIBLINEAR_INSTALLED = False +try: + from .bench_r_glmnet import r_glmnet_bench # isort:skip + + R_GLMNET_INSTALLED = True +except ImportError: + R_GLMNET_INSTALLED = False + +try: + from .bench_sklearn import sklearn_bench # isort:skip + + SKLEARN_INSTALLED = True +except ImportError: + SKLEARN_INSTALLED = False + +try: + from .bench_statsmodels import statsmodels_bench # isort:skip + + STATSMODELS_INSTALLED = True +except ImportError: + STATSMODELS_INSTALLED = False + +try: + from .bench_skglm import skglm_bench # isort:skip + + SKGLM_INSTALLED = True +except ImportError: + SKGLM_INSTALLED = False + +try: + from .bench_celer import celer_bench # isort:skip + + CELER_INSTALLED = True +except ImportError: + CELER_INSTALLED = False + @click.command() @click.option( @@ -188,6 +223,21 @@ def get_all_libraries() -> dict[str, Any]: if LIBLINEAR_INSTALLED: all_libraries["liblinear"] = liblinear_bench + if R_GLMNET_INSTALLED: + all_libraries["r-glmnet"] = r_glmnet_bench + + if SKLEARN_INSTALLED: + all_libraries["sklearn"] = sklearn_bench + + if STATSMODELS_INSTALLED: + all_libraries["statsmodels"] = statsmodels_bench + + if SKGLM_INSTALLED: + all_libraries["skglm"] = skglm_bench + + if CELER_INSTALLED: + all_libraries["celer"] = celer_bench + return all_libraries From 351e0611a89b9437d76304c991ecc12548c0cac0 Mon Sep 17 00:00:00 2001 From: David Eiglsperger Date: Thu, 22 Jan 2026 10:20:07 +0100 Subject: [PATCH 03/42] new benchmark adaption --- src/glum_benchmarks/bench_celer.py | 17 +++++------- src/glum_benchmarks/bench_skglm.py | 7 ++--- src/glum_benchmarks/bench_sklearn.py | 39 ++++++++++++++++++++++------ src/glum_benchmarks/cli_run.py | 10 ------- 4 files changed, 41 insertions(+), 32 deletions(-) diff --git a/src/glum_benchmarks/bench_celer.py b/src/glum_benchmarks/bench_celer.py index 0e4b40892..f484e84f2 100644 --- a/src/glum_benchmarks/bench_celer.py +++ b/src/glum_benchmarks/bench_celer.py @@ -7,12 +7,10 @@ from .util import benchmark_convergence_tolerance, runtime -# TODO: Add weights -# TODO: ADD CV -# TODO: Different results (due to objective function?) +# TODO: For CV the found alpha values differ significantly from glum -def _build_and_fit(model_args, fit_args, cv: bool): +def _build_and_fit(model_args, fit_args): model_class = model_args.pop("_model_class") reg = model_class(**model_args) return reg.fit(**fit_args) @@ -29,8 +27,10 @@ def celer_bench( **kwargs, ): result: dict[str, Any] = {} + fit_args = {"X": dat["X"], "y": dat["y"]} reg_strength = alpha if reg_multiplier is None else alpha * reg_multiplier + # LogisticRegression doesnt 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 {} @@ -40,8 +40,7 @@ def celer_bench( return {} if "sample_weight" in dat: - warnings.warn("Celer sample_weight not implemented in benchmark, skipping.") - return {} + fit_args.update({"sample_weight": dat["sample_weight"]}) if distribution == "gaussian": if l1_ratio == 0.0: @@ -65,12 +64,8 @@ def celer_bench( if not cv: model_args["alpha"] = reg_strength - fit_args = {"X": dat["X"], "y": dat["y"]} - try: - result["runtime"], m = runtime( - _build_and_fit, iterations, model_args, fit_args, cv - ) + result["runtime"], m = runtime(_build_and_fit, iterations, model_args, fit_args) except Exception as e: warnings.warn(f"Celer failed: {e}") return {} diff --git a/src/glum_benchmarks/bench_skglm.py b/src/glum_benchmarks/bench_skglm.py index edccbb2af..3c7ee4ed9 100644 --- a/src/glum_benchmarks/bench_skglm.py +++ b/src/glum_benchmarks/bench_skglm.py @@ -10,11 +10,11 @@ from .util import benchmark_convergence_tolerance, runtime -# TODO: For p = 1.5 Tweedie, add custom datafit +# TODO: For Tweedie (p = 1.5), add custom datafit +# TODO: For CV the found alpha values differ significantly from glum def _build_and_fit(model_args, fit_args, cv: bool): - """Internal helper to switch between standard and CV estimators.""" if cv: return GeneralizedLinearEstimatorCV(**model_args).fit(**fit_args) return GeneralizedLinearEstimator(**model_args).fit(**fit_args) @@ -84,7 +84,8 @@ def skglm_bench( } if cv: - model_args.update({"cv": 5, "n_alphas": 100, "n_jobs": -1}) + # Same CV parameters as glum + model_args.update({"cv": 5, "n_alphas": 100, "n_jobs": 1}) # Data Conversion X = ( diff --git a/src/glum_benchmarks/bench_sklearn.py b/src/glum_benchmarks/bench_sklearn.py index 8736fa4c6..6fc1f180a 100644 --- a/src/glum_benchmarks/bench_sklearn.py +++ b/src/glum_benchmarks/bench_sklearn.py @@ -3,13 +3,17 @@ import numpy as np from scipy import sparse as sps -from sklearn.linear_model import ElasticNet, LogisticRegression, TweedieRegressor +from sklearn.linear_model import ( + ElasticNet, + LogisticRegression, + TweedieRegressor, +) from .util import benchmark_convergence_tolerance, runtime -# TODO: Standardize features? # TODO: Add cv for logistic and elastic net -# TODO: Design decision: do we set precompute = True for elastic net? +# TODO: Design decision: do we set precompute = True ? +# TODO: Design decision: do we set copy_X = True ? def _build_and_fit(model_args, fit_args): @@ -55,6 +59,8 @@ def sklearn_bench( warnings.warn("sklearn doesn't support offset, skipping this problem.") return result + n_samples = dat["X"].shape[0] + if distribution == "gaussian": model_args = { "_model_class": ElasticNet, @@ -65,17 +71,34 @@ def sklearn_bench( "tol": benchmark_convergence_tolerance, } elif distribution == "binomial": - C_value = 1.0 / reg_strength if reg_strength > 0 else 1e10 + # sklearn's LogisticRegression uses C = 1/lambda where lambda is the + # regularization strength. However, sklearn does NOT scale the regularization + # by n_samples, while glum's objective is: mean(loss) + alpha * penalty. + # To match glum's objective, we need: C = 1 / (alpha * n_samples) + C_value = 1.0 / (reg_strength * n_samples) if reg_strength > 0 else 1e10 + # Determine penalty type based on l1_ratio + # Penalty is deprecated but we use it here as we run on sk learn 1.6.1 due + # to the fact that h2o-py requires Python <3.10 + if l1_ratio == 0.0: + penalty = "l2" + elif l1_ratio == 1.0: + penalty = "l1" + else: + penalty = "elasticnet" model_args = { "_model_class": LogisticRegression, "C": C_value, - "solver": "saga", - "l1_ratio": l1_ratio, + "penalty": penalty, + # Use lbfgs for L2 (faster and more reliable), saga for L1/elasticnet + "solver": "lbfgs" if penalty == "l2" else "saga", "fit_intercept": True, "max_iter": 1000, "tol": benchmark_convergence_tolerance, } + # Only pass l1_ratio for elastic net + if penalty == "elasticnet": + model_args["l1_ratio"] = l1_ratio else: if "tweedie" in distribution: power = float(distribution.split("-p=")[1]) @@ -84,9 +107,10 @@ def sklearn_bench( elif distribution == "gamma": power = 2.0 + # sklearn's TweedieRegressor only supports L2 regularization if l1_ratio > 0: warnings.warn( - f"sklearn doesn't support elastic net for {distribution}, skipping." + f"sklearn only supports L2 regularization for {distribution}, skipping." ) return result @@ -111,7 +135,6 @@ def sklearn_bench( warnings.warn(f"Problem failed with this error: {e}") return result - # Extract results (handle LogisticRegression's different output format) if distribution == "binomial": result["intercept"] = m.intercept_[0] if m.intercept_.ndim > 0 else m.intercept_ result["coef"] = m.coef_.ravel() diff --git a/src/glum_benchmarks/cli_run.py b/src/glum_benchmarks/cli_run.py index f21501f8a..12198c735 100644 --- a/src/glum_benchmarks/cli_run.py +++ b/src/glum_benchmarks/cli_run.py @@ -43,13 +43,6 @@ except ImportError: SKLEARN_INSTALLED = False -try: - from .bench_statsmodels import statsmodels_bench # isort:skip - - STATSMODELS_INSTALLED = True -except ImportError: - STATSMODELS_INSTALLED = False - try: from .bench_skglm import skglm_bench # isort:skip @@ -229,9 +222,6 @@ def get_all_libraries() -> dict[str, Any]: if SKLEARN_INSTALLED: all_libraries["sklearn"] = sklearn_bench - if STATSMODELS_INSTALLED: - all_libraries["statsmodels"] = statsmodels_bench - if SKGLM_INSTALLED: all_libraries["skglm"] = skglm_bench From 171e695ffdc11b9127ebef1343028ca114f55253 Mon Sep 17 00:00:00 2001 From: David Eiglsperger Date: Thu, 22 Jan 2026 13:34:24 +0100 Subject: [PATCH 04/42] exclude statsmodels --- src/glum_benchmarks/bench_statsmodels.py | 206 ----------------------- 1 file changed, 206 deletions(-) delete mode 100644 src/glum_benchmarks/bench_statsmodels.py diff --git a/src/glum_benchmarks/bench_statsmodels.py b/src/glum_benchmarks/bench_statsmodels.py deleted file mode 100644 index f642d49f9..000000000 --- a/src/glum_benchmarks/bench_statsmodels.py +++ /dev/null @@ -1,206 +0,0 @@ -import warnings -from typing import Any, Optional, Union - -import numpy as np -import statsmodels.api as sm -from scipy import sparse as sps - -from .util import benchmark_convergence_tolerance, runtime - -# TODO: Add scaling/centering to prevent numerical overflow - - -def _fit_statsmodels(model, alpha, l1_ratio, fit_args): - """ - Use fit_regularized if alpha is provided, else standard fit. - - alpha can be a scalar or vector. When vector, position 0 (intercept) should be 0. - """ - if alpha is not None: - return model.fit_regularized(alpha=alpha, L1_wt=l1_ratio, **fit_args) - return model.fit(**fit_args) - - -def statsmodels_bench( - dat: dict[str, Union[np.ndarray, sps.spmatrix]], - distribution: str, - alpha: float, - l1_ratio: float, - iterations: int, - cv: bool, - reg_multiplier: Optional[float] = None, - **kwargs, -): - """ - Benchmark statsmodels GLM implementation. - - Parameters - ---------- - dat - distribution - alpha - l1_ratio - iterations - cv - reg_multiplier - kwargs - - Returns - ------- - dict - - """ - if cv: - warnings.warn( - "statsmodels benchmarks do not support cross-validation, skipping." - ) - return {} - - result: dict[str, Any] = {} - X = dat["X"] - reg_strength = alpha if reg_multiplier is None else alpha * reg_multiplier - - # Skip regularized problems for distributions where statsmodels.fit_regularized() - # produces poor results despite our fixes (alpha vector + target scaling) - if reg_strength > 0: - if distribution == "gaussian": - warnings.warn( - "statsmodels.fit_regularized() produces poor results for Gaussian " - "distributions (objective values thousands of times worse). Skipping." - ) - return {} - if distribution == "gamma": - warnings.warn( - "statsmodels.fit_regularized() is unreliable for Gamma distributions " - "(numerical issues, convergence failures). Skipping." - ) - return {} - - # Map family distributions with explicit canonical links for stability - fam_map = { - "gaussian": sm.families.Gaussian(link=sm.families.links.Identity()), - "poisson": sm.families.Poisson(link=sm.families.links.Log()), - "gamma": sm.families.Gamma(link=sm.families.links.Log()), - "binomial": sm.families.Binomial(link=sm.families.links.Logit()), - } - - if "tweedie" in distribution: - p = float(distribution.split("-p=")[1]) - family = sm.families.Tweedie(var_power=p, link=sm.families.links.Log()) - else: - family = fam_map.get(distribution) - - if family is None: - warnings.warn( - f"Distribution {distribution} not supported by statsmodels, skipping." - ) - return {} - - # Convert to numpy/sparse and add intercept manually - if sps.issparse(X): - # Handle sparse matrices by adding intercept column manually - n_samples = X.shape[0] - intercept_col = sps.csr_matrix(np.ones((n_samples, 1))) - X = sps.hstack([intercept_col, X], format="csr") - else: - if hasattr(X, "values"): - X = X.values - elif not isinstance(X, np.ndarray): - X = np.asarray(X) - - # Ensure proper dtype and add intercept - X = np.asarray(X, dtype=np.float64) - n_samples = X.shape[0] - intercept_col = np.ones((n_samples, 1)) - X = np.hstack([intercept_col, X]) - - # Scale target for numerical stability (but not for binomial) - y = dat["y"] - y_mean = np.mean(y) - - # Only scale for continuous distributions, not binomial - if distribution == "binomial": - y_scaled = y - scale_factor = 1.0 - else: - y_scaled = y / y_mean if y_mean > 0 else y - scale_factor = float(y_mean) - - model = sm.GLM( - y_scaled, # Use scaled y for better numerical stability - X, - family=family, - offset=dat.get("offset"), - freq_weights=dat.get("sample_weight"), - ) - - # Initialize start_params to help with convergence - # Initialize start_params at 0 - start_params = np.zeros(X.shape[1]) - - # Set the intercept (index 0) to a reasonable guess based on scaled y - if distribution in ["poisson", "gamma"] or "tweedie" in distribution: - # For Log link with scaled y, intercept near 0 (y_scaled mean ~1) - start_params[0] = 0.0 - elif distribution == "binomial": - # For Logit link, intercept approx logit(mean(y)) - p_mean = np.clip(y_mean, 1e-6, 1 - 1e-6) - start_params[0] = np.log(p_mean / (1 - p_mean)) - else: - # For Gaussian / Identity link with scaled y, intercept should be near 1 - start_params[0] = 1.0 - - # fit_regularized has different arg names than fit - if reg_strength > 0: - # Create alpha vector: 0 for intercept (position 0), reg_strength for others - # This prevents statsmodels from shrinking the intercept - alpha_vector = np.full(X.shape[1], reg_strength) - alpha_vector[0] = 0.0 - fit_args = {"maxiter": 10000, "start_params": start_params} - alpha_param = alpha_vector - else: - fit_args = { - "maxiter": 10000, - "tol": benchmark_convergence_tolerance, - "start_params": start_params, - } - alpha_param = None - - try: - result["runtime"], m = runtime( - _fit_statsmodels, iterations, model, alpha_param, l1_ratio, fit_args - ) - except Exception as e: - warnings.warn(f"statsmodels failed with error: {e}") - return {} - - # Adjust coefficients back to original scale - if distribution in ["poisson", "gamma"] or "tweedie" in distribution: - # For Log link: log(y/c) = X*beta', so log(y) = X*beta' + log(c) - # Therefore: intercept_true = intercept_scaled + log(scale_factor) - result["intercept"] = m.params[0] + np.log(scale_factor) - result["coef"] = m.params[1:] # Other coefficients unchanged - elif distribution == "binomial": - # Binomial y is not scaled, so no adjustment needed - result["intercept"] = m.params[0] - result["coef"] = m.params[1:] - else: - # For Gaussian / Identity link: y/c = X*beta', so y = X*(c*beta') - # Therefore: all coefficients scale by scale_factor - result["intercept"] = m.params[0] * scale_factor - result["coef"] = m.params[1:] * scale_factor - - # Check for non-finite values (NaN or Inf) which can occur with numerical issues - if not np.isfinite(result["intercept"]) or not np.all(np.isfinite(result["coef"])): - warnings.warn( - "statsmodels returned non-finite values (NaN/Inf), skipping this problem." - ) - return {} - - # fit_regularized doesn't expose iteration count, use None for regularized - if reg_strength > 0: - result["n_iter"] = None - else: - result["n_iter"] = getattr(m, "iteration", None) - - return result From 6d49048ff7292d36a25d99f0d7443b00895463a3 Mon Sep 17 00:00:00 2001 From: David Eiglsperger Date: Mon, 26 Jan 2026 11:36:23 +0100 Subject: [PATCH 05/42] adjust benchmarks --- src/glum_benchmarks/bench_skglm.py | 73 ++++++++++++------------------ 1 file changed, 29 insertions(+), 44 deletions(-) diff --git a/src/glum_benchmarks/bench_skglm.py b/src/glum_benchmarks/bench_skglm.py index 3c7ee4ed9..8a5e7cf19 100644 --- a/src/glum_benchmarks/bench_skglm.py +++ b/src/glum_benchmarks/bench_skglm.py @@ -1,5 +1,5 @@ import warnings -from typing import Any, Optional, Union +from typing import Any, Optional, Union, cast import numpy as np from scipy import sparse as sps @@ -10,8 +10,8 @@ from .util import benchmark_convergence_tolerance, runtime -# TODO: For Tweedie (p = 1.5), add custom datafit # TODO: For CV the found alpha values differ significantly from glum +# TODO: Keep the data conversion? Runtime/2 for binomial def _build_and_fit(model_args, fit_args, cv: bool): @@ -37,6 +37,10 @@ def skglm_bench( warnings.warn("skglm doesn't support offsets, skipping.") return result + if "tweedie" in distribution: + warnings.warn("skglm doesn't support Tweedie, skipping.") + return result + DATAFITS = { "gaussian": Quadratic(), "poisson": Poisson(), @@ -44,38 +48,30 @@ def skglm_bench( "gamma": Gamma(), } - if distribution not in DATAFITS: - warnings.warn(f"skglm doesn't support {distribution}, skipping.") - return {} + if "sample_weight" in dat.keys(): + if distribution == "gaussian": + weights = np.asarray(dat["sample_weight"], dtype=np.float64) + datafit = WeightedQuadratic(weights) + else: + warnings.warn(f"skglm doesn't support Weighted {distribution}, skipping.") + return result + else: + datafit = DATAFITS[distribution] # For CV, alpha is determined internally, for non-CV, use reg_strength - # We use L1_plus_L2(l1_ratio=0) for pure L2 to ensure prox_1d is available p_alpha = 1.0 if cv else reg_strength if l1_ratio == 1: penalty = L1(alpha=p_alpha) else: + # We use L1_plus_L2(l1_ratio=0) for pure L2 to ensure prox_1d is available penalty = L1_plus_L2(alpha=p_alpha, l1_ratio=l1_ratio) - # ProxNewton is required for Poisson/Gamma or smooth L2 problems. + # ProxNewton is required for Poisson/Gamma or L2 problems + solver_tol = 1.0 if cv else benchmark_convergence_tolerance if distribution in ["poisson", "gamma"] or l1_ratio == 0: - solver = ProxNewton( - tol=benchmark_convergence_tolerance, fit_intercept=True, max_iter=1000 - ) + solver = ProxNewton(tol=solver_tol, fit_intercept=True, max_iter=1000) else: - solver = AndersonCD( - tol=benchmark_convergence_tolerance, fit_intercept=True, max_iter=1000 - ) - - # Sample Weights currently only supported for Gaussian - if "sample_weight" in dat: - if distribution == "gaussian": - weights = np.asarray(dat["sample_weight"], dtype=np.float64) - datafit = WeightedQuadratic(weights) - else: - warnings.warn(f"Weighted {distribution} not natively supported, skipping.") - return {} - else: - datafit = DATAFITS[distribution] + solver = AndersonCD(tol=solver_tol, fit_intercept=True, max_iter=1000) model_args = { "datafit": datafit, @@ -85,29 +81,18 @@ def skglm_bench( if cv: # Same CV parameters as glum - model_args.update({"cv": 5, "n_alphas": 100, "n_jobs": 1}) - - # Data Conversion - X = ( - dat["X"] - if sps.issparse(dat["X"]) - else np.ascontiguousarray(dat["X"], dtype=np.float64) - ) + model_args.update({"cv": 5, "n_alphas": 100, "eps": 1e-6, "n_jobs": 1}) + + # Data Conversion optimized for Coordinate Descent + X_raw = dat["X"] + if sps.issparse(X_raw): + X = cast(sps.spmatrix, X_raw).tocsc() + else: + X = np.asfortranarray(X_raw, dtype=np.float64) + y = np.asarray(dat["y"], dtype=np.float64).ravel() fit_args = {"X": X, "y": y} - # Numba Warm-up: Fit on a tiny subset to trigger JIT compilation before measurement - try: - warmup_df = ( - WeightedQuadratic(weights[:10]) if "sample_weight" in dat else datafit - ) - warmup_model = GeneralizedLinearEstimator( - datafit=warmup_df, penalty=penalty, solver=solver - ) - warmup_model.fit(X[:10], y[:10]) - except Exception: - pass - try: result["runtime"], m = runtime( _build_and_fit, iterations, model_args, fit_args, cv From 71edc78626d9f8f5390f31623ede502714bd506a Mon Sep 17 00:00:00 2001 From: David Eiglsperger Date: Mon, 26 Jan 2026 14:19:41 +0100 Subject: [PATCH 06/42] adapt parameters --- src/glum_benchmarks/bench_liblinear.py | 1 + src/glum_benchmarks/bench_sklearn.py | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/src/glum_benchmarks/bench_liblinear.py b/src/glum_benchmarks/bench_liblinear.py index 1cb14366e..6889d0633 100644 --- a/src/glum_benchmarks/bench_liblinear.py +++ b/src/glum_benchmarks/bench_liblinear.py @@ -82,6 +82,7 @@ def liblinear_bench( if reg_multiplier is None else 1 / (X.shape[0] * alpha * reg_multiplier) ), + max_iter=1000, # 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 diff --git a/src/glum_benchmarks/bench_sklearn.py b/src/glum_benchmarks/bench_sklearn.py index 6fc1f180a..61ff31e95 100644 --- a/src/glum_benchmarks/bench_sklearn.py +++ b/src/glum_benchmarks/bench_sklearn.py @@ -13,7 +13,6 @@ # TODO: Add cv for logistic and elastic net # TODO: Design decision: do we set precompute = True ? -# TODO: Design decision: do we set copy_X = True ? def _build_and_fit(model_args, fit_args): @@ -69,6 +68,7 @@ def sklearn_bench( "fit_intercept": True, "max_iter": 1000, "tol": benchmark_convergence_tolerance, + "precompute": True, } elif distribution == "binomial": # sklearn's LogisticRegression uses C = 1/lambda where lambda is the From 4cae856d187919291f6473841e85aeac43009938 Mon Sep 17 00:00:00 2001 From: David Eiglsperger Date: Mon, 26 Jan 2026 16:38:51 +0100 Subject: [PATCH 07/42] Delete cv and adjust plotting --- docs/benchmarks/benchmark_figure.py | 11 +- setup.py | 1 + src/glum_benchmarks/bench_celer.py | 21 +- src/glum_benchmarks/bench_glum.py | 27 +-- src/glum_benchmarks/bench_liblinear.py | 6 - src/glum_benchmarks/bench_skglm.py | 42 ++-- src/glum_benchmarks/bench_sklearn.py | 3 - src/glum_benchmarks/cli_analyze.py | 23 +-- src/glum_benchmarks/cli_plot.py | 268 +++++++++++++++++++++++++ src/glum_benchmarks/cli_run.py | 1 - src/glum_benchmarks/problems.py | 7 +- src/glum_benchmarks/util.py | 7 - src/glum_benchmarks/zeros_benchmark.py | 5 +- tests/benchmark/test_cli.py | 13 +- 14 files changed, 318 insertions(+), 117 deletions(-) create mode 100644 src/glum_benchmarks/cli_plot.py diff --git a/docs/benchmarks/benchmark_figure.py b/docs/benchmarks/benchmark_figure.py index a5094576b..34fb296e8 100644 --- a/docs/benchmarks/benchmark_figure.py +++ b/docs/benchmarks/benchmark_figure.py @@ -70,7 +70,6 @@ "offset", "threads", "single_precision", - "cv", "hessian_approx", "diagnostics_level", ], @@ -81,11 +80,19 @@ df["problem_name"].str.split("-").apply(lambda x: x[-2] if "5" in x[-1] else x[-1]) ) +# Remove duplicates (keep last run if same problem was run multiple times) +df = df.drop_duplicates(subset=["problem_name", "library_name"], keep="last") + # %% # %config InlineBackend.figure_format='retina' # %% -for prob_name in ["narrow-insurance", "intermediate-insurance", "intermediate-housing"]: +for prob_name in [ + "narrow-insurance", + "intermediate-insurance", + "intermediate-housing", + "wide-insurance", +]: for reg in ["l2", "lasso"]: plot_df = ( df[ diff --git a/setup.py b/setup.py index 46b0b2485..c24766441 100644 --- a/setup.py +++ b/setup.py @@ -94,6 +94,7 @@ [console_scripts] glm_benchmarks_run = glum_benchmarks.cli_run:cli_run glm_benchmarks_analyze = glum_benchmarks.cli_analyze:cli_analyze + glm_benchmarks_plot = glum_benchmarks.cli_plot:cli_plot """ ), ext_modules=cythonize( diff --git a/src/glum_benchmarks/bench_celer.py b/src/glum_benchmarks/bench_celer.py index f484e84f2..628389e52 100644 --- a/src/glum_benchmarks/bench_celer.py +++ b/src/glum_benchmarks/bench_celer.py @@ -2,13 +2,11 @@ from typing import Any, Optional, Union import numpy as np -from celer import ElasticNet, ElasticNetCV, Lasso, LassoCV +from celer import ElasticNet, Lasso from scipy import sparse as sps from .util import benchmark_convergence_tolerance, runtime -# TODO: For CV the found alpha values differ significantly from glum - def _build_and_fit(model_args, fit_args): model_class = model_args.pop("_model_class") @@ -22,7 +20,6 @@ def celer_bench( alpha: float, l1_ratio: float, iterations: int, - cv: bool, reg_multiplier: Optional[float] = None, **kwargs, ): @@ -47,23 +44,21 @@ def celer_bench( warnings.warn("Celer doesn't support Ridge (l1_ratio=0), skipping.") return {} elif l1_ratio < 1.0: - model_class = ElasticNetCV if cv else ElasticNet + model_class = ElasticNet else: - model_class = LassoCV if cv else Lasso + model_class = Lasso model_args = { "_model_class": model_class, "tol": benchmark_convergence_tolerance, "fit_intercept": True, "max_iter": 1000, + "alpha": reg_strength, } - if model_class in (ElasticNet, ElasticNetCV): + if model_class == ElasticNet: model_args["l1_ratio"] = l1_ratio - if not cv: - model_args["alpha"] = reg_strength - try: result["runtime"], m = runtime(_build_and_fit, iterations, model_args, fit_args) except Exception as e: @@ -74,10 +69,4 @@ def celer_bench( result["coef"] = np.array(m.coef_).ravel() result["n_iter"] = getattr(m, "n_iter_", None) - if cv: - result["best_alpha"] = m.alpha_ - result["n_alphas"] = len(m.alphas_) - result["max_alpha"] = m.alphas_.max() - result["min_alpha"] = m.alphas_.min() - return result diff --git a/src/glum_benchmarks/bench_glum.py b/src/glum_benchmarks/bench_glum.py index 098c637d3..aa91a3e7c 100644 --- a/src/glum_benchmarks/bench_glum.py +++ b/src/glum_benchmarks/bench_glum.py @@ -5,16 +5,14 @@ import pandas as pd from scipy import sparse as sps -from glum import GeneralizedLinearRegressor, GeneralizedLinearRegressorCV +from glum import GeneralizedLinearRegressor from .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) @@ -24,7 +22,6 @@ 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, @@ -40,7 +37,6 @@ def glum_bench( alpha l1_ratio iterations - cv diagnostics_level reg_multiplier hessian_approx @@ -74,25 +70,16 @@ 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 if reg_multiplier is None else alpha * reg_multiplier - 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"}) + result["runtime"], m = runtime(_build_and_fit, iterations, model_args, fit_args) + + # 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_ with pd.option_context( "display.expand_frame_repr", diff --git a/src/glum_benchmarks/bench_liblinear.py b/src/glum_benchmarks/bench_liblinear.py index 6889d0633..88245e621 100644 --- a/src/glum_benchmarks/bench_liblinear.py +++ b/src/glum_benchmarks/bench_liblinear.py @@ -19,7 +19,6 @@ def liblinear_bench( alpha: float, l1_ratio: float, iterations: int, - cv: bool, reg_multiplier: Optional[float] = None, **kwargs, ) -> dict[str, Any]: @@ -33,7 +32,6 @@ def liblinear_bench( alpha l1_ratio iterations - cv reg_multiplier kwargs @@ -70,10 +68,6 @@ def liblinear_bench( 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, diff --git a/src/glum_benchmarks/bench_skglm.py b/src/glum_benchmarks/bench_skglm.py index 8a5e7cf19..f4af2d7aa 100644 --- a/src/glum_benchmarks/bench_skglm.py +++ b/src/glum_benchmarks/bench_skglm.py @@ -3,20 +3,17 @@ import numpy as np from scipy import sparse as sps -from skglm import GeneralizedLinearEstimator, GeneralizedLinearEstimatorCV +from skglm import GeneralizedLinearEstimator from skglm.datafits import Gamma, Logistic, Poisson, Quadratic, WeightedQuadratic from skglm.penalties import L1, L1_plus_L2 from skglm.solvers import AndersonCD, ProxNewton from .util import benchmark_convergence_tolerance, runtime -# TODO: For CV the found alpha values differ significantly from glum # TODO: Keep the data conversion? Runtime/2 for binomial -def _build_and_fit(model_args, fit_args, cv: bool): - if cv: - return GeneralizedLinearEstimatorCV(**model_args).fit(**fit_args) +def _build_and_fit(model_args, fit_args): return GeneralizedLinearEstimator(**model_args).fit(**fit_args) @@ -26,7 +23,6 @@ def skglm_bench( alpha: float, l1_ratio: float, iterations: int, - cv: bool, reg_multiplier: Optional[float] = None, **kwargs, ): @@ -58,20 +54,21 @@ def skglm_bench( else: datafit = DATAFITS[distribution] - # For CV, alpha is determined internally, for non-CV, use reg_strength - p_alpha = 1.0 if cv else reg_strength if l1_ratio == 1: - penalty = L1(alpha=p_alpha) + penalty = L1(alpha=reg_strength) else: # We use L1_plus_L2(l1_ratio=0) for pure L2 to ensure prox_1d is available - penalty = L1_plus_L2(alpha=p_alpha, l1_ratio=l1_ratio) + penalty = L1_plus_L2(alpha=reg_strength, l1_ratio=l1_ratio) # ProxNewton is required for Poisson/Gamma or L2 problems - solver_tol = 1.0 if cv else benchmark_convergence_tolerance if distribution in ["poisson", "gamma"] or l1_ratio == 0: - solver = ProxNewton(tol=solver_tol, fit_intercept=True, max_iter=1000) + solver = ProxNewton( + tol=benchmark_convergence_tolerance, fit_intercept=True, max_iter=1000 + ) else: - solver = AndersonCD(tol=solver_tol, fit_intercept=True, max_iter=1000) + solver = AndersonCD( + tol=benchmark_convergence_tolerance, fit_intercept=True, max_iter=1000 + ) model_args = { "datafit": datafit, @@ -79,10 +76,6 @@ def skglm_bench( "solver": solver, } - if cv: - # Same CV parameters as glum - model_args.update({"cv": 5, "n_alphas": 100, "eps": 1e-6, "n_jobs": 1}) - # Data Conversion optimized for Coordinate Descent X_raw = dat["X"] if sps.issparse(X_raw): @@ -94,24 +87,13 @@ def skglm_bench( fit_args = {"X": X, "y": y} try: - result["runtime"], m = runtime( - _build_and_fit, iterations, model_args, fit_args, cv - ) + result["runtime"], m = runtime(_build_and_fit, iterations, model_args, fit_args) 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() - if cv: - n_iter_data = getattr(m, "n_iter_", None) - result["n_iter"] = np.sum(n_iter_data) if n_iter_data is not None else None - result["best_alpha"] = getattr(m, "alpha_", None) - if hasattr(m, "alphas_"): - result["n_alphas"] = len(m.alphas_) - result["max_alpha"] = m.alphas_.max() - result["min_alpha"] = m.alphas_.min() - else: - result["n_iter"] = getattr(m, "n_iter_", None) + result["n_iter"] = getattr(m, "n_iter_", None) return result diff --git a/src/glum_benchmarks/bench_sklearn.py b/src/glum_benchmarks/bench_sklearn.py index 61ff31e95..9cbbf9f25 100644 --- a/src/glum_benchmarks/bench_sklearn.py +++ b/src/glum_benchmarks/bench_sklearn.py @@ -11,7 +11,6 @@ from .util import benchmark_convergence_tolerance, runtime -# TODO: Add cv for logistic and elastic net # TODO: Design decision: do we set precompute = True ? @@ -28,7 +27,6 @@ def sklearn_bench( alpha: float, l1_ratio: float, iterations: int, - cv: bool, reg_multiplier: Optional[float] = None, **kwargs, ): @@ -42,7 +40,6 @@ def sklearn_bench( alpha l1_ratio iterations - cv reg_multiplier kwargs diff --git a/src/glum_benchmarks/cli_analyze.py b/src/glum_benchmarks/cli_analyze.py index 12ad11a57..78e5441e4 100644 --- a/src/glum_benchmarks/cli_analyze.py +++ b/src/glum_benchmarks/cli_analyze.py @@ -75,9 +75,6 @@ def cli_analyze( ] 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() @@ -101,16 +98,11 @@ def cli_analyze( "single_precision", "n_iter", "runtime", + "intercept", + "num_nonzero_coef", + "obj_val", + "rel_obj_val", ] - 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]) @@ -152,13 +144,6 @@ def _extract_dict_results_to_pd_series( 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} ) diff --git a/src/glum_benchmarks/cli_plot.py b/src/glum_benchmarks/cli_plot.py new file mode 100644 index 000000000..a6954d8aa --- /dev/null +++ b/src/glum_benchmarks/cli_plot.py @@ -0,0 +1,268 @@ +"""Generate benchmark comparison plots from benchmark output directory.""" + +import os +from pathlib import Path +from typing import Optional + +import click +import matplotlib.pyplot as plt +import pandas as pd + +from .cli_analyze import ( + _extract_dict_results_to_pd_series, + _identify_parameter_fnames, + _load_benchmark_results, +) +from .util import BenchmarkParams + + +@click.command() +@click.option( + "--output_dir", + type=str, + default="benchmark_output", + help="Directory containing benchmark results (default: benchmark_output)", +) +@click.option( + "--figure_dir", + type=str, + default="benchmark_figures", + help="Directory to save generated figures (default: benchmark_figures)", +) +@click.option( + "--problems", + type=str, + default=None, + help="Comma-separated list of problem prefixes to plot " + "(e.g., 'narrow-insurance,intermediate-housing'). If None, plots all.", +) +@click.option( + "--dpi", + type=int, + default=300, + help="DPI for saved PNG figures (default: 300)", +) +def cli_plot(output_dir: str, figure_dir: str, problems: Optional[str], dpi: int): + """ + Generate benchmark comparison plots from results directory. + + Examples: + # Plot all benchmarks from default directory + glm_benchmarks_plot + + # Plot from specific directory + glm_benchmarks_plot --output_dir my_benchmarks --figure_dir my_figures + + # Plot only specific problems + glm_benchmarks_plot --problems narrow-insurance,intermediate-housing + """ + # Create output directory + os.makedirs(figure_dir, exist_ok=True) + + # Load benchmark results + print(f"Loading benchmark results from {output_dir}...") + file_names = _identify_parameter_fnames(output_dir, BenchmarkParams()) + + 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: + print("No benchmark results found!") + return + + df = pd.DataFrame.from_records(formatted_results) + + # Extract distribution from problem name + df["distribution"] = ( + df["problem_name"] + .str.split("-") + .apply(lambda x: x[-2] if "5" in x[-1] else x[-1]) + ) + + # Remove duplicates + df = df.drop_duplicates(subset=["problem_name", "library_name"], keep="last") + + # Filter problems if specified + if problems: + problem_list = [p.strip() for p in problems.split(",")] + df = df[df["problem_name"].str.contains("|".join(problem_list))] + if df.empty: + print(f"No results found for problems: {problems}") + return + + # Determine which problem sets we have + problem_prefixes = set() + for idx, row in df.iterrows(): + parts = row["problem_name"].split("-") + # Extract dataset name (e.g., "narrow-insurance", "intermediate-housing") + if len(parts) >= 3: + dataset = "-".join( + parts[:-3] + ) # Everything before "no-weights-{reg}-{dist}" + problem_prefixes.add(dataset) + + print(f"Found datasets: {', '.join(sorted(problem_prefixes))}") + + # Generate plots for each problem/regularization combination + plot_count = 0 + for prob_name in sorted(problem_prefixes): + for reg in ["l2", "lasso"]: + plot_df = df[ + df["problem_name"].str.contains(reg) + & df["problem_name"].str.contains(prob_name) + ].copy() + + if plot_df.empty: + continue + + plot_df = plot_df.set_index(["distribution"])[["runtime", "library_name"]] + + try: + 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) + + # Drop columns (libraries) that have ALL NaN values (never ran) + plot_df = plot_df.dropna(axis=1, how="all") + + # Skip if no data remains + if plot_df.empty: + print(f"Skipping {prob_name}-{reg}: No data available") + continue + + # Replace NaN with 0 for plotting (matplotlib will show as no bar) + plot_df = plot_df.fillna(0) + + plot_df.index = [x.title() for x in plot_df.index] + except ValueError as e: + print(f"Skipping {prob_name}-{reg}: {e}") + continue + + # Hard limit y-axis to 15 seconds + ylim = 15 + + # Clean title: just dataset name + regularization type + clean_prob_name = prob_name.replace("-no-weights", "").replace("-no", "") + title = ( + clean_prob_name.title() + "-" + ("Lasso" if reg == "lasso" else "Ridge") + ) + + # Create figure manually to avoid gaps for missing data + fig, ax = plt.subplots(figsize=(6, 3)) + + # Get unique libraries and assign consistent colors using Paired colormap + libraries = plot_df.columns + cmap = plt.cm.Paired + n_colors = len(libraries) + colors = { + lib: cmap(i / max(n_colors - 1, 1)) for i, lib in enumerate(libraries) + } + + # Plot bars distribution by distribution + x_pos: float = 0 + x_labels: list[str] = [] + x_ticks: list[float] = [] + bar_width = 0.8 / len(libraries) + + for dist_idx, dist in enumerate(plot_df.index): + # Only plot libraries with data for this distribution + dist_data = plot_df.loc[dist] + libs_with_data = dist_data[dist_data > 0] + + if len(libs_with_data) > 0: + # Plot bars for this distribution + for lib_idx, (lib, runtime) in enumerate(libs_with_data.items()): + color = colors[lib] + ax.bar( + x_pos + lib_idx * bar_width, + runtime, + bar_width, + color=color, + label=lib if dist_idx == 0 else "", + ) + + # Center tick position for this group + x_ticks.append(x_pos + (len(libs_with_data) - 1) * bar_width / 2) + x_labels.append(dist) + x_pos += ( + len(libs_with_data) * bar_width + 0.2 + ) # Gap between distributions + + ax.set_ylim([0, ylim]) + ax.set_ylabel("run time (s)") + ax.set_title(title) + ax.set_xticks(x_ticks) + ax.set_xticklabels(x_labels, rotation=45, ha="right") + + # Add legend (remove duplicates) + handles, labels = ax.get_legend_handles_labels() + by_label = dict(zip(labels, handles)) + ax.legend( + by_label.values(), + by_label.keys(), + bbox_to_anchor=(1, 1), + loc="upper left", + ncol=1, + ) + + ax = plt.gca() + ax.spines["right"].set_visible(False) + ax.spines["top"].set_visible(False) + ax.yaxis.set_ticks_position("left") + ax.xaxis.set_ticks_position("bottom") + + # Annotate bars that exceed y-limit + for p in ax.patches: + x = p.get_x() + width = p.get_width() + y = p.get_height() + if y > ylim + 0.1: + arrow_x = x + width / 2 + + # Arrow at the very top - simple arrow style like in reference + ax.annotate( + "", + xy=(arrow_x, ylim - 0.1), # Arrow tip near top + xytext=(arrow_x, ylim - 1.0), # Arrow starts lower + arrowprops=dict( + arrowstyle="simple", + fc="black", + ec="black", + ), + ) + + # Text below the arrow, extending downward from arrow base + ax.annotate( + f"{y:.1f}", + (arrow_x, ylim - 1.1), # Start just below arrow + fontsize=14, + rotation="vertical", + ha="center", + va="top", # Text extends downward + ) + + plt.tight_layout() + + # Save figure + base_name = f"{prob_name}-{reg}" + png_path = Path(figure_dir) / f"{base_name}.png" + pdf_path = Path(figure_dir) / f"{base_name}.pdf" + + plt.savefig(png_path, dpi=dpi) + plt.savefig(pdf_path) + plt.close() + + plot_count += 1 + print(f"Saved: {base_name}.png, {base_name}.pdf") + + print(f"\nGenerated {plot_count} plots in {figure_dir}/") + + +if __name__ == "__main__": + cli_plot() diff --git a/src/glum_benchmarks/cli_run.py b/src/glum_benchmarks/cli_run.py index 12198c735..040777d9f 100644 --- a/src/glum_benchmarks/cli_run.py +++ b/src/glum_benchmarks/cli_run.py @@ -172,7 +172,6 @@ def execute_problem_library( 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, diff --git a/src/glum_benchmarks/problems.py b/src/glum_benchmarks/problems.py index ca53af4ae..84eb7b5dc 100644 --- a/src/glum_benchmarks/problems.py +++ b/src/glum_benchmarks/problems.py @@ -111,11 +111,14 @@ def transform_col(i: int, dtype) -> Union[pd.DataFrame, tm.CategoricalMatrix]: + cat_indices_in_expanded_arr, ) elif storage == "sparse": - X = csc_matrix(pd.concat(mat_parts, axis=1, ignore_index=True)) + dtype = np.float32 if single_precision else np.float64 + X = csc_matrix(pd.concat(mat_parts, axis=1, ignore_index=True).astype(dtype)) elif storage.startswith("split"): threshold = float(storage.split("split")[1]) + dtype = np.float32 if single_precision else np.float64 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(dtype)), + threshold, ) else: # Fall back to using a dense matrix. X = pd.concat(mat_parts, axis=1, ignore_index=True) diff --git a/src/glum_benchmarks/util.py b/src/glum_benchmarks/util.py index c023d2438..37fb96e61 100644 --- a/src/glum_benchmarks/util.py +++ b/src/glum_benchmarks/util.py @@ -199,7 +199,6 @@ def __init__( threads: Optional[int] = None, single_precision: Optional[bool] = None, regularization_strength: Optional[float] = None, - cv: Optional[bool] = None, hessian_approx: Optional[float] = None, diagnostics_level: Optional[str] = None, ): @@ -210,7 +209,6 @@ def __init__( self.threads = threads self.single_precision = single_precision self.regularization_strength = regularization_strength - self.cv = cv self.hessian_approx = hessian_approx self.diagnostics_level = diagnostics_level @@ -222,7 +220,6 @@ def __init__( "threads", "single_precision", "regularization_strength", - "cv", "hessian_approx", "diagnostics_level", ] @@ -265,7 +262,6 @@ def get_result_fname(self) -> str: num_rows=None, regularization_strength=None, storage="dense", - cv=False, single_precision=False, hessian_approx=0.0, diagnostics_level="basic", @@ -317,7 +313,6 @@ def benchmark_params_cli(func: Callable) -> Callable: 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", @@ -343,7 +338,6 @@ def wrapped_func( 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], @@ -359,7 +353,6 @@ def wrapped_func( threads, single_precision, regularization_strength, - cv, hessian_approx, diagnostics_level, ) diff --git a/src/glum_benchmarks/zeros_benchmark.py b/src/glum_benchmarks/zeros_benchmark.py index 80b4288c3..366157c32 100644 --- a/src/glum_benchmarks/zeros_benchmark.py +++ b/src/glum_benchmarks/zeros_benchmark.