From 67dcbeab70ea70245895b75c9bfdbb354585d5e1 Mon Sep 17 00:00:00 2001 From: Diptesh Basak Date: Tue, 7 Sep 2021 20:52:40 +0530 Subject: [PATCH 01/30] v0.4.0 changelog: - added udf to create lag variables for time series input with associated tests --- README.md | 2 +- data/input/test_lag_var.csv | 13 ++++ data/input/test_timeseries.csv | 105 +++++++++++++++++++++++++++++ log/cov.out | 15 +++-- log/pip.out | 2 +- log/pylint/lib-model-py.out | 4 ++ log/pylint/tests-test_model-py.out | 4 ++ mllib/__main__.py | 3 +- mllib/lib/{glmnet.py => model.py} | 67 +++++++++++++++--- requirements.txt | 2 +- tests/test_model.py | 88 ++++++++++++++++++++++++ 11 files changed, 285 insertions(+), 20 deletions(-) create mode 100644 data/input/test_lag_var.csv create mode 100644 data/input/test_timeseries.csv create mode 100644 log/pylint/lib-model-py.out create mode 100644 log/pylint/tests-test_model-py.out rename mllib/lib/{glmnet.py => model.py} (59%) create mode 100644 tests/test_model.py diff --git a/README.md b/README.md index bd1074c..d1e0f33 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ [![Python](https://github.com/bdiptesh/mllib/actions/workflows/python.yml/badge.svg)](https://github.com/bdiptesh/mllib/actions/workflows/python.yml) [![pylint Score](https://mperlet.github.io/pybadge/badges/10.0.svg)](./log/pylint/) -[![Coverage score](https://img.shields.io/badge/coverage-100%25-dagreen.svg)](./log/cov.out) +[![Coverage score](https://img.shields.io/badge/coverage-95%25-red.svg)](./log/cov.out) [![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause) *** diff --git a/data/input/test_lag_var.csv b/data/input/test_lag_var.csv new file mode 100644 index 0000000..e5ba31d --- /dev/null +++ b/data/input/test_lag_var.csv @@ -0,0 +1,13 @@ +week,y,lag_6,lag_4,lag_3,lag_2,lag_1,x1,x2 +1,14,,,,,,2,18 +2,12,,,,,,2,15 +3,14,,,,,,1,13 +4,11,,,,,,1,13 +5,15,,,,,,1,16 +6,17,,,,,,1,17 +7,16,14,14,11,15,17,3,20 +8,14,12,11,15,17,16,2,18 +9,19,14,15,17,16,14,2,15 +10,19,11,17,16,14,19,2,11 +11,21,15,16,14,19,19,1,19 +12,15,17,14,19,19,21,3,14 diff --git a/data/input/test_timeseries.csv b/data/input/test_timeseries.csv new file mode 100644 index 0000000..8d3f020 --- /dev/null +++ b/data/input/test_timeseries.csv @@ -0,0 +1,105 @@ +week,y,x1,x2 +1,14,2,18 +2,12,2,15 +3,14,1,13 +4,11,1,13 +5,15,1,16 +6,17,1,17 +7,16,3,20 +8,14,2,18 +9,19,2,15 +10,19,2,11 +11,21,1,19 +12,15,3,14 +13,21,2,13 +14,22,2,17 +15,25,1,13 +16,19,2,19 +17,23,3,16 +18,27,3,11 +19,31,1,18 +20,21,3,11 +21,25,2,16 +22,30,3,17 +23,36,2,10 +24,23,3,16 +25,31,1,14 +26,32,1,14 +27,39,2,11 +28,27,2,15 +29,33,3,12 +30,34,2,20 +31,41,1,13 +32,30,2,10 +33,36,1,17 +34,39,2,14 +35,44,1,14 +36,34,2,15 +37,41,1,20 +38,41,2,11 +39,50,3,18 +40,40,3,20 +41,46,2,17 +42,48,1,19 +43,56,2,11 +44,45,1,11 +45,51,3,18 +46,52,2,16 +47,61,1,11 +48,51,3,15 +49,58,3,13 +50,56,1,17 +51,68,1,11 +52,57,3,14 +53,65,3,20 +54,62,3,13 +55,72,3,15 +56,61,3,16 +57,69,2,18 +58,70,1,18 +59,79,1,12 +60,67,2,10 +61,77,1,11 +62,74,2,19 +63,84,3,18 +64,73,1,20 +65,83,2,15 +66,82,3,20 +67,91,1,12 +68,78,1,10 +69,88,1,18 +70,87,3,13 +71,99,2,15 +72,85,1,18 +73,94,1,16 +74,94,3,13 +75,105,2,19 +76,92,1,13 +77,101,1,13 +78,99,1,16 +79,113,3,17 +80,100,1,17 +81,107,3,17 +82,108,3,13 +83,123,1,10 +84,109,1,12 +85,115,1,16 +86,116,1,16 +87,132,1,13 +88,118,3,15 +89,121,2,14 +90,123,1,19 +91,139,1,20 +92,128,1,12 +93,129,2,10 +94,134,3,18 +95,150,2,15 +96,135,3,14 +97,138,3,13 +98,143,2,17 +99,160,1,15 +100,146,3,16 +101,146,3,17 +102,152,3,20 +103,171,3,19 +104,155,3,10 diff --git a/log/cov.out b/log/cov.out index a6e7cb8..cfe714a 100644 --- a/log/cov.out +++ b/log/cov.out @@ -1,7 +1,8 @@ -Name Stmts Miss Cover Missing ------------------------------------------------------ -mllib/__init__.py 7 0 100% -mllib/lib/__init__.py 7 0 100% -mllib/lib/cluster.py 103 0 100% ------------------------------------------------------ -TOTAL 117 0 100% +Name Stmts Miss Cover Missing +-------------------------------------------------------------------------------------------- +/media/ph33r/Data/Project/mllib/GitHub/mllib/__init__.py 7 0 100% +/media/ph33r/Data/Project/mllib/GitHub/mllib/lib/__init__.py 7 0 100% +/media/ph33r/Data/Project/mllib/GitHub/mllib/lib/cluster.py 103 0 100% +/media/ph33r/Data/Project/mllib/GitHub/mllib/lib/model.py 31 8 74% 129-144 +-------------------------------------------------------------------------------------------- +TOTAL 148 8 95% diff --git a/log/pip.out b/log/pip.out index 6b4200b..b25fea1 100644 --- a/log/pip.out +++ b/log/pip.out @@ -1 +1 @@ -INFO: Successfully saved requirements file in /media/ph33r/Data/Project/mllib/dev/requirements.txt +INFO: Successfully saved requirements file in /media/ph33r/Data/Project/mllib/GitHub/requirements.txt diff --git a/log/pylint/lib-model-py.out b/log/pylint/lib-model-py.out new file mode 100644 index 0000000..d7495ee --- /dev/null +++ b/log/pylint/lib-model-py.out @@ -0,0 +1,4 @@ + +-------------------------------------------------------------------- +Your code has been rated at 10.00/10 (previous run: 10.00/10, +0.00) + diff --git a/log/pylint/tests-test_model-py.out b/log/pylint/tests-test_model-py.out new file mode 100644 index 0000000..d7495ee --- /dev/null +++ b/log/pylint/tests-test_model-py.out @@ -0,0 +1,4 @@ + +-------------------------------------------------------------------- +Your code has been rated at 10.00/10 (previous run: 10.00/10, +0.00) + diff --git a/mllib/__main__.py b/mllib/__main__.py index 6c6fb9e..50bf96e 100644 --- a/mllib/__main__.py +++ b/mllib/__main__.py @@ -27,7 +27,7 @@ from lib import cfg, utils # noqa: F841 from lib.cluster import Cluster # noqa: F841 -from lib.glmnet import GLMNet # noqa: F841 +from lib.model import GLMNet # noqa: F841 # ============================================================================= # --- DO NOT CHANGE ANYTHING FROM HERE @@ -58,6 +58,7 @@ args = CLI.parse_args() fn_ip = args.filename[0] +fn_ip = "store.csv" # ============================================================================= # --- Main diff --git a/mllib/lib/glmnet.py b/mllib/lib/model.py similarity index 59% rename from mllib/lib/glmnet.py rename to mllib/lib/model.py index 2855247..2d7b097 100644 --- a/mllib/lib/glmnet.py +++ b/mllib/lib/model.py @@ -1,10 +1,9 @@ """ -GLMNet module. +Module for commonly used machine learning modelling algorithms. -Objective: - - Build - `GLMNet `_ - model using optimal alpha and lambda +**Available routines:** + +- class ``GLMNet``: Builds GLMnet model using cross validation. Credits ------- @@ -29,6 +28,56 @@ # ============================================================================= +def create_lag_vars(df: pd.DataFrame, + y_var: List[str], + x_var: List[str], + n_interval: str = None) -> pd.DataFrame: + """Create lag variables for time series data. + + Parameters + ---------- + df : pd.DataFrame + + Pandas dataframe containing `y_var`, `x_var` and `n_interval` + (if provided). + + y_var : List[str] + + Dependant variable. + + x_var : List[str] + Independant variables. + + n_interval : str, optional + + Column name of the time interval variable (the default is None). + + Returns + ------- + pd.DataFrame + + Pandas dataframe containing `y_var`, lag variables (`lag_xx`) and + `x_var`. + + """ + if n_interval is None: + y_lag = df[y_var].reset_index(drop=True) + else: + y_lag = df.sort_values(by=n_interval) + y_lag = y_lag[y_var].reset_index(drop=True) + time_int = len(y_lag) + lag_interval = [] + while time_int > 8: + time_int = int(np.floor(time_int/2)) + lag_interval.extend([time_int]) + lag_interval.extend([4, 3, 2, 1]) + for lag in lag_interval: + y_lag.loc[:, "lag_" + str(lag)] = y_lag["y"].shift(lag) + y_lag = y_lag.join(df[x_var]) + op = y_lag.dropna().reset_index(drop=True) + return op + + class GLMNet(): """GLMNet module. @@ -45,7 +94,7 @@ class GLMNet(): y_var : List[str] - Dependant variables. + Dependant variable. x_var : List[str] @@ -89,9 +138,9 @@ def __init__(self, "lambda_param": [1e-5, 1e-4, 1e-3, 1e-2, 1e-1, 1.0, 10.0, 100.0]} self.param = param - self.param["l1_range"] = list(np.array(range(5, 105, - int(self.param["a_inc"] - * 100.0))) / 100.0) + self.param["l1_range"] = list(np.round(np.arange(0.0, 1.01, + self.param["a_inc"]), + 10)) self.param["timeseries"] = timeseries def fit(self): diff --git a/requirements.txt b/requirements.txt index 33253fa..362a3a8 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,3 +1,3 @@ -numpy==1.19.5 pandas==1.1.3 +numpy==1.19.5 scikit_learn==0.24.2 diff --git a/tests/test_model.py b/tests/test_model.py new file mode 100644 index 0000000..ae8bcd9 --- /dev/null +++ b/tests/test_model.py @@ -0,0 +1,88 @@ +""" +Test suite module for ``model``. + +Credits +------- +:: + + Authors: + - Diptesh + + Date: Sep 07, 2021 +""" + +# pylint: disable=invalid-name +# pylint: disable=wrong-import-position + +import unittest +import warnings +import re +import sys + +from inspect import getsourcefile +from os.path import abspath + +import pandas as pd + +# Set base path +path = abspath(getsourcefile(lambda: 0)) +path = re.sub(r"(.+)(\/tests.*)", "\\1", path) + +sys.path.insert(0, path) + +from mllib.lib.model import create_lag_vars # noqa: F841 + +# ============================================================================= +# --- DO NOT CHANGE ANYTHING FROM HERE +# ============================================================================= + +path = path + "/data/input/" + +# ============================================================================= +# --- User defined functions +# ============================================================================= + + +def ignore_warnings(test_func): + """Suppress deprecation warnings of pulp.""" + + def do_test(self, *args, **kwargs): + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + test_func(self, *args, **kwargs) + return do_test + + +class TestCreateLagVars(unittest.TestCase): + """Test suite for module ``metric``.""" + + def setUp(self): + """Set up for module ``metric``.""" + + def test_no_interval_specified(self): + """Lag vars: Test when no interval is specified.""" + df_ip = pd.read_csv(path + "test_lag_var.csv") + df_op = create_lag_vars(df=df_ip, + y_var=["y"], + x_var=["x1", "x2"]) + exp_op = df_ip[list(df_ip.columns[1:])].dropna().reset_index(drop=True) + self.assertEqual(df_op.equals(exp_op), True) + + def test_interval_specified(self): + """Lag vars: Test when interval is specified.""" + df_ip = pd.read_csv(path + "test_lag_var.csv") + df_op = create_lag_vars(df=df_ip, + y_var=["y"], + x_var=["x1", "x2"], + n_interval="week") + exp_op = df_ip[list(df_ip.columns[1:])].dropna().reset_index(drop=True) + self.assertEqual(df_op.equals(exp_op), True) + + +# ============================================================================= +# --- Main +# ============================================================================= + + +if __name__ == '__main__': + unittest.main() From 8061cf2f3d0fd52a402fc619f5f1ec30bb068b76 Mon Sep 17 00:00:00 2001 From: Diptesh Basak Date: Tue, 7 Sep 2021 20:54:25 +0530 Subject: [PATCH 02/30] v0.4.0 --- mllib/lib/model.py | 1 + 1 file changed, 1 insertion(+) diff --git a/mllib/lib/model.py b/mllib/lib/model.py index 2d7b097..b5b0f3f 100644 --- a/mllib/lib/model.py +++ b/mllib/lib/model.py @@ -3,6 +3,7 @@ **Available routines:** +- udf ``create_lag_vars``: Create lag variables for time series data. - class ``GLMNet``: Builds GLMnet model using cross validation. Credits From 0e3ef0de5b275065b8c79cc7c38a5c6be056802e Mon Sep 17 00:00:00 2001 From: MadhuTangudu Date: Tue, 7 Sep 2021 22:57:37 +0530 Subject: [PATCH 03/30] v0.4.0 changelog: - glmnet class added to model.py --- mllib/__main__.py | 1 + mllib/lib/model.py | 72 +++++++++++++++++++++++++++++++++++++++++----- 2 files changed, 66 insertions(+), 7 deletions(-) diff --git a/mllib/__main__.py b/mllib/__main__.py index 50bf96e..e44bb42 100644 --- a/mllib/__main__.py +++ b/mllib/__main__.py @@ -79,3 +79,4 @@ glm_mod = GLMNet(df=df_ip, y_var=["y"], x_var=["x1", "x2", "x3"]) + glm_mod.fit() diff --git a/mllib/lib/model.py b/mllib/lib/model.py index b5b0f3f..10ff0c2 100644 --- a/mllib/lib/model.py +++ b/mllib/lib/model.py @@ -23,7 +23,10 @@ import pandas as pd import numpy as np - +from sklearn.model_selection import TimeSeriesSplit as ts_split +from sklearn.linear_model import ElasticNet +from sklearn.model_selection import RandomizedSearchCV +from sklearn.model_selection import GridSearchCV # ============================================================================= # --- DO NOT CHANGE ANYTHING FROM HERE # ============================================================================= @@ -105,6 +108,15 @@ class GLMNet(): Boolean value to indicate time-series inputs (the default is False). + search_method : str, optional + + String to indicate the hyper parameter search method. Possible values + are "grid", "random" (the default is "random") + + n_interval : str, optional + + Column name of the time interval variable (the default is None). + param : Dict, optional GLMNet parameters (the default is None). @@ -117,6 +129,7 @@ class GLMNet(): k_fold: 10 lambda_param: [1e-5, 1e-4, 1e-3, 1e-2, 1e-1, 1.0, 10.0, 100.0] timeseries: False + search_method: "random" """ @@ -125,9 +138,11 @@ def __init__(self, y_var: List[str], x_var: List[str], timeseries: bool = False, + search_method: str = "random", + n_interval: str = None, param: Dict = None): """Initialize variables for module ``GLMNet``.""" - self.df = df + self.df = df[y_var].join(df[x_var]) self.y_var = y_var self.x_var = x_var if param is None: @@ -137,15 +152,52 @@ def __init__(self, "n_jobs": -1, "k_fold": 10, "lambda_param": [1e-5, 1e-4, 1e-3, 1e-2, 1e-1, - 1.0, 10.0, 100.0]} + 1.0, 10.0, 100.0]} self.param = param - self.param["l1_range"] = list(np.round(np.arange(0.0, 1.01, - self.param["a_inc"]), - 10)) + self.param["l1_range"] = \ + [x*self.param["a_inc"] for x in range(\ + 1, int(1/self.param["a_inc"])+1)] self.param["timeseries"] = timeseries - + self.param["search_method"] = search_method + # if self.param["timeseries"]: + # self.df = create_lag_vars(self.df, y_var, x_var, n_interval) + # self.x_var = list(self.df) + # self.y_var = [self.x_var.pop(0)] + def fit(self): """Fit the best GLMNet model.""" + train_x = self.df[self.x_var] + train_x = pd.get_dummies(data=train_x, drop_first=True) + train_y = self.df[self.y_var] + if self.param["timeseries"]: + folds = ts_split(n_splits=self.param["k_fold"]) + folds = folds.split(X=train_y) + else: + folds = self.param["k_fold"] + est_glmnet = ElasticNet(random_state=self.param["seed"]) + grid = {"l1_ratio":self.param["l1_range"], + "alpha":self.param["lambda_param"]} + if self.param["search_method"]=="grid": + self.model = GridSearchCV(estimator=est_glmnet, + param_grid=grid, + n_jobs=-1, + cv=folds, + verbose=0, + scoring="neg_mean_squared_error") + self.model.fit(train_x, train_y) + if self.param["search_method"]=="random": + # n_iter = 30% of grid hyper parameter combinations + sample_perc = 0.3 + n_iter = int(np.ceil(len(self.param["l1_range"])*\ + len(self.param["lambda_param"])*sample_perc)) + self.model = RandomizedSearchCV(estimator=est_glmnet, + param_distributions=grid, + n_jobs=self.param["n_jobs"], + n_iter=n_iter, + cv=folds, + verbose=1, + scoring="neg_mean_squared_error") + self.model.fit(train_x, train_y) def predict(self, df_predict: pd.DataFrame) -> pd.DataFrame: """Short summary. @@ -163,3 +215,9 @@ def predict(self, df_predict: pd.DataFrame) -> pd.DataFrame: Pandas dataframe containing predicted `y_var` and `x_var`. """ + df_predict_cp = df_predict.copy(deep=True) + df_predict = pd.get_dummies(data=df_predict, drop_first=True) + df_op = pd.DataFrame(self.model.predict(df_predict)) + df_op.columns = ["y_hat"] + df_op = df_op.join(df_predict_cp) + return df_op From 1238736ed42d2248e7fdafd49df975221ecc7412 Mon Sep 17 00:00:00 2001 From: Diptesh Basak Date: Wed, 8 Sep 2021 12:12:59 +0530 Subject: [PATCH 04/30] v0.4.0 --- README.md | 2 +- log/cov.out | 4 ++-- mllib/lib/model.py | 38 +++++++++++++++++++------------------- requirements.txt | 2 +- 4 files changed, 23 insertions(+), 23 deletions(-) diff --git a/README.md b/README.md index d1e0f33..f41ff89 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ [![Python](https://github.com/bdiptesh/mllib/actions/workflows/python.yml/badge.svg)](https://github.com/bdiptesh/mllib/actions/workflows/python.yml) [![pylint Score](https://mperlet.github.io/pybadge/badges/10.0.svg)](./log/pylint/) -[![Coverage score](https://img.shields.io/badge/coverage-95%25-red.svg)](./log/cov.out) +[![Coverage score](https://img.shields.io/badge/coverage-81%25-red.svg)](./log/cov.out) [![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause) *** diff --git a/log/cov.out b/log/cov.out index cfe714a..a66fd78 100644 --- a/log/cov.out +++ b/log/cov.out @@ -3,6 +3,6 @@ Name Stmts Miss Cov /media/ph33r/Data/Project/mllib/GitHub/mllib/__init__.py 7 0 100% /media/ph33r/Data/Project/mllib/GitHub/mllib/lib/__init__.py 7 0 100% /media/ph33r/Data/Project/mllib/GitHub/mllib/lib/cluster.py 103 0 100% -/media/ph33r/Data/Project/mllib/GitHub/mllib/lib/model.py 31 8 74% 129-144 +/media/ph33r/Data/Project/mllib/GitHub/mllib/lib/model.py 61 34 44% 146-164, 168-200, 218-223 -------------------------------------------------------------------------------------------- -TOTAL 148 8 95% +TOTAL 178 34 81% diff --git a/mllib/lib/model.py b/mllib/lib/model.py index 10ff0c2..9af74ea 100644 --- a/mllib/lib/model.py +++ b/mllib/lib/model.py @@ -27,6 +27,7 @@ from sklearn.linear_model import ElasticNet from sklearn.model_selection import RandomizedSearchCV from sklearn.model_selection import GridSearchCV + # ============================================================================= # --- DO NOT CHANGE ANYTHING FROM HERE # ============================================================================= @@ -110,9 +111,9 @@ class GLMNet(): search_method : str, optional - String to indicate the hyper parameter search method. Possible values - are "grid", "random" (the default is "random") - + String to indicate the hyper parameter search method. Possible values + are "grid", "random" (the default is "random") + n_interval : str, optional Column name of the time interval variable (the default is None). @@ -145,6 +146,8 @@ def __init__(self, self.df = df[y_var].join(df[x_var]) self.y_var = y_var self.x_var = x_var + self.model = None + self.n_interval = n_interval if param is None: param = {"seed": 1, "a_inc": 0.05, @@ -152,18 +155,14 @@ def __init__(self, "n_jobs": -1, "k_fold": 10, "lambda_param": [1e-5, 1e-4, 1e-3, 1e-2, 1e-1, - 1.0, 10.0, 100.0]} + 1.0, 10.0, 100.0]} self.param = param - self.param["l1_range"] = \ - [x*self.param["a_inc"] for x in range(\ - 1, int(1/self.param["a_inc"])+1)] + self.param["l1_range"] = list(np.round(np.arange(0.0, 1.01, + self.param["a_inc"]), + 10)) self.param["timeseries"] = timeseries self.param["search_method"] = search_method - # if self.param["timeseries"]: - # self.df = create_lag_vars(self.df, y_var, x_var, n_interval) - # self.x_var = list(self.df) - # self.y_var = [self.x_var.pop(0)] - + def fit(self): """Fit the best GLMNet model.""" train_x = self.df[self.x_var] @@ -175,9 +174,9 @@ def fit(self): else: folds = self.param["k_fold"] est_glmnet = ElasticNet(random_state=self.param["seed"]) - grid = {"l1_ratio":self.param["l1_range"], - "alpha":self.param["lambda_param"]} - if self.param["search_method"]=="grid": + grid = {"l1_ratio": self.param["l1_range"], + "alpha": self.param["lambda_param"]} + if self.param["search_method"] == "grid": self.model = GridSearchCV(estimator=est_glmnet, param_grid=grid, n_jobs=-1, @@ -185,11 +184,12 @@ def fit(self): verbose=0, scoring="neg_mean_squared_error") self.model.fit(train_x, train_y) - if self.param["search_method"]=="random": + if self.param["search_method"] == "random": # n_iter = 30% of grid hyper parameter combinations sample_perc = 0.3 - n_iter = int(np.ceil(len(self.param["l1_range"])*\ - len(self.param["lambda_param"])*sample_perc)) + n_iter = int(np.ceil(len(self.param["l1_range"]) + * len(self.param["lambda_param"]) + * sample_perc)) self.model = RandomizedSearchCV(estimator=est_glmnet, param_distributions=grid, n_jobs=self.param["n_jobs"], @@ -200,7 +200,7 @@ def fit(self): self.model.fit(train_x, train_y) def predict(self, df_predict: pd.DataFrame) -> pd.DataFrame: - """Short summary. + """Predict y_var/target variable. Parameters ---------- diff --git a/requirements.txt b/requirements.txt index 362a3a8..33253fa 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,3 +1,3 @@ -pandas==1.1.3 numpy==1.19.5 +pandas==1.1.3 scikit_learn==0.24.2 From 4d551908bdf8d8d5f0221105a3cfe9dbf952e061 Mon Sep 17 00:00:00 2001 From: Diptesh Basak Date: Wed, 8 Sep 2021 21:02:49 +0530 Subject: [PATCH 05/30] v0.4.0 changelog: - added GLMNet module with associated test cases --- README.md | 4 +- log/cov.out | 4 +- mllib/__main__.py | 10 +++- mllib/lib/cluster.py | 4 +- mllib/lib/model.py | 109 ++++++++++++++++--------------------------- requirements.txt | 2 +- tests/test_model.py | 42 +++++++++++++++-- 7 files changed, 92 insertions(+), 83 deletions(-) diff --git a/README.md b/README.md index f41ff89..addf166 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,7 @@ [![Python](https://github.com/bdiptesh/mllib/actions/workflows/python.yml/badge.svg)](https://github.com/bdiptesh/mllib/actions/workflows/python.yml) [![pylint Score](https://mperlet.github.io/pybadge/badges/10.0.svg)](./log/pylint/) -[![Coverage score](https://img.shields.io/badge/coverage-81%25-red.svg)](./log/cov.out) -[![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause) +[![Coverage score](https://img.shields.io/badge/coverage-100%25-dagreen.svg)](./log/cov.out) +[![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](./LICENSE) *** ## Table of contents diff --git a/log/cov.out b/log/cov.out index a66fd78..f87518b 100644 --- a/log/cov.out +++ b/log/cov.out @@ -3,6 +3,6 @@ Name Stmts Miss Cov /media/ph33r/Data/Project/mllib/GitHub/mllib/__init__.py 7 0 100% /media/ph33r/Data/Project/mllib/GitHub/mllib/lib/__init__.py 7 0 100% /media/ph33r/Data/Project/mllib/GitHub/mllib/lib/cluster.py 103 0 100% -/media/ph33r/Data/Project/mllib/GitHub/mllib/lib/model.py 61 34 44% 146-164, 168-200, 218-223 +/media/ph33r/Data/Project/mllib/GitHub/mllib/lib/model.py 44 0 100% -------------------------------------------------------------------------------------------- -TOTAL 178 34 81% +TOTAL 161 0 100% diff --git a/mllib/__main__.py b/mllib/__main__.py index e44bb42..ef53af1 100644 --- a/mllib/__main__.py +++ b/mllib/__main__.py @@ -67,16 +67,22 @@ if __name__ == '__main__': start = time.time_ns() # --- Clustering + start_t = time.time_ns() df_ip = pd.read_csv(path + "input/" + fn_ip) clus_sol = Cluster(df=df_ip, x_var=["x1"]) clus_sol.opt_k() print("Clustering\n", "optimal k = " + str(clus_sol.optimal_k), - elapsed_time("Time", start), + elapsed_time("Time", start_t), sep="\n") # --- GLMNet + start_t = time.time_ns() df_ip = pd.read_csv(path + "input/test_glmnet.csv") glm_mod = GLMNet(df=df_ip, y_var=["y"], x_var=["x1", "x2", "x3"]) - glm_mod.fit() + print("\nGLMNet\n", + elapsed_time("Time", start_t), + sep="\n") + # --- EOF + print(sep, elapsed_time("Total time", start), sep, sep="\n") diff --git a/mllib/lib/cluster.py b/mllib/lib/cluster.py index b2844b9..b0e9d14 100644 --- a/mllib/lib/cluster.py +++ b/mllib/lib/cluster.py @@ -65,8 +65,8 @@ class Cluster(): Stopping criterion: `one_se` or `gap_max` (the default is "one_se"). - README - ------ + Notes + ----- Points to be noted for `method`: - Default method is `one_se`. diff --git a/mllib/lib/model.py b/mllib/lib/model.py index 9af74ea..ab283b8 100644 --- a/mllib/lib/model.py +++ b/mllib/lib/model.py @@ -23,10 +23,9 @@ import pandas as pd import numpy as np -from sklearn.model_selection import TimeSeriesSplit as ts_split -from sklearn.linear_model import ElasticNet -from sklearn.model_selection import RandomizedSearchCV -from sklearn.model_selection import GridSearchCV + +from sklearn.linear_model import ElasticNetCV +from sklearn.model_selection import train_test_split as split # ============================================================================= # --- DO NOT CHANGE ANYTHING FROM HERE @@ -105,18 +104,9 @@ class GLMNet(): Independant variables. - timeseries : bool, optional - - Boolean value to indicate time-series inputs (the default is False). - - search_method : str, optional - - String to indicate the hyper parameter search method. Possible values - are "grid", "random" (the default is "random") + strata : pd.DataFrame, optional - n_interval : str, optional - - Column name of the time interval variable (the default is None). + A pandas dataframe column defining the strata (the default is None). param : Dict, optional @@ -128,9 +118,6 @@ class GLMNet(): test_perc: 0.25 n_jobs: -1 k_fold: 10 - lambda_param: [1e-5, 1e-4, 1e-3, 1e-2, 1e-1, 1.0, 10.0, 100.0] - timeseries: False - search_method: "random" """ @@ -138,66 +125,50 @@ def __init__(self, df: pd.DataFrame, y_var: List[str], x_var: List[str], - timeseries: bool = False, - search_method: str = "random", - n_interval: str = None, + strata: str = None, param: Dict = None): """Initialize variables for module ``GLMNet``.""" - self.df = df[y_var].join(df[x_var]) + self.df = df[y_var + x_var] self.y_var = y_var self.x_var = x_var - self.model = None - self.n_interval = n_interval + self.strata = strata if param is None: param = {"seed": 1, "a_inc": 0.05, "test_perc": 0.25, "n_jobs": -1, - "k_fold": 10, - "lambda_param": [1e-5, 1e-4, 1e-3, 1e-2, 1e-1, - 1.0, 10.0, 100.0]} + "k_fold": 10} self.param = param - self.param["l1_range"] = list(np.round(np.arange(0.0, 1.01, + self.param["l1_range"] = list(np.round(np.arange(0.0001, 1.01, self.param["a_inc"]), 10)) - self.param["timeseries"] = timeseries - self.param["search_method"] = search_method + self._fit() - def fit(self): + def _fit(self) -> None: """Fit the best GLMNet model.""" - train_x = self.df[self.x_var] - train_x = pd.get_dummies(data=train_x, drop_first=True) - train_y = self.df[self.y_var] - if self.param["timeseries"]: - folds = ts_split(n_splits=self.param["k_fold"]) - folds = folds.split(X=train_y) - else: - folds = self.param["k_fold"] - est_glmnet = ElasticNet(random_state=self.param["seed"]) - grid = {"l1_ratio": self.param["l1_range"], - "alpha": self.param["lambda_param"]} - if self.param["search_method"] == "grid": - self.model = GridSearchCV(estimator=est_glmnet, - param_grid=grid, - n_jobs=-1, - cv=folds, - verbose=0, - scoring="neg_mean_squared_error") - self.model.fit(train_x, train_y) - if self.param["search_method"] == "random": - # n_iter = 30% of grid hyper parameter combinations - sample_perc = 0.3 - n_iter = int(np.ceil(len(self.param["l1_range"]) - * len(self.param["lambda_param"]) - * sample_perc)) - self.model = RandomizedSearchCV(estimator=est_glmnet, - param_distributions=grid, - n_jobs=self.param["n_jobs"], - n_iter=n_iter, - cv=folds, - verbose=1, - scoring="neg_mean_squared_error") - self.model.fit(train_x, train_y) + train_x, test_x,\ + train_y, test_y = split(self.df[self.x_var], + self.df[self.y_var], + test_size=self.param["test_perc"], + random_state=self.param["seed"], + stratify=self.strata) + mod = ElasticNetCV(l1_ratio=self.param["l1_range"], + fit_intercept=True, + alphas=[1e-5, 1e-4, 1e-3, 1e-2, 1e-1, + 1.0, 10.0, 100.0], + normalize=True, + cv=self.param["k_fold"], + n_jobs=self.param["n_jobs"], + random_state=self.param["seed"]) + mod.fit(train_x, train_y.values.ravel()) + opt = {"alpha": mod.l1_ratio_, + "lambda": mod.alpha_, + "intercept": mod.intercept_, + "coef": mod.coef_, + "train_v": mod.score(train_x, train_y), + "test_v": mod.score(test_x, test_y)} + self.model = mod + self.opt = opt def predict(self, df_predict: pd.DataFrame) -> pd.DataFrame: """Predict y_var/target variable. @@ -215,9 +186,7 @@ def predict(self, df_predict: pd.DataFrame) -> pd.DataFrame: Pandas dataframe containing predicted `y_var` and `x_var`. """ - df_predict_cp = df_predict.copy(deep=True) - df_predict = pd.get_dummies(data=df_predict, drop_first=True) - df_op = pd.DataFrame(self.model.predict(df_predict)) - df_op.columns = ["y_hat"] - df_op = df_op.join(df_predict_cp) - return df_op + y_hat = self.model.predict(df_predict) + df_predict = df_predict.copy() + df_predict["y"] = y_hat + return df_predict diff --git a/requirements.txt b/requirements.txt index 33253fa..362a3a8 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,3 +1,3 @@ -numpy==1.19.5 pandas==1.1.3 +numpy==1.19.5 scikit_learn==0.24.2 diff --git a/tests/test_model.py b/tests/test_model.py index ae8bcd9..f505633 100644 --- a/tests/test_model.py +++ b/tests/test_model.py @@ -23,6 +23,7 @@ from os.path import abspath import pandas as pd +import numpy as np # Set base path path = abspath(getsourcefile(lambda: 0)) @@ -31,6 +32,7 @@ sys.path.insert(0, path) from mllib.lib.model import create_lag_vars # noqa: F841 +from mllib.lib.model import GLMNet # noqa: F841 # ============================================================================= # --- DO NOT CHANGE ANYTHING FROM HERE @@ -44,7 +46,7 @@ def ignore_warnings(test_func): - """Suppress deprecation warnings of pulp.""" + """Suppress deprecation warnings.""" def do_test(self, *args, **kwargs): with warnings.catch_warnings(): @@ -54,10 +56,10 @@ def do_test(self, *args, **kwargs): class TestCreateLagVars(unittest.TestCase): - """Test suite for module ``metric``.""" + """Test suite for UDF ``create_lag_vars``.""" def setUp(self): - """Set up for module ``metric``.""" + """Set up for UDF ``create_lag_vars``.""" def test_no_interval_specified(self): """Lag vars: Test when no interval is specified.""" @@ -79,10 +81,42 @@ def test_interval_specified(self): self.assertEqual(df_op.equals(exp_op), True) +class TestGLMNet(unittest.TestCase): + """Test suite for module ``GLMNet``.""" + + def setUp(self): + """Set up for module ``GLMNet``.""" + + def test_known_equation(self): + """GLMNet: Test a known equation.""" + df_ip = pd.read_csv(path + "test_glmnet.csv") + mod = GLMNet(df=df_ip, + y_var=["y"], + x_var=["x1", "x2", "x3"]) + op = mod.opt + self.assertEqual(np.round(op.get('intercept'), 0), 100.0) + self.assertEqual(np.round(op.get('coef')[0], 0), 2.0) + self.assertEqual(np.round(op.get('coef')[1], 0), 3.0) + self.assertEqual(np.round(op.get('coef')[2], 0), 0.0) + + def test_predict_target_variable(self): + """GLMNet: Test to predict a target variable.""" + df_ip = pd.read_csv(path + "test_glmnet.csv") + mod = GLMNet(df=df_ip, + y_var=["y"], + x_var=["x1", "x2", "x3"]) + df_predict = pd.DataFrame({"x1": [10, 20], + "x2": [5, 10], + "x3": [100, 0]}) + op = mod.predict(df_predict) + op = np.round(np.array(op["y"]), 1) + exp_op = np.array([135.0, 170.0]) + self.assertEqual((op == exp_op).all(), True) + + # ============================================================================= # --- Main # ============================================================================= - if __name__ == '__main__': unittest.main() From 68fba90eb8f80f9107ed790853690d3f832873aa Mon Sep 17 00:00:00 2001 From: MadhuTangudu Date: Fri, 10 Sep 2021 01:02:16 +0530 Subject: [PATCH 06/30] v0.4.0 changelog: - glmnet_ts.py module is added for tim series --- mllib/lib/glmnet_ts.py | 150 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 150 insertions(+) create mode 100644 mllib/lib/glmnet_ts.py diff --git a/mllib/lib/glmnet_ts.py b/mllib/lib/glmnet_ts.py new file mode 100644 index 0000000..9e4de01 --- /dev/null +++ b/mllib/lib/glmnet_ts.py @@ -0,0 +1,150 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Thu Sep 9 15:25:51 2021 + +@author: madhu +""" + +from typing import List, Dict + +import pandas as pd +import numpy as np + +from sklearn.linear_model import ElasticNetCV +from sklearn.model_selection import train_test_split as split +from sklearn.model_selection import TimeSeriesSplit as ts_split + +def create_lag_vars(df: pd.DataFrame, + y_var: List[str], + x_var: List[str], + n_interval: str = None) -> pd.DataFrame: + """Create lag variables for time series data. + + Parameters + ---------- + df : pd.DataFrame + + Pandas dataframe containing `y_var`, `x_var` and `n_interval` + (if provided). + + y_var : List[str] + + Dependant variable. + + x_var : List[str] + Independant variables. + + n_interval : str, optional + + Column name of the time interval variable (the default is None). + + Returns + ------- + pd.DataFrame + + Pandas dataframe containing `y_var`, lag variables (`lag_xx`) and + `x_var`. + + """ + if n_interval is None: + y_lag = df[y_var].reset_index(drop=True) + else: + y_lag = df.sort_values(by=n_interval) + y_lag = y_lag[y_var].reset_index(drop=True) + time_int = len(y_lag) + lag_interval = [] + while time_int > 8: + time_int = int(np.floor(time_int/2)) + lag_interval.extend([time_int]) + lag_interval.extend([4, 3, 2, 1]) + for lag in lag_interval: + y_lag.loc[:, "lag_" + str(lag)] = y_lag["y"].shift(lag) + y_lag = y_lag.join(df[x_var]) + op = y_lag.dropna().reset_index(drop=True) + return op + + +df_ip = pd.read_csv("/media/madhu/Data/gitHub_kubuntu/mllib/data/" + "input/test_timeseries.csv") + +y_var=["y"] +x_var=["x1", "x2"] + +param = {} +param["a_inc"] = 0.015 +param["k_fold"] = 5 +param["test_perc"] = 0.2 +param["n_jobs"] = -1 +param["seed"] = 1 +param["l1_range"] = \ + [x*param["a_inc"] for x in range(\ + 1, int(1/param["a_inc"])+1)] + + +df_ip = create_lag_vars(df_ip, y_var, x_var, "week") +# modify create lag function to get lag list +lag_var = [52, 26, 13, 6, 4, 3, 2, 1] +x_var = list(df_ip.columns) +x_var.remove(y_var[0]) + +max_epoch = df_ip.index.max() + 1 + +# For prediction +df_pred_data = df_ip[y_var] + +df_train = df_ip[df_ip.index <= max_epoch *(1-param["test_perc"])] +df_test = df_ip[df_ip.index > (max_epoch) *(1-param["test_perc"])] + +train_x = df_train[x_var] +train_y = df_train[y_var] + +test_x = df_train[x_var] +test_y = df_train[y_var] + +param["k_fold"] = ts_split(n_splits=param["k_fold"]) +param["k_fold"] = param["k_fold"].split(X=train_y) + + +mod = ElasticNetCV(l1_ratio=param["l1_range"], + fit_intercept=True, + alphas=[1e-5, 1e-4, 1e-3, 1e-2, 1e-1, + 1.0, 10.0, 100.0], + normalize=True, + cv=param["k_fold"], + n_jobs=param["n_jobs"], + random_state=param["seed"]) + +mod.fit(train_x, train_y.values.ravel()) + +opt = {"alpha": mod.l1_ratio_, + "lambda": mod.alpha_, + "intercept": mod.intercept_, + "coef": mod.coef_, + "train_v": mod.score(train_x, train_y), + "test_v": mod.score(test_x, test_y)} +model = mod +opt = opt + + +# Prediction +df_predict = df_test.copy(deep=True) +# reset index +df_predict = df_predict.reset_index(drop=True) +df_predict = df_predict[["x1", "x2"]] +df_predict["y"] = -1 + +for i in range(0,len(df_test)): + df_pred = df_predict[df_predict.index == i].reset_index(drop=True) + df_pred = df_pred[["x1", "x2"]] + df_pred_x = pd.DataFrame({"lag_"+str(lag_var[0]):df_pred_data.iloc[len(df_pred_data)-lag_var[0]]}) + for j in range(1,len(lag_var)): + df_tmp = pd.DataFrame({"lag_"+str(lag_var[j]):df_pred_data.iloc[len(df_pred_data)-lag_var[j]]}) + df_pred_x = df_pred_x.join(df_tmp) + df_pred_x = df_pred_x.reset_index(drop=True) + df_pred_x = df_pred_x.join(df_pred) + y_hat = model.predict(df_pred_x) + df_tmp = pd.DataFrame() + df_tmp['y'] = y_hat + df_pred_data=df_pred_data.append(df_tmp).reset_index(drop=True) + df_predict["y"][i] = y_hat + From 63d603f7d4d490ccda54536ebf0eb95ba168a67b Mon Sep 17 00:00:00 2001 From: Diptesh Basak Date: Fri, 10 Sep 2021 02:19:40 +0530 Subject: [PATCH 07/30] v0.4.0 changelog: - added binary for module metrics --- README.md | 2 +- bin/metrics/build.sh | 27 + .../metrics.cpython-37m-x86_64-linux-gnu.so | Bin 0 -> 181888 bytes .../build/temp.linux-x86_64-3.7/metrics.o | Bin 0 -> 395416 bytes bin/metrics/metrics.c | 4319 +++++++++++++++++ bin/metrics/metrics.pyx | 181 + bin/metrics/metrics.so | Bin 0 -> 181888 bytes bin/metrics/setup.py | 7 + log/pylint/lib-glmnet_ts-py.out | 55 + log/pylint/metrics-setup-py.out | 4 + log/pylint/tests-test_metrics-py.out | 10 + mllib/lib/metrics.so | Bin 0 -> 181888 bytes mllib/lib/model.py | 1 + requirements.txt | 3 +- tests/test_metrics.py | 102 + 15 files changed, 4709 insertions(+), 2 deletions(-) create mode 100644 bin/metrics/build.sh create mode 100644 bin/metrics/build/lib.linux-x86_64-3.7/metrics.cpython-37m-x86_64-linux-gnu.so create mode 100644 bin/metrics/build/temp.linux-x86_64-3.7/metrics.o create mode 100644 bin/metrics/metrics.c create mode 100644 bin/metrics/metrics.pyx create mode 100644 bin/metrics/metrics.so create mode 100644 bin/metrics/setup.py create mode 100644 log/pylint/lib-glmnet_ts-py.out create mode 100644 log/pylint/metrics-setup-py.out create mode 100644 log/pylint/tests-test_metrics-py.out create mode 100644 mllib/lib/metrics.so create mode 100644 tests/test_metrics.py diff --git a/README.md b/README.md index addf166..f4fd7de 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,5 @@ [![Python](https://github.com/bdiptesh/mllib/actions/workflows/python.yml/badge.svg)](https://github.com/bdiptesh/mllib/actions/workflows/python.yml) -[![pylint Score](https://mperlet.github.io/pybadge/badges/10.0.svg)](./log/pylint/) +[![pylint Score](https://mperlet.github.io/pybadge/badges/9.5.svg)](./log/pylint/) [![Coverage score](https://img.shields.io/badge/coverage-100%25-dagreen.svg)](./log/cov.out) [![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](./LICENSE) *** diff --git a/bin/metrics/build.sh b/bin/metrics/build.sh new file mode 100644 index 0000000..c233a63 --- /dev/null +++ b/bin/metrics/build.sh @@ -0,0 +1,27 @@ +#!/bin/bash +# ============================================================================= +# Shared objects +# +# Objective: Build compiled shared objects (*.so files) from *pyx files. +# +# Version: 0.1.0 +# +# Author: Diptesh +# +# Date: Apr 17, 2020 +# +# ============================================================================= + +python setup.py build_ext --inplace + +pat="\.\/[^\/]+\.so" + +for i in $(find -name "*.so") +do + if [[ $i =~ $pat ]] + then + file_new=$(sed -E 's/(\.\/)([a-z0-9]+)(\..+\.so)/\2.so/' <<< $i) + file_old=$(sed -E 's/(\.\/)(.+)/\2/' <<< $i) + mv $file_old $file_new + fi +done diff --git a/bin/metrics/build/lib.linux-x86_64-3.7/metrics.cpython-37m-x86_64-linux-gnu.so b/bin/metrics/build/lib.linux-x86_64-3.7/metrics.cpython-37m-x86_64-linux-gnu.so new file mode 100644 index 0000000000000000000000000000000000000000..be2e20a9dc98f48bf2a6abb303aef09021bc1a31 GIT binary patch literal 181888 zcmeFa3wRV&(l_3nOp{5P%ycpd441$F2?U&k1dK_*XaWgLB!O^K@Ro!mkSHWEnJBn| zxJg{baTIse#p|N$1$Es;U6n<}fau_^7x0R=HQr~$3*xG*7xMk8PWPlUl6QB%|MPtR z?|r^^o`>$LI(6#QsZ*y;ozorq(z1#Nv(6h9H8Ly}Z16-X+s z8B#j)S|TMs2a3lCnsJvjfm0g_fiAk>=i+PRexOp|#@$?x+iTVNqwb%nf^OW+^@gCR zX1yq{2@JeTc7Z!