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/tests/benchmark/test_cli.py b/tests/benchmark/test_cli.py index b9672b0e3..e02eda716 100644 --- a/tests/benchmark/test_cli.py +++ b/tests/benchmark/test_cli.py @@ -16,7 +16,7 @@ "cli_options, expected_params", [ ([], {}), - (["--num_rows", "1000", "--cv", "True"], {"num_rows": 1000, "cv": True}), + (["--num_rows", "1000"], {"num_rows": 1000}), ], ) def test_make_params(cli_options: list[str], expected_params: dict[str, Any]): @@ -95,7 +95,6 @@ def test_correct_problems_run(): "storage", "threads", "single_precision", - "cv", "hessian_approx", "diagnostics_level", ] @@ -109,14 +108,14 @@ def test_correct_problems_run(): 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_zeros_20_dense_{n_threads}_False_1000.0" + "_0.0_basic.pkl", f"narrow-insurance-weights-l2-gamma_glum_20_dense_{n_threads}_False_10" - "00.0_False_0.0_basic.pkl", + "00.0_0.0_basic.pkl", f"wide-insurance-no-weights-net-poisson_zeros_20_dense_{n_threads}_False_1000.0" - "_False_0.0_basic.pkl", + "_0.0_basic.pkl", f"wide-insurance-no-weights-net-poisson_glum_20_dense_{n_threads}_False" - "_1000.0_False_0.0_basic.pkl", + "_1000.0_0.0_basic.pkl", ] assert sorted(problems_run) == sorted(expected_problems_run) From 42411b7f5e8f16a43e447f2e3ce35bb3e3df71cb Mon Sep 17 00:00:00 2001 From: David Eiglsperger Date: Mon, 26 Jan 2026 17:11:17 +0100 Subject: [PATCH 08/42] Resolve conflicts --- src/glum_benchmarks/bench_h2o.py | 10 +--------- src/glum_benchmarks/bench_liblinear.py | 1 - 2 files changed, 1 insertion(+), 10 deletions(-) diff --git a/src/glum_benchmarks/bench_h2o.py b/src/glum_benchmarks/bench_h2o.py index 2094f99db..3a0626c97 100644 --- a/src/glum_benchmarks/bench_h2o.py +++ b/src/glum_benchmarks/bench_h2o.py @@ -30,7 +30,6 @@ def h2o_bench( alpha: float, l1_ratio: float, iterations: int, - cv: bool, reg_multiplier: Optional[float] = None, **kwargs, ): @@ -44,7 +43,6 @@ def h2o_bench( alpha l1_ratio iterations - cv reg_multiplier kwargs @@ -101,9 +99,6 @@ def h2o_bench( max_iterations=1000, gainslift_bins=0, ) - if cv: - model_args["lambda_search"] = True - model_args["nfolds"] = 5 if tweedie: p = float(distribution.split("=")[-1]) @@ -148,12 +143,9 @@ 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"] return result diff --git a/src/glum_benchmarks/bench_liblinear.py b/src/glum_benchmarks/bench_liblinear.py index 88245e621..0a8752313 100644 --- a/src/glum_benchmarks/bench_liblinear.py +++ b/src/glum_benchmarks/bench_liblinear.py @@ -76,7 +76,6 @@ def liblinear_bench( if reg_multiplier is None else 1 / (X.shape[0] * alpha * reg_multiplier) ), - max_iter=1000, # 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 From ac74924a249ff9adfe76d933ef715fa51ee83f49 Mon Sep 17 00:00:00 2001 From: David Eiglsperger Date: Mon, 26 Jan 2026 22:30:54 +0100 Subject: [PATCH 09/42] Add scaling --- src/glum_benchmarks/bench_celer.py | 2 +- src/glum_benchmarks/bench_skglm.py | 2 -- src/glum_benchmarks/problems.py | 13 ++++++++- src/glum_benchmarks/util.py | 43 ++++++++++++++++++++++++++++++ 4 files changed, 56 insertions(+), 4 deletions(-) diff --git a/src/glum_benchmarks/bench_celer.py b/src/glum_benchmarks/bench_celer.py index 628389e52..6ea8c29a0 100644 --- a/src/glum_benchmarks/bench_celer.py +++ b/src/glum_benchmarks/bench_celer.py @@ -41,7 +41,7 @@ def celer_bench( if distribution == "gaussian": if l1_ratio == 0.0: - warnings.warn("Celer doesn't support Ridge (l1_ratio=0), skipping.") + warnings.warn("Celer doesn't support Ridge Regression, skipping.") return {} elif l1_ratio < 1.0: model_class = ElasticNet diff --git a/src/glum_benchmarks/bench_skglm.py b/src/glum_benchmarks/bench_skglm.py index f4af2d7aa..7d5e54e93 100644 --- a/src/glum_benchmarks/bench_skglm.py +++ b/src/glum_benchmarks/bench_skglm.py @@ -10,8 +10,6 @@ from .util import benchmark_convergence_tolerance, runtime -# TODO: Keep the data conversion? Runtime/2 for binomial - def _build_and_fit(model_args, fit_args): return GeneralizedLinearEstimator(**model_args).fit(**fit_args) diff --git a/src/glum_benchmarks/problems.py b/src/glum_benchmarks/problems.py index 84eb7b5dc..e814169a0 100644 --- a/src/glum_benchmarks/problems.py +++ b/src/glum_benchmarks/problems.py @@ -17,7 +17,14 @@ generate_real_insurance_dataset, generate_wide_insurance_dataset, ) -from .util import cache_location, exposure_and_offset_to_weights, get_tweedie_p +from .util import ( + _standardize_features, + cache_location, + exposure_and_offset_to_weights, + get_tweedie_p, +) + +# TODO: Add a second regularization strength (would double the number of problems) joblib_memory = Memory(cache_location, verbose=0) @@ -123,6 +130,10 @@ def transform_col(i: int, dtype) -> Union[pd.DataFrame, tm.CategoricalMatrix]: else: # Fall back to using a dense matrix. X = pd.concat(mat_parts, axis=1, ignore_index=True) + # Step 4b) Standardize features (center and scale to unit variance) + # This ensures all libraries start with the same pre-processed data + X = _standardize_features(X, single_precision) + # Step 5) Handle weights or offsets if needed. if data_setup == "weights": # The exposure correction doesn't make sense for these distributions since diff --git a/src/glum_benchmarks/util.py b/src/glum_benchmarks/util.py index 37fb96e61..717c4b42c 100644 --- a/src/glum_benchmarks/util.py +++ b/src/glum_benchmarks/util.py @@ -10,6 +10,7 @@ import pandas as pd import tabmat as tm from scipy import sparse as sps +from scipy.sparse import csc_matrix from glum import GeneralizedLinearRegressor, TweedieDistribution from glum._solvers import eta_mu_objective @@ -431,3 +432,45 @@ def get_tweedie_p(distribution: str) -> float: return 0 else: raise ValueError("Not a Tweedie distribution.") + + +def _standardize_features( + X: Union[np.ndarray, pd.DataFrame, csc_matrix, tm.MatrixBase], + single_precision: bool = False, +) -> Union[np.ndarray, csc_matrix, tm.MatrixBase]: + """ + Standardize features by scaling to unit L2 norm per column. + + For consistency across sparse/dense, we use L2 norm scaling (no centering). + This ensures all benchmark libraries start with the same pre-processed data. + """ + dtype = np.float32 if single_precision else np.float64 + + if isinstance(X, pd.DataFrame): + X = X.values.astype(dtype) + + if isinstance(X, np.ndarray): + # Dense: scale columns to unit L2 norm + X = np.asarray(X, dtype=dtype) + col_norms = np.linalg.norm(X, axis=0) + col_norms[col_norms == 0] = 1.0 + return X / col_norms + + elif isinstance(X, csc_matrix): + # Sparse: scale columns to unit L2 norm + X = X.astype(dtype) + col_norms = np.sqrt(X.power(2).sum(axis=0)).A1 + col_norms[col_norms == 0] = 1.0 + from scipy.sparse import diags + + return X @ diags(1.0 / col_norms) + + elif isinstance(X, tm.MatrixBase): + # tabmat matrices: skip standardization + # glum handles tabmat well, and StandardizedMatrix has compatibility issues + # with some benchmark libraries + return X + + else: + # Unknown type, return as-is + return X From e31b96e707e557c2b65e342dce99f7a46f7c3a11 Mon Sep 17 00:00:00 2001 From: David Eiglsperger Date: Tue, 27 Jan 2026 10:19:15 +0100 Subject: [PATCH 10/42] Scaling only for benchmarks, not for golden master tests --- src/glum_benchmarks/cli_run.py | 9 +++++++++ src/glum_benchmarks/problems.py | 11 +---------- src/glum_benchmarks/util.py | 12 ++++++++---- tests/glm/test_benchmark_golden_master.py | 4 +++- 4 files changed, 21 insertions(+), 15 deletions(-) diff --git a/src/glum_benchmarks/cli_run.py b/src/glum_benchmarks/cli_run.py index 040777d9f..2888d3fe2 100644 --- a/src/glum_benchmarks/cli_run.py +++ b/src/glum_benchmarks/cli_run.py @@ -8,6 +8,7 @@ from glum_benchmarks.problems import Problem, get_all_problems from glum_benchmarks.util import ( BenchmarkParams, + _standardize_features, benchmark_params_cli, clear_cache, defaults, @@ -124,6 +125,7 @@ def execute_problem_library( params: BenchmarkParams, iterations: int = 1, diagnostics_level: str = "basic", + standardize: bool = True, **kwargs, ): """ @@ -134,6 +136,8 @@ def execute_problem_library( params iterations diagnostics_level + standardize + Whether to standardize features before fitting. Default True for benchmarks. kwargs Returns @@ -154,6 +158,11 @@ def execute_problem_library( storage=params.storage, single_precision=params.single_precision, ) + + # Standardize features for better convergence across all libraries + if standardize: + dat["X"] = _standardize_features(dat["X"], params.single_precision or False) + os.environ["OMP_NUM_THREADS"] = str(params.threads) if params.regularization_strength is None: diff --git a/src/glum_benchmarks/problems.py b/src/glum_benchmarks/problems.py index e814169a0..b362e7d0f 100644 --- a/src/glum_benchmarks/problems.py +++ b/src/glum_benchmarks/problems.py @@ -17,12 +17,7 @@ generate_real_insurance_dataset, generate_wide_insurance_dataset, ) -from .util import ( - _standardize_features, - cache_location, - exposure_and_offset_to_weights, - get_tweedie_p, -) +from .util import cache_location, exposure_and_offset_to_weights, get_tweedie_p # TODO: Add a second regularization strength (would double the number of problems) @@ -130,10 +125,6 @@ def transform_col(i: int, dtype) -> Union[pd.DataFrame, tm.CategoricalMatrix]: else: # Fall back to using a dense matrix. X = pd.concat(mat_parts, axis=1, ignore_index=True) - # Step 4b) Standardize features (center and scale to unit variance) - # This ensures all libraries start with the same pre-processed data - X = _standardize_features(X, single_precision) - # Step 5) Handle weights or offsets if needed. if data_setup == "weights": # The exposure correction doesn't make sense for these distributions since diff --git a/src/glum_benchmarks/util.py b/src/glum_benchmarks/util.py index 717c4b42c..9ba41d82d 100644 --- a/src/glum_benchmarks/util.py +++ b/src/glum_benchmarks/util.py @@ -437,7 +437,7 @@ def get_tweedie_p(distribution: str) -> float: def _standardize_features( X: Union[np.ndarray, pd.DataFrame, csc_matrix, tm.MatrixBase], single_precision: bool = False, -) -> Union[np.ndarray, csc_matrix, tm.MatrixBase]: +) -> Union[np.ndarray, pd.DataFrame, csc_matrix, tm.MatrixBase]: """ Standardize features by scaling to unit L2 norm per column. @@ -447,7 +447,11 @@ def _standardize_features( dtype = np.float32 if single_precision else np.float64 if isinstance(X, pd.DataFrame): - X = X.values.astype(dtype) + # Preserve DataFrame type + X_arr = X.values.astype(dtype) + col_norms = np.linalg.norm(X_arr, axis=0) + col_norms[col_norms == 0] = 1.0 + return pd.DataFrame(X_arr / col_norms, columns=X.columns, index=X.index) if isinstance(X, np.ndarray): # Dense: scale columns to unit L2 norm @@ -467,8 +471,8 @@ def _standardize_features( elif isinstance(X, tm.MatrixBase): # tabmat matrices: skip standardization - # glum handles tabmat well, and StandardizedMatrix has compatibility issues - # with some benchmark libraries + # glum is the only library that can use tabmat directly, and it handles + # standardization internally. Other libraries would need conversion anyway. return X else: diff --git a/tests/glm/test_benchmark_golden_master.py b/tests/glm/test_benchmark_golden_master.py index 2a7e435c8..0b630798c 100644 --- a/tests/glm/test_benchmark_golden_master.py +++ b/tests/glm/test_benchmark_golden_master.py @@ -128,7 +128,9 @@ 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 From bd09f9dbbbea65f0e8dfa7fbcdef33743e9d8ffc Mon Sep 17 00:00:00 2001 From: David Eiglsperger Date: Tue, 27 Jan 2026 13:39:10 +0100 Subject: [PATCH 11/42] Closed form solution for l2-gaussian --- src/glum_benchmarks/bench_skglm.py | 10 ++++-- src/glum_benchmarks/bench_sklearn.py | 49 +++++++++++++++++++++------- src/glum_benchmarks/cli_analyze.py | 5 +-- src/glum_benchmarks/problems.py | 2 ++ 4 files changed, 50 insertions(+), 16 deletions(-) diff --git a/src/glum_benchmarks/bench_skglm.py b/src/glum_benchmarks/bench_skglm.py index 7d5e54e93..ac54dd116 100644 --- a/src/glum_benchmarks/bench_skglm.py +++ b/src/glum_benchmarks/bench_skglm.py @@ -58,8 +58,9 @@ def skglm_bench( # We use L1_plus_L2(l1_ratio=0) for pure L2 to ensure prox_1d is available penalty = L1_plus_L2(alpha=reg_strength, l1_ratio=l1_ratio) - # ProxNewton is required for Poisson/Gamma or L2 problems - if distribution in ["poisson", "gamma"] or l1_ratio == 0: + # 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, max_iter=1000 ) @@ -82,6 +83,11 @@ def skglm_bench( X = np.asfortranarray(X_raw, dtype=np.float64) y = np.asarray(dat["y"], dtype=np.float64).ravel() + + # skglm's Logistic datafit expects labels in {-1, 1}, not {0, 1} + if distribution == "binomial": + y = 2 * y - 1 + fit_args = {"X": X, "y": y} try: diff --git a/src/glum_benchmarks/bench_sklearn.py b/src/glum_benchmarks/bench_sklearn.py index 9cbbf9f25..b1b0fb6da 100644 --- a/src/glum_benchmarks/bench_sklearn.py +++ b/src/glum_benchmarks/bench_sklearn.py @@ -5,14 +5,14 @@ from scipy import sparse as sps from sklearn.linear_model import ( ElasticNet, + Lasso, LogisticRegression, + Ridge, TweedieRegressor, ) from .util import benchmark_convergence_tolerance, runtime -# TODO: Design decision: do we set precompute = True ? - def _build_and_fit(model_args, fit_args): """Build and fit a sklearn regressor.""" @@ -58,15 +58,38 @@ def sklearn_bench( n_samples = dat["X"].shape[0] if distribution == "gaussian": - model_args = { - "_model_class": ElasticNet, - "alpha": reg_strength, - "l1_ratio": l1_ratio, - "fit_intercept": True, - "max_iter": 1000, - "tol": benchmark_convergence_tolerance, - "precompute": True, - } + if l1_ratio == 0.0: + # Pure L2 (Ridge): use closed-form solution + # sklearn's Ridge uses alpha directly but with sum(loss) not mean(loss) + model_args = { + "_model_class": Ridge, + "alpha": reg_strength * n_samples, + "fit_intercept": True, + "max_iter": 1000, + "tol": benchmark_convergence_tolerance, + "solver": "auto", + } + elif l1_ratio == 1.0: + # Pure L1 (Lasso) + model_args = { + "_model_class": Lasso, + "alpha": reg_strength * n_samples, + "fit_intercept": True, + "max_iter": 1000, + "tol": benchmark_convergence_tolerance, + "precompute": True, + } + else: + # Elastic Net: mixed L1/L2 + model_args = { + "_model_class": ElasticNet, + "alpha": reg_strength, + "l1_ratio": l1_ratio, + "fit_intercept": True, + "max_iter": 1000, + "tol": benchmark_convergence_tolerance, + "precompute": True, + } elif distribution == "binomial": # sklearn's LogisticRegression uses C = 1/lambda where lambda is the # regularization strength. However, sklearn does NOT scale the regularization @@ -139,6 +162,8 @@ def sklearn_bench( else: result["intercept"] = m.intercept_ result["coef"] = m.coef_ - result["n_iter"] = m.n_iter_ if hasattr(m, "n_iter_") else None + # Ridge has n_iter_=None (closed-form), treat as 0 iterations (direct solve) + n_iter = getattr(m, "n_iter_", None) + result["n_iter"] = 0 if n_iter is None else n_iter return result diff --git a/src/glum_benchmarks/cli_analyze.py b/src/glum_benchmarks/cli_analyze.py index 78e5441e4..428d05d9f 100644 --- a/src/glum_benchmarks/cli_analyze.py +++ b/src/glum_benchmarks/cli_analyze.py @@ -125,10 +125,11 @@ def _extract_dict_results_to_pd_series( assert params.problem_name is not None coefs = results["coef"] - if results["n_iter"] is not None: + if results["n_iter"] is not None and results["n_iter"] > 0: runtime_per_iter = results["runtime"] / results["n_iter"] else: - runtime_per_iter = None + # n_iter=0 means immediate convergence or closed-form solution + runtime_per_iter = results["runtime"] 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 diff --git a/src/glum_benchmarks/problems.py b/src/glum_benchmarks/problems.py index b362e7d0f..67c8ef13e 100644 --- a/src/glum_benchmarks/problems.py +++ b/src/glum_benchmarks/problems.py @@ -20,6 +20,8 @@ from .util import cache_location, exposure_and_offset_to_weights, get_tweedie_p # TODO: Add a second regularization strength (would double the number of problems) +# TODO: Skip the Gaussian Ridge Regression as those have a closed form solution +# and can be solved immediately by e.g. sklearn joblib_memory = Memory(cache_location, verbose=0) From 4abe0750dda4b7130fef30ad255c1deda025c944 Mon Sep 17 00:00:00 2001 From: David Eiglsperger Date: Wed, 28 Jan 2026 15:09:53 +0100 Subject: [PATCH 12/42] Benchmark CLI replacement and separation of benchmarking --- .gitignore | 12 +- .pre-commit-config.yaml | 2 +- docs/benchmarks/benchmark_data.csv | 108 - docs/benchmarks/benchmark_figure.py | 317 --- glum_benchmarks/README.md | 109 + glum_benchmarks/__init__.py | 1 + .../data/__init__.py | 0 .../data/create_housing.py | 0 .../data/create_insurance.py | 0 .../data/simulated_glm.py | 0 glum_benchmarks/libraries/__init__.py | 41 + .../libraries}/bench_celer.py | 9 +- .../libraries}/bench_glum.py | 7 +- .../libraries}/bench_h2o.py | 48 +- .../libraries}/bench_liblinear.py | 7 +- .../libraries}/bench_skglm.py | 18 +- .../libraries}/bench_sklearn.py | 6 +- .../libraries/bench_zeros.py | 0 .../problems.py | 32 +- glum_benchmarks/run_benchmarks.py | 435 ++++ glum_benchmarks/tests/test_benchmarks.py | 135 ++ .../util.py | 279 +-- pixi.lock | 1791 +++++++++-------- pixi.toml | 16 +- pyproject.toml | 8 +- setup.py | 18 +- src/glum_benchmarks/README.md | 67 - src/glum_benchmarks/__init__.py | 9 - .../benchmark_dense_sandwich.py | 146 -- .../benchmark_sparse_sandwich.py | 115 -- src/glum_benchmarks/cli_analyze.py | 207 -- src/glum_benchmarks/cli_plot.py | 268 --- src/glum_benchmarks/cli_run.py | 307 --- .../scripts/run_intermediate_benchmarks.sh | 40 - .../scripts/run_narrow_benchmarks.sh | 40 - .../scripts/run_real_benchmarks.sh | 40 - .../scripts/run_wide_benchmarks.sh | 40 - tests/benchmark/test_cli.py | 139 -- tests/glm/performance/test_performance.py | 2 +- tests/glm/test_benchmark_golden_master.py | 3 +- .../glm/test_offset_weight_obj_equivalent.py | 5 +- 41 files changed, 1853 insertions(+), 2974 deletions(-) delete mode 100644 docs/benchmarks/benchmark_data.csv delete mode 100644 docs/benchmarks/benchmark_figure.py create mode 100644 glum_benchmarks/README.md create mode 100644 glum_benchmarks/__init__.py rename {src/glum_benchmarks => glum_benchmarks}/data/__init__.py (100%) rename {src/glum_benchmarks => glum_benchmarks}/data/create_housing.py (100%) rename {src/glum_benchmarks => glum_benchmarks}/data/create_insurance.py (100%) rename {src/glum_benchmarks => glum_benchmarks}/data/simulated_glm.py (100%) create mode 100644 glum_benchmarks/libraries/__init__.py rename {src/glum_benchmarks => glum_benchmarks/libraries}/bench_celer.py (87%) rename {src/glum_benchmarks => glum_benchmarks/libraries}/bench_glum.py (96%) rename {src/glum_benchmarks => glum_benchmarks/libraries}/bench_h2o.py (68%) rename {src/glum_benchmarks => glum_benchmarks/libraries}/bench_liblinear.py (92%) rename {src/glum_benchmarks => glum_benchmarks/libraries}/bench_skglm.py (80%) rename {src/glum_benchmarks => glum_benchmarks/libraries}/bench_sklearn.py (96%) rename src/glum_benchmarks/zeros_benchmark.py => glum_benchmarks/libraries/bench_zeros.py (100%) rename {src/glum_benchmarks => glum_benchmarks}/problems.py (87%) create mode 100644 glum_benchmarks/run_benchmarks.py create mode 100644 glum_benchmarks/tests/test_benchmarks.py rename {src/glum_benchmarks => glum_benchmarks}/util.py (68%) delete mode 100644 src/glum_benchmarks/README.md delete mode 100644 src/glum_benchmarks/__init__.py delete mode 100644 src/glum_benchmarks/benchmark_dense_sandwich.py delete mode 100644 src/glum_benchmarks/benchmark_sparse_sandwich.py delete mode 100644 src/glum_benchmarks/cli_analyze.py delete mode 100644 src/glum_benchmarks/cli_plot.py delete mode 100644 src/glum_benchmarks/cli_run.py delete mode 100755 src/glum_benchmarks/scripts/run_intermediate_benchmarks.sh delete mode 100755 src/glum_benchmarks/scripts/run_narrow_benchmarks.sh delete mode 100755 src/glum_benchmarks/scripts/run_real_benchmarks.sh delete mode 100755 src/glum_benchmarks/scripts/run_wide_benchmarks.sh delete mode 100644 tests/benchmark/test_cli.py diff --git a/.gitignore b/.gitignore index 5e3a68f95..c1146075d 100644 --- a/.gitignore +++ b/.gitignore @@ -1,5 +1,10 @@ -# Project-specific -benchmark_output/ +# Benchmark outputs, only track docs/results.csv +glum_benchmarks/results/* +!glum_benchmarks/results/docs/ +!glum_benchmarks/results/docs/** +glum_benchmarks/results/docs/* +!glum_benchmarks/results/docs/results.csv +glum_benchmarks/.cache/ # Files created by templating dense.cpp @@ -137,9 +142,6 @@ 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/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 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-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 34fb296e8..000000000 --- a/docs/benchmarks/benchmark_figure.py +++ /dev/null @@ -1,317 +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", - "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]) -) - -# Remove duplicates (keep last run if same problem was run multiple times) -df = df.drop_duplicates(subset=["problem_name", "library_name"], keep="last") - -# %% -# %config InlineBackend.figure_format='retina' - -# %% -for prob_name in [ - "narrow-insurance", - "intermediate-insurance", - "intermediate-housing", - "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, 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/glum_benchmarks/README.md b/glum_benchmarks/README.md new file mode 100644 index 000000000..946a09e44 --- /dev/null +++ b/glum_benchmarks/README.md @@ -0,0 +1,109 @@ +# glum_benchmarks + +Python module to benchmark GLM implementations. + +## Running the benchmarks + +```bash +pixi run -e benchmark run-benchmarks +``` + +The benchmark script runs in three steps that can be controlled independently. + +### Step 1: Run Benchmarks (`RUN_BENCHMARKS`) + +Executes each problem/library combination and saves raw results. + +- **Input:** Configuration parameters +- **Output:** Pickle files in `results//pickles/` +- **Set `RUN_BENCHMARKS = False`** to skip (e.g. to generate cvs from existing results) + +### 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`** to skip (e.g. to generate plots from existing csv) + +### 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`** to skip + +### Workflow examples + +**Full run (default):** + +```python +RUN_BENCHMARKS = True +ANALYZE_RESULTS = True +GENERATE_PLOTS = True +``` + +**Regenerate plots without re-running benchmarks:** + +```python +RUN_BENCHMARKS = False +ANALYZE_RESULTS = True # Rebuild CSV from existing pickles +GENERATE_PLOTS = True +``` + +**Run benchmarks and analyze results without creating figures:** + +```python +RUN_BENCHMARKS = True +ANALYZE_RESULTS = True +GENERATE_PLOTS = False +``` + +## Output structure + +Results are organized by `RUN_NAME` (default: `"docs"`): + +``` +glum_benchmarks/ +├── results/ +│ └── docs/ # RUN_NAME = "docs" +│ ├── pickles/ # Step 1 output (gitignored) +│ ├── figures/ # Step 3 output (gitignored) +│ └── results.csv # Step 2 output (tracked for docs/) +└── .cache/ # Shared data cache (gitignored) +``` + +Only `results/docs/results.csv` is tracked in git. Change `RUN_NAME` for experiments. + +## Configuration + +Edit the CONFIGURATION section at the top of `run_benchmarks.py`. +The script is pre-configured for the documentation benchmark run. + +| 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) | +| `RUN_NAME` | Subfolder in `results/` | +| `LIBRARIES` | Which libraries to benchmark | +| `DATASETS` | Which datasets to run | +| `REGULARIZATIONS` | Regularization types | +| `DISTRIBUTIONS` | Distribution families | +| `NUM_THREADS` | Number of threads for parallel execution | +| `REG_STRENGTH` | Regularization strength | +| `STANDARDIZE` | Whether to standardize features before fitting | +| `ITERATIONS` | Runs per benchmark (>=2 required for skglm) | +| `NUM_ROWS` | Limit rows per dataset (`None` = full dataset) | +| `CACHE_DATA` | Cache data loading in `.cache/` | +| `CLEAR_DATA_CACHE` | Clear data cache before running | +| `CLEAR_OUTPUT` | Clear pickles before running | + +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/src/glum_benchmarks/data/__init__.py b/glum_benchmarks/data/__init__.py similarity index 100% rename from src/glum_benchmarks/data/__init__.py rename to glum_benchmarks/data/__init__.py 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 100% rename from src/glum_benchmarks/data/create_insurance.py rename to glum_benchmarks/data/create_insurance.py diff --git a/src/glum_benchmarks/data/simulated_glm.py b/glum_benchmarks/data/simulated_glm.py similarity index 100% rename from src/glum_benchmarks/data/simulated_glm.py rename to glum_benchmarks/data/simulated_glm.py diff --git a/glum_benchmarks/libraries/__init__.py b/glum_benchmarks/libraries/__init__.py new file mode 100644 index 000000000..29d683836 --- /dev/null +++ b/glum_benchmarks/libraries/__init__.py @@ -0,0 +1,41 @@ +"""Benchmark functions for different GLM libraries.""" + +# Always available +from .bench_glum import glum_bench +from .bench_zeros import zeros_bench + +# Optional dependencies, only import if available +try: + from .bench_celer import celer_bench +except ImportError: + celer_bench = None # type: ignore + +try: + from .bench_h2o import h2o_bench +except ImportError: + h2o_bench = None # type: ignore + +try: + from .bench_liblinear import liblinear_bench +except ImportError: + liblinear_bench = None # type: ignore + +try: + from .bench_skglm import skglm_bench +except ImportError: + skglm_bench = None # type: ignore + +try: + from .bench_sklearn import sklearn_bench +except ImportError: + sklearn_bench = None # type: ignore + +__all__ = [ + "celer_bench", + "glum_bench", + "h2o_bench", + "liblinear_bench", + "skglm_bench", + "sklearn_bench", + "zeros_bench", +] diff --git a/src/glum_benchmarks/bench_celer.py b/glum_benchmarks/libraries/bench_celer.py similarity index 87% rename from src/glum_benchmarks/bench_celer.py rename to glum_benchmarks/libraries/bench_celer.py index 6ea8c29a0..c21048025 100644 --- a/src/glum_benchmarks/bench_celer.py +++ b/glum_benchmarks/libraries/bench_celer.py @@ -5,7 +5,7 @@ from celer import ElasticNet, Lasso from scipy import sparse as sps -from .util import benchmark_convergence_tolerance, runtime +from glum_benchmarks.util import benchmark_convergence_tolerance, runtime def _build_and_fit(model_args, fit_args): @@ -32,13 +32,6 @@ def celer_bench( warnings.warn(f"Celer doesn't support {distribution}, skipping.") return {} - if "offset" in dat: - warnings.warn("Celer doesn't support offsets, skipping.") - return {} - - if "sample_weight" in dat: - fit_args.update({"sample_weight": dat["sample_weight"]}) - if distribution == "gaussian": if l1_ratio == 0.0: warnings.warn("Celer doesn't support Ridge Regression, skipping.") diff --git a/src/glum_benchmarks/bench_glum.py b/glum_benchmarks/libraries/bench_glum.py similarity index 96% rename from src/glum_benchmarks/bench_glum.py rename to glum_benchmarks/libraries/bench_glum.py index aa91a3e7c..b50734471 100644 --- a/src/glum_benchmarks/bench_glum.py +++ b/glum_benchmarks/libraries/bench_glum.py @@ -6,8 +6,11 @@ from scipy import sparse as sps from glum import GeneralizedLinearRegressor - -from .util import benchmark_convergence_tolerance, get_sklearn_family, runtime +from glum_benchmarks.util import ( + benchmark_convergence_tolerance, + get_sklearn_family, + runtime, +) random_seed = 110 diff --git a/src/glum_benchmarks/bench_h2o.py b/glum_benchmarks/libraries/bench_h2o.py similarity index 68% rename from src/glum_benchmarks/bench_h2o.py rename to glum_benchmarks/libraries/bench_h2o.py index 3a0626c97..c223a4dda 100644 --- a/src/glum_benchmarks/bench_h2o.py +++ b/glum_benchmarks/libraries/bench_h2o.py @@ -8,7 +8,7 @@ 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 def _build_and_fit(model_args, train_args): @@ -62,21 +62,10 @@ def h2o_bench( h2o.init(nthreads=int(os.environ.get("OMP_NUM_THREADS", os.cpu_count()))) # type: ignore 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": @@ -107,35 +96,18 @@ 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) # 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 diff --git a/src/glum_benchmarks/bench_liblinear.py b/glum_benchmarks/libraries/bench_liblinear.py similarity index 92% rename from src/glum_benchmarks/bench_liblinear.py rename to glum_benchmarks/libraries/bench_liblinear.py index 0a8752313..35199f7e9 100644 --- a/src/glum_benchmarks/bench_liblinear.py +++ b/glum_benchmarks/libraries/bench_liblinear.py @@ -6,7 +6,7 @@ from scipy import sparse as sps from sklearn.linear_model import LogisticRegression -from .util import benchmark_convergence_tolerance, runtime +from glum_benchmarks.util import benchmark_convergence_tolerance, runtime def _build_and_fit(model_args, train_args): @@ -64,10 +64,6 @@ def liblinear_bench( ) return result - if "offset" in dat.keys(): - warnings.warn("liblinear does not support offsets") - return result - model_args = dict( l1_ratio=sklearn_l1_ratio, tol=benchmark_convergence_tolerance, @@ -90,7 +86,6 @@ def liblinear_bench( 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) diff --git a/src/glum_benchmarks/bench_skglm.py b/glum_benchmarks/libraries/bench_skglm.py similarity index 80% rename from src/glum_benchmarks/bench_skglm.py rename to glum_benchmarks/libraries/bench_skglm.py index ac54dd116..0e36a2254 100644 --- a/src/glum_benchmarks/bench_skglm.py +++ b/glum_benchmarks/libraries/bench_skglm.py @@ -4,11 +4,11 @@ import numpy as np from scipy import sparse as sps from skglm import GeneralizedLinearEstimator -from skglm.datafits import Gamma, Logistic, Poisson, Quadratic, WeightedQuadratic +from skglm.datafits import Gamma, Logistic, Poisson, Quadratic from skglm.penalties import L1, L1_plus_L2 from skglm.solvers import AndersonCD, ProxNewton -from .util import benchmark_convergence_tolerance, runtime +from glum_benchmarks.util import benchmark_convergence_tolerance, runtime def _build_and_fit(model_args, fit_args): @@ -27,10 +27,6 @@ def skglm_bench( result: dict[str, Any] = {} reg_strength = alpha if reg_multiplier is None else alpha * reg_multiplier - if "offset" in dat: - warnings.warn("skglm doesn't support offsets, skipping.") - return result - if "tweedie" in distribution: warnings.warn("skglm doesn't support Tweedie, skipping.") return result @@ -42,15 +38,7 @@ def skglm_bench( "gamma": Gamma(), } - if "sample_weight" in dat.keys(): - if distribution == "gaussian": - weights = np.asarray(dat["sample_weight"], dtype=np.float64) - datafit = WeightedQuadratic(weights) - else: - warnings.warn(f"skglm doesn't support Weighted {distribution}, skipping.") - return result - else: - datafit = DATAFITS[distribution] + datafit = DATAFITS[distribution] if l1_ratio == 1: penalty = L1(alpha=reg_strength) diff --git a/src/glum_benchmarks/bench_sklearn.py b/glum_benchmarks/libraries/bench_sklearn.py similarity index 96% rename from src/glum_benchmarks/bench_sklearn.py rename to glum_benchmarks/libraries/bench_sklearn.py index b1b0fb6da..3e7b2aff8 100644 --- a/src/glum_benchmarks/bench_sklearn.py +++ b/glum_benchmarks/libraries/bench_sklearn.py @@ -11,7 +11,7 @@ TweedieRegressor, ) -from .util import benchmark_convergence_tolerance, runtime +from glum_benchmarks.util import benchmark_convergence_tolerance, runtime def _build_and_fit(model_args, fit_args): @@ -51,10 +51,6 @@ def sklearn_bench( result: dict[str, Any] = {} reg_strength = alpha if reg_multiplier is None else alpha * reg_multiplier - if "offset" in dat.keys(): - warnings.warn("sklearn doesn't support offset, skipping this problem.") - return result - n_samples = dat["X"].shape[0] if distribution == "gaussian": diff --git a/src/glum_benchmarks/zeros_benchmark.py b/glum_benchmarks/libraries/bench_zeros.py similarity index 100% rename from src/glum_benchmarks/zeros_benchmark.py rename to glum_benchmarks/libraries/bench_zeros.py diff --git a/src/glum_benchmarks/problems.py b/glum_benchmarks/problems.py similarity index 87% rename from src/glum_benchmarks/problems.py rename to glum_benchmarks/problems.py index 67c8ef13e..e31596bac 100644 --- a/src/glum_benchmarks/problems.py +++ b/glum_benchmarks/problems.py @@ -20,8 +20,8 @@ from .util import cache_location, exposure_and_offset_to_weights, get_tweedie_p # TODO: Add a second regularization strength (would double the number of problems) -# TODO: Skip the Gaussian Ridge Regression as those have a closed form solution -# and can be solved immediately by e.g. sklearn +# TODO: Implement closed form solution for l2-gaussian and put a note in the results +# TODO: Add glm_data problems? joblib_memory = Memory(cache_location, verbose=0) @@ -44,10 +44,9 @@ 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", ) -> dict[str, np.ndarray]: """ Load the data. @@ -56,20 +55,12 @@ def load_data( 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) - # 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) - - # Step 3) One hot encode columns if we are not using CategoricalMatrix + # 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": @@ -81,10 +72,9 @@ def transform_col(i: int, dtype) -> Union[pd.DataFrame, tm.CategoricalMatrix]: # 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] = [] @@ -115,13 +105,15 @@ def transform_col(i: int, dtype) -> Union[pd.DataFrame, tm.CategoricalMatrix]: + cat_indices_in_expanded_arr, ) elif storage == "sparse": - dtype = np.float32 if single_precision else np.float64 - X = csc_matrix(pd.concat(mat_parts, axis=1, ignore_index=True).astype(dtype)) + X = csc_matrix( + pd.concat(mat_parts, axis=1, ignore_index=True).astype(np.float64) + ) elif storage.startswith("split"): threshold = float(storage.split("split")[1]) - dtype = np.float32 if single_precision else np.float64 X = tm.from_csc( - csc_matrix(pd.concat(mat_parts, axis=1, ignore_index=True).astype(dtype)), + csc_matrix( + pd.concat(mat_parts, axis=1, ignore_index=True).astype(np.float64) + ), threshold, ) else: # Fall back to using a dense matrix. diff --git a/glum_benchmarks/run_benchmarks.py b/glum_benchmarks/run_benchmarks.py new file mode 100644 index 000000000..f6b06d4e8 --- /dev/null +++ b/glum_benchmarks/run_benchmarks.py @@ -0,0 +1,435 @@ +#!/usr/bin/env python +""" +Benchmark runner script for comparing GLM libraries. + +Usage: + pixi run -e benchmark run-benchmarks + +Configuration: + Edit the CONFIGURATION section below to select which libraries, datasets, + regularizations, and distributions to benchmark. You can also control which + steps to run (RUN_BENCHMARKS, ANALYZE_RESULTS, GENERATE_PLOTS). + +Output: + - glum_benchmarks/results/pickles/: Pickle files with detailed results + - glum_benchmarks/results/figures/: PNG plots comparing library performance + - glum_benchmarks/results/results.csv: Summary CSV for reproducibility +""" + +from __future__ import annotations + +import os +import pickle +import shutil +from pathlib import Path + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd + +from glum_benchmarks.problems import get_all_problems +from glum_benchmarks.util import ( + BenchmarkParams, + execute_problem_library, + get_all_libraries, + get_params_from_fname, +) + +# TODO: Rerun the benchmarks +# TODO: update README and the documentation + +# CONFIGURATION + +# Keep as-is to run the same benchmark suite as in the documentation. In particular +# if you just want to update the benchmark results, +# you do not need to change anything here. + +# Steps to run +RUN_BENCHMARKS = True # Run the benchmarks (can be slow) +ANALYZE_RESULTS = True # Analyze and print results (writes CSV_FILE) +GENERATE_PLOTS = True # Generate comparison plots (reads from CSV_FILE and writes PNGs) + +# Output directories (relative to this file) +# Change RUN_NAME to separate different benchmark runs, e.g.: +# RUN_NAME = "housing_lasso" +# RUN_NAME = "insurance_full" +# Note: Only "docs" folder has its CSV tracked in git (for documentation) +_SCRIPT_DIR = Path(__file__).parent +RUN_NAME = "docs" # Subfolder name within results/ ("docs" CSV is git-tracked) +RESULTS_DIR = _SCRIPT_DIR / "results" / RUN_NAME +PICKLE_DIR = RESULTS_DIR / "pickles" +FIGURE_DIR = RESULTS_DIR / "figures" +CSV_FILE = RESULTS_DIR / "results.csv" + +# Cache settings (shared across all runs) +DATA_CACHE_DIR = _SCRIPT_DIR / ".cache" # Where to store the data cache +CACHE_DATA = True # Cache data loading across library runs +CLEAR_DATA_CACHE = False # Clear data cache before running +CLEAR_OUTPUT = True # Clear pickle output directory before running + +# Libraries to benchmark: "glum", "sklearn", "h2o", "liblinear", +# "skglm", "celer", "zeros" +LIBRARIES = [ + "glum", + "sklearn", + "h2o", + "liblinear", + "skglm", + "celer", +] + +# Datasets to run: "intermediate-housing", "intermediate-insurance", +# "narrow-insurance", "wide-insurance" +DATASETS = [ + "intermediate-housing", + "intermediate-insurance", + "narrow-insurance", + "wide-insurance", +] + +# Regularization types to include: "lasso", "l2", "net" +REGULARIZATIONS = ["lasso", "l2"] + +# Distributions to include: "gaussian", "gamma", "binomial", "poisson", "tweedie-p=1.5" +DISTRIBUTIONS = ["gaussian", "gamma", "binomial", "poisson", "tweedie-p=1.5"] + +# Benchmark settings +NUM_THREADS = 16 +REG_STRENGTH = 0.001 +STANDARDIZE = True +ITERATIONS = 2 # Run each benchmark N times, report minimum runtime (>=2 for skglm) +NUM_ROWS = None # None = use full dataset, or set an int for quick test runs + + +def get_problems_to_run() -> list[str]: + """Get list of problem names matching the configuration.""" + all_problems = get_all_problems() + selected = [] + + for name in all_problems.keys(): + # Filter by dataset + if DATASETS is not None: + if not any(d in name for d in DATASETS): + continue + + # Filter by regularization + if REGULARIZATIONS is not None: + if not any(reg in name for reg in REGULARIZATIONS): + continue + + # Filter by distribution + if DISTRIBUTIONS is not None: + if not any(dist in name for dist in DISTRIBUTIONS): + continue + + selected.append(name) + + return sorted(selected) + + +def run_single_benchmark( + problem_name: str, library_name: str +) -> tuple[dict, BenchmarkParams]: + """Run a single benchmark and return results.""" + params = BenchmarkParams( + problem_name=problem_name, + library_name=library_name, + num_rows=NUM_ROWS, + storage="dense", + threads=NUM_THREADS, + regularization_strength=REG_STRENGTH, + ) + + result, _ = execute_problem_library( + params, + iterations=ITERATIONS, + standardize=STANDARDIZE, + ) + + return result, params + + +def run_all_benchmarks(): + """Run all configured benchmarks.""" + # Set up data caching + if CACHE_DATA: + os.environ["GLM_BENCHMARKS_CACHE"] = str(DATA_CACHE_DIR.absolute()) + if CLEAR_DATA_CACHE and DATA_CACHE_DIR.exists(): + print(f"Clearing data cache: {DATA_CACHE_DIR}") + shutil.rmtree(DATA_CACHE_DIR) + + # Set up output directory + if CLEAR_OUTPUT and PICKLE_DIR.exists(): + print(f"Clearing output directory: {PICKLE_DIR}") + shutil.rmtree(PICKLE_DIR) + PICKLE_DIR.mkdir(parents=True, exist_ok=True) + + problems = get_problems_to_run() + available = get_all_libraries() + libraries = [lib for lib in LIBRARIES if lib in available] + + print(f"Problems: {len(problems)}") + print(f"Libraries: {libraries}") + print() + + total = len(problems) * len(libraries) + current = 0 + + for problem_name in problems: + for library_name in libraries: + current += 1 + print(f"[{current}/{total}] {library_name} / {problem_name}", end=" ") + + try: + result, params = run_single_benchmark(problem_name, library_name) + + # Save result + fname = params.get_result_fname() + ".pkl" + with open(PICKLE_DIR / fname, "wb") as f: + pickle.dump(result, f) + + if len(result) > 0 and "runtime" in result: + print(f"-> {result['runtime']:.4f}s") + else: + print("-> (no result)") + + except Exception as e: + print(f"-> ERROR: {e}") + + +def analyze_results() -> 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 PICKLE_DIR.glob("*.pkl"): + with open(fname, "rb") as f: + data = pickle.load(f) + + if not data or "coef" not in data: + continue + + params = get_params_from_fname(fname.name) + if params.problem_name is None: + continue + problem = get_all_problems()[params.problem_name] + coefs = data["coef"] + + # 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 + 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) + + # Get regularization strength from params or problem default + reg_strength = ( + problem.regularization_strength + if params.regularization_strength is None + else params.regularization_strength + ) + + results.append( + { + "problem_name": params.problem_name, + "library_name": params.library_name, + "num_rows": data.get("num_rows"), + "regularization_strength": reg_strength, + "storage": params.storage, + "threads": params.threads, + "n_iter": n_iter, + "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", "regularization_strength"] + df = df.set_index(problem_id_cols).sort_values("library_name").sort_index() + + # Calculate relative objective value (how far from best) + df["rel_obj_val"] = df[["obj_val"]] - df.groupby(level=[0, 1, 2])[["obj_val"]].min() + + # Display columns + cols_to_show = [ + "library_name", + "storage", + "threads", + "n_iter", + "runtime", + "intercept", + "num_nonzero_coef", + "obj_val", + "rel_obj_val", + ] + + with pd.option_context( + "display.expand_frame_repr", + False, + "display.max_columns", + None, + "display.max_rows", + None, + ): + print(df[cols_to_show]) + + # Export to CSV for figure generation and reproducibility + CSV_FILE.parent.mkdir(parents=True, exist_ok=True) + df.reset_index().to_csv(CSV_FILE, index=False) + print(f"\nExported results to: {CSV_FILE}") + + return df.reset_index() + + +def plot_results(): + """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 CSV_FILE.exists(): + print(f"CSV file not found: {CSV_FILE}") + print("Run ANALYZE_RESULTS first to generate the CSV.") + return + + df = pd.read_csv(CSV_FILE) + print(f"Reading results from: {CSV_FILE}") + + if df.empty: + print("No data to plot!") + return + + FIGURE_DIR.mkdir(exist_ok=True) + + # Extract distribution and regularization from problem name + df = df.copy() + df["distribution"] = df["problem_name"].apply(lambda x: x.split("-")[-1]) + df["regularization"] = df["problem_name"].apply( + lambda x: "lasso" if "lasso" in x else "l2" + ) + df["dataset"] = df["problem_name"].apply( + lambda x: "-".join(x.split("-")[:-3]) # e.g., "intermediate-housing-no" + ) + + # Drop duplicates (keep latest result for each problem/library combo) + df = df.drop_duplicates(subset=["problem_name", "library_name"], keep="last") + + # Generate one plot per dataset/regularization combo + for dataset in df["dataset"].unique(): + for reg in df["regularization"].unique(): + subset = df[(df["dataset"] == dataset) & (df["regularization"] == reg)] + + if subset.empty: + continue + + # Pivot for plotting + pivot = subset.pivot( + index="distribution", + columns="library_name", + values="runtime", + ).fillna(0) + + # Create bar chart + fig, ax = plt.subplots(figsize=(10, 5)) + pivot.plot(kind="bar", ax=ax) + + reg_label = "Lasso" if reg == "lasso" else "Ridge" + ax.set_title(f"{dataset.replace('-', ' ').title()} - {reg_label}") + ax.set_ylabel("Runtime (s)") + ax.set_xlabel("Distribution") + ax.legend(title="Library", bbox_to_anchor=(1.02, 1), loc="upper left") + plt.xticks(rotation=45, ha="right") + plt.tight_layout() + + # Save + fname = f"{dataset}-{reg}" + plt.savefig(FIGURE_DIR / f"{fname}.png", dpi=300) + plt.close() + print(f"Saved: {fname}.png") + + # Generate normalized plot (glum = 1.0) + if "glum" in pivot.columns: + pivot_norm = pivot.div(pivot["glum"], axis=0) + + fig, ax = plt.subplots(figsize=(10, 5)) + pivot_norm.plot(kind="bar", ax=ax) + + ax.set_title( + f"{dataset.replace('-', ' ').title()} - {reg_label} (normalized)" + ) + ax.set_ylabel("Runtime relative to glum (1.0 = glum)") + ax.set_xlabel("Distribution") + ax.axhline(y=1.0, color="gray", linestyle="--", linewidth=1, alpha=0.7) + ax.legend(title="Library", bbox_to_anchor=(1.02, 1), loc="upper left") + plt.xticks(rotation=45, ha="right") + plt.tight_layout() + + fname_norm = f"{dataset}-{reg}-normalized" + plt.savefig(FIGURE_DIR / f"{fname_norm}.png", dpi=300) + plt.close() + print(f"Saved: {fname_norm}.png") + + +def main(): + """Run benchmarks, analyze, and plot based on configuration. + + Workflow: + 1. RUN_BENCHMARKS: Execute benchmarks, save pickle files to PICKLE_DIR + 2. ANALYZE_RESULTS: Analyze pickles, print summary, write CSV_FILE + 3. GENERATE_PLOTS: Read CSV_FILE, generate figures to FIGURE_DIR + + You can run steps independently: + - Run benchmarks once, then regenerate figures later from CSV + - Commit CSV to repo for reproducibility + """ + if RUN_BENCHMARKS: + run_all_benchmarks() + + if ANALYZE_RESULTS: + analyze_results() + + if GENERATE_PLOTS: + plot_results() + + print() + print("=" * 60) + print("DONE") + print("=" * 60) + if RUN_BENCHMARKS or ANALYZE_RESULTS: + print(f"Results saved to: {RESULTS_DIR}/") + if GENERATE_PLOTS: + print(f"Figures saved to: {FIGURE_DIR}/") + + +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..726208817 --- /dev/null +++ b/glum_benchmarks/tests/test_benchmarks.py @@ -0,0 +1,135 @@ +""" +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, +) + + +class TestProblems: + """Tests for problem definitions.""" + + def test_get_all_problems_returns_dict(self): + problems = get_all_problems() + assert isinstance(problems, dict) + assert len(problems) > 0 + + def test_all_problems_have_required_attributes(self): + problems = get_all_problems() + for name, problem in problems.items(): + assert hasattr(problem, "data_loader") + assert hasattr(problem, "distribution") + assert hasattr(problem, "regularization_strength") + + +class TestLibraries: + """Tests for library availability.""" + + def test_get_all_libraries_returns_dict(self): + libraries = get_all_libraries() + assert isinstance(libraries, dict) + + def test_glum_always_available(self): + libraries = get_all_libraries() + assert "glum" in libraries + + +class TestBenchmarkParams: + """Tests for BenchmarkParams.""" + + def test_params_creation(self): + params = BenchmarkParams( + problem_name="test-problem", + library_name="glum", + num_rows=100, + storage="dense", + threads=1, + regularization_strength=0.01, + ) + assert params.problem_name == "test-problem" + assert params.library_name == "glum" + + def test_get_result_fname(self): + params = BenchmarkParams( + problem_name="test-problem", + library_name="glum", + num_rows=100, + storage="dense", + threads=1, + regularization_strength=0.01, + ) + fname = params.get_result_fname() + assert "test-problem" in fname + assert "glum" in fname + + def test_params_roundtrip(self): + """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, + regularization_strength=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 + + +class TestExecuteProblem: + """Tests for execute_problem_library.""" + + def test_execute_simple_problem(self): + """Test that a simple problem runs without error.""" + # Use a small, fast problem + problems = get_all_problems() + # Find a gaussian problem (fastest) + 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, # Small for speed + storage="dense", + threads=1, + regularization_strength=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) + + +class TestUtilityFunctions: + """Tests for utility functions.""" + + def test_get_tweedie_p_poisson(self): + assert get_tweedie_p("poisson") == 1 + + def test_get_tweedie_p_gamma(self): + assert get_tweedie_p("gamma") == 2 + + def test_get_tweedie_p_gaussian(self): + assert get_tweedie_p("gaussian") == 0 + + def test_get_tweedie_p_explicit(self): + 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 68% rename from src/glum_benchmarks/util.py rename to glum_benchmarks/util.py index 9ba41d82d..f4ff6755d 100644 --- a/src/glum_benchmarks/util.py +++ b/glum_benchmarks/util.py @@ -1,11 +1,7 @@ -import glob import os -import shutil 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 @@ -198,7 +194,6 @@ def __init__( num_rows: Optional[int] = None, storage: Optional[str] = None, threads: Optional[int] = None, - single_precision: Optional[bool] = None, regularization_strength: Optional[float] = None, hessian_approx: Optional[float] = None, diagnostics_level: Optional[str] = None, @@ -208,7 +203,6 @@ def __init__( self.num_rows = num_rows self.storage = storage self.threads = threads - self.single_precision = single_precision self.regularization_strength = regularization_strength self.hessian_approx = hessian_approx self.diagnostics_level = diagnostics_level @@ -219,7 +213,6 @@ def __init__( "num_rows", "storage", "threads", - "single_precision", "regularization_strength", "hessian_approx", "diagnostics_level", @@ -263,115 +256,18 @@ def get_result_fname(self) -> str: num_rows=None, regularization_strength=None, storage="dense", - 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("--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], - 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, - 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_reg_hessian_diag.pkl + Parameters ---------- fname: file name @@ -379,35 +275,29 @@ 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, 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: @@ -436,7 +326,6 @@ def get_tweedie_p(distribution: str) -> float: def _standardize_features( X: Union[np.ndarray, pd.DataFrame, csc_matrix, tm.MatrixBase], - single_precision: bool = False, ) -> Union[np.ndarray, pd.DataFrame, csc_matrix, tm.MatrixBase]: """ Standardize features by scaling to unit L2 norm per column. @@ -444,7 +333,7 @@ def _standardize_features( For consistency across sparse/dense, we use L2 norm scaling (no centering). This ensures all benchmark libraries start with the same pre-processed data. """ - dtype = np.float32 if single_precision else np.float64 + dtype = np.float64 if isinstance(X, pd.DataFrame): # Preserve DataFrame type @@ -478,3 +367,125 @@ def _standardize_features( else: # Unknown type, return as-is return X + + +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, + glum_bench, + h2o_bench, + liblinear_bench, + skglm_bench, + sklearn_bench, + zeros_bench, + ) + + all_libraries = { + "glum": glum_bench, + "zeros": zeros_bench, + "celer": celer_bench, + "h2o": h2o_bench, + "liblinear": liblinear_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: str = "basic", + standardize: bool = True, + **kwargs, +): + """ + Run the benchmark problem specified by 'params', 'iterations' times. + + Parameters + ---------- + params + iterations + diagnostics_level + standardize + Whether to standardize features before fitting. Default True for benchmarks. + kwargs + + Returns + ------- + Tuple: Result data on this run, and the regularization 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, + storage=params.storage, + ) + + # Standardize features for better convergence across all libraries + if standardize: + dat["X"] = _standardize_features(dat["X"]) + + 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, + diagnostics_level=diagnostics_level, + reg_multiplier=reg_multiplier, + hessian_approx=params.hessian_approx, + **kwargs, + ) + + if len(result) > 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 diff --git a/pixi.lock b/pixi.lock index fece64121..2fe971a4c 100644 --- a/pixi.lock +++ b/pixi.lock @@ -34,19 +34,23 @@ environments: - 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/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-h3394656_0.conda - 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vc14_runtime >=14.44.35208 license: Zlib license_family: Other + purls: [] size: 123890 timestamp: 1766076739436 - conda: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.7-hb78ec9c_6.conda diff --git a/pixi.toml b/pixi.toml index d736ca2f1..123ca1861 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" @@ -119,17 +119,17 @@ shapely = "*" # used in docs/tutorials/regularization_housing_data [feature.benchmark.dependencies] attrs = "*" -click = "*" git_root = "*" +matplotlib-base = "*" openjdk = "*" -python = ">=3.9,<3.10" # skglm requires Python <3.10 +pytest = "*" r-base = "*" rpy2 = "*" +skglm = "*" [feature.benchmark.pypi-dependencies] celer = "*" h2o = "*" # Latest version from PyPI -skglm = "*" [feature.benchmark.target.win-64.dependencies] blas = { build = "*mkl" } diff --git a/pyproject.toml b/pyproject.toml index 11f90e81d..b9225c4b9 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"] @@ -32,15 +31,16 @@ 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 namespace_packages = true ignore_missing_imports = true +follow_imports = "skip" [[tool.mypy.overrides]] # https://github.com/scikit-learn/scikit-learn/issues/16705 diff --git a/setup.py b/setup.py index c24766441..