}{fv6lYpU0(*K5`58TVf52!6)hDo}}Zq<`CVvqj%sI-hZmuD4IG 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CYTHON_USE_PYTYPE_LOOKUP 0 + #if PY_VERSION_HEX < 0x03050000 + #undef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 0 + #elif !defined(CYTHON_USE_ASYNC_SLOTS) + #define CYTHON_USE_ASYNC_SLOTS 1 + #endif + #undef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 0 + #undef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 0 + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #undef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 1 + #undef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 0 + #undef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 0 + #undef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 0 + #undef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 0 + #undef CYTHON_PEP489_MULTI_PHASE_INIT + #define CYTHON_PEP489_MULTI_PHASE_INIT 0 + #undef CYTHON_USE_TP_FINALIZE + #define CYTHON_USE_TP_FINALIZE 0 + #undef CYTHON_USE_DICT_VERSIONS + #define CYTHON_USE_DICT_VERSIONS 0 + #undef CYTHON_USE_EXC_INFO_STACK + #define CYTHON_USE_EXC_INFO_STACK 0 +#elif defined(PYSTON_VERSION) + #define CYTHON_COMPILING_IN_PYPY 0 + #define CYTHON_COMPILING_IN_PYSTON 1 + #define CYTHON_COMPILING_IN_CPYTHON 0 + #ifndef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 1 + #endif + #undef CYTHON_USE_PYTYPE_LOOKUP + #define CYTHON_USE_PYTYPE_LOOKUP 0 + #undef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 0 + #undef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 0 + #ifndef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 1 + #endif + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #ifndef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 0 + #endif + #ifndef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 1 + #endif + #ifndef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 1 + #endif + #undef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 0 + #undef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 0 + #undef CYTHON_PEP489_MULTI_PHASE_INIT + #define CYTHON_PEP489_MULTI_PHASE_INIT 0 + #undef CYTHON_USE_TP_FINALIZE + #define CYTHON_USE_TP_FINALIZE 0 + #undef CYTHON_USE_DICT_VERSIONS + #define CYTHON_USE_DICT_VERSIONS 0 + #undef CYTHON_USE_EXC_INFO_STACK + #define CYTHON_USE_EXC_INFO_STACK 0 +#else + #define CYTHON_COMPILING_IN_PYPY 0 + #define CYTHON_COMPILING_IN_PYSTON 0 + #define CYTHON_COMPILING_IN_CPYTHON 1 + #ifndef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 1 + #endif + #if PY_VERSION_HEX < 0x02070000 + #undef CYTHON_USE_PYTYPE_LOOKUP + #define CYTHON_USE_PYTYPE_LOOKUP 0 + #elif !defined(CYTHON_USE_PYTYPE_LOOKUP) + #define CYTHON_USE_PYTYPE_LOOKUP 1 + #endif + #if PY_MAJOR_VERSION < 3 + #undef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 0 + #elif !defined(CYTHON_USE_ASYNC_SLOTS) + #define CYTHON_USE_ASYNC_SLOTS 1 + #endif + #if PY_VERSION_HEX < 0x02070000 + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #elif !defined(CYTHON_USE_PYLONG_INTERNALS) + #define CYTHON_USE_PYLONG_INTERNALS 1 + #endif + #ifndef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 1 + #endif + #ifndef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 1 + #endif + #if PY_VERSION_HEX < 0x030300F0 + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #elif !defined(CYTHON_USE_UNICODE_WRITER) + #define CYTHON_USE_UNICODE_WRITER 1 + #endif + #ifndef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 0 + #endif + #ifndef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 1 + #endif + #ifndef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 1 + #endif + #ifndef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 1 + #endif + #ifndef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 1 + #endif + #ifndef CYTHON_PEP489_MULTI_PHASE_INIT + #define CYTHON_PEP489_MULTI_PHASE_INIT (PY_VERSION_HEX >= 0x03050000) + #endif + #ifndef CYTHON_USE_TP_FINALIZE + #define CYTHON_USE_TP_FINALIZE (PY_VERSION_HEX >= 0x030400a1) + #endif + #ifndef CYTHON_USE_DICT_VERSIONS + #define CYTHON_USE_DICT_VERSIONS (PY_VERSION_HEX >= 0x030600B1) + #endif + #ifndef CYTHON_USE_EXC_INFO_STACK + #define CYTHON_USE_EXC_INFO_STACK (PY_VERSION_HEX >= 0x030700A3) + #endif +#endif +#if !defined(CYTHON_FAST_PYCCALL) +#define CYTHON_FAST_PYCCALL (CYTHON_FAST_PYCALL && PY_VERSION_HEX >= 0x030600B1) +#endif +#if CYTHON_USE_PYLONG_INTERNALS + #include "longintrepr.h" + #undef SHIFT + #undef BASE + #undef MASK + #ifdef SIZEOF_VOID_P + enum { __pyx_check_sizeof_voidp = 1 / (int)(SIZEOF_VOID_P == sizeof(void*)) }; + #endif +#endif +#ifndef __has_attribute + #define __has_attribute(x) 0 +#endif +#ifndef __has_cpp_attribute + #define __has_cpp_attribute(x) 0 +#endif +#ifndef CYTHON_RESTRICT + #if defined(__GNUC__) + #define CYTHON_RESTRICT __restrict__ + #elif defined(_MSC_VER) && _MSC_VER >= 1400 + #define CYTHON_RESTRICT __restrict + #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define CYTHON_RESTRICT restrict + #else + #define CYTHON_RESTRICT + #endif +#endif +#ifndef CYTHON_UNUSED +# if defined(__GNUC__) +# if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4)) +# define CYTHON_UNUSED __attribute__ ((__unused__)) +# else +# define CYTHON_UNUSED +# endif +# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER)) +# define CYTHON_UNUSED __attribute__ ((__unused__)) +# else +# define CYTHON_UNUSED +# endif +#endif +#ifndef CYTHON_MAYBE_UNUSED_VAR +# if defined(__cplusplus) + template void CYTHON_MAYBE_UNUSED_VAR( const T& ) { } +# else +# define CYTHON_MAYBE_UNUSED_VAR(x) (void)(x) +# endif +#endif +#ifndef CYTHON_NCP_UNUSED +# if CYTHON_COMPILING_IN_CPYTHON +# define CYTHON_NCP_UNUSED +# else +# define CYTHON_NCP_UNUSED CYTHON_UNUSED +# endif +#endif +#define __Pyx_void_to_None(void_result) ((void)(void_result), Py_INCREF(Py_None), Py_None) +#ifdef _MSC_VER + #ifndef _MSC_STDINT_H_ + #if _MSC_VER < 1300 + typedef unsigned char uint8_t; + typedef unsigned int uint32_t; + #else + typedef unsigned __int8 uint8_t; + typedef unsigned __int32 uint32_t; + #endif + #endif +#else + #include +#endif +#ifndef CYTHON_FALLTHROUGH + #if defined(__cplusplus) && __cplusplus >= 201103L + #if __has_cpp_attribute(fallthrough) + #define CYTHON_FALLTHROUGH [[fallthrough]] + #elif __has_cpp_attribute(clang::fallthrough) + #define CYTHON_FALLTHROUGH [[clang::fallthrough]] + #elif __has_cpp_attribute(gnu::fallthrough) + #define CYTHON_FALLTHROUGH [[gnu::fallthrough]] + #endif + #endif + #ifndef CYTHON_FALLTHROUGH + #if __has_attribute(fallthrough) + #define CYTHON_FALLTHROUGH __attribute__((fallthrough)) + #else + #define CYTHON_FALLTHROUGH + #endif + #endif + #if defined(__clang__ ) && defined(__apple_build_version__) + #if __apple_build_version__ < 7000000 + #undef CYTHON_FALLTHROUGH + #define CYTHON_FALLTHROUGH + #endif + #endif +#endif + +#ifndef CYTHON_INLINE + #if defined(__clang__) + #define CYTHON_INLINE __inline__ __attribute__ ((__unused__)) + #elif defined(__GNUC__) + #define CYTHON_INLINE __inline__ + #elif defined(_MSC_VER) + #define CYTHON_INLINE __inline + #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define CYTHON_INLINE inline + #else + #define CYTHON_INLINE + #endif +#endif + +#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x02070600 && !defined(Py_OptimizeFlag) + #define Py_OptimizeFlag 0 +#endif +#define __PYX_BUILD_PY_SSIZE_T "n" +#define CYTHON_FORMAT_SSIZE_T "z" +#if PY_MAJOR_VERSION < 3 + #define __Pyx_BUILTIN_MODULE_NAME "__builtin__" + #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ + PyCode_New(a+k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) + #define __Pyx_DefaultClassType PyClass_Type +#else + #define __Pyx_BUILTIN_MODULE_NAME "builtins" +#if PY_VERSION_HEX >= 0x030800A4 && PY_VERSION_HEX < 0x030800B2 + #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ + PyCode_New(a, 0, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) +#else + #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ + PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) +#endif + #define __Pyx_DefaultClassType PyType_Type +#endif +#ifndef Py_TPFLAGS_CHECKTYPES + #define Py_TPFLAGS_CHECKTYPES 0 +#endif +#ifndef Py_TPFLAGS_HAVE_INDEX + #define Py_TPFLAGS_HAVE_INDEX 0 +#endif +#ifndef Py_TPFLAGS_HAVE_NEWBUFFER + #define Py_TPFLAGS_HAVE_NEWBUFFER 0 +#endif +#ifndef Py_TPFLAGS_HAVE_FINALIZE + #define Py_TPFLAGS_HAVE_FINALIZE 0 +#endif +#ifndef METH_STACKLESS + #define METH_STACKLESS 0 +#endif +#if PY_VERSION_HEX <= 0x030700A3 || !defined(METH_FASTCALL) + #ifndef METH_FASTCALL + #define METH_FASTCALL 0x80 + #endif + typedef PyObject *(*__Pyx_PyCFunctionFast) (PyObject *self, PyObject *const *args, Py_ssize_t nargs); + typedef PyObject *(*__Pyx_PyCFunctionFastWithKeywords) (PyObject *self, PyObject *const *args, + Py_ssize_t nargs, PyObject *kwnames); +#else + #define __Pyx_PyCFunctionFast _PyCFunctionFast + #define __Pyx_PyCFunctionFastWithKeywords _PyCFunctionFastWithKeywords +#endif +#if CYTHON_FAST_PYCCALL +#define __Pyx_PyFastCFunction_Check(func)\ + ((PyCFunction_Check(func) && (METH_FASTCALL == (PyCFunction_GET_FLAGS(func) & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS | METH_STACKLESS))))) +#else +#define __Pyx_PyFastCFunction_Check(func) 0 +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Malloc) + #define PyObject_Malloc(s) PyMem_Malloc(s) + #define PyObject_Free(p) PyMem_Free(p) + #define PyObject_Realloc(p) PyMem_Realloc(p) +#endif +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030400A1 + #define PyMem_RawMalloc(n) PyMem_Malloc(n) + #define PyMem_RawRealloc(p, n) PyMem_Realloc(p, n) + #define PyMem_RawFree(p) PyMem_Free(p) +#endif +#if CYTHON_COMPILING_IN_PYSTON + #define __Pyx_PyCode_HasFreeVars(co) PyCode_HasFreeVars(co) + #define __Pyx_PyFrame_SetLineNumber(frame, lineno) PyFrame_SetLineNumber(frame, lineno) +#else + #define __Pyx_PyCode_HasFreeVars(co) (PyCode_GetNumFree(co) > 0) + #define __Pyx_PyFrame_SetLineNumber(frame, lineno) (frame)->f_lineno = (lineno) +#endif +#if !CYTHON_FAST_THREAD_STATE || PY_VERSION_HEX < 0x02070000 + #define __Pyx_PyThreadState_Current PyThreadState_GET() +#elif PY_VERSION_HEX >= 0x03060000 + #define __Pyx_PyThreadState_Current _PyThreadState_UncheckedGet() +#elif PY_VERSION_HEX >= 0x03000000 + #define __Pyx_PyThreadState_Current PyThreadState_GET() +#else + #define __Pyx_PyThreadState_Current _PyThreadState_Current +#endif +#if PY_VERSION_HEX < 0x030700A2 && !defined(PyThread_tss_create) && !defined(Py_tss_NEEDS_INIT) +#include "pythread.h" +#define Py_tss_NEEDS_INIT 0 +typedef int Py_tss_t; +static CYTHON_INLINE int PyThread_tss_create(Py_tss_t *key) { + *key = PyThread_create_key(); + return 0; +} +static CYTHON_INLINE Py_tss_t * PyThread_tss_alloc(void) { + Py_tss_t *key = (Py_tss_t *)PyObject_Malloc(sizeof(Py_tss_t)); + *key = Py_tss_NEEDS_INIT; + return key; +} +static CYTHON_INLINE void PyThread_tss_free(Py_tss_t *key) { + PyObject_Free(key); +} +static CYTHON_INLINE int PyThread_tss_is_created(Py_tss_t *key) { + return *key != Py_tss_NEEDS_INIT; +} +static CYTHON_INLINE void PyThread_tss_delete(Py_tss_t *key) { + PyThread_delete_key(*key); + *key = Py_tss_NEEDS_INIT; +} +static CYTHON_INLINE int PyThread_tss_set(Py_tss_t *key, void *value) { + return PyThread_set_key_value(*key, value); +} +static CYTHON_INLINE void * PyThread_tss_get(Py_tss_t *key) { + return PyThread_get_key_value(*key); +} +#endif +#if CYTHON_COMPILING_IN_CPYTHON || defined(_PyDict_NewPresized) +#define __Pyx_PyDict_NewPresized(n) ((n <= 8) ? PyDict_New() : _PyDict_NewPresized(n)) +#else +#define __Pyx_PyDict_NewPresized(n) PyDict_New() +#endif +#if PY_MAJOR_VERSION >= 3 || CYTHON_FUTURE_DIVISION + #define __Pyx_PyNumber_Divide(x,y) PyNumber_TrueDivide(x,y) + #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceTrueDivide(x,y) +#else + #define __Pyx_PyNumber_Divide(x,y) PyNumber_Divide(x,y) + #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceDivide(x,y) +#endif +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1 && CYTHON_USE_UNICODE_INTERNALS +#define __Pyx_PyDict_GetItemStr(dict, name) _PyDict_GetItem_KnownHash(dict, name, ((PyASCIIObject *) name)->hash) +#else +#define __Pyx_PyDict_GetItemStr(dict, name) PyDict_GetItem(dict, name) +#endif +#if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND) + #define CYTHON_PEP393_ENABLED 1 + #define __Pyx_PyUnicode_READY(op) (likely(PyUnicode_IS_READY(op)) ?\ + 0 : _PyUnicode_Ready((PyObject *)(op))) + #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_LENGTH(u) + #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i) + #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) PyUnicode_MAX_CHAR_VALUE(u) + #define __Pyx_PyUnicode_KIND(u) PyUnicode_KIND(u) + #define __Pyx_PyUnicode_DATA(u) PyUnicode_DATA(u) + #define __Pyx_PyUnicode_READ(k, d, i) PyUnicode_READ(k, d, i) + #define __Pyx_PyUnicode_WRITE(k, d, i, ch) PyUnicode_WRITE(k, d, i, ch) + #define __Pyx_PyUnicode_IS_TRUE(u) (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : PyUnicode_GET_SIZE(u))) +#else + #define CYTHON_PEP393_ENABLED 0 + #define PyUnicode_1BYTE_KIND 1 + #define PyUnicode_2BYTE_KIND 2 + #define PyUnicode_4BYTE_KIND 4 + #define __Pyx_PyUnicode_READY(op) (0) + #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_SIZE(u) + #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i])) + #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) ((sizeof(Py_UNICODE) == 2) ? 65535 : 1114111) + #define __Pyx_PyUnicode_KIND(u) (sizeof(Py_UNICODE)) + #define __Pyx_PyUnicode_DATA(u) ((void*)PyUnicode_AS_UNICODE(u)) + #define __Pyx_PyUnicode_READ(k, d, i) ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i])) + #define __Pyx_PyUnicode_WRITE(k, d, i, ch) (((void)(k)), ((Py_UNICODE*)d)[i] = ch) + #define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GET_SIZE(u)) +#endif +#if CYTHON_COMPILING_IN_PYPY + #define __Pyx_PyUnicode_Concat(a, b) PyNumber_Add(a, b) + #define __Pyx_PyUnicode_ConcatSafe(a, b) PyNumber_Add(a, b) +#else + #define __Pyx_PyUnicode_Concat(a, b) PyUnicode_Concat(a, b) + #define __Pyx_PyUnicode_ConcatSafe(a, b) ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ?\ + PyNumber_Add(a, b) : __Pyx_PyUnicode_Concat(a, b)) +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyUnicode_Contains) + #define PyUnicode_Contains(u, s) PySequence_Contains(u, s) +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyByteArray_Check) + #define PyByteArray_Check(obj) PyObject_TypeCheck(obj, &PyByteArray_Type) +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Format) + #define PyObject_Format(obj, fmt) PyObject_CallMethod(obj, "__format__", "O", fmt) +#endif +#define __Pyx_PyString_FormatSafe(a, b) ((unlikely((a) == Py_None || (PyString_Check(b) && !PyString_CheckExact(b)))) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b)) +#define __Pyx_PyUnicode_FormatSafe(a, b) ((unlikely((a) == Py_None || (PyUnicode_Check(b) && !PyUnicode_CheckExact(b)))) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b)) +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyString_Format(a, b) PyUnicode_Format(a, b) +#else + #define __Pyx_PyString_Format(a, b) PyString_Format(a, b) +#endif +#if PY_MAJOR_VERSION < 3 && !defined(PyObject_ASCII) + #define PyObject_ASCII(o) PyObject_Repr(o) +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyBaseString_Type PyUnicode_Type + #define PyStringObject PyUnicodeObject + #define PyString_Type PyUnicode_Type + #define PyString_Check PyUnicode_Check + #define PyString_CheckExact PyUnicode_CheckExact + #define PyObject_Unicode PyObject_Str +#endif +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj) + #define __Pyx_PyBaseString_CheckExact(obj) PyUnicode_CheckExact(obj) +#else + #define __Pyx_PyBaseString_Check(obj) (PyString_Check(obj) || PyUnicode_Check(obj)) + #define __Pyx_PyBaseString_CheckExact(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj)) +#endif +#ifndef PySet_CheckExact + #define PySet_CheckExact(obj) (Py_TYPE(obj) == &PySet_Type) +#endif +#if CYTHON_ASSUME_SAFE_MACROS + #define __Pyx_PySequence_SIZE(seq) Py_SIZE(seq) +#else + #define __Pyx_PySequence_SIZE(seq) PySequence_Size(seq) +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyIntObject PyLongObject + #define PyInt_Type PyLong_Type + #define PyInt_Check(op) PyLong_Check(op) + #define PyInt_CheckExact(op) PyLong_CheckExact(op) + #define PyInt_FromString PyLong_FromString + #define PyInt_FromUnicode PyLong_FromUnicode + #define PyInt_FromLong PyLong_FromLong + #define PyInt_FromSize_t PyLong_FromSize_t + #define PyInt_FromSsize_t PyLong_FromSsize_t + #define PyInt_AsLong PyLong_AsLong + #define PyInt_AS_LONG PyLong_AS_LONG + #define PyInt_AsSsize_t PyLong_AsSsize_t + #define PyInt_AsUnsignedLongMask PyLong_AsUnsignedLongMask + #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask + #define PyNumber_Int PyNumber_Long +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyBoolObject PyLongObject +#endif +#if PY_MAJOR_VERSION >= 3 && CYTHON_COMPILING_IN_PYPY + #ifndef PyUnicode_InternFromString + #define PyUnicode_InternFromString(s) PyUnicode_FromString(s) + #endif +#endif +#if PY_VERSION_HEX < 0x030200A4 + typedef long Py_hash_t; + #define __Pyx_PyInt_FromHash_t PyInt_FromLong + #define __Pyx_PyInt_AsHash_t PyInt_AsLong +#else + #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t + #define __Pyx_PyInt_AsHash_t PyInt_AsSsize_t +#endif +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyMethod_New(func, self, klass) ((self) ? PyMethod_New(func, self) : (Py_INCREF(func), func)) +#else + #define __Pyx_PyMethod_New(func, self, klass) PyMethod_New(func, self, klass) +#endif +#if CYTHON_USE_ASYNC_SLOTS + #if PY_VERSION_HEX >= 0x030500B1 + #define __Pyx_PyAsyncMethodsStruct PyAsyncMethods + #define __Pyx_PyType_AsAsync(obj) (Py_TYPE(obj)->tp_as_async) + #else + #define __Pyx_PyType_AsAsync(obj) ((__Pyx_PyAsyncMethodsStruct*) (Py_TYPE(obj)->tp_reserved)) + #endif +#else + #define __Pyx_PyType_AsAsync(obj) NULL +#endif +#ifndef __Pyx_PyAsyncMethodsStruct + typedef struct { + unaryfunc am_await; + unaryfunc am_aiter; + unaryfunc am_anext; + } __Pyx_PyAsyncMethodsStruct; +#endif + +#if defined(WIN32) || defined(MS_WINDOWS) + #define _USE_MATH_DEFINES +#endif +#include +#ifdef NAN +#define __PYX_NAN() ((float) NAN) +#else +static CYTHON_INLINE float __PYX_NAN() { + float value; + memset(&value, 0xFF, sizeof(value)); + return value; +} +#endif +#if defined(__CYGWIN__) && defined(_LDBL_EQ_DBL) +#define __Pyx_truncl trunc +#else +#define __Pyx_truncl truncl +#endif + + +#define __PYX_ERR(f_index, lineno, Ln_error) \ +{ \ + __pyx_filename = __pyx_f[f_index]; __pyx_lineno = lineno; __pyx_clineno = __LINE__; goto Ln_error; \ +} + +#ifndef __PYX_EXTERN_C + #ifdef __cplusplus + #define __PYX_EXTERN_C extern "C" + #else + #define __PYX_EXTERN_C extern + #endif +#endif + +#define __PYX_HAVE__metrics +#define __PYX_HAVE_API__metrics +/* Early includes */ +#ifdef _OPENMP +#include +#endif /* _OPENMP */ + +#if defined(PYREX_WITHOUT_ASSERTIONS) && !defined(CYTHON_WITHOUT_ASSERTIONS) +#define CYTHON_WITHOUT_ASSERTIONS +#endif + +typedef struct {PyObject **p; const char *s; const Py_ssize_t n; const char* encoding; + const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; + +#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0 +#define __PYX_DEFAULT_STRING_ENCODING_IS_UTF8 0 +#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT (PY_MAJOR_VERSION >= 3 && __PYX_DEFAULT_STRING_ENCODING_IS_UTF8) +#define __PYX_DEFAULT_STRING_ENCODING "" +#define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString +#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize +#define __Pyx_uchar_cast(c) ((unsigned char)c) +#define __Pyx_long_cast(x) ((long)x) +#define __Pyx_fits_Py_ssize_t(v, type, is_signed) (\ + (sizeof(type) < sizeof(Py_ssize_t)) ||\ + (sizeof(type) > sizeof(Py_ssize_t) &&\ + likely(v < (type)PY_SSIZE_T_MAX ||\ + v == (type)PY_SSIZE_T_MAX) &&\ + (!is_signed || likely(v > (type)PY_SSIZE_T_MIN ||\ + v == (type)PY_SSIZE_T_MIN))) ||\ + (sizeof(type) == sizeof(Py_ssize_t) &&\ + (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||\ + v == (type)PY_SSIZE_T_MAX))) ) +static CYTHON_INLINE int __Pyx_is_valid_index(Py_ssize_t i, Py_ssize_t limit) { + return (size_t) i < (size_t) limit; +} +#if defined (__cplusplus) && __cplusplus >= 201103L + #include + #define __Pyx_sst_abs(value) std::abs(value) +#elif SIZEOF_INT >= SIZEOF_SIZE_T + #define __Pyx_sst_abs(value) abs(value) +#elif SIZEOF_LONG >= SIZEOF_SIZE_T + #define __Pyx_sst_abs(value) labs(value) +#elif defined (_MSC_VER) + #define __Pyx_sst_abs(value) ((Py_ssize_t)_abs64(value)) +#elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define __Pyx_sst_abs(value) llabs(value) +#elif defined (__GNUC__) + #define __Pyx_sst_abs(value) __builtin_llabs(value) +#else + #define __Pyx_sst_abs(value) ((value<0) ? -value : value) +#endif +static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject*); +static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length); +#define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s)) +#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l) +#define __Pyx_PyBytes_FromString PyBytes_FromString +#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*); +#if PY_MAJOR_VERSION < 3 + #define __Pyx_PyStr_FromString __Pyx_PyBytes_FromString + #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize +#else + #define __Pyx_PyStr_FromString __Pyx_PyUnicode_FromString + #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize +#endif +#define __Pyx_PyBytes_AsWritableString(s) ((char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsWritableSString(s) ((signed char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsWritableUString(s) ((unsigned char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsString(s) ((const char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsSString(s) ((const signed char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsUString(s) ((const unsigned char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyObject_AsWritableString(s) ((char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsWritableSString(s) ((signed char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsWritableUString(s) ((unsigned char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsSString(s) ((const signed char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsUString(s) ((const unsigned char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_FromCString(s) __Pyx_PyObject_FromString((const char*)s) +#define __Pyx_PyBytes_FromCString(s) __Pyx_PyBytes_FromString((const char*)s) +#define __Pyx_PyByteArray_FromCString(s) __Pyx_PyByteArray_FromString((const char*)s) +#define __Pyx_PyStr_FromCString(s) __Pyx_PyStr_FromString((const char*)s) +#define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s) +static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) { + const Py_UNICODE *u_end = u; + while (*u_end++) ; + return (size_t)(u_end - u - 1); +} +#define __Pyx_PyUnicode_FromUnicode(u) PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u)) +#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode +#define __Pyx_PyUnicode_AsUnicode PyUnicode_AsUnicode +#define __Pyx_NewRef(obj) (Py_INCREF(obj), obj) +#define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None) +static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b); +static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*); +static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject*); +static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x); +#define __Pyx_PySequence_Tuple(obj)\ + (likely(PyTuple_CheckExact(obj)) ? __Pyx_NewRef(obj) : PySequence_Tuple(obj)) +static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*); +static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t); +#if CYTHON_ASSUME_SAFE_MACROS +#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x)) +#else +#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x) +#endif +#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x)) +#if PY_MAJOR_VERSION >= 3 +#define __Pyx_PyNumber_Int(x) (PyLong_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Long(x)) +#else +#define __Pyx_PyNumber_Int(x) (PyInt_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Int(x)) +#endif +#define __Pyx_PyNumber_Float(x) (PyFloat_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Float(x)) +#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII +static int __Pyx_sys_getdefaultencoding_not_ascii; +static int __Pyx_init_sys_getdefaultencoding_params(void) { + PyObject* sys; + PyObject* default_encoding = NULL; + PyObject* ascii_chars_u = NULL; + PyObject* ascii_chars_b = NULL; + const char* default_encoding_c; + sys = PyImport_ImportModule("sys"); + if (!sys) goto bad; + default_encoding = PyObject_CallMethod(sys, (char*) "getdefaultencoding", NULL); + Py_DECREF(sys); + if (!default_encoding) goto bad; + default_encoding_c = PyBytes_AsString(default_encoding); + if (!default_encoding_c) goto bad; + if (strcmp(default_encoding_c, "ascii") == 0) { + __Pyx_sys_getdefaultencoding_not_ascii = 0; + } else { + char ascii_chars[128]; + int c; + for (c = 0; c < 128; c++) { + ascii_chars[c] = c; + } + __Pyx_sys_getdefaultencoding_not_ascii = 1; + ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL); + if (!ascii_chars_u) goto bad; + ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL); + if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) { + PyErr_Format( + PyExc_ValueError, + "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.", + default_encoding_c); + goto bad; + } + Py_DECREF(ascii_chars_u); + Py_DECREF(ascii_chars_b); + } + Py_DECREF(default_encoding); + return 0; +bad: + Py_XDECREF(default_encoding); + Py_XDECREF(ascii_chars_u); + Py_XDECREF(ascii_chars_b); + return -1; +} +#endif +#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3 +#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL) +#else +#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL) +#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT +static char* __PYX_DEFAULT_STRING_ENCODING; +static int __Pyx_init_sys_getdefaultencoding_params(void) { + PyObject* sys; + PyObject* default_encoding = NULL; + char* default_encoding_c; + sys = PyImport_ImportModule("sys"); + if (!sys) goto bad; + default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL); + Py_DECREF(sys); + if (!default_encoding) goto bad; + default_encoding_c = PyBytes_AsString(default_encoding); + if (!default_encoding_c) goto bad; + __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c) + 1); + if (!__PYX_DEFAULT_STRING_ENCODING) goto bad; + strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c); + Py_DECREF(default_encoding); + return 0; +bad: + Py_XDECREF(default_encoding); + return -1; +} +#endif +#endif + + +/* Test for GCC > 2.95 */ +#if defined(__GNUC__) && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95))) + #define likely(x) __builtin_expect(!!(x), 1) + #define unlikely(x) __builtin_expect(!!(x), 0) +#else /* !__GNUC__ or GCC < 2.95 */ + #define likely(x) (x) + #define unlikely(x) (x) +#endif /* __GNUC__ */ +static CYTHON_INLINE void __Pyx_pretend_to_initialize(void* ptr) { (void)ptr; } + +static PyObject *__pyx_m = NULL; +static PyObject *__pyx_d; +static PyObject *__pyx_b; +static PyObject *__pyx_cython_runtime = NULL; +static PyObject *__pyx_empty_tuple; +static PyObject *__pyx_empty_bytes; +static PyObject *__pyx_empty_unicode; +static int __pyx_lineno; +static int __pyx_clineno = 0; +static const char * __pyx_cfilenm= __FILE__; +static const char *__pyx_filename; + + +static const char *__pyx_f[] = { + "metrics.pyx", +}; 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r = NULL; __Pyx_DECREF(tmp);}} while(0) + +/* PyObjectGetAttrStr.proto */ +#if CYTHON_USE_TYPE_SLOTS +static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name); +#else +#define __Pyx_PyObject_GetAttrStr(o,n) PyObject_GetAttr(o,n) +#endif + +/* GetBuiltinName.proto */ +static PyObject *__Pyx_GetBuiltinName(PyObject *name); + +/* PyDictVersioning.proto */ +#if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_TYPE_SLOTS +#define __PYX_DICT_VERSION_INIT ((PY_UINT64_T) -1) +#define __PYX_GET_DICT_VERSION(dict) (((PyDictObject*)(dict))->ma_version_tag) +#define __PYX_UPDATE_DICT_CACHE(dict, value, cache_var, version_var)\ + (version_var) = __PYX_GET_DICT_VERSION(dict);\ + (cache_var) = (value); +#define __PYX_PY_DICT_LOOKUP_IF_MODIFIED(VAR, DICT, LOOKUP) {\ + static PY_UINT64_T __pyx_dict_version = 0;\ + static PyObject *__pyx_dict_cached_value = NULL;\ + if (likely(__PYX_GET_DICT_VERSION(DICT) == __pyx_dict_version)) {\ + (VAR) = __pyx_dict_cached_value;\ + } else {\ + (VAR) = __pyx_dict_cached_value = (LOOKUP);\ + __pyx_dict_version = __PYX_GET_DICT_VERSION(DICT);\ + }\ +} +static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj); 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+#else +#define __Pyx_PyCFunction_FastCall(func, args, nargs) (assert(0), NULL) +#endif + +/* PyObjectCall.proto */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw); +#else +#define __Pyx_PyObject_Call(func, arg, kw) PyObject_Call(func, arg, kw) +#endif + +/* GetItemInt.proto */ +#define __Pyx_GetItemInt(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ + (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ + __Pyx_GetItemInt_Fast(o, (Py_ssize_t)i, is_list, wraparound, boundscheck) :\ + (is_list ? (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL) :\ + __Pyx_GetItemInt_Generic(o, to_py_func(i)))) +#define __Pyx_GetItemInt_List(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ + (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ + __Pyx_GetItemInt_List_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\ + (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL)) +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, + int wraparound, int boundscheck); +#define __Pyx_GetItemInt_Tuple(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ + (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ + __Pyx_GetItemInt_Tuple_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\ + (PyErr_SetString(PyExc_IndexError, "tuple index out of range"), (PyObject*)NULL)) +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, + int wraparound, int boundscheck); +static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j); +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, + int is_list, int wraparound, int boundscheck); + +/* RaiseArgTupleInvalid.proto */ +static void __Pyx_RaiseArgtupleInvalid(const char* func_name, int exact, + Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found); + +/* RaiseDoubleKeywords.proto */ +static void __Pyx_RaiseDoubleKeywordsError(const char* func_name, PyObject* kw_name); + +/* ParseKeywords.proto */ +static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject **argnames[],\ + PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args,\ + const char* function_name); + +/* ArgTypeTest.proto */ +#define __Pyx_ArgTypeTest(obj, type, none_allowed, name, exact)\ + ((likely((Py_TYPE(obj) == type) | (none_allowed && (obj == Py_None)))) ? 1 :\ + __Pyx__ArgTypeTest(obj, type, name, exact)) +static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact); + +/* Import.proto */ +static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level); + +/* PyThreadStateGet.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_PyThreadState_declare PyThreadState *__pyx_tstate; +#define __Pyx_PyThreadState_assign __pyx_tstate = __Pyx_PyThreadState_Current; +#define __Pyx_PyErr_Occurred() __pyx_tstate->curexc_type +#else +#define __Pyx_PyThreadState_declare +#define __Pyx_PyThreadState_assign +#define __Pyx_PyErr_Occurred() PyErr_Occurred() +#endif + +/* PyErrFetchRestore.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_PyErr_Clear() __Pyx_ErrRestore(NULL, NULL, NULL) +#define __Pyx_ErrRestoreWithState(type, value, tb) __Pyx_ErrRestoreInState(PyThreadState_GET(), type, value, tb) +#define __Pyx_ErrFetchWithState(type, value, tb) __Pyx_ErrFetchInState(PyThreadState_GET(), type, value, tb) +#define __Pyx_ErrRestore(type, value, tb) __Pyx_ErrRestoreInState(__pyx_tstate, type, value, tb) +#define __Pyx_ErrFetch(type, value, tb) __Pyx_ErrFetchInState(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb); +static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#if CYTHON_COMPILING_IN_CPYTHON +#define __Pyx_PyErr_SetNone(exc) (Py_INCREF(exc), __Pyx_ErrRestore((exc), NULL, NULL)) +#else +#define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc) +#endif +#else +#define __Pyx_PyErr_Clear() PyErr_Clear() +#define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc) +#define __Pyx_ErrRestoreWithState(type, value, tb) PyErr_Restore(type, value, tb) +#define __Pyx_ErrFetchWithState(type, value, tb) PyErr_Fetch(type, value, tb) +#define __Pyx_ErrRestoreInState(tstate, type, value, tb) PyErr_Restore(type, value, tb) +#define __Pyx_ErrFetchInState(tstate, type, value, tb) PyErr_Fetch(type, value, tb) +#define __Pyx_ErrRestore(type, value, tb) PyErr_Restore(type, value, tb) +#define __Pyx_ErrFetch(type, value, tb) PyErr_Fetch(type, value, tb) +#endif + +/* CLineInTraceback.proto */ +#ifdef CYTHON_CLINE_IN_TRACEBACK +#define __Pyx_CLineForTraceback(tstate, c_line) (((CYTHON_CLINE_IN_TRACEBACK)) ? c_line : 0) +#else +static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line); +#endif + +/* CodeObjectCache.proto */ +typedef struct { + PyCodeObject* code_object; + int code_line; +} __Pyx_CodeObjectCacheEntry; +struct __Pyx_CodeObjectCache { + int count; + int max_count; + __Pyx_CodeObjectCacheEntry* entries; +}; +static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL}; +static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line); +static PyCodeObject *__pyx_find_code_object(int code_line); +static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object); + +/* AddTraceback.proto */ +static void __Pyx_AddTraceback(const char *funcname, int c_line, + int py_line, const char *filename); + +/* CIntToPy.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value); + +/* CIntToPy.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value); + +/* CIntFromPy.proto */ +static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *); + +/* CIntFromPy.proto */ +static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *); + +/* FastTypeChecks.proto */ +#if CYTHON_COMPILING_IN_CPYTHON +#define __Pyx_TypeCheck(obj, type) __Pyx_IsSubtype(Py_TYPE(obj), (PyTypeObject *)type) +static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b); +static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject *type); +static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *type1, PyObject *type2); +#else +#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type) +#define __Pyx_PyErr_GivenExceptionMatches(err, type) PyErr_GivenExceptionMatches(err, type) +#define __Pyx_PyErr_GivenExceptionMatches2(err, type1, type2) (PyErr_GivenExceptionMatches(err, type1) || PyErr_GivenExceptionMatches(err, type2)) +#endif +#define __Pyx_PyException_Check(obj) __Pyx_TypeCheck(obj, PyExc_Exception) + +/* CheckBinaryVersion.proto */ +static int __Pyx_check_binary_version(void); + +/* InitStrings.proto */ +static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); + + +/* Module declarations from 'metrics' */ +static PyObject *__pyx_f_7metrics_rsq(PyObject *, PyObject *, int __pyx_skip_dispatch); /*proto*/ +static PyObject *__pyx_f_7metrics_mse(PyObject *, PyObject *, int __pyx_skip_dispatch); /*proto*/ +static PyObject *__pyx_f_7metrics_rmse(PyObject *, PyObject *, int __pyx_skip_dispatch); /*proto*/ +static PyObject *__pyx_f_7metrics_mae(PyObject *, PyObject *, int __pyx_skip_dispatch); /*proto*/ +static PyObject *__pyx_f_7metrics_mape(PyObject *, PyObject *, int __pyx_skip_dispatch); /*proto*/ +#define __Pyx_MODULE_NAME "metrics" +extern int __pyx_module_is_main_metrics; +int __pyx_module_is_main_metrics = 0; + +/* Implementation of 'metrics' */ +static PyObject *__pyx_builtin_range; +static const char __pyx_k_y[] = "y"; +static const char __pyx_k_np[] = "_np"; +static const char __pyx_k_main[] = "__main__"; +static const char __pyx_k_name[] = "__name__"; +static const char __pyx_k_test[] = "__test__"; +static const char __pyx_k_numpy[] = "numpy"; +static const char __pyx_k_range[] = "range"; +static const char __pyx_k_round[] = "round"; +static const char __pyx_k_y_hat[] = "y_hat"; +static const char __pyx_k_import[] = "__import__"; +static const char __pyx_k_corrcoef[] = "corrcoef"; +static const char __pyx_k_cline_in_traceback[] = "cline_in_traceback"; +static const char __pyx_k_Common_metrics_required_in_mach[] = "\nCommon metrics required in machine learning modules.\n\nAvailable functions:\n - ``rsq``: R-Squared\n - ``mse``: Mean squared error\n - ``rmse``: Root mean squared error\n - ``mae``: Mean absolute error\n - ``mape``: Mean absolute percentage error\n\nAuthor\n------\n::\n\n Author: Diptesh Basak\n Date: Sep 10, 2021\n"; +static PyObject *__pyx_n_s_cline_in_traceback; +static PyObject *__pyx_n_s_corrcoef; +static PyObject *__pyx_n_s_import; +static PyObject *__pyx_n_s_main; +static PyObject *__pyx_n_s_name; +static PyObject *__pyx_n_s_np; +static PyObject *__pyx_n_s_numpy; +static PyObject *__pyx_n_s_range; +static PyObject *__pyx_n_s_round; 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*/ + PyEval_InitThreads(); + #endif + #endif + /*--- Module creation code ---*/ + #if CYTHON_PEP489_MULTI_PHASE_INIT + __pyx_m = __pyx_pyinit_module; + Py_INCREF(__pyx_m); + #else + #if PY_MAJOR_VERSION < 3 + __pyx_m = Py_InitModule4("metrics", __pyx_methods, __pyx_k_Common_metrics_required_in_mach, 0, PYTHON_API_VERSION); Py_XINCREF(__pyx_m); + #else + __pyx_m = PyModule_Create(&__pyx_moduledef); + #endif + if (unlikely(!__pyx_m)) __PYX_ERR(0, 1, __pyx_L1_error) + #endif + __pyx_d = PyModule_GetDict(__pyx_m); if (unlikely(!__pyx_d)) __PYX_ERR(0, 1, __pyx_L1_error) + Py_INCREF(__pyx_d); + __pyx_b = PyImport_AddModule(__Pyx_BUILTIN_MODULE_NAME); if (unlikely(!__pyx_b)) __PYX_ERR(0, 1, __pyx_L1_error) + Py_INCREF(__pyx_b); + __pyx_cython_runtime = PyImport_AddModule((char *) "cython_runtime"); if (unlikely(!__pyx_cython_runtime)) __PYX_ERR(0, 1, __pyx_L1_error) + Py_INCREF(__pyx_cython_runtime); + if (PyObject_SetAttrString(__pyx_m, "__builtins__", __pyx_b) < 0) __PYX_ERR(0, 1, __pyx_L1_error); + /*--- Initialize various global constants etc. ---*/ + if (__Pyx_InitGlobals() < 0) __PYX_ERR(0, 1, __pyx_L1_error) + #if PY_MAJOR_VERSION < 3 && (__PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT) + if (__Pyx_init_sys_getdefaultencoding_params() < 0) __PYX_ERR(0, 1, __pyx_L1_error) + #endif + if (__pyx_module_is_main_metrics) { + if (PyObject_SetAttr(__pyx_m, __pyx_n_s_name, __pyx_n_s_main) < 0) __PYX_ERR(0, 1, __pyx_L1_error) + } + #if PY_MAJOR_VERSION >= 3 + { + PyObject *modules = PyImport_GetModuleDict(); if (unlikely(!modules)) __PYX_ERR(0, 1, __pyx_L1_error) + if (!PyDict_GetItemString(modules, "metrics")) { + if (unlikely(PyDict_SetItemString(modules, "metrics", __pyx_m) < 0)) __PYX_ERR(0, 1, __pyx_L1_error) + } + } + #endif + /*--- Builtin init code ---*/ + if (__Pyx_InitCachedBuiltins() < 0) goto __pyx_L1_error; + /*--- Constants init code ---*/ + if (__Pyx_InitCachedConstants() < 0) goto __pyx_L1_error; + /*--- Global type/function init code ---*/ + (void)__Pyx_modinit_global_init_code(); + (void)__Pyx_modinit_variable_export_code(); + (void)__Pyx_modinit_function_export_code(); + (void)__Pyx_modinit_type_init_code(); + (void)__Pyx_modinit_type_import_code(); + (void)__Pyx_modinit_variable_import_code(); + (void)__Pyx_modinit_function_import_code(); + /*--- Execution code ---*/ + #if defined(__Pyx_Generator_USED) || defined(__Pyx_Coroutine_USED) + if (__Pyx_patch_abc() < 0) __PYX_ERR(0, 1, __pyx_L1_error) + #endif + + /* "metrics.pyx":19 + * """ + * + * import numpy as _np # <<<<<<<<<<<<<< + * + * # ============================================================================= + */ + __pyx_t_1 = __Pyx_Import(__pyx_n_s_numpy, 0, -1); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 19, __pyx_L1_error) + __Pyx_GOTREF(__pyx_t_1); + if (PyDict_SetItem(__pyx_d, __pyx_n_s_np, __pyx_t_1) < 0) __PYX_ERR(0, 19, __pyx_L1_error) + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + + /* "metrics.pyx":1 + * """ # <<<<<<<<<<<<<< + * Common metrics required in machine learning modules. + * + */ + __pyx_t_1 = __Pyx_PyDict_NewPresized(0); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 1, __pyx_L1_error) + __Pyx_GOTREF(__pyx_t_1); + if (PyDict_SetItem(__pyx_d, __pyx_n_s_test, __pyx_t_1) < 0) __PYX_ERR(0, 1, __pyx_L1_error) + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + + /*--- Wrapped vars code ---*/ + + goto __pyx_L0; + __pyx_L1_error:; + __Pyx_XDECREF(__pyx_t_1); + if (__pyx_m) { + if (__pyx_d) { + __Pyx_AddTraceback("init metrics", __pyx_clineno, __pyx_lineno, __pyx_filename); + } + Py_CLEAR(__pyx_m); + } else if (!PyErr_Occurred()) { + PyErr_SetString(PyExc_ImportError, "init metrics"); + } + __pyx_L0:; + __Pyx_RefNannyFinishContext(); + #if CYTHON_PEP489_MULTI_PHASE_INIT + return (__pyx_m != NULL) ? 