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", @@ -87,16 +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 - glm_benchmarks_plot = glum_benchmarks.cli_plot:cli_plot - """ - ), + entry_points=None, ext_modules=cythonize( ext_modules, annotate=False, 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/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 428d05d9f..000000000 --- a/src/glum_benchmarks/cli_analyze.py +++ /dev/null @@ -1,207 +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() - - 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", - "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 and results["n_iter"] > 0: - runtime_per_iter = results["runtime"] / results["n_iter"] - else: - # n_iter=0 means immediate convergence or closed-form solution - runtime_per_iter = results["runtime"] - 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"] - 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_plot.py b/src/glum_benchmarks/cli_plot.py deleted file mode 100644 index a6954d8aa..000000000 --- a/src/glum_benchmarks/cli_plot.py +++ /dev/null @@ -1,268 +0,0 @@ -"""Generate benchmark comparison plots from benchmark output directory.""" - -import os -from pathlib import Path -from typing import Optional - -import click -import matplotlib.pyplot as plt -import pandas as pd - -from .cli_analyze import ( - _extract_dict_results_to_pd_series, - _identify_parameter_fnames, - _load_benchmark_results, -) -from .util import BenchmarkParams - - -@click.command() -@click.option( - "--output_dir", - type=str, - default="benchmark_output", - help="Directory containing benchmark results (default: benchmark_output)", -) -@click.option( - "--figure_dir", - type=str, - default="benchmark_figures", - help="Directory to save generated figures (default: benchmark_figures)", -) -@click.option( - "--problems", - type=str, - default=None, - help="Comma-separated list of problem prefixes to plot " - "(e.g., 'narrow-insurance,intermediate-housing'). If None, plots all.", -) -@click.option( - "--dpi", - type=int, - default=300, - help="DPI for saved PNG figures (default: 300)", -) -def cli_plot(output_dir: str, figure_dir: str, problems: Optional[str], dpi: int): - """ - Generate benchmark comparison plots from results directory. - - Examples: - # Plot all benchmarks from default directory - glm_benchmarks_plot - - # Plot from specific directory - glm_benchmarks_plot --output_dir my_benchmarks --figure_dir my_figures - - # Plot only specific problems - glm_benchmarks_plot --problems narrow-insurance,intermediate-housing - """ - # Create output directory - os.makedirs(figure_dir, exist_ok=True) - - # Load benchmark results - print(f"Loading benchmark results from {output_dir}...") - file_names = _identify_parameter_fnames(output_dir, BenchmarkParams()) - - 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: - print("No benchmark results found!") - return - - df = pd.DataFrame.from_records(formatted_results) - - # Extract distribution from problem name - df["distribution"] = ( - df["problem_name"] - .str.split("-") - .apply(lambda x: x[-2] if "5" in x[-1] else x[-1]) - ) - - # Remove duplicates - df = df.drop_duplicates(subset=["problem_name", "library_name"], keep="last") - - # Filter problems if specified - if problems: - problem_list = [p.strip() for p in problems.split(",")] - df = df[df["problem_name"].str.contains("|".join(problem_list))] - if df.empty: - print(f"No results found for problems: {problems}") - return - - # Determine which problem sets we have - problem_prefixes = set() - for idx, row in df.iterrows(): - parts = row["problem_name"].split("-") - # Extract dataset name (e.g., "narrow-insurance", "intermediate-housing") - if len(parts) >= 3: - dataset = "-".join( - parts[:-3] - ) # Everything before "no-weights-{reg}-{dist}" - problem_prefixes.add(dataset) - - print(f"Found datasets: {', '.join(sorted(problem_prefixes))}") - - # Generate plots for each problem/regularization combination - plot_count = 0 - for prob_name in sorted(problem_prefixes): - for reg in ["l2", "lasso"]: - plot_df = df[ - df["problem_name"].str.contains(reg) - & df["problem_name"].str.contains(prob_name) - ].copy() - - if plot_df.empty: - continue - - plot_df = plot_df.set_index(["distribution"])[["runtime", "library_name"]] - - try: - 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) - - # Drop columns (libraries) that have ALL NaN values (never ran) - plot_df = plot_df.dropna(axis=1, how="all") - - # Skip if no data remains - if plot_df.empty: - print(f"Skipping {prob_name}-{reg}: No data available") - continue - - # Replace NaN with 0 for plotting (matplotlib will show as no bar) - plot_df = plot_df.fillna(0) - - plot_df.index = [x.title() for x in plot_df.index] - except ValueError as e: - print(f"Skipping {prob_name}-{reg}: {e}") - continue - - # Hard limit y-axis to 15 seconds - ylim = 15 - - # Clean title: just dataset name + regularization type - clean_prob_name = prob_name.replace("-no-weights", "").replace("-no", "") - title = ( - clean_prob_name.title() + "-" + ("Lasso" if reg == "lasso" else "Ridge") - ) - - # Create figure manually to avoid gaps for missing data - fig, ax = plt.subplots(figsize=(6, 3)) - - # Get unique libraries and assign consistent colors using Paired colormap - libraries = plot_df.columns - cmap = plt.cm.Paired - n_colors = len(libraries) - colors = { - lib: cmap(i / max(n_colors - 1, 1)) for i, lib in enumerate(libraries) - } - - # Plot bars distribution by distribution - x_pos: float = 0 - x_labels: list[str] = [] - x_ticks: list[float] = [] - bar_width = 0.8 / len(libraries) - - for dist_idx, dist in enumerate(plot_df.index): - # Only plot libraries with data for this distribution - dist_data = plot_df.loc[dist] - libs_with_data = dist_data[dist_data > 0] - - if len(libs_with_data) > 0: - # Plot bars for this distribution - for lib_idx, (lib, runtime) in enumerate(libs_with_data.items()): - color = colors[lib] - ax.bar( - x_pos + lib_idx * bar_width, - runtime, - bar_width, - color=color, - label=lib if dist_idx == 0 else "", - ) - - # Center tick position for this group - x_ticks.append(x_pos + (len(libs_with_data) - 1) * bar_width / 2) - x_labels.append(dist) - x_pos += ( - len(libs_with_data) * bar_width + 0.2 - ) # Gap between distributions - - ax.set_ylim([0, ylim]) - ax.set_ylabel("run time (s)") - ax.set_title(title) - ax.set_xticks(x_ticks) - ax.set_xticklabels(x_labels, rotation=45, ha="right") - - # Add legend (remove duplicates) - handles, labels = ax.get_legend_handles_labels() - by_label = dict(zip(labels, handles)) - ax.legend( - by_label.values(), - by_label.keys(), - bbox_to_anchor=(1, 1), - loc="upper left", - ncol=1, - ) - - ax = plt.gca() - ax.spines["right"].set_visible(False) - ax.spines["top"].set_visible(False) - ax.yaxis.set_ticks_position("left") - ax.xaxis.set_ticks_position("bottom") - - # Annotate bars that exceed y-limit - for p in ax.patches: - x = p.get_x() - width = p.get_width() - y = p.get_height() - if y > ylim + 0.1: - arrow_x = x + width / 2 - - # Arrow at the very top - simple arrow style like in reference - ax.annotate( - "", - xy=(arrow_x, ylim - 0.1), # Arrow tip near top - xytext=(arrow_x, ylim - 1.0), # Arrow starts lower - arrowprops=dict( - arrowstyle="simple", - fc="black", - ec="black", - ), - ) - - # Text below the arrow, extending downward from arrow base - ax.annotate( - f"{y:.1f}", - (arrow_x, ylim - 1.1), # Start just below arrow - fontsize=14, - rotation="vertical", - ha="center", - va="top", # Text extends downward - ) - - plt.tight_layout() - - # Save figure - base_name = f"{prob_name}-{reg}" - png_path = Path(figure_dir) / f"{base_name}.png" - pdf_path = Path(figure_dir) / f"{base_name}.pdf" - - plt.savefig(png_path, dpi=dpi) - plt.savefig(pdf_path) - plt.close() - - plot_count += 1 - print(f"Saved: {base_name}.png, {base_name}.pdf") - - print(f"\nGenerated {plot_count} plots in {figure_dir}/") - - -if __name__ == "__main__": - cli_plot() diff --git a/src/glum_benchmarks/cli_run.py b/src/glum_benchmarks/cli_run.py deleted file mode 100644 index 2888d3fe2..000000000 --- a/src/glum_benchmarks/cli_run.py +++ /dev/null @@ -1,307 +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, - _standardize_features, - 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 - -try: - from .bench_r_glmnet import r_glmnet_bench # isort:skip - - R_GLMNET_INSTALLED = True -except ImportError: - R_GLMNET_INSTALLED = False - -try: - from .bench_sklearn import sklearn_bench # isort:skip - - SKLEARN_INSTALLED = True -except ImportError: - SKLEARN_INSTALLED = False - -try: - from .bench_skglm import skglm_bench # isort:skip - - SKGLM_INSTALLED = True -except ImportError: - SKGLM_INSTALLED = False - -try: - from .bench_celer import celer_bench # isort:skip - - CELER_INSTALLED = True -except ImportError: - CELER_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", - standardize: bool = True, - **kwargs, -): - """ - Run the benchmark problem specified by 'params', 'iterations' times. - - Parameters - ---------- - params - iterations - diagnostics_level - standardize - Whether to standardize features before fitting. Default True for benchmarks. - 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, - ) - - # Standardize features for better convergence across all libraries - if standardize: - dat["X"] = _standardize_features(dat["X"], params.single_precision or False) - - 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, - 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 - - if R_GLMNET_INSTALLED: - all_libraries["r-glmnet"] = r_glmnet_bench - - if SKLEARN_INSTALLED: - all_libraries["sklearn"] = sklearn_bench - - if SKGLM_INSTALLED: - all_libraries["skglm"] = skglm_bench - - if CELER_INSTALLED: - all_libraries["celer"] = celer_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/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 e02eda716..000000000 --- a/tests/benchmark/test_cli.py +++ /dev/null @@ -1,139 +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"], {"num_rows": 1000}), - ], -) -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", - "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" - "_0.0_basic.pkl", - f"narrow-insurance-weights-l2-gamma_glum_20_dense_{n_threads}_False_10" - "00.0_0.0_basic.pkl", - f"wide-insurance-no-weights-net-poisson_zeros_20_dense_{n_threads}_False_1000.0" - "_0.0_basic.pkl", - f"wide-insurance-no-weights-net-poisson_glum_20_dense_{n_threads}_False" - "_1000.0_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/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 0b630798c..aed366bc2 100644 --- a/tests/glm/test_benchmark_golden_master.py +++ b/tests/glm/test_benchmark_golden_master.py @@ -6,9 +6,8 @@ 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") diff --git a/tests/glm/test_offset_weight_obj_equivalent.py b/tests/glm/test_offset_weight_obj_equivalent.py index 791a82792..324e49c82 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, @@ -37,7 +36,7 @@ def test_offset_solution_matches_weights_solution( ): params = BenchmarkParams( problem_name=Pn, - library_name="sklearn-fork", + library_name="glum", # storage=storage, **bench_cfg, ) From c35bb1b2bf1ae43454bd0d56e4159af2c1389ce7 Mon Sep 17 00:00:00 2001 From: David Eiglsperger Date: Wed, 28 Jan 2026 15:36:15 +0100 Subject: [PATCH 13/42] fix CI --- src/glum/_distribution.py | 2 +- src/glum/_glm.py | 6 +++--- 2 files changed, 4 insertions(+), 4 deletions(-) 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) From eee3877be0802436961b9d15ec9f6ada92bede1e Mon Sep 17 00:00:00 2001 From: David Eiglsperger Date: Thu, 29 Jan 2026 10:37:16 +0100 Subject: [PATCH 14/42] Improved plotting and some small adaptions/fixes --- glum_benchmarks/README.md | 15 +- glum_benchmarks/libraries/bench_celer.py | 11 +- glum_benchmarks/libraries/bench_h2o.py | 10 +- glum_benchmarks/libraries/bench_sklearn.py | 42 ++-- glum_benchmarks/run_benchmarks.py | 226 +++++++++++++++------ 5 files changed, 199 insertions(+), 105 deletions(-) diff --git a/glum_benchmarks/README.md b/glum_benchmarks/README.md index 946a09e44..efe68cd6a 100644 --- a/glum_benchmarks/README.md +++ b/glum_benchmarks/README.md @@ -66,12 +66,11 @@ Results are organized by `RUN_NAME` (default: `"docs"`): ``` glum_benchmarks/ -├── results/ -│ └── docs/ # RUN_NAME = "docs" -│ ├── pickles/ # Step 1 output (gitignored) -│ ├── figures/ # Step 3 output (gitignored) -│ └── results.csv # Step 2 output (tracked for docs/) -└── .cache/ # Shared data cache (gitignored) +└── results/ + └── docs/ # RUN_NAME = "docs" + ├── pickles/ # Step 1 output (gitignored) + ├── figures/ # Step 3 output (gitignored) + └── results.csv # Step 2 output (tracked for docs/) ``` Only `results/docs/results.csv` is tracked in git. Change `RUN_NAME` for experiments. @@ -96,9 +95,7 @@ The script is pre-configured for the documentation benchmark run. | `STANDARDIZE` | Whether to standardize features before fitting | | `ITERATIONS` | Runs per benchmark (>=2 required for skglm) | | `NUM_ROWS` | Limit rows per dataset (`None` = full dataset) | -| `CACHE_DATA` | Cache data loading in `.cache/` | -| `CLEAR_DATA_CACHE` | Clear data cache before running | -| `CLEAR_OUTPUT` | Clear pickles before running | +| `CLEAR_OUTPUT` | Clear entire `RUN_NAME` directory before running | See `problems.py` for available datasets and problem definitions. diff --git a/glum_benchmarks/libraries/bench_celer.py b/glum_benchmarks/libraries/bench_celer.py index c21048025..25d5a72de 100644 --- a/glum_benchmarks/libraries/bench_celer.py +++ b/glum_benchmarks/libraries/bench_celer.py @@ -8,10 +8,8 @@ from glum_benchmarks.util import benchmark_convergence_tolerance, runtime -def _build_and_fit(model_args, fit_args): - model_class = model_args.pop("_model_class") - reg = model_class(**model_args) - return reg.fit(**fit_args) +def _build_and_fit(model_class, model_args, fit_args): + return model_class(**model_args).fit(**fit_args) def celer_bench( @@ -42,7 +40,6 @@ def celer_bench( model_class = Lasso model_args = { - "_model_class": model_class, "tol": benchmark_convergence_tolerance, "fit_intercept": True, "max_iter": 1000, @@ -53,7 +50,9 @@ def celer_bench( model_args["l1_ratio"] = l1_ratio try: - result["runtime"], m = runtime(_build_and_fit, iterations, model_args, fit_args) + result["runtime"], m = runtime( + _build_and_fit, iterations, model_class, model_args, fit_args + ) except Exception as e: warnings.warn(f"Celer failed: {e}") return {} diff --git a/glum_benchmarks/libraries/bench_h2o.py b/glum_benchmarks/libraries/bench_h2o.py index c223a4dda..f105f9470 100644 --- a/glum_benchmarks/libraries/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 @@ -10,6 +11,9 @@ 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): glm = H2OGeneralizedLinearEstimator(**model_args) @@ -59,7 +63,11 @@ 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])) train_h2o = h2o.H2OFrame(train_mat) diff --git a/glum_benchmarks/libraries/bench_sklearn.py b/glum_benchmarks/libraries/bench_sklearn.py index 3e7b2aff8..445299317 100644 --- a/glum_benchmarks/libraries/bench_sklearn.py +++ b/glum_benchmarks/libraries/bench_sklearn.py @@ -14,11 +14,9 @@ from glum_benchmarks.util import benchmark_convergence_tolerance, runtime -def _build_and_fit(model_args, fit_args): +def _build_and_fit(model_class, model_args, fit_args): """Build and fit a sklearn regressor.""" - model_class = model_args.pop("_model_class") - reg = model_class(**model_args) - return reg.fit(**fit_args) + return model_class(**model_args).fit(**fit_args) def sklearn_bench( @@ -52,13 +50,14 @@ def sklearn_bench( reg_strength = alpha if reg_multiplier is None else alpha * reg_multiplier n_samples = dat["X"].shape[0] + model_class = None if distribution == "gaussian": if l1_ratio == 0.0: # Pure L2 (Ridge): use closed-form solution # sklearn's Ridge uses alpha directly but with sum(loss) not mean(loss) + model_class = Ridge model_args = { - "_model_class": Ridge, "alpha": reg_strength * n_samples, "fit_intercept": True, "max_iter": 1000, @@ -67,8 +66,8 @@ def sklearn_bench( } elif l1_ratio == 1.0: # Pure L1 (Lasso) + model_class = Lasso model_args = { - "_model_class": Lasso, "alpha": reg_strength * n_samples, "fit_intercept": True, "max_iter": 1000, @@ -77,8 +76,8 @@ def sklearn_bench( } else: # Elastic Net: mixed L1/L2 + model_class = ElasticNet model_args = { - "_model_class": ElasticNet, "alpha": reg_strength, "l1_ratio": l1_ratio, "fit_intercept": True, @@ -89,32 +88,21 @@ def sklearn_bench( elif distribution == "binomial": # sklearn's LogisticRegression uses C = 1/lambda where lambda is the # regularization strength. However, sklearn does NOT scale the regularization - # by n_samples, while glum's objective is: mean(loss) + alpha * penalty. + # by n_samples, while glum's objective is: mean(loss) + alpha * reg_term. # To match glum's objective, we need: C = 1 / (alpha * n_samples) C_value = 1.0 / (reg_strength * n_samples) if reg_strength > 0 else 1e10 - # Determine penalty type based on l1_ratio - # Penalty is deprecated but we use it here as we run on sk learn 1.6.1 due - # to the fact that h2o-py requires Python <3.10 - if l1_ratio == 0.0: - penalty = "l2" - elif l1_ratio == 1.0: - penalty = "l1" - else: - penalty = "elasticnet" + # Use lbfgs for L2 (faster and more reliable), saga for L1/elasticnet + solver = "lbfgs" if l1_ratio == 0.0 else "saga" + model_class = LogisticRegression model_args = { - "_model_class": LogisticRegression, "C": C_value, - "penalty": penalty, - # Use lbfgs for L2 (faster and more reliable), saga for L1/elasticnet - "solver": "lbfgs" if penalty == "l2" else "saga", + "l1_ratio": l1_ratio, + "solver": solver, "fit_intercept": True, "max_iter": 1000, "tol": benchmark_convergence_tolerance, } - # Only pass l1_ratio for elastic net - if penalty == "elasticnet": - model_args["l1_ratio"] = l1_ratio else: if "tweedie" in distribution: power = float(distribution.split("-p=")[1]) @@ -130,8 +118,8 @@ def sklearn_bench( ) return result + model_class = TweedieRegressor model_args = { - "_model_class": TweedieRegressor, "power": power, "alpha": reg_strength, "fit_intercept": True, @@ -146,7 +134,9 @@ def sklearn_bench( fit_args["sample_weight"] = dat["sample_weight"] try: - result["runtime"], m = runtime(_build_and_fit, iterations, model_args, fit_args) + result["runtime"], m = runtime( + _build_and_fit, iterations, model_class, model_args, fit_args + ) except ValueError as e: warnings.warn(f"Problem failed with this error: {e}") return result diff --git a/glum_benchmarks/run_benchmarks.py b/glum_benchmarks/run_benchmarks.py index f6b06d4e8..5a0aee773 100644 --- a/glum_benchmarks/run_benchmarks.py +++ b/glum_benchmarks/run_benchmarks.py @@ -11,16 +11,16 @@ steps to run (RUN_BENCHMARKS, ANALYZE_RESULTS, GENERATE_PLOTS). Output: - - glum_benchmarks/results/pickles/: Pickle files with detailed results - - glum_benchmarks/results/figures/: PNG plots comparing library performance - - glum_benchmarks/results/results.csv: Summary CSV for reproducibility + - 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 os import pickle import shutil +import warnings from pathlib import Path import matplotlib.pyplot as plt @@ -37,6 +37,7 @@ # TODO: Rerun the benchmarks # TODO: update README and the documentation +# TODO: Rework plotting # CONFIGURATION @@ -49,22 +50,14 @@ ANALYZE_RESULTS = True # Analyze and print results (writes CSV_FILE) GENERATE_PLOTS = True # Generate comparison plots (reads from CSV_FILE and writes PNGs) -# Output directories (relative to this file) -# Change RUN_NAME to separate different benchmark runs, e.g.: -# RUN_NAME = "housing_lasso" -# RUN_NAME = "insurance_full" -# Note: Only "docs" folder has its CSV tracked in git (for documentation) + +# Change RUN_NAME to separate different benchmark runs into different folders. _SCRIPT_DIR = Path(__file__).parent RUN_NAME = "docs" # Subfolder name within results/ ("docs" CSV is git-tracked) RESULTS_DIR = _SCRIPT_DIR / "results" / RUN_NAME PICKLE_DIR = RESULTS_DIR / "pickles" FIGURE_DIR = RESULTS_DIR / "figures" CSV_FILE = RESULTS_DIR / "results.csv" - -# Cache settings (shared across all runs) -DATA_CACHE_DIR = _SCRIPT_DIR / ".cache" # Where to store the data cache -CACHE_DATA = True # Cache data loading across library runs -CLEAR_DATA_CACHE = False # Clear data cache before running CLEAR_OUTPUT = True # Clear pickle output directory before running # Libraries to benchmark: "glum", "sklearn", "h2o", "liblinear", @@ -107,6 +100,10 @@ def get_problems_to_run() -> list[str]: selected = [] for name in all_problems.keys(): + # Only benchmark "-no-weights" problems (skip offset and weighted variants) + if "-no-weights-" not in name: + continue + # Filter by dataset if DATASETS is not None: if not any(d in name for d in DATASETS): @@ -143,6 +140,7 @@ def run_single_benchmark( result, _ = execute_problem_library( params, iterations=ITERATIONS, + diagnostics_level=None, standardize=STANDARDIZE, ) @@ -151,17 +149,10 @@ def run_single_benchmark( def run_all_benchmarks(): """Run all configured benchmarks.""" - # Set up data caching - if CACHE_DATA: - os.environ["GLM_BENCHMARKS_CACHE"] = str(DATA_CACHE_DIR.absolute()) - if CLEAR_DATA_CACHE and DATA_CACHE_DIR.exists(): - print(f"Clearing data cache: {DATA_CACHE_DIR}") - shutil.rmtree(DATA_CACHE_DIR) - # Set up output directory - if CLEAR_OUTPUT and PICKLE_DIR.exists(): - print(f"Clearing output directory: {PICKLE_DIR}") - shutil.rmtree(PICKLE_DIR) + if CLEAR_OUTPUT and RESULTS_DIR.exists(): + print(f"Clearing output directory: {RESULTS_DIR}") + shutil.rmtree(RESULTS_DIR) PICKLE_DIR.mkdir(parents=True, exist_ok=True) problems = get_problems_to_run() @@ -178,10 +169,22 @@ def run_all_benchmarks(): for problem_name in problems: for library_name in libraries: current += 1 - print(f"[{current}/{total}] {library_name} / {problem_name}", end=" ") + print( + f"[{current}/{total}] {library_name} / {problem_name}", + end=" ", + flush=True, + ) try: - result, params = run_single_benchmark(problem_name, library_name) + # 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) # Save result fname = params.get_result_fname() + ".pkl" @@ -191,7 +194,11 @@ def run_all_benchmarks(): if len(result) > 0 and "runtime" in result: print(f"-> {result['runtime']:.4f}s") else: - print("-> (no result)") + print("-> (skipped)") + + # Print captured warnings + for w in caught_warnings: + print(f"{w.message}") except Exception as e: print(f"-> ERROR: {e}") @@ -331,18 +338,43 @@ def plot_results(): FIGURE_DIR.mkdir(exist_ok=True) - # Extract distribution and regularization from problem name + # Extract distribution, regularization and dataset from problem_name + # Format: {dataset}-no-weights-{regularization}-{distribution} + # where dataset is 2 parts, regularization is 1 part (lasso/l2/net) + # and distribution may contain hyphens (e.g., "tweedie-p=1.5") df = df.copy() - df["distribution"] = df["problem_name"].apply(lambda x: x.split("-")[-1]) - df["regularization"] = df["problem_name"].apply( - lambda x: "lasso" if "lasso" in x else "l2" - ) - df["dataset"] = df["problem_name"].apply( - lambda x: "-".join(x.split("-")[:-3]) # e.g., "intermediate-housing-no" + + def parse_problem_name(name): + """Parse problem name into (dataset, regularization, distribution).""" + parts = name.split("-") + dataset = "-".join(parts[:2]) + reg = parts[4] + dist = "-".join(parts[5:]) + return dataset, reg, dist + + 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]) + + # Drop duplicates (keep latest result for each unique combo) + # Use all derived columns to ensure no duplicates in pivot + df = df.drop_duplicates( + subset=["dataset", "regularization", "distribution", "library_name"], + keep="last", ) - # Drop duplicates (keep latest result for each problem/library combo) - df = df.drop_duplicates(subset=["problem_name", "library_name"], keep="last") + # Ensure library colors are consistent across plots + colors = { + "glum": "#a6cee3", + "h2o": "#fdbf6f", + "glmnet": "#b15928", + "sklearn": "#b15928", + "liblinear": "#33a02c", + "skglm": "#fb9a99", + "celer": "#cab2d6", + } # Generate one plot per dataset/regularization combo for dataset in df["dataset"].unique(): @@ -359,15 +391,59 @@ def plot_results(): values="runtime", ).fillna(0) - # Create bar chart - fig, ax = plt.subplots(figsize=(10, 5)) - pivot.plot(kind="bar", ax=ax) + # Calculate y-axis limit (10x fastest runtime) + min_runtime = pivot.values[pivot.values > 0].min() + y_max = min_runtime * 10 - reg_label = "Lasso" if reg == "lasso" else "Ridge" - ax.set_title(f"{dataset.replace('-', ' ').title()} - {reg_label}") - ax.set_ylabel("Runtime (s)") - ax.set_xlabel("Distribution") - ax.legend(title="Library", bbox_to_anchor=(1.02, 1), loc="upper left") + # Get colors for the libraries in this plot + plot_colors = [colors.get(lib, "#999999") for lib in pivot.columns] + + # Create bar chart with clipped bars and annotations + fig, ax = plt.subplots(figsize=(10, 5)) + pivot_clipped = pivot.clip(upper=y_max) + pivot_clipped.plot(kind="bar", ax=ax, color=plot_colors) + + # Add annotations for clipped bars (show original value on bar with arrow) + n_dists = len(pivot.index) + bars = ax.patches + for i, dist in enumerate(pivot.index): + for j, lib in enumerate(pivot.columns): + original_val = pivot.loc[dist, lib] + if original_val > y_max: + bar_idx = j * n_dists + i + bar = bars[bar_idx] + x = bar.get_x() + bar.get_width() / 2 + # Number on the bar + ax.text( + x, + y_max * 0.75, + f"{original_val:.4f}", + ha="center", + va="center", + fontsize=9, + fontweight="bold", + rotation=90, + ) + # Arrow starting inside bar, pointing up to top of bar + ax.annotate( + "", + xy=(x, y_max), + xytext=(x, y_max * 0.88), + arrowprops=dict(arrowstyle="->", color="black", lw=1.5), + ) + + ax.set_ylim(0, y_max * 1.08) + reg_label = reg.replace("-", " ").title().replace(" ", "-") + # Title with hyphens like in the example + title_dataset = dataset.replace(" ", "-").title() + ax.set_title(f"{title_dataset}-{reg_label}") + ax.set_ylabel("run time (s)") + ax.set_xlabel("") # No x-label, distribution names are self-explanatory + ax.legend(title="", bbox_to_anchor=(1.02, 1), loc="upper left") + # Capitalize x-tick labels + ax.set_xticklabels( + [label.get_text().title() for label in ax.get_xticklabels()] + ) plt.xticks(rotation=45, ha="right") plt.tight_layout() @@ -381,16 +457,51 @@ def plot_results(): if "glum" in pivot.columns: pivot_norm = pivot.div(pivot["glum"], axis=0) - fig, ax = plt.subplots(figsize=(10, 5)) - pivot_norm.plot(kind="bar", ax=ax) + # Limit to 10x glum (so y_max = 10) + norm_y_max = 10.0 + pivot_norm_clipped = pivot_norm.clip(upper=norm_y_max) - ax.set_title( - f"{dataset.replace('-', ' ').title()} - {reg_label} (normalized)" - ) - ax.set_ylabel("Runtime relative to glum (1.0 = glum)") - ax.set_xlabel("Distribution") + fig, ax = plt.subplots(figsize=(10, 5)) + pivot_norm_clipped.plot(kind="bar", ax=ax, color=plot_colors) + + # Add annotations for clipped bars (show original value on bar) + n_dists = len(pivot_norm.index) + bars = ax.patches + for i, dist in enumerate(pivot_norm.index): + for j, lib in enumerate(pivot_norm.columns): + original_val = pivot_norm.loc[dist, lib] + if original_val > norm_y_max: + bar_idx = j * n_dists + i + bar = bars[bar_idx] + x = bar.get_x() + bar.get_width() / 2 + # Number on the bar + ax.text( + x, + norm_y_max * 0.75, + f"{original_val:.1f}x", + ha="center", + va="center", + fontsize=9, + fontweight="bold", + rotation=90, + ) + # Arrow starting inside bar, pointing up to top of bar + ax.annotate( + "", + xy=(x, norm_y_max), + xytext=(x, norm_y_max * 0.88), + arrowprops=dict(arrowstyle="->", color="black", lw=1.5), + ) + + ax.set_ylim(0, norm_y_max * 1.08) + ax.set_title(f"{title_dataset}-{reg_label} (normalized)") + ax.set_ylabel("run time relative to glum") + ax.set_xlabel("") ax.axhline(y=1.0, color="gray", linestyle="--", linewidth=1, alpha=0.7) - ax.legend(title="Library", bbox_to_anchor=(1.02, 1), loc="upper left") + ax.legend(title="", bbox_to_anchor=(1.02, 1), loc="upper left") + ax.set_xticklabels( + [label.get_text().title() for label in ax.get_xticklabels()] + ) plt.xticks(rotation=45, ha="right") plt.tight_layout() @@ -401,17 +512,6 @@ def plot_results(): def main(): - """Run benchmarks, analyze, and plot based on configuration. - - Workflow: - 1. RUN_BENCHMARKS: Execute benchmarks, save pickle files to PICKLE_DIR - 2. ANALYZE_RESULTS: Analyze pickles, print summary, write CSV_FILE - 3. GENERATE_PLOTS: Read CSV_FILE, generate figures to FIGURE_DIR - - You can run steps independently: - - Run benchmarks once, then regenerate figures later from CSV - - Commit CSV to repo for reproducibility - """ if RUN_BENCHMARKS: run_all_benchmarks() From 67bbb1778499c9694fc06dfa18139d62c6620da7 Mon Sep 17 00:00:00 2001 From: David Eiglsperger Date: Thu, 29 Jan 2026 10:46:31 +0100 Subject: [PATCH 15/42] update comments and fix CI --- glum_benchmarks/problems.py | 1 - glum_benchmarks/run_benchmarks.py | 5 ++--- glum_benchmarks/util.py | 2 +- 3 files changed, 3 insertions(+), 5 deletions(-) diff --git a/glum_benchmarks/problems.py b/glum_benchmarks/problems.py index e31596bac..8e8391f34 100644 --- a/glum_benchmarks/problems.py +++ b/glum_benchmarks/problems.py @@ -19,7 +19,6 @@ ) from .util import cache_location, exposure_and_offset_to_weights, get_tweedie_p -# TODO: Add a second regularization strength (would double the number of problems) # TODO: Implement closed form solution for l2-gaussian and put a note in the results # TODO: Add glm_data problems? diff --git a/glum_benchmarks/run_benchmarks.py b/glum_benchmarks/run_benchmarks.py index 5a0aee773..87d149811 100644 --- a/glum_benchmarks/run_benchmarks.py +++ b/glum_benchmarks/run_benchmarks.py @@ -35,9 +35,8 @@ get_params_from_fname, ) -# TODO: Rerun the benchmarks -# TODO: update README and the documentation -# TODO: Rework plotting +# TODO: update README and documentation +# TODO: improve plotting to handle "not converged" and "doesnt supported" cases better # CONFIGURATION diff --git a/glum_benchmarks/util.py b/glum_benchmarks/util.py index f4ff6755d..1b907264a 100644 --- a/glum_benchmarks/util.py +++ b/glum_benchmarks/util.py @@ -407,7 +407,7 @@ def get_all_libraries() -> dict: def execute_problem_library( params: BenchmarkParams, iterations: int = 1, - diagnostics_level: str = "basic", + diagnostics_level: Optional[str] = "basic", standardize: bool = True, **kwargs, ): From e11a106a80ac747c743ca2ce7211bc4b7bbe43e5 Mon Sep 17 00:00:00 2001 From: David Eiglsperger Date: Fri, 30 Jan 2026 14:09:39 +0100 Subject: [PATCH 16/42] Config in yaml, advanced plotting & storage=auto for glum --- .gitignore | 3 +- glum_benchmarks/README.md | 106 +++-- glum_benchmarks/config.yaml | 52 +++ glum_benchmarks/libraries/bench_celer.py | 17 +- glum_benchmarks/libraries/bench_glum.py | 4 +- glum_benchmarks/libraries/bench_h2o.py | 16 +- glum_benchmarks/libraries/bench_liblinear.py | 16 +- glum_benchmarks/libraries/bench_skglm.py | 17 +- glum_benchmarks/libraries/bench_sklearn.py | 23 +- glum_benchmarks/run_benchmarks.py | 439 ++++++++++++++----- glum_benchmarks/util.py | 5 +- pixi.lock | 54 +++ pixi.toml | 2 + 13 files changed, 569 insertions(+), 185 deletions(-) create mode 100644 glum_benchmarks/config.yaml diff --git a/.gitignore b/.gitignore index c1146075d..2cbb0a96e 100644 --- a/.gitignore +++ b/.gitignore @@ -1,9 +1,10 @@ -# Benchmark outputs, only track docs/results.csv +# 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 diff --git a/glum_benchmarks/README.md b/glum_benchmarks/README.md index efe68cd6a..3d126aadb 100644 --- a/glum_benchmarks/README.md +++ b/glum_benchmarks/README.md @@ -2,6 +2,15 @@ Python module to benchmark GLM implementations. +## Benchmarked Libraries + +- [glum](https://github.com/Quantco/glum) - High-performance GLMs with L1/L2 regularization +- [scikit-learn](https://scikit-learn.org/) - Machine learning in Python +- [H2O](https://h2o.ai/) - Distributed machine learning platform +- [LIBLINEAR](https://www.csie.ntu.edu.tw/~cjlin/liblinear/) - Linear classification library +- [skglm](https://contrib.scikit-learn.org/skglm/) - Fast sklearn-compatible GLM solvers +- [celer](https://mathurinm.github.io/celer/) - Fast Lasso solver with dual extrapolation + ## Running the benchmarks ```bash @@ -10,92 +19,95 @@ pixi run -e benchmark run-benchmarks The benchmark script runs in three steps that can be controlled independently. -### Step 1: Run Benchmarks (`RUN_BENCHMARKS`) +### Step 1: Run Benchmarks (`run_benchmarks`) Executes each problem/library combination and saves raw results. -- **Input:** Configuration parameters -- **Output:** Pickle files in `results//pickles/` -- **Set `RUN_BENCHMARKS = False`** to skip (e.g. to generate cvs from existing 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`) +### 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`** to skip (e.g. to generate plots from existing csv) +- **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`) +### 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`** to skip +- **Input:** `results//results.csv` +- **Output:** PNG files in `results//figures/` +- **Set `generate_plots: false`** in config.yaml to skip ### Workflow examples **Full run (default):** -```python -RUN_BENCHMARKS = True -ANALYZE_RESULTS = True -GENERATE_PLOTS = True +```yaml +run_benchmarks: true +analyze_results: true +generate_plots: true ``` **Regenerate plots without re-running benchmarks:** -```python -RUN_BENCHMARKS = False -ANALYZE_RESULTS = True # Rebuild CSV from existing pickles -GENERATE_PLOTS = True +```yaml +run_benchmarks: false +analyze_results: true # Rebuild CSV from existing pickles +generate_plots: true ``` **Run benchmarks and analyze results without creating figures:** -```python -RUN_BENCHMARKS = True -ANALYZE_RESULTS = True -GENERATE_PLOTS = False +```yaml +run_benchmarks: true +analyze_results: true +generate_plots: false ``` ## Output structure -Results are organized by `RUN_NAME` (default: `"docs"`): +Results are organized by `run_name` (default: `"docs"`): ``` glum_benchmarks/ └── results/ - └── docs/ # RUN_NAME = "docs" + └── 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 for docs/) + └── results.csv # Step 2 output (tracked) ``` -Only `results/docs/results.csv` is tracked in git. Change `RUN_NAME` for experiments. +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 the CONFIGURATION section at the top of `run_benchmarks.py`. -The script is pre-configured for the documentation benchmark run. - -| 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) | -| `RUN_NAME` | Subfolder in `results/` | -| `LIBRARIES` | Which libraries to benchmark | -| `DATASETS` | Which datasets to run | -| `REGULARIZATIONS` | Regularization types | -| `DISTRIBUTIONS` | Distribution families | -| `NUM_THREADS` | Number of threads for parallel execution | -| `REG_STRENGTH` | Regularization strength | -| `STANDARDIZE` | Whether to standardize features before fitting | -| `ITERATIONS` | Runs per benchmark (>=2 required for skglm) | -| `NUM_ROWS` | Limit rows per dataset (`None` = full dataset) | -| `CLEAR_OUTPUT` | Clear entire `RUN_NAME` directory before running | +Edit `config.yaml` to customize benchmark parameters. + +| 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) | +| `run_name` | Subfolder in `results/` | +| `libraries` | Which libraries to benchmark | +| `datasets` | Which datasets to run | +| `regularizations` | Regularization types | +| `distributions` | Distribution families | +| `num_threads` | Number of threads for parallel execution | +| `reg_strength` | Regularization strength (alpha) | +| `standardize` | Whether to standardize features before fitting | +| `iterations` | Runs per benchmark (>=2 required for skglm) | +| `num_rows` | Limit rows per dataset (`null` = full dataset) | +| `max_iter` | Maximum iterations (for convergence detection) | +| `clear_output` | Clear entire `run_name` directory before running | See `problems.py` for available datasets and problem definitions. diff --git a/glum_benchmarks/config.yaml b/glum_benchmarks/config.yaml new file mode 100644 index 000000000..f4f7145e4 --- /dev/null +++ b/glum_benchmarks/config.yaml @@ -0,0 +1,52 @@ +# 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) + +# Output settings +run_name: "docs" # Subfolder name within results/ ("docs" CSV is git-tracked) +clear_output: true # Clear entire run_name directory before running + +# Libraries to benchmark +# Available: "glum", "sklearn", "h2o", "liblinear", "skglm", "celer", "zeros" +libraries: + - glum + - sklearn + - h2o + - liblinear + - skglm + - celer + +# Datasets to run +# Available: "intermediate-housing", "intermediate-insurance", "narrow-insurance", "wide-insurance" +datasets: + - intermediate-insurance + - intermediate-housing + - narrow-insurance + - wide-insurance + +# Regularization types to include +# Available: "lasso", "l2", "net" +regularizations: + - lasso + - l2 + +# Distributions to include +# Available: "gaussian", "gamma", "binomial", "poisson", "tweedie-p=1.5" +distributions: + - gaussian + - gamma + - binomial + - poisson + - tweedie-p=1.5 + +# Benchmark settings +standardize: true # Whether to standardize features before fitting +iterations: 2 # Run each benchmark N times, report minimum (>=2 for skglm) +reg_strength: 0.001 # Regularization strength (alpha) +num_threads: 16 # Number of threads for parallel execution +num_rows: null # Limit rows per dataset (null = full dataset) +max_iter: 1000 # Maximum iterations (for convergence detection) diff --git a/glum_benchmarks/libraries/bench_celer.py b/glum_benchmarks/libraries/bench_celer.py index 25d5a72de..d81759108 100644 --- a/glum_benchmarks/libraries/bench_celer.py +++ b/glum_benchmarks/libraries/bench_celer.py @@ -5,7 +5,11 @@ from celer import ElasticNet, Lasso from scipy import sparse as sps -from glum_benchmarks.util import benchmark_convergence_tolerance, runtime +from glum_benchmarks.util import ( + _standardize_features, + benchmark_convergence_tolerance, + runtime, +) def _build_and_fit(model_class, model_args, fit_args): @@ -19,13 +23,20 @@ def celer_bench( l1_ratio: float, iterations: int, reg_multiplier: Optional[float] = None, + standardize: bool = True, + max_iter: int = 1000, **kwargs, ): + # Standardize features if requested + if standardize: + dat = dat.copy() + dat["X"] = _standardize_features(dat["X"]) + result: dict[str, Any] = {} fit_args = {"X": dat["X"], "y": dat["y"]} reg_strength = alpha if reg_multiplier is None else alpha * reg_multiplier - # LogisticRegression doesnt support fitting an intercept yet, so we also skip it + # 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 {} @@ -42,7 +53,7 @@ def celer_bench( model_args = { "tol": benchmark_convergence_tolerance, "fit_intercept": True, - "max_iter": 1000, + "max_iter": max_iter, "alpha": reg_strength, } diff --git a/glum_benchmarks/libraries/bench_glum.py b/glum_benchmarks/libraries/bench_glum.py index b50734471..0ea457869 100644 --- a/glum_benchmarks/libraries/bench_glum.py +++ b/glum_benchmarks/libraries/bench_glum.py @@ -28,6 +28,7 @@ def glum_bench( diagnostics_level: str = "basic", reg_multiplier: Optional[float] = None, hessian_approx: float = 0.0, + max_iter: int = 1000, **kwargs, ): """ @@ -44,6 +45,7 @@ def glum_bench( reg_multiplier hessian_approx kwargs + Note: glum handles standardization internally via scale_predictors. Returns ------- @@ -62,7 +64,7 @@ def glum_bench( model_args = dict( family=get_sklearn_family(distribution), l1_ratio=l1_ratio, - max_iter=1000, + max_iter=max_iter, random_state=random_seed, copy_X=False, selection="cyclic", diff --git a/glum_benchmarks/libraries/bench_h2o.py b/glum_benchmarks/libraries/bench_h2o.py index f105f9470..e63cf5b3a 100644 --- a/glum_benchmarks/libraries/bench_h2o.py +++ b/glum_benchmarks/libraries/bench_h2o.py @@ -9,7 +9,11 @@ from h2o.estimators.glm import H2OGeneralizedLinearEstimator from scipy import sparse as sps -from glum_benchmarks.util import benchmark_convergence_tolerance, runtime +from glum_benchmarks.util import ( + _standardize_features, + benchmark_convergence_tolerance, + runtime, +) # Suppress H2O's "Closing connection" messages at exit logging.getLogger("h2o").setLevel(logging.WARNING) @@ -35,6 +39,8 @@ def h2o_bench( l1_ratio: float, iterations: int, reg_multiplier: Optional[float] = None, + standardize: bool = True, + max_iter: int = 1000, **kwargs, ): """ @@ -48,12 +54,18 @@ def h2o_bench( l1_ratio iterations reg_multiplier + standardize kwargs Returns ------- dict of data about this run """ + # Standardize features if requested + if standardize: + dat = dat.copy() + dat["X"] = _standardize_features(dat["X"]) + result: dict = {} if not isinstance(dat["X"], (np.ndarray, sps.spmatrix, pd.DataFrame)): @@ -93,7 +105,7 @@ def h2o_bench( objective_epsilon=benchmark_convergence_tolerance, beta_epsilon=benchmark_convergence_tolerance, gradient_epsilon=benchmark_convergence_tolerance, - max_iterations=1000, + max_iterations=max_iter, gainslift_bins=0, ) diff --git a/glum_benchmarks/libraries/bench_liblinear.py b/glum_benchmarks/libraries/bench_liblinear.py index 35199f7e9..aebc482fd 100644 --- a/glum_benchmarks/libraries/bench_liblinear.py +++ b/glum_benchmarks/libraries/bench_liblinear.py @@ -6,7 +6,11 @@ from scipy import sparse as sps from sklearn.linear_model import LogisticRegression -from glum_benchmarks.util import benchmark_convergence_tolerance, runtime +from glum_benchmarks.util import ( + _standardize_features, + benchmark_convergence_tolerance, + runtime, +) def _build_and_fit(model_args, train_args): @@ -20,6 +24,8 @@ def liblinear_bench( l1_ratio: float, iterations: int, reg_multiplier: Optional[float] = None, + standardize: bool = True, + max_iter: int = 1000, **kwargs, ) -> dict[str, Any]: """ @@ -33,6 +39,7 @@ def liblinear_bench( l1_ratio iterations reg_multiplier + standardize kwargs Returns @@ -40,6 +47,11 @@ def liblinear_bench( dict """ + # Standardize features if requested + if standardize: + dat = dat.copy() + dat["X"] = _standardize_features(dat["X"]) + result: dict = {} X = dat["X"] @@ -80,7 +92,7 @@ def liblinear_bench( # sklearn.linear_model.LogisticRegression.html intercept_scaling=1e3, solver="liblinear", - max_iter=1000, + max_iter=max_iter, ) fit_args = dict( # type: ignore diff --git a/glum_benchmarks/libraries/bench_skglm.py b/glum_benchmarks/libraries/bench_skglm.py index 0e36a2254..08f149571 100644 --- a/glum_benchmarks/libraries/bench_skglm.py +++ b/glum_benchmarks/libraries/bench_skglm.py @@ -8,7 +8,11 @@ from skglm.penalties import L1, L1_plus_L2 from skglm.solvers import AndersonCD, ProxNewton -from glum_benchmarks.util import benchmark_convergence_tolerance, runtime +from glum_benchmarks.util import ( + _standardize_features, + benchmark_convergence_tolerance, + runtime, +) def _build_and_fit(model_args, fit_args): @@ -22,8 +26,15 @@ def skglm_bench( l1_ratio: float, iterations: int, reg_multiplier: Optional[float] = None, + standardize: bool = True, + max_iter: int = 1000, **kwargs, ): + # Standardize features if requested + if standardize: + dat = dat.copy() + dat["X"] = _standardize_features(dat["X"]) + result: dict[str, Any] = {} reg_strength = alpha if reg_multiplier is None else alpha * reg_multiplier @@ -50,11 +61,11 @@ def skglm_bench( # 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, max_iter=1000 + tol=benchmark_convergence_tolerance, fit_intercept=True, max_iter=max_iter ) else: solver = AndersonCD( - tol=benchmark_convergence_tolerance, fit_intercept=True, max_iter=1000 + tol=benchmark_convergence_tolerance, fit_intercept=True, max_iter=max_iter ) model_args = { diff --git a/glum_benchmarks/libraries/bench_sklearn.py b/glum_benchmarks/libraries/bench_sklearn.py index 445299317..56c060d5c 100644 --- a/glum_benchmarks/libraries/bench_sklearn.py +++ b/glum_benchmarks/libraries/bench_sklearn.py @@ -11,7 +11,11 @@ TweedieRegressor, ) -from glum_benchmarks.util import benchmark_convergence_tolerance, runtime +from glum_benchmarks.util import ( + _standardize_features, + benchmark_convergence_tolerance, + runtime, +) def _build_and_fit(model_class, model_args, fit_args): @@ -26,6 +30,8 @@ def sklearn_bench( l1_ratio: float, iterations: int, reg_multiplier: Optional[float] = None, + standardize: bool = True, + max_iter: int = 1000, **kwargs, ): """ @@ -39,12 +45,17 @@ def sklearn_bench( l1_ratio iterations reg_multiplier + standardize kwargs Returns ------- Dict of """ + # Standardize features if requested + if standardize: + dat = dat.copy() + dat["X"] = _standardize_features(dat["X"]) result: dict[str, Any] = {} reg_strength = alpha if reg_multiplier is None else alpha * reg_multiplier @@ -60,7 +71,7 @@ def sklearn_bench( model_args = { "alpha": reg_strength * n_samples, "fit_intercept": True, - "max_iter": 1000, + "max_iter": max_iter, "tol": benchmark_convergence_tolerance, "solver": "auto", } @@ -70,7 +81,7 @@ def sklearn_bench( model_args = { "alpha": reg_strength * n_samples, "fit_intercept": True, - "max_iter": 1000, + "max_iter": max_iter, "tol": benchmark_convergence_tolerance, "precompute": True, } @@ -81,7 +92,7 @@ def sklearn_bench( "alpha": reg_strength, "l1_ratio": l1_ratio, "fit_intercept": True, - "max_iter": 1000, + "max_iter": max_iter, "tol": benchmark_convergence_tolerance, "precompute": True, } @@ -100,7 +111,7 @@ def sklearn_bench( "l1_ratio": l1_ratio, "solver": solver, "fit_intercept": True, - "max_iter": 1000, + "max_iter": max_iter, "tol": benchmark_convergence_tolerance, } else: @@ -123,7 +134,7 @@ def sklearn_bench( "power": power, "alpha": reg_strength, "fit_intercept": True, - "max_iter": 1000, + "max_iter": max_iter, "tol": benchmark_convergence_tolerance, "solver": "newton-cholesky", } diff --git a/glum_benchmarks/run_benchmarks.py b/glum_benchmarks/run_benchmarks.py index 87d149811..b22ae73e7 100644 --- a/glum_benchmarks/run_benchmarks.py +++ b/glum_benchmarks/run_benchmarks.py @@ -6,9 +6,9 @@ pixi run -e benchmark run-benchmarks Configuration: - Edit the CONFIGURATION section below to select which libraries, datasets, - regularizations, and distributions to benchmark. You can also control which - steps to run (RUN_BENCHMARKS, ANALYZE_RESULTS, GENERATE_PLOTS). + Edit config.yaml to select which libraries, datasets, regularizations, + and distributions to benchmark. You can also control which steps to run + (run_benchmarks, analyze_results, generate_plots). Output: - glum_benchmarks/results/RUN_NAME/pickles/: Pickle files with detailed results @@ -21,11 +21,14 @@ import pickle import shutil import warnings +from dataclasses import dataclass from pathlib import Path import matplotlib.pyplot as plt import numpy as np import pandas as pd +import yaml +from matplotlib.patches import Patch from glum_benchmarks.problems import get_all_problems from glum_benchmarks.util import ( @@ -36,64 +39,77 @@ ) # TODO: update README and documentation -# TODO: improve plotting to handle "not converged" and "doesnt supported" cases better - -# CONFIGURATION - -# Keep as-is to run the same benchmark suite as in the documentation. In particular -# if you just want to update the benchmark results, -# you do not need to change anything here. - -# Steps to run -RUN_BENCHMARKS = True # Run the benchmarks (can be slow) -ANALYZE_RESULTS = True # Analyze and print results (writes CSV_FILE) -GENERATE_PLOTS = True # Generate comparison plots (reads from CSV_FILE and writes PNGs) - - -# Change RUN_NAME to separate different benchmark runs into different folders. -_SCRIPT_DIR = Path(__file__).parent -RUN_NAME = "docs" # Subfolder name within results/ ("docs" CSV is git-tracked) -RESULTS_DIR = _SCRIPT_DIR / "results" / RUN_NAME -PICKLE_DIR = RESULTS_DIR / "pickles" -FIGURE_DIR = RESULTS_DIR / "figures" -CSV_FILE = RESULTS_DIR / "results.csv" -CLEAR_OUTPUT = True # Clear pickle output directory before running - -# Libraries to benchmark: "glum", "sklearn", "h2o", "liblinear", -# "skglm", "celer", "zeros" -LIBRARIES = [ - "glum", - "sklearn", - "h2o", - "liblinear", - "skglm", - "celer", -] - -# Datasets to run: "intermediate-housing", "intermediate-insurance", -# "narrow-insurance", "wide-insurance" -DATASETS = [ - "intermediate-housing", - "intermediate-insurance", - "narrow-insurance", - "wide-insurance", -] - -# Regularization types to include: "lasso", "l2", "net" -REGULARIZATIONS = ["lasso", "l2"] - -# Distributions to include: "gaussian", "gamma", "binomial", "poisson", "tweedie-p=1.5" -DISTRIBUTIONS = ["gaussian", "gamma", "binomial", "poisson", "tweedie-p=1.5"] - -# Benchmark settings -NUM_THREADS = 16 -REG_STRENGTH = 0.001 -STANDARDIZE = True -ITERATIONS = 2 # Run each benchmark N times, report minimum runtime (>=2 for skglm) -NUM_ROWS = None # None = use full dataset, or set an int for quick test runs - - -def get_problems_to_run() -> list[str]: +# TODO: improve plotting to handle "not converged" and "not supported" cases better + + +@dataclass +class BenchmarkConfig: + """Configuration for benchmark runs.""" + + # Steps to run + run_benchmarks: bool + analyze_results: bool + generate_plots: bool + + # Output settings + run_name: str + clear_output: bool + + # Problem selection + libraries: list[str] + datasets: list[str] + regularizations: list[str] + distributions: list[str] + + # Benchmark settings + num_threads: int + reg_strength: float + standardize: bool + iterations: int + num_rows: int | None + max_iter: int + + # Derived paths (computed after init) + script_dir: Path + results_dir: Path + pickle_dir: Path + figure_dir: Path + csv_file: Path + + @classmethod + def from_yaml(cls, yaml_path: Path) -> BenchmarkConfig: + """Load configuration from YAML file.""" + with open(yaml_path) as f: + data = yaml.safe_load(f) + + script_dir = yaml_path.parent + results_dir = script_dir / "results" / data["run_name"] + + return cls( + run_benchmarks=data["run_benchmarks"], + analyze_results=data["analyze_results"], + generate_plots=data["generate_plots"], + run_name=data["run_name"], + clear_output=data["clear_output"], + libraries=data["libraries"], + datasets=data["datasets"], + regularizations=data["regularizations"], + distributions=data["distributions"], + num_threads=data["num_threads"], + reg_strength=data["reg_strength"], + standardize=data["standardize"], + iterations=data["iterations"], + num_rows=data["num_rows"], + max_iter=data["max_iter"], + script_dir=script_dir, + results_dir=results_dir, + pickle_dir=results_dir / "pickles", + figure_dir=results_dir / "figures", + csv_file=results_dir / "results.csv", + ) + + +def get_problems_to_run(config: BenchmarkConfig) -> list[str]: """Get list of problem names matching the configuration.""" all_problems = get_all_problems() selected = [] @@ -104,18 +120,18 @@ def get_problems_to_run() -> list[str]: continue # Filter by dataset - if DATASETS is not None: - if not any(d in name for d in DATASETS): + if config.datasets is not None: + if not any(d in name for d in config.datasets): continue # Filter by regularization - if REGULARIZATIONS is not None: - if not any(reg in name for reg in REGULARIZATIONS): + if config.regularizations is not None: + if not any(reg in name for reg in config.regularizations): continue # Filter by distribution - if DISTRIBUTIONS is not None: - if not any(dist in name for dist in DISTRIBUTIONS): + if config.distributions is not None: + if not any(dist in name for dist in config.distributions): continue selected.append(name) @@ -124,39 +140,44 @@ def get_problems_to_run() -> list[str]: def run_single_benchmark( - problem_name: str, library_name: str + problem_name: str, library_name: str, config: BenchmarkConfig ) -> tuple[dict, BenchmarkParams]: """Run a single benchmark and return results.""" + # Use "auto" storage for glum (enables categorical algorithm via tabmat) + # Use "dense" for other libraries + storage = "auto" if library_name == "glum" else "dense" + params = BenchmarkParams( problem_name=problem_name, library_name=library_name, - num_rows=NUM_ROWS, - storage="dense", - threads=NUM_THREADS, - regularization_strength=REG_STRENGTH, + num_rows=config.num_rows, + storage=storage, + threads=config.num_threads, + regularization_strength=config.reg_strength, ) result, _ = execute_problem_library( params, - iterations=ITERATIONS, + iterations=config.iterations, diagnostics_level=None, - standardize=STANDARDIZE, + standardize=config.standardize, + max_iter=config.max_iter, ) return result, params -def run_all_benchmarks(): +def run_all_benchmarks(config: BenchmarkConfig): """Run all configured benchmarks.""" # Set up output directory - if CLEAR_OUTPUT and RESULTS_DIR.exists(): - print(f"Clearing output directory: {RESULTS_DIR}") - shutil.rmtree(RESULTS_DIR) - PICKLE_DIR.mkdir(parents=True, exist_ok=True) + 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) - problems = get_problems_to_run() + problems = get_problems_to_run(config) available = get_all_libraries() - libraries = [lib for lib in LIBRARIES if lib in available] + libraries = [lib for lib in config.libraries if lib in available] print(f"Problems: {len(problems)}") print(f"Libraries: {libraries}") @@ -183,11 +204,13 @@ def run_all_benchmarks(): "ignore", message=".*asyncio.iscoroutinefunction.*", ) - result, params = run_single_benchmark(problem_name, library_name) + result, params = run_single_benchmark( + problem_name, library_name, config + ) # Save result fname = params.get_result_fname() + ".pkl" - with open(PICKLE_DIR / fname, "wb") as f: + with open(config.pickle_dir / fname, "wb") as f: pickle.dump(result, f) if len(result) > 0 and "runtime" in result: @@ -203,7 +226,7 @@ def run_all_benchmarks(): print(f"-> ERROR: {e}") -def analyze_results() -> pd.DataFrame: +def analyze_results(config: BenchmarkConfig) -> pd.DataFrame: """Analyze benchmark results and print summary.""" print() print("=" * 60) @@ -216,7 +239,7 @@ def analyze_results() -> pd.DataFrame: results = [] - for fname in PICKLE_DIR.glob("*.pkl"): + for fname in config.pickle_dir.glob("*.pkl"): with open(fname, "rb") as f: data = pickle.load(f) @@ -249,6 +272,11 @@ def analyze_results() -> pd.DataFrame: else params.regularization_strength ) + # Check convergence (n_iter < max_iter means converged) + converged = True + if n_iter is not None and n_iter >= config.max_iter: + converged = False + results.append( { "problem_name": params.problem_name, @@ -258,6 +286,7 @@ def analyze_results() -> pd.DataFrame: "storage": params.storage, "threads": params.threads, "n_iter": n_iter, + "converged": converged, "runtime": runtime, "runtime per iter": runtime_per_iter, "intercept": data.get("intercept"), @@ -305,14 +334,14 @@ def analyze_results() -> pd.DataFrame: print(df[cols_to_show]) # Export to CSV for figure generation and reproducibility - CSV_FILE.parent.mkdir(parents=True, exist_ok=True) - df.reset_index().to_csv(CSV_FILE, index=False) - print(f"\nExported results to: {CSV_FILE}") + 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 plot_results(): +def plot_results(config: BenchmarkConfig): """Generate benchmark comparison plots from CSV file. Reads from CSV_FILE, which allows regenerating figures without @@ -323,19 +352,22 @@ def plot_results(): print("GENERATING PLOTS") print("=" * 60) - if not CSV_FILE.exists(): - print(f"CSV file not found: {CSV_FILE}") + 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(CSV_FILE) - print(f"Reading results from: {CSV_FILE}") + 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 - FIGURE_DIR.mkdir(exist_ok=True) + config.figure_dir.mkdir(exist_ok=True) # Extract distribution, regularization and dataset from problem_name # Format: {dataset}-no-weights-{regularization}-{distribution} @@ -384,11 +416,26 @@ def parse_problem_name(name): continue # Pivot for plotting - pivot = subset.pivot( + pivot_raw = subset.pivot( index="distribution", columns="library_name", values="runtime", - ).fillna(0) + ) + # 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="distribution", + 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() @@ -402,13 +449,73 @@ def parse_problem_name(name): pivot_clipped = pivot.clip(upper=y_max) pivot_clipped.plot(kind="bar", ax=ax, color=plot_colors) - # Add annotations for clipped bars (show original value on bar with arrow) + # Draw hatched bars for unsupported library/distribution combos n_dists = len(pivot.index) bars = ax.patches + for i, dist in enumerate(pivot.index): + for j, lib in enumerate(pivot.columns): + if unsupported.loc[dist, lib]: + bar_idx = j * n_dists + i + bar = bars[bar_idx] + x = bar.get_x() + width = bar.get_width() + lib_color = colors.get(lib, "#999999") + # Draw bar with library color + hatch pattern + ax.bar( + x + width / 2, + y_max, + width=width, + color="white", + edgecolor=lib_color, + linewidth=2, + hatch="//", + ) + # Add "N/A" label + ax.text( + x + width / 2, + y_max / 2, + "N/A", + ha="center", + va="center", + fontsize=8, + color="#666666", + fontweight="bold", + ) + # Add hatch overlay for non-converged results (keep the runtime bar) + elif not_converged.loc[dist, lib]: + bar_idx = j * n_dists + i + bar = bars[bar_idx] + x = bar.get_x() + width = bar.get_width() + bar_height = min(pivot.loc[dist, lib], y_max) + # Overlay hatch pattern on existing bar + ax.bar( + x + width / 2, + bar_height, + width=width, + color="none", + edgecolor="black", + linewidth=0.5, + hatch="//", + alpha=0.5, + ) + # Add "NC" (not converged) label at top of bar + ax.text( + x + width / 2, + bar_height + y_max * 0.02, + "NC", + ha="center", + va="bottom", + fontsize=7, + color="#cc0000", + fontweight="bold", + ) + + # Add annotations for clipped bars (show original value on bar with arrow) for i, dist in enumerate(pivot.index): for j, lib in enumerate(pivot.columns): original_val = pivot.loc[dist, lib] - if original_val > y_max: + if original_val > y_max and not unsupported.loc[dist, lib]: bar_idx = j * n_dists + i bar = bars[bar_idx] x = bar.get_x() + bar.get_width() / 2 @@ -438,7 +545,15 @@ def parse_problem_name(name): ax.set_title(f"{title_dataset}-{reg_label}") ax.set_ylabel("run time (s)") ax.set_xlabel("") # No x-label, distribution names are self-explanatory - ax.legend(title="", bbox_to_anchor=(1.02, 1), loc="upper left") + # Add custom legend entries for N/A and NC + handles, labels = ax.get_legend_handles_labels() + handles.append(Patch(facecolor="white", edgecolor="gray", hatch="//")) + labels.append("N/A (not supported)") + handles.append(Patch(facecolor="white", edgecolor="black", hatch="//")) + labels.append("NC (not converged)") + ax.legend( + handles, labels, title="", bbox_to_anchor=(1.02, 1), loc="upper left" + ) # Capitalize x-tick labels ax.set_xticklabels( [label.get_text().title() for label in ax.get_xticklabels()] @@ -448,7 +563,7 @@ def parse_problem_name(name): # Save fname = f"{dataset}-{reg}" - plt.savefig(FIGURE_DIR / f"{fname}.png", dpi=300) + plt.savefig(config.figure_dir / f"{fname}.png", dpi=300) plt.close() print(f"Saved: {fname}.png") @@ -463,13 +578,73 @@ def parse_problem_name(name): fig, ax = plt.subplots(figsize=(10, 5)) pivot_norm_clipped.plot(kind="bar", ax=ax, color=plot_colors) - # Add annotations for clipped bars (show original value on bar) + # Draw hatched bars for unsupported library/distribution combos n_dists = len(pivot_norm.index) bars = ax.patches + for i, dist in enumerate(pivot_norm.index): + for j, lib in enumerate(pivot_norm.columns): + if unsupported.loc[dist, lib]: + bar_idx = j * n_dists + i + bar = bars[bar_idx] + x = bar.get_x() + width = bar.get_width() + lib_color = colors.get(lib, "#999999") + # Draw bar with library color + hatch pattern + ax.bar( + x + width / 2, + norm_y_max, + width=width, + color="white", + edgecolor=lib_color, + linewidth=2, + hatch="//", + ) + # Add "N/A" label + ax.text( + x + width / 2, + norm_y_max / 2, + "N/A", + ha="center", + va="center", + fontsize=8, + color="#666666", + fontweight="bold", + ) + # Add hatch overlay for non-converged results + elif not_converged.loc[dist, lib]: + bar_idx = j * n_dists + i + bar = bars[bar_idx] + x = bar.get_x() + width = bar.get_width() + bar_height = min(pivot_norm.loc[dist, lib], norm_y_max) + # Overlay hatch pattern on existing bar + ax.bar( + x + width / 2, + bar_height, + width=width, + color="none", + edgecolor="black", + linewidth=0.5, + hatch="//", + alpha=0.5, + ) + # Add "NC" (not converged) label at top of bar + ax.text( + x + width / 2, + bar_height + norm_y_max * 0.02, + "NC", + ha="center", + va="bottom", + fontsize=7, + color="#cc0000", + fontweight="bold", + ) + + # Add annotations for clipped bars (show original value on bar) for i, dist in enumerate(pivot_norm.index): for j, lib in enumerate(pivot_norm.columns): original_val = pivot_norm.loc[dist, lib] - if original_val > norm_y_max: + if original_val > norm_y_max and not unsupported.loc[dist, lib]: bar_idx = j * n_dists + i bar = bars[bar_idx] x = bar.get_x() + bar.get_width() / 2 @@ -497,37 +672,69 @@ def parse_problem_name(name): ax.set_ylabel("run time relative to glum") ax.set_xlabel("") ax.axhline(y=1.0, color="gray", linestyle="--", linewidth=1, alpha=0.7) - ax.legend(title="", bbox_to_anchor=(1.02, 1), loc="upper left") - ax.set_xticklabels( - [label.get_text().title() for label in ax.get_xticklabels()] + # Add custom legend entries for N/A and NC + handles, labels = ax.get_legend_handles_labels() + handles.append(Patch(facecolor="white", edgecolor="gray", hatch="//")) + labels.append("N/A (not supported)") + handles.append(Patch(facecolor="white", edgecolor="black", hatch="//")) + labels.append("NC (not converged)") + ax.legend( + handles, + labels, + title="", + bbox_to_anchor=(1.02, 1), + loc="upper left", ) + # X-tick labels: show distribution name + glum runtime + new_labels = [] + for dist in pivot.index: + glum_runtime = pivot.loc[dist, "glum"] + if glum_runtime > 0: + new_labels.append( + f"{dist.title()}\n(glum = {glum_runtime:.3f}s)" + ) + else: + new_labels.append(dist.title()) + ax.set_xticklabels(new_labels) plt.xticks(rotation=45, ha="right") plt.tight_layout() fname_norm = f"{dataset}-{reg}-normalized" - plt.savefig(FIGURE_DIR / f"{fname_norm}.png", dpi=300) + plt.savefig(config.figure_dir / f"{fname_norm}.png", dpi=300) plt.close() print(f"Saved: {fname_norm}.png") def main(): - if RUN_BENCHMARKS: - run_all_benchmarks() + # 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 ANALYZE_RESULTS: - analyze_results() + if config.analyze_results: + analyze_results(config) - if GENERATE_PLOTS: - plot_results() + if config.generate_plots: + plot_results(config) + # Print summary print() print("=" * 60) print("DONE") print("=" * 60) - if RUN_BENCHMARKS or ANALYZE_RESULTS: - print(f"Results saved to: {RESULTS_DIR}/") - if GENERATE_PLOTS: - print(f"Figures saved to: {FIGURE_DIR}/") + 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}/") + + # Snapshot config for reproducibility + 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__": diff --git a/glum_benchmarks/util.py b/glum_benchmarks/util.py index 1b907264a..de64aed8f 100644 --- a/glum_benchmarks/util.py +++ b/glum_benchmarks/util.py @@ -443,10 +443,6 @@ def execute_problem_library( storage=params.storage, ) - # Standardize features for better convergence across all libraries - if standardize: - dat["X"] = _standardize_features(dat["X"]) - os.environ["OMP_NUM_THREADS"] = str(params.threads) if params.regularization_strength is None: @@ -470,6 +466,7 @@ def execute_problem_library( diagnostics_level=diagnostics_level, reg_multiplier=reg_multiplier, hessian_approx=params.hessian_approx, + standardize=standardize, **kwargs, ) diff --git a/pixi.lock b/pixi.lock index 2fe971a4c..fce728211 100644 --- a/pixi.lock +++ b/pixi.lock @@ -191,6 +191,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/python-3.14.2-h32b2ec7_101_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/noarch/pyyaml-6.0.3-pyh7db6752_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/r-base-4.5.2-h835929b_3.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/re2-2025.11.05-h5301d42_0.conda @@ -232,6 +233,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxrender-0.9.12-hb9d3cd8_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxt-1.3.1-hb9d3cd8_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxtst-1.2.5-hb9d3cd8_3.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/yaml-0.2.5-h280c20c_3.conda - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.23.0-pyhd8ed1ab_0.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 @@ -436,6 +438,7 @@ environments: - 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/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/pyyaml-6.0.3-pyh7db6752_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/r-base-4.5.2-hb2e0d2d_3.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/re2-2025.11.05-h64b956e_0.conda @@ -466,6 +469,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/osx-arm64/wrapt-2.0.1-py314h0612a62_1.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/osx-arm64/yaml-0.2.5-h925e9cb_3.conda - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.23.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/osx-arm64/zlib-1.3.1-h8359307_2.conda - 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python >=3.10 + license: Apache-2.0 AND MIT + size: 22060 + timestamp: 1762942278334 - conda: https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.14.1-h4440ef1_0.conda sha256: 349951278fa8d0860ec6b61fcdc1e6f604e6fce74fabf73af2e39a37979d0223 md5: 75be1a943e0a7f99fcf118309092c635 @@ -24622,6 +24673,7 @@ packages: - __glibc >=2.17,<3.0.a0 license: MIT license_family: MIT + purls: [] size: 85189 timestamp: 1753484064210 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/yaml-0.2.5-h925e9cb_3.conda @@ -24631,6 +24683,7 @@ packages: - __osx >=11.0 license: MIT license_family: MIT + purls: [] size: 83386 timestamp: 1753484079473 - conda: https://conda.anaconda.org/conda-forge/win-64/yaml-0.2.5-h6a83c73_3.conda @@ -24645,6 +24698,7 @@ packages: - ucrt >=10.0.20348.0 license: MIT license_family: MIT + purls: [] size: 63944 timestamp: 1753484092156 - conda: https://conda.anaconda.org/conda-forge/linux-64/zeromq-4.3.5-h387f397_9.conda diff --git a/pixi.toml b/pixi.toml index 123ca1861..08a028222 100644 --- a/pixi.toml +++ b/pixi.toml @@ -81,6 +81,7 @@ psutil = "*" pytest = "*" pytest-xdist = "*" statsmodels = "*" +types-pyyaml = "*" [feature.dev.dependencies] ipdb = "*" @@ -123,6 +124,7 @@ git_root = "*" matplotlib-base = "*" openjdk = "*" pytest = "*" +pyyaml = ">=6.0.3,<7" r-base = "*" rpy2 = "*" skglm = "*" From 3e2d7acdbc447dda996f12307c375dd862ee7bf1 Mon Sep 17 00:00:00 2001 From: David Eiglsperger Date: Mon, 2 Feb 2026 21:01:42 +0100 Subject: [PATCH 17/42] Incorporate feedback and add some further functionalities --- README.md | 2 + docs/benchmarks.rst | 4 + glum_benchmarks/README.md | 65 +- glum_benchmarks/config.yaml | 85 +- glum_benchmarks/data/__init__.py | 3 +- glum_benchmarks/data/simulated_glm.py | 56 + glum_benchmarks/libraries/__init__.py | 6 - glum_benchmarks/libraries/bench_celer.py | 22 +- glum_benchmarks/libraries/bench_glum.py | 7 +- glum_benchmarks/libraries/bench_h2o.py | 11 +- glum_benchmarks/libraries/bench_liblinear.py | 108 - glum_benchmarks/libraries/bench_skglm.py | 24 +- glum_benchmarks/libraries/bench_sklearn.py | 37 +- glum_benchmarks/problems.py | 23 +- glum_benchmarks/run_benchmarks.py | 1164 +++--- glum_benchmarks/util.py | 124 +- pixi.lock | 3402 ++++++++---------- pixi.toml | 4 +- tests/glm/test_benchmark_golden_master.py | 8 +- 19 files changed, 2558 insertions(+), 2597 deletions(-) delete mode 100644 glum_benchmarks/libraries/bench_liblinear.py diff --git a/README.md b/README.md index 2e127d992..35961d6bd 100644 --- a/README.md +++ b/README.md @@ -24,8 +24,10 @@ The goal of `glum` is to be at least as feature-complete as existing GLM librari 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 benchmarks](docs/_static/headline_benchmark.png#gh-light-mode-only) ![Performance benchmarks](docs/_static/headline_benchmark_dark.png#gh-dark-mode-only) + For more information on `glum`, including tutorials and API reference, please see [the documentation](https://glum.readthedocs.io/en/latest/). diff --git a/docs/benchmarks.rst b/docs/benchmarks.rst index e50b36adf..6780819ae 100644 --- a/docs/benchmarks.rst +++ b/docs/benchmarks.rst @@ -9,6 +9,8 @@ Note that glum was originally developed to solve problems where N >> K (number o 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. +.. BENCHMARK_FIGURES_START + .. image:: _static/narrow-insurance-l2.png :width: 700 .. image:: _static/narrow-insurance-lasso.png @@ -28,3 +30,5 @@ Note that the ``r-glmnet`` result for the ``wide-insurance-ridge`` Poisson bench :width: 700 .. image:: _static/intermediate-housing-lasso.png :width: 700 + +.. BENCHMARK_FIGURES_END diff --git a/glum_benchmarks/README.md b/glum_benchmarks/README.md index 3d126aadb..16d9be0f1 100644 --- a/glum_benchmarks/README.md +++ b/glum_benchmarks/README.md @@ -7,7 +7,6 @@ Python module to benchmark GLM implementations. - [glum](https://github.com/Quantco/glum) - High-performance GLMs with L1/L2 regularization - [scikit-learn](https://scikit-learn.org/) - Machine learning in Python - [H2O](https://h2o.ai/) - Distributed machine learning platform -- [LIBLINEAR](https://www.csie.ntu.edu.tw/~cjlin/liblinear/) - Linear classification library - [skglm](https://contrib.scikit-learn.org/skglm/) - Fast sklearn-compatible GLM solvers - [celer](https://mathurinm.github.io/celer/) - Fast Lasso solver with dual extrapolation @@ -57,7 +56,7 @@ generate_plots: true ```yaml run_benchmarks: false -analyze_results: true # Rebuild CSV from existing pickles +analyze_results: true # Rebuild CSV from existing pickles generate_plots: true ``` @@ -91,23 +90,51 @@ For the `docs` run, both `results.csv` and `config.yaml` are tracked in git. Cha Edit `config.yaml` to customize benchmark parameters. -| 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) | -| `run_name` | Subfolder in `results/` | -| `libraries` | Which libraries to benchmark | -| `datasets` | Which datasets to run | -| `regularizations` | Regularization types | -| `distributions` | Distribution families | -| `num_threads` | Number of threads for parallel execution | -| `reg_strength` | Regularization strength (alpha) | -| `standardize` | Whether to standardize features before fitting | -| `iterations` | Runs per benchmark (>=2 required for skglm) | -| `num_rows` | Limit rows per dataset (`null` = full dataset) | -| `max_iter` | Maximum iterations (for convergence detection) | -| `clear_output` | Clear entire `run_name` directory before running | +### 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` | Copy figures to docs and update documentation | +| `run_name` | Subfolder in `results/` (`"docs"` is git-tracked) | +| `clear_output` | Clear entire `run_name` directory before running | + +### Benchmark Settings + +| Option | Description | +| ------------- | ---------------------------------------------- | +| `standardize` | Whether to standardize features before fitting | +| `iterations` | Runs per benchmark (>=2 required for skglm) | +| `num_threads` | Number of threads for parallel execution | +| `num_rows` | Limit rows per dataset (`null` = full dataset) | +| `timeout` | Timeout in seconds per benchmark run | + +### 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"] + reg_strengths: [0.001] +``` + +Each entry computes a Cartesian product. Multiple entries are unioned (not crossed). + +**Available values:** + +- `libraries`: `["glum", "sklearn", "h2o", "skglm", "celer", "zeros"]` +- `datasets`: `["intermediate-housing", "intermediate-insurance", "narrow-insurance", "wide-insurance", "square-simulated"]` +- `regularizations`: `["lasso", "l2", "net"]` +- `distributions`: `["gaussian", "gamma", "binomial", "poisson", "tweedie-p=1.5"]` +- `reg_strengths`: `[0.0001, 0.001, 0.01]` + +When an entry is omitted or set to `null` all available values are taken --> if you want to run all combinations possiblem, just leave the `param_grid` entry empty. See `problems.py` for available datasets and problem definitions. diff --git a/glum_benchmarks/config.yaml b/glum_benchmarks/config.yaml index f4f7145e4..31e572360 100644 --- a/glum_benchmarks/config.yaml +++ b/glum_benchmarks/config.yaml @@ -5,48 +5,61 @@ 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: false # Copy figures to docs/_static and update benchmarks.rst and README.md + +# Figures to include in docs/README (null = use defaults) +# Default for docs: all figures +# Default for readme: first non-normalized figure only +# Available: null, or list of figure filenames in the format "-.png" +# Example: intermediate-insurance-gaussian.png +docs_figures: null +readme_figures: null # Output settings run_name: "docs" # Subfolder name within results/ ("docs" CSV is git-tracked) -clear_output: true # Clear entire run_name directory before running - -# Libraries to benchmark -# Available: "glum", "sklearn", "h2o", "liblinear", "skglm", "celer", "zeros" -libraries: - - glum - - sklearn - - h2o - - liblinear - - skglm - - celer - -# Datasets to run -# Available: "intermediate-housing", "intermediate-insurance", "narrow-insurance", "wide-insurance" -datasets: - - intermediate-insurance - - intermediate-housing - - narrow-insurance - - wide-insurance - -# Regularization types to include -# Available: "lasso", "l2", "net" -regularizations: - - lasso - - l2 - -# Distributions to include -# Available: "gaussian", "gamma", "binomial", "poisson", "tweedie-p=1.5" -distributions: - - gaussian - - gamma - - binomial - - poisson - - tweedie-p=1.5 +clear_output: True # Clear entire run_name directory before running # Benchmark settings standardize: true # Whether to standardize features before fitting iterations: 2 # Run each benchmark N times, report minimum (>=2 for skglm) -reg_strength: 0.001 # Regularization strength (alpha) num_threads: 16 # Number of threads for parallel execution num_rows: null # Limit rows per dataset (null = full dataset) -max_iter: 1000 # Maximum iterations (for convergence detection) +timeout: 100 # Timeout in seconds per benchmark run (reports as "not converged" if exceeded) + +# Storage format per library ("dense", "sparse", or "auto") +storage: + glum: auto # Auto selects best format + h2o: sparse # Better performance with sparse + celer: sparse # Optimized for sparse matrices + sklearn: dense # Can use either, configure as needed + skglm: dense # Can use either, configure as needed + +# 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"] +# datasets: ["intermediate-housing", "intermediate-insurance", "narrow-insurance", "wide-insurance","square-simulated"] +# regularizations: ["lasso", "l2", "net"] +# reg_strengths: [0.0001, 0.001, 0.01] +# distributions: ["gaussian", "gamma", "binomial", "poisson", "tweedie-p=1.5"] +# +# Use null (or omit) for any field to use its default value. Default refers to using all available values. +# +# Example: Run all regularizations for gaussian, but only lasso for gamma: +# param_grid: +# - distributions: [gaussian] +# regularizations: [lasso, l2, net] +# - distributions: [gamma] +# regularizations: [lasso] + +param_grid: + - distributions: [gaussian] + datasets: ["intermediate-housing"] + regularizations: [lasso, l2] + libraries: [glum, celer] + - distributions: [gamma] + datasets: ["intermediate-insurance"] + regularizations: [lasso] + libraries: [glum, h2o] diff --git a/glum_benchmarks/data/__init__.py b/glum_benchmarks/data/__init__.py index 7474e1514..450231b3a 100644 --- a/glum_benchmarks/data/__init__.py +++ b/glum_benchmarks/data/__init__.py @@ -6,7 +6,7 @@ generate_real_insurance_dataset, generate_wide_insurance_dataset, ) -from .simulated_glm import simulate_glm_data +from .simulated_glm import generate_square_dataset, simulate_glm_data __all__ = [ "generate_intermediate_insurance_dataset", @@ -15,6 +15,7 @@ "generate_real_insurance_dataset", "create_insurance_raw_data", "simulate_glm_data", + "generate_square_dataset", "generate_housing_dataset", "create_housing_raw_data", ] diff --git a/glum_benchmarks/data/simulated_glm.py b/glum_benchmarks/data/simulated_glm.py index 137b99f58..09a49a095 100644 --- a/glum_benchmarks/data/simulated_glm.py +++ b/glum_benchmarks/data/simulated_glm.py @@ -1,4 +1,5 @@ from functools import partial +from typing import Optional import numpy as np import pandas as pd @@ -149,3 +150,58 @@ def simulate_glm_data( "coefs": coefs, } return data + + +def generate_square_dataset( + num_rows: Optional[int] = None, + _noise=None, # unused, required by load_data signature + distribution: str = "poisson", +) -> tuple[pd.DataFrame, np.ndarray, np.ndarray]: + """Generate a square dataset where n_rows = n_cols. + + Parameters + ---------- + num_rows + Number of rows (and columns). Defaults to 500. + _noise + Unused, present to match load_data signature. + distribution + The GLM family: "gaussian", "poisson", "gamma", or "binomial". + + Returns + ------- + tuple[pd.DataFrame, np.ndarray, np.ndarray] + (X, y, exposure) where X is square. + """ + n = num_rows if num_rows is not None else 5000 + rand = np.random.default_rng(42) + + # Generate standardized features + X = pd.DataFrame( + data=rand.normal(0, 1, size=(n, n)), + columns=[f"x{i}" for i in range(n)], + ) + + # Sparse coefficients (~10% non-zero) + coefs = rand.choice([0] * 9 + [1], size=n) * rand.normal(0, 1, size=n) + + # Linear predictor + eta = X.to_numpy() @ coefs + + # Generate y based on distribution + if distribution == "gaussian": + y = eta + rand.normal(0, 1, size=n) + elif distribution == "poisson": + mu = np.exp(np.clip(eta, -5, 5)) # Clamp to avoid overflow + y = rand.poisson(mu) + elif distribution == "gamma": + mu = np.exp(np.clip(eta, -5, 5)) + y = rand.gamma(shape=1.0, scale=mu) + elif distribution == "binomial": + p = 1 / (1 + np.exp(-eta)) # Logistic + y = rand.binomial(1, p) + else: + raise ValueError(f"Unknown distribution: {distribution}") + + exposure = np.ones(n) + return X, y, exposure diff --git a/glum_benchmarks/libraries/__init__.py b/glum_benchmarks/libraries/__init__.py index 29d683836..ccf7d605f 100644 --- a/glum_benchmarks/libraries/__init__.py +++ b/glum_benchmarks/libraries/__init__.py @@ -15,11 +15,6 @@ except ImportError: h2o_bench = None # type: ignore -try: - from .bench_liblinear import liblinear_bench -except ImportError: - liblinear_bench = None # type: ignore - try: from .bench_skglm import skglm_bench except ImportError: @@ -34,7 +29,6 @@ "celer_bench", "glum_bench", "h2o_bench", - "liblinear_bench", "skglm_bench", "sklearn_bench", "zeros_bench", diff --git a/glum_benchmarks/libraries/bench_celer.py b/glum_benchmarks/libraries/bench_celer.py index d81759108..cafc0a0de 100644 --- a/glum_benchmarks/libraries/bench_celer.py +++ b/glum_benchmarks/libraries/bench_celer.py @@ -7,6 +7,7 @@ from glum_benchmarks.util import ( _standardize_features, + _unstandardize_coefficients, benchmark_convergence_tolerance, runtime, ) @@ -24,17 +25,19 @@ def celer_bench( iterations: int, reg_multiplier: Optional[float] = None, standardize: bool = True, - max_iter: int = 1000, **kwargs, ): # Standardize features if requested + scaler = None + scaled_indices = None if standardize: dat = dat.copy() - dat["X"] = _standardize_features(dat["X"]) + dat["X"], scaler, scaled_indices = _standardize_features(dat["X"]) result: dict[str, Any] = {} fit_args = {"X": dat["X"], "y": dat["y"]} reg_strength = alpha if reg_multiplier is None else alpha * reg_multiplier + n_samples = dat["X"].shape[0] # LogisticRegression doesn't support fitting an intercept yet, so we also skip it if distribution not in ["gaussian"]: @@ -50,11 +53,12 @@ def celer_bench( else: model_class = Lasso + # Celer uses same alpha convention as sklearn Lasso/ElasticNet + # Scale by n_samples to match glum's objective model_args = { "tol": benchmark_convergence_tolerance, "fit_intercept": True, - "max_iter": max_iter, - "alpha": reg_strength, + "alpha": reg_strength * n_samples, } if model_class == ElasticNet: @@ -68,8 +72,14 @@ def celer_bench( warnings.warn(f"Celer failed: {e}") return {} - result["intercept"] = np.array(m.intercept_).ravel()[0] - result["coef"] = np.array(m.coef_).ravel() + intercept = np.array(m.intercept_).ravel()[0] + coef = np.array(m.coef_).ravel() + + # Unstandardize coefficients to match original data scale + result["intercept"], result["coef"] = _unstandardize_coefficients( + intercept, coef, scaler, scaled_indices + ) 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_glum.py b/glum_benchmarks/libraries/bench_glum.py index 0ea457869..5f3cfd6a1 100644 --- a/glum_benchmarks/libraries/bench_glum.py +++ b/glum_benchmarks/libraries/bench_glum.py @@ -28,7 +28,7 @@ def glum_bench( diagnostics_level: str = "basic", reg_multiplier: Optional[float] = None, hessian_approx: float = 0.0, - max_iter: int = 1000, + standardize: bool = True, **kwargs, ): """ @@ -44,8 +44,8 @@ def glum_bench( diagnostics_level reg_multiplier hessian_approx + standardize kwargs - Note: glum handles standardization internally via scale_predictors. Returns ------- @@ -64,7 +64,6 @@ def glum_bench( model_args = dict( family=get_sklearn_family(distribution), l1_ratio=l1_ratio, - max_iter=max_iter, random_state=random_seed, copy_X=False, selection="cyclic", @@ -72,6 +71,7 @@ def glum_bench( step_size_tol=0.01 * benchmark_convergence_tolerance, force_all_finite=False, hessian_approx=hessian_approx, + scale_predictors=standardize, verbose=False, ) @@ -85,6 +85,7 @@ def glum_bench( result["intercept"] = m.intercept_ result["coef"] = m.coef_ result["n_iter"] = m.n_iter_ + result["max_iter"] = m.max_iter # For convergence detection with pd.option_context( "display.expand_frame_repr", diff --git a/glum_benchmarks/libraries/bench_h2o.py b/glum_benchmarks/libraries/bench_h2o.py index e63cf5b3a..f50a6bc93 100644 --- a/glum_benchmarks/libraries/bench_h2o.py +++ b/glum_benchmarks/libraries/bench_h2o.py @@ -10,7 +10,6 @@ from scipy import sparse as sps from glum_benchmarks.util import ( - _standardize_features, benchmark_convergence_tolerance, runtime, ) @@ -40,7 +39,6 @@ def h2o_bench( iterations: int, reg_multiplier: Optional[float] = None, standardize: bool = True, - max_iter: int = 1000, **kwargs, ): """ @@ -61,10 +59,6 @@ def h2o_bench( ------- dict of data about this run """ - # Standardize features if requested - if standardize: - dat = dat.copy() - dat["X"] = _standardize_features(dat["X"]) result: dict = {} @@ -100,12 +94,11 @@ def h2o_bench( family="tweedie" if tweedie else distribution, alpha=l1_ratio, lambda_=alpha if reg_multiplier is None else alpha * reg_multiplier, - standardize=False, + standardize=standardize, # Let h2o handle standardization internally solver="IRLSM", objective_epsilon=benchmark_convergence_tolerance, beta_epsilon=benchmark_convergence_tolerance, gradient_epsilon=benchmark_convergence_tolerance, - max_iterations=max_iter, gainslift_bins=0, ) @@ -140,4 +133,6 @@ def h2o_bench( result["coef"] = standardized_coefs result["n_iter"] = m.score_history().iloc[-1]["iterations"] + # h2o default max_iterations is very high, but we can get actual from params + result["max_iter"] = m.actual_params.get("max_iterations", 100) return result diff --git a/glum_benchmarks/libraries/bench_liblinear.py b/glum_benchmarks/libraries/bench_liblinear.py deleted file mode 100644 index aebc482fd..000000000 --- a/glum_benchmarks/libraries/bench_liblinear.py +++ /dev/null @@ -1,108 +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 glum_benchmarks.util import ( - _standardize_features, - 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, - reg_multiplier: Optional[float] = None, - standardize: bool = True, - max_iter: int = 1000, - **kwargs, -) -> dict[str, Any]: - """ - Run