0 : -1; + #elif PY_MAJOR_VERSION >= 3 + return __pyx_m; + #else + return; + #endif +} + +/* --- Runtime support code --- */ +/* Refnanny */ +#if CYTHON_REFNANNY +static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname) { + PyObject *m = NULL, *p = NULL; + void *r = NULL; + m = PyImport_ImportModule(modname); + if (!m) goto end; + p = PyObject_GetAttrString(m, "RefNannyAPI"); + if (!p) goto end; + r = PyLong_AsVoidPtr(p); +end: + Py_XDECREF(p); + Py_XDECREF(m); + return (__Pyx_RefNannyAPIStruct *)r; +} +#endif + +/* PyObjectGetAttrStr */ +#if CYTHON_USE_TYPE_SLOTS +static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name) { + PyTypeObject* tp = Py_TYPE(obj); + if (likely(tp->tp_getattro)) + return tp->tp_getattro(obj, attr_name); +#if PY_MAJOR_VERSION < 3 + if (likely(tp->tp_getattr)) + return tp->tp_getattr(obj, PyString_AS_STRING(attr_name)); +#endif + return PyObject_GetAttr(obj, attr_name); +} +#endif + +/* GetBuiltinName */ +static PyObject *__Pyx_GetBuiltinName(PyObject *name) { + PyObject* result = __Pyx_PyObject_GetAttrStr(__pyx_b, name); + if (unlikely(!result)) { + PyErr_Format(PyExc_NameError, +#if PY_MAJOR_VERSION >= 3 + "name '%U' is not defined", name); +#else + "name '%.200s' is not defined", PyString_AS_STRING(name)); +#endif + } + return result; +} + +/* PyDictVersioning */ +#if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_TYPE_SLOTS +static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj) { + PyObject *dict = Py_TYPE(obj)->tp_dict; + return likely(dict) ? __PYX_GET_DICT_VERSION(dict) : 0; +} +static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj) { + PyObject **dictptr = NULL; + Py_ssize_t offset = Py_TYPE(obj)->tp_dictoffset; + if (offset) { +#if CYTHON_COMPILING_IN_CPYTHON + dictptr = (likely(offset > 0)) ? (PyObject **) ((char *)obj + offset) : _PyObject_GetDictPtr(obj); +#else + dictptr = _PyObject_GetDictPtr(obj); +#endif + } + return (dictptr && *dictptr) ? __PYX_GET_DICT_VERSION(*dictptr) : 0; +} +static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version) { + PyObject *dict = Py_TYPE(obj)->tp_dict; + if (unlikely(!dict) || unlikely(tp_dict_version != __PYX_GET_DICT_VERSION(dict))) + return 0; + return obj_dict_version == __Pyx_get_object_dict_version(obj); +} +#endif + +/* GetModuleGlobalName */ +#if CYTHON_USE_DICT_VERSIONS +static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value) +#else +static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name) +#endif +{ + PyObject *result; +#if !CYTHON_AVOID_BORROWED_REFS +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1 + result = _PyDict_GetItem_KnownHash(__pyx_d, name, ((PyASCIIObject *) name)->hash); + __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) + if (likely(result)) { + return __Pyx_NewRef(result); + } else if (unlikely(PyErr_Occurred())) { + return NULL; + } +#else + result = PyDict_GetItem(__pyx_d, name); + __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) + if (likely(result)) { + return __Pyx_NewRef(result); + } +#endif +#else + result = PyObject_GetItem(__pyx_d, name); + __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) + if (likely(result)) { + return __Pyx_NewRef(result); + } + PyErr_Clear(); +#endif + return __Pyx_GetBuiltinName(name); +} + +/* PyFunctionFastCall */ +#if CYTHON_FAST_PYCALL +static PyObject* __Pyx_PyFunction_FastCallNoKw(PyCodeObject *co, PyObject **args, Py_ssize_t na, + PyObject *globals) { + PyFrameObject *f; + PyThreadState *tstate = __Pyx_PyThreadState_Current; + PyObject **fastlocals; + Py_ssize_t i; + PyObject *result; + assert(globals != NULL); + /* XXX Perhaps we should create a specialized + PyFrame_New() that doesn't take locals, but does + take builtins without sanity checking them. + */ + assert(tstate != NULL); + f = PyFrame_New(tstate, co, globals, NULL); + if (f == NULL) { + return NULL; + } + fastlocals = __Pyx_PyFrame_GetLocalsplus(f); + for (i = 0; i < na; i++) { + Py_INCREF(*args); + fastlocals[i] = *args++; + } + result = PyEval_EvalFrameEx(f,0); + ++tstate->recursion_depth; + Py_DECREF(f); + --tstate->recursion_depth; + return result; +} +#if 1 || PY_VERSION_HEX < 0x030600B1 +static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs) { + PyCodeObject *co = (PyCodeObject *)PyFunction_GET_CODE(func); + PyObject *globals = PyFunction_GET_GLOBALS(func); + PyObject *argdefs = PyFunction_GET_DEFAULTS(func); + PyObject *closure; +#if PY_MAJOR_VERSION >= 3 + PyObject *kwdefs; +#endif + PyObject *kwtuple, **k; + PyObject **d; + Py_ssize_t nd; + Py_ssize_t nk; + PyObject *result; + assert(kwargs == NULL || PyDict_Check(kwargs)); + nk = kwargs ? PyDict_Size(kwargs) : 0; + if (Py_EnterRecursiveCall((char*)" while calling a Python object")) { + return NULL; + } + if ( +#if PY_MAJOR_VERSION >= 3 + co->co_kwonlyargcount == 0 && +#endif + likely(kwargs == NULL || nk == 0) && + co->co_flags == (CO_OPTIMIZED | CO_NEWLOCALS | CO_NOFREE)) { + if (argdefs == NULL && co->co_argcount == nargs) { + result = __Pyx_PyFunction_FastCallNoKw(co, args, nargs, globals); + goto done; + } + else if (nargs == 0 && argdefs != NULL + && co->co_argcount == Py_SIZE(argdefs)) { + /* function called with no arguments, but all parameters have + a default value: use default values as arguments .*/ + args = &PyTuple_GET_ITEM(argdefs, 0); + result =__Pyx_PyFunction_FastCallNoKw(co, args, Py_SIZE(argdefs), globals); + goto done; + } + } + if (kwargs != NULL) { + Py_ssize_t pos, i; + kwtuple = PyTuple_New(2 * nk); + if (kwtuple == NULL) { + result = NULL; + goto done; + } + k = &PyTuple_GET_ITEM(kwtuple, 0); + pos = i = 0; + while (PyDict_Next(kwargs, &pos, &k[i], &k[i+1])) { + Py_INCREF(k[i]); + Py_INCREF(k[i+1]); + i += 2; + } + nk = i / 2; + } + else { + kwtuple = NULL; + k = NULL; + } + closure = PyFunction_GET_CLOSURE(func); +#if PY_MAJOR_VERSION >= 3 + kwdefs = PyFunction_GET_KW_DEFAULTS(func); +#endif + if (argdefs != NULL) { + d = &PyTuple_GET_ITEM(argdefs, 0); + nd = Py_SIZE(argdefs); + } + else { + d = NULL; + nd = 0; + } +#if PY_MAJOR_VERSION >= 3 + result = PyEval_EvalCodeEx((PyObject*)co, globals, (PyObject *)NULL, + args, (int)nargs, + k, (int)nk, + d, (int)nd, kwdefs, closure); +#else + result = PyEval_EvalCodeEx(co, globals, (PyObject *)NULL, + args, (int)nargs, + k, (int)nk, + d, (int)nd, closure); +#endif + Py_XDECREF(kwtuple); +done: + Py_LeaveRecursiveCall(); + return result; +} +#endif +#endif + +/* PyCFunctionFastCall */ +#if CYTHON_FAST_PYCCALL +static CYTHON_INLINE PyObject * __Pyx_PyCFunction_FastCall(PyObject *func_obj, PyObject **args, Py_ssize_t nargs) { + PyCFunctionObject *func = (PyCFunctionObject*)func_obj; + PyCFunction meth = PyCFunction_GET_FUNCTION(func); + PyObject *self = PyCFunction_GET_SELF(func); + int flags = PyCFunction_GET_FLAGS(func); + assert(PyCFunction_Check(func)); + assert(METH_FASTCALL == (flags & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS | METH_STACKLESS))); + assert(nargs >= 0); + assert(nargs == 0 || args != NULL); + /* _PyCFunction_FastCallDict() must not be called with an exception set, + because it may clear it (directly or indirectly) and so the + caller loses its exception */ + assert(!PyErr_Occurred()); + if ((PY_VERSION_HEX < 0x030700A0) || unlikely(flags & METH_KEYWORDS)) { + return (*((__Pyx_PyCFunctionFastWithKeywords)(void*)meth)) (self, args, nargs, NULL); + } else { + return (*((__Pyx_PyCFunctionFast)(void*)meth)) (self, args, nargs); + } +} +#endif + +/* PyObjectCall */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) { + PyObject *result; + ternaryfunc call = func->ob_type->tp_call; + if (unlikely(!call)) + return PyObject_Call(func, arg, kw); + if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) + return NULL; + result = (*call)(func, arg, kw); + Py_LeaveRecursiveCall(); + if (unlikely(!result) && unlikely(!PyErr_Occurred())) { + PyErr_SetString( + PyExc_SystemError, + "NULL result without error in PyObject_Call"); + } + return result; +} +#endif + +/* GetItemInt */ +static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) { + PyObject *r; + if (!j) return NULL; + r = PyObject_GetItem(o, j); + Py_DECREF(j); + return r; +} +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, + CYTHON_NCP_UNUSED int wraparound, + CYTHON_NCP_UNUSED int boundscheck) { +#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS + Py_ssize_t wrapped_i = i; + if (wraparound & unlikely(i < 0)) { + wrapped_i += PyList_GET_SIZE(o); + } + if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyList_GET_SIZE(o)))) { + PyObject *r = PyList_GET_ITEM(o, wrapped_i); + Py_INCREF(r); + return r; + } + return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); +#else + return PySequence_GetItem(o, i); +#endif +} +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, + CYTHON_NCP_UNUSED int wraparound, + CYTHON_NCP_UNUSED int boundscheck) { +#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS + Py_ssize_t wrapped_i = i; + if (wraparound & unlikely(i < 0)) { + wrapped_i += PyTuple_GET_SIZE(o); + } + if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyTuple_GET_SIZE(o)))) { + PyObject *r = PyTuple_GET_ITEM(o, wrapped_i); + Py_INCREF(r); + return r; + } + return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); +#else + return PySequence_GetItem(o, i); +#endif +} +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list, + CYTHON_NCP_UNUSED int wraparound, + CYTHON_NCP_UNUSED int boundscheck) { +#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS + if (is_list || PyList_CheckExact(o)) { + Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o); + if ((!boundscheck) || (likely(__Pyx_is_valid_index(n, PyList_GET_SIZE(o))))) { + PyObject *r = PyList_GET_ITEM(o, n); + Py_INCREF(r); + return r; + } + } + else if (PyTuple_CheckExact(o)) { + Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o); + if ((!boundscheck) || likely(__Pyx_is_valid_index(n, PyTuple_GET_SIZE(o)))) { + PyObject *r = PyTuple_GET_ITEM(o, n); + Py_INCREF(r); + return r; + } + } else { + PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence; + if (likely(m && m->sq_item)) { + if (wraparound && unlikely(i < 0) && likely(m->sq_length)) { + Py_ssize_t l = m->sq_length(o); + if (likely(l >= 0)) { + i += l; + } else { + if (!PyErr_ExceptionMatches(PyExc_OverflowError)) + return NULL; + PyErr_Clear(); + } + } + return m->sq_item(o, i); + } + } +#else + if (is_list || PySequence_Check(o)) { + return PySequence_GetItem(o, i); + } +#endif + return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); +} + +/* RaiseArgTupleInvalid */ +static void __Pyx_RaiseArgtupleInvalid( + const char* func_name, + int exact, + Py_ssize_t num_min, + Py_ssize_t num_max, + Py_ssize_t num_found) +{ + Py_ssize_t num_expected; + const char *more_or_less; + if (num_found < num_min) { + num_expected = num_min; + more_or_less = "at least"; + } else { + num_expected = num_max; + more_or_less = "at most"; + } + if (exact) { + more_or_less = "exactly"; + } + PyErr_Format(PyExc_TypeError, + "%.200s() takes %.8s %" CYTHON_FORMAT_SSIZE_T "d positional argument%.1s (%" CYTHON_FORMAT_SSIZE_T "d given)", + func_name, more_or_less, num_expected, + (num_expected == 1) ? "" : "s", num_found); +} + +/* RaiseDoubleKeywords */ +static void __Pyx_RaiseDoubleKeywordsError( + const char* func_name, + PyObject* kw_name) +{ + PyErr_Format(PyExc_TypeError, + #if PY_MAJOR_VERSION >= 3 + "%s() got multiple values for keyword argument '%U'", func_name, kw_name); + #else + "%s() got multiple values for keyword argument '%s'", func_name, + PyString_AsString(kw_name)); + #endif +} + +/* ParseKeywords */ +static int __Pyx_ParseOptionalKeywords( + PyObject *kwds, + PyObject **argnames[], + PyObject *kwds2, + PyObject *values[], + Py_ssize_t num_pos_args, + const char* function_name) +{ + PyObject *key = 0, *value = 0; + Py_ssize_t pos = 0; + PyObject*** name; + PyObject*** first_kw_arg = argnames + num_pos_args; + while (PyDict_Next(kwds, &pos, &key, &value)) { + name = first_kw_arg; + while (*name && (**name != key)) name++; + if (*name) { + values[name-argnames] = value; + continue; + } + name = first_kw_arg; + #if PY_MAJOR_VERSION < 3 + if (likely(PyString_CheckExact(key)) || likely(PyString_Check(key))) { + while (*name) { + if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key)) + && _PyString_Eq(**name, key)) { + values[name-argnames] = value; + break; + } + name++; + } + if (*name) continue; + else { + PyObject*** argname = argnames; + while (argname != first_kw_arg) { + if ((**argname == key) || ( + (CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**argname) == PyString_GET_SIZE(key)) + && _PyString_Eq(**argname, key))) { + goto arg_passed_twice; + } + argname++; + } + } + } else + #endif + if (likely(PyUnicode_Check(key))) { + while (*name) { + int cmp = (**name == key) ? 0 : + #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 + (PyUnicode_GET_SIZE(**name) != PyUnicode_GET_SIZE(key)) ? 1 : + #endif + PyUnicode_Compare(**name, key); + if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; + if (cmp == 0) { + values[name-argnames] = value; + break; + } + name++; + } + if (*name) continue; + else { + PyObject*** argname = argnames; + while (argname != first_kw_arg) { + int cmp = (**argname == key) ? 0 : + #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 + (PyUnicode_GET_SIZE(**argname) != PyUnicode_GET_SIZE(key)) ? 1 : + #endif + PyUnicode_Compare(**argname, key); + if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; + if (cmp == 0) goto arg_passed_twice; + argname++; + } + } + } else + goto invalid_keyword_type; + if (kwds2) { + if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad; + } else { + goto invalid_keyword; + } + } + return 0; +arg_passed_twice: + __Pyx_RaiseDoubleKeywordsError(function_name, key); + goto bad; +invalid_keyword_type: + PyErr_Format(PyExc_TypeError, + "%.200s() keywords must be strings", function_name); + goto bad; +invalid_keyword: + PyErr_Format(PyExc_TypeError, + #if PY_MAJOR_VERSION < 3 + "%.200s() got an unexpected keyword argument '%.200s'", + function_name, PyString_AsString(key)); + #else + "%s() got an unexpected keyword argument '%U'", + function_name, key); + #endif +bad: + return -1; +} + +/* ArgTypeTest */ +static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact) +{ + if (unlikely(!type)) { + PyErr_SetString(PyExc_SystemError, "Missing type object"); + return 0; + } + else if (exact) { + #if PY_MAJOR_VERSION == 2 + if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1; + #endif + } + else { + if (likely(__Pyx_TypeCheck(obj, type))) return 1; + } + PyErr_Format(PyExc_TypeError, + "Argument '%.200s' has incorrect type (expected %.200s, got %.200s)", + name, type->tp_name, Py_TYPE(obj)->tp_name); + return 0; +} + +/* Import */ +static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) { + PyObject *empty_list = 0; + PyObject *module = 0; + PyObject *global_dict = 0; + PyObject *empty_dict = 0; + PyObject *list; + #if PY_MAJOR_VERSION < 3 + PyObject *py_import; + py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import); + if (!py_import) + goto bad; + #endif + if (from_list) + list = from_list; + else { + empty_list = PyList_New(0); + if (!empty_list) + goto bad; + list = empty_list; + } + global_dict = PyModule_GetDict(__pyx_m); + if (!global_dict) + goto bad; + empty_dict = PyDict_New(); + if (!empty_dict) + goto bad; + { + #if PY_MAJOR_VERSION >= 3 + if (level == -1) { + if (strchr(__Pyx_MODULE_NAME, '.')) { + module = PyImport_ImportModuleLevelObject( + name, global_dict, empty_dict, list, 1); + if (!module) { + if (!PyErr_ExceptionMatches(PyExc_ImportError)) + goto bad; + PyErr_Clear(); + } + } + level = 0; + } + #endif + if (!module) { + #if PY_MAJOR_VERSION < 3 + PyObject *py_level = PyInt_FromLong(level); + if (!py_level) + goto bad; + module = PyObject_CallFunctionObjArgs(py_import, + name, global_dict, empty_dict, list, py_level, (PyObject *)NULL); + Py_DECREF(py_level); + #else + module = PyImport_ImportModuleLevelObject( + name, global_dict, empty_dict, list, level); + #endif + } + } +bad: + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(py_import); + #endif + Py_XDECREF(empty_list); + Py_XDECREF(empty_dict); + return module; +} + +/* PyErrFetchRestore */ +#if CYTHON_FAST_THREAD_STATE +static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { + PyObject *tmp_type, *tmp_value, *tmp_tb; + tmp_type = tstate->curexc_type; + tmp_value = tstate->curexc_value; + tmp_tb = tstate->curexc_traceback; + tstate->curexc_type = type; + tstate->curexc_value = value; + tstate->curexc_traceback = tb; + Py_XDECREF(tmp_type); + Py_XDECREF(tmp_value); + Py_XDECREF(tmp_tb); +} +static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { + *type = tstate->curexc_type; + *value = tstate->curexc_value; + *tb = tstate->curexc_traceback; + tstate->curexc_type = 0; + tstate->curexc_value = 0; + tstate->curexc_traceback = 0; +} +#endif + +/* CLineInTraceback */ +#ifndef CYTHON_CLINE_IN_TRACEBACK +static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line) { + PyObject *use_cline; + PyObject *ptype, *pvalue, *ptraceback; +#if CYTHON_COMPILING_IN_CPYTHON + PyObject **cython_runtime_dict; +#endif + if (unlikely(!__pyx_cython_runtime)) { + return c_line; + } + __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback); +#if CYTHON_COMPILING_IN_CPYTHON + cython_runtime_dict = _PyObject_GetDictPtr(__pyx_cython_runtime); + if (likely(cython_runtime_dict)) { + __PYX_PY_DICT_LOOKUP_IF_MODIFIED( + use_cline, *cython_runtime_dict, + __Pyx_PyDict_GetItemStr(*cython_runtime_dict, __pyx_n_s_cline_in_traceback)) + } else +#endif + { + PyObject *use_cline_obj = __Pyx_PyObject_GetAttrStr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback); + if (use_cline_obj) { + use_cline = PyObject_Not(use_cline_obj) ? Py_False : Py_True; + Py_DECREF(use_cline_obj); + } else { + PyErr_Clear(); + use_cline = NULL; + } + } + if (!use_cline) { + c_line = 0; + PyObject_SetAttr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback, Py_False); + } + else if (use_cline == Py_False || (use_cline != Py_True && PyObject_Not(use_cline) != 0)) { + c_line = 0; + } + __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback); + return c_line; +} +#endif + +/* CodeObjectCache */ +static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) { + int start = 0, mid = 0, end = count - 1; + if (end >= 0 && code_line > entries[end].code_line) { + return count; + } + while (start < end) { + mid = start + (end - start) / 2; + if (code_line < entries[mid].code_line) { + end = mid; + } else if (code_line > entries[mid].code_line) { + start = mid + 1; + } else { + return mid; + } + } + if (code_line <= entries[mid].code_line) { + return mid; + } else { + return mid + 1; + } +} +static PyCodeObject *__pyx_find_code_object(int code_line) { + PyCodeObject* code_object; + int pos; + if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) { + return NULL; + } + pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); + if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) { + return NULL; + } + code_object = __pyx_code_cache.entries[pos].code_object; + Py_INCREF(code_object); + return code_object; +} +static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) { + int pos, i; + __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries; + if (unlikely(!code_line)) { + return; + } + if (unlikely(!entries)) { + entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry)); + if (likely(entries)) { + __pyx_code_cache.entries = entries; + __pyx_code_cache.max_count = 64; + __pyx_code_cache.count = 1; + entries[0].code_line = code_line; + entries[0].code_object = code_object; + Py_INCREF(code_object); + } + return; + } + pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); + if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) { + PyCodeObject* tmp = entries[pos].code_object; + entries[pos].code_object = code_object; + Py_DECREF(tmp); + return; + } + if (__pyx_code_cache.count == __pyx_code_cache.max_count) { + int new_max = __pyx_code_cache.max_count + 64; + entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc( + __pyx_code_cache.entries, (size_t)new_max*sizeof(__Pyx_CodeObjectCacheEntry)); + if (unlikely(!entries)) { + return; + } + __pyx_code_cache.entries = entries; + __pyx_code_cache.max_count = new_max; + } + for (i=__pyx_code_cache.count; i>pos; i--) { + entries[i] = entries[i-1]; + } + entries[pos].code_line = code_line; + entries[pos].code_object = code_object; + __pyx_code_cache.count++; + Py_INCREF(code_object); +} + +/* AddTraceback */ +#include "compile.h" +#include "frameobject.h" +#include "traceback.h" +static PyCodeObject* __Pyx_CreateCodeObjectForTraceback( + const char *funcname, int c_line, + int py_line, const char *filename) { + PyCodeObject *py_code = 0; + PyObject *py_srcfile = 0; + PyObject *py_funcname = 0; + #if PY_MAJOR_VERSION < 3 + py_srcfile = PyString_FromString(filename); + #else + py_srcfile = PyUnicode_FromString(filename); + #endif + if (!py_srcfile) goto bad; + if (c_line) { + #if PY_MAJOR_VERSION < 3 + py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); + #else + py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); + #endif + } + else { + #if PY_MAJOR_VERSION < 3 + py_funcname = PyString_FromString(funcname); + #else + py_funcname = PyUnicode_FromString(funcname); + #endif + } + if (!py_funcname) goto bad; + py_code = __Pyx_PyCode_New( + 0, + 0, + 0, + 0, + 0, + __pyx_empty_bytes, /*PyObject *code,*/ + __pyx_empty_tuple, /*PyObject *consts,*/ + __pyx_empty_tuple, /*PyObject *names,*/ + __pyx_empty_tuple, /*PyObject *varnames,*/ + __pyx_empty_tuple, /*PyObject *freevars,*/ + __pyx_empty_tuple, /*PyObject *cellvars,*/ + py_srcfile, /*PyObject *filename,*/ + py_funcname, /*PyObject *name,*/ + py_line, + __pyx_empty_bytes /*PyObject *lnotab*/ + ); + Py_DECREF(py_srcfile); + Py_DECREF(py_funcname); + return py_code; +bad: + Py_XDECREF(py_srcfile); + Py_XDECREF(py_funcname); + return NULL; +} +static void __Pyx_AddTraceback(const char *funcname, int c_line, + int py_line, const char *filename) { + PyCodeObject *py_code = 0; + PyFrameObject *py_frame = 0; + PyThreadState *tstate = __Pyx_PyThreadState_Current; + if (c_line) { + c_line = __Pyx_CLineForTraceback(tstate, c_line); + } + py_code = __pyx_find_code_object(c_line ? -c_line : py_line); + if (!py_code) { + py_code = __Pyx_CreateCodeObjectForTraceback( + funcname, c_line, py_line, filename); + if (!py_code) goto bad; + __pyx_insert_code_object(c_line ? -c_line : py_line, py_code); + } + py_frame = PyFrame_New( + tstate, /*PyThreadState *tstate,*/ + py_code, /*PyCodeObject *code,*/ + __pyx_d, /*PyObject *globals,*/ + 0 /*PyObject *locals*/ + ); + if (!py_frame) goto bad; + __Pyx_PyFrame_SetLineNumber(py_frame, py_line); + PyTraceBack_Here(py_frame); +bad: + Py_XDECREF(py_code); + Py_XDECREF(py_frame); +} + +/* CIntToPy */ +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) { + const long neg_one = (long) ((long) 0 - (long) 1), const_zero = (long) 0; + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(long) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(long) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); +#endif + } + } else { + if (sizeof(long) <= sizeof(long)) { + return PyInt_FromLong((long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); +#endif + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; + return _PyLong_FromByteArray(bytes, sizeof(long), + little, !is_unsigned); + } +} + +/* CIntToPy */ +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) { + const int neg_one = (int) ((int) 0 - (int) 1), const_zero = (int) 0; + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(int) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(int) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); +#endif + } + } else { + if (sizeof(int) <= sizeof(long)) { + return PyInt_FromLong((long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); +#endif + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; + return _PyLong_FromByteArray(bytes, sizeof(int), + little, !is_unsigned); + } +} + +/* CIntFromPyVerify */ +#define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\ + __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0) +#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\ + __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1) +#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\ + {\ + func_type value = func_value;\ + if (sizeof(target_type) < sizeof(func_type)) {\ + if (unlikely(value != (func_type) (target_type) value)) {\ + func_type zero = 0;\ + if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\ + return (target_type) -1;\ + if (is_unsigned && unlikely(value < zero))\ + goto raise_neg_overflow;\ + else\ + goto raise_overflow;\ + }\ + }\ + return (target_type) value;\ + } + +/* CIntFromPy */ +static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) { + const int neg_one = (int) ((int) 0 - (int) 1), const_zero = (int) 0; + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if (sizeof(int) < sizeof(long)) { + __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x)) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + goto raise_neg_overflow; + } + return (int) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (int) 0; + case 1: __PYX_VERIFY_RETURN_INT(int, digit, digits[0]) + case 2: + if (8 * sizeof(int) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) >= 2 * PyLong_SHIFT) { + return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + case 3: + if (8 * sizeof(int) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) >= 3 * PyLong_SHIFT) { + return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + case 4: + if (8 * sizeof(int) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) >= 4 * PyLong_SHIFT) { + return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + } +#endif +#if CYTHON_COMPILING_IN_CPYTHON + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (int) -1; + if (unlikely(result == 1)) + goto raise_neg_overflow; + } +#endif + if (sizeof(int) <= sizeof(unsigned long)) { + __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) +#endif + } + } else { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (int) 0; + case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, (sdigit) (-(sdigit)digits[0])) + case 1: __PYX_VERIFY_RETURN_INT(int, digit, +digits[0]) + case -2: + if (8 * sizeof(int) - 1 > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { + return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 2: + if (8 * sizeof(int) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { + return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case -3: + if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { + return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 3: + if (8 * sizeof(int) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { + return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case -4: + if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { + return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 4: + if (8 * sizeof(int) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { + return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + } +#endif + if (sizeof(int) <= sizeof(long)) { + __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x)) +#endif + } + } + { +#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) + PyErr_SetString(PyExc_RuntimeError, + "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); +#else + int val; + PyObject *v = __Pyx_PyNumber_IntOrLong(x); + #if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } + #endif + if (likely(v)) { + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + int ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); + Py_DECREF(v); + if (likely(!ret)) + return val; + } +#endif + return (int) -1; + } + } else { + int val; + PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); + if (!tmp) return (int) -1; + val = __Pyx_PyInt_As_int(tmp); + Py_DECREF(tmp); + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to int"); + return (int) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to int"); + return (int) -1; +} + +/* CIntFromPy */ +static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) { + const long neg_one = (long) ((long) 0 - (long) 1), const_zero = (long) 0; + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if (sizeof(long) < sizeof(long)) { + __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x)) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + goto raise_neg_overflow; + } + return (long) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (long) 0; + case 1: __PYX_VERIFY_RETURN_INT(long, digit, digits[0]) + case 2: + if (8 * sizeof(long) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) >= 2 * PyLong_SHIFT) { + return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + case 3: + if (8 * sizeof(long) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) >= 3 * PyLong_SHIFT) { + return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + case 4: + if (8 * sizeof(long) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) >= 4 * PyLong_SHIFT) { + return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + } +#endif +#if CYTHON_COMPILING_IN_CPYTHON + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (long) -1; + if (unlikely(result == 1)) + goto raise_neg_overflow; + } +#endif + if (sizeof(long) <= sizeof(unsigned long)) { + __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) +#endif + } + } else { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (long) 0; + case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, (sdigit) (-(sdigit)digits[0])) + case 1: __PYX_VERIFY_RETURN_INT(long, digit, +digits[0]) + case -2: + if (8 * sizeof(long) - 1 > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 2: + if (8 * sizeof(long) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case -3: + if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 3: + if (8 * sizeof(long) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case -4: + if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { + return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 4: + if (8 * sizeof(long) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { + return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + } +#endif + if (sizeof(long) <= sizeof(long)) { + __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x)) +#endif + } + } + { +#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) + PyErr_SetString(PyExc_RuntimeError, + "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); +#else + long val; + PyObject *v = __Pyx_PyNumber_IntOrLong(x); + #if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } + #endif + if (likely(v)) { + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + int ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); + Py_DECREF(v); + if (likely(!ret)) + return val; + } +#endif + return (long) -1; + } + } else { + long val; + PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); + if (!tmp) return (long) -1; + val = __Pyx_PyInt_As_long(tmp); + Py_DECREF(tmp); + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to long"); + return (long) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to long"); + return (long) -1; +} + +/* FastTypeChecks */ +#if CYTHON_COMPILING_IN_CPYTHON +static int __Pyx_InBases(PyTypeObject *a, PyTypeObject *b) { + while (a) { + a = a->tp_base; + if (a == b) + return 1; + } + return b == &PyBaseObject_Type; +} +static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b) { + PyObject *mro; + if (a == b) return 1; + mro = a->tp_mro; + if (likely(mro)) { + Py_ssize_t i, n; + n = PyTuple_GET_SIZE(mro); + for (i = 0; i < n; i++) { + if (PyTuple_GET_ITEM(mro, i) == (PyObject *)b) + return 1; + } + return 0; + } + return __Pyx_InBases(a, b); +} +#if PY_MAJOR_VERSION == 2 +static int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject* exc_type2) { + PyObject *exception, *value, *tb; + int res; + __Pyx_PyThreadState_declare + __Pyx_PyThreadState_assign + __Pyx_ErrFetch(&exception, &value, &tb); + res = exc_type1 ? PyObject_IsSubclass(err, exc_type1) : 0; + if (unlikely(res == -1)) { + PyErr_WriteUnraisable(err); + res = 0; + } + if (!res) { + res = PyObject_IsSubclass(err, exc_type2); + if (unlikely(res == -1)) { + PyErr_WriteUnraisable(err); + res = 0; + } + } + __Pyx_ErrRestore(exception, value, tb); + return res; +} +#else +static CYTHON_INLINE int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject *exc_type2) { + int res = exc_type1 ? __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type1) : 0; + if (!res) { + res = __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type2); + } + return res; +} +#endif +static int __Pyx_PyErr_GivenExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) { + Py_ssize_t i, n; + assert(PyExceptionClass_Check(exc_type)); + n = PyTuple_GET_SIZE(tuple); +#if PY_MAJOR_VERSION >= 3 + for (i=0; ip) { + #if PY_MAJOR_VERSION < 3 + if (t->is_unicode) { + *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL); + } else if (t->intern) { + *t->p = PyString_InternFromString(t->s); + } else { + *t->p = PyString_FromStringAndSize(t->s, t->n - 1); + } + #else + if (t->is_unicode | t->is_str) { + if (t->intern) { + *t->p = PyUnicode_InternFromString(t->s); + } else if (t->encoding) { + *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL); + } else { + *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1); + } + } else { + *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1); + } + #endif + if (!*t->p) + return -1; + if (PyObject_Hash(*t->p) == -1) + return -1; + ++t; + } + return 0; +} + +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) { + return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str)); +} +static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject* o) { + Py_ssize_t ignore; + return __Pyx_PyObject_AsStringAndSize(o, &ignore); +} +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT +#if !CYTHON_PEP393_ENABLED +static const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) { + char* defenc_c; + PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL); + if (!defenc) return NULL; + defenc_c = PyBytes_AS_STRING(defenc); +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + { + char* end = defenc_c + PyBytes_GET_SIZE(defenc); + char* c; + for (c = defenc_c; c < end; c++) { + if ((unsigned char) (*c) >= 128) { + PyUnicode_AsASCIIString(o); + return NULL; + } + } + } +#endif + *length = PyBytes_GET_SIZE(defenc); + return defenc_c; +} +#else +static CYTHON_INLINE const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) { + if (unlikely(__Pyx_PyUnicode_READY(o) == -1)) return NULL; +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + if (likely(PyUnicode_IS_ASCII(o))) { + *length = PyUnicode_GET_LENGTH(o); + return PyUnicode_AsUTF8(o); + } else { + PyUnicode_AsASCIIString(o); + return NULL; + } +#else + return PyUnicode_AsUTF8AndSize(o, length); +#endif +} +#endif +#endif +static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) { +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT + if ( +#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + __Pyx_sys_getdefaultencoding_not_ascii && +#endif + PyUnicode_Check(o)) { + return __Pyx_PyUnicode_AsStringAndSize(o, length); + } else +#endif +#if (!CYTHON_COMPILING_IN_PYPY) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE)) + if (PyByteArray_Check(o)) { + *length = PyByteArray_GET_SIZE(o); + return PyByteArray_AS_STRING(o); + } else +#endif + { + char* result; + int r = PyBytes_AsStringAndSize(o, &result, length); + if (unlikely(r < 0)) { + return NULL; + } else { + return result; + } + } +} +static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { + int is_true = x == Py_True; + if (is_true | (x == Py_False) | (x == Py_None)) return is_true; + else return PyObject_IsTrue(x); +} +static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject* x) { + int retval; + if (unlikely(!x)) return -1; + retval = __Pyx_PyObject_IsTrue(x); + Py_DECREF(x); + return retval; +} +static PyObject* __Pyx_PyNumber_IntOrLongWrongResultType(PyObject* result, const char* type_name) { +#if PY_MAJOR_VERSION >= 3 + if (PyLong_Check(result)) { + if (PyErr_WarnFormat(PyExc_DeprecationWarning, 1, + "__int__ returned non-int (type %.200s). " + "The ability to return an instance of a strict subclass of int " + "is deprecated, and may be removed in a future version of Python.", + Py_TYPE(result)->tp_name)) { + Py_DECREF(result); + return NULL; + } + return result; + } +#endif + PyErr_Format(PyExc_TypeError, + "__%.4s__ returned non-%.4s (type %.200s)", + type_name, type_name, Py_TYPE(result)->tp_name); + Py_DECREF(result); + return NULL; +} +static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) { +#if CYTHON_USE_TYPE_SLOTS + PyNumberMethods *m; +#endif + const char *name = NULL; + PyObject *res = NULL; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x) || PyLong_Check(x))) +#else + if (likely(PyLong_Check(x))) +#endif + return __Pyx_NewRef(x); +#if CYTHON_USE_TYPE_SLOTS + m = Py_TYPE(x)->tp_as_number; + #if PY_MAJOR_VERSION < 3 + if (m && m->nb_int) { + name = "int"; + res = m->nb_int(x); + } + else if (m && m->nb_long) { + name = "long"; + res = m->nb_long(x); + } + #else + if (likely(m && m->nb_int)) { + name = "int"; + res = m->nb_int(x); + } + #endif +#else + if (!PyBytes_CheckExact(x) && !PyUnicode_CheckExact(x)) { + res = PyNumber_Int(x); + } +#endif + if (likely(res)) { +#if PY_MAJOR_VERSION < 3 + if (unlikely(!PyInt_Check(res) && !PyLong_Check(res))) { +#else + if (unlikely(!PyLong_CheckExact(res))) { +#endif + return __Pyx_PyNumber_IntOrLongWrongResultType(res, name); + } + } + else if (!PyErr_Occurred()) { + PyErr_SetString(PyExc_TypeError, + "an integer is required"); + } + return res; +} +static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) { + Py_ssize_t ival; + PyObject *x; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_CheckExact(b))) { + if (sizeof(Py_ssize_t) >= sizeof(long)) + return PyInt_AS_LONG(b); + else + return PyInt_AsSsize_t(b); + } +#endif + if (likely(PyLong_CheckExact(b))) { + #if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)b)->ob_digit; + const Py_ssize_t size = Py_SIZE(b); + if (likely(__Pyx_sst_abs(size) <= 1)) { + ival = likely(size) ? digits[0] : 0; + if (size == -1) ival = -ival; + return ival; + } else { + switch (size) { + case 2: + if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { + return (Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -2: + if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case 3: + if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { + return (Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -3: + if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case 4: + if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { + return (Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -4: + if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + } + } + #endif + return PyLong_AsSsize_t(b); + } + x = PyNumber_Index(b); + if (!x) return -1; + ival = PyInt_AsSsize_t(x); + Py_DECREF(x); + return ival; +} +static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b) { + return b ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False); +} +static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) { + return PyInt_FromSize_t(ival); +} + + +#endif /* Py_PYTHON_H */ diff --git a/bin/metrics/metrics.pyx b/bin/metrics/metrics.pyx new file mode 100644 index 0000000..cab0a7f --- /dev/null +++ b/bin/metrics/metrics.pyx @@ -0,0 +1,181 @@ +""" +Common metrics required in machine learning modules. + +Available functions: + - ``rsq``: R-Squared + - ``mse``: Mean squared error + - ``rmse``: Root mean squared error + - ``mae``: Mean absolute error + - ``mape``: Mean absolute percentage error + +Author +------ +:: + + Author: Diptesh Basak + Date: Sep 10, 2021 +""" + +import numpy as _np + +# ============================================================================= +# --- User defined functions +# ============================================================================= + + +cpdef rsq(list y, list y_hat): + """ + Compute `Coefficient of determination + `_ + or R-Squared. + + Parameters + ---------- + y : list + + Actual values. + + y_hat : list + + Predicted values. + + Returns + ------- + op : float + + R-Squared value. + + """ + return _np.round(_np.corrcoef(y, y_hat)[0][1] ** 2, 3) + + +cpdef mse(list y, list y_hat): + """ + Compute `Mean squared error + `_. + + Parameters + ---------- + :y: list + + Actual values. + + :y_hat: list + + Predicted values. + + Returns + ------- + :op: float + + Mean squared error. + + """ + cdef int i + cdef int arr_len + cdef double a + cdef double b + cdef double op = 0.0 + arr_len = len(y) + for i in range(0, arr_len, 1): + a = y[i] + b = y_hat[i] + op = op + (a - b) ** 2 + op = op * arr_len ** -1.0 + return op + +cpdef rmse(list y, list y_hat): + """ + Compute `Root mean square error + `_. + + Parameters + ---------- + y : list + + Actual values. + + y_hat : list + + Predicted values. + + Returns + ------- + op : float + + Root mean square error. + + """ + return mse(y, y_hat) ** 0.5 + + +cpdef mae(list y, list y_hat): + """ + Compute `Mean absolute error + `_. + + Parameters + ---------- + y : list + + Actual values. + + y_hat : list + + Predicted values. + + Returns + ------- + op : float + + Mean absolute error. + + """ + cdef int i + cdef int arr_len + cdef double a + cdef double b + cdef double op = 0.0 + arr_len = len(y) + for i in range(0, arr_len, 1): + a = y[i] + b = y_hat[i] + op += abs(a - b) + op = op * arr_len ** -1.0 + return op + + +cpdef mape(list y, list y_hat): + """ + Compute `Mean absolute percentage error + `_. + + Parameters + ---------- + y : list + + Actual values. + + y_hat : list + + Predicted values. + + Returns + ------- + op : float + + Mean absolute percentage error. + + """ + cdef int i + cdef int arr_len + cdef double a + cdef double b + cdef double op = 0.0 + arr_len = len(y) + for i in range(0, arr_len, 1): + a = y[i] + b = y_hat[i] + op += abs(1 - (b * a ** -1.0)) + op = op * arr_len ** -1.0 + return op diff --git a/bin/metrics/metrics.so b/bin/metrics/metrics.so new file mode 100644 index 0000000000000000000000000000000000000000..be2e20a9dc98f48bf2a6abb303aef09021bc1a31 GIT binary patch literal 181888 zcmeFa3wRV&(l_3nOp{5P%ycpd441$F2?U&k1dK_*XaWgLB!O^K@Ro!mkSHWEnJBn| zxJg{baTIse#p|N$1$Es;U6n<}fau_^7x0R=HQr~$3*xG*7xMk8PWPlUl6QB%|MPtR z?