the benchmark for sklearn.linear_model.LogisticRegression. - - Parameters - ---------- - dat - distribution - alpha - l1_ratio - iterations - reg_multiplier - standardize - kwargs - - Returns - ------- - dict - - """ - # Standardize features if requested - if standardize: - dat = dat.copy() - dat["X"] = _standardize_features(dat["X"]) - - 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 - - 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=max_iter, - ) - - fit_args = dict( # type: ignore - X=X, - y=dat["y"].astype(np.int64).copy(), - ) - - 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/glum_benchmarks/libraries/bench_skglm.py b/glum_benchmarks/libraries/bench_skglm.py index 08f149571..fced0c8e6 100644 --- a/glum_benchmarks/libraries/bench_skglm.py +++ b/glum_benchmarks/libraries/bench_skglm.py @@ -10,6 +10,7 @@ from glum_benchmarks.util import ( _standardize_features, + _unstandardize_coefficients, benchmark_convergence_tolerance, runtime, ) @@ -27,13 +28,14 @@ def skglm_bench( iterations: int, reg_multiplier: Optional[float] = None, standardize: bool = True, - max_iter: int = 1000, **kwargs, ): # Standardize features if requested + scaler = None + scaled_indices = None if standardize: dat = dat.copy() - dat["X"] = _standardize_features(dat["X"]) + dat["X"], scaler, scaled_indices = _standardize_features(dat["X"]) result: dict[str, Any] = {} reg_strength = alpha if reg_multiplier is None else alpha * reg_multiplier @@ -60,13 +62,9 @@ def skglm_bench( # 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, max_iter=max_iter - ) + solver = ProxNewton(tol=benchmark_convergence_tolerance, fit_intercept=True) else: - solver = AndersonCD( - tol=benchmark_convergence_tolerance, fit_intercept=True, max_iter=max_iter - ) + solver = AndersonCD(tol=benchmark_convergence_tolerance, fit_intercept=True) model_args = { "datafit": datafit, @@ -95,8 +93,14 @@ def skglm_bench( warnings.warn(f"skglm failed: {e}") return {} - result["intercept"] = np.array(m.intercept_).ravel()[0] - result["coef"] = np.array(m.coef_).ravel() + intercept = np.array(m.intercept_).ravel()[0] + coef = np.array(m.coef_).ravel() + + # Unstandardize coefficients to match original data scale + result["intercept"], result["coef"] = _unstandardize_coefficients( + intercept, coef, scaler, scaled_indices + ) 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 index 56c060d5c..3c282e1f9 100644 --- a/glum_benchmarks/libraries/bench_sklearn.py +++ b/glum_benchmarks/libraries/bench_sklearn.py @@ -13,6 +13,7 @@ from glum_benchmarks.util import ( _standardize_features, + _unstandardize_coefficients, benchmark_convergence_tolerance, runtime, ) @@ -31,7 +32,6 @@ def sklearn_bench( iterations: int, reg_multiplier: Optional[float] = None, standardize: bool = True, - max_iter: int = 1000, **kwargs, ): """ @@ -46,6 +46,7 @@ def sklearn_bench( iterations reg_multiplier standardize + If True, standardize continuous features using sklearn's StandardScaler. kwargs Returns @@ -53,9 +54,11 @@ def sklearn_bench( Dict of """ # Standardize features if requested + scaler = None + scaled_indices = None if standardize: dat = dat.copy() - dat["X"] = _standardize_features(dat["X"]) + dat["X"], scaler, scaled_indices = _standardize_features(dat["X"]) result: dict[str, Any] = {} reg_strength = alpha if reg_multiplier is None else alpha * reg_multiplier @@ -66,12 +69,10 @@ def sklearn_bench( if distribution == "gaussian": if l1_ratio == 0.0: # Pure L2 (Ridge): use closed-form solution - # sklearn's Ridge uses alpha directly but with sum(loss) not mean(loss) model_class = Ridge model_args = { "alpha": reg_strength * n_samples, "fit_intercept": True, - "max_iter": max_iter, "tol": benchmark_convergence_tolerance, "solver": "auto", } @@ -81,18 +82,16 @@ def sklearn_bench( model_args = { "alpha": reg_strength * n_samples, "fit_intercept": True, - "max_iter": max_iter, "tol": benchmark_convergence_tolerance, "precompute": True, } else: - # Elastic Net: mixed L1/L2 + # Scale by n_samples to match glum's objective model_class = ElasticNet model_args = { - "alpha": reg_strength, + "alpha": reg_strength * n_samples, "l1_ratio": l1_ratio, "fit_intercept": True, - "max_iter": max_iter, "tol": benchmark_convergence_tolerance, "precompute": True, } @@ -111,7 +110,6 @@ def sklearn_bench( "l1_ratio": l1_ratio, "solver": solver, "fit_intercept": True, - "max_iter": max_iter, "tol": benchmark_convergence_tolerance, } else: @@ -134,7 +132,6 @@ def sklearn_bench( "power": power, "alpha": reg_strength, "fit_intercept": True, - "max_iter": max_iter, "tol": benchmark_convergence_tolerance, "solver": "newton-cholesky", } @@ -153,14 +150,22 @@ def sklearn_bench( return result if distribution == "binomial": - result["intercept"] = m.intercept_[0] if m.intercept_.ndim > 0 else m.intercept_ - result["coef"] = m.coef_.ravel() - result["n_iter"] = m.n_iter_[0] if hasattr(m.n_iter_, "__len__") else m.n_iter_ + 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: - result["intercept"] = m.intercept_ - result["coef"] = m.coef_ + 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) - result["n_iter"] = 0 if n_iter is None else n_iter + n_iter = 0 if n_iter is None else n_iter + + # Unstandardize coefficients to match original data scale + result["intercept"], result["coef"] = _unstandardize_coefficients( + intercept, coef, scaler, scaled_indices + ) + 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/glum_benchmarks/problems.py b/glum_benchmarks/problems.py index 8e8391f34..8cc3fe347 100644 --- a/glum_benchmarks/problems.py +++ b/glum_benchmarks/problems.py @@ -15,13 +15,11 @@ generate_intermediate_insurance_dataset, generate_narrow_insurance_dataset, generate_real_insurance_dataset, + generate_square_dataset, generate_wide_insurance_dataset, ) from .util import cache_location, exposure_and_offset_to_weights, get_tweedie_p -# TODO: Implement closed form solution for l2-gaussian and put a note in the results -# TODO: Add glm_data problems? - joblib_memory = Memory(cache_location, verbose=0) @@ -172,6 +170,12 @@ 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 datasets (square: n_rows ≈ n_cols) + simulated_distributions = ["gaussian", "poisson", "gamma", "binomial"] + simulated_load_funcs = { + "square-simulated": generate_square_dataset, + } + problems = {} for penalty_str, l1_ratio in [("l2", 0.0), ("net", 0.5), ("lasso", 1.0)]: # Add housing problems @@ -202,5 +206,18 @@ def get_all_problems() -> dict[str, Problem]: regularization_strength=regularization_strength, 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, + regularization_strength=regularization_strength, + l1_ratio=l1_ratio, + ) return problems diff --git a/glum_benchmarks/run_benchmarks.py b/glum_benchmarks/run_benchmarks.py index b22ae73e7..d4f7a1cf8 100644 --- a/glum_benchmarks/run_benchmarks.py +++ b/glum_benchmarks/run_benchmarks.py @@ -6,9 +6,10 @@ pixi run -e benchmark run-benchmarks Configuration: - Edit config.yaml to select which libraries, datasets, regularizations, - and distributions to benchmark. You can also control which steps to run - (run_benchmarks, analyze_results, generate_plots). + Edit config.yaml to configure which benchmarks to run. Use param_grid to + specify combinations of libraries, datasets, regularizations, distributions, + and reg_strengths. 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 @@ -19,16 +20,27 @@ from __future__ import annotations import pickle +import re import shutil import warnings -from dataclasses import dataclass +from concurrent.futures import ThreadPoolExecutor +from concurrent.futures import TimeoutError as FuturesTimeoutError +from contextlib import nullcontext +from itertools import product from pathlib import Path +from typing import Literal import matplotlib.pyplot as plt import numpy as np import pandas as pd -import yaml from matplotlib.patches import Patch +from pydantic import ( + BaseModel, + ConfigDict, + Field, + model_validator, +) +from ruamel.yaml import YAML from glum_benchmarks.problems import get_all_problems from glum_benchmarks.util import ( @@ -38,114 +50,278 @@ get_params_from_fname, ) -# TODO: update README and documentation -# TODO: improve plotting to handle "not converged" and "not supported" cases better +# TODO: Update README and documentation +# TODO: Implement closed form solution for l2-gaussian and put a note in the results +# TODO: Scaling +# TODO: Determine optimal storage type for each library for a fair comparison + +# Type aliases for configuration options +Library = Literal["glum", "sklearn", "h2o", "skglm", "celer", "zeros"] +Dataset = Literal[ + "intermediate-insurance", + "intermediate-housing", + "narrow-insurance", + "wide-insurance", + "square-simulated", +] +Regularization = Literal["lasso", "l2", "net"] +RegStrength = float # Valid values: 0.0001, 0.001, 0.01 +REG_STRENGTH_VALUES = (0.0001, 0.001, 0.01) +Distribution = Literal["gaussian", "gamma", "binomial", "poisson", "tweedie-p=1.5"] +StorageFormat = Literal["auto", "dense", "sparse"] + + +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)" + ) + reg_strengths: list[RegStrength] | None = Field( + default=None, description="Regularization strengths (None = all)" + ) + distributions: list[Distribution] | None = Field( + default=None, description="Distributions (None = all)" + ) -@dataclass -class BenchmarkConfig: +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 - analyze_results: bool - generate_plots: bool + 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[str] | None = Field( + default=None, + description="Figure names to include in docs/benchmarks.rst (None = all )", + ) + 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 - clear_output: bool + 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" + ) - # Problem selection - libraries: list[str] - datasets: list[str] - regularizations: list[str] - distributions: list[str] + # 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 - reg_strength: float - standardize: bool - iterations: int - num_rows: int | None - max_iter: int - - # Derived paths (computed after init) - script_dir: Path - results_dir: Path - pickle_dir: Path - figure_dir: Path - csv_file: Path + num_threads: int = Field( + default=16, ge=1, description="Number of threads for parallel execution" + ) + standardize: bool = Field( + default=True, description="Whether to standardize before fitting" + ) + iterations: int = Field( + default=2, ge=1, description="Run each benchmark N times, report minimum" + ) + num_rows: int | None = Field( + default=None, ge=1, description="Limit rows per dataset (None = full dataset)" + ) + 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 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.safe_load(f) - - script_dir = yaml_path.parent - results_dir = script_dir / "results" / data["run_name"] - - return cls( - run_benchmarks=data["run_benchmarks"], - analyze_results=data["analyze_results"], - generate_plots=data["generate_plots"], - run_name=data["run_name"], - clear_output=data["clear_output"], - libraries=data["libraries"], - datasets=data["datasets"], - regularizations=data["regularizations"], - distributions=data["distributions"], - num_threads=data["num_threads"], - reg_strength=data["reg_strength"], - standardize=data["standardize"], - iterations=data["iterations"], - num_rows=data["num_rows"], - max_iter=data["max_iter"], - script_dir=script_dir, - results_dir=results_dir, - pickle_dir=results_dir / "pickles", - figure_dir=results_dir / "figures", - csv_file=results_dir / "results.csv", - ) + data = yaml.load(f) + # Add script_dir for path computation + data["script_dir"] = yaml_path.parent -def get_problems_to_run(config: BenchmarkConfig) -> list[str]: - """Get list of problem names matching the configuration.""" - all_problems = get_all_problems() - selected = [] + return cls.model_validate(data) - for name in all_problems.keys(): - # Only benchmark "-no-weights" problems (skip offset and weighted variants) - if "-no-weights-" not in name: - continue - # Filter by dataset - if config.datasets is not None: - if not any(d in name for d in config.datasets): - continue +def _parse_problem_name(name: str) -> tuple[str, str, str]: + """Parse problem name into (dataset, regularization, distribution). - # Filter by regularization - if config.regularizations is not None: - if not any(reg in name for reg in config.regularizations): - continue + 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 - # Filter by distribution - if config.distributions is not None: - if not any(dist in name for dist in config.distributions): - continue - selected.append(name) +def get_benchmark_combinations( + config: BenchmarkConfig, +) -> list[tuple[str, str, float]]: + """Get list of (problem_name, library, reg_strength) tuples to benchmark. + + Cartesian product within each entry, union across entries. + + Returns: + List of (problem_name, library_name, reg_strength) tuples to run. + """ + + all_problems = get_all_problems() + available_libraries = list(get_all_libraries()) + + # 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_reg_strengths = list(REG_STRENGTH_VALUES) + + combinations: set[tuple[str, str, 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 available_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 + strengths = entry.reg_strengths if entry.reg_strengths else all_reg_strengths + + print(f"Parameter Set {i}:") + print(f" Libraries: {libraries}") + print(f" Datasets: {datasets}") + print(f" Regularizations: {regs}") + print(f" Distributions: {dists}") + print(f" Reg strengths: {strengths}") + print() + + # Cartesian product within this entry + for lib, dataset, reg, dist, strength in product( + libraries, datasets, regs, dists, strengths + ): + key = (dataset, reg, dist) + if key in problem_lookup: + # Only add if library is actually available + if lib in available_libraries: + combinations.add((problem_lookup[key], lib, strength)) + + print(f"Total benchmark runs: {len(combinations)}") + print("=" * 70) + print() - return sorted(selected) + return sorted(combinations) def run_single_benchmark( - problem_name: str, library_name: str, config: BenchmarkConfig + problem_name: str, library_name: str, reg_strength: float, config: BenchmarkConfig ) -> tuple[dict, BenchmarkParams]: - """Run a single benchmark and return results.""" - # Use "auto" storage for glum (enables categorical algorithm via tabmat) - # Use "dense" for other libraries - storage = "auto" if library_name == "glum" else "dense" + """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 storage from config, default to "dense" + lib_key: Library = library_name # type: ignore[assignment] + storage = config.storage.get(lib_key, "dense") params = BenchmarkParams( problem_name=problem_name, @@ -153,16 +329,35 @@ def run_single_benchmark( num_rows=config.num_rows, storage=storage, threads=config.num_threads, - regularization_strength=config.reg_strength, + regularization_strength=reg_strength, ) - result, _ = execute_problem_library( + # Use ThreadPoolExecutor for cross-platform timeout support + # Note: We don't use the context manager because its __exit__ waits for + # the thread to complete, which defeats the purpose of the timeout. + executor = ThreadPoolExecutor(max_workers=1) + future = executor.submit( + execute_problem_library, params, iterations=config.iterations, diagnostics_level=None, standardize=config.standardize, - max_iter=config.max_iter, ) + try: + result, _ = future.result(timeout=config.timeout) + result["timed_out"] = False + except FuturesTimeoutError: + # Return timeout result + result = { + "runtime": float(config.timeout), + "timed_out": True, + "intercept": None, + "coef": None, + "n_iter": None, + } + # Shutdown without waiting for the thread to finish + # The thread will continue in the background but we move on + executor.shutdown(wait=False, cancel_futures=True) return result, params @@ -175,55 +370,48 @@ def run_all_benchmarks(config: BenchmarkConfig): shutil.rmtree(config.results_dir) config.pickle_dir.mkdir(parents=True, exist_ok=True) - problems = get_problems_to_run(config) - available = get_all_libraries() - libraries = [lib for lib in config.libraries if lib in available] + # Get benchmark combinations (problem, library, reg_strength) from param_grid + combinations = get_benchmark_combinations(config) - print(f"Problems: {len(problems)}") - print(f"Libraries: {libraries}") - print() - - total = len(problems) * len(libraries) + total = len(combinations) current = 0 - for problem_name in problems: - for library_name in libraries: - current += 1 - print( - f"[{current}/{total}] {library_name} / {problem_name}", - 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, config - ) + for problem_name, library_name, reg_strength in combinations: + current += 1 + label = f"{library_name} / {problem_name} (α={reg_strength})" + 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, reg_strength, config + ) - # Save result - fname = params.get_result_fname() + ".pkl" - with open(config.pickle_dir / fname, "wb") as f: - pickle.dump(result, f) + # Save result + fname = params.get_result_fname() + ".pkl" + with open(config.pickle_dir / fname, "wb") as f: + pickle.dump(result, f) - if len(result) > 0 and "runtime" in result: - print(f"-> {result['runtime']:.4f}s") - else: - print("-> (skipped)") + 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"{w.message}") + # Print captured warnings + for w in caught_warnings: + print(f" Warning: {w.message}") - except Exception as e: - print(f"-> ERROR: {e}") + except Exception as e: + print(f"-> ERROR: {e}") def analyze_results(config: BenchmarkConfig) -> pd.DataFrame: @@ -243,14 +431,21 @@ def analyze_results(config: BenchmarkConfig) -> pd.DataFrame: with open(fname, "rb") as f: data = pickle.load(f) - if not data or "coef" not in data: + 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] - coefs = data["coef"] + + # 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") @@ -260,10 +455,15 @@ def analyze_results(config: BenchmarkConfig) -> pd.DataFrame: else: runtime_per_iter = runtime - # Calculate coefficient norms - 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) + # 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 reg_strength = ( @@ -272,10 +472,14 @@ def analyze_results(config: BenchmarkConfig) -> pd.DataFrame: else params.regularization_strength ) - # Check convergence (n_iter < max_iter means converged) - converged = True - if n_iter is not None and n_iter >= config.max_iter: - converged = False + # 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( { @@ -341,6 +545,149 @@ def analyze_results(config: BenchmarkConfig) -> pd.DataFrame: return df.reset_index() +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) + + 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" + + # 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, + width=width, + color=na_bg, + edgecolor=lib_color, + linewidth=2, + 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() + bar_height = min(pivot.loc[combo, lib], y_max) + ax.bar( + x + width / 2, + bar_height, + width=width, + color="none", + edgecolor=nc_edge, + linewidth=0.5, + hatch="//", + alpha=0.5, + ) + ax.text( + x + width / 2, + bar_height + 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]: + 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. @@ -370,29 +717,28 @@ def plot_results(config: BenchmarkConfig): config.figure_dir.mkdir(exist_ok=True) # Extract distribution, regularization and dataset from problem_name - # Format: {dataset}-no-weights-{regularization}-{distribution} - # where dataset is 2 parts, regularization is 1 part (lasso/l2/net) - # and distribution may contain hyphens (e.g., "tweedie-p=1.5") df = df.copy() - - def parse_problem_name(name): - """Parse problem name into (dataset, regularization, distribution).""" - parts = name.split("-") - dataset = "-".join(parts[:2]) - reg = parts[4] - dist = "-".join(parts[5:]) - return dataset, reg, dist - - parsed = df["problem_name"].apply(parse_problem_name) + 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['regularization_strength']})", + axis=1, + ) + # Drop duplicates (keep latest result for each unique combo) - # Use all derived columns to ensure no duplicates in pivot df = df.drop_duplicates( - subset=["dataset", "regularization", "distribution", "library_name"], + subset=[ + "dataset", + "distribution", + "regularization", + "regularization_strength", + "library_name", + ], keep="last", ) @@ -402,22 +748,24 @@ def parse_problem_name(name): "h2o": "#fdbf6f", "glmnet": "#b15928", "sklearn": "#b15928", - "liblinear": "#33a02c", - "skglm": "#fb9a99", - "celer": "#cab2d6", + "skglm": "#33a02c", + "celer": "#fb9a99", } - # Generate one plot per dataset/regularization combo + # Generate one plot per dataset/distribution combo for dataset in df["dataset"].unique(): - for reg in df["regularization"].unique(): - subset = df[(df["dataset"] == dataset) & (df["regularization"] == reg)] + for dist in df["distribution"].unique(): + subset = df[(df["dataset"] == dataset) & (df["distribution"] == dist)] if subset.empty: continue - # Pivot for plotting + print(f" Plotting {dataset}-{dist}: {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="distribution", + index="reg_combo", columns="library_name", values="runtime", ) @@ -428,7 +776,7 @@ def parse_problem_name(name): # Track which cells did not converge pivot_converged = ( subset.pivot( - index="distribution", + index="reg_combo", columns="library_name", values="converged", ) @@ -441,269 +789,247 @@ def parse_problem_name(name): min_runtime = pivot.values[pivot.values > 0].min() y_max = min_runtime * 10 - # Get colors for the libraries in this plot - plot_colors = [colors.get(lib, "#999999") for lib in pivot.columns] - - # Create bar chart with clipped bars and annotations - fig, ax = plt.subplots(figsize=(10, 5)) - pivot_clipped = pivot.clip(upper=y_max) - pivot_clipped.plot(kind="bar", ax=ax, color=plot_colors) - - # Draw hatched bars for unsupported library/distribution combos - n_dists = len(pivot.index) - bars = ax.patches - for i, dist in enumerate(pivot.index): - for j, lib in enumerate(pivot.columns): - if unsupported.loc[dist, lib]: - bar_idx = j * n_dists + i - bar = bars[bar_idx] - x = bar.get_x() - width = bar.get_width() - lib_color = colors.get(lib, "#999999") - # Draw bar with library color + hatch pattern - ax.bar( - x + width / 2, - y_max, - width=width, - color="white", - edgecolor=lib_color, - linewidth=2, - hatch="//", - ) - # Add "N/A" label - ax.text( - x + width / 2, - y_max / 2, - "N/A", - ha="center", - va="center", - fontsize=8, - color="#666666", - fontweight="bold", - ) - # Add hatch overlay for non-converged results (keep the runtime bar) - elif not_converged.loc[dist, lib]: - bar_idx = j * n_dists + i - bar = bars[bar_idx] - x = bar.get_x() - width = bar.get_width() - bar_height = min(pivot.loc[dist, lib], y_max) - # Overlay hatch pattern on existing bar - ax.bar( - x + width / 2, - bar_height, - width=width, - color="none", - edgecolor="black", - linewidth=0.5, - hatch="//", - alpha=0.5, - ) - # Add "NC" (not converged) label at top of bar - ax.text( - x + width / 2, - bar_height + y_max * 0.02, - "NC", - ha="center", - va="bottom", - fontsize=7, - color="#cc0000", - fontweight="bold", - ) - - # Add annotations for clipped bars (show original value on bar with arrow) - for i, dist in enumerate(pivot.index): - for j, lib in enumerate(pivot.columns): - original_val = pivot.loc[dist, lib] - if original_val > y_max and not unsupported.loc[dist, lib]: - bar_idx = j * n_dists + i - bar = bars[bar_idx] - x = bar.get_x() + bar.get_width() / 2 - # Number on the bar - ax.text( - x, - y_max * 0.75, - f"{original_val:.4f}", - ha="center", - va="center", - fontsize=9, - fontweight="bold", - rotation=90, - ) - # Arrow starting inside bar, pointing up to top of bar - ax.annotate( - "", - xy=(x, y_max), - xytext=(x, y_max * 0.88), - arrowprops=dict(arrowstyle="->", color="black", lw=1.5), - ) - - ax.set_ylim(0, y_max * 1.08) - reg_label = reg.replace("-", " ").title().replace(" ", "-") - # Title with hyphens like in the example + # Title title_dataset = dataset.replace(" ", "-").title() - ax.set_title(f"{title_dataset}-{reg_label}") - ax.set_ylabel("run time (s)") - ax.set_xlabel("") # No x-label, distribution names are self-explanatory - # Add custom legend entries for N/A and NC - handles, labels = ax.get_legend_handles_labels() - handles.append(Patch(facecolor="white", edgecolor="gray", hatch="//")) - labels.append("N/A (not supported)") - handles.append(Patch(facecolor="white", edgecolor="black", hatch="//")) - labels.append("NC (not converged)") - ax.legend( - handles, labels, title="", bbox_to_anchor=(1.02, 1), loc="upper left" - ) - # Capitalize x-tick labels - ax.set_xticklabels( - [label.get_text().title() for label in ax.get_xticklabels()] + title_dist = dist.replace(" ", "-").title() + title = f"{title_dataset}-{title_dist}" + + # Determine if this figure needs a dark mode version (for README) + fname = f"{dataset}-{dist}" + 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, ) - plt.xticks(rotation=45, ha="right") - plt.tight_layout() - - # Save - fname = f"{dataset}-{reg}" - plt.savefig(config.figure_dir / f"{fname}.png", dpi=300) - plt.close() + 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) - - # Limit to 10x glum (so y_max = 10) norm_y_max = 10.0 - pivot_norm_clipped = pivot_norm.clip(upper=norm_y_max) - - fig, ax = plt.subplots(figsize=(10, 5)) - pivot_norm_clipped.plot(kind="bar", ax=ax, color=plot_colors) - - # Draw hatched bars for unsupported library/distribution combos - n_dists = len(pivot_norm.index) - bars = ax.patches - for i, dist in enumerate(pivot_norm.index): - for j, lib in enumerate(pivot_norm.columns): - if unsupported.loc[dist, lib]: - bar_idx = j * n_dists + i - bar = bars[bar_idx] - x = bar.get_x() - width = bar.get_width() - lib_color = colors.get(lib, "#999999") - # Draw bar with library color + hatch pattern - ax.bar( - x + width / 2, - norm_y_max, - width=width, - color="white", - edgecolor=lib_color, - linewidth=2, - hatch="//", - ) - # Add "N/A" label - ax.text( - x + width / 2, - norm_y_max / 2, - "N/A", - ha="center", - va="center", - fontsize=8, - color="#666666", - fontweight="bold", - ) - # Add hatch overlay for non-converged results - elif not_converged.loc[dist, lib]: - bar_idx = j * n_dists + i - bar = bars[bar_idx] - x = bar.get_x() - width = bar.get_width() - bar_height = min(pivot_norm.loc[dist, lib], norm_y_max) - # Overlay hatch pattern on existing bar - ax.bar( - x + width / 2, - bar_height, - width=width, - color="none", - edgecolor="black", - linewidth=0.5, - hatch="//", - alpha=0.5, - ) - # Add "NC" (not converged) label at top of bar - ax.text( - x + width / 2, - bar_height + norm_y_max * 0.02, - "NC", - ha="center", - va="bottom", - fontsize=7, - color="#cc0000", - fontweight="bold", - ) - - # Add annotations for clipped bars (show original value on bar) - for i, dist in enumerate(pivot_norm.index): - for j, lib in enumerate(pivot_norm.columns): - original_val = pivot_norm.loc[dist, lib] - if original_val > norm_y_max and not unsupported.loc[dist, lib]: - bar_idx = j * n_dists + i - bar = bars[bar_idx] - x = bar.get_x() + bar.get_width() / 2 - # Number on the bar - ax.text( - x, - norm_y_max * 0.75, - f"{original_val:.1f}x", - ha="center", - va="center", - fontsize=9, - fontweight="bold", - rotation=90, - ) - # Arrow starting inside bar, pointing up to top of bar - ax.annotate( - "", - xy=(x, norm_y_max), - xytext=(x, norm_y_max * 0.88), - arrowprops=dict(arrowstyle="->", color="black", lw=1.5), - ) - - ax.set_ylim(0, norm_y_max * 1.08) - ax.set_title(f"{title_dataset}-{reg_label} (normalized)") - ax.set_ylabel("run time relative to glum") - ax.set_xlabel("") - ax.axhline(y=1.0, color="gray", linestyle="--", linewidth=1, alpha=0.7) - # Add custom legend entries for N/A and NC - handles, labels = ax.get_legend_handles_labels() - handles.append(Patch(facecolor="white", edgecolor="gray", hatch="//")) - labels.append("N/A (not supported)") - handles.append(Patch(facecolor="white", edgecolor="black", hatch="//")) - labels.append("NC (not converged)") - ax.legend( - handles, - labels, - title="", - bbox_to_anchor=(1.02, 1), - loc="upper left", - ) - # X-tick labels: show distribution name + glum runtime - new_labels = [] - for dist in pivot.index: - glum_runtime = pivot.loc[dist, "glum"] + + # X-tick labels with glum runtime + x_labels = [] + for combo in pivot.index: + glum_runtime = pivot.loc[combo, "glum"] if glum_runtime > 0: - new_labels.append( - f"{dist.title()}\n(glum = {glum_runtime:.3f}s)" - ) + x_labels.append(f"{combo}\n(glum = {glum_runtime:.3f}s)") else: - new_labels.append(dist.title()) - ax.set_xticklabels(new_labels) - plt.xticks(rotation=45, ha="right") - plt.tight_layout() - - fname_norm = f"{dataset}-{reg}-normalized" - plt.savefig(config.figure_dir / f"{fname_norm}.png", dpi=300) - plt.close() + x_labels.append(combo) + + fname_norm = f"{dataset}-{dist}-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 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 figures to use for docs + if config.docs_figures is not None: + # Use explicitly specified figures + docs_fig_names = [f for f in config.docs_figures if f in available] + missing = set(config.docs_figures) - available + if missing: + print(f"Warning: docs_figures not found: {missing}") + else: + # Default: all generated figures + docs_fig_names = 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_needed = set(docs_fig_names) | 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 + if docs_fig_names: + rst_lines = [RST_START_MARKER, ""] + for fig_name in docs_fig_names: + rst_lines.append(f".. image:: _static/{fig_name}") + rst_lines.append(" :width: 700") + rst_lines.append("") + rst_lines.append(RST_END_MARKER) + rst_new_content = "\n".join(rst_lines) + + if config.benchmarks_rst.exists(): + with open(config.benchmarks_rst) as f: + rst_content = f.read() + + if RST_START_MARKER in rst_content and RST_END_MARKER in rst_content: + pattern = re.compile( + rf"{re.escape(RST_START_MARKER)}.*?{re.escape(RST_END_MARKER)}", + re.DOTALL, + ) + updated_rst = pattern.sub(rst_new_content, rst_content) + with open(config.benchmarks_rst, "w") as f: + f.write(updated_rst) + print(f"\nUpdated: {config.benchmarks_rst}") + print(f"Inserted {len(docs_fig_names)} figure references.") + else: + print(f"\nMarkers not found in {config.benchmarks_rst}") + print(f"Add: {RST_START_MARKER} and {RST_END_MARKER}") + else: + print(f"\nbenchmarks.rst not found: {config.benchmarks_rst}") + + # Update README.md + # Use GitHub's light/dark mode syntax with actual dark images when available + 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/") + # Use actual dark image + light_ref = f"docs/_static/{fig_name}#gh-light-mode-only" + dark_ref = f"docs/_static/{dark_name}#gh-dark-mode-only" + else: + # Use same image for both modes + 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]({light_ref})") + md_lines.append(f"![Benchmark results]({dark_ref})") + 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}") + def main(): # Load configuration @@ -721,6 +1047,9 @@ def main(): if config.generate_plots: plot_results(config) + if config.update_docs: + update_docs(config) + # Print summary print() print("=" * 60) @@ -730,6 +1059,9 @@ def main(): 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.benchmarks_rst}") + print(f"README updated: {config.readme_file}") # Snapshot config for reproducibility config.results_dir.mkdir(parents=True, exist_ok=True) diff --git a/glum_benchmarks/util.py b/glum_benchmarks/util.py index de64aed8f..a7d68c504 100644 --- a/glum_benchmarks/util.py +++ b/glum_benchmarks/util.py @@ -7,6 +7,7 @@ import tabmat as tm from scipy import sparse as sps from scipy.sparse import csc_matrix +from sklearn.preprocessing import StandardScaler from glum import GeneralizedLinearRegressor, TweedieDistribution from glum._solvers import eta_mu_objective @@ -326,47 +327,98 @@ def get_tweedie_p(distribution: str) -> float: def _standardize_features( X: Union[np.ndarray, pd.DataFrame, csc_matrix, tm.MatrixBase], -) -> Union[np.ndarray, pd.DataFrame, csc_matrix, tm.MatrixBase]: +) -> tuple[ + Union[np.ndarray, pd.DataFrame, csc_matrix, tm.MatrixBase], + Optional[StandardScaler], + Optional[np.ndarray], +]: """ - Standardize features by scaling to unit L2 norm per column. + Standardize only numerical columns using StandardScaler. - For consistency across sparse/dense, we use L2 norm scaling (no centering). - This ensures all benchmark libraries start with the same pre-processed data. - """ - dtype = np.float64 + For dense data: mean=0, std=1 + For sparse data: std=1 only (with_mean=False to preserve sparsity) + Returns + ------- + X_scaled : array-like + The scaled feature matrix. + scaler : StandardScaler or None + The fitted scaler (for later unstandardization). + scaled_indices : ndarray or None + Indices of the columns that were scaled (for DataFrames with mixed types). + None if all columns were scaled or no scaling was performed. + """ if isinstance(X, pd.DataFrame): - # Preserve DataFrame type - X_arr = X.values.astype(dtype) - col_norms = np.linalg.norm(X_arr, axis=0) - col_norms[col_norms == 0] = 1.0 - return pd.DataFrame(X_arr / col_norms, columns=X.columns, index=X.index) - - if isinstance(X, np.ndarray): - # Dense: scale columns to unit L2 norm - X = np.asarray(X, dtype=dtype) - col_norms = np.linalg.norm(X, axis=0) - col_norms[col_norms == 0] = 1.0 - return X / col_norms - - elif isinstance(X, csc_matrix): - # Sparse: scale columns to unit L2 norm - X = X.astype(dtype) - col_norms = np.sqrt(X.power(2).sum(axis=0)).A1 - col_norms[col_norms == 0] = 1.0 - from scipy.sparse import diags - - return X @ diags(1.0 / col_norms) - - elif isinstance(X, tm.MatrixBase): - # tabmat matrices: skip standardization - # glum is the only library that can use tabmat directly, and it handles - # standardization internally. Other libraries would need conversion anyway. - return X + # Identify numerical columns + numerical_cols = X.select_dtypes(include=["number"]).columns + # Get the integer indices of numerical columns + scaled_indices = np.array([X.columns.get_loc(c) for c in numerical_cols]) + scaler = StandardScaler() + X_scaled = X.copy() + X_scaled[numerical_cols] = scaler.fit_transform(X[numerical_cols]) + return X_scaled, scaler, scaled_indices + + if sps.issparse(X): + # For sparse matrices, we can only scale (not center) to preserve sparsity + scaler = StandardScaler(with_mean=False) + X_scaled = scaler.fit_transform(X) + return X_scaled, scaler, None # All columns scaled + + # Fallback: return unchanged for other types (tabmat) + return X, None, None + + +def _unstandardize_coefficients( + intercept: float, + coef: np.ndarray, + scaler: Optional[StandardScaler], + scaled_indices: Optional[np.ndarray] = None, +) -> tuple[float, np.ndarray]: + """ + Unstandardize coefficients to match the original (unscaled) data. + This reverses the StandardScaler transformation so coefficients can be + compared with libraries that handle standardization internally (glum, h2o). + + Parameters + ---------- + intercept : float + The intercept from the fitted model. + coef : ndarray + The coefficients from the fitted model. + scaler : StandardScaler or None + The scaler used to standardize features. + scaled_indices : ndarray or None + Indices of the columns that were scaled. If None, assumes all columns + were scaled (or no scaling was performed if scaler is also None). + """ + if scaler is None: + return intercept, coef + + scale = scaler.scale_ + coef_original = coef.copy() + + if scaled_indices is not None: + # Only unstandardize the coefficients corresponding to scaled columns + coef_original[scaled_indices] = coef[scaled_indices] / scale + + # intercept_original = intercept_scaled - sum(mean / scale * coef_scaled) + if scaler.with_mean and scaler.mean_ is not None: + intercept_original = intercept - np.dot( + scaler.mean_ / scale, coef[scaled_indices] + ) + else: + intercept_original = intercept else: - # Unknown type, return as-is - return X + # All columns were scaled + coef_original = coef / scale + + if scaler.with_mean and scaler.mean_ is not None: + intercept_original = intercept - np.dot(scaler.mean_ / scale, coef) + else: + intercept_original = intercept + + return intercept_original, coef_original def get_all_libraries() -> dict: @@ -384,7 +436,6 @@ def get_all_libraries() -> dict: celer_bench, glum_bench, h2o_bench, - liblinear_bench, skglm_bench, sklearn_bench, zeros_bench, @@ -395,7 +446,6 @@ def get_all_libraries() -> dict: "zeros": zeros_bench, "celer": celer_bench, "h2o": h2o_bench, - "liblinear": liblinear_bench, "skglm": skglm_bench, "sklearn": sklearn_bench, } diff --git a/pixi.lock b/pixi.lock index fce728211..5d9f7345c 100644 --- a/pixi.lock +++ b/pixi.lock @@ -10,7 +10,8 @@ environments: - 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/attrs-25.3.0-pyh71513ae_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 - 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__osx >=11.0 license: MIT license_family: MIT - purls: [] size: 83386 timestamp: 1753484079473 - conda: https://conda.anaconda.org/conda-forge/win-64/yaml-0.2.5-h6a83c73_3.conda @@ -24698,7 +24253,6 @@ packages: - ucrt >=10.0.20348.0 license: MIT license_family: MIT - purls: [] size: 63944 timestamp: 1753484092156 - conda: https://conda.anaconda.org/conda-forge/linux-64/zeromq-4.3.5-h387f397_9.conda @@ -24760,6 +24314,8 @@ packages: - python license: MIT license_family: MIT + purls: + - 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 @@ -24769,8 +24325,6 @@ packages: - python >=3.9 license: MIT license_family: MIT - purls: - - pkg:pypi/zipp?source=hash-mapping size: 22963 timestamp: 1749421737203 - conda: https://conda.anaconda.org/conda-forge/linux-64/zlib-1.3.1-hb9d3cd8_2.conda diff --git a/pixi.toml b/pixi.toml index 08a028222..1ec1fe3ae 100644 --- a/pixi.toml +++ b/pixi.toml @@ -81,7 +81,6 @@ psutil = "*" pytest = "*" pytest-xdist = "*" statsmodels = "*" -types-pyyaml = "*" [feature.dev.dependencies] ipdb = "*" @@ -123,8 +122,9 @@ attrs = "*" git_root = "*" matplotlib-base = "*" openjdk = "*" +pydantic = "*" pytest = "*" -pyyaml = ">=6.0.3,<7" +"ruamel.yaml" = "*" r-base = "*" rpy2 = "*" skglm = "*" diff --git a/tests/glm/test_benchmark_golden_master.py b/tests/glm/test_benchmark_golden_master.py index aed366bc2..dcd2ca7f4 100644 --- a/tests/glm/test_benchmark_golden_master.py +++ b/tests/glm/test_benchmark_golden_master.py @@ -32,13 +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) From de723adaaeb944c2eaebf8e0664684aeae302225 Mon Sep 17 00:00:00 2001 From: David Eiglsperger Date: Mon, 2 Feb 2026 21:16:11 +0100 Subject: [PATCH 18/42] chore: retrigger CI From b2119d3cff213d99b175b8390ccb883d45778174 Mon Sep 17 00:00:00 2001 From: David Eiglsperger Date: Tue, 3 Feb 2026 11:49:39 +0100 Subject: [PATCH 19/42] change timeout logic --- glum_benchmarks/README.md | 74 ++++++++++++++-------- glum_benchmarks/libraries/bench_celer.py | 11 +++- glum_benchmarks/libraries/bench_glum.py | 5 +- glum_benchmarks/libraries/bench_h2o.py | 5 +- glum_benchmarks/libraries/bench_skglm.py | 8 ++- glum_benchmarks/libraries/bench_sklearn.py | 8 ++- glum_benchmarks/run_benchmarks.py | 29 +++------ glum_benchmarks/util.py | 56 ++++++++++++---- 8 files changed, 135 insertions(+), 61 deletions(-) diff --git a/glum_benchmarks/README.md b/glum_benchmarks/README.md index 16d9be0f1..4bbc3cbec 100644 --- a/glum_benchmarks/README.md +++ b/glum_benchmarks/README.md @@ -1,14 +1,14 @@ # glum_benchmarks -Python module to benchmark GLM implementations. +Module to benchmark glum against similar libraries. ## Benchmarked Libraries -- [glum](https://github.com/Quantco/glum) - High-performance GLMs with L1/L2 regularization -- [scikit-learn](https://scikit-learn.org/) - Machine learning in Python -- [H2O](https://h2o.ai/) - Distributed machine learning platform -- [skglm](https://contrib.scikit-learn.org/skglm/) - Fast sklearn-compatible GLM solvers -- [celer](https://mathurinm.github.io/celer/) - Fast Lasso solver with dual extrapolation +- [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/) ## Running the benchmarks @@ -16,7 +16,7 @@ Python module to benchmark GLM implementations. pixi run -e benchmark run-benchmarks ``` -The benchmark script runs in three steps that can be controlled independently. +The benchmark script runs in four steps that can be controlled independently. ### Step 1: Run Benchmarks (`run_benchmarks`) @@ -42,6 +42,16 @@ Creates comparison charts from the CSV produced by Step 2. - **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). + ### Workflow examples **Full run (default):** @@ -50,6 +60,7 @@ Creates comparison charts from the CSV produced by Step 2. 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:** @@ -58,14 +69,16 @@ generate_plots: true run_benchmarks: false analyze_results: true # Rebuild CSV from existing pickles generate_plots: true +update_docs: false ``` -**Run benchmarks and analyze results without creating figures:** +**Update documentation with existing figures:** ```yaml -run_benchmarks: true -analyze_results: true +run_benchmarks: false +analyze_results: false generate_plots: false +update_docs: true # Only copy figures and update docs ``` ## Output structure @@ -92,24 +105,33 @@ 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` | Copy figures to docs and update documentation | -| `run_name` | Subfolder in `results/` (`"docs"` is git-tracked) | -| `clear_output` | Clear entire `run_name` directory before running | +| 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 | -| ------------- | ---------------------------------------------- | -| `standardize` | Whether to standardize features before fitting | -| `iterations` | Runs per benchmark (>=2 required for skglm) | -| `num_threads` | Number of threads for parallel execution | -| `num_rows` | Limit rows per dataset (`null` = full dataset) | -| `timeout` | Timeout in seconds per benchmark run | +| Option | Description | +| ------------- | ---------------------------------------------------------------------------- | +| `standardize` | Whether to standardize features (glum/h2o: internal, others: StandardScaler) | +| `iterations` | Runs per benchmark (>=2 required for skglm) | +| `num_threads` | Number of threads for parallel execution | +| `num_rows` | Limit rows per dataset (`null` = full dataset) | +| `timeout` | Timeout in seconds (benchmarks timing out are marked "not converged") | +| `storage` | Storage format per library: `{"glum": "auto", "sklearn": "dense", ...