|r^^o`>$LI(6#QsZ*y;ozorq(z1#Nv(6h9H8Ly}Z16-X+s z8B#j)S|TMs2a3lCnsJvjfm0g_fiAk>=i+PRexOp|#@$?x+iTVNqwb%nf^OW+^@gCR 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zM2qm3^;v!!-(>5D`=1VK74l~TtjF>{qWZi~>cLlPjf2=f&VH6%U&*_d*xpdm&j#0xMbng%O_3u{_c0M`Z3+<3AaF!FohLt3&3Dt6p%*jSD)#U*IBoPTjF6l^bB h|El+E{nuf?JNt>{m+6kSo2C2PKhpYRQHAKz{a*(Ntt|im literal 0 HcmV?d00001 diff --git a/bin/metrics/setup.py b/bin/metrics/setup.py new file mode 100644 index 0000000..abd9145 --- /dev/null +++ b/bin/metrics/setup.py @@ -0,0 +1,7 @@ +"""Setup file.""" + +from setuptools import setup +from Cython.Build import cythonize + +setup(name="Shared objects", + ext_modules=cythonize("*.pyx")) diff --git a/log/pylint/lib-glmnet_ts-py.out b/log/pylint/lib-glmnet_ts-py.out new file mode 100644 index 0000000..c86d78e --- /dev/null +++ b/log/pylint/lib-glmnet_ts-py.out @@ -0,0 +1,55 @@ +************* Module mllib.lib.glmnet_ts +glmnet_ts.py:70:5: C0326: Exactly one space required around assignment +y_var=["y"] + ^ (bad-whitespace) +glmnet_ts.py:71:5: C0326: Exactly one space required around assignment +x_var=["x1", "x2"] + ^ (bad-whitespace) +glmnet_ts.py:82:0: C0303: Trailing whitespace (trailing-whitespace) +glmnet_ts.py:116:0: C0303: Trailing whitespace (trailing-whitespace) +glmnet_ts.py:131:13: C0303: Trailing whitespace (trailing-whitespace) +glmnet_ts.py:136:16: C0326: Exactly one space required after comma +for i in range(0,len(df_test)): + ^ (bad-whitespace) +glmnet_ts.py:139:0: C0301: Line too long (102/100) (line-too-long) +glmnet_ts.py:140:20: C0326: Exactly one space required after comma + for j in range(1,len(lag_var)): + ^ (bad-whitespace) +glmnet_ts.py:141:0: C0301: Line too long (103/100) (line-too-long) +glmnet_ts.py:148:16: C0326: Exactly one space required around assignment + df_pred_data=df_pred_data.append(df_tmp).reset_index(drop=True) + ^ (bad-whitespace) +glmnet_ts.py:150:0: C0305: Trailing newlines (trailing-newlines) +glmnet_ts.py:19:20: W0621: Redefining name 'y_var' from outer scope (line 70) (redefined-outer-name) +glmnet_ts.py:20:20: W0621: Redefining name 'x_var' from outer scope (line 71) (redefined-outer-name) +glmnet_ts.py:18:0: C0103: Argument name "df" doesn't conform to snake_case naming style (invalid-name) +glmnet_ts.py:64:4: C0103: Variable name "op" doesn't conform to snake_case naming style (invalid-name) +glmnet_ts.py:68:0: C0103: Constant name "df_ip" doesn't conform to UPPER_CASE naming style (invalid-name) +glmnet_ts.py:70:0: C0103: Constant name "y_var" doesn't conform to UPPER_CASE naming style (invalid-name) +glmnet_ts.py:71:0: C0103: Constant name "x_var" doesn't conform to UPPER_CASE naming style (invalid-name) +glmnet_ts.py:73:0: C0103: Constant name "param" doesn't conform to UPPER_CASE naming style (invalid-name) +glmnet_ts.py:84:0: C0103: Constant name "df_ip" doesn't conform to UPPER_CASE naming style (invalid-name) +glmnet_ts.py:86:0: C0103: Constant name "lag_var" doesn't conform to UPPER_CASE naming style (invalid-name) +glmnet_ts.py:87:0: C0103: Constant name "x_var" doesn't conform to UPPER_CASE naming style (invalid-name) +glmnet_ts.py:90:0: C0103: Constant name "max_epoch" doesn't conform to UPPER_CASE naming style (invalid-name) +glmnet_ts.py:93:0: C0103: Constant name "df_pred_data" doesn't conform to UPPER_CASE naming style (invalid-name) +glmnet_ts.py:95:0: C0103: Constant name "df_train" doesn't conform to UPPER_CASE naming style (invalid-name) +glmnet_ts.py:96:0: C0103: Constant name "df_test" doesn't conform to UPPER_CASE naming style (invalid-name) +glmnet_ts.py:98:0: C0103: Constant name "train_x" doesn't conform to UPPER_CASE naming style (invalid-name) +glmnet_ts.py:99:0: C0103: Constant name "train_y" doesn't conform to UPPER_CASE naming style (invalid-name) +glmnet_ts.py:101:0: C0103: Constant name "test_x" doesn't conform to UPPER_CASE naming style (invalid-name) +glmnet_ts.py:102:0: C0103: Constant name "test_y" doesn't conform to UPPER_CASE naming style (invalid-name) +glmnet_ts.py:108:0: C0103: Constant name "mod" doesn't conform to UPPER_CASE naming style (invalid-name) +glmnet_ts.py:119:0: C0103: Constant name "opt" doesn't conform to UPPER_CASE naming style (invalid-name) +glmnet_ts.py:125:0: C0103: Constant name "model" doesn't conform to UPPER_CASE naming style (invalid-name) +glmnet_ts.py:126:0: W0127: Assigning the same variable 'opt' to itself (self-assigning-variable) +glmnet_ts.py:126:0: C0103: Constant name "opt" doesn't conform to UPPER_CASE naming style (invalid-name) +glmnet_ts.py:130:0: C0103: Constant name "df_predict" doesn't conform to UPPER_CASE naming style (invalid-name) +glmnet_ts.py:132:0: C0103: Constant name "df_predict" doesn't conform to UPPER_CASE naming style (invalid-name) +glmnet_ts.py:133:0: C0103: Constant name "df_predict" doesn't conform to UPPER_CASE naming style (invalid-name) +glmnet_ts.py:9:0: W0611: Unused Dict imported from typing (unused-import) +glmnet_ts.py:15:0: W0611: Unused train_test_split imported from sklearn.model_selection as split (unused-import) + +------------------------------------------------------------------ +Your code has been rated at 4.20/10 (previous run: 4.20/10, +0.00) + diff --git a/log/pylint/metrics-setup-py.out b/log/pylint/metrics-setup-py.out new file mode 100644 index 0000000..d7495ee --- /dev/null +++ b/log/pylint/metrics-setup-py.out @@ -0,0 +1,4 @@ + +-------------------------------------------------------------------- +Your code has been rated at 10.00/10 (previous run: 10.00/10, +0.00) + diff --git a/log/pylint/tests-test_metrics-py.out b/log/pylint/tests-test_metrics-py.out new file mode 100644 index 0000000..d40a4ad --- /dev/null +++ b/log/pylint/tests-test_metrics-py.out @@ -0,0 +1,10 @@ +************* Module tests.test_metrics +test_metrics.py:61:22: I1101: Module 'mllib.lib.metrics' has no 'rsq' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member) +test_metrics.py:69:22: I1101: Module 'mllib.lib.metrics' has no 'mse' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member) +test_metrics.py:77:22: I1101: Module 'mllib.lib.metrics' has no 'rmse' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member) +test_metrics.py:85:22: I1101: Module 'mllib.lib.metrics' has no 'mae' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member) +test_metrics.py:93:22: I1101: Module 'mllib.lib.metrics' has no 'mape' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. 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a/requirements.txt b/requirements.txt index 362a3a8..878016e 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,3 +1,4 @@ -pandas==1.1.3 numpy==1.19.5 +Cython==0.29.15 +pandas==1.1.3 scikit_learn==0.24.2 diff --git a/tests/test_metrics.py b/tests/test_metrics.py new file mode 100644 index 0000000..39f8f1c --- /dev/null +++ b/tests/test_metrics.py @@ -0,0 +1,102 @@ +""" +Test suite module for ``metrics``. + +Credits +------- +:: + + Authors: + - Diptesh + + Date: Sep 01, 2021 +""" + +# pylint: disable=invalid-name +# pylint: disable=wrong-import-position + +import unittest +import warnings +import re +import sys + +from inspect import getsourcefile +from os.path import abspath + +import numpy as np + +# Set base path +path = abspath(getsourcefile(lambda: 0)) +path = re.sub(r"(.+)(\/tests.*)", "\\1", path) + +sys.path.insert(0, path) + +from mllib.lib import metrics # noqa: F841 + +# ============================================================================= +# --- User defined functions +# ============================================================================= + + +def ignore_warnings(test_func): + """Suppress deprecation warnings of pulp.""" + + def do_test(self, *args, **kwargs): + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + test_func(self, *args, **kwargs) + return do_test + + +class TestMetrics(unittest.TestCase): + """Test suite for module ``metrics``.""" + + def setUp(self): + """Set up for module ``metrics``.""" + + def test_rsq(self): + """Metrics: Test for R-squared.""" + y = [3, 8, 10, 17, 24, 27] + y_hat = [2, 8, 10, 13, 18, 20] + exp_op = 0.973 + op = np.round(metrics.rsq(y, y_hat), 3) + self.assertEqual(op, exp_op) + + def test_mse(self): + """Metrics: Test for MSE.""" + y = [34, 37, 44, 47, 48, 48, 46, 43, 32, 27, 26, 24] + y_hat = [37, 40, 46, 44, 46, 50, 45, 44, 34, 30, 22, 23] + exp_op = 5.917 + op = np.round(metrics.mse(y, y_hat), 3) + self.assertEqual(op, exp_op) + + def test_rmse(self): + """Metrics: Test for RMSE.""" + y = [34, 37, 44, 47, 48, 48, 46, 43, 32, 27, 26, 24] + y_hat = [37, 40, 46, 44, 46, 50, 45, 44, 34, 30, 22, 23] + exp_op = 2.432 + op = np.round(metrics.rmse(y, y_hat), 3) + self.assertEqual(op, exp_op) + + def test_mae(self): + """Metrics: Test for MAE.""" + y = [12, 13, 14, 15, 15, 22, 27] + y_hat = [11, 13, 14, 14, 15, 16, 18] + exp_op = 2.429 + op = np.round(metrics.mae(y, y_hat), 3) + self.assertEqual(op, exp_op) + + def test_mape(self): + """Metrics: Test for MAPE.""" + y = [34, 37, 44, 47, 48, 48, 46, 43, 32, 27, 26, 24] + y_hat = [37, 40, 46, 44, 46, 50, 45, 44, 34, 30, 22, 23] + exp_op = 0.065 + op = np.round(metrics.mape(y, y_hat), 3) + self.assertEqual(op, exp_op) + + +# ============================================================================= +# --- Main +# ============================================================================= + +if __name__ == '__main__': + unittest.main() From d7b9bb9aa455bb4c32dc04733a6f52e528fefde0 Mon Sep 17 00:00:00 2001 From: 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a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ -numpy==1.19.5 -Cython==0.29.15 pandas==1.1.3 +Cython==0.29.15 +numpy==1.19.5 scikit_learn==0.24.2 diff --git a/tests/test_metrics.py b/tests/test_metrics.py index 39f8f1c..d9b7eac 100644 --- a/tests/test_metrics.py +++ b/tests/test_metrics.py @@ -8,7 +8,7 @@ Authors: - Diptesh - Date: Sep 01, 2021 + Date: Sep 07, 2021 """ # pylint: disable=invalid-name From c0e5f27804b90492abd8635980134750565f8fdf Mon Sep 17 00:00:00 2001 From: Diptesh Basak Date: Fri, 10 Sep 2021 02:42:51 +0530 Subject: [PATCH 09/30] v0.4.0 --- docs/Directory_structure.md | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/docs/Directory_structure.md b/docs/Directory_structure.md index 6095e7b..816dc0d 100644 --- a/docs/Directory_structure.md +++ b/docs/Directory_structure.md @@ -31,7 +31,14 @@ project_name/ │ ├── bin/ │ ├── hive_queries.sh -│ └── run_tests.sh +│ ├── run_tests.sh +│ └── metrics/ +│ ├── build/ +│ ├── metrics.pyx +│ ├── metrics.so +│ ├── metrics.c +│ ├── setup.py +│ └── build.sh │ ├── data/ │ ├── input/ @@ -42,7 +49,7 @@ project_name/ │ └── model_diagnostics.csv │ ├── docs/ -│ ├── problem_statement.md +│ ├── Branch.md │ ├── Approach.pdf │ └── latex/ │ @@ -67,14 +74,7 @@ project_name/ │ ├── stat.py │ ├── opt.py │ ├── utils.py -│ ├── data_types.py -│ └── tmp/ -│ ├── build/ -│ ├── metrics.pyx -│ ├── metrics.so -│ ├── metrics.c -│ ├── setup.py -│ └── build.sh +│ └── data_types.py │ ├── tests/ │ ├── __init__.py From c4b7bd771dc4845bdcd22b814b67c4f816408d78 Mon Sep 17 00:00:00 2001 From: Diptesh Basak Date: Sat, 11 Sep 2021 00:41:27 +0530 Subject: [PATCH 10/30] v0.4.0 --- log/cov.out | 4 +-- log/pylint/lib-model-py.out | 5 ++++ mllib/__main__.py | 7 +++-- mllib/lib/glmnet_ts.py | 57 ++++++++++++++++++++++++++----------- mllib/lib/model.py | 28 ++++++++++++++++-- requirements.txt | 4 +-- 6 files changed, 80 insertions(+), 25 deletions(-) diff --git a/log/cov.out b/log/cov.out index f87518b..5995bfe 100644 --- a/log/cov.out +++ b/log/cov.out @@ -3,6 +3,6 @@ Name Stmts Miss Cov /media/ph33r/Data/Project/mllib/GitHub/mllib/__init__.py 7 0 100% /media/ph33r/Data/Project/mllib/GitHub/mllib/lib/__init__.py 7 0 100% /media/ph33r/Data/Project/mllib/GitHub/mllib/lib/cluster.py 103 0 100% -/media/ph33r/Data/Project/mllib/GitHub/mllib/lib/model.py 44 0 100% +/media/ph33r/Data/Project/mllib/GitHub/mllib/lib/model.py 60 0 100% -------------------------------------------------------------------------------------------- -TOTAL 161 0 100% +TOTAL 177 0 100% diff --git a/log/pylint/lib-model-py.out b/log/pylint/lib-model-py.out index d7495ee..436caf9 100644 --- a/log/pylint/lib-model-py.out +++ b/log/pylint/lib-model-py.out @@ -1,3 +1,8 @@ +************* Module mllib.lib.model +model.py:191:32: I1101: Module 'metrics' has no 'rsq' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member) +model.py:192:32: I1101: Module 'metrics' has no 'mae' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member) +model.py:193:33: I1101: Module 'metrics' has no 'mape' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member) +model.py:194:33: I1101: Module 'metrics' has no 'rmse' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member) -------------------------------------------------------------------- Your code has been rated at 10.00/10 (previous run: 10.00/10, +0.00) diff --git a/mllib/__main__.py b/mllib/__main__.py index ef53af1..c6d583e 100644 --- a/mllib/__main__.py +++ b/mllib/__main__.py @@ -81,8 +81,11 @@ glm_mod = GLMNet(df=df_ip, y_var=["y"], x_var=["x1", "x2", "x3"]) - print("\nGLMNet\n", - elapsed_time("Time", start_t), + print("\nGLMNet\n") + model_summary = glm_mod.model_summary + for k, v in model_summary.items(): + print(k, v) + print(elapsed_time("Time", start_t), sep="\n") # --- EOF print(sep, elapsed_time("Total time", start), sep, sep="\n") diff --git a/mllib/lib/glmnet_ts.py b/mllib/lib/glmnet_ts.py index 9e4de01..b3b57ac 100644 --- a/mllib/lib/glmnet_ts.py +++ b/mllib/lib/glmnet_ts.py @@ -15,6 +15,9 @@ from sklearn.model_selection import train_test_split as split from sklearn.model_selection import TimeSeriesSplit as ts_split +import metrics + + def create_lag_vars(df: pd.DataFrame, y_var: List[str], x_var: List[str], @@ -65,21 +68,20 @@ def create_lag_vars(df: pd.DataFrame, return op -df_ip = pd.read_csv("/media/madhu/Data/gitHub_kubuntu/mllib/data/" + "input/test_timeseries.csv") +df_ip = pd.read_csv( + "/media/ph33r/Data/Project/mllib/GitHub/data/input/test_timeseries.csv") -y_var=["y"] -x_var=["x1", "x2"] +y_var = ["y"] +x_var = ["x1", "x2"] param = {} -param["a_inc"] = 0.015 +param["a_inc"] = 0.05 param["k_fold"] = 5 param["test_perc"] = 0.2 param["n_jobs"] = -1 param["seed"] = 1 -param["l1_range"] = \ - [x*param["a_inc"] for x in range(\ - 1, int(1/param["a_inc"])+1)] - +param["l1_range"] = list(np.round(np.arange(0.0001, 1.01, param["a_inc"]), 2)) + df_ip = create_lag_vars(df_ip, y_var, x_var, "week") # modify create lag function to get lag list @@ -87,20 +89,26 @@ def create_lag_vars(df: pd.DataFrame, x_var = list(df_ip.columns) x_var.remove(y_var[0]) +# Use len? max_epoch = df_ip.index.max() + 1 # For prediction df_pred_data = df_ip[y_var] -df_train = df_ip[df_ip.index <= max_epoch *(1-param["test_perc"])] -df_test = df_ip[df_ip.index > (max_epoch) *(1-param["test_perc"])] +# Use iloc +df_train = df_ip[df_ip.index <= max_epoch * (1-param["test_perc"])] +df_test = df_ip[df_ip.index > (max_epoch) * (1-param["test_perc"])] train_x = df_train[x_var] train_y = df_train[y_var] +# Should it not be df_test? test_x = df_train[x_var] test_y = df_train[y_var] +test_x = df_test[x_var] +test_y = df_test[y_var] + param["k_fold"] = ts_split(n_splits=param["k_fold"]) param["k_fold"] = param["k_fold"].split(X=train_y) @@ -113,7 +121,7 @@ def create_lag_vars(df: pd.DataFrame, cv=param["k_fold"], n_jobs=param["n_jobs"], random_state=param["seed"]) - + mod.fit(train_x, train_y.values.ravel()) opt = {"alpha": mod.l1_ratio_, @@ -128,23 +136,38 @@ def create_lag_vars(df: pd.DataFrame, # Prediction df_predict = df_test.copy(deep=True) -# reset index +df_predict = df_ip.copy(deep=True) + +# reset index df_predict = df_predict.reset_index(drop=True) df_predict = df_predict[["x1", "x2"]] df_predict["y"] = -1 -for i in range(0,len(df_test)): +# Is there a way to improve this? +for i in range(0, len(df_test)): + # for i in range(0, len(df_ip)): df_pred = df_predict[df_predict.index == i].reset_index(drop=True) df_pred = df_pred[["x1", "x2"]] - df_pred_x = pd.DataFrame({"lag_"+str(lag_var[0]):df_pred_data.iloc[len(df_pred_data)-lag_var[0]]}) - for j in range(1,len(lag_var)): - df_tmp = pd.DataFrame({"lag_"+str(lag_var[j]):df_pred_data.iloc[len(df_pred_data)-lag_var[j]]}) + df_pred_x = pd.DataFrame( + {"lag_"+str(lag_var[0]): df_pred_data.iloc[len(df_pred_data)-lag_var[0]]}) + for j in range(1, len(lag_var)): + df_tmp = pd.DataFrame( + {"lag_"+str(lag_var[j]): df_pred_data.iloc[len(df_pred_data)-lag_var[j]]}) df_pred_x = df_pred_x.join(df_tmp) df_pred_x = df_pred_x.reset_index(drop=True) df_pred_x = df_pred_x.join(df_pred) y_hat = model.predict(df_pred_x) df_tmp = pd.DataFrame() df_tmp['y'] = y_hat - df_pred_data=df_pred_data.append(df_tmp).reset_index(drop=True) + df_pred_data = df_pred_data.append(df_tmp).reset_index(drop=True) df_predict["y"][i] = y_hat + +y = list(df_ip["y"]) +y_hat = list(df_predict["y"]) +model_summary = {"rsq": metrics.rsq(y, y_hat), + "mae": metrics.mae(y, y_hat), + "mape": metrics.mape(y, y_hat), + "rmse": metrics.rmse(y, y_hat)} +model_summary["mse"] = model_summary["rmse"] ** 2 +model_summary diff --git a/mllib/lib/model.py b/mllib/lib/model.py index fb04cb4..a8c7a61 100644 --- a/mllib/lib/model.py +++ b/mllib/lib/model.py @@ -17,16 +17,27 @@ """ # pylint: disable=invalid-name -# pylint: disable=R0902,R0903,R0913,R0914 +# pylint: disable=R0902,R0903,R0913,R0914,C0413 from typing import List, Dict +import re +import sys +from inspect import getsourcefile +from os.path import abspath + import pandas as pd import numpy as np from sklearn.linear_model import ElasticNetCV from sklearn.model_selection import train_test_split as split +path = abspath(getsourcefile(lambda: 0)) +path = re.sub(r"(.+\/)(.+.py)", "\\1", path) +sys.path.insert(0, path) + +import metrics # noqa: F841 + # ============================================================================= # --- DO NOT CHANGE ANYTHING FROM HERE # ============================================================================= @@ -133,6 +144,7 @@ def __init__(self, self.y_var = y_var self.x_var = x_var self.strata = strata + self.model_summary = None if param is None: param = {"seed": 1, "a_inc": 0.05, @@ -142,8 +154,9 @@ def __init__(self, self.param = param self.param["l1_range"] = list(np.round(np.arange(0.0001, 1.01, self.param["a_inc"]), - 10)) + 2)) self._fit() + self._compute_metrics() def _fit(self) -> None: """Fit the best GLMNet model.""" @@ -171,6 +184,17 @@ def _fit(self) -> None: self.model = mod self.opt = opt + def _compute_metrics(self): + """Compute commonly used metrics to evaluate the model.""" + y = self.df[self.y_var].iloc[:, 0].values.tolist() + y_hat = list(self.predict(self.df[self.x_var])["y"].values) + model_summary = {"rsq": metrics.rsq(y, y_hat), + "mae": metrics.mae(y, y_hat), + "mape": metrics.mape(y, y_hat), + "rmse": metrics.rmse(y, y_hat)} + model_summary["mse"] = model_summary["rmse"] ** 2 + self.model_summary = model_summary + def predict(self, df_predict: pd.DataFrame) -> pd.DataFrame: """Predict y_var/target variable. diff --git a/requirements.txt b/requirements.txt index c5b832f..878016e 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ -pandas==1.1.3 -Cython==0.29.15 numpy==1.19.5 +Cython==0.29.15 +pandas==1.1.3 scikit_learn==0.24.2 From 554e3a3ffbc890d9bf963e490c75231fa944672f Mon Sep 17 00:00:00 2001 From: Diptesh Date: Mon, 13 Sep 2021 20:43:18 +0530 Subject: [PATCH 11/30] v0.4.0 --- .github/workflows/python.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.github/workflows/python.yml b/.github/workflows/python.yml index 99d197b..53fe8c0 100644 --- a/.github/workflows/python.yml +++ b/.github/workflows/python.yml @@ -9,12 +9,14 @@ on: - 'stable' - 'testing' - 'feature*' + - 'bug*' - '!maintenance*' pull_request: branches: - 'stable' - 'testing' - 'feature*' + - 'bug*' - '!maintenance*' jobs: From 46afd58c92ef443ccf13e1fdd4445acdac7019dd Mon Sep 17 00:00:00 2001 From: Diptesh Date: Tue, 14 Sep 2021 23:27:10 +0530 Subject: [PATCH 12/30] Update python.yml --- .github/workflows/python.yml | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/.github/workflows/python.yml b/.github/workflows/python.yml index 53fe8c0..5571b99 100644 --- a/.github/workflows/python.yml +++ b/.github/workflows/python.yml @@ -37,12 +37,13 @@ jobs: - name: Install dependencies run: | python -m pip install --upgrade pip - python -m pip install flake8 pytest + python -m pip install flake8 pytest pylint if [ -f requirements.txt ]; then pip install -r requirements.txt; fi - name: Lint with flake8 run: | # stop the build if there are Python syntax errors or undefined names - flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics + # flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics + flake8 . --count --show-source --statistics # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics - name: Test with pytest From 5fbecc45a60b6795278224e6ebea80160f692f00 Mon Sep 17 00:00:00 2001 From: Diptesh Date: Tue, 14 Sep 2021 23:38:14 +0530 Subject: [PATCH 13/30] v0.4.0 --- .github/workflows/python.yml | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/.github/workflows/python.yml b/.github/workflows/python.yml index 5571b99..f0a54c1 100644 --- a/.github/workflows/python.yml +++ b/.github/workflows/python.yml @@ -42,8 +42,7 @@ jobs: - name: Lint with flake8 run: | # stop the build if there are Python syntax errors or undefined names - # flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics - flake8 . --count --show-source --statistics + flake8 . --count --extend-ignore=E402 --show-source --statistics # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics - name: Test with pytest From cd5aea0153bb71196a6ac743b6c137dbd04b5102 Mon Sep 17 00:00:00 2001 From: Diptesh Basak Date: Wed, 15 Sep 2021 00:05:34 +0530 Subject: [PATCH 14/30] v0.4.0 --- mllib/__main__.py | 7 +++---- mllib/lib/model.py | 13 +++++++------ 2 files changed, 10 insertions(+), 10 deletions(-) diff --git a/mllib/__main__.py b/mllib/__main__.py index c6d583e..2fbd285 100644 --- a/mllib/__main__.py +++ b/mllib/__main__.py @@ -80,11 +80,10 @@ df_ip = pd.read_csv(path + "input/test_glmnet.csv") glm_mod = GLMNet(df=df_ip, y_var=["y"], - x_var=["x1", "x2", "x3"]) + x_var=["x1", "x3"]) print("\nGLMNet\n") - model_summary = glm_mod.model_summary - for k, v in model_summary.items(): - print(k, v) + for k, v in glm_mod.model_summary.items(): + print(k, str(v).rjust(69 - len(k))) print(elapsed_time("Time", start_t), sep="\n") # --- EOF diff --git a/mllib/lib/model.py b/mllib/lib/model.py index a8c7a61..49cf649 100644 --- a/mllib/lib/model.py +++ b/mllib/lib/model.py @@ -152,7 +152,8 @@ def __init__(self, "n_jobs": -1, "k_fold": 10} self.param = param - self.param["l1_range"] = list(np.round(np.arange(0.0001, 1.01, + self.param["l1_range"] = list(np.round(np.arange(self.param["a_inc"], + 1.01, self.param["a_inc"]), 2)) self._fit() @@ -188,11 +189,11 @@ def _compute_metrics(self): """Compute commonly used metrics to evaluate the model.""" y = self.df[self.y_var].iloc[:, 0].values.tolist() y_hat = list(self.predict(self.df[self.x_var])["y"].values) - model_summary = {"rsq": metrics.rsq(y, y_hat), - "mae": metrics.mae(y, y_hat), - "mape": metrics.mape(y, y_hat), - "rmse": metrics.rmse(y, y_hat)} - model_summary["mse"] = model_summary["rmse"] ** 2 + model_summary = {"rsq": np.round(metrics.rsq(y, y_hat), 3), + "mae": np.round(metrics.mae(y, y_hat), 3), + "mape": np.round(metrics.mape(y, y_hat), 3), + "rmse": np.round(metrics.rmse(y, y_hat), 3)} + model_summary["mse"] = np.round(model_summary["rmse"] ** 2, 3) self.model_summary = model_summary def predict(self, df_predict: pd.DataFrame) -> pd.DataFrame: From 657584b0a7bc5e765074ad183e841308474eeb07 Mon Sep 17 00:00:00 2001 From: Diptesh Basak Date: Wed, 15 Sep 2021 14:59:49 +0530 Subject: [PATCH 15/30] v0.4.0 --- .github/workflows/{python.yml => build.yml} | 23 +++++++++++++++------ README.md | 2 +- 2 files changed, 18 insertions(+), 7 deletions(-) rename .github/workflows/{python.yml => build.yml} (75%) diff --git a/.github/workflows/python.yml b/.github/workflows/build.yml similarity index 75% rename from .github/workflows/python.yml rename to .github/workflows/build.yml index f0a54c1..56bcd95 100644 --- a/.github/workflows/python.yml +++ b/.github/workflows/build.yml @@ -1,7 +1,20 @@ -# This workflow will install Python dependencies, run tests and lint with a variety of Python versions -# For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions +# ============================================================================= +# Build file +# +# Objective: +# - Install python dependencies +# - Run linter +# - Run tests +# +# Version: 0.1.0 +# +# Author: Diptesh +# +# Date: May 03, 2020 +# +# ============================================================================= -name: Python +name: Build on: push: @@ -9,14 +22,12 @@ on: - 'stable' - 'testing' - 'feature*' - - 'bug*' - '!maintenance*' pull_request: branches: - 'stable' - 'testing' - 'feature*' - - 'bug*' - '!maintenance*' jobs: @@ -37,7 +48,7 @@ jobs: - name: Install dependencies run: | python -m pip install --upgrade pip - python -m pip install flake8 pytest pylint + python -m pip install flake8 pytest if [ -f requirements.txt ]; then pip install -r requirements.txt; fi - name: Lint with flake8 run: | diff --git a/README.md b/README.md index f4fd7de..3b7789a 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -[![Python](https://github.com/bdiptesh/mllib/actions/workflows/python.yml/badge.svg)](https://github.com/bdiptesh/mllib/actions/workflows/python.yml) +[![Build](../../actions/workflows/build.yml/badge.svg)](../../actions/workflows/build.yml) [![pylint Score](https://mperlet.github.io/pybadge/badges/9.5.svg)](./log/pylint/) [![Coverage score](https://img.shields.io/badge/coverage-100%25-dagreen.svg)](./log/cov.out) [![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](./LICENSE) From d72ef39f5e91bc23476aab8644a15928add41612 Mon Sep 17 00:00:00 2001 From: Diptesh Basak Date: Wed, 15 Sep 2021 15:01:14 +0530 Subject: [PATCH 16/30] v0.4.0 --- .github/workflows/build.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 56bcd95..1b3369a 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -22,6 +22,7 @@ on: - 'stable' - 'testing' - 'feature*' + - 'dev*' - '!maintenance*' pull_request: branches: From 00c9b1e63543d0f50a2aa6dcaaf7dbdcda3f0741 Mon Sep 17 00:00:00 2001 From: Diptesh Basak Date: Wed, 15 Sep 2021 15:05:54 +0530 Subject: [PATCH 17/30] v0.4.0 --- .github/workflows/build.yml | 1 - 1 file changed, 1 deletion(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 1b3369a..56bcd95 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -22,7 +22,6 @@ on: - 'stable' - 'testing' - 'feature*' - - 'dev*' - '!maintenance*' pull_request: branches: From 97316946bc197655a8b063a76a9dfcf18ccaa3ad Mon Sep 17 00:00:00 2001 From: Diptesh Basak Date: Wed, 15 Sep 2021 15:19:45 +0530 Subject: [PATCH 18/30] v0.4.0 --- .github/PULL_REQUEST_TEMPLATE.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md index 6e3bc20..9652cab 100644 --- a/.github/PULL_REQUEST_TEMPLATE.md +++ b/.github/PULL_REQUEST_TEMPLATE.md @@ -19,7 +19,7 @@ Please select option(s) that are relevant. Please describe the tests that you ran to verify your changes. Provide instructions so we can reproduce. Please also list any relevant details for your test configuration -- [ ] Unit tests in the code +- [ ] Unit/Integration tests in the code - [ ] Code runs locally without any warnings/errors with test files ## Checklist: From 72bce4e072e1361369b885cc056d3403cfb5f743 Mon Sep 17 00:00:00 2001 From: Diptesh Basak Date: Fri, 17 Sep 2021 10:56:53 +0530 Subject: [PATCH 19/30] v0.4.0 --- .github/CONTRIBUTING.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/CONTRIBUTING.md b/.github/CONTRIBUTING.md index df5a6ad..fd35775 100644 --- a/.github/CONTRIBUTING.md +++ b/.github/CONTRIBUTING.md @@ -24,7 +24,7 @@ standards. rating of 10/10. - Ensure to profile your modules and move any bottlenecks to a low latency system/module. - Ensure to add unit tests with corner cases and achieve 100% code coverage. -- Limit the use of third party libraries. If you do have to use them, ensure that it exists in [bigRED environment](https://wiki.target.com/tgtwiki/index.php/Portal:BigRED/_Software_Libraries). +- Limit the use of third party libraries. If you do have to use them, ensure that it exists in production environment. - Follow [pull request](PULL_REQUEST_TEMPLATE.md) guidelines. ### Guidelines From 5a72566ba7e57e88441c9c0eb3d4e409afc2e35b Mon Sep 17 00:00:00 2001 From: MadhuTangudu Date: Fri, 17 Sep 2021 11:07:42 +0530 Subject: [PATCH 20/30] v0.4.0 changelog: - glmnet_ts class added to model.py - glmnet_ts.py file removed --- mllib/lib/glmnet_ts.py | 173 --------------------------------- mllib/lib/model.py | 215 ++++++++++++++++++++++++++++++++++++++--- 2 files changed, 203 insertions(+), 185 deletions(-) delete mode 100644 mllib/lib/glmnet_ts.py diff --git a/mllib/lib/glmnet_ts.py b/mllib/lib/glmnet_ts.py deleted file mode 100644 index b3b57ac..0000000 --- a/mllib/lib/glmnet_ts.py +++ /dev/null @@ -1,173 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Thu Sep 9 15:25:51 2021 - -@author: madhu -""" - -from typing import List, Dict - -import pandas as pd -import numpy as np - -from sklearn.linear_model import ElasticNetCV -from sklearn.model_selection import train_test_split as split -from sklearn.model_selection import TimeSeriesSplit as ts_split - -import metrics - - -def create_lag_vars(df: pd.DataFrame, - y_var: List[str], - x_var: List[str], - n_interval: str = None) -> pd.DataFrame: - """Create lag variables for time series data. - - Parameters - ---------- - df : pd.DataFrame - - Pandas dataframe containing `y_var`, `x_var` and `n_interval` - (if provided). - - y_var : List[str] - - Dependant variable. - - x_var : List[str] - Independant variables. - - n_interval : str, optional - - Column name of the time interval variable (the default is None). - - Returns - ------- - pd.DataFrame - - Pandas dataframe containing `y_var`, lag variables (`lag_xx`) and - `x_var`. - - """ - if n_interval is None: - y_lag = df[y_var].reset_index(drop=True) - else: - y_lag = df.sort_values(by=n_interval) - y_lag = y_lag[y_var].reset_index(drop=True) - time_int = len(y_lag) - lag_interval = [] - while time_int > 8: - time_int = int(np.floor(time_int/2)) - lag_interval.extend([time_int]) - lag_interval.extend([4, 3, 2, 1]) - for lag in lag_interval: - y_lag.loc[:, "lag_" + str(lag)] = y_lag["y"].shift(lag) - y_lag = y_lag.join(df[x_var]) - op = y_lag.dropna().reset_index(drop=True) - return op - - -df_ip = pd.read_csv( - "/media/ph33r/Data/Project/mllib/GitHub/data/input/test_timeseries.csv") - -y_var = ["y"] -x_var = ["x1", "x2"] - -param = {} -param["a_inc"] = 0.05 -param["k_fold"] = 5 -param["test_perc"] = 0.2 -param["n_jobs"] = -1 -param["seed"] = 1 -param["l1_range"] = list(np.round(np.arange(0.0001, 1.01, param["a_inc"]), 2)) - - -df_ip = create_lag_vars(df_ip, y_var, x_var, "week") -# modify create lag function to get lag list -lag_var = [52, 26, 13, 6, 4, 3, 2, 1] -x_var = list(df_ip.columns) -x_var.remove(y_var[0]) - -# Use len? -max_epoch = df_ip.index.max() + 1 - -# For prediction -df_pred_data = df_ip[y_var] - -# Use iloc -df_train = df_ip[df_ip.index <= max_epoch * (1-param["test_perc"])] -df_test = df_ip[df_ip.index > (max_epoch) * (1-param["test_perc"])] - -train_x = df_train[x_var] -train_y = df_train[y_var] - -# Should it not be df_test? -test_x = df_train[x_var] -test_y = df_train[y_var] - -test_x = df_test[x_var] -test_y = df_test[y_var] - -param["k_fold"] = ts_split(n_splits=param["k_fold"]) -param["k_fold"] = param["k_fold"].split(X=train_y) - - -mod = ElasticNetCV(l1_ratio=param["l1_range"], - fit_intercept=True, - alphas=[1e-5, 1e-4, 1e-3, 1e-2, 1e-1, - 1.0, 10.0, 100.0], - normalize=True, - cv=param["k_fold"], - n_jobs=param["n_jobs"], - random_state=param["seed"]) - -mod.fit(train_x, train_y.values.ravel()) - -opt = {"alpha": mod.l1_ratio_, - "lambda": mod.alpha_, - "intercept": mod.intercept_, - "coef": mod.coef_, - "train_v": mod.score(train_x, train_y), - "test_v": mod.score(test_x, test_y)} -model = mod -opt = opt - - -# Prediction -df_predict = df_test.copy(deep=True) -df_predict = df_ip.copy(deep=True) - -# reset index -df_predict = df_predict.reset_index(drop=True) -df_predict = df_predict[["x1", "x2"]] -df_predict["y"] = -1 - -# Is there a way to improve this? -for i in range(0, len(df_test)): - # for i in range(0, len(df_ip)): - df_pred = df_predict[df_predict.index == i].reset_index(drop=True) - df_pred = df_pred[["x1", "x2"]] - df_pred_x = pd.DataFrame( - {"lag_"+str(lag_var[0]): df_pred_data.iloc[len(df_pred_data)-lag_var[0]]}) - for j in range(1, len(lag_var)): - df_tmp = pd.DataFrame( - {"lag_"+str(lag_var[j]): df_pred_data.iloc[len(df_pred_data)-lag_var[j]]}) - df_pred_x = df_pred_x.join(df_tmp) - df_pred_x = df_pred_x.reset_index(drop=True) - df_pred_x = df_pred_x.join(df_pred) - y_hat = model.predict(df_pred_x) - df_tmp = pd.DataFrame() - df_tmp['y'] = y_hat - df_pred_data = df_pred_data.append(df_tmp).reset_index(drop=True) - df_predict["y"][i] = y_hat - - -y = list(df_ip["y"]) -y_hat = list(df_predict["y"]) -model_summary = {"rsq": metrics.rsq(y, y_hat), - "mae": metrics.mae(y, y_hat), - "mape": metrics.mape(y, y_hat), - "rmse": metrics.rmse(y, y_hat)} -model_summary["mse"] = model_summary["rmse"] ** 2 -model_summary diff --git a/mllib/lib/model.py b/mllib/lib/model.py index 49cf649..b9767f8 100644 --- a/mllib/lib/model.py +++ b/mllib/lib/model.py @@ -31,6 +31,7 @@ from sklearn.linear_model import ElasticNetCV from sklearn.model_selection import train_test_split as split +from sklearn.model_selection import TimeSeriesSplit as ts_split path = abspath(getsourcefile(lambda: 0)) path = re.sub(r"(.+\/)(.