}` | + +**Notes:** + +- **Standardization**: glum and h2o handle scaling internally. sklearn, skglm, and celer use `StandardScaler`. Only numerical columns are scaled (categorical/one-hot columns are not). +- **Convergence**: A benchmark is marked "not converged" if it either (1) hits the timeout, or (2) reaches the library's internal `max_iter` limit. +- **Storage formats**: `"auto"`, `"dense"`, or `"sparse"`. Configured per library for optimal performance. ### Parameter Grid @@ -134,7 +156,7 @@ Each entry computes a Cartesian product. Multiple entries are unioned (not cross - `distributions`: `["gaussian", "gamma", "binomial", "poisson", "tweedie-p=1.5"]` - `reg_strengths`: `[0.0001, 0.001, 0.01]` -When an entry is omitted or set to `null` all available values are taken --> if you want to run all combinations possiblem, just leave the `param_grid` entry empty. +When an entry is omitted or set to `null` all available values are taken --> if you want to run all combinations possible, just leave the `param_grid` entry empty. See `problems.py` for available datasets and problem definitions. diff --git a/glum_benchmarks/libraries/bench_celer.py b/glum_benchmarks/libraries/bench_celer.py index cafc0a0de..2fbfb9f08 100644 --- a/glum_benchmarks/libraries/bench_celer.py +++ b/glum_benchmarks/libraries/bench_celer.py @@ -25,6 +25,7 @@ def celer_bench( iterations: int, reg_multiplier: Optional[float] = None, standardize: bool = True, + timeout: Optional[float] = None, **kwargs, ): # Standardize features if requested @@ -66,8 +67,16 @@ def celer_bench( try: result["runtime"], m = runtime( - _build_and_fit, iterations, model_class, model_args, fit_args + _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 {} diff --git a/glum_benchmarks/libraries/bench_glum.py b/glum_benchmarks/libraries/bench_glum.py index 5f3cfd6a1..29edf99f5 100644 --- a/glum_benchmarks/libraries/bench_glum.py +++ b/glum_benchmarks/libraries/bench_glum.py @@ -29,6 +29,7 @@ def glum_bench( reg_multiplier: Optional[float] = None, hessian_approx: float = 0.0, standardize: bool = True, + timeout: Optional[float] = None, **kwargs, ): """ @@ -77,7 +78,9 @@ def glum_bench( model_args["alpha"] = alpha if reg_multiplier is None else alpha * reg_multiplier - result["runtime"], m = runtime(_build_and_fit, iterations, model_args, fit_args) + result["runtime"], m = runtime( + _build_and_fit, iterations, model_args, fit_args, timeout=timeout + ) # 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"}) diff --git a/glum_benchmarks/libraries/bench_h2o.py b/glum_benchmarks/libraries/bench_h2o.py index f50a6bc93..a14056524 100644 --- a/glum_benchmarks/libraries/bench_h2o.py +++ b/glum_benchmarks/libraries/bench_h2o.py @@ -39,6 +39,7 @@ def h2o_bench( iterations: int, reg_multiplier: Optional[float] = None, standardize: bool = True, + timeout: Optional[float] = None, **kwargs, ): """ @@ -115,7 +116,9 @@ def h2o_bench( 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"] diff --git a/glum_benchmarks/libraries/bench_skglm.py b/glum_benchmarks/libraries/bench_skglm.py index fced0c8e6..c43fc05ad 100644 --- a/glum_benchmarks/libraries/bench_skglm.py +++ b/glum_benchmarks/libraries/bench_skglm.py @@ -28,6 +28,7 @@ def skglm_bench( iterations: int, reg_multiplier: Optional[float] = None, standardize: bool = True, + timeout: Optional[float] = None, **kwargs, ): # Standardize features if requested @@ -88,7 +89,12 @@ def skglm_bench( fit_args = {"X": X, "y": y} try: - result["runtime"], m = runtime(_build_and_fit, iterations, model_args, fit_args) + 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 {} diff --git a/glum_benchmarks/libraries/bench_sklearn.py b/glum_benchmarks/libraries/bench_sklearn.py index 3c282e1f9..b00feb231 100644 --- a/glum_benchmarks/libraries/bench_sklearn.py +++ b/glum_benchmarks/libraries/bench_sklearn.py @@ -32,6 +32,7 @@ def sklearn_bench( iterations: int, reg_multiplier: Optional[float] = None, standardize: bool = True, + timeout: Optional[float] = None, **kwargs, ): """ @@ -143,7 +144,12 @@ def sklearn_bench( try: result["runtime"], m = runtime( - _build_and_fit, iterations, model_class, model_args, fit_args + _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}") diff --git a/glum_benchmarks/run_benchmarks.py b/glum_benchmarks/run_benchmarks.py index d4f7a1cf8..62f9c6989 100644 --- a/glum_benchmarks/run_benchmarks.py +++ b/glum_benchmarks/run_benchmarks.py @@ -23,8 +23,6 @@ import re import shutil import warnings -from concurrent.futures import ThreadPoolExecutor -from concurrent.futures import TimeoutError as FuturesTimeoutError from contextlib import nullcontext from itertools import product from pathlib import Path @@ -332,22 +330,18 @@ def run_single_benchmark( regularization_strength=reg_strength, ) - # Use ThreadPoolExecutor for cross-platform timeout support - # Note: We don't use the context manager because its __exit__ waits for - # the thread to complete, which defeats the purpose of the timeout. - executor = ThreadPoolExecutor(max_workers=1) - future = executor.submit( - execute_problem_library, - params, - iterations=config.iterations, - diagnostics_level=None, - standardize=config.standardize, - ) + # Pass timeout to execute_problem_library for per-iteration timeout handling try: - result, _ = future.result(timeout=config.timeout) + result, _ = execute_problem_library( + params, + iterations=config.iterations, + diagnostics_level=None, + standardize=config.standardize, + timeout=config.timeout, + ) result["timed_out"] = False - except FuturesTimeoutError: - # Return timeout result + except TimeoutError: + # All iterations timed out result = { "runtime": float(config.timeout), "timed_out": True, @@ -355,9 +349,6 @@ def run_single_benchmark( "coef": None, "n_iter": None, } - # Shutdown without waiting for the thread to finish - # The thread will continue in the background but we move on - executor.shutdown(wait=False, cancel_futures=True) return result, params diff --git a/glum_benchmarks/util.py b/glum_benchmarks/util.py index a7d68c504..415ec31c6 100644 --- a/glum_benchmarks/util.py +++ b/glum_benchmarks/util.py @@ -1,4 +1,5 @@ import os +import signal import time from typing import Optional, Union @@ -16,29 +17,60 @@ 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. Parameters ---------- f: function - iterations + iterations: int + Number of times to run the function 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: (Minimum runtime across successful iterations, output frombest iteration) + 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) pairs + + 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)) + 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") + + # Return the run with minimum runtime + min_runtime, min_output = min(successful_runs, key=lambda x: x[0]) + return min_runtime, min_output def get_sklearn_family(distribution): @@ -459,6 +491,7 @@ def execute_problem_library( iterations: int = 1, diagnostics_level: Optional[str] = "basic", standardize: bool = True, + timeout: Optional[float] = None, **kwargs, ): """ @@ -517,6 +550,7 @@ def execute_problem_library( reg_multiplier=reg_multiplier, hessian_approx=params.hessian_approx, standardize=standardize, + timeout=timeout, **kwargs, ) From 7f169b3c659aa5a329d3a608209148b3f84159c1 Mon Sep 17 00:00:00 2001 From: David Eiglsperger Date: Fri, 6 Feb 2026 18:00:27 +0100 Subject: [PATCH 20/42] Refinements & new features --- glum_benchmarks/README.md | 10 +- glum_benchmarks/config.yaml | 11 +- glum_benchmarks/data/__init__.py | 11 +- glum_benchmarks/data/simulated_glm.py | 122 +- glum_benchmarks/libraries/__init__.py | 6 + glum_benchmarks/libraries/bench_celer.py | 20 +- glum_benchmarks/libraries/bench_glmnet.py | 194 + glum_benchmarks/libraries/bench_glum.py | 4 +- glum_benchmarks/libraries/bench_h2o.py | 6 +- glum_benchmarks/libraries/bench_skglm.py | 37 +- glum_benchmarks/libraries/bench_sklearn.py | 49 +- glum_benchmarks/problems.py | 32 +- glum_benchmarks/run_benchmarks.py | 65 +- glum_benchmarks/tests/test_benchmarks.py | 10 +- glum_benchmarks/util.py | 49 +- pixi.lock | 3385 ++++++++++------- pixi.toml | 1 + tests/glm/test_benchmark_golden_master.py | 6 +- tests/glm/test_golden_master.py | 20 +- .../glm/test_offset_weight_obj_equivalent.py | 10 +- 20 files changed, 2501 insertions(+), 1547 deletions(-) create mode 100644 glum_benchmarks/libraries/bench_glmnet.py diff --git a/glum_benchmarks/README.md b/glum_benchmarks/README.md index 4bbc3cbec..909963a0b 100644 --- a/glum_benchmarks/README.md +++ b/glum_benchmarks/README.md @@ -9,6 +9,7 @@ Module to benchmark glum against similar libraries. - [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 @@ -130,6 +131,7 @@ Edit `config.yaml` to customize benchmark parameters. **Notes:** - **Standardization**: glum and h2o handle scaling internally. sklearn, skglm, and celer use `StandardScaler`. Only numerical columns are scaled (categorical/one-hot columns are not). +- **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. - **Storage formats**: `"auto"`, `"dense"`, or `"sparse"`. Configured per library for optimal performance. @@ -143,21 +145,23 @@ param_grid: datasets: ["intermediate-insurance"] regularizations: ["lasso", "l2"] distributions: ["gaussian", "poisson"] - reg_strengths: [0.001] + alphas: [0.001] ``` Each entry computes a Cartesian product. Multiple entries are unioned (not crossed). **Available values:** -- `libraries`: `["glum", "sklearn", "h2o", "skglm", "celer", "zeros"]` +- `libraries`: `["glum", "sklearn", "h2o", "skglm", "celer", "zeros", "glmnet"]` - `datasets`: `["intermediate-housing", "intermediate-insurance", "narrow-insurance", "wide-insurance", "square-simulated"]` - `regularizations`: `["lasso", "l2", "net"]` - `distributions`: `["gaussian", "gamma", "binomial", "poisson", "tweedie-p=1.5"]` -- `reg_strengths`: `[0.0001, 0.001, 0.01]` +- `alphas`: `[0.0001, 0.001, 0.01]` When an entry is omitted or set to `null` all available values are taken --> if you want to run all combinations possible, just 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 diff --git a/glum_benchmarks/config.yaml b/glum_benchmarks/config.yaml index 31e572360..ead2192c3 100644 --- a/glum_benchmarks/config.yaml +++ b/glum_benchmarks/config.yaml @@ -29,20 +29,21 @@ timeout: 100 # Timeout in seconds per benchmark run (reports as "not converged" # Storage format per library ("dense", "sparse", or "auto") storage: glum: auto # Auto selects best format - h2o: sparse # Better performance with sparse + sklearn: dense # Can use either + h2o: sparse # Optimized for sparse matrices celer: sparse # Optimized for sparse matrices - sklearn: dense # Can use either, configure as needed - skglm: dense # Can use either, configure as needed + skglm: sparse # Faster for dense matrices + glmnet: sparse # R glmnet supports sparse matrices # 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"] +# libraries: ["glum", "sklearn", "h2o", "skglm", "celer", "zeros", "glmnet"] # datasets: ["intermediate-housing", "intermediate-insurance", "narrow-insurance", "wide-insurance","square-simulated"] # regularizations: ["lasso", "l2", "net"] -# reg_strengths: [0.0001, 0.001, 0.01] +# alphas: [0.0001, 0.001, 0.01] # distributions: ["gaussian", "gamma", "binomial", "poisson", "tweedie-p=1.5"] # # Use null (or omit) for any field to use its default value. Default refers to using all available values. diff --git a/glum_benchmarks/data/__init__.py b/glum_benchmarks/data/__init__.py index 450231b3a..4db72d1e9 100644 --- a/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 generate_square_dataset, simulate_glm_data +from .simulated_glm import ( + simulate_categorical_dataset, + simulate_mixed_data, + simulate_square_dataset, +) __all__ = [ "generate_intermediate_insurance_dataset", @@ -14,8 +18,9 @@ "generate_wide_insurance_dataset", "generate_real_insurance_dataset", "create_insurance_raw_data", - "simulate_glm_data", - "generate_square_dataset", + "simulate_categorical_dataset", + "simulate_mixed_data", + "simulate_square_dataset", "generate_housing_dataset", "create_housing_raw_data", ] diff --git a/glum_benchmarks/data/simulated_glm.py b/glum_benchmarks/data/simulated_glm.py index 09a49a095..f5e759002 100644 --- a/glum_benchmarks/data/simulated_glm.py +++ b/glum_benchmarks/data/simulated_glm.py @@ -5,9 +5,18 @@ 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. @@ -29,6 +38,30 @@ def tweedie_rv(mu, sigma2=1, p=1.5): 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, @@ -50,7 +83,7 @@ def _get_family_rv(family, rand: np.random._generator.Generator): ) -def simulate_glm_data( +def simulate_mixed_data( family: str = "poisson", link: str = "auto", n_rows: int = 5000, @@ -116,6 +149,9 @@ def simulate_glm_data( 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 ) @@ -130,7 +166,7 @@ def simulate_glm_data( intercept = intercept - link_inst = get_link(link=link, family=get_family("poisson")) + 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())) @@ -152,7 +188,7 @@ def simulate_glm_data( return data -def generate_square_dataset( +def simulate_square_dataset( num_rows: Optional[int] = None, _noise=None, # unused, required by load_data signature distribution: str = "poisson", @@ -188,20 +224,74 @@ def generate_square_dataset( # Linear predictor eta = X.to_numpy() @ coefs - # Generate y based on distribution - if distribution == "gaussian": - y = eta + rand.normal(0, 1, size=n) - elif distribution == "poisson": - mu = np.exp(np.clip(eta, -5, 5)) # Clamp to avoid overflow - y = rand.poisson(mu) - elif distribution == "gamma": - mu = np.exp(np.clip(eta, -5, 5)) - y = rand.gamma(shape=1.0, scale=mu) - elif distribution == "binomial": - p = 1 / (1 + np.exp(-eta)) # Logistic - y = rand.binomial(1, p) + # 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: - raise ValueError(f"Unknown distribution: {distribution}") + family_rv = _get_family_rv(family, rand) + y = family_rv(mu) exposure = np.ones(n) return X, y, exposure + + +def simulate_categorical_dataset( + num_rows: Optional[int] = None, + _noise=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 5000. + _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 index ccf7d605f..9f6b92802 100644 --- a/glum_benchmarks/libraries/__init__.py +++ b/glum_benchmarks/libraries/__init__.py @@ -25,11 +25,17 @@ except ImportError: sklearn_bench = None # type: ignore +try: + from .bench_glmnet import glmnet_bench +except ImportError: + glmnet_bench = None # type: ignore + __all__ = [ "celer_bench", "glum_bench", "h2o_bench", "skglm_bench", "sklearn_bench", + "glmnet_bench", "zeros_bench", ] diff --git a/glum_benchmarks/libraries/bench_celer.py b/glum_benchmarks/libraries/bench_celer.py index 2fbfb9f08..ec1f622e0 100644 --- a/glum_benchmarks/libraries/bench_celer.py +++ b/glum_benchmarks/libraries/bench_celer.py @@ -23,7 +23,6 @@ def celer_bench( alpha: float, l1_ratio: float, iterations: int, - reg_multiplier: Optional[float] = None, standardize: bool = True, timeout: Optional[float] = None, **kwargs, @@ -37,8 +36,6 @@ def celer_bench( result: dict[str, Any] = {} fit_args = {"X": dat["X"], "y": dat["y"]} - reg_strength = alpha if reg_multiplier is None else alpha * reg_multiplier - n_samples = dat["X"].shape[0] # LogisticRegression doesn't support fitting an intercept yet, so we also skip it if distribution not in ["gaussian"]: @@ -54,12 +51,10 @@ def celer_bench( else: model_class = Lasso - # Celer uses same alpha convention as sklearn Lasso/ElasticNet - # Scale by n_samples to match glum's objective model_args = { "tol": benchmark_convergence_tolerance, "fit_intercept": True, - "alpha": reg_strength * n_samples, + "alpha": alpha, } if model_class == ElasticNet: @@ -84,10 +79,15 @@ def celer_bench( intercept = np.array(m.intercept_).ravel()[0] coef = np.array(m.coef_).ravel() - # Unstandardize coefficients to match original data scale - result["intercept"], result["coef"] = _unstandardize_coefficients( - intercept, coef, scaler, scaled_indices - ) + if standardize: + # Unstandardize coefficients to match original data scale + result["intercept"], result["coef"] = _unstandardize_coefficients( + intercept, coef, scaler, scaled_indices + ) + else: + result["intercept"] = intercept + result["coef"] = coef + result["n_iter"] = getattr(m, "n_iter_", None) result["max_iter"] = m.max_iter # For convergence detection diff --git a/glum_benchmarks/libraries/bench_glmnet.py b/glum_benchmarks/libraries/bench_glmnet.py new file mode 100644 index 000000000..ae4a70b5f --- /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 + + +def _setup_and_fit(X_np, y, distribution, l1_ratio, alpha, standardize, thresh): + """ + 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=1e-15) + + # 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=standardize, + thresh=thresh, + **{"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, + "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, + standardize: bool = True, + 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() + + # glmnet's thresh is a *relative* convergence criterion (scaled internally + # by the null deviance). We use a fixed tight value so glmnet actually + # converges to the same quality as the other libraries. + thresh = 1e-12 + + fit_args = (X, y, distribution, l1_ratio, alpha, standardize, thresh) + + # 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": None, + } diff --git a/glum_benchmarks/libraries/bench_glum.py b/glum_benchmarks/libraries/bench_glum.py index 29edf99f5..ef9619ad2 100644 --- a/glum_benchmarks/libraries/bench_glum.py +++ b/glum_benchmarks/libraries/bench_glum.py @@ -26,7 +26,6 @@ def glum_bench( l1_ratio: float, iterations: int, diagnostics_level: str = "basic", - reg_multiplier: Optional[float] = None, hessian_approx: float = 0.0, standardize: bool = True, timeout: Optional[float] = None, @@ -43,7 +42,6 @@ def glum_bench( l1_ratio iterations diagnostics_level - reg_multiplier hessian_approx standardize kwargs @@ -76,7 +74,7 @@ def glum_bench( verbose=False, ) - 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 diff --git a/glum_benchmarks/libraries/bench_h2o.py b/glum_benchmarks/libraries/bench_h2o.py index a14056524..726c18139 100644 --- a/glum_benchmarks/libraries/bench_h2o.py +++ b/glum_benchmarks/libraries/bench_h2o.py @@ -37,7 +37,6 @@ def h2o_bench( alpha: float, l1_ratio: float, iterations: int, - reg_multiplier: Optional[float] = None, standardize: bool = True, timeout: Optional[float] = None, **kwargs, @@ -52,7 +51,6 @@ def h2o_bench( alpha l1_ratio iterations - reg_multiplier standardize kwargs @@ -91,10 +89,9 @@ 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, + lambda_=alpha, standardize=standardize, # Let h2o handle standardization internally solver="IRLSM", objective_epsilon=benchmark_convergence_tolerance, @@ -136,6 +133,5 @@ def h2o_bench( result["coef"] = standardized_coefs result["n_iter"] = m.score_history().iloc[-1]["iterations"] - # h2o default max_iterations is very high, but we can get actual from params 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 index c43fc05ad..6342294e0 100644 --- a/glum_benchmarks/libraries/bench_skglm.py +++ b/glum_benchmarks/libraries/bench_skglm.py @@ -1,5 +1,5 @@ import warnings -from typing import Any, Optional, Union, cast +from typing import Any, Optional, Union import numpy as np from scipy import sparse as sps @@ -26,7 +26,6 @@ def skglm_bench( alpha: float, l1_ratio: float, iterations: int, - reg_multiplier: Optional[float] = None, standardize: bool = True, timeout: Optional[float] = None, **kwargs, @@ -39,8 +38,6 @@ def skglm_bench( dat["X"], scaler, scaled_indices = _standardize_features(dat["X"]) result: dict[str, Any] = {} - reg_strength = alpha if reg_multiplier is None else alpha * reg_multiplier - if "tweedie" in distribution: warnings.warn("skglm doesn't support Tweedie, skipping.") return result @@ -55,10 +52,10 @@ def skglm_bench( datafit = DATAFITS[distribution] if l1_ratio == 1: - penalty = L1(alpha=reg_strength) + 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=reg_strength, l1_ratio=l1_ratio) + # 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) # ProxNewton is faster for non-Gaussian distributions (Poisson, Gamma, Binomial) # and required for L2 problems. AndersonCD is better for Gaussian. @@ -73,20 +70,11 @@ def skglm_bench( "solver": solver, } - # Data Conversion optimized for Coordinate Descent - X_raw = dat["X"] - if sps.issparse(X_raw): - X = cast(sps.spmatrix, X_raw).tocsc() - else: - X = np.asfortranarray(X_raw, dtype=np.float64) - - y = np.asarray(dat["y"], dtype=np.float64).ravel() + fit_args = {"X": dat["X"], "y": dat["y"]} # skglm's Logistic datafit expects labels in {-1, 1}, not {0, 1} if distribution == "binomial": - y = 2 * y - 1 - - fit_args = {"X": X, "y": y} + fit_args["y"] = 2 * fit_args["y"] - 1 try: result["runtime"], m = runtime( @@ -102,10 +90,15 @@ def skglm_bench( intercept = np.array(m.intercept_).ravel()[0] coef = np.array(m.coef_).ravel() - # Unstandardize coefficients to match original data scale - result["intercept"], result["coef"] = _unstandardize_coefficients( - intercept, coef, scaler, scaled_indices - ) + if standardize: + # Unstandardize coefficients to match original data scale + result["intercept"], result["coef"] = _unstandardize_coefficients( + intercept, coef, scaler, scaled_indices + ) + else: + result["intercept"] = intercept + result["coef"] = coef + result["n_iter"] = getattr(m, "n_iter_", None) result["max_iter"] = solver.max_iter # For convergence detection diff --git a/glum_benchmarks/libraries/bench_sklearn.py b/glum_benchmarks/libraries/bench_sklearn.py index b00feb231..6bec45451 100644 --- a/glum_benchmarks/libraries/bench_sklearn.py +++ b/glum_benchmarks/libraries/bench_sklearn.py @@ -30,7 +30,6 @@ def sklearn_bench( alpha: float, l1_ratio: float, iterations: int, - reg_multiplier: Optional[float] = None, standardize: bool = True, timeout: Optional[float] = None, **kwargs, @@ -45,7 +44,6 @@ def sklearn_bench( alpha l1_ratio iterations - reg_multiplier standardize If True, standardize continuous features using sklearn's StandardScaler. kwargs @@ -62,48 +60,46 @@ def sklearn_bench( dat["X"], scaler, scaled_indices = _standardize_features(dat["X"]) result: dict[str, Any] = {} - reg_strength = alpha if reg_multiplier is None else alpha * reg_multiplier - n_samples = dat["X"].shape[0] model_class = None if distribution == "gaussian": if l1_ratio == 0.0: - # Pure L2 (Ridge): use closed-form solution + # 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": reg_strength * n_samples, + "alpha": alpha * n_samples, "fit_intercept": True, "tol": benchmark_convergence_tolerance, "solver": "auto", } elif l1_ratio == 1.0: - # Pure L1 (Lasso) + # Pure L1 model_class = Lasso model_args = { - "alpha": reg_strength * n_samples, + "alpha": alpha, "fit_intercept": True, "tol": benchmark_convergence_tolerance, "precompute": True, } else: - # Scale by n_samples to match glum's objective model_class = ElasticNet model_args = { - "alpha": reg_strength * n_samples, + "alpha": alpha, "l1_ratio": l1_ratio, "fit_intercept": True, "tol": benchmark_convergence_tolerance, "precompute": True, } elif distribution == "binomial": - # sklearn's LogisticRegression uses C = 1/lambda where lambda is the - # regularization strength. However, sklearn does NOT scale the regularization - # by n_samples, while glum's objective is: mean(loss) + alpha * reg_term. - # To match glum's objective, we need: C = 1 / (alpha * n_samples) - C_value = 1.0 / (reg_strength * n_samples) if reg_strength > 0 else 1e10 - # Use lbfgs for L2 (faster and more reliable), saga for L1/elasticnet - solver = "lbfgs" if l1_ratio == 0.0 else "saga" + # # 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 = { @@ -128,10 +124,12 @@ def sklearn_bench( ) 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": reg_strength, + "alpha": alpha, "fit_intercept": True, "tol": benchmark_convergence_tolerance, "solver": "newton-cholesky", @@ -139,9 +137,6 @@ def sklearn_bench( fit_args = {"X": dat["X"], "y": dat["y"]} - if "sample_weight" in dat.keys(): - fit_args["sample_weight"] = dat["sample_weight"] - try: result["runtime"], m = runtime( _build_and_fit, @@ -166,10 +161,14 @@ def sklearn_bench( n_iter = getattr(m, "n_iter_", None) n_iter = 0 if n_iter is None else n_iter - # Unstandardize coefficients to match original data scale - result["intercept"], result["coef"] = _unstandardize_coefficients( - intercept, coef, scaler, scaled_indices - ) + # Unstandardize coefficients if we standardized the features + if standardize and scaler is not None: + result["intercept"], result["coef"] = _unstandardize_coefficients( + intercept, coef, scaler, scaled_indices + ) + else: + 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) diff --git a/glum_benchmarks/problems.py b/glum_benchmarks/problems.py index 8cc3fe347..e3021d3ed 100644 --- a/glum_benchmarks/problems.py +++ b/glum_benchmarks/problems.py @@ -15,8 +15,9 @@ generate_intermediate_insurance_dataset, generate_narrow_insurance_dataset, generate_real_insurance_dataset, - generate_square_dataset, generate_wide_insurance_dataset, + simulate_categorical_dataset, + simulate_square_dataset, ) from .util import cache_location, exposure_and_offset_to_weights, get_tweedie_p @@ -29,7 +30,7 @@ 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) @@ -148,7 +149,7 @@ 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 = { @@ -173,7 +174,7 @@ def get_all_problems() -> dict[str, Problem]: # Simulated datasets (square: n_rows ≈ n_cols) simulated_distributions = ["gaussian", "poisson", "gamma", "binomial"] simulated_load_funcs = { - "square-simulated": generate_square_dataset, + "square-simulated": simulate_square_dataset, } problems = {} @@ -189,7 +190,7 @@ 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 @@ -203,7 +204,7 @@ 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 simulated problems @@ -216,8 +217,25 @@ def get_all_problems() -> dict[str, Problem]: load_data, load_fn, distribution=dist, data_setup="no-weights" ), distribution=distribution, - regularization_strength=regularization_strength, + 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/run_benchmarks.py b/glum_benchmarks/run_benchmarks.py index 62f9c6989..dbf49d8c5 100644 --- a/glum_benchmarks/run_benchmarks.py +++ b/glum_benchmarks/run_benchmarks.py @@ -8,7 +8,7 @@ Configuration: Edit config.yaml to configure which benchmarks to run. Use param_grid to specify combinations of libraries, datasets, regularizations, distributions, - and reg_strengths. You can also control which steps to run (run_benchmarks, + and alphas. You can also control which steps to run (run_benchmarks, analyze_results, generate_plots, update_docs). Output: @@ -54,19 +54,20 @@ # TODO: Determine optimal storage type for each library for a fair comparison # Type aliases for configuration options -Library = Literal["glum", "sklearn", "h2o", "skglm", "celer", "zeros"] +Library = Literal["glum", "sklearn", "h2o", "skglm", "celer", "zeros", "glmnet"] Dataset = Literal[ "intermediate-insurance", "intermediate-housing", "narrow-insurance", "wide-insurance", "square-simulated", + "categorical-simulated", ] Regularization = Literal["lasso", "l2", "net"] -RegStrength = float # Valid values: 0.0001, 0.001, 0.01 -REG_STRENGTH_VALUES = (0.0001, 0.001, 0.01) +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", "sparse"] +StorageFormat = Literal["auto", "dense", "sparse", "cat"] class ParamGridEntry(BaseModel): @@ -87,8 +88,8 @@ class ParamGridEntry(BaseModel): regularizations: list[Regularization] | None = Field( default=None, description="Regularization types (None = all)" ) - reg_strengths: list[RegStrength] | None = Field( - default=None, description="Regularization strengths (None = all)" + alphas: list[Alpha] | None = Field( + default=None, description="Per-observation alpha values (None = all)" ) distributions: list[Distribution] | None = Field( default=None, description="Distributions (None = all)" @@ -244,12 +245,12 @@ def _parse_problem_name(name: str) -> tuple[str, str, str]: def get_benchmark_combinations( config: BenchmarkConfig, ) -> list[tuple[str, str, float]]: - """Get list of (problem_name, library, reg_strength) tuples to benchmark. + """Get list of (problem_name, library, alpha) tuples to benchmark. Cartesian product within each entry, union across entries. Returns: - List of (problem_name, library_name, reg_strength) tuples to run. + List of (problem_name, library_name, alpha) tuples to run. """ all_problems = get_all_problems() @@ -268,7 +269,7 @@ def get_benchmark_combinations( 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_reg_strengths = list(REG_STRENGTH_VALUES) + all_alphas = list(ALPHA_VALUES) combinations: set[tuple[str, str, float]] = set() @@ -282,25 +283,25 @@ def get_benchmark_combinations( 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 - strengths = entry.reg_strengths if entry.reg_strengths else all_reg_strengths + alphas = entry.alphas if entry.alphas else all_alphas print(f"Parameter Set {i}:") print(f" Libraries: {libraries}") print(f" Datasets: {datasets}") print(f" Regularizations: {regs}") print(f" Distributions: {dists}") - print(f" Reg strengths: {strengths}") + print(f" Alphas: {alphas}") print() # Cartesian product within this entry - for lib, dataset, reg, dist, strength in product( - libraries, datasets, regs, dists, strengths + for lib, dataset, reg, dist, alpha in product( + libraries, datasets, regs, dists, alphas ): key = (dataset, reg, dist) if key in problem_lookup: # Only add if library is actually available if lib in available_libraries: - combinations.add((problem_lookup[key], lib, strength)) + combinations.add((problem_lookup[key], lib, alpha)) print(f"Total benchmark runs: {len(combinations)}") print("=" * 70) @@ -310,7 +311,7 @@ def get_benchmark_combinations( def run_single_benchmark( - problem_name: str, library_name: str, reg_strength: float, config: BenchmarkConfig + problem_name: str, library_name: str, alpha: float, config: BenchmarkConfig ) -> tuple[dict, BenchmarkParams]: """Run a single benchmark and return results. @@ -327,7 +328,7 @@ def run_single_benchmark( num_rows=config.num_rows, storage=storage, threads=config.num_threads, - regularization_strength=reg_strength, + alpha=alpha, ) # Pass timeout to execute_problem_library for per-iteration timeout handling @@ -361,15 +362,15 @@ def run_all_benchmarks(config: BenchmarkConfig): shutil.rmtree(config.results_dir) config.pickle_dir.mkdir(parents=True, exist_ok=True) - # Get benchmark combinations (problem, library, reg_strength) from param_grid + # Get benchmark combinations (problem, library, alpha) from param_grid combinations = get_benchmark_combinations(config) total = len(combinations) current = 0 - for problem_name, library_name, reg_strength in combinations: + for problem_name, library_name, alpha in combinations: current += 1 - label = f"{library_name} / {problem_name} (α={reg_strength})" + label = f"{library_name} / {problem_name} (α={alpha})" print(f"[{current}/{total}] {label}", end=" ", flush=True) try: @@ -382,7 +383,7 @@ def run_all_benchmarks(config: BenchmarkConfig): message=".*asyncio.iscoroutinefunction.*", ) result, params = run_single_benchmark( - problem_name, library_name, reg_strength, config + problem_name, library_name, alpha, config ) # Save result @@ -457,11 +458,7 @@ def analyze_results(config: BenchmarkConfig) -> pd.DataFrame: num_nonzero_coef = 0 # Get regularization strength from params or problem default - reg_strength = ( - problem.regularization_strength - if params.regularization_strength is None - else params.regularization_strength - ) + alpha = problem.alpha if params.alpha is None else params.alpha # Check convergence: # 1. timed_out=True means we hit the benchmark timeout @@ -477,7 +474,7 @@ def analyze_results(config: BenchmarkConfig) -> pd.DataFrame: "problem_name": params.problem_name, "library_name": params.library_name, "num_rows": data.get("num_rows"), - "regularization_strength": reg_strength, + "alpha": alpha, "storage": params.storage, "threads": params.threads, "n_iter": n_iter, @@ -499,11 +496,13 @@ def analyze_results(config: BenchmarkConfig) -> pd.DataFrame: df = pd.DataFrame(results) # Format: set index and sort - problem_id_cols = ["problem_name", "num_rows", "regularization_strength"] + problem_id_cols = ["problem_name", "num_rows", "alpha"] df = df.set_index(problem_id_cols).sort_values("library_name").sort_index() - # Calculate relative objective value (how far from best) - df["rel_obj_val"] = df[["obj_val"]] - df.groupby(level=[0, 1, 2])[["obj_val"]].min() + # Calculate relative objective value (how far from best). + # Use transform to preserve alignment when index has duplicates. + best_obj = df.groupby(level=[0, 1, 2])["obj_val"].transform("min") + df["rel_obj_val"] = df["obj_val"] - best_obj # Display columns cols_to_show = [ @@ -717,7 +716,7 @@ def plot_results(config: BenchmarkConfig): # Create a combined regularization column (type + strength) df["reg_combo"] = df.apply( - lambda row: f"{row['regularization']} (α={row['regularization_strength']})", + lambda row: f"{row['regularization']} (α={row['alpha']})", axis=1, ) @@ -727,7 +726,7 @@ def plot_results(config: BenchmarkConfig): "dataset", "distribution", "regularization", - "regularization_strength", + "alpha", "library_name", ], keep="last", @@ -738,7 +737,7 @@ def plot_results(config: BenchmarkConfig): "glum": "#a6cee3", "h2o": "#fdbf6f", "glmnet": "#b15928", - "sklearn": "#b15928", + "sklearn": "#1f78b4", "skglm": "#33a02c", "celer": "#fb9a99", } diff --git a/glum_benchmarks/tests/test_benchmarks.py b/glum_benchmarks/tests/test_benchmarks.py index 726208817..c0861ad0f 100644 --- a/glum_benchmarks/tests/test_benchmarks.py +++ b/glum_benchmarks/tests/test_benchmarks.py @@ -29,7 +29,7 @@ def test_all_problems_have_required_attributes(self): for name, problem in problems.items(): assert hasattr(problem, "data_loader") assert hasattr(problem, "distribution") - assert hasattr(problem, "regularization_strength") + assert hasattr(problem, "alpha") class TestLibraries: @@ -54,7 +54,7 @@ def test_params_creation(self): num_rows=100, storage="dense", threads=1, - regularization_strength=0.01, + alpha=0.01, ) assert params.problem_name == "test-problem" assert params.library_name == "glum" @@ -66,7 +66,7 @@ def test_get_result_fname(self): num_rows=100, storage="dense", threads=1, - regularization_strength=0.01, + alpha=0.01, ) fname = params.get_result_fname() assert "test-problem" in fname @@ -80,7 +80,7 @@ def test_params_roundtrip(self): num_rows=1000, storage="dense", threads=4, - regularization_strength=0.001, + alpha=0.001, ) fname = params.get_result_fname() + ".pkl" recovered = get_params_from_fname(fname) @@ -108,7 +108,7 @@ def test_execute_simple_problem(self): num_rows=100, # Small for speed storage="dense", threads=1, - regularization_strength=0.1, + alpha=0.1, ) result, _ = execute_problem_library(params, iterations=1) diff --git a/glum_benchmarks/util.py b/glum_benchmarks/util.py index 415ec31c6..97df8e8e1 100644 --- a/glum_benchmarks/util.py +++ b/glum_benchmarks/util.py @@ -33,7 +33,7 @@ def runtime(f, iterations, *args, timeout=None, **kwargs): Returns ------- - Tuple: (Minimum runtime across successful iterations, output frombest iteration) + Tuple: (Minimum runtime across successful iterations, output from fastest iteration) If all iterations timeout, raises TimeoutError. """ @@ -227,7 +227,7 @@ def __init__( num_rows: Optional[int] = None, storage: Optional[str] = None, threads: Optional[int] = None, - regularization_strength: Optional[float] = None, + alpha: Optional[float] = None, hessian_approx: Optional[float] = None, diagnostics_level: Optional[str] = None, ): @@ -236,7 +236,7 @@ def __init__( self.num_rows = num_rows self.storage = storage self.threads = threads - self.regularization_strength = regularization_strength + self.alpha = alpha self.hessian_approx = hessian_approx self.diagnostics_level = diagnostics_level @@ -246,7 +246,7 @@ def __init__( "num_rows", "storage", "threads", - "regularization_strength", + "alpha", "hessian_approx", "diagnostics_level", ] @@ -287,7 +287,7 @@ def get_result_fname(self) -> str: problem_name=None, library_name=None, num_rows=None, - regularization_strength=None, + alpha=None, storage="dense", hessian_approx=0.0, diagnostics_level="basic", @@ -299,7 +299,7 @@ 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_reg_hessian_diag.pkl + problem_library_numrows_storage_threads_alpha_hessian_diag.pkl Parameters ---------- @@ -391,10 +391,10 @@ def _standardize_features( return X_scaled, scaler, scaled_indices if sps.issparse(X): - # For sparse matrices, we can only scale (not center) to preserve sparsity + # For sparse matrices, use with_mean=False to preserve sparsity scaler = StandardScaler(with_mean=False) X_scaled = scaler.fit_transform(X) - return X_scaled, scaler, None # All columns scaled + return X_scaled, scaler, None # Fallback: return unchanged for other types (tabmat) return X, None, None @@ -466,6 +466,7 @@ def get_all_libraries() -> dict: """ from glum_benchmarks.libraries import ( celer_bench, + glmnet_bench, glum_bench, h2o_bench, skglm_bench, @@ -478,6 +479,7 @@ def get_all_libraries() -> dict: "zeros": zeros_bench, "celer": celer_bench, "h2o": h2o_bench, + "glmnet": glmnet_bench, "skglm": skglm_bench, "sklearn": sklearn_bench, } @@ -508,7 +510,7 @@ def execute_problem_library( Returns ------- - Tuple: Result data on this run, and the regularization applied + Tuple: Result data on this run, and the alpha applied """ from glum_benchmarks.problems import get_all_problems @@ -528,26 +530,22 @@ def execute_problem_library( os.environ["OMP_NUM_THREADS"] = str(params.threads) - if params.regularization_strength is None: - params.regularization_strength = P.regularization_strength + if params.alpha is None: + params.alpha = P.alpha - # 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 + alpha = ( + params.alpha / np.asarray(dat["sample_weight"]).mean() + if "sample_weight" in dat + else params.alpha ) result = L( dat, distribution=P.distribution, - alpha=params.regularization_strength, + alpha=alpha, l1_ratio=P.l1_ratio, iterations=iterations, diagnostics_level=diagnostics_level, - reg_multiplier=reg_multiplier, hessian_approx=params.hessian_approx, standardize=standardize, timeout=timeout, @@ -555,8 +553,13 @@ def execute_problem_library( ) if len(result) > 0: - # Use best_alpha from CV if available, otherwise use regularization_strength - alpha_for_obj = result.get("best_alpha", P.regularization_strength) + # 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, @@ -569,4 +572,4 @@ def execute_problem_library( result["obj_val"] = obj_val result["num_rows"] = dat["y"].shape[0] - return result, params.regularization_strength + return result, params.alpha diff --git a/pixi.lock b/pixi.lock index 5d9f7345c..87ad10efd 100644 --- a/pixi.lock +++ b/pixi.lock @@ -30,9 +30,9 @@ 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/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 @@ -43,14 +43,14 @@ environments: - 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-h3394656_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/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 - 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ucrt >=10.0.20348.0 - vc >=14.3,<15 @@ -24395,8 +25042,8 @@ packages: license: Zlib license_family: Other purls: [] - size: 123890 - timestamp: 1766076739436 + 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 1ec1fe3ae..b8d68c123 100644 --- a/pixi.toml +++ b/pixi.toml @@ -126,6 +126,7 @@ pydantic = "*" pytest = "*" "ruamel.yaml" = "*" r-base = "*" +r-glmnet = "*" rpy2 = "*" skglm = "*" diff --git a/tests/glm/test_benchmark_golden_master.py b/tests/glm/test_benchmark_golden_master.py index dcd2ca7f4..a8132c19c 100644 --- a/tests/glm/test_benchmark_golden_master.py +++ b/tests/glm/test_benchmark_golden_master.py @@ -9,7 +9,7 @@ from glum_benchmarks.problems import Problem, get_all_problems 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() @@ -64,7 +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:], @@ -72,7 +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:], 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 324e49c82..015add2a1 100644 --- a/tests/glm/test_offset_weight_obj_equivalent.py +++ b/tests/glm/test_offset_weight_obj_equivalent.py @@ -17,7 +17,7 @@ bench_cfg = dict( num_rows=10000, - regularization_strength=0.1, + alpha=0.1, storage="dense", ) @@ -60,20 +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) From 4fae115184ba601ab3644111542f772da2b17a27 Mon Sep 17 00:00:00 2001 From: David Eiglsperger Date: Mon, 9 Feb 2026 13:18:01 +0100 Subject: [PATCH 21/42] Bug fixes, parameter tuning and scaling refinement --- glum_benchmarks/config.yaml | 25 +- glum_benchmarks/data/create_insurance.py | 10 +- glum_benchmarks/libraries/bench_celer.py | 23 +- glum_benchmarks/libraries/bench_glmnet.py | 15 +- glum_benchmarks/libraries/bench_skglm.py | 23 +- glum_benchmarks/libraries/bench_sklearn.py | 22 +- glum_benchmarks/problems.py | 30 +- glum_benchmarks/run_benchmarks.py | 23 +- glum_benchmarks/util.py | 106 +- tests/glm/golden_master/benchmark_gm.json | 207041 +++++++++++++++++- 10 files changed, 199301 insertions(+), 8017 deletions(-) diff --git a/glum_benchmarks/config.yaml b/glum_benchmarks/config.yaml index ead2192c3..f0dcefb99 100644 --- a/glum_benchmarks/config.yaml +++ b/glum_benchmarks/config.yaml @@ -20,20 +20,33 @@ run_name: "docs" # Subfolder name within results/ ("docs" CSV is git-tracked) clear_output: True # Clear entire run_name directory before running # Benchmark settings -standardize: true # Whether to standardize features before fitting iterations: 2 # Run each benchmark N times, report minimum (>=2 for skglm) num_threads: 16 # Number of threads for parallel execution num_rows: null # Limit rows per dataset (null = full dataset) timeout: 100 # Timeout in seconds per benchmark run (reports as "not converged" if exceeded) +# Standardization approach per library: +# "pre" = pre-standardize continuous features in data loader (before OHE/format +# conversion). Only continuous columns are scaled. +# "internal" = library handles standardization internally (e.g. glum's scale_predictors) +# "none" = no standardization +# TODO: we might want to ommit this as a config option and just do the scaling in the data loader as a default +standardize: + glum: pre # glum handles via scale_predictors + sklearn: pre # pre-standardize in data loader + h2o: pre # h2o handles via standardize param + celer: pre # pre-standardize in data loader + skglm: pre # pre-standardize in data loader + glmnet: pre # R glmnet handles via standardize param + # Storage format per library ("dense", "sparse", or "auto") storage: glum: auto # Auto selects best format - sklearn: dense # Can use either - h2o: sparse # Optimized for sparse matrices - celer: sparse # Optimized for sparse matrices - skglm: sparse # Faster for dense matrices - glmnet: sparse # R glmnet supports sparse matrices + sklearn: csr + h2o: csr + celer: csc + skglm: csc + glmnet: csc # R glmnet supports sparse matrices # PARAMETER GRID # Each entry specifies a parameter set. Within each entry, the Cartesian diff --git a/glum_benchmarks/data/create_insurance.py b/glum_benchmarks/data/create_insurance.py index ee7c6bf25..2f9636af3 100644 --- a/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/libraries/bench_celer.py b/glum_benchmarks/libraries/bench_celer.py index ec1f622e0..13a70fe8c 100644 --- a/glum_benchmarks/libraries/bench_celer.py +++ b/glum_benchmarks/libraries/bench_celer.py @@ -6,8 +6,6 @@ from scipy import sparse as sps from glum_benchmarks.util import ( - _standardize_features, - _unstandardize_coefficients, benchmark_convergence_tolerance, runtime, ) @@ -23,17 +21,9 @@ def celer_bench( alpha: float, l1_ratio: float, iterations: int, - standardize: bool = True, timeout: Optional[float] = None, **kwargs, ): - # Standardize features if requested - scaler = None - scaled_indices = None - if standardize: - dat = dat.copy() - dat["X"], scaler, scaled_indices = _standardize_features(dat["X"]) - result: dict[str, Any] = {} fit_args = {"X": dat["X"], "y": dat["y"]} @@ -76,17 +66,8 @@ def celer_bench( warnings.warn(f"Celer failed: {e}") return {} - intercept = np.array(m.intercept_).ravel()[0] - coef = np.array(m.coef_).ravel() - - if standardize: - # Unstandardize coefficients to match original data scale - result["intercept"], result["coef"] = _unstandardize_coefficients( - intercept, coef, scaler, scaled_indices - ) - else: - result["intercept"] = intercept - result["coef"] = coef + 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 diff --git a/glum_benchmarks/libraries/bench_glmnet.py b/glum_benchmarks/libraries/bench_glmnet.py index ae4a70b5f..4800a150e 100644 --- a/glum_benchmarks/libraries/bench_glmnet.py +++ b/glum_benchmarks/libraries/bench_glmnet.py @@ -7,8 +7,10 @@ import pandas as pd from scipy import sparse as sps +from glum_benchmarks.util import benchmark_convergence_tolerance -def _setup_and_fit(X_np, y, distribution, l1_ratio, alpha, standardize, thresh): + +def _setup_and_fit(X_np, y, distribution, l1_ratio, alpha, standardize): """ Run in a child process: set up R/glmnet, fit, return results. @@ -27,7 +29,7 @@ def _setup_and_fit(X_np, y, distribution, l1_ratio, alpha, standardize, thresh): ro.r(f'suppressPackageStartupMessages(library("{pkg}"))') glmnet_pkg = importr("glmnet") - ro.r["glmnet.control"](epsnr=1e-15) + ro.r["glmnet.control"](epsnr=benchmark_convergence_tolerance * 10.0) # Build R family object if distribution in ("gaussian", "binomial", "poisson"): @@ -66,7 +68,7 @@ def _setup_and_fit(X_np, y, distribution, l1_ratio, alpha, standardize, thresh): alpha=l1_ratio, intercept=True, standardize=standardize, - thresh=thresh, + thresh=benchmark_convergence_tolerance, **{"lambda": lambda_vec}, ) fit_runtime = time.time() - start @@ -157,12 +159,7 @@ def glmnet_bench( y = np.asarray(dat["y"], dtype=float).ravel() - # glmnet's thresh is a *relative* convergence criterion (scaled internally - # by the null deviance). We use a fixed tight value so glmnet actually - # converges to the same quality as the other libraries. - thresh = 1e-12 - - fit_args = (X, y, distribution, l1_ratio, alpha, standardize, thresh) + fit_args = (X, y, distribution, l1_ratio, alpha, standardize) # Run iterations, keeping the fastest. # The child process times data conversion + glmnet() (excludes R startup). diff --git a/glum_benchmarks/libraries/bench_skglm.py b/glum_benchmarks/libraries/bench_skglm.py index 6342294e0..1e02a2fcb 100644 --- a/glum_benchmarks/libraries/bench_skglm.py +++ b/glum_benchmarks/libraries/bench_skglm.py @@ -9,8 +9,6 @@ from skglm.solvers import AndersonCD, ProxNewton from glum_benchmarks.util import ( - _standardize_features, - _unstandardize_coefficients, benchmark_convergence_tolerance, runtime, ) @@ -26,17 +24,9 @@ def skglm_bench( alpha: float, l1_ratio: float, iterations: int, - standardize: bool = True, timeout: Optional[float] = None, **kwargs, ): - # Standardize features if requested - scaler = None - scaled_indices = None - if standardize: - dat = dat.copy() - dat["X"], scaler, scaled_indices = _standardize_features(dat["X"]) - result: dict[str, Any] = {} if "tweedie" in distribution: warnings.warn("skglm doesn't support Tweedie, skipping.") @@ -87,17 +77,8 @@ def skglm_bench( warnings.warn(f"skglm failed: {e}") return {} - intercept = np.array(m.intercept_).ravel()[0] - coef = np.array(m.coef_).ravel() - - if standardize: - # Unstandardize coefficients to match original data scale - result["intercept"], result["coef"] = _unstandardize_coefficients( - intercept, coef, scaler, scaled_indices - ) - else: - result["intercept"] = intercept - result["coef"] = coef + 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 diff --git a/glum_benchmarks/libraries/bench_sklearn.py b/glum_benchmarks/libraries/bench_sklearn.py index 6bec45451..296093eae 100644 --- a/glum_benchmarks/libraries/bench_sklearn.py +++ b/glum_benchmarks/libraries/bench_sklearn.py @@ -12,8 +12,6 @@ ) from glum_benchmarks.util import ( - _standardize_features, - _unstandardize_coefficients, benchmark_convergence_tolerance, runtime, ) @@ -30,7 +28,6 @@ def sklearn_bench( alpha: float, l1_ratio: float, iterations: int, - standardize: bool = True, timeout: Optional[float] = None, **kwargs, ): @@ -44,21 +41,12 @@ def sklearn_bench( alpha l1_ratio iterations - standardize - If True, standardize continuous features using sklearn's StandardScaler. kwargs Returns ------- Dict of """ - # Standardize features if requested - scaler = None - scaled_indices = None - if standardize: - dat = dat.copy() - dat["X"], scaler, scaled_indices = _standardize_features(dat["X"]) - result: dict[str, Any] = {} n_samples = dat["X"].shape[0] model_class = None @@ -161,14 +149,8 @@ def sklearn_bench( n_iter = getattr(m, "n_iter_", None) n_iter = 0 if n_iter is None else n_iter - # Unstandardize coefficients if we standardized the features - if standardize and scaler is not None: - result["intercept"], result["coef"] = _unstandardize_coefficients( - intercept, coef, scaler, scaled_indices - ) - else: - result["intercept"] = intercept - result["coef"] = coef + 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) diff --git a/glum_benchmarks/problems.py b/glum_benchmarks/problems.py index e3021d3ed..54454d362 100644 --- a/glum_benchmarks/problems.py +++ b/glum_benchmarks/problems.py @@ -8,7 +8,8 @@ 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, @@ -45,6 +46,7 @@ def load_data( noise: Optional[float] = None, distribution: str = "poisson", data_setup: str = "no-weights", + standardize: bool = False, ) -> dict[str, np.ndarray]: """ Load the data. @@ -58,12 +60,28 @@ def load_data( raise NotImplementedError X_in, y, exposure = loader_func(num_rows, noise, distribution) + # 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() # wichtig wegen joblib / in-place + + continuous_cols = [ + c + for c in X_in.select_dtypes(include=[np.number]).columns + if X_in[c].nunique(dropna=False) > 2 + ] + + 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)] @@ -102,9 +120,13 @@ 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": + 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).astype(np.float64) + pd.concat(mat_parts, axis=1, ignore_index=True).to_numpy(dtype=np.float64) ) elif storage.startswith("split"): threshold = float(storage.split("split")[1]) diff --git a/glum_benchmarks/run_benchmarks.py b/glum_benchmarks/run_benchmarks.py index dbf49d8c5..4e536481a 100644 --- a/glum_benchmarks/run_benchmarks.py +++ b/glum_benchmarks/run_benchmarks.py @@ -67,7 +67,8 @@ 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", "sparse", "cat"] +StorageFormat = Literal["auto", "dense", "sparse", "cat", "csr", "csc"] +StandardizeMethod = Literal["pre", "internal", "none"] class ParamGridEntry(BaseModel): @@ -148,8 +149,14 @@ class BenchmarkConfig(BaseModel): num_threads: int = Field( default=16, ge=1, description="Number of threads for parallel execution" ) - standardize: bool = Field( - default=True, description="Whether to standardize before fitting" + standardize: dict[Library, StandardizeMethod] = Field( + default_factory=dict, + description=( + "Standardization approach per library: " + "'pre' = pre-standardize in data loader, " + "'internal' = library handles internally, " + "'none' = no standardization" + ), ) iterations: int = Field( default=2, ge=1, description="Run each benchmark N times, report minimum" @@ -318,9 +325,14 @@ def run_single_benchmark( If the benchmark exceeds the configured timeout, returns a result with runtime=timeout and timed_out=True. """ - # Get library-specific storage from config, default to "dense" + # Get library-specific settings from config lib_key: Library = library_name # type: ignore[assignment] storage = config.storage.get(lib_key, "dense") + std_method = config.standardize.get(lib_key, "none") + + # Derive standardization flags per library + pre_standardize = std_method == "pre" + library_standardize = std_method == "internal" params = BenchmarkParams( problem_name=problem_name, @@ -337,7 +349,8 @@ def run_single_benchmark( params, iterations=config.iterations, diagnostics_level=None, - standardize=config.standardize, + pre_standardize=pre_standardize, + standardize=library_standardize, timeout=config.timeout, ) result["timed_out"] = False diff --git a/glum_benchmarks/util.py b/glum_benchmarks/util.py index 97df8e8e1..262b9f9da 100644 --- a/glum_benchmarks/util.py +++ b/glum_benchmarks/util.py @@ -7,8 +7,6 @@ import pandas as pd import tabmat as tm from scipy import sparse as sps -from scipy.sparse import csc_matrix -from sklearn.preprocessing import StandardScaler from glum import GeneralizedLinearRegressor, TweedieDistribution from glum._solvers import eta_mu_objective @@ -357,102 +355,6 @@ def get_tweedie_p(distribution: str) -> float: raise ValueError("Not a Tweedie distribution.") -def _standardize_features( - X: Union[np.ndarray, pd.DataFrame, csc_matrix, tm.MatrixBase], -) -> tuple[ - Union[np.ndarray, pd.DataFrame, csc_matrix, tm.MatrixBase], - Optional[StandardScaler], - Optional[np.ndarray], -]: - """ - Standardize only numerical columns using StandardScaler. - - For dense data: mean=0, std=1 - For sparse data: std=1 only (with_mean=False to preserve sparsity) - - Returns - ------- - X_scaled : array-like - The scaled feature matrix. - scaler : StandardScaler or None - The fitted scaler (for later unstandardization). - scaled_indices : ndarray or None - Indices of the columns that were scaled (for DataFrames with mixed types). - None if all columns were scaled or no scaling was performed. - """ - if isinstance(X, pd.DataFrame): - # Identify numerical columns - numerical_cols = X.select_dtypes(include=["number"]).columns - # Get the integer indices of numerical columns - scaled_indices = np.array([X.columns.get_loc(c) for c in numerical_cols]) - scaler = StandardScaler() - X_scaled = X.copy() - X_scaled[numerical_cols] = scaler.fit_transform(X[numerical_cols]) - return X_scaled, scaler, scaled_indices - - if sps.issparse(X): - # For sparse matrices, use with_mean=False to preserve sparsity - scaler = StandardScaler(with_mean=False) - X_scaled = scaler.fit_transform(X) - return X_scaled, scaler, None - - # Fallback: return unchanged for other types (tabmat) - return X, None, None - - -def _unstandardize_coefficients( - intercept: float, - coef: np.ndarray, - scaler: Optional[StandardScaler], - scaled_indices: Optional[np.ndarray] = None, -) -> tuple[float, np.ndarray]: - """ - Unstandardize coefficients to match the original (unscaled) data. - - This reverses the StandardScaler transformation so coefficients can be - compared with libraries that handle standardization internally (glum, h2o). - - Parameters - ---------- - intercept : float - The intercept from the fitted model. - coef : ndarray - The coefficients from the fitted model. - scaler : StandardScaler or None - The scaler used to standardize features. - scaled_indices : ndarray or None - Indices of the columns that were scaled. If None, assumes all columns - were scaled (or no scaling was performed if scaler is also None). - """ - if scaler is None: - return intercept, coef - - scale = scaler.scale_ - coef_original = coef.copy() - - if scaled_indices is not None: - # Only unstandardize the coefficients corresponding to scaled columns - coef_original[scaled_indices] = coef[scaled_indices] / scale - - # intercept_original = intercept_scaled - sum(mean / scale * coef_scaled) - if scaler.with_mean and scaler.mean_ is not None: - intercept_original = intercept - np.dot( - scaler.mean_ / scale, coef[scaled_indices] - ) - else: - intercept_original = intercept - else: - # All columns were scaled - coef_original = coef / scale - - if scaler.with_mean and scaler.mean_ is not None: - intercept_original = intercept - np.dot(scaler.mean_ / scale, coef) - else: - intercept_original = intercept - - return intercept_original, coef_original - - def get_all_libraries() -> dict: """ Get the names of all available libraries and the functions to benchmark them. @@ -492,6 +394,7 @@ def execute_problem_library( params: BenchmarkParams, iterations: int = 1, diagnostics_level: Optional[str] = "basic", + pre_standardize: bool = False, standardize: bool = True, timeout: Optional[float] = None, **kwargs, @@ -504,8 +407,12 @@ def execute_problem_library( params iterations diagnostics_level + pre_standardize + Whether to pre-standardize continuous features in the data loader, + before OHE and format conversion. standardize - Whether to standardize features before fitting. Default True for benchmarks. + Whether to pass a standardize flag to the library function. Can be used for + libraries that handle standardization internally (e.g. glum, h2o, glmnet). kwargs Returns @@ -526,6 +433,7 @@ def execute_problem_library( dat = P.data_loader( num_rows=params.num_rows, storage=params.storage, + standardize=pre_standardize, ) os.environ["OMP_NUM_THREADS"] = str(params.threads) diff --git a/tests/glm/golden_master/benchmark_gm.json b/tests/glm/golden_master/benchmark_gm.json index 547d46a91..c1fda0dd4 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, - 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0.01403507088150894, 0.0 ], - "intercept": -23.899507643941142 + "intercept": 0.8983678245040168 } } From 0db1cb210d3acf89d97db73da711ee89cb45399f Mon Sep 17 00:00:00 2001 From: David Eiglsperger Date: Mon, 9 Feb 2026 13:21:08 +0100 Subject: [PATCH 22/42] CI fix --- glum_benchmarks/data/simulated_glm.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/glum_benchmarks/data/simulated_glm.py b/glum_benchmarks/data/simulated_glm.py index f5e759002..6cc273949 100644 --- a/glum_benchmarks/data/simulated_glm.py +++ b/glum_benchmarks/data/simulated_glm.py @@ -190,7 +190,7 @@ def simulate_mixed_data( def simulate_square_dataset( num_rows: Optional[int] = None, - _noise=None, # unused, required by load_data signature + noise: Optional[float] = None, # unused, required by load_data signature distribution: str = "poisson", ) -> tuple[pd.DataFrame, np.ndarray, np.ndarray]: """Generate a square dataset where n_rows = n_cols. @@ -199,7 +199,7 @@ def simulate_square_dataset( ---------- num_rows Number of rows (and columns). Defaults to 500. - _noise + noise Unused, present to match load_data signature. distribution The GLM family: "gaussian", "poisson", "gamma", or "binomial". @@ -248,7 +248,7 @@ def simulate_square_dataset( def simulate_categorical_dataset( num_rows: Optional[int] = None, - _noise=None, # unused, required by load_data signature + 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]: @@ -261,7 +261,7 @@ def simulate_categorical_dataset( ---------- num_rows Number of rows. Defaults to 5000. - _noise + noise Unused, present to match load_data signature. distribution The GLM family: "gaussian", "poisson", "gamma", or "binomial". From 80d9ed2d6d3fd9785ab5a8d51d75f16375ca2333 Mon Sep 17 00:00:00 2001 From: David Eiglsperger Date: Mon, 9 Feb 2026 14:34:17 +0100 Subject: [PATCH 23/42] Minor cleaning --- glum_benchmarks/README.md | 9 ++++----- glum_benchmarks/config.yaml | 29 ++++++++++++++--------------- glum_benchmarks/run_benchmarks.py | 5 +---- 3 files changed, 19 insertions(+), 24 deletions(-) diff --git a/glum_benchmarks/README.md b/glum_benchmarks/README.md index 909963a0b..5778e778d 100644 --- a/glum_benchmarks/README.md +++ b/glum_benchmarks/README.md @@ -121,19 +121,18 @@ Edit `config.yaml` to customize benchmark parameters. | Option | Description | | ------------- | ---------------------------------------------------------------------------- | -| `standardize` | Whether to standardize features (glum/h2o: internal, others: StandardScaler) | +| `standardize` | Standardization strategy per library (`pre`, `internal`, `none`) | | `iterations` | Runs per benchmark (>=2 required for skglm) | | `num_threads` | Number of threads for parallel execution | | `num_rows` | Limit rows per dataset (`null` = full dataset) | | `timeout` | Timeout in seconds (benchmarks timing out are marked "not converged") | -| `storage` | Storage format per library: `{"glum": "auto", "sklearn": "dense", ...}` | +| `storage` | Storage format per library: (`auto`, `dense`, `cat`, `csr`, `csc`) | **Notes:** -- **Standardization**: glum and h2o handle scaling internally. sklearn, skglm, and celer use `StandardScaler`. Only numerical columns are scaled (categorical/one-hot columns are not). +- **Standardization**: `pre` standardizes continuous columns in the data loader before OHE/format conversion; `internal` delegates to the library; `none` skips scaling. - **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. -- **Storage formats**: `"auto"`, `"dense"`, or `"sparse"`. Configured per library for optimal performance. ### Parameter Grid @@ -153,7 +152,7 @@ Each entry computes a Cartesian product. Multiple entries are unioned (not cross **Available values:** - `libraries`: `["glum", "sklearn", "h2o", "skglm", "celer", "zeros", "glmnet"]` -- `datasets`: `["intermediate-housing", "intermediate-insurance", "narrow-insurance", "wide-insurance", "square-simulated"]` +- `datasets`: `["intermediate-housing", "intermediate-insurance", "narrow-insurance", "wide-insurance", "square-simulated", "categorical-simulated"]` - `regularizations`: `["lasso", "l2", "net"]` - `distributions`: `["gaussian", "gamma", "binomial", "poisson", "tweedie-p=1.5"]` - `alphas`: `[0.0001, 0.001, 0.01]` diff --git a/glum_benchmarks/config.yaml b/glum_benchmarks/config.yaml index f0dcefb99..e7c2b086d 100644 --- a/glum_benchmarks/config.yaml +++ b/glum_benchmarks/config.yaml @@ -26,27 +26,26 @@ num_rows: null # Limit rows per dataset (null = full dataset) timeout: 100 # Timeout in seconds per benchmark run (reports as "not converged" if exceeded) # Standardization approach per library: -# "pre" = pre-standardize continuous features in data loader (before OHE/format -# conversion). Only continuous columns are scaled. -# "internal" = library handles standardization internally (e.g. glum's scale_predictors) +# "pre" = pre-standardize continuous features in data loader +# "internal" = library handles standardization internally # "none" = no standardization # TODO: we might want to ommit this as a config option and just do the scaling in the data loader as a default standardize: - glum: pre # glum handles via scale_predictors - sklearn: pre # pre-standardize in data loader - h2o: pre # h2o handles via standardize param - celer: pre # pre-standardize in data loader - skglm: pre # pre-standardize in data loader - glmnet: pre # R glmnet handles via standardize param + glum: pre + sklearn: pre + h2o: pre + celer: pre + skglm: pre + glmnet: pre -# Storage format per library ("dense", "sparse", or "auto") +# Storage format per library [auto, dense, cat, csr, csc] storage: - glum: auto # Auto selects best format + glum: auto sklearn: csr h2o: csr celer: csc skglm: csc - glmnet: csc # R glmnet supports sparse matrices + glmnet: csc # PARAMETER GRID # Each entry specifies a parameter set. Within each entry, the Cartesian @@ -54,12 +53,12 @@ storage: # # Available values: # libraries: ["glum", "sklearn", "h2o", "skglm", "celer", "zeros", "glmnet"] -# datasets: ["intermediate-housing", "intermediate-insurance", "narrow-insurance", "wide-insurance","square-simulated"] +# datasets: ["intermediate-housing", "intermediate-insurance", "narrow-insurance", "wide-insurance","square-simulated", "categorical-simulated"] # regularizations: ["lasso", "l2", "net"] -# alphas: [0.0001, 0.001, 0.01] +# alphas: [0.001, 0.01, 0.1] # distributions: ["gaussian", "gamma", "binomial", "poisson", "tweedie-p=1.5"] # -# Use null (or omit) for any field to use its default value. Default refers to using all available values. +# Use null for any field (or omit) to use its default value. Default refers to using all available values. # # Example: Run all regularizations for gaussian, but only lasso for gamma: # param_grid: diff --git a/glum_benchmarks/run_benchmarks.py b/glum_benchmarks/run_benchmarks.py index 4e536481a..8832f72d0 100644 --- a/glum_benchmarks/run_benchmarks.py +++ b/glum_benchmarks/run_benchmarks.py @@ -49,9 +49,6 @@ ) # TODO: Update README and documentation -# TODO: Implement closed form solution for l2-gaussian and put a note in the results -# TODO: Scaling -# TODO: Determine optimal storage type for each library for a fair comparison # Type aliases for configuration options Library = Literal["glum", "sklearn", "h2o", "skglm", "celer", "zeros", "glmnet"] @@ -67,7 +64,7 @@ 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", "sparse", "cat", "csr", "csc"] +StorageFormat = Literal["auto", "dense", "cat", "csr", "csc"] StandardizeMethod = Literal["pre", "internal", "none"] From 863e4877f3e25766505bcb41284613c5a3485418 Mon Sep 17 00:00:00 2001 From: David Eiglsperger Date: Tue, 10 Feb 2026 11:10:56 +0100 Subject: [PATCH 24/42] Allow flexible K/N ratio for the simulated problems --- glum_benchmarks/README.md | 19 +++++++++-------- glum_benchmarks/config.yaml | 3 ++- glum_benchmarks/data/__init__.py | 4 ++-- glum_benchmarks/data/simulated_glm.py | 30 +++++++++++++++++++-------- glum_benchmarks/problems.py | 15 +++++++++----- glum_benchmarks/run_benchmarks.py | 11 +++++++++- glum_benchmarks/util.py | 7 ++++++- 7 files changed, 61 insertions(+), 28 deletions(-) diff --git a/glum_benchmarks/README.md b/glum_benchmarks/README.md index 5778e778d..80fd6f4a4 100644 --- a/glum_benchmarks/README.md +++ b/glum_benchmarks/README.md @@ -119,14 +119,15 @@ Edit `config.yaml` to customize benchmark parameters. ### Benchmark Settings -| Option | Description | -| ------------- | ---------------------------------------------------------------------------- | -| `standardize` | Standardization strategy per library (`pre`, `internal`, `none`) | -| `iterations` | Runs per benchmark (>=2 required for skglm) | -| `num_threads` | Number of threads for parallel execution | -| `num_rows` | Limit rows per dataset (`null` = full dataset) | -| `timeout` | Timeout in seconds (benchmarks timing out are marked "not converged") | -| `storage` | Storage format per library: (`auto`, `dense`, `cat`, `csr`, `csc`) | +| Option | Description | +| ------------- | ---------------------------------------------------------------------------- | +| `standardize` | Standardization strategy per library (`pre`, `internal`, `none`) | +| `iterations` | Runs per benchmark (>=2 required for skglm) | +| `num_threads` | Number of threads for parallel execution | +| `num_rows` | Limit rows per dataset (`null` = full dataset) | +| `k_over_n_ratio` | Feature/row ratio (K/N) for `simulated-glm` (`>1` gives `K>N`) | +| `timeout` | Timeout in seconds (benchmarks timing out are marked "not converged") | +| `storage` | Storage format per library: (`auto`, `dense`, `cat`, `csr`, `csc`) | **Notes:** @@ -152,7 +153,7 @@ Each entry computes a Cartesian product. Multiple entries are unioned (not cross **Available values:** - `libraries`: `["glum", "sklearn", "h2o", "skglm", "celer", "zeros", "glmnet"]` -- `datasets`: `["intermediate-housing", "intermediate-insurance", "narrow-insurance", "wide-insurance", "square-simulated", "categorical-simulated"]` +- `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]` diff --git a/glum_benchmarks/config.yaml b/glum_benchmarks/config.yaml index e7c2b086d..fa7c34503 100644 --- a/glum_benchmarks/config.yaml +++ b/glum_benchmarks/config.yaml @@ -23,6 +23,7 @@ clear_output: True # Clear entire run_name directory before running iterations: 2 # Run each benchmark N times, report minimum (>=2 for skglm) num_threads: 16 # Number of threads for parallel execution num_rows: null # Limit rows per dataset (null = full dataset) +k_over_n_ratio: 0.5 # K/N ratio for simulated-glm only (>1: K>N, <1: K tuple[pd.DataFrame, np.ndarray, np.ndarray]: - """Generate a square dataset where n_rows = n_cols. + """Generate a simulated GLM dataset with configurable K/N ratio. Parameters ---------- num_rows - Number of rows (and columns). Defaults to 500. + Number of rows. Defaults to 5000. 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) where X is square. + (X, y, exposure). """ - n = num_rows if num_rows is not None else 5000 + n_rows = num_rows if num_rows is not None else 5000 + 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, n)), - columns=[f"x{i}" for i in range(n)], + 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) * rand.normal(0, 1, size=n) + coefs = rand.choice([0] * 9 + [1], size=n_features) * rand.normal( + 0, 1, size=n_features + ) # Linear predictor eta = X.to_numpy() @ coefs @@ -242,7 +254,7 @@ def simulate_square_dataset( family_rv = _get_family_rv(family, rand) y = family_rv(mu) - exposure = np.ones(n) + exposure = np.ones(n_rows) return X, y, exposure diff --git a/glum_benchmarks/problems.py b/glum_benchmarks/problems.py index 54454d362..d25c9dd47 100644 --- a/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 @@ -18,7 +19,7 @@ generate_real_insurance_dataset, generate_wide_insurance_dataset, simulate_categorical_dataset, - simulate_square_dataset, + simulate_glm_dataset, ) from .util import cache_location, exposure_and_offset_to_weights, get_tweedie_p @@ -47,6 +48,7 @@ def load_data( distribution: str = "poisson", data_setup: str = "no-weights", standardize: bool = False, + k_over_n_ratio: Optional[float] = None, ) -> dict[str, np.ndarray]: """ Load the data. @@ -58,7 +60,10 @@ def load_data( # 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 @@ -193,10 +198,10 @@ 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 datasets (square: n_rows ≈ n_cols) + # Simulated dataset with configurable K/N ratio. simulated_distributions = ["gaussian", "poisson", "gamma", "binomial"] simulated_load_funcs = { - "square-simulated": simulate_square_dataset, + "simulated-glm": simulate_glm_dataset, } problems = {} diff --git a/glum_benchmarks/run_benchmarks.py b/glum_benchmarks/run_benchmarks.py index 8832f72d0..f10b66e8e 100644 --- a/glum_benchmarks/run_benchmarks.py +++ b/glum_benchmarks/run_benchmarks.py @@ -57,7 +57,7 @@ "intermediate-housing", "narrow-insurance", "wide-insurance", - "square-simulated", + "simulated-glm", "categorical-simulated", ] Regularization = Literal["lasso", "l2", "net"] @@ -161,6 +161,14 @@ class BenchmarkConfig(BaseModel): num_rows: int | None = Field( default=None, ge=1, description="Limit rows per dataset (None = full dataset)" ) + k_over_n_ratio: float = Field( + default=1.0, + gt=0, + description=( + "Feature-to-row ratio (K/N) for simulated-glm dataset. " + "Values >1 give K>N; values <1 give K str: problem_name=None, library_name=None, num_rows=None, + k_over_n_ratio=1.0, alpha=None, storage="dense", hessian_approx=0.0, @@ -321,7 +325,7 @@ def parse_value(value: str, dtype=str): # Map parts to parameter names with appropriate types # Order matches BenchmarkParams.param_names - param_types = [str, str, int, str, int, float, float, str] + param_types = [str, str, int, float, str, int, float, float, str] kwargs = {} for i, (name, dtype) in enumerate(zip(BenchmarkParams.param_names, param_types)): @@ -432,6 +436,7 @@ def execute_problem_library( dat = P.data_loader( num_rows=params.num_rows, + k_over_n_ratio=params.k_over_n_ratio, storage=params.storage, standardize=pre_standardize, ) From 423fb4ff2e35b0487990d5e998680efb029e7996 Mon Sep 17 00:00:00 2001 From: David Eiglsperger Date: Tue, 10 Feb 2026 11:48:14 +0100 Subject: [PATCH 25/42] adjust defaults and available distributions --- glum_benchmarks/README.md | 2 +- glum_benchmarks/config.yaml | 3 ++- glum_benchmarks/problems.py | 4 +--- glum_benchmarks/run_benchmarks.py | 3 ++- 4 files changed, 6 insertions(+), 6 deletions(-) diff --git a/glum_benchmarks/README.md b/glum_benchmarks/README.md index 80fd6f4a4..5fe509975 100644 --- a/glum_benchmarks/README.md +++ b/glum_benchmarks/README.md @@ -158,7 +158,7 @@ Each entry computes a Cartesian product. Multiple entries are unioned (not cross - `distributions`: `["gaussian", "gamma", "binomial", "poisson", "tweedie-p=1.5"]` - `alphas`: `[0.0001, 0.001, 0.01]` -When an entry is omitted or set to `null` all available values are taken --> if you want to run all combinations possible, just leave the `param_grid` entry empty. +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). 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. diff --git a/glum_benchmarks/config.yaml b/glum_benchmarks/config.yaml index fa7c34503..96d57bad6 100644 --- a/glum_benchmarks/config.yaml +++ b/glum_benchmarks/config.yaml @@ -59,7 +59,8 @@ storage: # alphas: [0.001, 0.01, 0.1] # distributions: ["gaussian", "gamma", "binomial", "poisson", "tweedie-p=1.5"] # -# Use null for any field (or omit) to use its default value. Default refers to using all available values. +# 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). # # Example: Run all regularizations for gaussian, but only lasso for gamma: # param_grid: diff --git a/glum_benchmarks/problems.py b/glum_benchmarks/problems.py index d25c9dd47..433911174 100644 --- a/glum_benchmarks/problems.py +++ b/glum_benchmarks/problems.py @@ -178,17 +178,15 @@ def get_all_problems() -> dict[str, Problem]: """ alpha = 0.001 - housing_distributions = ["gaussian", "gamma", "binomial"] + housing_distributions = ["gaussian", "gamma"] housing_load_funcs = { "intermediate-housing": generate_housing_dataset, } insurance_distributions = [ - "gaussian", "poisson", "gamma", "tweedie-p=1.5", - "binomial", ] insurance_load_funcs = { "intermediate-insurance": generate_intermediate_insurance_dataset, diff --git a/glum_benchmarks/run_benchmarks.py b/glum_benchmarks/run_benchmarks.py index f10b66e8e..53963cc0c 100644 --- a/glum_benchmarks/run_benchmarks.py +++ b/glum_benchmarks/run_benchmarks.py @@ -267,6 +267,7 @@ def get_benchmark_combinations( 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] @@ -291,7 +292,7 @@ def get_benchmark_combinations( print("=" * 70) for i, entry in enumerate(config.param_grid, 1): # Use entry values or defaults (all) - libraries = entry.libraries if entry.libraries else available_libraries + 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 From 68212f7ab32d5e5db527f46301344828fc8dd15f Mon Sep 17 00:00:00 2001 From: David Eiglsperger Date: Wed, 11 Feb 2026 11:27:49 +0100 Subject: [PATCH 26/42] k_over_n as entry in param grid and rich for tables --- glum_benchmarks/README.md | 9 +- glum_benchmarks/config.yaml | 13 +- glum_benchmarks/run_benchmarks.py | 377 +++++++++++++++++++----------- pixi.lock | 28 +++ pixi.toml | 3 +- 5 files changed, 291 insertions(+), 139 deletions(-) diff --git a/glum_benchmarks/README.md b/glum_benchmarks/README.md index 5fe509975..0f62d6bf8 100644 --- a/glum_benchmarks/README.md +++ b/glum_benchmarks/README.md @@ -51,7 +51,7 @@ Copies figures to `docs/_static/` and updates figure references in documentation - **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). +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 @@ -125,7 +125,6 @@ Edit `config.yaml` to customize benchmark parameters. | `iterations` | Runs per benchmark (>=2 required for skglm) | | `num_threads` | Number of threads for parallel execution | | `num_rows` | Limit rows per dataset (`null` = full dataset) | -| `k_over_n_ratio` | Feature/row ratio (K/N) for `simulated-glm` (`>1` gives `K>N`) | | `timeout` | Timeout in seconds (benchmarks timing out are marked "not converged") | | `storage` | Storage format per library: (`auto`, `dense`, `cat`, `csr`, `csc`) | @@ -146,6 +145,9 @@ param_grid: regularizations: ["lasso", "l2"] distributions: ["gaussian", "poisson"] alphas: [0.001] + - datasets: ["simulated-glm"] + distributions: ["gaussian", "poisson"] + k_over_n_ratios: [0.5, 0.7, 1.2] ``` Each entry computes a Cartesian product. Multiple entries are unioned (not crossed). @@ -157,8 +159,9 @@ Each entry computes a Cartesian product. Multiple entries are unioned (not cross - `regularizations`: `["lasso", "l2", "net"]` - `distributions`: `["gaussian", "gamma", "binomial", "poisson", "tweedie-p=1.5"]` - `alphas`: `[0.0001, 0.001, 0.01]` +- `k_over_n_ratios`: `[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). If you want to run all default combinations, leave the `param_grid` entry empty. +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. 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. diff --git a/glum_benchmarks/config.yaml b/glum_benchmarks/config.yaml index 96d57bad6..0bc97ddae 100644 --- a/glum_benchmarks/config.yaml +++ b/glum_benchmarks/config.yaml @@ -10,8 +10,11 @@ update_docs: false # Copy figures to docs/_static and update benchmarks.rst and # Figures to include in docs/README (null = use defaults) # Default for docs: all figures # Default for readme: first non-normalized figure only -# Available: null, or list of figure filenames in the format "-.png" +# Available: null, or list of figure filenames. +# Standard format: "-.png" +# Simulated format with K/N split: "--k-over-n-.png" # Example: intermediate-insurance-gaussian.png +# Example: simulated-glm-gaussian-k-over-n-0.7.png docs_figures: null readme_figures: null @@ -23,7 +26,6 @@ clear_output: True # Clear entire run_name directory before running iterations: 2 # Run each benchmark N times, report minimum (>=2 for skglm) num_threads: 16 # Number of threads for parallel execution num_rows: null # Limit rows per dataset (null = full dataset) -k_over_n_ratio: 0.5 # K/N ratio for simulated-glm only (>1: K>N, <1: K1 give K>N; values <1 give K tuple[str, str, str]: def get_benchmark_combinations( config: BenchmarkConfig, -) -> list[tuple[str, str, float]]: - """Get list of (problem_name, library, alpha) tuples to benchmark. +) -> list[tuple[str, str, float, float]]: + """Get list of (problem_name, library, alpha, k_over_n_ratio) tuples. Cartesian product within each entry, union across entries. Returns: - List of (problem_name, library_name, alpha) tuples to run. + List of (problem_name, library_name, alpha, k_over_n_ratio) tuples to run. """ all_problems = get_all_problems() @@ -284,7 +287,7 @@ def get_benchmark_combinations( all_dists = sorted({_parse_problem_name(n)[2] for n in base_problems}) all_alphas = list(ALPHA_VALUES) - combinations: set[tuple[str, str, float]] = set() + combinations: set[tuple[str, str, float, float]] = set() # Print configuration per parameter grid entry print("=" * 70) @@ -297,6 +300,9 @@ def get_benchmark_combinations( 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 + 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}") @@ -304,6 +310,7 @@ def get_benchmark_combinations( print(f" Regularizations: {regs}") print(f" Distributions: {dists}") print(f" Alphas: {alphas}") + print(f" K/N ratios (simulated-glm): {k_over_n_ratios}") print() # Cartesian product within this entry @@ -314,7 +321,13 @@ def get_benchmark_combinations( if key in problem_lookup: # Only add if library is actually available if lib in available_libraries: - combinations.add((problem_lookup[key], lib, alpha)) + 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, k_over_n_ratio) + ) print(f"Total benchmark runs: {len(combinations)}") print("=" * 70) @@ -324,7 +337,11 @@ def get_benchmark_combinations( def run_single_benchmark( - problem_name: str, library_name: str, alpha: float, config: BenchmarkConfig + problem_name: str, + library_name: str, + alpha: float, + k_over_n_ratio: float, + config: BenchmarkConfig, ) -> tuple[dict, BenchmarkParams]: """Run a single benchmark and return results. @@ -344,7 +361,7 @@ def run_single_benchmark( problem_name=problem_name, library_name=library_name, num_rows=config.num_rows, - k_over_n_ratio=config.k_over_n_ratio, + k_over_n_ratio=k_over_n_ratio, storage=storage, threads=config.num_threads, alpha=alpha, @@ -382,15 +399,21 @@ def run_all_benchmarks(config: BenchmarkConfig): shutil.rmtree(config.results_dir) config.pickle_dir.mkdir(parents=True, exist_ok=True) - # Get benchmark combinations (problem, library, alpha) from param_grid + # Get benchmark combinations (problem, library, alpha, k_over_n_ratio) + # from param_grid. combinations = get_benchmark_combinations(config) total = len(combinations) current = 0 - for problem_name, library_name, alpha in combinations: + for problem_name, library_name, alpha, k_over_n_ratio in combinations: current += 1 - label = f"{library_name} / {problem_name} (α={alpha})" + 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}{ratio_label})" print(f"[{current}/{total}] {label}", end=" ", flush=True) try: @@ -403,7 +426,7 @@ def run_all_benchmarks(config: BenchmarkConfig): message=".*asyncio.iscoroutinefunction.*", ) result, params = run_single_benchmark( - problem_name, library_name, alpha, config + problem_name, library_name, alpha, k_over_n_ratio, config ) # Save result @@ -494,6 +517,7 @@ def analyze_results(config: BenchmarkConfig) -> pd.DataFrame: "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, @@ -516,7 +540,7 @@ def analyze_results(config: BenchmarkConfig) -> pd.DataFrame: df = pd.DataFrame(results) # Format: set index and sort - problem_id_cols = ["problem_name", "num_rows", "alpha"] + 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 relative objective value (how far from best). @@ -537,15 +561,13 @@ def analyze_results(config: BenchmarkConfig) -> pd.DataFrame: "rel_obj_val", ] - with pd.option_context( - "display.expand_frame_repr", - False, - "display.max_columns", - None, - "display.max_rows", - None, - ): - print(df[cols_to_show]) + # Group by each problem key: problem_name + alpha + num_rows. + table_df = df.loc[:, cols_to_show].reset_index() + _print_dataframe_table( + table_df, + title="Benchmark summary", + group_by=["problem_name", "alpha", "num_rows", "k_over_n_ratio"], + ) # Export to CSV for figure generation and reproducibility config.csv_file.parent.mkdir(parents=True, exist_ok=True) @@ -555,6 +577,73 @@ def analyze_results(config: BenchmarkConfig) -> pd.DataFrame: 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_cols = grouping_cols + [c for c in ["library_name"] if c in rows.columns] + 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, @@ -741,16 +830,16 @@ def plot_results(config: BenchmarkConfig): ) # Drop duplicates (keep latest result for each unique combo) - df = df.drop_duplicates( - subset=[ - "dataset", - "distribution", - "regularization", - "alpha", - "library_name", - ], - keep="last", - ) + 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 = { @@ -762,70 +851,74 @@ def plot_results(config: BenchmarkConfig): "celer": "#fb9a99", } - # Generate one plot per dataset/distribution combo + # 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(): - subset = df[(df["dataset"] == dataset) & (df["distribution"] == dist)] - - if subset.empty: + base_subset = df[(df["dataset"] == dataset) & (df["distribution"] == dist)] + if base_subset.empty: continue - print(f" Plotting {dataset}-{dist}: {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", + split_by_ratio = ( + dataset == "simulated-glm" and "k_over_n_ratio" in df.columns ) - # Track which cells are unsupported (NaN) before filling - unsupported = pivot_raw.isna() - pivot = pivot_raw.fillna(0) + 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 - # Track which cells did not converge - pivot_converged = ( - subset.pivot( + 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="converged", + values="runtime", ) - .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}" - - # Determine if this figure needs a dark mode version (for README) - fname = f"{dataset}-{dist}" - 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") + # 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 - # Generate dark mode version if needed for README - if needs_dark: + # 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, @@ -834,48 +927,46 @@ def plot_results(config: BenchmarkConfig): 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}-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) + fig.savefig(config.figure_dir / f"{fname}.png", dpi=300) plt.close(fig) - print(f"Saved: {fname_norm}.png") + print(f"Saved: {fname}.png") # Generate dark mode version if needed for README - if needs_dark_norm: + 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, @@ -884,13 +975,33 @@ def plot_results(config: BenchmarkConfig): y_max=norm_y_max, title=f"{title} (normalized)", ylabel="run time relative to glum", - dark_mode=True, + dark_mode=False, x_labels=x_labels, show_baseline=True, ) - fig.savefig(config.figure_dir / f"{fname_norm}_dark.png", dpi=300) + fig.savefig(config.figure_dir / f"{fname_norm}.png", dpi=300) plt.close(fig) - print(f"Saved: {fname_norm}_dark.png") + 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 diff --git a/pixi.lock b/pixi.lock index 87ad10efd..9e7a8321c 100644 --- a/pixi.lock +++ b/pixi.lock @@ -157,8 +157,10 @@ environments: - 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 @@ -208,6 +210,7 @@ environments: - 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 @@ -415,8 +418,10 @@ environments: - 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/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 @@ -465,6 +470,7 @@ environments: - 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 @@ -631,8 +637,10 @@ 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/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 @@ -682,6 +690,7 @@ environments: - 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 @@ -14774,6 +14783,8 @@ 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-py312h8a5da7c_0.conda @@ -14965,6 +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 @@ -21228,6 +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 diff --git a/pixi.toml b/pixi.toml index b8d68c123..29d6f9581 100644 --- a/pixi.toml +++ b/pixi.toml @@ -124,10 +124,11 @@ matplotlib-base = "*" openjdk = "*" pydantic = "*" pytest = "*" -"ruamel.yaml" = "*" r-base = "*" r-glmnet = "*" +rich = "*" rpy2 = "*" +"ruamel.yaml" = "*" skglm = "*" [feature.benchmark.pypi-dependencies] From 5a744241e05e1d9d08c04dac9233fc0b7dd9b923 Mon Sep 17 00:00:00 2001 From: David Eiglsperger Date: Wed, 11 Feb 2026 13:05:00 +0100 Subject: [PATCH 27/42] small adjustments --- glum_benchmarks/README.md | 2 +- glum_benchmarks/data/simulated_glm.py | 6 +++--- glum_benchmarks/run_benchmarks.py | 28 +++++++++++++++++++++------ 3 files changed, 26 insertions(+), 10 deletions(-) diff --git a/glum_benchmarks/README.md b/glum_benchmarks/README.md index 0f62d6bf8..410fb401d 100644 --- a/glum_benchmarks/README.md +++ b/glum_benchmarks/README.md @@ -159,7 +159,7 @@ Each entry computes a Cartesian product. Multiple entries are unioned (not cross - `regularizations`: `["lasso", "l2", "net"]` - `distributions`: `["gaussian", "gamma", "binomial", "poisson", "tweedie-p=1.5"]` - `alphas`: `[0.0001, 0.001, 0.01]` -- `k_over_n_ratios`: `[0.5, 0.7, 1.2]` (applies to `simulated-glm` only) +- `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. If you want to run all default combinations, leave the `param_grid` entry empty. diff --git a/glum_benchmarks/data/simulated_glm.py b/glum_benchmarks/data/simulated_glm.py index dca6ce176..6d945aa2b 100644 --- a/glum_benchmarks/data/simulated_glm.py +++ b/glum_benchmarks/data/simulated_glm.py @@ -199,7 +199,7 @@ def simulate_glm_dataset( Parameters ---------- num_rows - Number of rows. Defaults to 5000. + Number of rows. Defaults to 1000. noise Unused, present to match load_data signature. distribution @@ -215,7 +215,7 @@ def simulate_glm_dataset( tuple[pd.DataFrame, np.ndarray, np.ndarray] (X, y, exposure). """ - n_rows = num_rows if num_rows is not None else 5000 + 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.") @@ -272,7 +272,7 @@ def simulate_categorical_dataset( Parameters ---------- num_rows - Number of rows. Defaults to 5000. + Number of rows. Defaults to 1000. noise Unused, present to match load_data signature. distribution diff --git a/glum_benchmarks/run_benchmarks.py b/glum_benchmarks/run_benchmarks.py index 4e4ff47a0..8f44ff050 100644 --- a/glum_benchmarks/run_benchmarks.py +++ b/glum_benchmarks/run_benchmarks.py @@ -543,10 +543,18 @@ def analyze_results(config: BenchmarkConfig) -> pd.DataFrame: 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 relative objective value (how far from best). + # 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=[0, 1, 2])["obj_val"].transform("min") - df["rel_obj_val"] = df["obj_val"] - best_obj + 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 = [ @@ -558,15 +566,17 @@ def analyze_results(config: BenchmarkConfig) -> pd.DataFrame: "intercept", "num_nonzero_coef", "obj_val", + "obj_gap", "rel_obj_val", ] - # Group by each problem key: problem_name + alpha + num_rows. + # 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", "alpha", "num_rows", "k_over_n_ratio"], + group_by=["problem_name", "k_over_n_ratio"], ) # Export to CSV for figure generation and reproducibility @@ -588,7 +598,13 @@ def _print_dataframe_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_cols = grouping_cols + [c for c in ["library_name"] if c in rows.columns] + # 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: From 08fdb2db435a6f27fccd8a9ea251ed65f176aeb3 Mon Sep 17 00:00:00 2001 From: David Eiglsperger Date: Wed, 11 Feb 2026 15:28:40 +0100 Subject: [PATCH 28/42] pass glmnets max_iter also in cases it doesnt converge, --- glum_benchmarks/libraries/bench_glmnet.