+.py)", "\\1", path) @@ -46,6 +47,7 @@ def create_lag_vars(df: pd.DataFrame, y_var: List[str], x_var: List[str], + lst_lag: List[int] = None, n_interval: str = None) -> pd.DataFrame: """Create lag variables for time series data. @@ -61,9 +63,12 @@ def create_lag_vars(df: pd.DataFrame, Dependant variable. x_var : List[str] - Independant variables. + lst_lag : List[int] + + Lag values list (the default is None) + n_interval : str, optional Column name of the time interval variable (the default is None). @@ -77,21 +82,29 @@ def create_lag_vars(df: pd.DataFrame, """ if n_interval is None: - y_lag = df[y_var].reset_index(drop=True) + df = df.reset_index(drop=True) + elif len(df) != (df[n_interval].max() - df[n_interval].min() + 1): + sys.exit("Missing/duplicate time instance found in input data") else: - y_lag = df.sort_values(by=n_interval) - y_lag = y_lag[y_var].reset_index(drop=True) + df = df.sort_values(by=n_interval) + df = df.reset_index(drop=True) + y_lag = df[y_var].copy(deep=True) time_int = len(y_lag) - lag_interval = [] - while time_int > 8: - time_int = int(np.floor(time_int/2)) - lag_interval.extend([time_int]) - lag_interval.extend([4, 3, 2, 1]) - for lag in lag_interval: + if lst_lag is None: + lst_lag = [] + while time_int > 8: + time_int = int(np.floor(time_int/2)) + lst_lag.extend([time_int]) + lst_lag.extend([4, 3, 2, 1]) + for lag in lst_lag: y_lag.loc[:, "lag_" + str(lag)] = y_lag["y"].shift(lag) y_lag = y_lag.join(df[x_var]) - op = y_lag.dropna().reset_index(drop=True) - return op + if n_interval: + y_lag = y_lag.join(df[n_interval]) + y_lag = y_lag.set_index(n_interval) + op = y_lag.dropna() + return lst_lag, op + class GLMNet(): @@ -216,3 +229,181 @@ def predict(self, df_predict: pd.DataFrame) -> pd.DataFrame: df_predict = df_predict.copy() df_predict["y"] = y_hat return df_predict + +class GLMNet_ts(): + """GLMNet time series module. + + Objective: + - Build + `GLMNet `_ + model using optimal alpha and lambda + + Parameters + ---------- + df : pd.DataFrame + + Pandas dataframe containing `y_var` and `x_var` variables. + + y_var : List[str] + + Dependant variable. + + x_var : List[str] + + Independant variables. + + lst_lag : List[int] + + Lag values list (the default is None) + + n_interval : str, optional + + Column name of the time interval variable (the default is None). + + + param : Dict, optional + + GLMNet parameters (the default is None). + In case of None, the parameters will default to:: + + seed: 1 + a_inc: 0.05 + test_perc: 0.25 + n_jobs: -1 + k_fold: 10 + + """ + + def __init__(self, + df: pd.DataFrame, + y_var: List[str], + x_var: List[str], + lst_lag: List[int] = None, + n_interval: str = None, + param: Dict = None): + """Initialize variables for module ``GLMNet``.""" + self.df = df.sort_values(by=n_interval)[y_var + x_var] + self.y_var = y_var + self.x_var = x_var + self.lst_lag = lst_lag + self.n_interval = n_interval + self.model_summary = None + self.max_epoch = None + if param is None: + param = {"seed": 1, + "a_inc": 0.05, + "test_perc": 0.25, + "n_jobs": -1, + "k_fold": 10} + self.param = param + self.param["l1_range"] = list(np.round(np.arange(self.param["a_inc"], + 1.01, + self.param["a_inc"]), + 2)) + self._fit() + self._compute_metrics() + + def _fit(self) -> None: + """Fit the best GLMNet time series model.""" + if self.n_interval is None: + self.max_epoch = len(self.df) - 1 + else: + self.max_epoch = self.df[self.n_interval].max() + self.lag_var, df_ip = create_lag_vars(self.df, + self.y_var, + self.x_var, + self.lst_lag, + self.n_interval) + self.x_var = list(self.df.columns) + self.x_var.remove(self.y_var[0]) + df_train = df_ip.iloc[0:int(len(df_ip) * (1-self.param["test_perc"]))] + df_test = df_ip.iloc[int(len(df_ip) * (1-self.param["test_perc"])):] + train_x = df_train[self.x_var] + train_y = df_train[self.y_var] + test_x = df_test[self.x_var] + test_y = df_test[self.y_var] + self.param["k_fold"] = ts_split(n_splits=self.param["k_fold"]) + self.param["k_fold"] = self.param["k_fold"].split(X=train_y) + mod = ElasticNetCV(l1_ratio=self.param["l1_range"], + fit_intercept=True, + alphas=[1e-5, 1e-4, 1e-3, 1e-2, 1e-1, + 1.0, 10.0, 100.0], + normalize=True, + cv=self.param["k_fold"], + n_jobs=self.param["n_jobs"], + random_state=self.param["seed"]) + mod.fit(train_x, train_y.values.ravel()) + opt = {"alpha": mod.l1_ratio_, + "lambda": mod.alpha_, + "intercept": mod.intercept_, + "coef": mod.coef_, + "train_v": mod.score(train_x, train_y), + "test_v": mod.score(test_x, test_y)} + self.model = mod + self.opt = opt + + def _compute_metrics(self): + """Compute commonly used metrics to evaluate the model.""" + y = self.df[self.y_var].iloc[:, 0].values.tolist() + y_hat = list(self.predict(self.df[self.x_var])["y"].values) + model_summary = {"rsq": np.round(metrics.rsq(y, y_hat), 3), + "mae": np.round(metrics.mae(y, y_hat), 3), + "mape": np.round(metrics.mape(y, y_hat), 3), + "rmse": np.round(metrics.rmse(y, y_hat), 3)} + model_summary["mse"] = np.round(model_summary["rmse"] ** 2, 3) + self.model_summary = model_summary + + def predict(self, df_predict: pd.DataFrame) -> pd.DataFrame: + """Predict y_var/target variable. + + Parameters + ---------- + df_predict : pd.DataFrame + + Pandas dataframe containing `x_var`. + + Returns + ------- + pd.DataFrame + + Pandas dataframe containing predicted `y_var` and `x_var`. + + """ + if self.n_interval is None: + df_predict = df_predict.reset_index(drop=True) + df_predict = \ + df_predict.set_index(df_predict.index+self.max_epoch+1) + elif len(df_predict) != (df_predict[self.n_interval].max() \ + - df_predict[self.n_interval].min() + 1) \ + or df_predict[self.n_interval].min() \ + > self.max_epoch+1: + sys.exit("Missing time instance found in input data") + else: + df_ip = self.df[self.df[self.n_interval] \ + <= df_predict[self.n_interval].min()] + df_predict = df_predict.sort_values(by=self.n_interval) + df_predict = df_predict.set_index(self.n_interval) + df_predict = df_predict[self.x_var] + df_predict["y"] = -1 + for i in range(0, len(df_predict)): + # for i in range(0, len(df_ip)): + df_pred = pd.DataFrame(df_predict.iloc[i]) + df_pred = df_pred.T # Transpose + period_val = df_pred.index + df_pred = df_pred[self.x_var].reset_index(drop=True) + df_pred_x = pd.DataFrame( + {"lag_"+str(self.lst_lag[0]): df_ip.iloc[len(df_ip)\ + -self.lst_lag[0]]}) + for j in range(1, len(self.lst_lag)): + df_tmp = pd.DataFrame( + {"lag_"+str(self.lst_lag[j]): \ + df_ip.iloc[len(df_ip)-self.lst_lag[j]]}) + df_pred_x = df_pred_x.join(df_tmp) + df_pred_x = df_pred_x.reset_index(drop=True) + df_pred_x = df_pred_x.join(df_pred) + y_hat = self.model.predict(df_pred_x) + df_tmp = pd.DataFrame() + df_tmp['y'] = y_hat + df_ip = df_ip.append(df_tmp).reset_index(drop=True) + df_predict.loc[period_val, "y"] = y_hat + return df_predict From 4398d8a1e37055a869c0e34d89fcfecc66216482 Mon Sep 17 00:00:00 2001 From: Diptesh Basak Date: Fri, 17 Sep 2021 11:11:06 +0530 Subject: [PATCH 21/30] v0.4.0 changelog: - changed log to logs - removed redundant links in contribution guidelines --- README.md | 6 +- bin/run_tests.sh | 16 +++--- log/pylint/lib-glmnet_ts-py.out | 55 ------------------- {log => logs}/cov.out | 4 +- {log => logs}/pip.out | 0 {log => logs}/pylint/lib-__init__-py.out | 0 {log => logs}/pylint/lib-cfg-py.out | 0 {log => logs}/pylint/lib-cluster-py.out | 0 {log => logs}/pylint/lib-glmnet-py.out | 0 logs/pylint/lib-glmnet_ts-py.out | 45 +++++++++++++++ {log => logs}/pylint/lib-model-py.out | 8 +-- {log => logs}/pylint/lib-utils-py.out | 0 {log => logs}/pylint/metrics-setup-py.out | 0 {log => logs}/pylint/mllib-__init__-py.out | 0 {log => logs}/pylint/mllib-__main__-py.out | 0 {log => logs}/pylint/pylint.out | 0 {log => logs}/pylint/tests-__init__-py.out | 0 .../pylint/tests-test_cluster-py.out | 0 .../pylint/tests-test_metrics-py.out | 0 {log => logs}/pylint/tests-test_model-py.out | 0 requirements.txt | 2 +- 21 files changed, 63 insertions(+), 73 deletions(-) delete mode 100644 log/pylint/lib-glmnet_ts-py.out rename {log => logs}/cov.out (94%) rename {log => logs}/pip.out (100%) rename {log => logs}/pylint/lib-__init__-py.out (100%) rename {log => logs}/pylint/lib-cfg-py.out (100%) rename {log => logs}/pylint/lib-cluster-py.out (100%) rename {log => logs}/pylint/lib-glmnet-py.out (100%) create mode 100644 logs/pylint/lib-glmnet_ts-py.out rename {log => logs}/pylint/lib-model-py.out (78%) rename {log => logs}/pylint/lib-utils-py.out (100%) rename {log => logs}/pylint/metrics-setup-py.out (100%) rename {log => logs}/pylint/mllib-__init__-py.out (100%) rename {log => logs}/pylint/mllib-__main__-py.out (100%) rename {log => logs}/pylint/pylint.out (100%) rename {log => logs}/pylint/tests-__init__-py.out (100%) rename {log => logs}/pylint/tests-test_cluster-py.out (100%) rename {log => logs}/pylint/tests-test_metrics-py.out (100%) rename {log => logs}/pylint/tests-test_model-py.out (100%) diff --git a/README.md b/README.md index 3b7789a..685a5a6 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ [![Build](../../actions/workflows/build.yml/badge.svg)](../../actions/workflows/build.yml) -[![pylint Score](https://mperlet.github.io/pybadge/badges/9.5.svg)](./log/pylint/) -[![Coverage score](https://img.shields.io/badge/coverage-100%25-dagreen.svg)](./log/cov.out) +[![pylint Score](https://mperlet.github.io/pybadge/badges/9.6.svg)](./logs/pylint/) +[![Coverage score](https://img.shields.io/badge/coverage-100%25-dagreen.svg)](./logs/cov.out) [![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](./LICENSE) *** @@ -75,7 +75,7 @@ Available options: -r code rating. ``` The pylint ratings for each python script can be found at -[log/pylint/](./log/pylint/) +[logs/pylint/](./logs/pylint/) *** ## Important links diff --git a/bin/run_tests.sh b/bin/run_tests.sh index 6ca921f..ff63c0e 100644 --- a/bin/run_tests.sh +++ b/bin/run_tests.sh @@ -33,14 +33,14 @@ if [[ $module == "-a" || $module == "-u" ]] then printf "\nRunning unit & integration tests...\n\n" coverage run -m unittest discover -v -s $test_dir -p "test_*.py" - coverage report -m --omit="*/tests/test_*,*/opt/spark-*" > "$proj_dir/log/cov.out" - COV_SCORE=`grep "TOTAL" $proj_dir/log/cov.out | tail -1 | awk '{ printf("%d", $4) }'` + coverage report -m --omit="*/tests/test_*,*/opt/spark-*" > "$proj_dir/logs/cov.out" + COV_SCORE=`grep "TOTAL" $proj_dir/logs/cov.out | tail -1 | awk '{ printf("%d", $4) }'` COV_COLOR="red" if [[ $COV_SCORE == "100" ]] then COV_COLOR="dagreen" fi - sed -i "3s/.*/\[\!\[Coverage score\]\(\https\:\/\/img\.shields\.io\/badge\/coverage\-$COV_SCORE\%25\-$COV_COLOR.svg\)\]\(\.\/log\/cov\.out\)/" "$proj_dir/README.md" + sed -i "3s/.*/\[\!\[Coverage score\]\(\https\:\/\/img\.shields\.io\/badge\/coverage\-$COV_SCORE\%25\-$COV_COLOR.svg\)\]\(\.\/logs\/cov\.out\)/" "$proj_dir/README.md" printf "=%.0s" {1..70} printf "\n" fi @@ -57,11 +57,11 @@ then printf "%-67s %s" "$file" file_dir=$(sed -E 's/(.+\/)(.+\.py)/\1/' <<< $i) cd "$file_dir" - pylint "$i" > "$proj_dir/log/pylint/pylint.out" - PYLINT_SCORE=`grep "Your code has been rated" $proj_dir/log/pylint/pylint.out | cut -d" " -f7 | cut -d"." -f1` + pylint "$i" > "$proj_dir/logs/pylint/pylint.out" + PYLINT_SCORE=`grep "Your code has been rated" $proj_dir/logs/pylint/pylint.out | cut -d" " -f7 | cut -d"." -f1` file_name=$(sed -E 's/(\/)/-/' <<< $file) file_name=$(sed -E 's/(\.)/-/' <<< $file_name) - cp "$proj_dir/log/pylint/pylint.out" "$proj_dir/log/pylint/$file_name.out" + cp "$proj_dir/logs/pylint/pylint.out" "$proj_dir/logs/pylint/$file_name.out" score=$((score + PYLINT_SCORE)) cnt=$((cnt + 1)) printf "$PYLINT_SCORE\n" @@ -70,11 +70,11 @@ then tot_score=$(echo "scale=1; $score/$cnt" | bc) printf "\nTotal score: $tot_score\n" # Add pylint badge to README.md - sed -i "2s/.*/\[\!\[pylint Score\]\(https\:\/\/mperlet\.github\.io\/pybadge\/badges\/$tot_score.svg\)\]\(\.\/log\/pylint\/\)/" "$proj_dir/README.md" + sed -i "2s/.*/\[\!\[pylint Score\]\(https\:\/\/mperlet\.github\.io\/pybadge\/badges\/$tot_score.svg\)\]\(\.\/logs\/pylint\/\)/" "$proj_dir/README.md" printf "=%.0s" {1..70} printf "\n" fi -pipreqs --force $proj_dir &> $proj_dir/log/pip.out +pipreqs --force $proj_dir &> $proj_dir/logs/pip.out exit 0 diff --git a/log/pylint/lib-glmnet_ts-py.out b/log/pylint/lib-glmnet_ts-py.out deleted file mode 100644 index c86d78e..0000000 --- a/log/pylint/lib-glmnet_ts-py.out +++ /dev/null @@ -1,55 +0,0 @@ -************* Module mllib.lib.glmnet_ts -glmnet_ts.py:70:5: C0326: Exactly one space required around assignment -y_var=["y"] - ^ (bad-whitespace) -glmnet_ts.py:71:5: C0326: Exactly one space required around assignment -x_var=["x1", "x2"] - ^ (bad-whitespace) -glmnet_ts.py:82:0: C0303: Trailing whitespace (trailing-whitespace) -glmnet_ts.py:116:0: C0303: Trailing whitespace (trailing-whitespace) -glmnet_ts.py:131:13: C0303: Trailing whitespace (trailing-whitespace) -glmnet_ts.py:136:16: C0326: Exactly one space required after comma -for i in range(0,len(df_test)): - ^ (bad-whitespace) -glmnet_ts.py:139:0: C0301: Line too long (102/100) (line-too-long) -glmnet_ts.py:140:20: C0326: Exactly one space required after comma - for j in range(1,len(lag_var)): - ^ (bad-whitespace) -glmnet_ts.py:141:0: C0301: Line too long (103/100) (line-too-long) -glmnet_ts.py:148:16: C0326: Exactly one space required around assignment - df_pred_data=df_pred_data.append(df_tmp).reset_index(drop=True) - ^ (bad-whitespace) -glmnet_ts.py:150:0: C0305: Trailing newlines (trailing-newlines) -glmnet_ts.py:19:20: W0621: Redefining name 'y_var' from outer scope (line 70) (redefined-outer-name) -glmnet_ts.py:20:20: W0621: Redefining name 'x_var' from outer scope (line 71) (redefined-outer-name) -glmnet_ts.py:18:0: C0103: Argument name "df" doesn't conform to snake_case naming style (invalid-name) -glmnet_ts.py:64:4: C0103: Variable name "op" doesn't conform to snake_case naming style (invalid-name) -glmnet_ts.py:68:0: C0103: Constant name "df_ip" doesn't conform to UPPER_CASE naming style (invalid-name) -glmnet_ts.py:70:0: C0103: Constant name "y_var" doesn't conform to UPPER_CASE naming style (invalid-name) -glmnet_ts.py:71:0: C0103: Constant name "x_var" doesn't conform to UPPER_CASE naming style (invalid-name) -glmnet_ts.py:73:0: C0103: Constant name "param" doesn't conform to UPPER_CASE naming style (invalid-name) -glmnet_ts.py:84:0: C0103: Constant name "df_ip" doesn't conform to UPPER_CASE naming style (invalid-name) -glmnet_ts.py:86:0: C0103: Constant name "lag_var" doesn't conform to UPPER_CASE naming style (invalid-name) -glmnet_ts.py:87:0: C0103: Constant name "x_var" doesn't conform to UPPER_CASE naming style (invalid-name) -glmnet_ts.py:90:0: C0103: Constant name "max_epoch" doesn't conform to UPPER_CASE naming style (invalid-name) -glmnet_ts.py:93:0: C0103: Constant name "df_pred_data" doesn't conform to UPPER_CASE naming style (invalid-name) -glmnet_ts.py:95:0: C0103: Constant name "df_train" doesn't conform to UPPER_CASE naming style (invalid-name) -glmnet_ts.py:96:0: C0103: Constant name "df_test" doesn't conform to UPPER_CASE naming style (invalid-name) -glmnet_ts.py:98:0: C0103: Constant name "train_x" doesn't conform to UPPER_CASE naming style (invalid-name) -glmnet_ts.py:99:0: C0103: Constant name "train_y" doesn't conform to UPPER_CASE naming style (invalid-name) -glmnet_ts.py:101:0: C0103: Constant name "test_x" doesn't conform to UPPER_CASE naming style (invalid-name) -glmnet_ts.py:102:0: C0103: Constant name "test_y" doesn't conform to UPPER_CASE naming style (invalid-name) -glmnet_ts.py:108:0: C0103: Constant name "mod" doesn't conform to UPPER_CASE naming style (invalid-name) -glmnet_ts.py:119:0: C0103: Constant name "opt" doesn't conform to UPPER_CASE naming style (invalid-name) -glmnet_ts.py:125:0: C0103: Constant name "model" doesn't conform to UPPER_CASE naming style (invalid-name) -glmnet_ts.py:126:0: W0127: Assigning the same variable 'opt' to itself (self-assigning-variable) -glmnet_ts.py:126:0: C0103: Constant name "opt" doesn't conform to UPPER_CASE naming style (invalid-name) -glmnet_ts.py:130:0: C0103: Constant name "df_predict" doesn't conform to UPPER_CASE naming style (invalid-name) -glmnet_ts.py:132:0: C0103: Constant name "df_predict" doesn't conform to UPPER_CASE naming style (invalid-name) -glmnet_ts.py:133:0: C0103: Constant name "df_predict" doesn't conform to UPPER_CASE naming style (invalid-name) -glmnet_ts.py:9:0: W0611: Unused Dict imported from typing (unused-import) -glmnet_ts.py:15:0: W0611: Unused train_test_split imported from sklearn.model_selection as split (unused-import) - ------------------------------------------------------------------- -Your code has been rated at 4.20/10 (previous run: 4.20/10, +0.00) - diff --git a/log/cov.out b/logs/cov.out similarity index 94% rename from log/cov.out rename to logs/cov.out index 5995bfe..2023a09 100644 --- a/log/cov.out +++ b/logs/cov.out @@ -3,6 +3,6 @@ Name Stmts Miss Cov /media/ph33r/Data/Project/mllib/GitHub/mllib/__init__.py 7 0 100% /media/ph33r/Data/Project/mllib/GitHub/mllib/lib/__init__.py 7 0 100% /media/ph33r/Data/Project/mllib/GitHub/mllib/lib/cluster.py 103 0 100% -/media/ph33r/Data/Project/mllib/GitHub/mllib/lib/model.py 60 0 100% +/media/ph33r/Data/Project/mllib/GitHub/mllib/lib/model.py 59 0 100% -------------------------------------------------------------------------------------------- -TOTAL 177 0 100% +TOTAL 176 0 100% diff --git a/log/pip.out b/logs/pip.out similarity index 100% rename from log/pip.out rename to logs/pip.out diff --git a/log/pylint/lib-__init__-py.out b/logs/pylint/lib-__init__-py.out similarity index 100% rename from log/pylint/lib-__init__-py.out rename to logs/pylint/lib-__init__-py.out diff --git a/log/pylint/lib-cfg-py.out b/logs/pylint/lib-cfg-py.out similarity index 100% rename from log/pylint/lib-cfg-py.out rename to logs/pylint/lib-cfg-py.out diff --git a/log/pylint/lib-cluster-py.out b/logs/pylint/lib-cluster-py.out similarity index 100% rename from log/pylint/lib-cluster-py.out rename to logs/pylint/lib-cluster-py.out diff --git a/log/pylint/lib-glmnet-py.out b/logs/pylint/lib-glmnet-py.out similarity index 100% rename from log/pylint/lib-glmnet-py.out rename to logs/pylint/lib-glmnet-py.out diff --git a/logs/pylint/lib-glmnet_ts-py.out b/logs/pylint/lib-glmnet_ts-py.out new file mode 100644 index 0000000..3ecdf82 --- /dev/null +++ b/logs/pylint/lib-glmnet_ts-py.out @@ -0,0 +1,45 @@ +************* Module mllib.lib.glmnet_ts +glmnet_ts.py:22:20: W0621: Redefining name 'y_var' from outer scope (line 74) (redefined-outer-name) +glmnet_ts.py:23:20: W0621: Redefining name 'x_var' from outer scope (line 75) (redefined-outer-name) +glmnet_ts.py:21:0: C0103: Argument name "df" doesn't conform to snake_case naming style (invalid-name) +glmnet_ts.py:67:4: C0103: Variable name "op" doesn't conform to snake_case naming style (invalid-name) +glmnet_ts.py:71:0: C0103: Constant name "df_ip" doesn't conform to UPPER_CASE naming style (invalid-name) +glmnet_ts.py:74:0: C0103: Constant name "y_var" doesn't conform to UPPER_CASE naming style (invalid-name) +glmnet_ts.py:75:0: C0103: Constant name "x_var" doesn't conform to UPPER_CASE naming style (invalid-name) +glmnet_ts.py:77:0: C0103: Constant name "param" doesn't conform to UPPER_CASE naming style (invalid-name) +glmnet_ts.py:86:0: C0103: Constant name "df_ip" doesn't conform to UPPER_CASE naming style (invalid-name) +glmnet_ts.py:88:0: C0103: Constant name "lag_var" doesn't conform to UPPER_CASE naming style (invalid-name) +glmnet_ts.py:89:0: C0103: Constant name "x_var" doesn't conform to UPPER_CASE naming style (invalid-name) +glmnet_ts.py:93:0: C0103: Constant name "max_epoch" doesn't conform to UPPER_CASE naming style (invalid-name) +glmnet_ts.py:96:0: C0103: Constant name "df_pred_data" doesn't conform to UPPER_CASE naming style (invalid-name) +glmnet_ts.py:99:0: C0103: Constant name "df_train" doesn't conform to UPPER_CASE naming style (invalid-name) +glmnet_ts.py:100:0: C0103: Constant name "df_test" doesn't conform to UPPER_CASE naming style (invalid-name) +glmnet_ts.py:102:0: C0103: Constant name "train_x" doesn't conform to UPPER_CASE naming style (invalid-name) +glmnet_ts.py:103:0: C0103: Constant name "train_y" doesn't conform to UPPER_CASE naming style (invalid-name) +glmnet_ts.py:106:0: C0103: Constant name "test_x" doesn't conform to UPPER_CASE naming style (invalid-name) +glmnet_ts.py:107:0: C0103: Constant name "test_y" doesn't conform to UPPER_CASE naming style (invalid-name) +glmnet_ts.py:109:0: C0103: Constant name "test_x" doesn't conform to UPPER_CASE naming style (invalid-name) +glmnet_ts.py:110:0: C0103: Constant name "test_y" doesn't conform to UPPER_CASE naming style (invalid-name) +glmnet_ts.py:116:0: C0103: Constant name "mod" doesn't conform to UPPER_CASE naming style (invalid-name) +glmnet_ts.py:127:0: C0103: Constant name "opt" doesn't conform to UPPER_CASE naming style (invalid-name) +glmnet_ts.py:133:0: C0103: Constant name "model" doesn't conform to UPPER_CASE naming style (invalid-name) +glmnet_ts.py:134:0: W0127: Assigning the same variable 'opt' to itself (self-assigning-variable) +glmnet_ts.py:134:0: C0103: Constant name "opt" doesn't conform to UPPER_CASE naming style (invalid-name) +glmnet_ts.py:138:0: C0103: Constant name "df_predict" doesn't conform to UPPER_CASE naming style (invalid-name) +glmnet_ts.py:139:0: C0103: Constant name "df_predict" doesn't conform to UPPER_CASE naming style (invalid-name) +glmnet_ts.py:142:0: C0103: Constant name "df_predict" doesn't conform to UPPER_CASE naming style (invalid-name) +glmnet_ts.py:143:0: C0103: Constant name "df_predict" doesn't conform to UPPER_CASE naming style (invalid-name) +glmnet_ts.py:166:0: C0103: Constant name "y" doesn't conform to UPPER_CASE naming style (invalid-name) +glmnet_ts.py:167:0: C0103: Constant name "y_hat" doesn't conform to UPPER_CASE naming style (invalid-name) +glmnet_ts.py:168:0: C0103: Constant name "model_summary" doesn't conform to UPPER_CASE naming style (invalid-name) +glmnet_ts.py:168:24: I1101: Module 'metrics' has no 'rsq' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member) +glmnet_ts.py:169:24: I1101: Module 'metrics' has no 'mae' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member) +glmnet_ts.py:170:25: I1101: Module 'metrics' has no 'mape' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member) +glmnet_ts.py:171:25: I1101: Module 'metrics' has no 'rmse' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member) +glmnet_ts.py:173:0: W0104: Statement seems to have no effect (pointless-statement) +glmnet_ts.py:9:0: W0611: Unused Dict imported from typing (unused-import) +glmnet_ts.py:15:0: W0611: Unused train_test_split imported from sklearn.model_selection as split (unused-import) + +------------------------------------------------------------------ +Your code has been rated at 5.38/10 (previous run: 5.38/10, +0.00) + diff --git a/log/pylint/lib-model-py.out b/logs/pylint/lib-model-py.out similarity index 78% rename from log/pylint/lib-model-py.out rename to logs/pylint/lib-model-py.out index 436caf9..9727bb7 100644 --- a/log/pylint/lib-model-py.out +++ b/logs/pylint/lib-model-py.out @@ -1,8 +1,8 @@ ************* Module mllib.lib.model -model.py:191:32: I1101: Module 'metrics' has no 'rsq' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member) -model.py:192:32: I1101: Module 'metrics' has no 'mae' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member) -model.py:193:33: I1101: Module 'metrics' has no 'mape' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member) -model.py:194:33: I1101: Module 'metrics' has no 'rmse' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member) +model.py:190:41: I1101: Module 'metrics' has no 'rsq' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member) +model.py:191:41: I1101: Module 'metrics' has no 'mae' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member) +model.py:192:42: I1101: Module 'metrics' has no 'mape' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member) +model.py:193:42: I1101: Module 'metrics' has no 'rmse' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member) -------------------------------------------------------------------- Your code has been rated at 10.00/10 (previous run: 10.00/10, +0.00) diff --git a/log/pylint/lib-utils-py.out b/logs/pylint/lib-utils-py.out similarity index 100% rename from log/pylint/lib-utils-py.out rename to logs/pylint/lib-utils-py.out diff --git a/log/pylint/metrics-setup-py.out b/logs/pylint/metrics-setup-py.out similarity index 100% rename from log/pylint/metrics-setup-py.out rename to logs/pylint/metrics-setup-py.out diff --git a/log/pylint/mllib-__init__-py.out b/logs/pylint/mllib-__init__-py.out similarity index 100% rename from log/pylint/mllib-__init__-py.out rename to logs/pylint/mllib-__init__-py.out diff --git a/log/pylint/mllib-__main__-py.out b/logs/pylint/mllib-__main__-py.out similarity index 100% rename from log/pylint/mllib-__main__-py.out rename to logs/pylint/mllib-__main__-py.out diff --git a/log/pylint/pylint.out b/logs/pylint/pylint.out similarity index 100% rename from log/pylint/pylint.out rename to logs/pylint/pylint.out diff --git a/log/pylint/tests-__init__-py.out b/logs/pylint/tests-__init__-py.out similarity index 100% rename from log/pylint/tests-__init__-py.out rename to logs/pylint/tests-__init__-py.out diff --git a/log/pylint/tests-test_cluster-py.out b/logs/pylint/tests-test_cluster-py.out similarity index 100% rename from log/pylint/tests-test_cluster-py.out rename to logs/pylint/tests-test_cluster-py.out diff --git a/log/pylint/tests-test_metrics-py.out b/logs/pylint/tests-test_metrics-py.out similarity index 100% rename from log/pylint/tests-test_metrics-py.out rename to logs/pylint/tests-test_metrics-py.out diff --git a/log/pylint/tests-test_model-py.out b/logs/pylint/tests-test_model-py.out similarity index 100% rename from log/pylint/tests-test_model-py.out rename to logs/pylint/tests-test_model-py.out diff --git a/requirements.txt b/requirements.txt index 878016e..327fb8a 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ numpy==1.19.5 -Cython==0.29.15 pandas==1.1.3 +Cython==0.29.15 scikit_learn==0.24.2 From 67db07ca7bd1f1d76007c2642124f68de94d1434 Mon Sep 17 00:00:00 2001 From: Diptesh Basak Date: Fri, 17 Sep 2021 11:24:41 +0530 Subject: [PATCH 22/30] v0.4.0 changelog: - Change unit tests to match new outputs --- README.md | 4 ++-- logs/cov.out | 4 ++-- logs/pylint/lib-model-py.out | 12 ++++++++---- mllib/lib/model.py | 4 ++-- requirements.txt | 2 +- tests/test_model.py | 16 +++++++++------- 6 files changed, 24 insertions(+), 18 deletions(-) diff --git a/README.md b/README.md index 685a5a6..80fe418 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ [![Build](../../actions/workflows/build.yml/badge.svg)](../../actions/workflows/build.yml) -[![pylint Score](https://mperlet.github.io/pybadge/badges/9.6.svg)](./logs/pylint/) -[![Coverage score](https://img.shields.io/badge/coverage-100%25-dagreen.svg)](./logs/cov.out) +[![pylint Score](https://mperlet.github.io/pybadge/badges/10.0.svg)](./logs/pylint/) +[![Coverage score](https://img.shields.io/badge/coverage-74%25-red.svg)](./logs/cov.out) [![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](./LICENSE) *** diff --git a/logs/cov.out b/logs/cov.out index 2023a09..27f2c36 100644 --- a/logs/cov.out +++ b/logs/cov.out @@ -3,6 +3,6 @@ Name Stmts Miss Cov /media/ph33r/Data/Project/mllib/GitHub/mllib/__init__.py 7 0 100% /media/ph33r/Data/Project/mllib/GitHub/mllib/lib/__init__.py 7 0 100% /media/ph33r/Data/Project/mllib/GitHub/mllib/lib/cluster.py 103 0 100% -/media/ph33r/Data/Project/mllib/GitHub/mllib/lib/model.py 59 0 100% +/media/ph33r/Data/Project/mllib/GitHub/mllib/lib/model.py 137 65 53% 87, 285-304, 308-343, 347-354, 372-409 -------------------------------------------------------------------------------------------- -TOTAL 176 0 100% +TOTAL 254 65 74% diff --git a/logs/pylint/lib-model-py.out b/logs/pylint/lib-model-py.out index 9727bb7..20446f8 100644 --- a/logs/pylint/lib-model-py.out +++ b/logs/pylint/lib-model-py.out @@ -1,8 +1,12 @@ ************* Module mllib.lib.model -model.py:190:41: I1101: Module 'metrics' has no 'rsq' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member) -model.py:191:41: I1101: Module 'metrics' has no 'mae' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member) -model.py:192:42: I1101: Module 'metrics' has no 'mape' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member) -model.py:193:42: I1101: Module 'metrics' has no 'rmse' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member) +model.py:204:41: I1101: Module 'metrics' has no 'rsq' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member) +model.py:205:41: I1101: Module 'metrics' has no 'mae' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member) +model.py:206:42: I1101: Module 'metrics' has no 'mape' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member) +model.py:207:42: I1101: Module 'metrics' has no 'rmse' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member) +model.py:349:41: I1101: Module 'metrics' has no 'rsq' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member) +model.py:350:41: I1101: Module 'metrics' has no 'mae' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member) +model.py:351:42: I1101: Module 'metrics' has no 'mape' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member) +model.py:352:42: I1101: Module 'metrics' has no 'rmse' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member) -------------------------------------------------------------------- Your code has been rated at 10.00/10 (previous run: 10.00/10, +0.00) diff --git a/mllib/lib/model.py b/mllib/lib/model.py index b9767f8..967e50b 100644 --- a/mllib/lib/model.py +++ b/mllib/lib/model.py @@ -102,11 +102,10 @@ def create_lag_vars(df: pd.DataFrame, if n_interval: y_lag = y_lag.join(df[n_interval]) y_lag = y_lag.set_index(n_interval) - op = y_lag.dropna() + op = y_lag.dropna().reset_index(drop=True) return lst_lag, op - class GLMNet(): """GLMNet module. @@ -230,6 +229,7 @@ def predict(self, df_predict: pd.DataFrame) -> pd.DataFrame: df_predict["y"] = y_hat return df_predict + class GLMNet_ts(): """GLMNet time series module. diff --git a/requirements.txt b/requirements.txt index 327fb8a..ec389bf 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ +Cython==0.29.15 numpy==1.19.5 pandas==1.1.3 -Cython==0.29.15 scikit_learn==0.24.2 diff --git a/tests/test_model.py b/tests/test_model.py index f505633..0fdc731 100644 --- a/tests/test_model.py +++ b/tests/test_model.py @@ -64,21 +64,23 @@ def setUp(self): def test_no_interval_specified(self): """Lag vars: Test when no interval is specified.""" df_ip = pd.read_csv(path + "test_lag_var.csv") - df_op = create_lag_vars(df=df_ip, - y_var=["y"], - x_var=["x1", "x2"]) + lst_lag, df_op = create_lag_vars(df=df_ip, + y_var=["y"], + x_var=["x1", "x2"]) exp_op = df_ip[list(df_ip.columns[1:])].dropna().reset_index(drop=True) self.assertEqual(df_op.equals(exp_op), True) + self.assertEqual([6, 4, 3, 2, 1], lst_lag) def test_interval_specified(self): """Lag vars: Test when interval is specified.""" df_ip = pd.read_csv(path + "test_lag_var.csv") - df_op = create_lag_vars(df=df_ip, - y_var=["y"], - x_var=["x1", "x2"], - n_interval="week") + lst_lag, df_op = create_lag_vars(df=df_ip, + y_var=["y"], + x_var=["x1", "x2"], + n_interval="week") exp_op = df_ip[list(df_ip.columns[1:])].dropna().reset_index(drop=True) self.assertEqual(df_op.equals(exp_op), True) + self.assertEqual([6, 4, 3, 2, 1], lst_lag) class TestGLMNet(unittest.TestCase): From d9b7edf5e62a2292ab6bc442a85746e79721f2a5 Mon Sep 17 00:00:00 2001 From: MadhuTangudu Date: Fri, 17 Sep 2021 21:10:05 +0530 Subject: [PATCH 23/30] v0.4.0 changelog: - glmnet_ts.py and test_glmnet_ts.py modules added - model.py and test_model.py modules modified --- mllib/lib/glmnet_ts.py | 285 ++++++++++++++++++++++++++++++++++++++++ mllib/lib/model.py | 243 ---------------------------------- tests/test_glmnet_ts.py | 125 ++++++++++++++++++ tests/test_model.py | 29 ---- 4 files changed, 410 insertions(+), 272 deletions(-) create mode 100644 mllib/lib/glmnet_ts.py create mode 100644 tests/test_glmnet_ts.py diff --git a/mllib/lib/glmnet_ts.py b/mllib/lib/glmnet_ts.py new file mode 100644 index 0000000..fb06d58 --- /dev/null +++ b/mllib/lib/glmnet_ts.py @@ -0,0 +1,285 @@ +""" +Module for commonly used machine learning modelling algorithms. + +**Available routines:** + +- udf ``create_lag_vars``: Create lag variables for time series data. +- class ``GLMNet``: Builds GLMnet model using cross validation. + +Credits +------- +:: + + Authors: + - Madhu + - Diptesh + + Date: Sep 16, 2021 +""" + +# pylint: disable=invalid-name +# pylint: disable=R0902,R0903,R0913,R0914,C0413 + +from typing import List, Dict + +import re +import sys +from inspect import getsourcefile +from os.path import abspath + +import pandas as pd +import numpy as np + +from sklearn.linear_model import ElasticNetCV +from sklearn.model_selection import TimeSeriesSplit as ts_split + +path = abspath(getsourcefile(lambda: 0)) +path = re.sub(r"(.+\/)(.+.py)", "\\1", path) +sys.path.insert(0, path) + +import metrics # noqa: F841 + +# ============================================================================= +# --- DO NOT CHANGE ANYTHING FROM HERE +# ============================================================================= + + +def create_lag_vars(df: pd.DataFrame, + y_var: List[str], + x_var: List[str], + lst_lag: List[int] = None, + n_interval: str = None) -> pd.DataFrame: + """Create lag variables for time series data. + + Parameters + ---------- + df : pd.DataFrame + + Pandas dataframe containing `y_var`, `x_var` and `n_interval` + (if provided). + + y_var : List[str] + + Dependant variable. + + x_var : List[str] + Independant variables. + + lst_lag : List[int] + + Lag values list (the default is None) + + n_interval : str, optional + + Column name of the time interval variable (the default is None). + + Returns + ------- + pd.DataFrame + + Pandas dataframe containing `y_var`, lag variables (`lag_xx`) and + `x_var`. + + """ + if n_interval is None: + df = df.reset_index(drop=True) + elif len(df) != (df[n_interval].max() - df[n_interval].min() + 1): + sys.exit("Missing/duplicate time instance found in input data") + else: + df = df.sort_values(by=n_interval) + df = df.reset_index(drop=True) + y_lag = df[y_var].copy(deep=True) + time_int = len(y_lag) + if lst_lag is None: + lst_lag = [] + while time_int > 8: + time_int = int(np.floor(time_int/2)) + lst_lag.extend([time_int]) + lst_lag.extend([4, 3, 2, 1]) + for lag in lst_lag: + y_lag.loc[:, "lag_" + str(lag)] = y_lag["y"].shift(lag) + y_lag = y_lag.join(df[x_var]) + if n_interval: + y_lag = y_lag.join(df[n_interval]) + y_lag = y_lag.set_index(n_interval) + op = y_lag.dropna().reset_index(drop=True) + return lst_lag, op + + +class GLMNet_ts(): + """GLMNet time series module. + + Objective: + - Build + `GLMNet `_ + model using optimal alpha and lambda + + Parameters + ---------- + df : pd.DataFrame + + Pandas dataframe containing `y_var` and `x_var` variables. + + y_var : List[str] + + Dependant variable. + + x_var : List[str] + + Independant variables. + + lst_lag : List[int] + + Lag values list (the default is None) + + n_interval : str, optional + + Column name of the time interval variable (the default is None). + + + param : Dict, optional + + GLMNet parameters (the default is None). + In case of None, the parameters will default to:: + + seed: 1 + a_inc: 0.05 + test_perc: 0.25 + n_jobs: -1 + k_fold: 10 + + """ + + def __init__(self, + df: pd.DataFrame, + y_var: List[str], + x_var: List[str], + lst_lag: List[int] = None, + n_interval: str = None, + param: Dict = None): + """Initialize variables for module ``GLMNet``.""" + self.df = df.sort_values(by=n_interval)[y_var + x_var] + self.y_var = y_var + self.x_var = x_var + self.lst_lag = lst_lag + self.n_interval = n_interval + self.model_summary = None + self.max_epoch = None + if param is None: + param = {"seed": 1, + "a_inc": 0.05, + "test_perc": 0.25, + "n_jobs": -1, + "k_fold": 10} + self.param = param + self.param["l1_range"] = list(np.round(np.arange(self.param["a_inc"], + 1.01, + self.param["a_inc"]), + 2)) + self._fit() + self._compute_metrics() + + def _fit(self) -> None: + """Fit the best GLMNet time series model.""" + if self.n_interval is None: + self.max_epoch = len(self.df) - 1 + else: + self.max_epoch = self.df[self.n_interval].max() + self.lag_var, df_ip = create_lag_vars(self.df, + self.y_var, + self.x_var, + self.lst_lag, + self.n_interval) + self.x_var = list(self.df.columns) + self.x_var.remove(self.y_var[0]) + df_train = df_ip.iloc[0:int(len(df_ip) * (1-self.param["test_perc"]))] + df_test = df_ip.iloc[int(len(df_ip) * (1-self.param["test_perc"])):] + train_x = df_train[self.x_var] + train_y = df_train[self.y_var] + test_x = df_test[self.x_var] + test_y = df_test[self.y_var] + self.param["k_fold"] = ts_split(n_splits=self.param["k_fold"]) + self.param["k_fold"] = self.param["k_fold"].split(X=train_y) + mod = ElasticNetCV(l1_ratio=self.param["l1_range"], + fit_intercept=True, + alphas=[1e-5, 1e-4, 1e-3, 1e-2, 1e-1, + 1.0, 10.0, 100.0], + normalize=True, + cv=self.param["k_fold"], + n_jobs=self.param["n_jobs"], + random_state=self.param["seed"]) + mod.fit(train_x, train_y.values.ravel()) + opt = {"alpha": mod.l1_ratio_, + "lambda": mod.alpha_, + "intercept": mod.intercept_, + "coef": mod.coef_, + "train_v": mod.score(train_x, train_y), + "test_v": mod.score(test_x, test_y)} + self.model = mod + self.opt = opt + + def _compute_metrics(self): + """Compute commonly used metrics to evaluate the model.""" + y = self.df[self.y_var].iloc[:, 0].values.tolist() + y_hat = list(self.predict(self.df[self.x_var])["y"].values) + model_summary = {"rsq": np.round(metrics.rsq(y, y_hat), 3), + "mae": np.round(metrics.mae(y, y_hat), 3), + "mape": np.round(metrics.mape(y, y_hat), 3), + "rmse": np.round(metrics.rmse(y, y_hat), 3)} + model_summary["mse"] = np.round(model_summary["rmse"] ** 2, 3) + self.model_summary = model_summary + + def predict(self, df_predict: pd.DataFrame) -> pd.DataFrame: + """Predict y_var/target variable. + + Parameters + ---------- + df_predict : pd.DataFrame + + Pandas dataframe containing `x_var`. + + Returns + ------- + pd.DataFrame + + Pandas dataframe containing predicted `y_var` and `x_var`. + + """ + if self.n_interval is None: + df_predict = df_predict.reset_index(drop=True) + df_predict = \ + df_predict.set_index(df_predict.index+self.max_epoch+1) + elif len(df_predict) != (df_predict[self.n_interval].max() \ + - df_predict[self.n_interval].min() + 1) \ + or df_predict[self.n_interval].min() \ + > self.max_epoch+1: + sys.exit("Missing time instance found in input data") + else: + df_ip = self.df[self.df[self.n_interval] \ + <= df_predict[self.n_interval].min()] + df_predict = df_predict.sort_values(by=self.n_interval) + df_predict = df_predict.set_index(self.n_interval) + df_predict = df_predict[self.x_var] + df_predict["y"] = -1 + for i in range(0, len(df_predict)): + # for i in range(0, len(df_ip)): + df_pred = pd.DataFrame(df_predict.iloc[i]) + df_pred = df_pred.T # Transpose + period_val = df_pred.index + df_pred = df_pred[self.x_var].reset_index(drop=True) + df_pred_x = pd.DataFrame( + {"lag_"+str(self.lst_lag[0]): df_ip.iloc[len(df_ip)\ + -self.lst_lag[0]]}) + for j in range(1, len(self.lst_lag)): + df_tmp = pd.DataFrame( + {"lag_"+str(self.lst_lag[j]): \ + df_ip.iloc[len(df_ip)-self.lst_lag[j]]}) + df_pred_x = df_pred_x.join(df_tmp) + df_pred_x = df_pred_x.reset_index(drop=True) + df_pred_x = df_pred_x.join(df_pred) + y_hat = self.model.predict(df_pred_x) + df_tmp = pd.DataFrame() + df_tmp['y'] = y_hat + df_ip = df_ip.append(df_tmp).reset_index(drop=True) + df_predict.loc[period_val, "y"] = y_hat + return df_predict diff --git a/mllib/lib/model.py b/mllib/lib/model.py index 967e50b..a93c36d 100644 --- a/mllib/lib/model.py +++ b/mllib/lib/model.py @@ -3,7 +3,6 @@ **Available routines:** -- udf ``create_lag_vars``: Create lag variables for time series data. - class ``GLMNet``: Builds GLMnet model using cross validation. Credits @@ -31,7 +30,6 @@ from sklearn.linear_model import ElasticNetCV from sklearn.model_selection import train_test_split as split -from sklearn.model_selection import TimeSeriesSplit as ts_split path = abspath(getsourcefile(lambda: 0)) path = re.sub(r"(.+\/)(.+.py)", "\\1", path) @@ -44,68 +42,6 @@ # ============================================================================= -def create_lag_vars(df: pd.DataFrame, - y_var: List[str], - x_var: List[str], - lst_lag: List[int] = None, - n_interval: str = None) -> pd.DataFrame: - """Create lag variables for time series data. - - Parameters - ---------- - df : pd.DataFrame - - Pandas dataframe containing `y_var`, `x_var` and `n_interval` - (if provided). - - y_var : List[str] - - Dependant variable. - - x_var : List[str] - Independant variables. - - lst_lag : List[int] - - Lag values list (the default is None) - - n_interval : str, optional - - Column name of the time interval variable (the default is None). - - Returns - ------- - pd.DataFrame - - Pandas dataframe containing `y_var`, lag variables (`lag_xx`) and - `x_var`. - - """ - if n_interval is None: - df = df.reset_index(drop=True) - elif len(df) != (df[n_interval].max() - df[n_interval].min() + 1): - sys.exit("Missing/duplicate time instance found in input data") - else: - df = df.sort_values(by=n_interval) - df = df.reset_index(drop=True) - y_lag = df[y_var].copy(deep=True) - time_int = len(y_lag) - if lst_lag is None: - lst_lag = [] - while time_int > 8: - time_int = int(np.floor(time_int/2)) - lst_lag.extend([time_int]) - lst_lag.extend([4, 3, 2, 1]) - for lag in lst_lag: - y_lag.loc[:, "lag_" + str(lag)] = y_lag["y"].shift(lag) - y_lag = y_lag.join(df[x_var]) - if n_interval: - y_lag = y_lag.join(df[n_interval]) - y_lag = y_lag.set_index(n_interval) - op = y_lag.dropna().reset_index(drop=True) - return lst_lag, op - - class GLMNet(): """GLMNet module. @@ -228,182 +164,3 @@ def predict(self, df_predict: pd.DataFrame) -> pd.DataFrame: df_predict = df_predict.copy() df_predict["y"] = y_hat return df_predict - - -class GLMNet_ts(): - """GLMNet time series module. - - Objective: - - Build - `GLMNet `_ - model using optimal alpha and lambda - - Parameters - ---------- - df : pd.DataFrame - - Pandas dataframe containing `y_var` and `x_var` variables. - - y_var : List[str] - - Dependant variable. - - x_var : List[str] - - Independant variables. - - lst_lag : List[int] - - Lag values list (the default is None) - - n_interval : str, optional - - Column name of the time interval variable (the default is None). - - - param : Dict, optional - - GLMNet parameters (the default is None). - In case of None, the parameters will default to:: - - seed: 1 - a_inc: 0.05 - test_perc: 0.25 - n_jobs: -1 - k_fold: 10 - - """ - - def __init__(self, - df: pd.DataFrame, - y_var: List[str], - x_var: List[str], - lst_lag: List[int] = None, - n_interval: str = None, - param: Dict = None): - """Initialize variables for module ``GLMNet``.""" - self.df = df.sort_values(by=n_interval)[y_var + x_var] - self.y_var = y_var - self.x_var = x_var - self.lst_lag = lst_lag - self.n_interval = n_interval - self.model_summary = None - self.max_epoch = None - if param is None: - param = {"seed": 1, - "a_inc": 0.05, - "test_perc": 0.25, - "n_jobs": -1, - "k_fold": 10} - self.param = param - self.param["l1_range"] = list(np.round(np.arange(self.param["a_inc"], - 1.01, - self.param["a_inc"]), - 2)) - self._fit() - self._compute_metrics() - - def _fit(self) -> None: - """Fit the best GLMNet time series model.""" - if self.n_interval is None: - self.max_epoch = len(self.df) - 1 - else: - self.max_epoch = self.df[self.n_interval].max() - self.lag_var, df_ip = create_lag_vars(self.df, - self.y_var, - self.x_var, - self.lst_lag, - self.n_interval) - self.x_var = list(self.df.columns) - self.x_var.remove(self.y_var[0]) - df_train = df_ip.iloc[0:int(len(df_ip) * (1-self.param["test_perc"]))] - df_test = df_ip.iloc[int(len(df_ip) * (1-self.param["test_perc"])):] - train_x = df_train[self.x_var] - train_y = df_train[self.y_var] - test_x = df_test[self.x_var] - test_y = df_test[self.y_var] - self.param["k_fold"] = ts_split(n_splits=self.param["k_fold"]) - self.param["k_fold"] = self.param["k_fold"].split(X=train_y) - mod = ElasticNetCV(l1_ratio=self.param["l1_range"], - fit_intercept=True, - alphas=[1e-5, 1e-4, 1e-3, 1e-2, 1e-1, - 1.0, 10.0, 100.0], - normalize=True, - cv=self.param["k_fold"], - n_jobs=self.param["n_jobs"], - random_state=self.param["seed"]) - mod.fit(train_x, train_y.values.ravel()) - opt = {"alpha": mod.l1_ratio_, - "lambda": mod.alpha_, - "intercept": mod.intercept_, - "coef": mod.coef_, - "train_v": mod.score(train_x, train_y), - "test_v": mod.score(test_x, test_y)} - self.model = mod - self.opt = opt - - def _compute_metrics(self): - """Compute commonly used metrics to evaluate the model.""" - y = self.df[self.y_var].iloc[:, 0].values.tolist() - y_hat = list(self.predict(self.df[self.x_var])["y"].values) - model_summary = {"rsq": np.round(metrics.rsq(y, y_hat), 3), - "mae": np.round(metrics.mae(y, y_hat), 3), - "mape": np.round(metrics.mape(y, y_hat), 3), - "rmse": np.round(metrics.rmse(y, y_hat), 3)} - model_summary["mse"] = np.round(model_summary["rmse"] ** 2, 3) - self.model_summary = model_summary - - def predict(self, df_predict: pd.DataFrame) -> pd.DataFrame: - """Predict y_var/target variable. - - Parameters - ---------- - df_predict : pd.DataFrame - - Pandas dataframe containing `x_var`. - - Returns - ------- - pd.DataFrame - - Pandas dataframe containing predicted `y_var` and `x_var`. - - """ - if self.n_interval is None: - df_predict = df_predict.reset_index(drop=True) - df_predict = \ - df_predict.set_index(df_predict.index+self.max_epoch+1) - elif len(df_predict) != (df_predict[self.n_interval].max() \ - - df_predict[self.n_interval].min() + 1) \ - or df_predict[self.n_interval].min() \ - > self.max_epoch+1: - sys.exit("Missing time instance found in input data") - else: - df_ip = self.df[self.df[self.n_interval] \ - <= df_predict[self.n_interval].min()] - df_predict = df_predict.sort_values(by=self.n_interval) - df_predict = df_predict.set_index(self.n_interval) - df_predict = df_predict[self.x_var] - df_predict["y"] = -1 - for i in range(0, len(df_predict)): - # for i in range(0, len(df_ip)): - df_pred = pd.DataFrame(df_predict.iloc[i]) - df_pred = df_pred.T # Transpose - period_val = df_pred.index - df_pred = df_pred[self.x_var].reset_index(drop=True) - df_pred_x = pd.DataFrame( - {"lag_"+str(self.lst_lag[0]): df_ip.iloc[len(df_ip)\ - -self.lst_lag[0]]}) - for j in range(1, len(self.lst_lag)): - df_tmp = pd.DataFrame( - {"lag_"+str(self.lst_lag[j]): \ - df_ip.iloc[len(df_ip)-self.lst_lag[j]]}) - df_pred_x = df_pred_x.join(df_tmp) - df_pred_x = df_pred_x.reset_index(drop=True) - df_pred_x = df_pred_x.join(df_pred) - y_hat = self.model.predict(df_pred_x) - df_tmp = pd.DataFrame() - df_tmp['y'] = y_hat - df_ip = df_ip.append(df_tmp).reset_index(drop=True) - df_predict.loc[period_val, "y"] = y_hat - return df_predict diff --git a/tests/test_glmnet_ts.py b/tests/test_glmnet_ts.py new file mode 100644 index 0000000..2e49a75 --- /dev/null +++ b/tests/test_glmnet_ts.py @@ -0,0 +1,125 @@ +""" +Test suite module for ``glmnet_ts``. + +Credits +------- +:: + + Authors: + - Madhu + - Diptesh + + Date: Sep 07, 2021 +""" + +# pylint: disable=invalid-name +# pylint: disable=wrong-import-position + +import unittest +import warnings +import re +import sys + +from inspect import getsourcefile +from os.path import abspath + +import pandas as pd +import numpy as np + +# Set base path +path = abspath(getsourcefile(lambda: 0)) +path = re.sub(r"(.+)(\/tests.*)", "\\1", path) + +sys.path.insert(0, path) + +from mllib.lib.glmnet_ts import create_lag_vars # noqa: F841 +from mllib.lib.glmnet_ts import GLMNet_ts # noqa: F841 + +# ============================================================================= +# --- DO NOT CHANGE ANYTHING FROM HERE +# ============================================================================= + +path = path + "/data/input/" + +# ============================================================================= +# --- User defined functions +# ============================================================================= + + +def ignore_warnings(test_func): + """Suppress deprecation warnings.""" + + def do_test(self, *args, **kwargs): + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + test_func(self, *args, **kwargs) + return do_test + + +class TestCreateLagVars(unittest.TestCase): + """Test suite for UDF ``create_lag_vars``.""" + + def setUp(self): + """Set up for UDF ``create_lag_vars``.""" + + def test_no_interval_specified(self): + """Lag vars: Test when no interval is specified.""" + df_ip = pd.read_csv(path + "test_lag_var.csv") + lst_lag, df_op = create_lag_vars(df=df_ip, + y_var=["y"], + x_var=["x1", "x2"]) + exp_op = df_ip[list(df_ip.columns[1:])].dropna().reset_index(drop=True) + self.assertEqual(df_op.equals(exp_op), True) + self.assertEqual([6, 4, 3, 2, 1], lst_lag) + + def test_interval_specified(self): + """Lag vars: Test when interval is specified.""" + df_ip = pd.read_csv(path + "test_lag_var.csv") + lst_lag, df_op = create_lag_vars(df=df_ip, + y_var=["y"], + x_var=["x1", "x2"], + n_interval="week") + exp_op = df_ip[list(df_ip.columns[1:])].dropna().reset_index(drop=True) + self.assertEqual(df_op.equals(exp_op), True) + self.assertEqual([6, 4, 3, 2, 1], lst_lag) + + +class TestGLMNet(unittest.TestCase): + """Test suite for module ``GLMNet``.""" + + def setUp(self): + """Set up for module ``GLMNet``.""" + + def test_known_equation(self): + """GLMNet: Test a known equation.""" + df_ip = pd.read_csv(path + "test_glmnet.csv") + mod = GLMNet(df=df_ip, + y_var=["y"], + x_var=["x1", "x2", "x3"]) + op = mod.opt + self.assertEqual(np.round(op.get('intercept'), 0), 100.0) + self.assertEqual(np.round(op.get('coef')[0], 0), 2.0) + self.assertEqual(np.round(op.get('coef')[1], 0), 3.0) + self.assertEqual(np.round(op.get('coef')[2], 0), 0.0) + + def test_predict_target_variable(self): + """GLMNet: Test to predict a target variable.""" + df_ip = pd.read_csv(path + "test_glmnet.csv") + mod = GLMNet(df=df_ip, + y_var=["y"], + x_var=["x1", "x2", "x3"]) + df_predict = pd.DataFrame({"x1": [10, 20], + "x2": [5, 10], + "x3": [100, 0]}) + op = mod.predict(df_predict) + op = np.round(np.array(op["y"]), 1) + exp_op = np.array([135.0, 170.0]) + self.assertEqual((op == exp_op).all(), True) + + +# ============================================================================= +# --- Main +# ============================================================================= + +if __name__ == '__main__': + unittest.main() diff --git a/tests/test_model.py b/tests/test_model.py index 0fdc731..a73901c 100644 --- a/tests/test_model.py +++ b/tests/test_model.py @@ -31,7 +31,6 @@ sys.path.insert(0, path) -from mllib.lib.model import create_lag_vars # noqa: F841 from mllib.lib.model import GLMNet # noqa: F841 # ============================================================================= @@ -55,34 +54,6 @@ def do_test(self, *args, **kwargs): return do_test -class TestCreateLagVars(unittest.TestCase): - """Test suite for UDF ``create_lag_vars``.""" - - def setUp(self): - """Set up for UDF ``create_lag_vars``.""" - - def test_no_interval_specified(self): - """Lag vars: Test when no interval is specified.""" - df_ip = pd.read_csv(path + "test_lag_var.csv") - lst_lag, df_op = create_lag_vars(df=df_ip, - y_var=["y"], - x_var=["x1", "x2"]) - exp_op = df_ip[list(df_ip.columns[1:])].dropna().reset_index(drop=True) - self.assertEqual(df_op.equals(exp_op), True) - self.assertEqual([6, 4, 3, 2, 1], lst_lag) - - def test_interval_specified(self): - """Lag vars: Test when interval is specified.""" - df_ip = pd.read_csv(path + "test_lag_var.csv") - lst_lag, df_op = create_lag_vars(df=df_ip, - y_var=["y"], - x_var=["x1", "x2"], - n_interval="week") - exp_op = df_ip[list(df_ip.columns[1:])].dropna().reset_index(drop=True) - self.assertEqual(df_op.equals(exp_op), True) - self.assertEqual([6, 4, 3, 2, 1], lst_lag) - - class TestGLMNet(unittest.TestCase): """Test suite for module ``GLMNet``.""" From 4fbddd939a8b91a64b54228315999661a0d40d00 Mon Sep 17 00:00:00 2001 From: MadhuTangudu Date: Sun, 19 Sep 2021 23:15:53 +0530 Subject: [PATCH 24/30] v0.4.0 -changelog: - test_lag_var.csv file modified to handle one more test case - test_glmnet_ts.py file modified --- data/input/test_glmnet_ts.csv | 105 ++++++++++++++++++++++++++++++++++ data/input/test_lag_var.csv | 10 ++-- mllib/lib/glmnet_ts.py | 6 +- tests/test_glmnet_ts.py | 76 +++++++++++++----------- 4 files changed, 157 insertions(+), 40 deletions(-) create mode 100644 data/input/test_glmnet_ts.csv diff --git a/data/input/test_glmnet_ts.csv b/data/input/test_glmnet_ts.csv new file mode 100644 index 0000000..b6c121b --- /dev/null +++ b/data/input/test_glmnet_ts.csv @@ -0,0 +1,105 @@ +week,y,x1,x2 +1,14,2,18 +2,12,2,15 +3,14,1,13 +4,11,1,13 +5,15,1,16 +6,17,1,17 +7,16,3,20 +8,124.50,2,1.8 +9,221.10,2,1.5 +10,307.34,2,1.1 +11,384.56,1,1.9 +12,457.30,3,1.4 +13,520.72,2,1.3 +14,578.60,2,1.7 +15,638.64,1,1.3 +16,705.73,2,1.9 +17,776.10,3,1.6 +18,845.89,3,1.1 +19,913.73,1,1.8 +20,983.38,3,1.1 +21,1051.00,2,1.6 +22,1120.12,3,1.7 +23,1185.28,2,1 +24,1254.56,3,1.6 +25,1319.29,1,1.4 +26,1384.44,1,1.4 +27,1451.18,2,1.1 +28,1519.01,2,1.5 +29,1588.22,3,1.2 +30,1657.03,2,2 +31,1722.13,1,1.3 +32,1788.45,2,1 +33,1854.50,1,1.7 +34,1921.87,2,1.4 +35,1987.28,1,1.4 +36,2055.32,2,1.5 +37,2122.60,1,2 +38,2189.50,2,1.1 +39,2259.99,3,1.8 +40,2330.64,3,2 +41,2398.12,2,1.7 +42,2463.98,1,1.9 +43,2529.97,2,1.1 +44,2594.08,1,1.1 +45,2664.22,3,1.8 +46,2731.90,2,1.6 +47,2796.72,1,1.1 +48,2866.71,3,1.5 +49,2935.89,3,1.3 +50,3001.75,1,1.7 +51,3065.93,1,1.1 +52,3135.36,3,1.4 +53,3206.21,3,2 +54,3274.61,3,1.3 +55,3343.67,3,1.5 +56,3413.00,3,1.6 +57,3480.57,2,1.8 +58,3545.81,1,1.8 +59,3609.96,1,1.2 +60,3676.19,2,1 +61,3740.98,1,1.1 +62,3810.10,2,1.9 +63,3880.99,3,1.8 +64,3948.05,1,2 +65,4015.67,2,1.5 +66,4086.20,3,2 +67,4150.30,1,1.2 +68,4214.07,1,1 +69,4280.27,1,1.8 +70,4349.79,3,1.3 +71,4417.47,2,1.5 +72,4483.99,1,1.8 +73,4550.31,1,1.6 +74,4619.66,3,1.3 +75,4687.95,2,1.9 +76,4752.53,1,1.3 +77,4817.71,1,1.3 +78,4883.78,1,1.6 +79,4954.26,3,1.7 +80,5020.31,1,1.7 +81,5090.70,3,1.7 +82,5159.77,3,1.3 +83,5223.75,1,1 +84,5288.34,1,1.2 +85,5353.99,1,1.6 +86,5420.11,1,1.6 +87,5485.58,1,1.3 +88,5555.95,3,1.5 +89,5624.03,2,1.4 +90,5690.95,1,1.9 +91,5757.89,1,2 +92,5822.70,1,1.2 +93,5889.04,2,1 +94,5959.29,3,1.8 +95,6026.81,2,1.5 +96,6096.23,3,1.4 +97,6165.15,3,1.3 +98,6232.78,2,1.7 +99,6297.62,1,1.5 +100,6366.96,3,1.6 +101,6436.54,3,1.7 +102,6506.77,3,2 +103,6576.67,3,1.9 +104,6644.12,3,1 diff --git a/data/input/test_lag_var.csv b/data/input/test_lag_var.csv index e5ba31d..7a374be 100644 --- a/data/input/test_lag_var.csv +++ b/data/input/test_lag_var.csv @@ -1,10 +1,10 @@ week,y,lag_6,lag_4,lag_3,lag_2,lag_1,x1,x2 1,14,,,,,,2,18 -2,12,,,,,,2,15 -3,14,,,,,,1,13 -4,11,,,,,,1,13 -5,15,,,,,,1,16 -6,17,,,,,,1,17 +2,12,,,,,14,2,15 +3,14,,,,14,12,1,13 +4,11,,,14,12,14,1,13 +5,15,,14,12,14,11,1,16 +6,17,,12,14,11,15,1,17 7,16,14,14,11,15,17,3,20 8,14,12,11,15,17,16,2,18 9,19,14,15,17,16,14,2,15 diff --git a/mllib/lib/glmnet_ts.py b/mllib/lib/glmnet_ts.py index fb06d58..3279fc2 100644 --- a/mllib/lib/glmnet_ts.py +++ b/mllib/lib/glmnet_ts.py @@ -158,7 +158,7 @@ def __init__(self, n_interval: str = None, param: Dict = None): """Initialize variables for module ``GLMNet``.""" - self.df = df.sort_values(by=n_interval)[y_var + x_var] + self.df = df[y_var + x_var] self.y_var = y_var self.x_var = x_var self.lst_lag = lst_lag @@ -190,7 +190,7 @@ def _fit(self) -> None: self.x_var, self.lst_lag, self.n_interval) - self.x_var = list(self.df.columns) + self.x_var = list(df_ip.columns) self.x_var.remove(self.y_var[0]) df_train = df_ip.iloc[0:int(len(df_ip) * (1-self.param["test_perc"]))] df_test = df_ip.iloc[int(len(df_ip) * (1-self.param["test_perc"])):] @@ -236,7 +236,7 @@ def predict(self, df_predict: pd.DataFrame) -> pd.DataFrame: ---------- df_predict : pd.DataFrame - Pandas dataframe containing `x_var`. + Pandas dataframe containing `x_var`, 'n_interval' (optional) Returns ------- diff --git a/tests/test_glmnet_ts.py b/tests/test_glmnet_ts.py index 2e49a75..b4aa414 100644 --- a/tests/test_glmnet_ts.py +++ b/tests/test_glmnet_ts.py @@ -83,38 +83,50 @@ def test_interval_specified(self): self.assertEqual(df_op.equals(exp_op), True) self.assertEqual([6, 4, 3, 2, 1], lst_lag) - -class TestGLMNet(unittest.TestCase): - """Test suite for module ``GLMNet``.""" - - def setUp(self): - """Set up for module ``GLMNet``.""" - - def test_known_equation(self): - """GLMNet: Test a known equation.""" - df_ip = pd.read_csv(path + "test_glmnet.csv") - mod = GLMNet(df=df_ip, - y_var=["y"], - x_var=["x1", "x2", "x3"]) - op = mod.opt - self.assertEqual(np.round(op.get('intercept'), 0), 100.0) - self.assertEqual(np.round(op.get('coef')[0], 0), 2.0) - self.assertEqual(np.round(op.get('coef')[1], 0), 3.0) - self.assertEqual(np.round(op.get('coef')[2], 0), 0.0) - - def test_predict_target_variable(self): - """GLMNet: Test to predict a target variable.""" - df_ip = pd.read_csv(path + "test_glmnet.csv") - mod = GLMNet(df=df_ip, - y_var=["y"], - x_var=["x1", "x2", "x3"]) - df_predict = pd.DataFrame({"x1": [10, 20], - "x2": [5, 10], - "x3": [100, 0]}) - op = mod.predict(df_predict) - op = np.round(np.array(op["y"]), 1) - exp_op = np.array([135.0, 170.0]) - self.assertEqual((op == exp_op).all(), True) + def test_lag_vars_specified(self): + """Lag vars: Test when lags are specified.""" + df_ip = pd.read_csv(path + "test_lag_var.csv") + lst_lag, df_op = create_lag_vars(df=df_ip, + y_var=["y"], + x_var=["x1", "x2"], + lst_lag=[3, 2, 1]) + exp_op = df_ip.iloc[:, [1, 4, 5, 6, 7, 8]].dropna().reset_index(drop=True) + self.assertEqual(df_op.equals(exp_op), True) + self.assertEqual([3, 2, 1], lst_lag) + +# class TestGLMNet_ts(unittest.TestCase): +# """Test suite for module ``GLMNet_ts``.""" + +# def setUp(self): +# """Set up for module ``GLMNet_ts``.""" + +# def test_known_equation(self): +# """GLMNet_ts: Test a known equation.""" +# df_ip = pd.read_csv(path + "test_glmnet_ts.csv") +# df_train_ip = df_ip.iloc[7:100] +# mod = GLMNet_ts(df=df_train_ip, +# y_var=["y"], +# x_var=["x1", "x2"], +# lst_lag=[7,1]) +# op = mod.opt +# self.assertEqual(np.round(op.get('intercept'), 0), 100.0) +# self.assertEqual(np.round(op.get('coef')[0], 0), 2.0) +# self.assertEqual(np.round(op.get('coef')[1], 0), 3.0) +# self.assertEqual(np.round(op.get('coef')[2], 0), 0.0) + +# def test_predict_target_variable(self): +# """GLMNet_ts: Test to predict a target variable.""" +# df_ip = pd.read_csv(path + "test_glmnet.csv") +# mod = GLMNet_ts(df=df_ip, +# y_var=["y"], +# x_var=["x1", "x2", "x3"]) +# df_predict = pd.DataFrame({"x1": [10, 20], +# "x2": [5, 10], +# "x3": [100, 0]}) +# op = mod.predict(df_predict) +# op = np.round(np.array(op["y"]), 1) +# exp_op = np.array([135.0, 170.0]) +# self.assertEqual((op == exp_op).all(), True) # ============================================================================= From 72ef30d6d2f4bc876e954c345b0488bdd5ec3bcf Mon Sep 17 00:00:00 2001 From: Diptesh Basak Date: Fri, 24 Sep 2021 12:32:48 +0530 Subject: [PATCH 25/30] v0.4.0 --- .github/workflows/{build.yml => checks.yml} | 5 +++-- README.md | 2 +- 2 files changed, 4 insertions(+), 3 deletions(-) rename .github/workflows/{build.yml => checks.yml} (96%) diff --git a/.github/workflows/build.yml b/.github/workflows/checks.yml similarity index 96% rename from .github/workflows/build.yml rename to .github/workflows/checks.yml index 56bcd95..6d91104 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/checks.yml @@ -1,5 +1,5 @@ # ============================================================================= -# Build file +# Workflow for checks # # Objective: # - Install python dependencies @@ -14,13 +14,14 @@ # # ============================================================================= -name: Build +name: checks on: push: branches: - 'stable' - 'testing' + - 'dev*' - 'feature*' - '!maintenance*' pull_request: diff --git a/README.md b/README.md index 80fe418..0f0fb88 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -[![Build](../../actions/workflows/build.yml/badge.svg)](../../actions/workflows/build.yml) +[![checks](../../actions/workflows/checks.yml/badge.svg)](../../actions/workflows/checks.yml) [![pylint Score](https://mperlet.github.io/pybadge/badges/10.0.svg)](./logs/pylint/) [![Coverage score](https://img.shields.io/badge/coverage-74%25-red.svg)](./logs/cov.out) [![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](./LICENSE) From 6a20d03c72bb787ef4355cc4e8f36ae7be6508fc Mon Sep 17 00:00:00 2001 From: Diptesh Basak Date: Fri, 24 Sep 2021 12:36:53 +0530 Subject: [PATCH 26/30] v0.4.0 --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 0f0fb88..06a24c8 100644 --- a/README.md +++ b/README.md @@ -17,6 +17,7 @@ 1. [Initial setup](./README.md#initial-setup) 1. [Unit tests](./README.md#run-unit-tests-and-pylint-ratings) 1. [Important links](./README.md#important-links) +1. [License](./LICENSE) *** ## Introduction From b05862db6013a83d8f7d8df5711b51e7cf0506c3 Mon Sep 17 00:00:00 2001 From: Diptesh Basak Date: Fri, 24 Sep 2021 12:41:15 +0530 Subject: [PATCH 27/30] v0.4.0 --- .github/workflows/checks.yml | 1 - 1 file changed, 1 deletion(-) diff --git a/.github/workflows/checks.yml b/.github/workflows/checks.yml index 6d91104..7745894 100644 --- a/.github/workflows/checks.yml +++ b/.github/workflows/checks.yml @@ -21,7 +21,6 @@ on: branches: - 'stable' - 'testing' - - 'dev*' - 'feature*' - '!maintenance*' pull_request: From 6b7638d665f3e969b78553854e89aa93bcefe984 Mon Sep 17 00:00:00 2001 From: MadhuTangudu Date: Sat, 25 Sep 2021 00:51:53 +0530 Subject: [PATCH 28/30] v0.4.0 changelog: - glmnet_ts.py and test_glmnet_ts.py modified - test_glmnet_ts1.csv added --- data/input/test_glmnet_ts1.csv | 105 ++++++++++++++++++++++++++ mllib/lib/glmnet_ts.py | 40 ++++++---- tests/test_glmnet_ts.py | 130 ++++++++++++++++++++++++--------- 3 files changed, 228 insertions(+), 47 deletions(-) create mode 100644 data/input/test_glmnet_ts1.csv diff --git a/data/input/test_glmnet_ts1.csv b/data/input/test_glmnet_ts1.csv new file mode 100644 index 0000000..e9079fa --- /dev/null +++ b/data/input/test_glmnet_ts1.csv @@ -0,0 +1,105 @@ +week,y,x1,x2 +1,3.8,2,2 +2,6.11,2,1.5 +3,6.98,1,1.6 +4,8.59,1,1.9 +5,9.99,1,1.7 +6,11.44,1,2.1 +7,14.02,3,1.4 +8,15.41,2,1.8 +9,16.63,2,1.5 +10,17.79,2,1.1 +11,18.49,1,1.9 +12,20.57,3,1.4 +13,21.31,2,1.3 +14,22.26,2,1.7 +15,22.35,1,1.3 +16,23.65,2,1.9 +17,25.41,3,1.6 +18,26.41,3,1.1 +19,26.12,1,1.8 +20,27.41,3,1.1 +21,27.97,2,1.6 +22,29.15,3,1.7 +23,29.19,2,1 +24,30.33,3,1.6 +25,29.76,1,1.4 +26,29.37,1,1.4 +27,29.97,2,1.1 +28,30.38,2,1.5 +29,31.24,3,1.2 +30,31.66,2,2 +31,30.89,1,1.3 +32,31.17,2,1 +33,31,1,1.7 +34,31.28,2,1.4 +35,30.83,1,1.4 +36,31.33,2,1.5 +37,31.19,1,2 +38,31.25,2,1.1 +39,32.54,3,1.8 +40,33.52,3,2 +41,33.26,2,1.7 +42,32.74,1,1.9 +43,32.87,2,1.1 +44,32.11,1,1.1 +45,33.43,3,1.8 +46,33.47,2,1.6 +47,32.3,1,1.1 +48,33.55,3,1.5 +49,34.33,3,1.3 +50,33.34,1,1.7 +51,32.5,1,1.1 +52,33.81,3,1.4 +53,34.84,3,2 +54,35.14,3,1.3 +55,35.71,3,1.5 +56,36.26,3,1.6 +57,36.11,2,1.8 +58,35.32,1,1.8 +59,34.48,1,1.2 +60,34.66,2,1 +61,33.77,1,1.1 +62,34.29,2,1.9 +63,35.33,3,1.8 +64,34.39,1,2 +65,34.38,2,1.5 +66,35.63,3,2 +67,34.42,1,1.2 +68,33.37,1,1 +69,33.28,1,1.8 +70,34.33,3,1.3 +71,34.26,2,1.5 +72,33.54,1,1.8 +73,33.14,1,1.6 +74,34.2,3,1.3 +75,34.4,2,1.9 +76,33.36,1,1.3 +77,32.84,1,1.3 +78,32.57,1,1.6 +79,33.92,3,1.7 +80,33.16,1,1.7 +81,34.08,3,1.7 +82,34.89,3,1.3 +83,33.45,1,1 +84,32.83,1,1.2 +85,32.76,1,1.6 +86,32.42,1,1.6 +87,31.91,1,1.3 +88,33.14,3,1.5 +89,33.08,2,1.4 +90,32.49,1,1.9 +91,32.37,1,2 +92,31.88,1,1.2 +93,32.11,2,1 +94,33.35,3,1.8 +95,33.17,2,1.5 +96,33.84,3,1.4 +97,34.51,3,1.3 +98,34.34,2,1.7 +99,33.56,1,1.5 +100,34.79,3,1.6 +101,35.67,3,1.7 +102,36.28,3,2 +103,36.91,3,1.9 +104,36.97,3,1 diff --git a/mllib/lib/glmnet_ts.py b/mllib/lib/glmnet_ts.py index 3279fc2..3d37305 100644 --- a/mllib/lib/glmnet_ts.py +++ b/mllib/lib/glmnet_ts.py @@ -63,11 +63,12 @@ def create_lag_vars(df: pd.DataFrame, Dependant variable. x_var : List[str] + Independant variables. lst_lag : List[int] - Lag values list (the default is None) + Lag variables list (the default is None) n_interval : str, optional @@ -130,7 +131,7 @@ class GLMNet_ts(): lst_lag : List[int] - Lag values list (the default is None) + Lag variables list (the default is None) n_interval : str, optional @@ -158,7 +159,10 @@ def __init__(self, n_interval: str = None, param: Dict = None): """Initialize variables for module ``GLMNet``.""" - self.df = df[y_var + x_var] + if n_interval is None: + self.df = df[y_var + x_var] + else: + self.df = df[y_var + x_var + [n_interval]] self.y_var = y_var self.x_var = x_var self.lst_lag = lst_lag @@ -190,13 +194,13 @@ def _fit(self) -> None: self.x_var, self.lst_lag, self.n_interval) - self.x_var = list(df_ip.columns) - self.x_var.remove(self.y_var[0]) + x_var = list(df_ip.columns) + x_var.remove(self.y_var[0]) df_train = df_ip.iloc[0:int(len(df_ip) * (1-self.param["test_perc"]))] df_test = df_ip.iloc[int(len(df_ip) * (1-self.param["test_perc"])):] - train_x = df_train[self.x_var] + train_x = df_train[x_var] train_y = df_train[self.y_var] - test_x = df_test[self.x_var] + test_x = df_test[x_var] test_y = df_test[self.y_var] self.param["k_fold"] = ts_split(n_splits=self.param["k_fold"]) self.param["k_fold"] = self.param["k_fold"].split(X=train_y) @@ -204,7 +208,7 @@ def _fit(self) -> None: fit_intercept=True, alphas=[1e-5, 1e-4, 1e-3, 1e-2, 1e-1, 1.0, 10.0, 100.0], - normalize=True, + normalize=False, cv=self.param["k_fold"], n_jobs=self.param["n_jobs"], random_state=self.param["seed"]) @@ -220,8 +224,14 @@ def _fit(self) -> None: def _compute_metrics(self): """Compute commonly used metrics to evaluate the model.""" - y = self.df[self.y_var].iloc[:, 0].values.tolist() - y_hat = list(self.predict(self.df[self.x_var])["y"].values) + y = self.df[self.y_var].iloc[\ + max(self.lst_lag):len(self.df), 0].values.tolist() + if self.n_interval is None: + y_hat = list(self.predict(self.df[\ + self.x_var][max(self.lst_lag):len(self.df)])["y"].values) + else: + y_hat = list(self.predict(self.df[self.x_var +\ + [self.n_interval]][max(self.lst_lag):len(self.df)])["y"].values) model_summary = {"rsq": np.round(metrics.rsq(y, y_hat), 3), "mae": np.round(metrics.mae(y, y_hat), 3), "mape": np.round(metrics.mape(y, y_hat), 3), @@ -246,6 +256,11 @@ def predict(self, df_predict: pd.DataFrame) -> pd.DataFrame: """ if self.n_interval is None: + df_predict = df_predict[self.x_var] + else: + df_predict = df_predict[self.x_var + [self.n_interval]] + if self.n_interval is None: + df_ip = self.df df_predict = df_predict.reset_index(drop=True) df_predict = \ df_predict.set_index(df_predict.index+self.max_epoch+1) @@ -262,18 +277,17 @@ def predict(self, df_predict: pd.DataFrame) -> pd.DataFrame: df_predict = df_predict[self.x_var] df_predict["y"] = -1 for i in range(0, len(df_predict)): - # for i in range(0, len(df_ip)): df_pred = pd.DataFrame(df_predict.iloc[i]) df_pred = df_pred.T # Transpose period_val = df_pred.index df_pred = df_pred[self.x_var].reset_index(drop=True) df_pred_x = pd.DataFrame( {"lag_"+str(self.lst_lag[0]): df_ip.iloc[len(df_ip)\ - -self.lst_lag[0]]}) + -self.lst_lag[0]][self.y_var]}) for j in range(1, len(self.lst_lag)): df_tmp = pd.DataFrame( {"lag_"+str(self.lst_lag[j]): \ - df_ip.iloc[len(df_ip)-self.lst_lag[j]]}) + df_ip.iloc[len(df_ip)-self.lst_lag[j]][self.y_var]}) df_pred_x = df_pred_x.join(df_tmp) df_pred_x = df_pred_x.reset_index(drop=True) df_pred_x = df_pred_x.join(df_pred) diff --git a/tests/test_glmnet_ts.py b/tests/test_glmnet_ts.py index b4aa414..7168736 100644 --- a/tests/test_glmnet_ts.py +++ b/tests/test_glmnet_ts.py @@ -9,7 +9,7 @@ - Madhu - Diptesh - Date: Sep 07, 2021 + Date: Sep 24, 2021 """ # pylint: disable=invalid-name @@ -25,6 +25,7 @@ import pandas as pd import numpy as np +import pytest # Set base path path = abspath(getsourcefile(lambda: 0)) @@ -94,39 +95,100 @@ def test_lag_vars_specified(self): self.assertEqual(df_op.equals(exp_op), True) self.assertEqual([3, 2, 1], lst_lag) -# class TestGLMNet_ts(unittest.TestCase): -# """Test suite for module ``GLMNet_ts``.""" - -# def setUp(self): -# """Set up for module ``GLMNet_ts``.""" - -# def test_known_equation(self): -# """GLMNet_ts: Test a known equation.""" -# df_ip = pd.read_csv(path + "test_glmnet_ts.csv") -# df_train_ip = df_ip.iloc[7:100] -# mod = GLMNet_ts(df=df_train_ip, -# y_var=["y"], -# x_var=["x1", "x2"], -# lst_lag=[7,1]) -# op = mod.opt -# self.assertEqual(np.round(op.get('intercept'), 0), 100.0) -# self.assertEqual(np.round(op.get('coef')[0], 0), 2.0) -# self.assertEqual(np.round(op.get('coef')[1], 0), 3.0) -# self.assertEqual(np.round(op.get('coef')[2], 0), 0.0) - -# def test_predict_target_variable(self): -# """GLMNet_ts: Test to predict a target variable.""" -# df_ip = pd.read_csv(path + "test_glmnet.csv") -# mod = GLMNet_ts(df=df_ip, -# y_var=["y"], -# x_var=["x1", "x2", "x3"]) -# df_predict = pd.DataFrame({"x1": [10, 20], -# "x2": [5, 10], -# "x3": [100, 0]}) -# op = mod.predict(df_predict) -# op = np.round(np.array(op["y"]), 1) -# exp_op = np.array([135.0, 170.0]) -# self.assertEqual((op == exp_op).all(), True) +class TestGLMNet_ts(unittest.TestCase): + """Test suite for module ``GLMNet_ts``.""" + + def setUp(self): + """Set up for module ``GLMNet_ts``.""" + + def test_known_equation(self): + """GLMNet_ts: Test a known equation with/without n_interval.""" + df_ip = pd.read_csv(path + "test_glmnet_ts1.csv") + df_train_ip = df_ip.iloc[0:len(df_ip)] + mod = GLMNet_ts(df=df_train_ip, + y_var=["y"], + x_var=["x1", "x2"], + lst_lag=[3, 1]) + op = mod.opt + self.assertTrue(0.5 <= np.round(op.get('intercept'), 0) <= 1.5) + self.assertTrue(0.15 <= np.round(op.get('coef')[0], 2) <= 0.25) + self.assertTrue(0.65 <= np.round(op.get('coef')[1], 2) <= 0.75) + self.assertTrue(0.75 <= np.round(op.get('coef')[2], 2) <= 0.85) + self.assertTrue(0.45 <= np.round(op.get('coef')[3], 2) <= 0.55) + mod = GLMNet_ts(df=df_train_ip, + y_var=["y"], + x_var=["x1", "x2"], + lst_lag=[3, 1], + n_interval="week") + op = mod.opt + self.assertTrue(0.5 <= np.round(op.get('intercept'), 0) <= 1.5) + self.assertTrue(0.15 <= np.round(op.get('coef')[0], 2) <= 0.25) + self.assertTrue(0.65 <= np.round(op.get('coef')[1], 2) <= 0.75) + self.assertTrue(0.75 <= np.round(op.get('coef')[2], 2) <= 0.85) + self.assertTrue(0.45 <= np.round(op.get('coef')[3], 2) <= 0.55) + + + def test_predict_target_variable(self): + """GLMNet_ts: Test to predict a target variable with/without n_interval.""" + df_ip = pd.read_csv(path + "test_glmnet_ts1.csv") + # without n_interval + df_train_ip = df_ip.iloc[0:95] + mod = GLMNet_ts(df=df_train_ip, + y_var=["y"], + x_var=["x1", "x2"], + lst_lag=[3, 1]) + op = mod.opt + df_predict = df_ip.iloc[95:len(df_ip)] + y_pred = mod.predict(df_predict) + y_pred = np.round(np.array(y_pred["y"]), 1) + df_exp = df_ip.copy(deep=True) + df_exp['lag_3'] = df_exp["y"].shift(3) + df_exp['lag_1'] = df_exp["y"].shift(1) + df_exp = df_exp[["lag_3", "lag_1", "x1", "x2"]] + df_exp = df_exp.iloc[95:len(df_ip)] + df_exp["y"] = op.get('intercept') + op.get('coef')[0] * df_exp["lag_3"] \ + + op.get('coef')[1] * df_exp["lag_1"] \ + + op.get('coef')[2] * df_exp["x1"] \ + + op.get('coef')[3] * df_exp["x2"] + y_exp = np.round(np.array(df_exp["y"]), 1) + for i, j in zip(y_pred, y_exp): + self.assertTrue(j - 0.1 <= i <= j + 0.1) + # with n_interval + mod = GLMNet_ts(df=df_train_ip, + y_var=["y"], + x_var=["x1", "x2"], + lst_lag=[3, 1], + n_interval="week") + op = mod.opt + df_predict = df_ip.iloc[95:len(df_ip)] + y_pred = mod.predict(df_predict) + y_pred = np.round(np.array(y_pred["y"]), 1) + df_exp = df_ip.copy(deep=True) + df_exp['lag_3'] = df_exp["y"].shift(3) + df_exp['lag_1'] = df_exp["y"].shift(1) + df_exp = df_exp[["lag_3", "lag_1", "x1", "x2"]] + df_exp = df_exp.iloc[95:len(df_ip)] + df_exp["y"] = op.get('intercept') + op.get('coef')[0] * df_exp["lag_3"] \ + + op.get('coef')[1] * df_exp["lag_1"] \ + + op.get('coef')[2] * df_exp["x1"] \ + + op.get('coef')[3] * df_exp["x2"] + y_exp = np.round(np.array(df_exp["y"]), 1) + for i, j in zip(y_pred, y_exp): + self.assertTrue(j - 0.1 <= i <= j + 0.1) + + def test_exit(self): + """GLMNet_ts: Test for missing time instance.""" + df_ip = pd.read_csv(path + "test_glmnet_ts1.csv") + # without n_interval + df_train_ip = df_ip.iloc[0:95] + mod = GLMNet_ts(df=df_train_ip, + y_var=["y"], + x_var=["x1", "x2"], + lst_lag=[3, 1], + n_interval="week") + df_predict = df_ip.iloc[96:len(df_ip)] + with pytest.raises(SystemExit): + df_predict = mod.predict(df_predict) # ============================================================================= From efd835c397692b346df94e244744bf83241bb5c1 Mon Sep 17 00:00:00 2001 From: Diptesh Basak Date: Sat, 25 Sep 2021 12:50:18 +0530 Subject: [PATCH 29/30] v0.4.0 changelog: - cleanup of GLMNET time series modules --- README.md | 2 +- logs/cov.out | 17 ++++----- logs/pip.out | 2 +- logs/pylint/lib-glmnet_ts-py.out | 48 ++++--------------------- logs/pylint/lib-model-py.out | 12 +++---- logs/pylint/tests-test_glmnet_ts-py.out | 4 +++ mllib/lib/glmnet_ts.py | 33 +++++++++-------- requirements.txt | 3 +- tests/test_glmnet_ts.py | 28 ++++++++------- 9 files changed, 61 insertions(+), 88 deletions(-) create mode 100644 logs/pylint/tests-test_glmnet_ts-py.out diff --git a/README.md b/README.md index 06a24c8..c4feeaf 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ [![checks](../../actions/workflows/checks.yml/badge.svg)](../../actions/workflows/checks.yml) [![pylint Score](https://mperlet.github.io/pybadge/badges/10.0.svg)](./logs/pylint/) -[![Coverage score](https://img.shields.io/badge/coverage-74%25-red.svg)](./logs/cov.out) +[![Coverage score](https://img.shields.io/badge/coverage-99%25-red.svg)](./logs/cov.out) [![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](./LICENSE) *** diff --git a/logs/cov.out b/logs/cov.out index 27f2c36..78d80d2 100644 --- a/logs/cov.out +++ b/logs/cov.out @@ -1,8 +1,9 @@ -Name Stmts Miss Cover Missing --------------------------------------------------------------------------------------------- -/media/ph33r/Data/Project/mllib/GitHub/mllib/__init__.py 7 0 100% -/media/ph33r/Data/Project/mllib/GitHub/mllib/lib/__init__.py 7 0 100% -/media/ph33r/Data/Project/mllib/GitHub/mllib/lib/cluster.py 103 0 100% -/media/ph33r/Data/Project/mllib/GitHub/mllib/lib/model.py 137 65 53% 87, 285-304, 308-343, 347-354, 372-409 --------------------------------------------------------------------------------------------- -TOTAL 254 65 74% +Name Stmts Miss Cover Missing +------------------------------------------------------ +mllib/__init__.py 7 0 100% +mllib/lib/__init__.py 7 0 100% +mllib/lib/cluster.py 103 0 100% +mllib/lib/glmnet_ts.py 113 1 99% 88 +mllib/lib/model.py 44 0 100% +------------------------------------------------------ +TOTAL 274 1 99% diff --git a/logs/pip.out b/logs/pip.out index b25fea1..f61bf91 100644 --- a/logs/pip.out +++ b/logs/pip.out @@ -1 +1 @@ -INFO: Successfully saved requirements file in /media/ph33r/Data/Project/mllib/GitHub/requirements.txt +INFO: Successfully saved requirements file in /media/ph33r/Data/Project/mllib/Git/requirements.txt diff --git a/logs/pylint/lib-glmnet_ts-py.out b/logs/pylint/lib-glmnet_ts-py.out index 3ecdf82..77fd809 100644 --- a/logs/pylint/lib-glmnet_ts-py.out +++ b/logs/pylint/lib-glmnet_ts-py.out @@ -1,45 +1,9 @@ ************* Module mllib.lib.glmnet_ts -glmnet_ts.py:22:20: W0621: Redefining name 'y_var' from outer scope (line 74) (redefined-outer-name) -glmnet_ts.py:23:20: W0621: Redefining name 'x_var' from outer scope (line 75) (redefined-outer-name) -glmnet_ts.py:21:0: C0103: Argument name "df" doesn't conform to snake_case naming style (invalid-name) -glmnet_ts.py:67:4: C0103: Variable name "op" doesn't conform to snake_case naming style (invalid-name) -glmnet_ts.py:71:0: C0103: Constant name "df_ip" doesn't conform to UPPER_CASE naming style (invalid-name) -glmnet_ts.py:74:0: C0103: Constant name "y_var" doesn't conform to UPPER_CASE naming style (invalid-name) -glmnet_ts.py:75:0: C0103: Constant name "x_var" doesn't conform to UPPER_CASE naming style (invalid-name) -glmnet_ts.py:77:0: C0103: Constant name "param" doesn't conform to UPPER_CASE naming style (invalid-name) -glmnet_ts.py:86:0: C0103: Constant name "df_ip" doesn't conform to UPPER_CASE naming style (invalid-name) -glmnet_ts.py:88:0: C0103: Constant name "lag_var" doesn't conform to UPPER_CASE naming style (invalid-name) -glmnet_ts.py:89:0: C0103: Constant name "x_var" doesn't conform to UPPER_CASE naming style (invalid-name) -glmnet_ts.py:93:0: C0103: Constant name "max_epoch" doesn't conform to UPPER_CASE naming style (invalid-name) -glmnet_ts.py:96:0: C0103: Constant name "df_pred_data" doesn't conform to UPPER_CASE naming style (invalid-name) -glmnet_ts.py:99:0: C0103: Constant name "df_train" doesn't conform to UPPER_CASE naming style (invalid-name) -glmnet_ts.py:100:0: C0103: Constant name "df_test" doesn't conform to UPPER_CASE naming style (invalid-name) -glmnet_ts.py:102:0: C0103: Constant name "train_x" doesn't conform to UPPER_CASE naming style (invalid-name) -glmnet_ts.py:103:0: C0103: Constant name "train_y" doesn't conform to UPPER_CASE naming style (invalid-name) -glmnet_ts.py:106:0: C0103: Constant name "test_x" doesn't conform to UPPER_CASE naming style (invalid-name) -glmnet_ts.py:107:0: C0103: Constant name "test_y" doesn't conform to UPPER_CASE naming style (invalid-name) -glmnet_ts.py:109:0: C0103: Constant name "test_x" doesn't conform to UPPER_CASE naming style (invalid-name) -glmnet_ts.py:110:0: C0103: Constant name "test_y" doesn't conform to UPPER_CASE naming style (invalid-name) -glmnet_ts.py:116:0: C0103: Constant name "mod" doesn't conform to UPPER_CASE naming style (invalid-name) -glmnet_ts.py:127:0: C0103: Constant name "opt" doesn't conform to UPPER_CASE naming style (invalid-name) -glmnet_ts.py:133:0: C0103: Constant name "model" doesn't conform to UPPER_CASE naming style (invalid-name) -glmnet_ts.py:134:0: W0127: Assigning the same variable 'opt' to itself (self-assigning-variable) -glmnet_ts.py:134:0: C0103: Constant name "opt" doesn't conform to UPPER_CASE naming style (invalid-name) -glmnet_ts.py:138:0: C0103: Constant name "df_predict" doesn't conform to UPPER_CASE naming style (invalid-name) -glmnet_ts.py:139:0: C0103: Constant name "df_predict" doesn't conform to UPPER_CASE naming style (invalid-name) -glmnet_ts.py:142:0: C0103: Constant name "df_predict" doesn't conform to UPPER_CASE naming style (invalid-name) -glmnet_ts.py:143:0: C0103: Constant name "df_predict" doesn't conform to UPPER_CASE naming style (invalid-name) -glmnet_ts.py:166:0: C0103: Constant name "y" doesn't conform to UPPER_CASE naming style (invalid-name) -glmnet_ts.py:167:0: C0103: Constant name "y_hat" doesn't conform to UPPER_CASE naming style (invalid-name) -glmnet_ts.py:168:0: C0103: Constant name "model_summary" doesn't conform to UPPER_CASE naming style (invalid-name) -glmnet_ts.py:168:24: I1101: Module 'metrics' has no 'rsq' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member) -glmnet_ts.py:169:24: I1101: Module 'metrics' has no 'mae' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member) -glmnet_ts.py:170:25: I1101: Module 'metrics' has no 'mape' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member) -glmnet_ts.py:171:25: I1101: Module 'metrics' has no 'rmse' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member) -glmnet_ts.py:173:0: W0104: Statement seems to have no effect (pointless-statement) -glmnet_ts.py:9:0: W0611: Unused Dict imported from typing (unused-import) -glmnet_ts.py:15:0: W0611: Unused train_test_split imported from sklearn.model_selection as split (unused-import) +glmnet_ts.py:238:41: I1101: Module 'metrics' has no 'rsq' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member) +glmnet_ts.py:239:41: I1101: Module 'metrics' has no 'mae' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member) +glmnet_ts.py:240:42: I1101: Module 'metrics' has no 'mape' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member) +glmnet_ts.py:241:42: I1101: Module 'metrics' has no 'rmse' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member) ------------------------------------------------------------------- -Your code has been rated at 5.38/10 (previous run: 5.38/10, +0.00) +-------------------------------------------------------------------- +Your code has been rated at 10.00/10 (previous run: 10.00/10, +0.00) diff --git a/logs/pylint/lib-model-py.out b/logs/pylint/lib-model-py.out index 20446f8..8d71b59 100644 --- a/logs/pylint/lib-model-py.out +++ b/logs/pylint/lib-model-py.out @@ -1,12 +1,8 @@ ************* Module mllib.lib.model -model.py:204:41: I1101: Module 'metrics' has no 'rsq' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member) -model.py:205:41: I1101: Module 'metrics' has no 'mae' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member) -model.py:206:42: I1101: Module 'metrics' has no 'mape' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member) -model.py:207:42: I1101: Module 'metrics' has no 'rmse' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member) -model.py:349:41: I1101: Module 'metrics' has no 'rsq' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member) -model.py:350:41: I1101: Module 'metrics' has no 'mae' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member) -model.py:351:42: I1101: Module 'metrics' has no 'mape' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member) -model.py:352:42: I1101: Module 'metrics' has no 'rmse' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member) +model.py:140:41: I1101: Module 'metrics' has no 'rsq' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member) +model.py:141:41: I1101: Module 'metrics' has no 'mae' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member) +model.py:142:42: I1101: Module 'metrics' has no 'mape' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member) +model.py:143:42: I1101: Module 'metrics' has no 'rmse' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member) -------------------------------------------------------------------- Your code has been rated at 10.00/10 (previous run: 10.00/10, +0.00) diff --git a/logs/pylint/tests-test_glmnet_ts-py.out b/logs/pylint/tests-test_glmnet_ts-py.out new file mode 100644 index 0000000..d7495ee --- /dev/null +++ b/logs/pylint/tests-test_glmnet_ts-py.out @@ -0,0 +1,4 @@ + +-------------------------------------------------------------------- +Your code has been rated at 10.00/10 (previous run: 10.00/10, +0.00) + diff --git a/mllib/lib/glmnet_ts.py b/mllib/lib/glmnet_ts.py index 3d37305..9a1ddd3 100644 --- a/mllib/lib/glmnet_ts.py +++ b/mllib/lib/glmnet_ts.py @@ -224,14 +224,17 @@ def _fit(self) -> None: def _compute_metrics(self): """Compute commonly used metrics to evaluate the model.""" - y = self.df[self.y_var].iloc[\ - max(self.lst_lag):len(self.df), 0].values.tolist() + y = self.df[self.y_var].iloc[max(self.lst_lag): + len(self.df), 0].values.tolist() if self.n_interval is None: - y_hat = list(self.predict(self.df[\ - self.x_var][max(self.lst_lag):len(self.df)])["y"].values) + y_hat = list(self.predict(self.df[self.x_var][max(self.lst_lag): + len(self.df)])["y"] + .values) else: - y_hat = list(self.predict(self.df[self.x_var +\ - [self.n_interval]][max(self.lst_lag):len(self.df)])["y"].values) + y_hat = list(self.predict(self.df[self.x_var + + [self.n_interval]] + [max(self.lst_lag):len(self.df)])["y"] + .values) model_summary = {"rsq": np.round(metrics.rsq(y, y_hat), 3), "mae": np.round(metrics.mae(y, y_hat), 3), "mape": np.round(metrics.mape(y, y_hat), 3), @@ -264,13 +267,13 @@ def predict(self, df_predict: pd.DataFrame) -> pd.DataFrame: df_predict = df_predict.reset_index(drop=True) df_predict = \ df_predict.set_index(df_predict.index+self.max_epoch+1) - elif len(df_predict) != (df_predict[self.n_interval].max() \ - - df_predict[self.n_interval].min() + 1) \ - or df_predict[self.n_interval].min() \ - > self.max_epoch+1: + elif len(df_predict) != (df_predict[self.n_interval].max() + - df_predict[self.n_interval].min() + 1)\ + or df_predict[self.n_interval].min()\ + > self.max_epoch + 1: sys.exit("Missing time instance found in input data") else: - df_ip = self.df[self.df[self.n_interval] \ + df_ip = self.df[self.df[self.n_interval] <= df_predict[self.n_interval].min()] df_predict = df_predict.sort_values(by=self.n_interval) df_predict = df_predict.set_index(self.n_interval) @@ -278,15 +281,15 @@ def predict(self, df_predict: pd.DataFrame) -> pd.DataFrame: df_predict["y"] = -1 for i in range(0, len(df_predict)): df_pred = pd.DataFrame(df_predict.iloc[i]) - df_pred = df_pred.T # Transpose + df_pred = df_pred.T period_val = df_pred.index df_pred = df_pred[self.x_var].reset_index(drop=True) df_pred_x = pd.DataFrame( - {"lag_"+str(self.lst_lag[0]): df_ip.iloc[len(df_ip)\ - -self.lst_lag[0]][self.y_var]}) + {"lag_" + str(self.lst_lag[0]): + df_ip.iloc[len(df_ip) - self.lst_lag[0]][self.y_var]}) for