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/glum_benchmarks/libraries/bench_glmnet.py b/glum_benchmarks/libraries/bench_glmnet.py index 4800a150e..3fd619829 100644 --- a/glum_benchmarks/libraries/bench_glmnet.py +++ b/glum_benchmarks/libraries/bench_glmnet.py @@ -9,6 +9,8 @@ from glum_benchmarks.util import benchmark_convergence_tolerance +_GLMNET_MAXIT = 100_000 + def _setup_and_fit(X_np, y, distribution, l1_ratio, alpha, standardize): """ @@ -68,6 +70,7 @@ def _setup_and_fit(X_np, y, distribution, l1_ratio, alpha, standardize): alpha=l1_ratio, intercept=True, standardize=standardize, + maxit=_GLMNET_MAXIT, thresh=benchmark_convergence_tolerance, **{"lambda": lambda_vec}, ) @@ -89,6 +92,7 @@ def _setup_and_fit(X_np, y, distribution, l1_ratio, alpha, standardize): "intercept": float(coef_vec[0]), "coef": coef_vec[1:], "n_iter": n_iter, + "max_iter": _GLMNET_MAXIT, "fit_runtime": fit_runtime, } @@ -187,5 +191,5 @@ def glmnet_bench( "intercept": best_result["intercept"], "coef": best_result["coef"], "n_iter": best_result["n_iter"], - "max_iter": None, + "max_iter": best_result["max_iter"], } From 6b1780d261bdb493ad7e17236602ea63cb159bbb Mon Sep 17 00:00:00 2001 From: David Eiglsperger Date: Wed, 11 Feb 2026 20:24:39 +0100 Subject: [PATCH 29/42] distribution adjustments --- glum_benchmarks/config.yaml | 16 ++++++++-------- glum_benchmarks/problems.py | 4 +++- glum_benchmarks/run_benchmarks.py | 21 ++++++++++++++++++++- 3 files changed, 31 insertions(+), 10 deletions(-) diff --git a/glum_benchmarks/config.yaml b/glum_benchmarks/config.yaml index 0bc97ddae..db4a162ef 100644 --- a/glum_benchmarks/config.yaml +++ b/glum_benchmarks/config.yaml @@ -79,11 +79,11 @@ storage: # k_over_n_ratios: [0.5, 0.7, 1.2] param_grid: - - distributions: [gaussian] - datasets: ["intermediate-housing"] - regularizations: [lasso, l2] - libraries: [glum, celer] - - distributions: [gamma] - datasets: ["intermediate-insurance"] - regularizations: [lasso] - libraries: [glum, h2o] +- distributions: [gaussian] + datasets: ["intermediate-housing"] + egularizations: [lasso, l2] + libraries: [glum, celer] +- distributions: [gamma] + datasets: ["intermediate-insurance"] + regularizations: [lasso] + libraries: [glum, h2o] diff --git a/glum_benchmarks/problems.py b/glum_benchmarks/problems.py index 433911174..140ada64e 100644 --- a/glum_benchmarks/problems.py +++ b/glum_benchmarks/problems.py @@ -178,14 +178,16 @@ def get_all_problems() -> dict[str, Problem]: """ alpha = 0.001 - housing_distributions = ["gaussian", "gamma"] + housing_distributions = ["gaussian", "gamma", "binomial"] housing_load_funcs = { "intermediate-housing": generate_housing_dataset, } insurance_distributions = [ + "gaussian", "poisson", "gamma", + "binomial", "tweedie-p=1.5", ] insurance_load_funcs = { diff --git a/glum_benchmarks/run_benchmarks.py b/glum_benchmarks/run_benchmarks.py index 8f44ff050..9048e9ffa 100644 --- a/glum_benchmarks/run_benchmarks.py +++ b/glum_benchmarks/run_benchmarks.py @@ -26,7 +26,7 @@ from contextlib import nullcontext from itertools import product from pathlib import Path -from typing import Literal +from typing import Literal, cast import matplotlib.pyplot as plt import numpy as np @@ -72,6 +72,18 @@ StorageFormat = Literal["auto", "dense", "cat", "csr", "csc"] StandardizeMethod = Literal["pre", "internal", "none"] +# 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. @@ -286,6 +298,7 @@ def get_benchmark_combinations( 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, float]] = set() @@ -317,6 +330,12 @@ def get_benchmark_combinations( for lib, dataset, reg, dist, alpha in product( libraries, datasets, regs, dists, alphas ): + 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 From 2b403f367dc3b3f0e3fc2fe514640edb44bc7366 Mon Sep 17 00:00:00 2001 From: David Eiglsperger Date: Fri, 13 Feb 2026 16:02:46 +0100 Subject: [PATCH 30/42] Change goldenmaster generation to only include housing/insurance datasets --- glum_benchmarks/libraries/bench_skglm.py | 4 + glum_benchmarks/problems.py | 2 +- tests/glm/golden_master/benchmark_gm.json | 203864 +------------------ tests/glm/test_benchmark_golden_master.py | 4 +- 4 files changed, 5778 insertions(+), 198096 deletions(-) diff --git a/glum_benchmarks/libraries/bench_skglm.py b/glum_benchmarks/libraries/bench_skglm.py index 1e02a2fcb..c965b4ff8 100644 --- a/glum_benchmarks/libraries/bench_skglm.py +++ b/glum_benchmarks/libraries/bench_skglm.py @@ -47,6 +47,10 @@ def skglm_bench( # 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: diff --git a/glum_benchmarks/problems.py b/glum_benchmarks/problems.py index 140ada64e..601fb816a 100644 --- a/glum_benchmarks/problems.py +++ b/glum_benchmarks/problems.py @@ -69,7 +69,7 @@ def load_data( # At this point we still have dtype information, so we can reliably # distinguish continuous columns from categoricals. if standardize: - X_in = X_in.copy() # wichtig wegen joblib / in-place + X_in = X_in.copy() continuous_cols = [ c diff --git a/tests/glm/golden_master/benchmark_gm.json b/tests/glm/golden_master/benchmark_gm.json index c1fda0dd4..f9ee5dc33 100644 --- a/tests/glm/golden_master/benchmark_gm.json +++ b/tests/glm/golden_master/benchmark_gm.json @@ -2,513 +2,513 @@ "intermediate-insurance-weights-l2-gaussian": { "coef": [ -128.5966412621167, - 76.78121470762072, + 76.78121470762069, 270.1175741090245, - -56.12455423543153, - 55.639646250863535, - 44.50049683889796, - 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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: From d1f37144bc606f34a949eef381f05fd98ef7757a Mon Sep 17 00:00:00 2001 From: David Eiglsperger Date: Fri, 13 Feb 2026 20:33:19 +0100 Subject: [PATCH 31/42] show maximum bar length for not converged cases --- glum_benchmarks/run_benchmarks.py | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/glum_benchmarks/run_benchmarks.py b/glum_benchmarks/run_benchmarks.py index 9048e9ffa..0d5995f24 100644 --- a/glum_benchmarks/run_benchmarks.py +++ b/glum_benchmarks/run_benchmarks.py @@ -698,6 +698,7 @@ def _render_bar_chart( 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) @@ -744,7 +745,7 @@ def _render_bar_chart( bar = bars[bar_idx] x = bar.get_x() width = bar.get_width() - bar_height = min(pivot.loc[combo, lib], y_max) + bar_height = y_max ax.bar( x + width / 2, bar_height, @@ -770,7 +771,11 @@ def _render_bar_chart( 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]: + 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 From 4827a0e2597e475420280c93f6b9431f3c745e72 Mon Sep 17 00:00:00 2001 From: David Eiglsperger Date: Mon, 16 Feb 2026 13:19:22 +0100 Subject: [PATCH 32/42] Move num_rows into param_grid to allow for flexible number of rows across different problems --- glum_benchmarks/README.md | 19 ++++----- glum_benchmarks/config.yaml | 4 +- glum_benchmarks/run_benchmarks.py | 66 +++++++++++++++++++++---------- 3 files changed, 59 insertions(+), 30 deletions(-) diff --git a/glum_benchmarks/README.md b/glum_benchmarks/README.md index 410fb401d..1b8e78ba0 100644 --- a/glum_benchmarks/README.md +++ b/glum_benchmarks/README.md @@ -119,14 +119,13 @@ Edit `config.yaml` to customize benchmark parameters. ### Benchmark Settings -| Option | Description | -| ------------- | ---------------------------------------------------------------------------- | -| `standardize` | Standardization strategy per library (`pre`, `internal`, `none`) | -| `iterations` | Runs per benchmark (>=2 required for skglm) | -| `num_threads` | Number of threads for parallel execution | -| `num_rows` | Limit rows per dataset (`null` = full dataset) | -| `timeout` | Timeout in seconds (benchmarks timing out are marked "not converged") | -| `storage` | Storage format per library: (`auto`, `dense`, `cat`, `csr`, `csc`) | +| Option | Description | +| ------------- | --------------------------------------------------------------------- | +| `standardize` | Standardization strategy per library (`pre`, `internal`, `none`) | +| `iterations` | Runs per benchmark (>=2 required for skglm) | +| `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:** @@ -147,6 +146,7 @@ param_grid: alphas: [0.001] - datasets: ["simulated-glm"] distributions: ["gaussian", "poisson"] + num_rows: [1000, 5000] k_over_n_ratios: [0.5, 0.7, 1.2] ``` @@ -159,9 +159,10 @@ Each entry computes a Cartesian product. Multiple entries are unioned (not cross - `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. If you want to run all default combinations, leave the `param_grid` entry empty. +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. diff --git a/glum_benchmarks/config.yaml b/glum_benchmarks/config.yaml index db4a162ef..659f4ae16 100644 --- a/glum_benchmarks/config.yaml +++ b/glum_benchmarks/config.yaml @@ -25,7 +25,6 @@ clear_output: True # Clear entire run_name directory before running # Benchmark settings iterations: 2 # Run each benchmark N times, report minimum (>=2 for skglm) num_threads: 16 # Number of threads for parallel execution -num_rows: null # Limit rows per dataset (null = full dataset) timeout: 100 # Timeout in seconds per benchmark run (reports as "not converged" if exceeded) # Standardization approach per library: @@ -59,11 +58,13 @@ storage: # 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: @@ -76,6 +77,7 @@ storage: # param_grid: # - datasets: [simulated-glm] # distributions: [gaussian, poisson] +# num_rows: [1000, 5000] # k_over_n_ratios: [0.5, 0.7, 1.2] param_grid: diff --git a/glum_benchmarks/run_benchmarks.py b/glum_benchmarks/run_benchmarks.py index 0d5995f24..1fa886fb9 100644 --- a/glum_benchmarks/run_benchmarks.py +++ b/glum_benchmarks/run_benchmarks.py @@ -106,6 +106,13 @@ class ParamGridEntry(BaseModel): 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=( @@ -181,9 +188,6 @@ class BenchmarkConfig(BaseModel): iterations: int = Field( default=2, ge=1, description="Run each benchmark N times, report minimum" ) - num_rows: int | None = Field( - default=None, ge=1, description="Limit rows per dataset (None = full dataset)" - ) timeout: int = Field( default=100, ge=1, description="Timeout in seconds per benchmark run" ) @@ -271,13 +275,14 @@ def _parse_problem_name(name: str) -> tuple[str, str, str]: def get_benchmark_combinations( config: BenchmarkConfig, -) -> list[tuple[str, str, float, float]]: - """Get list of (problem_name, library, alpha, k_over_n_ratio) tuples. +) -> 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, k_over_n_ratio) tuples to run. + List of (problem_name, library_name, alpha, num_rows, k_over_n_ratio) + tuples to run. """ all_problems = get_all_problems() @@ -300,7 +305,7 @@ def get_benchmark_combinations( all_alphas = list(ALPHA_VALUES) allowed_dist_by_dataset = ALLOWED_DISTRIBUTIONS_BY_DATASET - combinations: set[tuple[str, str, float, float]] = set() + combinations: set[tuple[str, str, float, int | None, float]] = set() # Print configuration per parameter grid entry print("=" * 70) @@ -313,6 +318,7 @@ def get_benchmark_combinations( 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] ) @@ -323,12 +329,13 @@ def get_benchmark_combinations( 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 in product( - libraries, datasets, regs, dists, alphas + 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 ( @@ -345,20 +352,30 @@ def get_benchmark_combinations( ) for k_over_n_ratio in ratios_for_dataset: combinations.add( - (problem_lookup[key], lib, alpha, k_over_n_ratio) + (problem_lookup[key], lib, alpha, num_rows, k_over_n_ratio) ) print(f"Total benchmark runs: {len(combinations)}") print("=" * 70) print() - return sorted(combinations) + 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]: @@ -379,7 +396,7 @@ def run_single_benchmark( params = BenchmarkParams( problem_name=problem_name, library_name=library_name, - num_rows=config.num_rows, + num_rows=num_rows, k_over_n_ratio=k_over_n_ratio, storage=storage, threads=config.num_threads, @@ -418,21 +435,24 @@ def run_all_benchmarks(config: BenchmarkConfig): shutil.rmtree(config.results_dir) config.pickle_dir.mkdir(parents=True, exist_ok=True) - # Get benchmark combinations (problem, library, alpha, k_over_n_ratio) + # 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, k_over_n_ratio in combinations: + 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}{ratio_label})" + label = f"{library_name} / {problem_name} (α={alpha}{rows_label}{ratio_label})" print(f"[{current}/{total}] {label}", end=" ", flush=True) try: @@ -445,7 +465,12 @@ def run_all_benchmarks(config: BenchmarkConfig): message=".*asyncio.iscoroutinefunction.*", ) result, params = run_single_benchmark( - problem_name, library_name, alpha, k_over_n_ratio, config + problem_name, + library_name, + alpha, + num_rows, + k_over_n_ratio, + config, ) # Save result @@ -1224,10 +1249,11 @@ def main(): print(f"Docs updated: {config.benchmarks_rst}") print(f"README updated: {config.readme_file}") - # Snapshot config for reproducibility - 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'}") + # 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__": From a2620967344baa15ce27dfb56b119019fc244446 Mon Sep 17 00:00:00 2001 From: David Eiglsperger Date: Tue, 17 Feb 2026 15:32:21 +0100 Subject: [PATCH 33/42] Final changes --- README.md | 8 +- .../intermediate-housing-gamma-normalized.png | Bin 0 -> 176514 bytes ...termediate-housing-gaussian-normalized.png | Bin 0 -> 178202 bytes ...-glm-gaussian-k-over-n-0.01-normalized.png | Bin 0 -> 187861 bytes ...d-glm-gaussian-k-over-n-0.1-normalized.png | Bin 0 -> 181490 bytes ...d-glm-gaussian-k-over-n-0.5-normalized.png | Bin 0 -> 172432 bytes ...ted-glm-gaussian-k-over-n-1-normalized.png | Bin 0 -> 152091 bytes .../simulated-glm-gaussian-k-over-n-2.png | Bin 0 -> 168576 bytes .../wide-insurance-gamma-normalized.png | Bin 0 -> 179480 bytes .../wide-insurance-gamma-normalized_dark.png | Bin 0 -> 184176 bytes .../wide-insurance-poisson-normalized.png | Bin 0 -> 162995 bytes ...ide-insurance-tweedie-p=1.5-normalized.png | Bin 0 -> 173912 bytes docs/benchmarks.rst | 126 +++++++++++-- docs/contributing.rst | 11 +- docs/index.rst | 8 +- glum_benchmarks/README.md | 5 +- glum_benchmarks/config.yaml | 70 ++++---- glum_benchmarks/libraries/bench_glmnet.py | 7 +- glum_benchmarks/libraries/bench_glum.py | 3 - glum_benchmarks/libraries/bench_h2o.py | 3 - glum_benchmarks/problems.py | 6 +- glum_benchmarks/results/docs/config.yaml | 99 +++++++++++ glum_benchmarks/results/docs/results.csv | 142 +++++++++++++++ glum_benchmarks/run_benchmarks.py | 166 ++++++++++++------ glum_benchmarks/util.py | 49 ++++-- 25 files changed, 562 insertions(+), 141 deletions(-) create mode 100644 docs/_static/intermediate-housing-gamma-normalized.png create mode 100644 docs/_static/intermediate-housing-gaussian-normalized.png create mode 100644 docs/_static/simulated-glm-gaussian-k-over-n-0.01-normalized.png create mode 100644 docs/_static/simulated-glm-gaussian-k-over-n-0.1-normalized.png create mode 100644 docs/_static/simulated-glm-gaussian-k-over-n-0.5-normalized.png create mode 100644 docs/_static/simulated-glm-gaussian-k-over-n-1-normalized.png create mode 100644 docs/_static/simulated-glm-gaussian-k-over-n-2.png create mode 100644 docs/_static/wide-insurance-gamma-normalized.png create mode 100644 docs/_static/wide-insurance-gamma-normalized_dark.png create mode 100644 docs/_static/wide-insurance-poisson-normalized.png create mode 100644 docs/_static/wide-insurance-tweedie-p=1.5-normalized.png create mode 100644 glum_benchmarks/results/docs/config.yaml create mode 100644 glum_benchmarks/results/docs/results.csv diff --git a/README.md b/README.md index 35961d6bd..9eee14c16 100644 --- a/README.md +++ b/README.md @@ -13,7 +13,7 @@ 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 @@ -22,11 +22,11 @@ The goal of `glum` is to be at least as feature-complete as existing GLM librari * Normal, Poisson, logistic, gamma, and Tweedie distributions, plus varied and customizable link functions * 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 0000000000000000000000000000000000000000..50140188e863156732d950096bb51e858ad966fd GIT binary patch literal 176514 zcmeFZcT`j97d4FHSZN|i2T>FO1*sxcWh``*Dpi_D@4Y#rfC3>72q-NuQj{KgM}Z^) zLXZ|Z(xnr6=RKGCwg12M{qe5#veqmN1IfMjdCoce?7h$PMnmlZ^=a1A6ciNHiVuH( zOhIv`jDq5~fs@DKCk5*D&*7Vt+dVxuO~+?$o~ACA6so3fPIiuNcGhMWJS<&YtsNai z1%+=2N?yD0+|A9&RZ2+6{y)DV=;&f4^wq$<72f5P)59mO6cmD&kpGT&rOA6x9HF34 z{QZuWSHj%j2{-nxJ^4A?@{M3s;h*Br;SB5$9(eEXrOcoCh=QQ@}~ 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z{l|a5cF21B_aT2<7ymA)ug~J&4fOR<{QrfE`J*KADpB_xhjTQUifx^~@h_39b{_p7 DNl7st literal 0 HcmV?d00001 diff --git a/docs/benchmarks.rst b/docs/benchmarks.rst index 6780819ae..f243cb8ff 100644 --- a/docs/benchmarks.rst +++ b/docs/benchmarks.rst @@ -1,34 +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: ``elastic-net`` +- L2 (ridge): ``ridge`` +- L1 (lasso): ``lasso`` + +We evaluate distributions that are typical for each dataset, and adapt the target construction so it matches the expected distributional behavior (for example, poisson benchmarks model claim counts). + +Runtime plots are reported relative to ``glum``: for each benchmark case, ``glum`` is normalized to 1.0 and other libraries 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 originally developed for settings with N >> K (many more observations than predictors), which is representative of the ``wide-insurance`` benchmark (including high-cardinality categorical features). For insurance data, we evaluate gamma, poisson, and tweedie distributions. .. BENCHMARK_FIGURES_START -.. image:: _static/narrow-insurance-l2.png +.. 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, which are the most meaningful 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 + -.. image:: _static/wide-insurance-lasso.png +``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. + +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/simulated-glm-gaussian-k-over-n-0.1-normalized.png + :width: 700 + +.. image:: _static/simulated-glm-gaussian-k-over-n-0.5-normalized.png :width: 700 -.. image:: _static/intermediate-housing-l2.png + +.. image:: _static/simulated-glm-gaussian-k-over-n-1-normalized.png :width: 700 -.. image:: _static/intermediate-housing-lasso.png + +.. 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. For "Wide-Insurance-Gamma" we use oversampling with replacement (and some noise on top) in order to get reasonably big N, as after the filtering for ``ClaimAmountCut > 0`` only ~25,000 rows are left. This is also the reason why the number of columns after one-hot-encoding is smaller compared to the other distributions on this dataset (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) + - (678 013, 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 + - internally simulated + * - (simulated-glm, gaussian) with K/N = 0.1 + - (10 000, 1 000) + - 0 + - 1 000 + - 1 000 + - internally simulated + * - (simulated-glm, gaussian) with K/N = 0.5 + - (10 000, 5 000) + - 0 + - 5 000 + - 5 000 + - internally simulated + * - (simulated-glm, gaussian) with K/N = 1 + - (1 000, 1 000) + - 0 + - 1 000 + - 1 000 + - internally simulated + * - (simulated-glm, gaussian) with K/N = 2 + - (1 000, 2 000) + - 0 + - 2 000 + - 2 000 + - internally simulated 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..53e5e35ac 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -13,9 +13,13 @@ Welcome to glum's documentation! ``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 +28,7 @@ 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 index 1b8e78ba0..eca1e5ae5 100644 --- a/glum_benchmarks/README.md +++ b/glum_benchmarks/README.md @@ -121,15 +121,14 @@ Edit `config.yaml` to customize benchmark parameters. | Option | Description | | ------------- | --------------------------------------------------------------------- | -| `standardize` | Standardization strategy per library (`pre`, `internal`, `none`) | -| `iterations` | Runs per benchmark (>=2 required for skglm) | +| `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**: `pre` standardizes continuous columns in the data loader before OHE/format conversion; `internal` delegates to the library; `none` skips scaling. +- **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. diff --git a/glum_benchmarks/config.yaml b/glum_benchmarks/config.yaml index 659f4ae16..941ec2ec2 100644 --- a/glum_benchmarks/config.yaml +++ b/glum_benchmarks/config.yaml @@ -5,41 +5,39 @@ 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: false # Copy figures to docs/_static and update benchmarks.rst and README.md +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 docs: all figures # Default for readme: first non-normalized figure only -# Available: null, or list of figure filenames. -# Standard format: "-.png" -# Simulated format with K/N split: "--k-over-n-.png" -# Example: intermediate-insurance-gaussian.png -# Example: simulated-glm-gaussian-k-over-n-0.7.png -docs_figures: null -readme_figures: null +# 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: 2 # Run each benchmark N times, report minimum (>=2 for skglm) +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) -# Standardization approach per library: -# "pre" = pre-standardize continuous features in data loader -# "internal" = library handles standardization internally -# "none" = no standardization -# TODO: we might want to ommit this as a config option and just do the scaling in the data loader as a default -standardize: - glum: pre - sklearn: pre - h2o: pre - celer: pre - skglm: pre - glmnet: pre - # Storage format per library [auto, dense, cat, csr, csc] storage: glum: auto @@ -81,11 +79,21 @@ storage: # k_over_n_ratios: [0.5, 0.7, 1.2] param_grid: -- distributions: [gaussian] - datasets: ["intermediate-housing"] - egularizations: [lasso, l2] - libraries: [glum, celer] -- distributions: [gamma] - datasets: ["intermediate-insurance"] - regularizations: [lasso] - libraries: [glum, h2o] +- 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/libraries/bench_glmnet.py b/glum_benchmarks/libraries/bench_glmnet.py index 3fd619829..99cdc478d 100644 --- a/glum_benchmarks/libraries/bench_glmnet.py +++ b/glum_benchmarks/libraries/bench_glmnet.py @@ -12,7 +12,7 @@ _GLMNET_MAXIT = 100_000 -def _setup_and_fit(X_np, y, distribution, l1_ratio, alpha, standardize): +def _setup_and_fit(X_np, y, distribution, l1_ratio, alpha): """ Run in a child process: set up R/glmnet, fit, return results. @@ -69,7 +69,7 @@ def _setup_and_fit(X_np, y, distribution, l1_ratio, alpha, standardize): family=family, alpha=l1_ratio, intercept=True, - standardize=standardize, + standardize=False, # Data is pre-standardized in the data loader maxit=_GLMNET_MAXIT, thresh=benchmark_convergence_tolerance, **{"lambda": lambda_vec}, @@ -137,7 +137,6 @@ def glmnet_bench( alpha: float, l1_ratio: float, iterations: int, - standardize: bool = True, timeout: Optional[float] = None, **kwargs, ): @@ -163,7 +162,7 @@ def glmnet_bench( y = np.asarray(dat["y"], dtype=float).ravel() - fit_args = (X, y, distribution, l1_ratio, alpha, standardize) + fit_args = (X, y, distribution, l1_ratio, alpha) # Run iterations, keeping the fastest. # The child process times data conversion + glmnet() (excludes R startup). diff --git a/glum_benchmarks/libraries/bench_glum.py b/glum_benchmarks/libraries/bench_glum.py index ef9619ad2..fe6f45bdb 100644 --- a/glum_benchmarks/libraries/bench_glum.py +++ b/glum_benchmarks/libraries/bench_glum.py @@ -27,7 +27,6 @@ def glum_bench( iterations: int, diagnostics_level: str = "basic", hessian_approx: float = 0.0, - standardize: bool = True, timeout: Optional[float] = None, **kwargs, ): @@ -43,7 +42,6 @@ def glum_bench( iterations diagnostics_level hessian_approx - standardize kwargs Returns @@ -70,7 +68,6 @@ def glum_bench( step_size_tol=0.01 * benchmark_convergence_tolerance, force_all_finite=False, hessian_approx=hessian_approx, - scale_predictors=standardize, verbose=False, ) diff --git a/glum_benchmarks/libraries/bench_h2o.py b/glum_benchmarks/libraries/bench_h2o.py index 726c18139..5a27320f1 100644 --- a/glum_benchmarks/libraries/bench_h2o.py +++ b/glum_benchmarks/libraries/bench_h2o.py @@ -37,7 +37,6 @@ def h2o_bench( alpha: float, l1_ratio: float, iterations: int, - standardize: bool = True, timeout: Optional[float] = None, **kwargs, ): @@ -51,7 +50,6 @@ def h2o_bench( alpha l1_ratio iterations - standardize kwargs Returns @@ -92,7 +90,6 @@ def h2o_bench( family="tweedie" if tweedie else distribution, alpha=l1_ratio, lambda_=alpha, - standardize=standardize, # Let h2o handle standardization internally solver="IRLSM", objective_epsilon=benchmark_convergence_tolerance, beta_epsilon=benchmark_convergence_tolerance, diff --git a/glum_benchmarks/problems.py b/glum_benchmarks/problems.py index 601fb816a..e827012ca 100644 --- a/glum_benchmarks/problems.py +++ b/glum_benchmarks/problems.py @@ -47,12 +47,16 @@ def load_data( noise: Optional[float] = None, distribution: str = "poisson", data_setup: str = "no-weights", - standardize: bool = False, + 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. 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..8681b98ca --- /dev/null +++ b/glum_benchmarks/results/docs/results.csv @@ -0,0 +1,142 @@ 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+wide-insurance-no-weights-lasso-tweedie-p=1.5,600000,1.0,0.001,glmnet,csc,16,43,True,5.761547088623047,0.13398946717728016,4.948554147628833,170.51970522098122,1596.4954098238238,216,4807.155999355063,4746.616640368523,78.40546579662097 +wide-insurance-no-weights-lasso-tweedie-p=1.5,600000,1.0,0.001,glum,auto,16,6,True,0.2547159194946289,0.04245265324910482,4.849913246384275,106.64269634291581,90.34045028464396,221,60.53935898654003,0.0,0.0 +wide-insurance-no-weights-lasso-tweedie-p=1.5,600000,1.0,0.001,h2o,csr,16,4,True,0.6430718898773193,0.16076797246932983,6.027234481995983,663.2736278950679,3183.581053847547,307,61.05160611278306,0.5122471262430324,0.008461389991871609 +wide-insurance-no-weights-net-gamma,600000,1.0,0.001,glmnet,csc,16,12,True,3.45501708984375,0.2879180908203125,7.458395665026475,12.939078318809774,2.5748305149785495,146,8.498529135053142,0.0005807860978261914,6.834427252050662e-05 +wide-insurance-no-weights-net-gamma,600000,1.0,0.001,glum,auto,16,4,True,0.14071297645568848,0.03517824411392212,7.455068648663862,12.875594502991692,2.4560071246395534,143,8.497948348955315,0.0,0.0 +wide-insurance-no-weights-net-gamma,600000,1.0,0.001,h2o,csr,16,8,True,0.6330492496490479,0.07913115620613098,7.523323102848009,59.36067733566276,37.88155822867532,247,8.52570184370091,0.02775349474559441,0.003265905322783734 +wide-insurance-no-weights-net-gamma,600000,1.0,0.001,skglm,csc,16,14,True,1.6572988033294678,0.11837848595210484,7.455097803317209,12.686591376120965,2.389422446212486,143,8.498492993780213,0.0005446448248971336,6.40913315228715e-05 +wide-insurance-no-weights-net-poisson,600000,1.0,0.001,glmnet,csc,16,23,True,0.5576460361480713,0.024245479832524838,-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,0.1375138759613037,0.03437846899032593,-1.838583151107156,19.515968008804737,8.706242262103284,112,0.5002614049261365,0.0,0.0 +wide-insurance-no-weights-net-poisson,600000,1.0,0.001,h2o,csr,16,8,True,0.8549530506134033,0.10686913132667542,-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,0.6147739887237549,0.08782485553196498,-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,6.897139072418213,0.11891619090376229,4.988734364525118,142.98299162257783,164.7252477402239,236,60.600447635193824,0.093534094359633,0.0015458414400283335 +wide-insurance-no-weights-net-tweedie-p=1.5,600000,1.0,0.001,glum,auto,16,5,True,0.2761359214782715,0.0552271842956543,4.740615372823341,111.25673909142868,86.38619730557,238,60.50691354083419,0.0,0.0 +wide-insurance-no-weights-net-tweedie-p=1.5,600000,1.0,0.001,h2o,csr,16,4,True,0.4219048023223877,0.10547620058059692,6.08371556563129,700.0209332488993,3616.519892470872,307,61.64195875151962,1.1350452106854263,0.01875893421533426 diff --git a/glum_benchmarks/run_benchmarks.py b/glum_benchmarks/run_benchmarks.py index 1fa886fb9..198d3581a 100644 --- a/glum_benchmarks/run_benchmarks.py +++ b/glum_benchmarks/run_benchmarks.py @@ -70,7 +70,6 @@ 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"] -StandardizeMethod = Literal["pre", "internal", "none"] # Internal benchmark-only distribution restrictions by dataset. # This is intentionally not config-exposed, so test suites that rely on @@ -145,9 +144,13 @@ class BenchmarkConfig(BaseModel): default=False, description="Whether to copy figures to docs/_static and update benchmarks.rst", ) - docs_figures: list[str] | None = Field( + docs_figures: list[list[str]] | None = Field( default=None, - description="Figure names to include in docs/benchmarks.rst (None = all )", + 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, @@ -176,18 +179,14 @@ class BenchmarkConfig(BaseModel): num_threads: int = Field( default=16, ge=1, description="Number of threads for parallel execution" ) - standardize: dict[Library, StandardizeMethod] = Field( - default_factory=dict, + iterations: int = Field( + default=3, + ge=1, description=( - "Standardization approach per library: " - "'pre' = pre-standardize in data loader, " - "'internal' = library handles internally, " - "'none' = no standardization" + "Run each benchmark N times. When >= 2, the first iteration is " + "discarded as warmup and the median of the rest is reported." ), ) - iterations: int = Field( - default=2, ge=1, description="Run each benchmark N times, report minimum" - ) timeout: int = Field( default=100, ge=1, description="Timeout in seconds per benchmark run" ) @@ -227,6 +226,11 @@ 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.""" @@ -387,11 +391,6 @@ def run_single_benchmark( # Get library-specific settings from config lib_key: Library = library_name # type: ignore[assignment] storage = config.storage.get(lib_key, "dense") - std_method = config.standardize.get(lib_key, "none") - - # Derive standardization flags per library - pre_standardize = std_method == "pre" - library_standardize = std_method == "internal" params = BenchmarkParams( problem_name=problem_name, @@ -409,8 +408,6 @@ def run_single_benchmark( params, iterations=config.iterations, diagnostics_level=None, - pre_standardize=pre_standardize, - standardize=library_standardize, timeout=config.timeout, ) result["timed_out"] = False @@ -1077,7 +1074,7 @@ def plot_results(config: BenchmarkConfig): def update_docs(config: BenchmarkConfig): - """Copy figures to docs/_static and update benchmarks.rst and README.md. + """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 @@ -1102,16 +1099,19 @@ def update_docs(config: BenchmarkConfig): # Get available figure names (without path) available = {f.name for f in all_figures} - # Determine which figures to use for docs + # 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: - # Use explicitly specified figures - docs_fig_names = [f for f in config.docs_figures if f in available] - missing = set(config.docs_figures) - available - if missing: - print(f"Warning: docs_figures not found: {missing}") + 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 - docs_fig_names = sorted(f.name for f in all_figures) + # 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: @@ -1126,7 +1126,8 @@ def update_docs(config: BenchmarkConfig): readme_fig_names = [non_norm[0]] if non_norm else [] # Collect all figures needed for copying - all_needed = set(docs_fig_names) | set(readme_fig_names) + 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) @@ -1137,37 +1138,60 @@ def update_docs(config: BenchmarkConfig): print(f"Copied: {fig_name} -> docs/_static/") # Update benchmarks.rst - if docs_fig_names: - rst_lines = [RST_START_MARKER, ""] - for fig_name in docs_fig_names: - rst_lines.append(f".. image:: _static/{fig_name}") - rst_lines.append(" :width: 700") - rst_lines.append("") - rst_lines.append(RST_END_MARKER) - rst_new_content = "\n".join(rst_lines) - + # 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() - if RST_START_MARKER in rst_content and RST_END_MARKER in rst_content: - pattern = re.compile( - rf"{re.escape(RST_START_MARKER)}.*?{re.escape(RST_END_MARKER)}", - re.DOTALL, - ) - updated_rst = pattern.sub(rst_new_content, rst_content) + 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(updated_rst) + f.write(rst_content) print(f"\nUpdated: {config.benchmarks_rst}") - print(f"Inserted {len(docs_fig_names)} figure references.") - else: - print(f"\nMarkers not found in {config.benchmarks_rst}") - print(f"Add: {RST_START_MARKER} and {RST_END_MARKER}") + 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 GitHub's light/dark mode syntax with actual dark images when available + # 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: @@ -1183,16 +1207,18 @@ def update_docs(config: BenchmarkConfig): config.docs_static_dir / dark_name, ) print(f"Copied: {dark_name} -> docs/_static/") - # Use actual dark image light_ref = f"docs/_static/{fig_name}#gh-light-mode-only" dark_ref = f"docs/_static/{dark_name}#gh-dark-mode-only" else: - # Use same image for both modes 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]({light_ref})") - md_lines.append(f"![Benchmark results]({dark_ref})") + 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) @@ -1216,6 +1242,36 @@ def update_docs(config: BenchmarkConfig): 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 @@ -1246,7 +1302,7 @@ def main(): if config.generate_plots: print(f"Figures saved to: {config.figure_dir}/") if config.update_docs: - print(f"Docs updated: {config.benchmarks_rst}") + 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. diff --git a/glum_benchmarks/util.py b/glum_benchmarks/util.py index 084d2f886..a64865ae5 100644 --- a/glum_benchmarks/util.py +++ b/glum_benchmarks/util.py @@ -1,5 +1,6 @@ import os import signal +import statistics import time from typing import Optional, Union @@ -19,11 +20,20 @@ def runtime(f, iterations, *args, timeout=None, **kwargs): """ 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: int - Number of times to run the function + 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. @@ -31,11 +41,13 @@ def runtime(f, iterations, *args, timeout=None, **kwargs): Returns ------- - Tuple: (Minimum runtime across successful iterations, output from fastest iteration) + Tuple: (Median runtime after warmup, output from the run closest to + the median runtime) If all iterations timeout, raises TimeoutError. """ - successful_runs = [] # (runtime, output) pairs + + successful_runs = [] # (runtime, output, iteration_index) tuples for i in range(iterations): # Set up timeout for this iteration if requested @@ -52,7 +64,7 @@ def _iter_timeout_handler(signum, frame): out = f(*args, **kwargs) end = time.time() runtime_val = end - start - successful_runs.append((runtime_val, out)) + successful_runs.append((runtime_val, out, i)) except TimeoutError: # This iteration timed out, continue to next iteration pass @@ -66,9 +78,18 @@ def _iter_timeout_handler(signum, frame): # All iterations timed out raise TimeoutError(f"All {iterations} iterations exceeded {timeout}s timeout") - # Return the run with minimum runtime - min_runtime, min_output = min(successful_runs, key=lambda x: x[0]) - return min_runtime, min_output + # 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): @@ -398,7 +419,6 @@ def execute_problem_library( params: BenchmarkParams, iterations: int = 1, diagnostics_level: Optional[str] = "basic", - pre_standardize: bool = False, standardize: bool = True, timeout: Optional[float] = None, **kwargs, @@ -406,17 +426,17 @@ def execute_problem_library( """ 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 - pre_standardize - Whether to pre-standardize continuous features in the data loader, - before OHE and format conversion. standardize - Whether to pass a standardize flag to the library function. Can be used for - libraries that handle standardization internally (e.g. glum, h2o, glmnet). + Whether to pre-standardize continuous features in the data loader. kwargs Returns @@ -438,7 +458,7 @@ def execute_problem_library( num_rows=params.num_rows, k_over_n_ratio=params.k_over_n_ratio, storage=params.storage, - standardize=pre_standardize, + standardize=standardize, ) os.environ["OMP_NUM_THREADS"] = str(params.threads) @@ -460,7 +480,6 @@ def execute_problem_library( iterations=iterations, diagnostics_level=diagnostics_level, hessian_approx=params.hessian_approx, - standardize=standardize, timeout=timeout, **kwargs, ) From a191527ac1ed283b2c31c6f10becd898f2d469cd Mon Sep 17 00:00:00 2001 From: David Eiglsperger Date: Tue, 17 Feb 2026 15:39:33 +0100 Subject: [PATCH 34/42] Smal fix to benchmarks.rst --- docs/benchmarks.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/benchmarks.rst b/docs/benchmarks.rst index f243cb8ff..4221e771d 100644 --- a/docs/benchmarks.rst +++ b/docs/benchmarks.rst @@ -83,7 +83,7 @@ For ``simulated-glm`` we reduce N from 10 000 to 1 000 for K/N = 1 and K/N = 2 i - Columns (OHE) - Source * - (wide-insurance, poisson), (wide-insurance, tweedie) - - (678 013, 9) + - (600 000, 9) - 8 - 1 - 322 From dba960e7e1f4c2ac2d51acce03a4dacb0a07f2de Mon Sep 17 00:00:00 2001 From: David Eiglsperger Date: Tue, 17 Feb 2026 16:07:27 +0100 Subject: [PATCH 35/42] CI fix --- tests/glm/test_offset_weight_obj_equivalent.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/glm/test_offset_weight_obj_equivalent.py b/tests/glm/test_offset_weight_obj_equivalent.py index 015add2a1..138173bbb 100644 --- a/tests/glm/test_offset_weight_obj_equivalent.py +++ b/tests/glm/test_offset_weight_obj_equivalent.py @@ -43,7 +43,7 @@ def test_offset_solution_matches_weights_solution( 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"] From 15dcea8911c69987afe1470993a1785d274b5d01 Mon Sep 17 00:00:00 2001 From: David Eiglsperger Date: Tue, 17 Feb 2026 16:41:59 +0100 Subject: [PATCH 36/42] small changes --- README.md | 2 ++ docs/index.rst | 2 ++ glum_benchmarks/run_benchmarks.py | 2 -- 3 files changed, 4 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 9eee14c16..131c5aef5 100644 --- a/README.md +++ b/README.md @@ -20,6 +20,8 @@ We believe that for GLM development, broad support for distributions, regulariza * 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 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). diff --git a/docs/index.rst b/docs/index.rst index 53e5e35ac..36ffa97ce 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -7,6 +7,8 @@ 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 +* Built-in formula-based model specification using ``formulaic`` +* Classical statistical inference for unregularized models * Dispersion and standard errors * Box and linear inequality constraints, sample weights, offsets. * A scikit-learn-like API to fit smoothly into existing workflows. diff --git a/glum_benchmarks/run_benchmarks.py b/glum_benchmarks/run_benchmarks.py index 198d3581a..d4e4e572b 100644 --- a/glum_benchmarks/run_benchmarks.py +++ b/glum_benchmarks/run_benchmarks.py @@ -51,8 +51,6 @@ get_params_from_fname, ) -# TODO: Update README and documentation - _RICH_CONSOLE = Console() # Type aliases for configuration options From b5941d08ed5d69d6e97fb52c2e9159c67ae8fb29 Mon Sep 17 00:00:00 2001 From: David Eiglsperger Date: Fri, 20 Feb 2026 10:26:30 +0100 Subject: [PATCH 37/42] Improve wording --- docs/benchmarks.rst | 24 ++++++++++++------------ docs/index.rst | 7 +++---- 2 files changed, 15 insertions(+), 16 deletions(-) diff --git a/docs/benchmarks.rst b/docs/benchmarks.rst index 4221e771d..6bc695b7f 100644 --- a/docs/benchmarks.rst +++ b/docs/benchmarks.rst @@ -7,17 +7,17 @@ Each plot title indicates the dataset and distribution used. For example, "Wide- For each dataset/distribution pair, we benchmark three regularization types: -- Elastic net: ``elastic-net`` -- L2 (ridge): ``ridge`` -- L1 (lasso): ``lasso`` +- Elastic net (``l1_ratio=0.5``): ``elastic-net`` +- Ridge (``l1_ratio=0.0``): ``ridge`` +- Lasso (``l1_ratio=1.0``): ``lasso`` -We evaluate distributions that are typical for each dataset, and adapt the target construction so it matches the expected distributional behavior (for example, poisson benchmarks model claim counts). +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`` is normalized to 1.0 and other libraries are scaled accordingly. If a bar exceeds the plotting range, the exact runtime is printed on the bar and an arrow indicates truncation. +Runtime plots are reported relative to ``glum``: for each benchmark case, ``glum``'s runtime is normalized to 1.0 and other libraries 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 originally developed for settings with N >> K (many more observations than predictors), which is representative of the ``wide-insurance`` benchmark (including high-cardinality categorical features). For insurance data, we evaluate gamma, poisson, and tweedie distributions. +``glum`` was developed for settings with N >> K (many more observations than predictors, except for high-cardinality categorical ones), which is representative of the ``wide-insurance`` benchmark. For insurance data, we evaluate gamma, Poisson, and Tweedie distributions. .. BENCHMARK_FIGURES_START @@ -32,7 +32,7 @@ We compare ``glum`` against ``sklearn``, ``skglm``, ``glmnet``, ``h2o`` and ``ce .. 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, which are the most meaningful distributions. +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 @@ -68,7 +68,7 @@ For K/N = 2, we include an unnormalized runtime plot, because in the normalized .. BENCHMARK_FIGURES_END -In the following table more information about the used datasets can be found. For "Wide-Insurance-Gamma" we use oversampling with replacement (and some noise on top) in order to get reasonably big N, as after the filtering for ``ClaimAmountCut > 0`` only ~25,000 rows are left. This is also the reason why the number of columns after one-hot-encoding is smaller compared to the other distributions on this dataset (some category levels only exist in the dropped rows). +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). @@ -105,25 +105,25 @@ For ``simulated-glm`` we reduce N from 10 000 to 1 000 for K/N = 1 and K/N = 2 i - 0 - 100 - 100 - - internally simulated + - simulated * - (simulated-glm, gaussian) with K/N = 0.1 - (10 000, 1 000) - 0 - 1 000 - 1 000 - - internally simulated + - simulated * - (simulated-glm, gaussian) with K/N = 0.5 - (10 000, 5 000) - 0 - 5 000 - 5 000 - - internally simulated + - simulated * - (simulated-glm, gaussian) with K/N = 1 - (1 000, 1 000) - 0 - 1 000 - 1 000 - - internally simulated + - simulated * - (simulated-glm, gaussian) with K/N = 2 - (1 000, 2 000) - 0 diff --git a/docs/index.rst b/docs/index.rst index 36ffa97ce..c916b36c3 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -8,10 +8,9 @@ Welcome to glum's documentation! * 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 * Built-in formula-based model specification using ``formulaic`` -* Classical statistical inference for unregularized models -* Dispersion and standard errors -* Box and linear inequality constraints, sample weights, offsets. -* A scikit-learn-like API to fit smoothly into existing workflows. +* 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. From 616c92577c57e6ef148945d9747726a2824c1565 Mon Sep 17 00:00:00 2001 From: David Eiglsperger Date: Fri, 20 Feb 2026 11:22:19 +0100 Subject: [PATCH 38/42] Small fixes & updated figures --- .../intermediate-housing-gamma-normalized.png | Bin 176514 -> 177050 bytes ...termediate-housing-gaussian-normalized.png | Bin 178202 -> 178108 bytes ...-glm-gaussian-k-over-n-0.01-normalized.png | Bin 187861 -> 187734 bytes ...d-glm-gaussian-k-over-n-0.1-normalized.png | Bin 181490 -> 181422 bytes ...d-glm-gaussian-k-over-n-0.5-normalized.png | Bin 172432 -> 172293 bytes ...ted-glm-gaussian-k-over-n-1-normalized.png | Bin 152091 -> 152281 bytes .../simulated-glm-gaussian-k-over-n-2.png | Bin 168576 -> 168635 bytes .../wide-insurance-gamma-normalized.png | Bin 179480 -> 180054 bytes .../wide-insurance-gamma-normalized_dark.png | Bin 184176 -> 184657 bytes .../wide-insurance-poisson-normalized.png | Bin 162995 -> 163557 bytes ...ide-insurance-tweedie-p=1.5-normalized.png | Bin 173912 -> 174236 bytes 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