j in range(1, len(self.lst_lag)): df_tmp = pd.DataFrame( - {"lag_"+str(self.lst_lag[j]): \ + {"lag_"+str(self.lst_lag[j]): df_ip.iloc[len(df_ip)-self.lst_lag[j]][self.y_var]}) df_pred_x = df_pred_x.join(df_tmp) df_pred_x = df_pred_x.reset_index(drop=True) diff --git a/requirements.txt b/requirements.txt index ec389bf..25c2389 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,5 @@ +pytest==5.3.5 Cython==0.29.15 numpy==1.19.5 pandas==1.1.3 -scikit_learn==0.24.2 +scikit_learn==1.0 diff --git a/tests/test_glmnet_ts.py b/tests/test_glmnet_ts.py index 7168736..d09e0c3 100644 --- a/tests/test_glmnet_ts.py +++ b/tests/test_glmnet_ts.py @@ -91,10 +91,12 @@ def test_lag_vars_specified(self): y_var=["y"], x_var=["x1", "x2"], lst_lag=[3, 2, 1]) - exp_op = df_ip.iloc[:, [1, 4, 5, 6, 7, 8]].dropna().reset_index(drop=True) + exp_op = df_ip.iloc[:, [1, 4, 5, 6, 7, 8]]\ + .dropna().reset_index(drop=True) self.assertEqual(df_op.equals(exp_op), True) self.assertEqual([3, 2, 1], lst_lag) + class TestGLMNet_ts(unittest.TestCase): """Test suite for module ``GLMNet_ts``.""" @@ -127,9 +129,8 @@ def test_known_equation(self): self.assertTrue(0.75 <= np.round(op.get('coef')[2], 2) <= 0.85) self.assertTrue(0.45 <= np.round(op.get('coef')[3], 2) <= 0.55) - def test_predict_target_variable(self): - """GLMNet_ts: Test to predict a target variable with/without n_interval.""" + """GLMNet_ts: Test predictor with/without n_interval.""" df_ip = pd.read_csv(path + "test_glmnet_ts1.csv") # without n_interval df_train_ip = df_ip.iloc[0:95] @@ -146,10 +147,11 @@ def test_predict_target_variable(self): df_exp['lag_1'] = df_exp["y"].shift(1) df_exp = df_exp[["lag_3", "lag_1", "x1", "x2"]] df_exp = df_exp.iloc[95:len(df_ip)] - df_exp["y"] = op.get('intercept') + op.get('coef')[0] * df_exp["lag_3"] \ - + op.get('coef')[1] * df_exp["lag_1"] \ - + op.get('coef')[2] * df_exp["x1"] \ - + op.get('coef')[3] * df_exp["x2"] + df_exp["y"] = op.get('intercept')\ + + op.get('coef')[0] * df_exp["lag_3"]\ + + op.get('coef')[1] * df_exp["lag_1"]\ + + op.get('coef')[2] * df_exp["x1"]\ + + op.get('coef')[3] * df_exp["x2"] y_exp = np.round(np.array(df_exp["y"]), 1) for i, j in zip(y_pred, y_exp): self.assertTrue(j - 0.1 <= i <= j + 0.1) @@ -168,15 +170,17 @@ def test_predict_target_variable(self): df_exp['lag_1'] = df_exp["y"].shift(1) df_exp = df_exp[["lag_3", "lag_1", "x1", "x2"]] df_exp = df_exp.iloc[95:len(df_ip)] - df_exp["y"] = op.get('intercept') + op.get('coef')[0] * df_exp["lag_3"] \ - + op.get('coef')[1] * df_exp["lag_1"] \ - + op.get('coef')[2] * df_exp["x1"] \ - + op.get('coef')[3] * df_exp["x2"] + df_exp["y"] = op.get('intercept')\ + + op.get('coef')[0] * df_exp["lag_3"]\ + + op.get('coef')[1] * df_exp["lag_1"]\ + + op.get('coef')[2] * df_exp["x1"]\ + + op.get('coef')[3] * df_exp["x2"] y_exp = np.round(np.array(df_exp["y"]), 1) for i, j in zip(y_pred, y_exp): self.assertTrue(j - 0.1 <= i <= j + 0.1) - def test_exit(self): + @staticmethod + def test_for_exit(): """GLMNet_ts: Test for missing time instance.""" df_ip = pd.read_csv(path + "test_glmnet_ts1.csv") # without n_interval From 531efd1b6b97381576e4def8dc721ee8662033bf Mon Sep 17 00:00:00 2001 From: Diptesh Basak Date: Sat, 25 Sep 2021 17:24:12 +0530 Subject: [PATCH 30/30] v0.4.0 --- README.md | 2 +- data/input/iris.csv | 151 --- data/input/store.csv | 1843 -------------------------------- data/input/test_glmnet_ts.csv | 105 -- data/input/test_glmnet_ts1.csv | 105 -- data/input/test_lag_var.csv | 13 - data/input/test_timeseries.csv | 105 -- logs/cov.out | 17 +- mllib/lib/glmnet_ts.py | 302 ------ requirements.txt | 3 +- tests/test_glmnet_ts.py | 203 ---- 11 files changed, 10 insertions(+), 2839 deletions(-) delete mode 100644 data/input/iris.csv delete mode 100644 data/input/store.csv delete mode 100644 data/input/test_glmnet_ts.csv delete mode 100644 data/input/test_glmnet_ts1.csv delete mode 100644 data/input/test_lag_var.csv delete mode 100644 data/input/test_timeseries.csv delete mode 100644 mllib/lib/glmnet_ts.py delete mode 100644 tests/test_glmnet_ts.py diff --git a/README.md b/README.md index c4feeaf..9be7aef 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ [![checks](../../actions/workflows/checks.yml/badge.svg)](../../actions/workflows/checks.yml) [![pylint Score](https://mperlet.github.io/pybadge/badges/10.0.svg)](./logs/pylint/) -[![Coverage score](https://img.shields.io/badge/coverage-99%25-red.svg)](./logs/cov.out) +[![Coverage score](https://img.shields.io/badge/coverage-100%25-dagreen.svg)](./logs/cov.out) [![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](./LICENSE) *** diff --git a/data/input/iris.csv b/data/input/iris.csv deleted file mode 100644 index d93a29c..0000000 --- a/data/input/iris.csv +++ /dev/null @@ -1,151 +0,0 @@ -x3,x4,x1,x2,y -5.1,3.5,1.4,0.2,0 -4.9,3.0,1.4,0.2,0 -4.7,3.2,1.3,0.2,0 -4.6,3.1,1.5,0.2,0 -5.0,3.6,1.4,0.2,0 -5.4,3.9,1.7,0.4,0 -4.6,3.4,1.4,0.3,0 -5.0,3.4,1.5,0.2,0 -4.4,2.9,1.4,0.2,0 -4.9,3.1,1.5,0.1,0 -5.4,3.7,1.5,0.2,0 -4.8,3.4,1.6,0.2,0 -4.8,3.0,1.4,0.1,0 -4.3,3.0,1.1,0.1,0 -5.8,4.0,1.2,0.2,0 -5.7,4.4,1.5,0.4,0 -5.4,3.9,1.3,0.4,0 -5.1,3.5,1.4,0.3,0 -5.7,3.8,1.7,0.3,0 -5.1,3.8,1.5,0.3,0 -5.4,3.4,1.7,0.2,0 -5.1,3.7,1.5,0.4,0 -4.6,3.6,1.0,0.2,0 -5.1,3.3,1.7,0.5,0 -4.8,3.4,1.9,0.2,0 -5.0,3.0,1.6,0.2,0 -5.0,3.4,1.6,0.4,0 -5.2,3.5,1.5,0.2,0 -5.2,3.4,1.4,0.2,0 -4.7,3.2,1.6,0.2,0 -4.8,3.1,1.6,0.2,0 -5.4,3.4,1.5,0.4,0 -5.2,4.1,1.5,0.1,0 -5.5,4.2,1.4,0.2,0 -4.9,3.1,1.5,0.2,0 -5.0,3.2,1.2,0.2,0 -5.5,3.5,1.3,0.2,0 -4.9,3.6,1.4,0.1,0 -4.4,3.0,1.3,0.2,0 -5.1,3.4,1.5,0.2,0 -5.0,3.5,1.3,0.3,0 -4.5,2.3,1.3,0.3,0 -4.4,3.2,1.3,0.2,0 -5.0,3.5,1.6,0.6,0 -5.1,3.8,1.9,0.4,0 -4.8,3.0,1.4,0.3,0 -5.1,3.8,1.6,0.2,0 -4.6,3.2,1.4,0.2,0 -5.3,3.7,1.5,0.2,0 -5.0,3.3,1.4,0.2,0 -7.0,3.2,4.7,1.4,1 -6.4,3.2,4.5,1.5,1 -6.9,3.1,4.9,1.5,1 -5.5,2.3,4.0,1.3,1 -6.5,2.8,4.6,1.5,1 -5.7,2.8,4.5,1.3,1 -6.3,3.3,4.7,1.6,1 -4.9,2.4,3.3,1.0,1 -6.6,2.9,4.6,1.3,1 -5.2,2.7,3.9,1.4,1 -5.0,2.0,3.5,1.0,1 -5.9,3.0,4.2,1.5,1 -6.0,2.2,4.0,1.0,1 -6.1,2.9,4.7,1.4,1 -5.6,2.9,3.6,1.3,1 -6.7,3.1,4.4,1.4,1 -5.6,3.0,4.5,1.5,1 -5.8,2.7,4.1,1.0,1 -6.2,2.2,4.5,1.5,1 -5.6,2.5,3.9,1.1,1 -5.9,3.2,4.8,1.8,1 -6.1,2.8,4.0,1.3,1 -6.3,2.5,4.9,1.5,1 -6.1,2.8,4.7,1.2,1 -6.4,2.9,4.3,1.3,1 -6.6,3.0,4.4,1.4,1 -6.8,2.8,4.8,1.4,1 -6.7,3.0,5.0,1.7,1 -6.0,2.9,4.5,1.5,1 -5.7,2.6,3.5,1.0,1 -5.5,2.4,3.8,1.1,1 -5.5,2.4,3.7,1.0,1 -5.8,2.7,3.9,1.2,1 -6.0,2.7,5.1,1.6,1 -5.4,3.0,4.5,1.5,1 -6.0,3.4,4.5,1.6,1 -6.7,3.1,4.7,1.5,1 -6.3,2.3,4.4,1.3,1 -5.6,3.0,4.1,1.3,1 -5.5,2.5,4.0,1.3,1 -5.5,2.6,4.4,1.2,1 -6.1,3.0,4.6,1.4,1 -5.8,2.6,4.0,1.2,1 -5.0,2.3,3.3,1.0,1 -5.6,2.7,4.2,1.3,1 -5.7,3.0,4.2,1.2,1 -5.7,2.9,4.2,1.3,1 -6.2,2.9,4.3,1.3,1 -5.1,2.5,3.0,1.1,1 -5.7,2.8,4.1,1.3,1 -6.3,3.3,6.0,2.5,2 -5.8,2.7,5.1,1.9,2 -7.1,3.0,5.9,2.1,2 -6.3,2.9,5.6,1.8,2 -6.5,3.0,5.8,2.2,2 -7.6,3.0,6.6,2.1,2 -4.9,2.5,4.5,1.7,2 -7.3,2.9,6.3,1.8,2 -6.7,2.5,5.8,1.8,2 -7.2,3.6,6.1,2.5,2 -6.5,3.2,5.1,2.0,2 -6.4,2.7,5.3,1.9,2 -6.8,3.0,5.5,2.1,2 -5.7,2.5,5.0,2.0,2 -5.8,2.8,5.1,2.4,2 -6.4,3.2,5.3,2.3,2 -6.5,3.0,5.5,1.8,2 -7.7,3.8,6.7,2.2,2 -7.7,2.6,6.9,2.3,2 -6.0,2.2,5.0,1.5,2 -6.9,3.2,5.7,2.3,2 -5.6,2.8,4.9,2.0,2 -7.7,2.8,6.7,2.0,2 -6.3,2.7,4.9,1.8,2 -6.7,3.3,5.7,2.1,2 -7.2,3.2,6.0,1.8,2 -6.2,2.8,4.8,1.8,2 -6.1,3.0,4.9,1.8,2 -6.4,2.8,5.6,2.1,2 -7.2,3.0,5.8,1.6,2 -7.4,2.8,6.1,1.9,2 -7.9,3.8,6.4,2.0,2 -6.4,2.8,5.6,2.2,2 -6.3,2.8,5.1,1.5,2 -6.1,2.6,5.6,1.4,2 -7.7,3.0,6.1,2.3,2 -6.3,3.4,5.6,2.4,2 -6.4,3.1,5.5,1.8,2 -6.0,3.0,4.8,1.8,2 -6.9,3.1,5.4,2.1,2 -6.7,3.1,5.6,2.4,2 -6.9,3.1,5.1,2.3,2 -5.8,2.7,5.1,1.9,2 -6.8,3.2,5.9,2.3,2 -6.7,3.3,5.7,2.5,2 -6.7,3.0,5.2,2.3,2 -6.3,2.5,5.0,1.9,2 -6.5,3.0,5.2,2.0,2 -6.2,3.4,5.4,2.3,2 -5.9,3.0,5.1,1.8,2 diff --git a/data/input/store.csv b/data/input/store.csv deleted file mode 100644 index 55e3503..0000000 --- a/data/input/store.csv +++ /dev/null @@ -1,1843 +0,0 @@ -y,x1,x2,x3,x4,x5,x6,x7,x8 -3,R100 ,R131 ,17413.06,1.02,80844,5,10,A -4,R100 ,R114 ,23290.41,1.36,82980,5,10,A -5,R100 ,R163 ,18241.02,1.06,80933,5,10,A -12,R100 ,R117 ,20692.01,1.21,69184,7,10,A -13,R300 ,R352 ,8252.73,0.48,69674,6,10,A -19,R100 ,R129 ,16169.47,0.94,74595,6,10,A -26,R100 ,R117 ,20397.41,1.19,72984,7,10,A -43,R300 ,R301 ,18933.64,1.1,81050,6,10,A -48,R100 ,R175 ,14473.25,0.84,78121,8,10,A -52,R100 ,R126 ,17470.96,1.02,73182,5,10,A -55,R300 ,R352 ,11862.25,0.69,75618,6,10,A -61,R100 ,R143 ,31066.83,1.81,79900,8,10,A -64,R100 ,R176 ,26316.01,1.53,80978,8,10,A -67,R300 ,R326 ,8210.73,0.48,74609,6,10,A -68,R100 ,R102 ,13214,0.77,72394,5,10,A -69,R100 ,R105 ,16713.05,0.97,72221,7,10,A -75,R300 ,R311 ,11314.14,0.66,77492,6,10,A -76,R100 ,R143 ,22717.36,1.32,79855,8,10,A -78,R100 ,R147 ,11464.04,0.67,62523,5,10,A -79,R100 ,R176 ,16834.94,0.98,74041,8,10,A -80,R300 ,R326 ,11171.17,0.65,64431,6,10,A -82,R100 ,R110 ,13369.59,0.78,80681,5,10,A -83,R300 ,R352 ,8377.58,0.49,74591,6,10,A -85,R100 ,R143 ,17766.32,1.04,59142,8,10,A -86,R100 ,R127 ,14260.12,0.83,59850,7,10,A -90,R300 ,R332 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-304,R200 ,R231 ,13578.56,0.79,64076,9,10,A -305,R200 ,R231 ,15078.17,0.88,66946,9,10,A -307,R200 ,R205 ,12927.6,0.75,76103,9,10,A -309,R200 ,R240 ,13776.63,0.8,64878,9,10,A -310,R200 ,R246 ,14322.45,0.84,67605,9,10,A -311,R200 ,R220 ,18370.87,1.07,60950,9,10,A -312,R200 ,R220 ,17400.32,1.01,74884,9,10,A -313,R200 ,R246 ,22900.55,1.34,72559,9,10,A -314,R200 ,R213 ,13669.59,0.8,62438,9,10,A -317,R200 ,R208 ,23051.67,1.34,64525,9,10,A -318,R200 ,R208 ,18353.7,1.07,78674,9,10,A -319,R300 ,R379 ,13809.68,0.81,72008,9,10,A -320,R200 ,R210 ,27109.95,1.58,86991,9,10,A -321,R200 ,R210 ,16997.32,0.99,73450,9,10,A -322,R200 ,R210 ,17208.92,1,64752,9,10,A -323,R200 ,R233 ,15363.32,0.9,80548,9,10,A -324,R200 ,R233 ,21284.51,1.24,86940,9,10,A -328,R200 ,R237 ,43726.31,2.55,70781,9,10,A -330,R200 ,R247 ,12027.58,0.7,68082,9,10,A -331,R200 ,R247 ,17419.86,1.02,66921,9,10,A -332,R200 ,R247 ,12368.4,0.72,65852,9,10,A -335,R300 ,R305 ,11422.19,0.67,66727,6,10,A -336,R200 ,R202 ,21101.16,1.23,71527,9,10,A -337,R200 ,R264 ,14635.59,0.85,66550,10,10,A -338,R200 ,R264 ,16602.57,0.97,76473,10,10,A -339,R200 ,R265 ,16888.18,0.99,58755,10,10,A -341,R200 ,R259 ,22173.45,1.29,76337,10,10,A -342,R200 ,R259 ,15013.57,0.88,71806,10,10,A -343,R200 ,R258 ,13540.57,0.79,75090,10,10,A -344,R200 ,R258 ,19060.47,1.11,74577,10,10,A -345,R200 ,R258 ,19543.35,1.14,76378,10,10,A -346,R200 ,R260 ,22555.49,1.32,82049,10,10,A -347,R100 ,R135 ,11678.05,0.68,65415,5,10,A -348,R200 ,R264 ,16714.12,0.97,65578,10,10,A -349,R200 ,R262 ,14038.88,0.82,66290,10,10,A -350,R100 ,R135 ,12169.44,0.71,65379,5,10,A -351,R100 ,R134 ,12084.35,0.7,66018,5,10,A -353,R100 ,R137 ,12788.3,0.75,62972,5,10,A -354,R100 ,R148 ,13587.71,0.79,76345,5,10,A -355,R100 ,R144 ,11952.45,0.7,66017,7,10,A -356,R300 ,R378 ,16005.66,0.93,69067,6,10,A -357,R300 ,R378 ,16083.17,0.94,82732,6,10,A -358,R200 ,R213 ,15717.28,0.92,67402,9,10,A -359,R200 ,R205 ,22621.42,1.32,67096,9,10,A -360,R100 ,R163 ,13269.87,0.77,74426,5,10,A -361,R100 ,R137 ,10869.13,0.63,61750,5,10,A -362,R200 ,R258 ,11993.62,0.7,66740,10,10,A -363,R300 ,R377 ,17075.5,1,66706,9,10,A -364,R100 ,R146 ,15159.91,0.88,66271,5,10,A -365,R100 ,R137 ,13262.49,0.77,66062,5,10,A -366,R100 ,R168 ,17256.12,1.01,65990,4,10,A -373,R300 ,R334 ,14656.24,0.85,80648,4,10,A -397,R100 ,R136 ,12922.53,0.75,67248,5,10,A -530,R100 ,R107 ,19923.38,1.16,103457,7,10,A -531,R100 ,R144 ,16151.47,0.94,84704,7,10,A -532,R100 ,R107 ,17833.54,1.04,98036,7,10,A -533,R100 ,R127 ,34191.9,1.99,89039,7,10,A -604,R100 ,R136 ,14096.32,0.82,67111,5,10,A -606,R200 ,R262 ,17183.87,1,66151,10,10,A -607,R200 ,R262 ,14899.87,0.87,66057,10,10,A -608,R200 ,R260 ,15644.36,0.91,66405,10,10,A -609,R200 ,R260 ,11360.22,0.66,60279,10,10,A -610,R100 ,R136 ,11809.55,0.69,66444,5,10,A -611,R100 ,R137 ,11098.5,0.65,65653,5,10,A -612,R200 ,R260 ,16885.54,0.98,74348,10,10,A -613,R200 ,R260 ,12190.76,0.71,66333,10,10,A -614,R200 ,R250 ,12850.16,0.75,66231,9,10,A -615,R200 ,R208 ,13925.71,0.81,66575,9,10,A -616,R100 ,R137 ,10833.78,0.63,66576,5,10,A -617,R100 ,R161 ,20871.26,1.22,66088,10,10,A -618,R100 ,R177 ,12361,0.72,50149,8,10,A -619,R100 ,R126 ,19079.03,1.11,98794,5,10,A -620,R100 ,R111 ,14951.87,0.87,67544,5,10,A -622,R100 ,R148 ,11238.4,0.66,65458,5,10,A -623,R100 ,R148 ,14675.69,0.86,66573,5,10,A -624,R100 ,R148 ,11932.04,0.7,65750,5,10,A -625,R300 ,R378 ,15289.87,0.89,75376,6,10,A -626,R200 ,R237 ,13135.78,0.77,68252,9,10,A -627,R200 ,R259 ,14874.91,0.87,66207,10,10,A -628,R200 ,R262 ,18734.17,1.09,50594,10,10,A -632,R100 ,R134 ,11463.05,0.67,66097,5,10,A -634,R100 ,R148 ,15091.59,0.88,66189,5,10,A -636,R100 ,R161 ,16625.41,0.97,66748,10,10,A -637,R200 ,R259 ,13760.15,0.8,61785,10,10,A -638,R300 ,R318 ,12475.7,0.73,66168,4,10,A -639,R300 ,R374 ,12022.38,0.7,75334,9,10,A -641,R200 ,R213 ,15340.49,0.89,75407,9,10,A -642,R300 ,R310 ,16165.36,0.94,66799,4,10,A -643,R100 ,R163 ,19057.3,1.11,92458,5,10,A -645,R300 ,R315 ,11095.3,0.65,79182,4,10,A -647,R300 ,R313 ,13955.94,0.81,75669,4,10,A -649,R300 ,R324 ,15011.35,0.88,70602,4,10,A -650,R300 ,R365 ,10206.04,0.6,66253,4,10,A -652,R300 ,R314 ,13283.59,0.77,66845,4,10,A -654,R300 ,R354 ,15300.77,0.89,76687,4,10,A -655,R300 ,R314 ,13824.79,0.81,74609,4,10,A -656,R300 ,R314 ,11401.5,0.66,73212,4,10,A -657,R100 ,R114 ,16250.62,0.95,72026,5,10,A -658,R100 ,R143 ,13334.06,0.78,56072,5,10,A -659,R100 ,R114 ,14857.14,0.87,73784,5,10,A -660,R200 ,R248 ,18044.54,1.05,82029,9,10,A -661,R100 ,R131 ,11975.33,0.7,55260,5,10,A -662,R100 ,R102 ,12920.75,0.75,73838,5,10,A -663,R100 ,R111 ,14683.45,0.86,75436,5,10,A -664,R100 ,R131 ,28491.6,1.66,90014,5,10,A -665,R300 ,R354 ,12082.51,0.7,75447,4,10,A -666,R100 ,R173 ,18710.74,1.09,85468,5,10,A -669,R300 ,R315 ,13741.96,0.8,76207,4,10,A -670,R100 ,R147 ,17538.55,1.02,85753,5,10,A -671,R100 ,R136 ,15063.66,0.88,77104,5,10,A -672,R100 ,R135 ,9472.64,0.55,56980,5,10,A -673,R100 ,R148 ,12272.15,0.72,74994,5,10,A -674,R100 ,R117 ,11906.48,0.69,74825,5,10,A -675,R200 ,R247 ,12611.32,0.74,66790,9,10,A -676,R200 ,R245 ,12996.93,0.76,76516,9,10,A -677,R200 ,R244 ,15104.15,0.88,74961,9,10,A -679,R100 ,R161 ,13577.39,0.79,56269,10,10,A -680,R300 ,R380 ,12815.67,0.75,66975,9,10,A -681,R200 ,R259 ,14110.54,0.82,75520,10,10,A -682,R300 ,R332 ,14346.29,0.84,85748,4,10,A -684,R300 ,R304 ,14466.89,0.84,74003,6,10,A -685,R200 ,R219 ,14253.86,0.83,74776,9,10,A -686,R300 ,R359 ,13970.9,0.81,74939,4,10,A -687,R300 ,R324 ,22537.92,1.31,73964,4,10,A -688,R300 ,R313 ,13191.38,0.77,74889,4,10,A -689,R300 ,R360 ,11874.18,0.69,79256,4,10,A -690,R300 ,R320 ,12607.31,0.74,75475,4,10,A -692,R200 ,R212 ,16675.5,0.97,74657,9,10,A -693,R100 ,R131 ,14323.35,0.84,75068,5,10,A -694,R100 ,R102 ,19843.7,1.16,79452,5,10,A -695,R300 ,R369 ,18813.73,1.1,79827,4,10,A -696,R200 ,R264 ,11071.47,0.65,56332,10,10,A -699,R100 ,R178 ,15547.59,0.91,75653,10,10,A -700,R300 ,R376 ,13718.33,0.8,79122,9,10,A -731,R100 ,R133 ,14704.79,0.86,80185,5,10,A -732,R100 ,R121 ,16922.39,0.99,79893,5,10,A -733,R100 ,R147 ,11778.71,0.69,56463,5,10,A -734,R300 ,R348 ,13176.85,0.77,57593,4,10,A -735,R300 ,R376 ,9886.67,0.58,65587,9,10,A -736,R200 ,R232 ,14899.71,0.87,79654,9,10,A -737,R200 ,R211 ,11658.33,0.68,79226,9,10,A -738,R200 ,R218 ,12452.37,0.73,69457,9,10,A -739,R100 ,R177 ,15816.5,0.92,74947,8,10,A -740,R300 ,R348 ,13188.93,0.77,68100,4,10,A -746,R300 ,R349 ,17283.73,1.01,82780,4,10,A -747,R300 ,R346 ,12341.8,0.72,74525,4,10,A -749,R300 ,R335 ,10602.2,0.62,83248,4,10,A -750,R300 ,R348 ,16034.87,0.94,56541,4,10,A -751,R100 ,R126 ,12347.29,0.72,75151,5,10,A -752,R100 ,R118 ,12834.54,0.75,72085,7,10,A -753,R100 ,R112 ,14159.83,0.83,75137,5,10,A -754,R300 ,R331 ,13030.96,0.76,75437,4,10,A -755,R300 ,R371 ,17954.69,1.05,81291,4,10,A -756,R300 ,R351 ,12277.07,0.72,56090,4,10,A -757,R100 ,R140 ,11394.52,0.66,71676,4,10,A -758,R300 ,R321 ,9805.5,0.57,75314,6,10,A -759,R400 ,R466 ,13812.66,0.81,82346,3,10,A -760,R200 ,R265 ,12263.55,0.72,75243,10,10,A -761,R200 ,R205 ,12517.46,0.73,79627,9,10,A -762,R300 ,R355 ,13260.1,0.77,75079,4,10,A -766,R200 ,R258 ,15063.67,0.88,57588,10,10,A -767,R200 ,R206 ,11838.62,0.69,79816,9,10,A -768,R100 ,R178 ,14300.03,0.83,79041,8,10,A -769,R300 ,R374 ,12764.85,0.74,57193,6,10,A -770,R300 ,R321 ,33605.62,1.96,75023,6,10,A -771,R300 ,R327 ,12210.47,0.71,77443,6,10,A -772,R400 ,R466 ,14169.17,0.83,79977,3,10,A -773,R100 ,R140 ,10690.24,0.62,74470,5,10,A -774,R100 ,R167 ,13946.17,0.81,70113,7,10,A -775,R300 ,R302 ,11675,0.68,81270,6,10,A -778,R300 ,R336 ,11251.62,0.66,83741,4,10,A -779,R100 ,R168 ,10057.59,0.59,55763,4,10,A -780,R100 ,R168 ,14748.17,0.86,75680,4,10,A -792,R100 ,R174 ,12913.43,0.75,83730,5,10,A -793,R100 ,R171 ,12900.57,0.75,82156,5,10,A -794,R100 ,R171 ,18977.98,1.11,80586,5,10,A -795,R300 ,R351 ,12827.42,0.75,74892,4,10,A -796,R300 ,R335 ,12221.83,0.71,62396,4,10,A -797,R300 ,R359 ,11817.54,0.69,75447,4,10,A -798,R300 ,R314 ,11531.76,0.67,83273,4,10,A -799,R300 ,R354 ,14277.17,0.83,80716,4,10,A -800,R300 ,R304 ,15227.79,0.89,75364,6,10,A -801,R300 ,R305 ,12687.79,0.74,74903,6,10,A -802,R300 ,R305 ,10382.85,0.61,81104,6,10,A -803,R100 ,R105 ,12177.95,0.71,73609,7,10,A -804,R100 ,R111 ,14030.28,0.82,95593,7,10,A -805,R100 ,R146 ,11351.29,0.66,55502,5,10,A -806,R100 ,R146 ,11181.7,0.65,55848,5,10,A -807,R100 ,R128 ,14802.49,0.86,75358,5,10,A -808,R100 ,R128 ,14435.58,0.84,76023,5,10,A -809,R100 ,R146 ,14463.27,0.84,75443,5,10,A -810,R100 ,R150 ,18293.01,1.07,79359,5,10,A -811,R300 ,R348 ,11270.38,0.66,74535,4,10,A -812,R300 ,R354 ,13458.47,0.78,76779,4,10,A -813,R300 ,R320 ,12430.83,0.73,83627,4,10,A -815,R300 ,R318 ,18275.95,1.07,93937,4,10,A -816,R300 ,R360 ,16732.91,0.98,83497,4,10,A -817,R300 ,R354 ,12219.45,0.71,74986,4,10,A -818,R300 ,R320 ,16331.83,0.95,95399,4,10,A -819,R100 ,R134 ,14761.37,0.86,75749,5,10,A -820,R100 ,R124 ,13094,0.76,74449,5,10,A -821,R100 ,R114 ,13080.87,0.76,55736,5,10,A -822,R300 ,R374 ,13598.9,0.79,73133,6,10,A -823,R300 ,R374 ,10773.36,0.63,73354,6,10,A -824,R300 ,R305 ,16570.77,0.97,73531,6,10,A -825,R300 ,R375 ,21786.15,1.27,82399,9,10,A -826,R300 ,R373 ,14130.02,0.82,77967,9,10,A -827,R200 ,R247 ,14627.47,0.85,77096,9,10,A -828,R200 ,R237 ,14267.82,0.83,79853,9,10,A -830,R200 ,R265 ,14285.9,0.83,75195,10,10,A -831,R100 ,R176 ,9757.66,0.57,54546,7,10,A -832,R300 ,R378 ,10729.06,0.63,54430,6,10,A -833,R100 ,R112 ,21095.08,1.23,94531,5,10,A -834,R100 ,R122 ,10162.99,0.59,74093,5,10,A -835,R100 ,R122 ,17435.22,1.02,95756,5,10,A -836,R100 ,R122 ,18008.8,1.05,94990,5,10,A -837,R100 ,R141 ,18986.37,1.11,74725,5,10,A -838,R100 ,R115 ,16768.37,0.98,77118,5,10,A -839,R100 ,R115 ,16029.09,0.93,81683,5,10,A -840,R100 ,R115 ,18403.36,1.07,94895,5,10,A -841,R100 ,R145 ,14517.66,0.85,73806,5,10,A -842,R100 ,R121 ,13751.94,0.8,81006,5,10,A -843,R100 ,R121 ,19472.96,1.14,73299,5,10,A -844,R300 ,R348 ,18103.13,1.06,79801,4,10,A -845,R300 ,R313 ,12493.38,0.73,75701,4,10,A -847,R100 ,R114 ,17040.68,0.99,82758,5,10,A -848,R100 ,R143 ,11848.11,0.69,56972,8,10,A -849,R300 ,R374 ,15576.15,0.91,73085,6,10,A -850,R300 ,R373 ,12381.32,0.72,75112,9,10,A -851,R300 ,R375 ,10456.67,0.61,79594,9,10,A -852,R200 ,R212 ,13930.61,0.81,75152,9,10,A -853,R200 ,R246 ,14804.51,0.86,80007,9,10,A -854,R300 ,R376 ,12662.51,0.74,79382,9,10,A -855,R300 ,R376 ,12077.75,0.7,75102,9,10,A -856,R100 ,R107 ,11568.85,0.67,56822,7,10,A -857,R100 ,R107 ,12676.07,0.74,72247,7,10,A -858,R300 ,R364 ,12123.38,0.71,81664,6,10,A -859,R100 ,R143 ,11344.36,0.66,56053,8,10,A -860,R100 ,R127 ,10387.94,0.61,57143,7,10,A -861,R100 ,R131 ,14089.19,0.82,69349,5,10,A -862,R100 ,R101 ,17025.68,0.99,75690,5,10,A -863,R100 ,R128 ,16827.45,0.98,75267,5,10,A -864,R100 ,R110 ,13243.49,0.77,57323,5,10,A -865,R100 ,R150 ,16207.49,0.95,74598,5,10,A -866,R100 ,R121 ,17452.99,1.02,74472,5,10,A -867,R100 ,R115 ,15276.27,0.89,78734,5,10,A -868,R100 ,R145 ,13700.88,0.8,72395,5,10,A -870,R100 ,R133 ,14944.44,0.87,73340,5,10,A -871,R100 ,R147 ,14713.99,0.86,75927,5,10,A -872,R100 ,R137 ,19656.7,1.15,83744,5,10,A -873,R300 ,R324 ,14953.98,0.87,82790,4,10,A -874,R300 ,R313 ,11707.98,0.68,78991,4,10,A -875,R300 ,R352 ,8397.58,0.49,80019,6,10,A -876,R300 ,R321 ,7788.55,0.45,75175,6,10,A -877,R300 ,R367 ,15207.69,0.89,79749,4,10,A -878,R100 ,R105 ,12148.5,0.71,70402,7,10,A -879,R100 ,R107 ,10502.31,0.61,57554,7,10,A -880,R100 ,R122 ,14841.71,0.87,81730,5,10,A -881,R100 ,R138 ,10260.59,0.6,75977,5,10,A -882,R300 ,R303 ,13883.89,0.81,75518,6,10,A -883,R200 ,R236 ,25053.52,1.46,80938,9,10,A -884,R300 ,R377 ,12522.09,0.73,56068,9,10,A -885,R100 ,R161 ,22473.63,1.31,83090,8,10,A -887,R300 ,R303 ,11557.09,0.67,57918,6,10,A -888,R300 ,R311 ,11238.45,0.66,70353,6,10,A -891,R100 ,R127 ,10641.29,0.62,56494,5,10,A -893,R100 ,R122 ,9777.19,0.57,75476,5,10,A -894,R100 ,R121 ,12858.27,0.75,75096,5,10,A -895,R100 ,R147 ,14090.52,0.82,68150,5,10,A -896,R100 ,R135 ,15066.37,0.88,75471,5,10,A -897,R300 ,R313 ,15443.46,0.9,79188,4,10,A -898,R300 ,R313 ,11953.84,0.7,79408,4,10,A -899,R300 ,R320 ,15640.01,0.91,79414,4,10,A -901,R100 ,R136 ,13281.67,0.77,83241,5,10,A -904,R100 ,R114 ,15871.84,0.93,83109,5,10,A -905,R100 ,R129 ,11363.9,0.66,56016,7,10,A -906,R100 ,R129 ,9824.93,0.57,56320,7,10,A -907,R300 ,R364 ,12520.79,0.73,75033,6,10,A -909,R300 ,R379 ,20735.77,1.21,78678,9,10,A -910,R200 ,R258 ,17719.57,1.03,74972,10,10,A -911,R200 ,R213 ,14027.57,0.82,83015,9,10,A -912,R200 ,R206 ,12509.72,0.73,79722,9,10,A -913,R200 ,R261 ,21092.98,1.23,80793,9,10,A -914,R200 ,R261 ,17387.01,1.01,84371,9,10,A -915,R100 ,R161 ,12881.37,0.75,57131,10,10,A -917,R300 ,R346 ,16139.13,0.94,79750,4,10,A -918,R300 ,R355 ,16759.65,0.98,79977,4,10,A -919,R300 ,R314 ,10185.33,0.59,68358,4,10,A -920,R300 ,R367 ,13588.43,0.79,79375,4,10,A -921,R300 ,R332 ,15469.82,0.9,58807,4,10,A -922,R100 ,R137 ,14012.34,0.82,77171,5,10,A -923,R100 ,R148 ,14182.56,0.83,70222,5,10,A -924,R100 ,R136 ,10767.7,0.63,56887,5,10,A -926,R100 ,R127 ,13836.29,0.81,65842,5,10,A -927,R100 ,R141 ,15681.73,0.91,83590,5,10,A -928,R100 ,R141 ,15850.61,0.92,87622,5,10,A -929,R100 ,R147 ,15001.34,0.87,70197,5,10,A -930,R100 ,R114 ,14711.92,0.86,67846,5,10,A -931,R100 ,R102 ,15310.65,0.89,75660,5,10,A -935,R300 ,R377 ,17339.27,1.01,64122,9,10,A -936,R300 ,R377 ,16544.2,0.96,79521,9,10,A -937,R200 ,R212 ,20063.99,1.17,85010,9,10,A -938,R200 ,R218 ,13301.26,0.78,80066,9,10,A -939,R200 ,R248 ,16711.96,0.97,80185,9,10,A -940,R200 ,R205 ,16258.42,0.95,79973,9,10,A -941,R200 ,R245 ,16124.66,0.94,67592,9,10,A -942,R100 ,R116 ,38219.99,2.23,96940,5,10,A -943,R100 ,R147 ,18371.27,1.07,75469,5,10,A -945,R100 ,R134 ,11838.7,0.69,59155,5,10,A -947,R300 ,R352 ,8226.42,0.48,79824,6,10,A -949,R200 ,R237 ,17603.78,1.03,85097,9,10,A -950,R300 ,R379 ,13406.43,0.78,80012,9,10,A -952,R300 ,R378 ,11938.23,0.7,66270,6,10,A -953,R300 ,R304 ,10603.24,0.62,56116,6,10,A -955,R300 ,R311 ,19753.34,1.15,74365,6,10,A -957,R100 ,R115 ,11458.47,0.67,73816,5,10,A -958,R300 ,R333 ,10952.48,0.64,56754,4,10,A -959,R300 ,R333 ,9755.63,0.57,56667,4,10,A -961,R300 ,R372 ,17747.27,1.04,81510,4,10,A -962,R300 ,R371 ,11205.51,0.65,70079,4,10,A -963,R300 ,R350 ,11458.97,0.67,58489,4,10,A -964,R300 ,R371 ,11376.51,0.66,70045,4,10,A -966,R300 ,R362 ,9217.09,0.54,62214,4,10,A -967,R300 ,R315 ,14439.93,0.84,70385,4,10,A -968,R300 ,R349 ,20125.68,1.17,83082,4,10,A -969,R300 ,R334 ,9875.95,0.58,57011,4,10,A -970,R300 ,R335 ,14499.95,0.85,75908,4,10,A -981,R300 ,R334 ,13241.83,0.77,79540,4,10,A -982,R300 ,R335 ,11540.62,0.67,74821,4,10,A -984,R100 ,R171 ,14974.6,0.87,68595,5,10,A -985,R100 ,R174 ,14832.65,0.87,75672,5,10,A -986,R100 ,R171 ,11025.8,0.64,57584,5,10,A -988,R100 ,R170 ,12355.24,0.72,63054,5,10,A -989,R100 ,R169 ,11680.45,0.68,58275,5,10,A -990,R100 ,R169 ,10210.04,0.6,57146,5,10,A -992,R100 ,R117 ,14328.27,0.84,65440,7,10,A -993,R300 ,R364 ,13744.05,0.8,74276,6,10,A -995,R200 ,R264 ,20885.95,1.22,82546,10,10,A -996,R200 ,R265 ,16589.49,0.97,75233,10,10,A -997,R200 ,R201 ,15421.41,0.9,84558,9,10,A -1000,R400 ,R411 ,14057.39,0.82,74789,3,10,A -1001,R400 ,R411 ,13268.69,0.77,87398,3,10,A -1002,R400 ,R411 ,16054.98,0.94,84630,3,10,A -1003,R400 ,R415 ,14109.36,0.82,82540,3,10,A -1004,R400 ,R415 ,11784.21,0.69,79136,3,10,A -1005,R400 ,R466 ,12042.22,0.7,68204,3,10,A -1006,R400 ,R412 ,17199.14,1,82372,3,10,A -1007,R400 ,R448 ,14373.35,0.84,82088,3,10,A -1008,R400 ,R415 ,13985.06,0.82,83379,3,10,A -1009,R400 ,R441 ,14171.18,0.83,74705,3,10,A -1010,R400 ,R416 ,14718.4,0.86,82060,2,10,A -1011,R400 ,R416 ,12925.4,0.75,75375,2,10,A -1012,R400 ,R416 ,11941.58,0.7,74898,2,10,A -1013,R400 ,R416 ,13957.03,0.81,74932,2,10,A -1014,R400 ,R416 ,11156.08,0.65,75024,2,10,A -1015,R300 ,R371 ,15384.24,0.9,71263,4,10,A -1016,R400 ,R417 ,13455.75,0.78,75665,3,10,A -1017,R400 ,R417 ,12332.08,0.72,69990,3,10,A -1018,R400 ,R417 ,20712.37,1.21,82804,3,10,A -1019,R400 ,R417 ,11427,0.67,83081,3,10,A -1021,R300 ,R370 ,14852.7,0.87,76304,3,10,A -1022,R300 ,R371 ,13437.58,0.78,57277,4,10,A -1023,R300 ,R354 ,15593.41,0.91,85795,4,10,A -1024,R100 ,R115 ,21665.05,1.26,84615,5,10,A -1025,R200 ,R246 ,13903.17,0.81,81194,9,10,A -1026,R200 ,R247 ,12856.44,0.75,75357,9,10,A -1027,R200 ,R240 ,21268.54,1.24,81889,9,10,A -1028,R200 ,R217 ,15093.6,0.88,83790,9,10,A -1029,R200 ,R231 ,23800.31,1.39,87063,9,10,A -1030,R300 ,R332 ,15032.77,0.88,82969,4,10,A -1031,R100 ,R167 ,21315.47,1.24,83823,7,10,A -1032,R300 ,R352 ,9621.68,0.56,79793,6,10,A -1033,R200 ,R263 ,13752.57,0.8,74564,9,10,A -1034,R300 ,R378 ,15048.36,0.88,80008,6,10,A -1035,R100 ,R118 ,16421.83,0.96,79010,7,10,A -1036,R100 ,R150 ,15931.75,0.93,75424,5,10,A -1037,R100 ,R172 ,13312.31,0.78,72938,5,10,A -1038,R300 ,R367 ,20952.9,1.22,82276,4,10,A -1039,R300 ,R349 ,21585.09,1.26,75780,4,10,A -1040,R200 ,R261 ,17256.26,1.01,82212,9,10,A -1041,R400 ,R417 ,13848.16,0.81,83308,3,10,A -1042,R400 ,R448 ,22541.27,1.31,74488,3,10,A -1043,R400 ,R411 ,9674.19,0.56,57629,3,10,A -1044,R400 ,R448 ,18648.55,1.09,74381,3,10,A -1045,R400 ,R411 ,12697.34,0.74,57680,3,10,A -1046,R400 ,R448 ,15008.95,0.88,74546,3,10,A -1047,R300 ,R370 ,17068.33,1,84460,3,10,A -1048,R300 ,R370 ,12571.53,0.73,89661,3,10,A -1049,R400 ,R417 ,19411.15,1.13,83480,3,10,A -1050,R300 ,R360 ,13629.29,0.79,78543,4,10,A -1051,R300 ,R354 ,14358.95,0.84,57784,4,10,A -1052,R100 ,R136 ,13649.62,0.8,83128,5,10,A -1053,R300 ,R360 ,10981.1,0.64,81251,4,10,A -1054,R200 ,R210 ,16034.08,0.94,79288,9,10,A -1055,R400 ,R419 ,15761.67,0.92,74162,2,10,A -1056,R400 ,R467 ,19275.98,1.12,81604,2,10,A -1057,R400 ,R416 ,19422.2,1.13,85123,2,10,A -1058,R100 ,R169 ,26827.81,1.56,77525,5,10,A -1059,R300 ,R333 ,11100.21,0.65,76026,4,10,A -1060,R100 ,R146 ,18741.09,1.09,85682,5,10,A -1061,R300 ,R322 ,27634.12,1.61,77934,6,10,A -1062,R200 ,R245 ,19180.99,1.12,84643,9,10,A -1063,R100 ,R138 ,15725.63,0.92,75663,5,10,A -1064,R200 ,R265 ,11304.91,0.66,56919,10,10,A -1065,R300 ,R302 ,8036.52,0.47,78622,6,10,A -1066,R300 ,R341 ,10094.74,0.59,82977,6,10,A -1068,R100 ,R111 ,11222.77,0.65,54672,5,10,A -1069,R100 ,R146 ,13950.53,0.81,85092,5,10,A -1070,R100 ,R150 ,11085.66,0.65,58193,5,10,A -1071,R100 ,R168 ,14238.13,0.83,79308,4,10,A -1072,R100 ,R169 ,20588.88,1.2,84598,5,10,A -1073,R300 ,R336 ,10449.04,0.61,55976,4,10,A -1074,R300 ,R367 ,15783.12,0.92,75181,4,10,A -1075,R300 ,R349 ,11196.88,0.65,74087,4,10,A -1076,R400 ,R412 ,22054.33,1.29,79882,3,10,A -1077,R300 ,R358 ,12022.74,0.7,75830,4,10,A -1078,R300 ,R358 ,12216.56,0.71,80443,4,10,A -1079,R300 ,R358 ,10462.42,0.61,57122,4,10,A -1080,R300 ,R372 ,12041.19,0.7,79206,4,10,A -1081,R300 ,R362 ,10805.16,0.63,78940,4,10,A -1082,R100 ,R178 ,13987.89,0.82,56530,10,10,A -1083,R400 ,R451 ,10262.03,0.6,78939,2,10,A -1084,R400 ,R419 ,14913.21,0.87,73647,2,10,A -1085,R400 ,R422 ,17797.87,1.04,76086,2,10,A -1086,R300 ,R358 ,14044.87,0.82,79541,4,10,A -1087,R300 ,R355 ,14673.58,0.86,80952,4,10,A -1088,R400 ,R441 ,18484.7,1.08,81178,3,10,A -1090,R300 ,R337 ,15288.34,0.89,84845,4,10,A -1091,R100 ,R172 ,14574.08,0.85,81192,5,10,A -1092,R100 ,R172 ,16969.16,0.99,83984,5,10,A -1094,R100 ,R168 ,13793.7,0.8,78733,4,10,A -1095,R100 ,R126 ,14865.29,0.87,82374,5,10,A -1096,R100 ,R111 ,12782.47,0.75,66120,5,10,A -1097,R200 ,R208 ,14637.24,0.85,67586,9,10,A -1098,R200 ,R220 ,13887.15,0.81,83914,9,10,A -1100,R200 ,R240 ,21448.34,1.25,83303,9,10,A -1101,R100 ,R117 ,14793.07,0.86,78536,7,10,A -1102,R100 ,R118 ,23101.55,1.35,87697,7,10,A -1103,R300 ,R370 ,25616.65,1.49,84246,3,10,A -1104,R300 ,R371 ,11324.7,0.66,78459,4,10,A -1105,R300 ,R370 ,12581.38,0.73,79341,3,10,A -1106,R300 ,R334 ,11322.44,0.66,78249,4,10,A -1107,R300 ,R371 ,14886.03,0.87,79096,4,10,A -1108,R400 ,R424 ,11003.09,0.64,73761,2,10,A -1109,R400 ,R450 ,14145,0.83,79394,2,10,A -1110,R300 ,R310 ,12190.37,0.71,78641,4,10,A -1112,R100 ,R170 ,14990.05,0.87,77496,5,10,A -1113,R100 ,R127 ,20601.29,1.2,78812,7,10,A -1114,R100 ,R167 ,13789.7,0.8,83990,6,10,A -1115,R300 ,R304 ,12955.25,0.76,78319,6,10,A -1116,R300 ,R306 ,10543.9,0.61,79008,6,10,A -1117,R300 ,R378 ,13617.13,0.79,78255,6,10,A -1118,R200 ,R264 ,22466.87,1.31,79799,10,10,A -1120,R200 ,R240 ,13126.34,0.77,70788,9,10,A -1121,R200 ,R220 ,22210.7,1.3,83934,9,10,A -1122,R200 ,R210 ,18475.35,1.08,81029,9,10,A -1124,R300 ,R364 ,12002.15,0.7,79023,6,10,A -1125,R100 ,R112 ,12638.07,0.74,62204,5,10,A -1126,R300 ,R333 ,13139.84,0.77,78886,4,10,A -1128,R100 ,R173 ,14548.73,0.85,80305,5,10,A -1129,R100 ,R173 ,13539.23,0.79,79721,5,10,A -1131,R300 ,R354 ,14374.63,0.84,78799,4,10,A -1132,R400 ,R422 ,14373.61,0.84,84626,2,10,A -1133,R400 ,R422 ,14022.38,0.82,79655,2,10,A -1134,R400 ,R453 ,15159.2,0.88,78859,3,10,A -1135,R400 ,R462 ,19665.56,1.15,85269,3,10,A -1136,R400 ,R453 ,13097.71,0.76,78977,3,10,A -1137,R400 ,R448 ,15243.67,0.89,78353,3,10,A -1138,R400 ,R448 ,17921.49,1.05,85901,3,10,A -1139,R400 ,R455 ,27999.72,1.63,89058,2,10,A -1140,R200 ,R201 ,16988.81,0.99,80860,9,10,A -1141,R300 ,R377 ,12812.43,0.75,79106,9,10,A -1142,R400 ,R411 ,15035.9,0.88,82658,3,10,A -1143,R200 ,R245 ,12793.1,0.75,79506,9,10,A -1144,R100 ,R124 ,21673.35,1.26,81623,5,10,A -1146,R400 ,R453 ,33031.59,1.93,80804,3,10,A -1147,R400 ,R455 ,14760.76,0.86,79867,2,10,A -1148,R400 ,R424 ,13470.5,0.79,81984,2,10,A -1150,R400 ,R469 ,25788.59,1.5,83349,2,10,A -1151,R400 ,R450 ,15274.37,0.89,79397,2,10,A -1152,R400 ,R419 ,13937.29,0.81,79011,2,10,A -1153,R400 ,R450 ,23008.64,1.34,84564,2,10,A -1154,R400 ,R450 ,16797.18,0.98,78819,2,10,A -1155,R400 ,R419 ,13177.32,0.77,80978,2,10,A -1156,R400 ,R467 ,17689.87,1.03,79459,2,10,A -1157,R400 ,R416 ,13734.36,0.8,79557,2,10,A -1158,R400 ,R422 ,13956.47,0.81,79348,2,10,A -1159,R400 ,R421 ,18608.6,1.09,82152,3,10,A -1160,R400 ,R430 ,11125.02,0.65,62480,3,10,A -1161,R300 ,R370 ,13171.81,0.77,79065,3,10,A -1162,R400 ,R457 ,14130.48,0.82,79301,3,10,A -1163,R300 ,R318 ,18493.36,1.08,84345,4,10,A -1164,R300 ,R336 ,10590.98,0.62,78743,4,10,A -1165,R300 ,R334 ,12077.48,0.7,79712,4,10,A -1166,R100 ,R150 ,14064.14,0.82,78521,5,10,A -1167,R100 ,R112 ,17379.38,1.01,80765,5,10,A -1168,R100 ,R112 ,18790.14,1.1,81642,5,10,A -1169,R300 ,R332 ,11280.83,0.66,78478,4,10,A -1170,R100 ,R105 ,15595.7,0.91,78914,7,10,A -1171,R300 ,R380 ,17415.95,1.02,82328,9,10,A -1172,R300 ,R336 ,9759.44,0.57,62950,4,10,A -1175,R400 ,R446 ,18480.61,1.08,76110,2,10,A -1176,R100 ,R112 ,17171.21,1,77791,5,10,A -1177,R100 ,R123 ,15118.3,0.88,79774,7,10,A -1178,R100 ,R176 ,12340.5,0.72,78861,8,10,A -1179,R300 ,R340 ,15139.54,0.88,78851,4,10,A -1180,R300 ,R358 ,13044.07,0.76,79698,4,10,A -1181,R300 ,R362 ,10450.84,0.61,79400,4,10,A -1182,R300 ,R337 ,16836.04,0.98,79479,4,10,A -1183,R400 ,R462 ,13107.52,0.76,79279,3,10,A -1184,R400 ,R450 ,9849.95,0.57,80286,2,10,A -1185,R100 ,R126 ,17363.88,1.01,81612,5,10,A -1186,R400 ,R452 ,15409.89,0.9,74371,1,10,A -1187,R400 ,R452 ,20348.66,1.19,77961,1,10,A -1188,R400 ,R428 ,13428.24,0.78,79648,1,10,A -1189,R400 ,R428 ,14250.59,0.83,79736,1,10,A -1190,R400 ,R470 ,12793.33,0.75,79611,1,10,A -1191,R400 ,R424 ,12364.48,0.72,80719,2,10,A -1192,R400 ,R450 ,14769.51,0.86,74289,2,10,A -1193,R400 ,R448 ,19389.97,1.13,85733,3,10,A -1194,R400 ,R416 ,13524.5,0.79,80037,2,10,A -1195,R400 ,R467 ,14231.23,0.83,80267,2,10,A -1196,R400 ,R421 ,15065.12,0.88,80017,3,10,A -1197,R300 ,R335 ,21077.27,1.23,80742,4,10,A -1198,R300 ,R337 ,13790.65,0.8,79458,4,10,A -1199,R300 ,R337 ,12523.74,0.73,79304,4,10,A -1200,R100 ,R172 ,13295.01,0.78,79646,4,10,A -1201,R100 ,R123 ,13544,0.79,81430,7,10,A -1204,R300 ,R327 ,11405.33,0.67,79878,6,10,A -1205,R200 ,R262 ,14477.44,0.84,80534,10,10,A -1206,R300 ,R346 ,14214.46,0.83,80243,4,10,A -1207,R300 ,R373 ,20865.33,1.22,82256,9,10,A -1208,R200 ,R211 ,22029.06,1.28,82114,9,10,A -1209,R300 ,R379 ,14094.86,0.82,80224,9,10,A -1210,R100 ,R131 ,10714.23,0.62,79360,5,10,A -1211,R100 ,R111 ,14078.84,0.82,80387,5,10,A -1212,R100 ,R128 ,14011.98,0.82,63259,5,10,A -1213,R100 ,R121 ,12974.21,0.76,79146,5,10,A -1214,R100 ,R140 ,8109.67,0.47,79013,5,10,A -1215,R100 ,R174 ,10601.42,0.62,79046,5,10,A -1216,R400 ,R454 ,14410.69,0.84,79294,3,10,A -1217,R400 ,R430 ,16143.12,0.94,80165,3,10,A -1218,R400 ,R430 ,11138.91,0.65,79979,3,10,A -1219,R400 ,R454 ,15455.74,0.9,79945,3,10,A -1220,R400 ,R454 ,12999.54,0.76,79354,3,10,A -1221,R400 ,R431 ,13904,0.81,79928,3,10,A -1222,R400 ,R431 ,16531.44,0.96,80225,3,10,A -1223,R300 ,R336 ,14408.13,0.84,80433,4,10,A -1224,R400 ,R419 ,10164.62,0.59,81838,2,10,A -1225,R400 ,R417 ,13041.87,0.76,80551,3,10,A -1226,R300 ,R371 ,11064.58,0.65,63020,4,10,A -1227,R400 ,R447 ,11871.08,0.69,79768,1,10,A -1228,R400 ,R468 ,17561.85,1.02,80552,1,10,A -1229,R400 ,R452 ,18859.41,1.1,83211,1,10,A -1230,R100 ,R161 ,19998.5,1.17,79519,10,10,A -1231,R300 ,R302 ,9206.64,0.54,79375,6,10,A -1232,R400 ,R439 ,14797.27,0.86,87986,1,10,A -1233,R400 ,R415 ,11725.46,0.68,62042,3,10,A -1234,R400 ,R457 ,13125.97,0.77,79367,3,10,A -1235,R100 ,R102 ,14789.14,0.86,78414,5,10,A -1236,R100 ,R173 ,16660.4,0.97,81797,5,10,A -1237,R100 ,R161 ,24438.29,1.43,78241,8,10,A -1238,R200 ,R202 ,18677.19,1.09,79905,9,10,A -1239,R200 ,R220 ,11286.18,0.66,79193,9,10,A -1241,R100 ,R118 ,14363.32,0.84,79052,7,10,A -1242,R300 ,R375 ,14528.31,0.85,81165,9,10,A -1244,R100 ,R124 ,13415.1,0.78,81303,5,10,A -1246,R100 ,R128 ,10999.6,0.64,63183,5,10,A -1247,R100 ,R128 ,16005.53,0.93,83242,5,10,A -1248,R100 ,R128 ,15318.3,0.89,79830,5,10,A -1249,R400 ,R439 ,14685.61,0.86,81453,1,10,A -1250,R300 ,R351 ,12324.57,0.72,80155,4,10,A -1251,R100 ,R134 ,14639.79,0.85,81741,5,10,A -1252,R100 ,R173 ,14030.26,0.82,81000,5,10,A -1253,R400 ,R454 ,14300.85,0.83,81697,3,10,A -1254,R400 ,R431 ,15113.43,0.88,80391,3,10,A -1255,R400 ,R439 ,16585.27,0.97,81167,1,10,A -1256,R400 ,R421 ,14669.22,0.86,82210,3,10,A -1257,R400 ,R448 ,13004.29,0.76,63664,3,10,A -1258,R400 ,R415 ,14006.86,0.82,80424,3,10,A -1259,R400 ,R430 ,14837.91,0.87,80493,3,10,A -1260,R400 ,R461 ,14254.66,0.83,79752,3,10,A -1261,R100 ,R169 ,17535.15,1.02,80978,3,10,A -1263,R400 ,R446 ,11517.84,0.67,71425,2,10,A -1264,R400 ,R455 ,17426.34,1.02,82748,2,10,A -1265,R400 ,R411 ,13676.07,0.8,80181,3,10,A -1266,R400 ,R452 ,17968.16,1.05,83686,1,10,A -1267,R400 ,R439 ,14506.19,0.85,80481,1,10,A -1268,R400 ,R438 ,16958.27,0.99,79959,2,10,A -1269,R400 ,R421 ,18921.78,1.1,82963,3,10,A -1270,R400 ,R454 ,13033.07,0.76,78340,3,10,A -1271,R400 ,R438 ,17143.28,1,80121,2,10,A -1272,R100 ,R101 ,15188.72,0.89,80758,5,10,A -1273,R300 ,R359 ,13815.79,0.81,79850,4,10,A -1274,R300 ,R359 ,12682.06,0.74,78868,4,10,A -1275,R400 ,R457 ,15737.15,0.92,80027,3,10,A -1278,R100 ,R117 ,13831.31,0.81,79474,7,10,A -1279,R100 ,R117 ,14764.1,0.86,81064,7,10,A -1280,R100 ,R118 ,16188.82,0.94,80452,7,10,A -1281,R400 ,R468 ,12969.39,0.76,81367,1,10,A -1282,R100 ,R161 ,12354.55,0.72,56298,8,10,A -1283,R200 ,R205 ,15097.95,0.88,79886,9,10,A -1284,R200 ,R264 ,25793.89,1.5,82810,10,10,A -1285,R100 ,R148 ,14530.65,0.85,80626,5,10,A -1286,R100 ,R133 ,15769.99,0.92,80906,5,10,A -1287,R100 ,R170 ,13921.28,0.81,81542,3,10,A -1288,R400 ,R430 ,15454.86,0.9,80472,3,10,A -1289,R400 ,R439 ,19364.94,1.13,81467,1,10,A -1290,R400 ,R452 ,11960.35,0.7,63834,1,10,A -1291,R400 ,R421 ,12250.01,0.71,80365,3,10,A -1292,R400 ,R457 ,16208.91,0.95,79934,3,10,A -1293,R200 ,R202 ,22852.88,1.33,85522,9,10,A -1294,R300 ,R313 ,17682.77,1.03,79679,4,10,A -1295,R400 ,R412 ,13334.85,0.78,79468,3,10,A -1296,R400 ,R451 ,16585.17,0.97,79249,2,10,A -1298,R300 ,R350 ,14447.75,0.84,79876,4,10,A -1299,R300 ,R314 ,15354.12,0.9,79715,4,10,A -1300,R300 ,R315 ,15217.94,0.89,80226,4,10,A -1301,R100 ,R171 ,14515.28,0.85,80208,5,10,A -1302,R300 ,R333 ,23175.96,1.35,79567,4,10,A -1303,R100 ,R124 ,13451.41,0.78,81107,5,10,A -1304,R200 ,R218 ,16774.45,0.98,81102,9,10,A -1305,R200 ,R222 ,20472.84,1.19,86040,9,10,A -1306,R200 ,R204 ,32559.93,1.9,91970,9,10,A -1307,R200 ,R209 ,21987.41,1.28,83980,9,10,A -1308,R400 ,R470 ,18482.67,1.08,83345,1,10,A -1309,R200 ,R209 ,15277.8,0.89,81646,9,10,A -1310,R300 ,R337 ,11012.4,0.64,79479,4,10,A -1311,R100 ,R110 ,14029.58,0.82,82227,5,10,A -1312,R400 ,R453 ,14622.92,0.85,80088,3,10,A -1313,R100 ,R135 ,23476.69,1.37,86317,5,10,A -1314,R100 ,R117 ,14457.01,0.84,80920,7,10,A -1315,R400 ,R419 ,11562.07,0.67,80625,2,10,A -1316,R300 ,R376 ,13610.25,0.79,80978,9,10,A -1317,R100 ,R148 ,12925.82,0.75,81033,5,10,A -1318,R400 ,R451 ,16736.78,0.98,78508,2,10,A -1319,R300 ,R337 ,12855.22,0.75,80331,4,10,A -1322,R300 ,R369 ,13086.18,0.76,79663,4,10,A -1323,R100 ,R122 ,14305.24,0.83,95195,5,10,A -1324,R100 ,R170 ,13496.36,0.79,81663,5,10,A -1325,R100 ,R174 ,14430.11,0.84,81330,5,10,A -1326,R100 ,R177 ,15237.48,0.89,79425,8,10,A -1327,R300 ,R377 ,12630.56,0.74,80721,9,10,A -1328,R200 ,R222 ,23358.4,1.36,81521,9,10,A -1329,R200 ,R239 ,17148.27,1,82561,9,10,A -1330,R400 ,R446 ,15806.71,0.92,80652,2,10,A -1331,R200 ,R264 ,14018.96,0.82,81655,10,10,A -1332,R200 ,R236 ,21445.53,1.25,82277,9,10,A -1333,R100 ,R161 ,16263.73,0.95,79126,8,10,A -1334,R100 ,R128 ,17864.74,1.04,80908,5,10,A -1335,R300 ,R375 ,15171.29,0.88,80519,9,10,A -1336,R300 ,R311 ,24842.32,1.45,95996,6,10,A -1337,R300 ,R318 ,12336.16,0.72,85393,4,10,A -1338,R400 ,R447 ,17778.52,1.04,80001,1,10,A -1339,R300 ,R306 ,21062.07,1.23,93983,6,10,A -1340,R200 ,R217 ,16611.88,0.97,84922,9,10,A -1341,R400 ,R466 ,18120.18,1.06,81178,3,10,A -1342,R100 ,R141 ,15531.83,0.91,81296,5,10,A -1344,R400 ,R469 ,22846.31,1.33,82594,2,10,A -1345,R400 ,R450 ,13408.56,0.78,80360,2,10,A -1346,R300 ,R369 ,14549.77,0.85,94357,4,10,A -1347,R300 ,R353 ,12193.9,0.71,94361,6,10,A -1348,R400 ,R468 ,15041.11,0.88,80072,1,10,A -1349,R300 ,R337 ,13841.34,0.81,79596,4,10,A -1350,R100 ,R138 ,30718.17,1.79,95521,5,10,A -1351,R100 ,R111 ,24733.96,1.44,100835,5,10,A -1352,R100 ,R101 ,19718.35,1.15,94257,5,10,A -1353,R100 ,R118 ,15134.69,0.88,84225,7,10,A -1354,R300 ,R327 ,17198.42,1,94215,6,10,A -1355,R200 ,R262 ,15925.03,0.93,80115,10,10,A -1356,R100 ,R101 ,21051.73,1.23,103397,5,10,A -1357,R300 ,R373 ,13590.44,0.79,79802,8,10,A -1358,R400 ,R459 ,10900.3,0.64,77528,2,10,A -1359,R300 ,R311 ,14125.21,0.82,95097,6,10,A -1360,R300 ,R377 ,18079.46,1.05,85527,9,10,A -1361,R300 ,R375 ,13041.25,0.76,82293,9,10,A -1362,R200 ,R236 ,31221.44,1.82,85097,9,10,A -1363,R200 ,R220 ,16885.45,0.98,85327,9,10,A -1364,R100 ,R140 ,15796.29,0.92,95279,5,10,A -1365,R400 ,R422 ,16904.97,0.99,79025,2,10,A -1366,R100 ,R138 ,19422.64,1.13,95278,5,10,A -1367,R300 ,R369 ,21935.64,1.28,95279,4,10,A -1368,R300 ,R321 ,15959.63,0.93,95107,6,10,A -1369,R300 ,R353 ,20938.86,1.22,97765,6,10,A -1370,R300 ,R351 ,21402.18,1.25,94830,4,10,A -1371,R300 ,R355 ,18402.2,1.07,78748,4,10,A -1372,R100 ,R175 ,18704.56,1.09,98021,8,10,A -1373,R400 ,R460 ,12884.8,0.75,80056,1,10,A -1374,R400 ,R428 ,15221.4,0.89,80795,1,10,A -1375,R100 ,R126 ,18618.96,1.09,83566,5,10,A -1376,R300 ,R359 ,17153.75,1,94993,4,10,A -1377,R300 ,R303 ,14410.68,0.84,95711,6,10,A -1378,R400 ,R450 ,19715.08,1.15,85219,2,10,A -1380,R300 ,R346 ,47074.8,2.75,79595,4,10,A -1381,R100 ,R171 ,12612.89,0.74,80521,5,10,A -1382,R300 ,R314 ,34903.38,2.04,94993,4,10,A -1383,R200 ,R244 ,15240.51,0.89,85622,9,10,A -1384,R200 ,R250 ,34506.5,2.01,85650,9,10,A -1385,R100 ,R112 ,15226.12,0.89,80772,5,10,A -1386,R300 ,R374 ,14175.13,0.83,80878,9,10,A -1388,R100 ,R123 ,13260.85,0.77,80152,7,10,A -1389,R400 ,R422 ,13918.33,0.81,81639,2,10,A -1390,R300 ,R346 ,15809.78,0.92,79934,4,10,A -1391,R300 ,R350 ,15032.69,0.88,79377,4,10,A -1392,R100 ,R123 ,31337.39,1.83,95000,7,10,A -1393,R100 ,R172 ,13678.93,0.8,79856,5,10,A -1394,R300 ,R335 ,14994.85,0.87,95605,4,10,A -1395,R300 ,R326 ,13013.86,0.76,95743,6,10,A -1396,R300 ,R303 ,17825.5,1.04,95078,6,10,A -1397,R300 ,R301 ,15724.42,0.92,94846,6,10,A -1398,R300 ,R301 ,24879.51,1.45,94864,6,10,A -1399,R300 ,R353 ,14691.16,0.86,79472,6,10,A -1400,R300 ,R336 ,13578.61,0.79,95628,4,10,A -1401,R400 ,R469 ,17203.32,1,84844,2,10,A -1402,R100 ,R115 ,13736.85,0.8,79895,5,10,A -1403,R100 ,R115 ,13620.81,0.79,84794,5,10,A -1404,R400 ,R428 ,13940.57,0.81,79683,1,10,A -1405,R200 ,R260 ,13101.65,0.76,79864,10,10,A -1406,R200 ,R260 ,15898.42,0.93,78002,10,10,A -1407,R200 ,R210 ,20547.2,1.2,76106,9,10,A -1408,R200 ,R236 ,27562.74,1.61,77741,9,10,A -1409,R200 ,R207 ,27878.23,1.63,84687,9,10,A -1410,R200 ,R201 ,32452.36,1.89,88936,9,10,A -1411,R200 ,R263 ,16017.4,0.93,79155,9,10,A -1413,R100 ,R175 ,15207.33,0.89,85438,8,10,A -1414,R400 ,R430 ,13957.06,0.81,77501,3,10,A -1415,R400 ,R448 ,18922.65,1.1,82904,3,10,A -1416,R400 ,R441 ,14708.29,0.86,80098,3,10,A -1417,R200 ,R213 ,15727.61,0.92,81041,9,10,A -1418,R200 ,R244 ,16708.99,0.97,78174,9,10,A -1419,R200 ,R260 ,18432.1,1.08,81868,10,10,A -1420,R300 ,R370 ,11546.59,0.67,62802,3,10,A -1421,R200 ,R260 ,17185.44,1,79427,9,10,A -1422,R200 ,R237 ,20517.5,1.2,78465,9,10,A -1423,R200 ,R206 ,13769.65,0.8,77332,9,10,A -1424,R200 ,R217 ,19904.37,1.16,80465,9,10,A -1425,R200 ,R263 ,20894.31,1.22,78662,9,10,A -1426,R200 ,R237 ,18938.16,1.1,79716,9,10,A -1427,R200 ,R233 ,20566.2,1.2,88324,9,10,A -1428,R200 ,R211 ,19987.72,1.17,78509,9,10,A -1429,R300 ,R379 ,12286.66,0.72,80337,9,10,A -1430,R300 ,R326 ,12633.06,0.74,94626,6,10,A -1431,R400 ,R441 ,26944.05,1.57,90013,3,10,A -1432,R300 ,R377 ,13568.45,0.79,79816,9,10,A -1435,R300 ,R364 ,15195.82,0.89,81361,6,10,A -1436,R100 ,R144 ,22996.24,1.34,78564,7,10,A -1437,R100 ,R141 ,20784.42,1.21,92520,5,10,A -1438,R200 ,R247 ,11378.66,0.66,59126,9,10,A -1439,R300 ,R376 ,16151.14,0.94,79653,9,10,A -1441,R400 ,R429 ,11597.57,0.68,60789,1,10,A -1442,R400 ,R429 ,31402.58,1.83,90993,1,10,A -1443,R400 ,R462 ,19582.85,1.14,73075,3,10,A -1444,R200 ,R258 ,41456.43,2.42,79606,10,10,A -1445,R100 ,R133 ,21046.09,1.23,94300,5,10,A -1446,R100 ,R133 ,12958.75,0.76,77910,5,10,A -1447,R100 ,R172 ,13750.87,0.8,79846,5,10,A -1448,R100 ,R124 ,20413.45,1.19,94866,5,10,A -1449,R300 ,R331 ,19998.13,1.17,82829,6,10,A -1450,R300 ,R331 ,15567.47,0.91,79546,6,10,A -1451,R300 ,R331 ,13618.26,0.79,79783,6,10,A -1452,R300 ,R337 ,13203.65,0.77,80883,4,10,A -1453,R300 ,R346 ,16843.49,0.98,80290,4,10,A -1454,R300 ,R320 ,15849.37,0.92,80019,4,10,A -1455,R100 ,R123 ,19161.4,1.12,94722,7,10,A -1456,R100 ,R124 ,17681.74,1.03,94306,5,10,A -1457,R300 ,R304 ,11364.39,0.66,94628,6,10,A -1458,R300 ,R304 ,17584.32,1.03,94658,6,10,A -1459,R300 ,R303 ,18111.48,1.06,94434,6,10,A -1460,R100 ,R145 ,11495.93,0.67,79898,5,10,A -1461,R300 ,R346 ,14904.54,0.87,94871,4,10,A -1462,R300 ,R373 ,14882.73,0.87,83934,9,10,A -1463,R100 ,R171 ,15408.63,0.9,80236,5,10,A -1464,R400 ,R461 ,11662.73,0.68,80767,2,10,A -1465,R100 ,R137 ,16060.82,0.94,80600,5,10,A -1466,R100 ,R134 ,15546.77,0.91,80184,5,10,A -1467,R400 ,R419 ,15155.51,0.88,80047,2,10,A -1468,R300 ,R348 ,18186.62,1.06,82108,4,10,A -1469,R300 ,R353 ,61312.57,3.58,80610,6,10,A -1470,R100 ,R167 ,16364.57,0.95,79786,6,10,A -1471,R100 ,R175 ,15954.86,0.93,95756,8,10,A -1472,R200 ,R211 ,13471.26,0.79,77852,9,10,A -1473,R300 ,R353 ,19010.14,1.11,95348,6,10,A -1474,R400 ,R461 ,15159.92,0.88,79873,3,10,A -1475,R400 ,R467 ,13767,0.8,80833,2,10,A -1476,R400 ,R467 ,14527.01,0.85,79280,2,10,A -1477,R400 ,R438 ,15080.73,0.88,79713,2,10,A -1478,R100 ,R169 ,13882.8,0.81,79796,3,10,A -1479,R100 ,R168 ,12891.86,0.75,79728,4,10,A -1480,R300 ,R367 ,13070.76,0.76,80034,4,10,A -1481,R100 ,R140 ,19755.47,1.15,84260,5,10,A -1482,R100 ,R136 ,14472.82,0.84,80809,5,10,A -1483,R100 ,R128 ,14561.18,0.85,79837,5,10,A -1484,R100 ,R163 ,18753.39,1.09,94822,5,10,A -1485,R200 ,R231 ,18068.32,1.05,80187,9,10,A -1486,R300 ,R335 ,18023.41,1.05,84388,4,10,A -1487,R100 ,R144 ,20972.67,1.22,84348,7,10,A -1489,R300 ,R302 ,12780.93,0.75,94372,6,10,A -1490,R300 ,R305 ,13568.24,0.79,79570,6,10,A -1491,R400 ,R446 ,16934.42,0.99,79942,2,10,A -1492,R400 ,R467 ,14625.56,0.85,79907,2,10,A -1493,R300 ,R350 ,16529.17,0.96,81017,4,10,A -1495,R400 ,R428 ,14986.81,0.87,79343,1,10,A -1496,R400 ,R468 ,13230.69,0.77,78045,1,10,A -1497,R300 ,R315 ,11006.6,0.64,79928,4,10,A -1499,R300 ,R340 ,14006.27,0.82,80166,4,10,A -1500,R100 ,R179 ,20110.3,1.17,94844,8,10,A -1501,R100 ,R177 ,20363.69,1.19,94939,8,10,A -1502,R200 ,R208 ,21349.7,1.25,85173,9,10,A -1504,R300 ,R372 ,19012.51,1.11,94142,4,10,A -1505,R300 ,R362 ,20788.14,1.21,94433,4,10,A -1506,R300 ,R321 ,28557.96,1.67,79612,6,10,A -1507,R200 ,R211 ,10823.89,0.63,82990,9,10,A -1508,R400 ,R467 ,17396.41,1.01,79605,2,10,A -1509,R100 ,R117 ,18392.96,1.07,79962,7,10,A -1510,R400 ,R461 ,11503.86,0.67,85690,3,10,A -1511,R300 ,R333 ,18266.55,1.07,80047,4,10,A -1512,R300 ,R367 ,18369.17,1.07,94925,4,10,A -1513,R100 ,R168 ,12302.6,0.72,79288,4,10,A -1514,R300 ,R321 ,11305.94,0.66,94433,6,10,A -1515,R100 ,R117 ,17495.95,1.02,82714,7,10,A -1516,R400 ,R468 ,10656.16,0.62,79472,1,10,A -1517,R300 ,R321 ,13371.28,0.78,94433,6,10,A -1518,R300 ,R365 ,17623.12,1.03,91271,4,10,A -1519,R300 ,R324 ,16814.27,0.98,94293,4,10,A -1520,R400 ,R447 ,13936.58,0.81,79488,1,10,A -1521,R400 ,R438 ,13481.91,0.79,79709,2,10,A -1522,R100 ,R111 ,12006.22,0.7,79527,5,10,A -1523,R300 ,R327 ,14753.24,0.86,94693,6,10,A -1524,R300 ,R380 ,18225.45,1.06,84745,9,10,A -1525,R100 ,R179 ,17651.41,1.03,79686,8,10,A -1526,R200 ,R218 ,15836.53,0.92,79652,9,10,A -1527,R200 ,R246 ,13418.91,0.78,79960,9,10,A -1528,R400 ,R459 ,12496,0.73,79482,1,10,A -1529,R300 ,R350 ,13856.34,0.81,79914,4,10,A -1530,R100 ,R138 ,14005.68,0.82,78964,5,10,A -1531,R300 ,R306 ,12549.21,0.73,85562,6,10,A -1532,R400 ,R468 ,14153.75,0.83,79596,1,10,A -1533,R400 ,R441 ,13093.09,0.76,79856,3,10,A -1534,R100 ,R172 ,14811.02,0.86,81127,5,10,A -1535,R300 ,R303 ,11991.54,0.7,79278,6,10,A -1536,R300 ,R306 ,13411.79,0.78,94415,6,10,A -1537,R100 ,R107 ,13158.31,0.77,79801,7,10,A -1538,R100 ,R123 ,13557.97,0.79,79756,7,10,A -1539,R100 ,R117 ,17090.09,1,79948,5,10,A -1540,R100 ,R161 ,20973.79,1.22,78853,8,10,A -1541,R400 ,R411 ,12628.49,0.74,80594,3,10,A -1542,R300 ,R341 ,11343.29,0.66,78324,6,10,A -1543,R100 ,R144 ,11447.45,0.67,79848,7,10,A -1544,R400 ,R459 ,18982.28,1.11,86453,1,10,A -1545,R100 ,R172 ,13674.89,0.8,80832,5,10,A -1546,R300 ,R336 ,13927.83,0.81,81677,4,10,A -1547,R200 ,R240 ,16454.87,0.96,79638,9,10,A -1548,R200 ,R232 ,14052.03,0.82,80193,9,10,A -1750,R100 ,R178 ,16773.24,0.98,86701,8,10,A -1751,R100 ,R178 ,25765.46,1.5,96518,8,10,A -1752,R100 ,R178 ,23523.94,1.37,90711,8,10,A -1753,R100 ,R178 ,17863.16,1.04,86804,8,10,A -1754,R100 ,R178 ,30769.6,1.79,95329,8,10,A -1755,R100 ,R178 ,19087.56,1.11,93951,8,10,A -1756,R100 ,R144 ,22257.67,1.3,94759,7,10,A -1757,R100 ,R144 ,18282.25,1.07,99554,7,10,A -1759,R100 ,R144 ,14250.34,0.83,105819,7,10,A -1760,R300 ,R365 ,22732.47,1.33,95022,4,10,A -1761,R300 ,R334 ,15610.46,0.91,114596,4,10,A -1762,R100 ,R138 ,17159.27,1,115418,5,10,A -1763,R300 ,R326 ,13275.82,0.77,114551,6,10,A -1764,R300 ,R326 ,17831.69,1.04,95616,6,10,A -1765,R300 ,R342 ,15134.86,0.88,95689,6,10,A -1766,R300 ,R321 ,10585.66,0.62,97674,6,10,A -1767,R100 ,R105 ,19927.12,1.16,114575,7,10,A -1768,R100 ,R127 ,21937.37,1.28,94945,7,10,A -1769,R100 ,R176 ,19421.32,1.13,95171,8,10,A -1770,R300 ,R342 ,14799.48,0.86,96337,6,10,A -1771,R100 ,R127 ,15920.12,0.93,90966,7,10,A -1772,R300 ,R340 ,19001.11,1.11,93637,4,10,A -1773,R300 ,R340 ,15456.68,0.9,93900,4,10,A -1774,R100 ,R111 ,18948.96,1.11,91645,5,10,A -1775,R300 ,R326 ,12125.78,0.71,94203,6,10,A -1776,R100 ,R179 ,24149.73,1.41,104154,8,10,A -1777,R100 ,R107 ,20862.76,1.22,106299,7,10,A -1780,R300 ,R334 ,14634.57,0.85,105923,4,10,A -1782,R100 ,R129 ,17875.93,1.04,94771,6,10,A -1783,R100 ,R143 ,28589.57,1.67,96198,8,10,A -1784,R300 ,R352 ,17720.69,1.03,99779,6,10,A -1785,R300 ,R327 ,12582.66,0.73,106014,6,10,A -1786,R300 ,R311 ,17185.12,1,106365,6,10,A -1787,R300 ,R340 ,23508.66,1.37,95013,4,10,A -1788,R100 ,R140 ,15308.04,0.89,94299,5,10,A -1789,R100 ,R140 ,15136.51,0.88,94300,5,10,A -1790,R300 ,R365 ,12762.2,0.74,95242,4,10,A -1791,R100 ,R105 ,22070.27,1.29,94718,7,10,A -1792,R100 ,R127 ,15316.64,0.89,93817,7,10,A -1793,R300 ,R355 ,17968.3,1.05,79483,4,10,A -1794,R300 ,R372 ,12133.4,0.71,83246,4,10,A -1795,R300 ,R313 ,15159.33,0.88,94246,4,10,A -1796,R400 ,R438 ,14209.43,0.83,81114,2,10,A -1797,R300 ,R341 ,11456.25,0.67,94441,6,10,A -1798,R400 ,R459 ,19742.26,1.15,83870,2,10,A -1799,R100 ,R150 ,14874.78,0.87,81010,5,10,A -1800,R100 ,R143 ,20985.04,1.22,84973,7,10,A -1801,R100 ,R150 ,20024.08,1.17,94203,5,10,A -1802,R400 ,R439 ,12412.8,0.72,81304,1,10,A -1803,R400 ,R452 ,9338.3,0.54,60555,1,10,A -1804,R100 ,R170 ,17151.46,1,85860,5,10,A -1805,R200 ,R250 ,18451.28,1.08,84906,9,10,A -1806,R100 ,R175 ,41996.76,2.45,100176,8,10,A -1807,R300 ,R362 ,13394.1,0.78,79707,4,10,A -1808,R400 ,R451 ,20509.07,1.2,77582,2,10,A -1809,R400 ,R462 ,13882.08,0.81,84651,3,10,A -1811,R300 ,R302 ,11217.03,0.65,79484,6,10,A -1812,R300 ,R322 ,12568.03,0.73,79062,6,10,A -1813,R100 ,R176 ,14444.1,0.84,80168,8,10,A -1814,R100 ,R178 ,20852.4,1.22,79720,8,10,A -1815,R200 ,R201 ,14787.12,0.86,79263,9,10,A -1816,R200 ,R201 ,12240.58,0.71,79941,9,10,A -1818,R400 ,R424 ,10957.5,0.64,79808,2,10,A -1819,R200 ,R247 ,16986.94,0.99,84587,9,10,A -1820,R300 ,R314 ,20989.38,1.22,94933,4,10,A -1821,R100 ,R144 ,16587.3,0.97,80224,7,10,A -1822,R400 ,R446 ,19931.96,1.16,82510,2,10,A -1823,R400 ,R450 ,14976.72,0.87,79982,2,10,A -1824,R300 ,R371 ,12920.74,0.75,80565,4,10,A -1825,R300 ,R340 ,13347.57,0.78,79593,4,10,A -1826,R300 ,R372 ,15716.27,0.92,94925,4,10,A -1827,R400 ,R441 ,13591.57,0.79,84993,3,10,A -1828,R400 ,R454 ,12540.36,0.73,79270,3,10,A -1829,R300 ,R350 ,13630.58,0.8,80245,4,10,A -1830,R400 ,R438 ,12367.52,0.72,80374,2,10,A -1831,R100 ,R124 ,21141.71,1.23,95562,5,10,A -1832,R100 ,R124 ,23621.85,1.38,96302,5,10,A -1833,R100 ,R101 ,19920.1,1.16,95199,5,10,A -1834,R200 ,R206 ,18203.97,1.06,79823,9,10,A -1835,R400 ,R468 ,16198.49,0.94,79307,1,10,A -1836,R300 ,R306 ,13824.79,0.81,94702,6,10,A -1837,R300 ,R303 ,22022.48,1.28,94604,6,10,A -1838,R300 ,R379 ,15196.1,0.89,84238,9,10,A -1839,R400 ,R439 ,17488.48,1.02,77649,1,10,A -1840,R100 ,R123 ,14105.68,0.82,94868,7,10,A -1841,R100 ,R170 ,14982.58,0.87,80132,5,10,A -1842,R100 ,R144 ,13679.32,0.8,80824,7,10,A -1843,R200 ,R232 ,13696.45,0.8,80081,9,10,A -1845,R400 ,R461 ,14200.1,0.83,79798,3,10,A -1846,R200 ,R201 ,15056.43,0.88,79264,9,10,A -1847,R200 ,R260 ,15996.21,0.93,79961,10,10,A -1848,R100 ,R138 ,14031.96,0.82,79899,5,10,A -1849,R400 ,R423 ,27990.17,1.63,83772,2,10,A -1850,R300 ,R326 ,11244.58,0.66,79175,6,10,A -1851,R200 ,R245 ,10151.56,0.59,79793,9,10,A -1852,R300 ,R327 ,9960.07,0.58,68749,6,10,A -1853,R300 ,R315 ,14860.54,0.87,94787,4,10,A -1854,R400 ,R448 ,19617.32,1.14,84253,3,10,A -1855,R400 ,R447 ,13971.34,0.81,79714,1,10,A -1856,R400 ,R451 ,14806.54,0.86,77462,2,10,A -1857,R400 ,R466 ,12368.36,0.72,79735,3,10,A -1858,R400 ,R457 ,17193.94,1,84751,3,10,A -1859,R300 ,R314 ,15253.33,0.89,79869,4,10,A -1860,R300 ,R301 ,13214.67,0.77,72279,6,10,A -1861,R300 ,R302 ,9311.67,0.54,79778,6,10,A -1862,R200 ,R246 ,11821.07,0.69,79669,9,10,A -1863,R300 ,R376 ,13649.29,0.8,85204,9,10,A -1864,R400 ,R461 ,17747.76,1.04,85456,2,10,A -1865,R400 ,R446 ,11907.42,0.69,77515,2,10,A -1866,R400 ,R424 ,10758.13,0.63,79831,2,10,A -1867,R200 ,R205 ,31419.28,1.83,79816,9,10,A -1868,R200 ,R258 ,14414.18,0.84,79799,10,10,A -1869,R200 ,R248 ,45817.24,2.67,80760,9,10,A -1870,R300 ,R337 ,18913.16,1.1,79925,4,10,A -1871,R400 ,R411 ,12212.86,0.71,85340,3,10,A -1872,R300 ,R372 ,19657.74,1.15,94874,4,10,A -1873,R400 ,R466 ,15246.9,0.89,94627,3,10,A -1874,R400 ,R441 ,24903.13,1.45,94237,3,10,A -1875,R400 ,R431 ,16289.11,0.95,79478,3,10,A -1876,R300 ,R331 ,13350.36,0.78,80856,6,10,A -1877,R300 ,R303 ,11647.39,0.68,80199,6,10,A -1878,R100 ,R140 ,16216.42,0.95,80705,5,10,A -1879,R100 ,R145 ,15869.97,0.93,79855,5,10,A -1880,R100 ,R128 ,12781.86,0.75,67129,5,10,A -1881,R100 ,R121 ,15113.88,0.88,80071,5,10,A -1882,R100 ,R121 ,19062.39,1.11,79353,5,10,A -1883,R200 ,R258 ,14822.47,0.86,80070,10,10,A -1884,R200 ,R204 ,26238.15,1.53,78984,9,10,A -1885,R400 ,R455 ,19118.93,1.12,82904,2,10,A -1886,R400 ,R446 ,31838.76,1.86,86008,2,10,A -1887,R400 ,R459 ,12041.74,0.7,81088,2,10,A -1888,R100 ,R141 ,19317.01,1.13,82762,5,10,A -1889,R100 ,R116 ,19939.43,1.16,82383,5,10,A -1890,R400 ,R412 ,17656.4,1.03,80944,3,10,A -1891,R100 ,R167 ,12439.11,0.73,79208,6,10,A -1892,R300 ,R372 ,25971.98,1.51,82176,4,10,A -1893,R400 ,R412 ,20082.28,1.17,87646,3,10,A -1894,R300 ,R364 ,12266.28,0.72,79414,6,10,A -1895,R100 ,R110 ,16457.66,0.96,80015,5,10,A -1896,R100 ,R122 ,20302.75,1.18,96907,5,10,A -1897,R400 ,R412 ,15691.74,0.92,79834,3,10,A -1898,R400 ,R429 ,20319.46,1.19,88165,1,10,A -1901,R100 ,R105 ,21258.65,1.24,96988,7,10,A -1902,R100 ,R133 ,13075.84,0.76,80221,5,10,A -1903,R100 ,R115 ,19720.66,1.15,96988,5,10,A -1904,R300 ,R364 ,11880.47,0.69,79922,6,10,A -1905,R300 ,R379 ,13713.69,0.8,79842,9,10,A -1906,R200 ,R250 ,12129.36,0.71,79837,9,10,A -1907,R300 ,R332 ,11750.72,0.69,79740,4,10,A -1908,R300 ,R364 ,21818.37,1.27,96878,6,10,A -1910,R300 ,R350 ,13878.69,0.81,79101,4,10,A -1911,R100 ,R140 ,15783.9,0.92,79857,5,10,A -1912,R100 ,R112 ,18813.41,1.1,96663,5,10,A -1913,R100 ,R133 ,16319.96,0.95,79888,5,10,A -1914,R100 ,R118 ,11945.36,0.7,79962,7,10,A -1915,R400 ,R438 ,14269.23,0.83,80280,2,10,A -1916,R400 ,R460 ,11931.3,0.7,89345,1,10,A -1917,R400 ,R422 ,21152.09,1.23,85221,2,10,A -1918,R300 ,R365 ,20249.03,1.18,97938,4,10,A -1919,R100 ,R167 ,13211.57,0.77,79993,6,10,A -1920,R300 ,R331 ,19359.99,1.13,79428,4,10,A -1921,R300 ,R315 ,13109.37,0.76,97974,4,10,A -1922,R300 ,R342 ,10731.77,0.63,79641,6,10,A -1923,R300 ,R337 ,20608.75,1.2,79484,4,10,A -1924,R100 ,R141 ,18602.58,1.09,81866,5,10,A -1925,R100 ,R150 ,14823.37,0.86,79914,5,10,A -1926,R200 ,R211 ,16035.38,0.94,87307,9,10,A -1927,R200 ,R233 ,18089.89,1.06,87498,9,10,A -1928,R100 ,R175 ,18484,1.08,96664,8,10,A -1929,R400 ,R419 ,13479.94,0.79,84656,2,10,A -1930,R400 ,R470 ,19137.52,1.12,84530,1,10,A -1931,R400 ,R417 ,13417.49,0.78,79855,3,10,A -1932,R300 ,R372 ,17498.3,1.02,96635,4,10,A -1933,R100 ,R133 ,15007.42,0.88,79753,5,10,A -1934,R300 ,R360 ,15013.3,0.88,96416,4,10,A -1935,R300 ,R310 ,17898.13,1.04,96890,4,10,A -1936,R200 ,R222 ,21679.63,1.26,78857,9,10,A -1937,R300 ,R337 ,13952.96,0.81,79869,4,10,A -1938,R400 ,R415 ,10906.67,0.64,79407,3,10,A -1939,R100 ,R105 ,12791.38,0.75,79737,7,10,A -1940,R100 ,R173 ,12149.04,0.71,80004,5,10,A -1941,R300 ,R310 ,17674.22,1.03,79343,4,10,A -1942,R400 ,R452 ,11022.64,0.64,79859,1,10,A -1943,R100 ,R129 ,13999,0.82,79891,7,10,A -1944,R100 ,R129 ,22245.22,1.3,96663,7,10,A -1945,R100 ,R129 ,15443.5,0.9,96892,7,10,A -1946,R100 ,R172 ,13466.53,0.79,79837,5,10,A -1947,R200 ,R259 ,13569.47,0.79,80057,10,10,A -1948,R400 ,R424 ,14464.63,0.84,84468,2,10,A -1949,R400 ,R454 ,15832.45,0.92,80170,3,10,A -1950,R100 ,R122 ,15450.59,0.9,103127,5,10,A -1951,R100 ,R147 ,14901.96,0.87,79811,5,10,A -1952,R100 ,R118 ,17005.88,0.99,82313,7,10,A -1953,R300 ,R341 ,10380.56,0.61,79516,6,10,A -1954,R400 ,R459 ,12301.22,0.72,72326,2,10,A -1955,R400 ,R460 ,10912.26,0.64,79797,1,10,A -1956,R400 ,R460 ,13294.29,0.78,81816,1,10,A -1957,R200 ,R259 ,14672.85,0.86,80130,10,10,A -1958,R200 ,R248 ,16870.99,0.98,79947,9,10,A -1959,R300 ,R374 ,17293.8,1.01,94245,9,10,A -1960,R300 ,R379 ,22912.25,1.34,94275,9,10,A -1961,R200 ,R232 ,18783.31,1.1,79962,9,10,A -1962,R300 ,R306 ,8816.03,0.51,79780,6,10,A -1963,R300 ,R369 ,12178.13,0.71,79891,4,10,A -1964,R300 ,R336 ,18641.11,1.09,81507,4,10,A -1965,R400 ,R468 ,16174.39,0.94,79939,1,10,A -1966,R300 ,R313 ,15802.73,0.92,103129,4,10,A -1967,R300 ,R314 ,14475.79,0.84,79983,4,10,A -1968,R400 ,R417 ,14314.36,0.83,80046,3,10,A -1969,R100 ,R173 ,15678.41,0.91,80005,5,10,A -1970,R400 ,R411 ,12912.32,0.75,80040,3,10,A -1971,R100 ,R137 ,11438.97,0.67,80160,5,10,A -1972,R100 ,R169 ,12317.64,0.72,79571,5,10,A -1973,R300 ,R348 ,12927.16,0.75,79813,4,10,A -1974,R300 ,R315 ,12648.88,0.74,79979,4,10,A -1975,R300 ,R311 ,16395.99,0.96,79191,6,10,A -1976,R100 ,R177 ,17236.3,1.01,103121,8,10,A -1977,R100 ,R123 ,12964.93,0.76,80167,7,10,A -1978,R100 ,R173 ,16261.24,0.95,79886,5,10,A -1979,R300 ,R327 ,10674.13,0.62,79771,6,10,A -1980,R200 ,R239 ,21183.4,1.24,85248,9,10,A -1981,R300 ,R342 ,8779.61,0.51,79952,6,10,A -1982,R300 ,R341 ,11215.11,0.65,79818,6,10,A -1983,R300 ,R369 ,17104.06,1,79918,4,10,A -1984,R200 ,R237 ,11877.35,0.69,79974,9,10,A -2006,R400 ,R419 ,20153.3,1.18,84532,2,10,A -2007,R400 ,R412 ,10893.06,0.64,80028,3,10,A -2008,R300 ,R342 ,7908.83,0.46,79922,6,10,A -2009,R400 ,R447 ,13109.58,0.76,79743,1,10,A -2010,R100 ,R107 ,11830.41,0.69,82264,7,10,A -2011,R300 ,R362 ,12315.06,0.72,79954,4,10,A -2014,R100 ,R136 ,18046.87,1.05,84554,5,10,A -2015,R100 ,R136 ,11773.91,0.69,79827,5,10,A -2016,R100 ,R174 ,14287.05,0.83,79738,5,10,A -2017,R400 ,R466 ,11668.63,0.68,79714,3,10,A -2018,R200 ,R213 ,18777.98,1.1,84121,9,10,A -2019,R200 ,R217 ,14515.48,0.85,84567,9,10,A -2020,R200 ,R209 ,15833.8,0.92,80485,9,10,A -2021,R100 ,R179 ,16619.28,0.97,96887,8,10,A -2022,R300 ,R367 ,22285.2,1.3,96822,4,10,A -2023,R100 ,R177 ,18884.33,1.1,96607,8,10,A -2024,R400 ,R451 ,14800.4,0.86,80050,2,10,A -2025,R100 ,R124 ,15418.57,0.9,84509,5,10,A -2026,R200 ,R207 ,16593.37,0.97,84476,9,10,A -2027,R300 ,R337 ,13010.79,0.76,80042,4,10,A -2028,R100 ,R145 ,13746.54,0.8,79786,5,10,A -2029,R100 ,R179 ,15920.81,0.93,80039,8,10,A -2030,R200 ,R219 ,21262.98,1.24,82356,9,10,A -2031,R300 ,R378 ,14390.38,0.84,79793,6,10,A -2032,R300 ,R365 ,13460.31,0.79,79763,4,10,A -2033,R100 ,R148 ,14546.27,0.85,79707,5,10,A -2034,R300 ,R354 ,21253.59,1.24,96696,4,10,A -2035,R100 ,R145 ,15469.15,0.9,96887,5,10,A -2036,R300 ,R335 ,22126.21,1.29,96990,4,10,A -2037,R300 ,R358 ,11518.8,0.67,79906,4,10,A -2038,R400 ,R467 ,14318.05,0.84,79753,2,10,A -2040,R300 ,R354 ,17063.27,1,79439,4,10,A -2041,R100 ,R105 ,12355.08,0.72,83548,7,10,A -2042,R300 ,R342 ,16031.24,0.94,96935,6,10,A -2043,R100 ,R144 ,12732.6,0.74,79972,7,10,A -2044,R100 ,R171 ,11796.18,0.69,80039,5,10,A -2045,R400 ,R460 ,13842.5,0.81,78700,1,10,A -2046,R100 ,R102 ,15801.65,0.92,97524,5,10,A -2048,R100 ,R133 ,15655.3,0.91,80039,5,10,A -2051,R200 ,R202 ,16135.06,0.94,82512,9,10,A -2052,R100 ,R175 ,17685.1,1.03,96997,8,10,A -2053,R300 ,R333 ,13709.08,0.8,80059,4,10,A -2055,R300 ,R331 ,13899.73,0.81,79476,4,10,A -2056,R300 ,R335 ,16237.46,0.95,96931,4,10,A -2057,R300 ,R351 ,21884.11,1.28,81995,4,10,A -2058,R300 ,R324 ,14026.92,0.82,79530,4,10,A -2059,R300 ,R320 ,15837.23,0.92,96931,4,10,A -2061,R300 ,R301 ,11518.39,0.67,79617,6,10,A -2062,R300 ,R324 ,15010.69,0.88,79725,4,10,A -2063,R300 ,R320 ,22742.94,1.33,97807,4,10,A -2064,R300 ,R354 ,12526.87,0.73,79854,4,10,A -2065,R300 ,R310 ,14059.85,0.82,96712,4,10,A -2066,R300 ,R364 ,13446.13,0.78,79811,6,10,A -2067,R300 ,R310 ,14790.46,0.86,95402,4,10,A -2068,R100 ,R168 ,12474.44,0.73,79974,5,10,A -2069,R300 ,R372 ,19794.26,1.15,96935,4,10,A -2070,R100 ,R169 ,18237.02,1.06,82352,5,10,A -2071,R400 ,R457 ,13884.61,0.81,79833,3,10,A -2072,R400 ,R453 ,15049.46,0.88,80252,3,10,A -2073,R400 ,R470 ,12944.49,0.75,79637,1,10,A -2074,R300 ,R355 ,10997.2,0.64,79476,4,10,A -2075,R400 ,R462 ,16673.55,0.97,85512,3,10,A -2076,R400 ,R451 ,11758,0.69,79795,2,10,A -2077,R400 ,R430 ,12219.16,0.71,79634,3,10,A -2078,R100 ,R116 ,18619.49,1.09,84892,5,10,A -2079,R100 ,R141 ,20343.1,1.19,85510,5,10,A -2080,R300 ,R362 ,12605.45,0.74,101681,4,10,A -2081,R100 ,R121 ,17503.82,1.02,96676,5,10,A -2082,R200 ,R244 ,14148.16,0.83,79830,9,10,A -2083,R300 ,R375 ,14948.88,0.87,84188,9,10,A -2084,R300 ,R369 ,11805.45,0.69,82776,4,10,A -2085,R300 ,R348 ,13948.75,0.81,79817,4,10,A -2086,R100 ,R169 ,13041.89,0.76,79795,5,10,A -2087,R100 ,R145 ,22546.95,1.32,81928,5,10,A -2088,R200 ,R233 ,18525.42,1.08,84224,9,10,A -2089,R300 ,R353 ,14135.17,0.82,96672,6,10,A -2090,R300 ,R355 ,13911.55,0.81,79848,4,10,A -2091,R300 ,R334 ,15409.45,0.9,97477,4,10,A -2092,R300 ,R318 ,19802.69,1.15,103290,4,10,A -2093,R300 ,R364 ,24946.24,1.45,81803,6,10,A -2095,R100 ,R129 ,14597.45,0.85,76959,6,10,A -2096,R200 ,R218 ,13918.17,0.81,79279,9,10,A -2098,R100 ,R167 ,11273.99,0.66,80161,7,10,A -2099,R400 ,R431 ,13274.74,0.77,80095,3,10,A -2100,R400 ,R430 ,11881.95,0.69,80161,3,10,A -2101,R100 ,R126 ,25991.63,1.52,92390,5,10,A -2102,R400 ,R424 ,11910.75,0.69,79940,2,10,A -2103,R100 ,R118 ,13708.19,0.8,82487,7,10,A -2104,R400 ,R422 ,14904.77,0.87,80190,2,10,A -2105,R100 ,R135 ,13635.68,0.8,82171,5,10,A -2106,R100 ,R146 ,21886.76,1.28,102399,5,10,A -2108,R300 ,R358 ,13209.06,0.77,80355,4,10,A -2109,R300 ,R349 ,13425.73,0.78,81724,4,10,A -2110,R200 ,R260 ,15506.9,0.9,80371,10,10,A -2111,R300 ,R371 ,12067.51,0.7,80126,4,10,A -2112,R300 ,R305 ,11248.22,0.66,80161,6,10,A -2113,R100 ,R134 ,16154.26,0.94,82041,5,10,A -2114,R400 ,R454 ,12706.22,0.74,79999,3,10,A -2115,R200 ,R208 ,12969.02,0.76,84158,9,10,A -2118,R300 ,R314 ,17591.29,1.03,101926,4,10,A -2119,R100 ,R171 ,13437.88,0.78,77429,5,10,A -2120,R400 ,R447 ,13448.13,0.78,80226,1,10,A -2121,R300 ,R334 ,15421.05,0.9,84352,4,10,A -2122,R100 ,R122 ,16351.93,0.95,79868,5,10,A -2123,R100 ,R178 ,18344.56,1.07,79905,8,10,A -2124,R400 ,R421 ,24831.76,1.45,86198,3,10,A -2125,R100 ,R107 ,16321.94,0.95,79771,7,10,A -2126,R300 ,R333 ,14207.28,0.83,80194,4,10,A -2127,R400 ,R439 ,14587.04,0.85,79317,1,10,A -2128,R200 ,R202 ,18280.45,1.07,81453,9,10,A -2129,R300 ,R336 ,13316.84,0.78,77316,4,10,A -2130,R400 ,R447 ,16133.54,0.94,80237,1,10,A -2131,R400 ,R421 ,16672.83,0.97,80356,3,10,A -2132,R300 ,R355 ,13031.92,0.76,96653,4,10,A -2133,R400 ,R415 ,13025,0.76,82355,3,10,A -2134,R300 ,R358 ,18768.16,1.09,80517,4,10,A -2135,R100 ,R126 ,11790.14,0.69,82548,5,10,A -2136,R200 ,R259 ,12598.14,0.73,81907,10,10,A -2137,R300 ,R336 ,29282.46,1.71,85373,4,10,A -2138,R400 ,R466 ,15357.32,0.9,96757,3,10,A -2139,R300 ,R311 ,11320.49,0.66,83197,6,10,A -2140,R300 ,R376 ,18635.02,1.09,101204,9,10,A -2141,R400 ,R419 ,17782.43,1.04,82185,2,10,A -2142,R300 ,R326 ,14030.84,0.82,95921,6,10,A -2143,R200 ,R209 ,18214.6,1.06,88818,9,10,A -2144,R300 ,R364 ,12214.72,0.71,82363,6,10,A -2145,R300 ,R321 ,15779.07,0.92,95958,6,10,A -2146,R300 ,R318 ,19087.89,1.11,100315,4,10,A -2147,R200 ,R217 ,15700.89,0.92,82594,9,10,A -2149,R300 ,R375 ,13284.48,0.77,82217,9,10,A -2150,R100 ,R178 ,14546.06,0.85,82403,8,10,A -2151,R200 ,R202 ,18247.93,1.06,84705,9,10,A -2152,R300 ,R305 ,12708.48,0.74,82672,6,10,A -2153,R300 ,R340 ,11820.02,0.69,82680,4,10,A -2154,R300 ,R359 ,13939.34,0.81,81971,4,10,A -2155,R300 ,R315 ,11028.44,0.64,82218,4,10,A -2156,R400 ,R460 ,15043.97,0.88,80276,1,10,A -2157,R100 ,R170 ,11986.52,0.7,82655,5,10,A -2158,R400 ,R467 ,12458.49,0.73,82241,2,10,A -2159,R100 ,R174 ,9543.22,0.56,83075,5,10,A -2161,R100 ,R170 ,15143.77,0.88,82722,5,10,A -2163,R200 ,R261 ,17502.73,1.02,82555,9,10,A -2164,R300 ,R380 ,13939.14,0.81,82340,9,10,A -2165,R200 ,R261 ,18428.88,1.07,84447,9,10,A -2166,R400 ,R447 ,12957.95,0.76,82709,1,10,A -2167,R400 ,R428 ,13447.78,0.78,82544,1,10,A -2168,R300 ,R371 ,11369.85,0.66,82675,4,10,A -2169,R300 ,R362 ,20325.35,1.19,95961,4,10,A -2170,R400 ,R422 ,12172.56,0.71,82272,2,10,A -2171,R300 ,R335 ,15182.74,0.89,82583,4,10,A -2172,R400 ,R453 ,14327.61,0.84,83077,3,10,A -2173,R400 ,R470 ,12794.02,0.75,82181,1,10,A -2174,R300 ,R346 ,12391.72,0.72,82601,4,10,A -2175,R400 ,R466 ,10997.7,0.64,82857,3,10,A -2176,R300 ,R379 ,25009.47,1.46,83900,9,10,A -2177,R100 ,R115 ,13191.28,0.77,82115,5,10,A -2178,R100 ,R135 ,21199.34,1.24,80171,5,10,A -2179,R200 ,R217 ,11191.94,0.65,82666,9,10,A -2180,R100 ,R114 ,19791.33,1.15,98251,5,10,A -2181,R400 ,R422 ,12618.57,0.74,84927,2,10,A -2182,R400 ,R447 ,12903.72,0.75,82275,1,10,A -2183,R100 ,R175 ,14774.68,0.86,95872,8,10,A -2184,R400 ,R454 ,14525.17,0.85,82530,3,10,A -2185,R200 ,R211 ,14338.59,0.84,84052,9,10,A -2186,R400 ,R431 ,13942.39,0.81,82838,3,10,A -2187,R300 ,R327 ,10701.3,0.62,81841,6,10,A -2188,R300 ,R349 ,21189.37,1.24,82204,4,10,A -2189,R100 ,R101 ,24105.11,1.41,98135,5,10,A -2190,R300 ,R352 ,12424.24,0.72,81751,6,10,A -2191,R400 ,R416 ,9003.96,0.53,81839,2,10,A -2192,R200 ,R264 ,13160.13,0.77,82140,10,10,A -2193,R100 ,R131 ,21700.84,1.27,104591,5,10,A -2194,R100 ,R143 ,21255.94,1.24,83071,8,10,A -2195,R200 ,R232 ,81115.94,4.73,81631,9,10,A -2196,R300 ,R367 ,11416.52,0.67,83971,4,10,A -2197,R100 ,R175 ,20485.02,1.19,95794,8,10,A -2199,R100 ,R110 ,15465.15,0.9,82252,5,10,A -2200,R100 ,R126 ,17905.31,1.04,98368,5,10,A -2201,R400 ,R430 ,15054.4,0.88,82198,3,10,A -2202,R400 ,R431 ,19492.92,1.14,84493,3,10,A -2203,R300 ,R370 ,12279.02,0.72,82433,3,10,A -2204,R100 ,R167 ,13945.06,0.81,82000,6,10,A -2205,R300 ,R311 ,11948.89,0.7,82517,6,10,A -2206,R100 ,R161 ,17124.9,1,82683,10,10,A -2207,R100 ,R135 ,13115.65,0.76,82368,5,10,A -2208,R300 ,R348 ,15921.09,0.93,82277,4,10,A -2210,R300 ,R310 ,20953.26,1.22,98033,4,10,A -2211,R400 ,R416 ,11981.49,0.7,82314,2,10,A -2212,R400 ,R423 ,24660.11,1.44,91947,2,10,A -2213,R400 ,R439 ,12549.89,0.73,82258,1,10,A -2214,R200 ,R208 ,14361.37,0.84,83147,9,10,A -2216,R300 ,R374 ,19632.56,1.15,95591,6,10,A -2217,R100 ,R133 ,15569.67,0.91,82555,5,10,A -2218,R100 ,R176 ,19129.39,1.12,95853,8,10,A -2219,R100 ,R177 ,20773.99,1.21,95921,8,10,A -2220,R300 ,R301 ,19264.01,1.12,95883,6,10,A -2221,R100 ,R177 ,19171.12,1.12,98530,8,10,A -2222,R100 ,R123 ,15223.32,0.89,82572,7,10,A -2223,R100 ,R131 ,12062.63,0.7,82769,5,10,A -2224,R300 ,R305 ,12026.39,0.7,82008,6,10,A -2225,R100 ,R179 ,15380.23,0.9,82815,8,10,A -2226,R100 ,R174 ,13300.03,0.78,79568,5,10,A -2227,R300 ,R375 ,18671.34,1.09,97381,9,10,A -2228,R100 ,R174 ,12586.78,0.73,79749,5,10,A -2229,R100 ,R102 ,28290.13,1.65,104399,5,10,A -2230,R100 ,R137 ,20244.76,1.18,82346,5,10,A -2231,R400 ,R454 ,12315.13,0.72,82023,3,10,A -2232,R200 ,R201 ,16960.15,0.99,84026,9,10,A -2233,R300 ,R315 ,14477.09,0.84,97840,4,10,A -2234,R300 ,R302 ,10622.44,0.62,102469,6,10,A -2235,R300 ,R354 ,19945.87,1.16,102327,4,10,A -2236,R300 ,R377 ,17565.23,1.02,98398,9,10,A -2237,R300 ,R305 ,14789.51,0.86,81809,6,10,A -2238,R200 ,R237 ,17540.29,1.02,81739,9,10,A -2239,R300 ,R327 ,16317.28,0.95,102368,6,10,A -2240,R300 ,R369 ,12964.15,0.76,82422,4,10,A -2241,R400 ,R431 ,14083.83,0.82,82689,3,10,A -2243,R300 ,R306 ,13982.8,0.82,98028,6,10,A -2244,R300 ,R355 ,15384.29,0.9,84647,4,10,A -2245,R200 ,R206 ,18086.36,1.05,82114,9,10,A -2246,R400 ,R461 ,13631.19,0.8,82987,3,10,A -2247,R400 ,R451 ,14377,0.84,81651,2,10,A -2248,R300 ,R360 ,12583.95,0.73,82718,4,10,A -2249,R400 ,R460 ,13104.62,0.76,82302,1,10,A -2250,R400 ,R457 ,12185.7,0.71,82698,3,10,A -2251,R100 ,R150 ,15613.2,0.91,82608,5,10,A -2252,R200 ,R245 ,12439.23,0.73,82391,9,10,A -2253,R400 ,R467 ,13349.91,0.78,82792,2,10,A -2254,R100 ,R134 ,13982.43,0.82,79882,5,10,A -2255,R100 ,R118 ,12695.01,0.74,82319,7,10,A -2256,R400 ,R450 ,11696.3,0.68,81964,2,10,A -2258,R400 ,R429 ,13082.13,0.76,85561,1,10,A -2259,R400 ,R412 ,23898.41,1.39,87461,3,10,A -2260,R200 ,R248 ,16211.26,0.95,98284,9,10,A -2261,R100 ,R179 ,21320.48,1.24,96619,8,10,A -2262,R400 ,R430 ,11808.71,0.69,82719,3,10,A -2264,R300 ,R324 ,27120.06,1.58,102684,4,10,A -2265,R300 ,R318 ,11890.78,0.69,82459,4,10,A -2266,R100 ,R174 ,18072.21,1.05,82387,5,10,A -2267,R400 ,R470 ,13125.4,0.77,82677,1,10,A -2268,R200 ,R246 ,12944.3,0.75,82302,9,10,A -2269,R300 ,R360 ,12743.29,0.74,82054,4,10,A -2270,R200 ,R220 ,17684.44,1.03,82589,9,10,A -2271,R400 ,R415 ,20206.73,1.18,84460,3,10,A -2272,R400 ,R441 ,15535.61,0.91,82290,3,10,A -2273,R300 ,R353 ,12140.39,0.71,82402,6,10,A -2274,R300 ,R340 ,12462.88,0.73,82754,4,10,A -2275,R200 ,R207 ,15040.35,0.88,82932,9,10,A -2276,R300 ,R340 ,13076.17,0.76,82715,4,10,A -2277,R300 ,R337 ,15381.21,0.9,79507,4,10,A -2278,R300 ,R306 ,8577.29,0.5,82776,6,10,A -2279,R300 ,R359 ,11524.71,0.67,82048,4,10,A -2280,R200 ,R239 ,18666.58,1.09,82375,9,10,A -2281,R200 ,R233 ,16828.93,0.98,82439,9,10,A -2283,R300 ,R302 ,10424.27,0.61,81779,6,10,A -2284,R100 ,R167 ,9327.15,0.54,82310,6,10,A -2287,R400 ,R452 ,14367.85,0.84,82476,1,10,A -2288,R300 ,R322 ,11427.84,0.67,98196,6,10,A -2289,R300 ,R314 ,13654.12,0.8,102718,4,10,A -2290,R200 ,R265 ,17523.14,1.02,82140,10,10,A -2292,R400 ,R428 ,13748.51,0.8,82304,1,10,A -2294,R400 ,R457 ,13577.8,0.79,82848,3,10,A -2295,R400 ,R467 ,11937.38,0.7,82713,2,10,A -2296,R300 ,R370 ,11253.68,0.66,82845,3,10,A -2297,R400 ,R457 ,13328.51,0.78,82301,3,10,A -2300,R100 ,R163 ,23676.61,1.38,102560,5,10,A -2301,R300 ,R315 ,12067.75,0.7,79580,4,10,A -2302,R400 ,R431 ,12003.96,0.7,82325,3,10,A -2303,R100 ,R107 ,21916.35,1.28,102791,7,10,A -2304,R200 ,R202 ,16984.39,0.99,92431,9,10,A -2305,R400 ,R460 ,13259.1,0.77,83617,1,10,A -2306,R200 ,R245 ,12004.62,0.7,81802,9,10,A -2307,R200 ,R236 ,21543.6,1.26,87697,9,10,A -2308,R300 ,R351 ,11655,0.68,82714,3,10,A -2309,R200 ,R232 ,14751.69,0.86,102578,9,10,A -2310,R400 ,R415 ,16000.5,0.93,81887,3,10,A -2312,R400 ,R453 ,23583.75,1.38,92086,3,10,A -2313,R100 ,R163 ,31423.53,1.83,104449,5,10,A -2314,R200 ,R265 ,15517.41,0.91,82921,10,10,A -2315,R400 ,R447 ,21727.61,1.27,82276,1,10,A -2316,R300 ,R320 ,15477.38,0.9,97942,4,10,A -2317,R300 ,R320 ,19986.85,1.17,103536,4,10,A -2319,R200 ,R207 ,19689.77,1.15,82769,9,10,A -2320,R300 ,R303 ,15413.78,0.9,103649,6,10,A -2321,R100 ,R111 ,16703.16,0.97,103492,5,10,A -2322,R100 ,R170 ,18035.07,1.05,82761,5,10,A -2323,R400 ,R466 ,13828.03,0.81,104043,3,10,A -2324,R400 ,R467 ,16118.75,0.94,82227,2,10,A -2325,R400 ,R452 ,11186.67,0.65,82428,1,10,A -2326,R100 ,R107 ,18733.23,1.09,103451,7,10,A -2327,R100 ,R136 ,13390.27,0.78,82875,5,10,A -2328,R200 ,R207 ,16998.92,0.99,83584,9,10,A -2329,R200 ,R219 ,20467.54,1.19,84992,9,10,A -2330,R100 ,R117 ,13410.92,0.78,82072,5,10,A -2331,R300 ,R350 ,17189.98,1,82050,4,10,A -2332,R300 ,R351 ,12842.81,0.75,82366,4,10,A -2333,R300 ,R335 ,15113.19,0.88,79522,4,10,A -2334,R300 ,R306 ,9482,0.55,82479,6,10,A -2335,R300 ,R302 ,14171.57,0.83,101330,6,10,A -2337,R400 ,R417 ,11256.92,0.66,82257,3,10,A -2338,R300 ,R326 ,19559.39,1.14,101316,6,10,A -2339,R200 ,R262 ,30081,1.75,88269,10,10,A -2340,R100 ,R163 ,18175.89,1.06,103620,5,10,A -2341,R300 ,R375 ,13774.94,0.8,82599,9,10,A -2342,R300 ,R341 ,14026.97,0.82,102964,6,10,A -2343,R100 ,R179 ,13508.68,0.79,82251,8,10,A -2345,R100 ,R118 ,14386.95,0.84,82330,7,10,A -2346,R100 ,R171 ,14719.41,0.86,82289,5,10,A -2347,R200 ,R218 ,11198.63,0.65,82277,9,10,A -2348,R100 ,R150 ,13182.36,0.77,82360,5,10,A -2349,R200 ,R250 ,17487.19,1.02,103132,9,10,A -2350,R200 ,R219 ,17382.19,1.01,97725,9,10,A -2351,R100 ,R174 ,13315.86,0.78,79494,5,10,A -2354,R300 ,R375 ,15840.96,0.92,103776,9,10,A -2355,R300 ,R340 ,10969.41,0.64,82363,4,10,A -2356,R300 ,R304 ,13665.86,0.8,82201,6,10,A -2357,R100 ,R129 ,21272.94,1.24,82116,6,10,A -2358,R100 ,R173 ,12503.48,0.73,82246,5,10,A -2359,R200 ,R213 ,13391.61,0.78,82322,9,10,A -2360,R300 ,R333 ,18988.72,1.11,103204,4,10,A -2361,R400 ,R460 ,17916.09,1.04,92397,1,10,A -2362,R300 ,R369 ,13196.91,0.77,103637,4,10,A -2363,R300 ,R320 ,15580.11,0.91,103715,4,10,A -2364,R300 ,R313 ,16405.13,0.96,111024,4,10,A -2365,R300 ,R374 ,18410.06,1.07,103609,9,10,A -2366,R300 ,R314 ,13287.57,0.78,82209,4,10,A -2367,R300 ,R333 ,13534.64,0.79,82289,4,10,A -2368,R300 ,R380 ,11108.29,0.65,82270,9,10,A -2369,R300 ,R320 ,11140.3,0.65,82334,4,10,A -2370,R300 ,R365 ,20821.61,1.21,100164,4,10,A -2371,R200 ,R262 ,29785.77,1.74,88020,10,10,A -2372,R200 ,R262 ,36310.74,2.12,87847,10,10,A -2373,R100 ,R141 ,23314.18,1.36,88792,5,10,A -2374,R300 ,R306 ,11208.65,0.65,82292,6,10,A -2375,R300 ,R340 ,11086.8,0.65,82382,4,10,A -2376,R300 ,R365 ,13742.95,0.8,101479,4,10,A -2377,R300 ,R353 ,11964.7,0.7,82341,6,10,A -2378,R100 ,R115 ,18158.26,1.06,102945,5,10,A -2379,R400 ,R457 ,13095.58,0.76,82376,3,10,A -2380,R400 ,R469 ,15786.46,0.92,83341,2,10,A -2381,R400 ,R446 ,20390.42,1.19,82841,2,10,A -2382,R400 ,R416 ,10784.53,0.63,82487,2,10,A -2383,R100 ,R107 ,19469.75,1.14,103679,7,10,A -2384,R400 ,R422 ,17722.2,1.03,82585,2,10,A -2385,R400 ,R430 ,14009.86,0.82,81934,3,10,A -2386,R200 ,R218 ,12348.87,0.72,101445,9,10,A -2387,R300 ,R335 ,12542.55,0.73,82295,4,10,A -2388,R100 ,R150 ,15505.85,0.9,82046,5,10,A -2389,R300 ,R304 ,20579.06,1.2,103125,6,10,A -2390,R100 ,R163 ,23943.77,1.4,105381,5,10,A -2391,R100 ,R138 ,13943.97,0.81,82922,5,10,A -2392,R400 ,R450 ,15908.74,0.93,82089,2,10,A -2394,R400 ,R415 ,13287.04,0.77,82259,3,10,A -2395,R300 ,R355 ,14161.52,0.83,82534,4,10,A -2396,R300 ,R370 ,11884.73,0.69,82555,3,10,A -2397,R200 ,R244 ,15628.89,0.91,83338,9,10,A -2398,R200 ,R240 ,17118.96,1,88129,9,10,A -2399,R400 ,R461 ,14808.26,0.86,82540,3,10,A -2400,R300 ,R375 ,15323.16,0.89,102712,9,10,A -2403,R100 ,R176 ,25435.33,1.48,102986,8,10,A -2404,R300 ,R380 ,15046.5,0.88,79480,9,10,A -2406,R100 ,R102 ,23477.23,1.37,97351,5,10,A -2407,R300 ,R358 ,10225.27,0.6,82339,3,10,A -2408,R200 ,R208 ,14322.74,0.84,82396,9,10,A -2409,R300 ,R341 ,14274.87,0.83,82387,6,10,A -2410,R200 ,R257 ,61584.24,3.59,89082,9,10,A -2411,R200 ,R257 ,50207.23,2.93,86764,9,10,A -2412,R200 ,R257 ,48383.53,2.82,86717,9,10,A -2414,R100 ,R117 ,13409.28,0.78,82272,5,10,A -2415,R100 ,R148 ,13091.96,0.76,79563,5,10,A -2418,R400 ,R462 ,16515.58,0.96,82365,3,10,A -2419,R300 ,R311 ,16511.15,0.96,103695,6,10,A -2420,R200 ,R250 ,13985.84,0.82,82255,9,10,A -2421,R200 ,R244 ,15128.36,0.88,82941,9,10,A -2422,R100 ,R129 ,13669.47,0.8,82291,6,10,A -2423,R100 ,R123 ,19437.21,1.13,82301,7,10,A -2424,R200 ,R207 ,17662.86,1.03,82183,9,10,A -2425,R300 ,R342 ,8287.99,0.48,82124,6,10,A -2426,R300 ,R327 ,14875.68,0.87,103658,6,10,A -2427,R300 ,R310 ,20554.44,1.2,87393,4,10,A -2428,R300 ,R304 ,14166.64,0.83,82240,6,10,A -2429,R300 ,R322 ,11837.59,0.69,82343,6,10,A -2430,R400 ,R428 ,14300.46,0.83,85134,1,10,A -2431,R300 ,R334 ,13900.18,0.81,102189,4,10,A -2432,R400 ,R439 ,12124.78,0.71,82311,1,10,A -2433,R400 ,R439 ,10645.27,0.62,82236,1,10,A -2434,R400 ,R460 ,16641.42,0.97,81961,1,10,A -2436,R400 ,R417 ,12524.26,0.73,82324,3,10,A -2437,R400 ,R431 ,13571.89,0.79,82214,3,10,A -2438,R300 ,R322 ,10645.93,0.62,82382,6,10,A -2439,R400 ,R431 ,13430.15,0.78,82287,3,10,A -2440,R100 ,R133 ,14561.68,0.85,82278,5,10,A -2442,R300 ,R332 ,14268.32,0.83,82355,4,10,A -2445,R300 ,R359 ,12342.84,0.72,82306,4,10,A -2446,R400 ,R461 ,13878.56,0.81,82109,2,10,A -2448,R100 ,R129 ,11575.58,0.68,79562,7,10,A -2449,R100 ,R101 ,16443.63,0.96,85996,5,10,A -2450,R100 ,R169 ,13559.36,0.79,85639,5,10,A -2451,R400 ,R469 ,27828.52,1.62,87111,2,10,A -2454,R100 ,R107 ,18016.56,1.05,82387,7,10,A -2455,R200 ,R208 ,15834.51,0.92,82590,9,10,A -2456,R100 ,R124 ,21347.96,1.25,107394,5,10,A -2457,R100 ,R176 ,21077.64,1.23,82430,8,10,A -2458,R100 ,R177 ,13321.8,0.78,82233,8,10,A -2459,R400 ,R438 ,12407.12,0.72,82135,2,10,A -2460,R300 ,R301 ,11315.18,0.66,81996,6,10,A -2461,R400 ,R438 ,13038.79,0.76,82284,2,10,A -2462,R200 ,R240 ,16201.64,0.94,85529,9,10,A -2463,R200 ,R246 ,17660.66,1.03,86038,9,10,A -2465,R200 ,R231 ,36242.28,2.11,84466,9,10,A -2467,R300 ,R327 ,10961.99,0.64,81855,6,10,A -2468,R200 ,R248 ,21042.87,1.23,103779,9,10,A -2469,R200 ,R250 ,13626.16,0.79,84451,9,10,A -2470,R200 ,R207 ,17761.54,1.04,82584,9,10,A -2471,R200 ,R205 ,17599.8,1.03,103431,9,10,A -2472,R200 ,R220 ,16118.77,0.94,81977,9,10,A -2473,R100 ,R168 ,20818.76,1.21,85348,4,10,A -2474,R300 ,R332 ,16935.71,0.99,85941,4,10,A -2475,R400 ,R459 ,25298.57,1.48,87391,2,10,A -2476,R300 ,R334 ,27801.12,1.62,108688,4,10,A -2478,R400 ,R417 ,13047.21,0.76,85748,3,10,A -2480,R400 ,R470 ,13099.1,0.76,86377,1,10,A -2482,R200 ,R244 ,17448.65,1.02,86586,9,10,A -2483,R100 ,R172 ,14827.66,0.86,85442,4,10,A -2485,R300 ,R359 ,12974.87,0.76,82918,4,10,A -2486,R100 ,R172 ,13628.4,0.79,85822,5,10,A -2488,R100 ,R172 ,16422.61,0.96,86006,5,10,A -2489,R300 ,R314 ,12425.12,0.72,85675,4,10,A -2490,R100 ,R121 ,19147.89,1.12,106766,5,10,A -2491,R100 ,R146 ,16506.59,0.96,85927,5,10,A -2492,R200 ,R246 ,16992.46,0.99,83759,9,10,A -2493,R300 ,R346 ,11287.76,0.66,85804,4,10,A -2494,R300 ,R303 ,12446.28,0.73,85769,6,10,A -2495,R300 ,R341 ,15332.87,0.89,107348,6,10,A -2496,R400 ,R453 ,13449.38,0.78,82821,3,10,A -2497,R300 ,R373 ,12544.15,0.73,85952,9,10,A -2498,R100 ,R167 ,15550.81,0.91,85803,6,10,A -2499,R200 ,R205 ,15762.52,0.92,107308,9,10,A -2501,R300 ,R370 ,14101.51,0.82,85787,3,10,A -2503,R300 ,R340 ,17639.27,1.03,103610,4,10,A -2516,R300 ,R302 ,14469.06,0.84,107351,6,10,A -2519,R100 ,R102 ,12477.91,0.73,86712,5,10,A -2520,R300 ,R326 ,10930.01,0.64,107443,6,10,A -2523,R200 ,R258 ,21739.93,1.27,85996,10,10,A -2524,R200 ,R250 ,18610.44,1.09,85127,9,10,A -2525,R100 ,R123 ,13790.05,0.8,85698,7,10,A -2526,R100 ,R127 ,12976.58,0.76,82494,7,10,A -2527,R400 ,R421 ,13644.45,0.8,85782,3,10,A -2528,R400 ,R416 ,11215.47,0.65,82825,2,10,A -2529,R400 ,R431 ,14783.81,0.86,85807,3,10,A -2530,R400 ,R447 ,12060.87,0.7,81769,1,10,A -2531,R300 ,R331 ,14006.88,0.82,82502,6,10,A -2532,R400 ,R428 ,18832.66,1.1,85689,1,10,A -2533,R300 ,R304 ,13237.56,0.77,82865,6,10,A -2534,R300 ,R331 ,12611.23,0.74,85262,6,10,A -2536,R400 ,R461 ,16009.96,0.93,85859,3,10,A -2537,R400 ,R421 ,14867.53,0.87,85862,3,10,A -2538,R400 ,R457 ,13153.08,0.77,82572,3,10,A -2540,R100 ,R143 ,17659.94,1.03,86399,8,10,A -2542,R100 ,R129 ,20273.52,1.18,103504,6,10,A -2544,R100 ,R135 ,14674.67,0.86,85980,5,10,A -2545,R400 ,R453 ,17078.18,1,85916,3,10,A -2546,R100 ,R110 ,14497.89,0.85,85943,5,10,A -2547,R300 ,R360 ,11536.51,0.67,85792,4,10,A -2548,R400 ,R421 ,14093.4,0.82,75582,3,10,A -2550,R300 ,R302 ,10857.29,0.63,103940,6,10,A -2557,R100 ,R172 ,14412.47,0.84,86062,5,10,A -2559,R100 ,R122 ,15308.18,0.89,82554,5,10,A -2565,R300 ,R362 ,6955.21,0.41,86031,4,10,A -2567,R400 ,R431 ,14185.38,0.83,86057,3,10,A -2568,R300 ,R380 ,20486.14,1.19,86392,9,10,A -2569,R300 ,R373 ,13379.83,0.78,85898,9,10,A -2570,R400 ,R468 ,14667.2,0.86,86060,1,10,A -2572,R300 ,R302 ,20958.4,1.22,101369,6,10,A -2581,R200 ,R233 ,15178.5,0.89,85236,9,10,A -2584,R200 ,R233 ,20568.84,1.2,87380,9,10,A -2586,R100 ,R110 ,19215.74,1.12,86289,5,10,A -2596,R400 ,R421 ,14129.75,0.82,86612,3,10,A -2601,R200 ,R212 ,18532.38,1.08,85641,9,10,A -2604,R200 ,R208 ,14991.21,0.87,86813,9,10,A -2605,R200 ,R233 ,20680.12,1.21,87313,9,10,A -2607,R400 ,R428 ,10917.01,0.64,86008,1,10,A -2608,R400 ,R461 ,14096.88,0.82,78437,3,10,A -2609,R100 ,R178 ,15797.52,0.92,86118,8,10,A -2613,R100 ,R116 ,26971.71,1.57,83519,5,10,A -2615,R200 ,R237 ,19736.55,1.15,83372,9,10,A -2627,R200 ,R263 ,16619.9,0.97,85914,9,10,A -2631,R400 ,R415 ,15121.29,0.88,83229,3,10,A -2632,R200 ,R239 ,20718.86,1.21,86716,9,10,A -2641,R100 ,R178 ,22049.41,1.29,85386,8,10,A -2649,R400 ,R429 ,17883.65,1.04,86430,1,10,A -2660,R200 ,R257 ,50337.47,2.94,85572,9,10,A -2682,R200 ,R257 ,60787.18,3.55,86688,9,10,A -2693,R400 ,R429 ,22348.01,1.3,78343,1,10,A -2697,R200 ,R257 ,49197.17,2.87,86717,9,10,A -2715,R200 ,R250 ,18163.03,1.06,85629,9,10,A -2716,R100 ,R179 ,26029.89,1.52,103599,8,10,A -2717,R100 ,R179 ,16868.81,0.98,84045,8,10,A -2721,R300 ,R372 ,15351.64,0.9,86076,4,10,A -2725,R300 ,R322 ,6638.75,0.39,82643,6,10,A -2727,R300 ,R301 ,12836.29,0.75,86223,6,10,A -2728,R100 ,R168 ,14313.97,0.83,82660,4,10,A -2729,R400 ,R470 ,14787.04,0.86,85163,1,10,A -2730,R200 ,R261 ,19808.13,1.16,85619,9,10,A -2737,R100 ,R167 ,14397.33,0.84,85650,6,10,A -2739,R300 ,R301 ,10245.67,0.6,85796,6,10,A -2742,R300 ,R350 ,17037.35,0.99,86116,4,10,A -2744,R200 ,R213 ,16037.01,0.94,86827,9,10,A -2747,R300 ,R379 ,15020.35,0.88,87031,9,10,A -2753,R400 ,R419 ,20767.47,1.21,82700,2,10,A -2754,R300 ,R342 ,11179.4,0.65,79305,6,10,A -2757,R400 ,R454 ,23500.78,1.37,87536,3,10,A -2759,R200 ,R240 ,21761.92,1.27,86306,9,10,A -2760,R200 ,R240 ,19140.67,1.12,86140,9,10,A -2764,R400 ,R453 ,20121.51,1.17,85110,3,10,A -2765,R100 ,R146 ,21144.9,1.23,86977,5,10,A -2766,R200 ,R210 ,10885.51,0.63,50670,9,10,A -2767,R200 ,R211 ,29795.47,1.74,81382,9,10,A -2768,R200 ,R212 ,12421.06,0.72,54031,9,10,A -2770,R300 ,R370 ,17177.56,1,86658,3,10,A -2771,R200 ,R237 ,15844.28,0.92,85407,9,10,A -2772,R200 ,R212 ,16456.86,0.96,81415,9,10,A -2774,R200 ,R204 ,13732.93,0.8,45825,9,10,A -2775,R200 ,R204 ,12790.63,0.75,58067,9,10,A -2776,R200 ,R204 ,11541.7,0.67,57332,9,10,A -2779,R200 ,R260 ,18343.08,1.07,86115,10,10,A -2780,R100 ,R110 ,19400.44,1.13,86169,5,10,A -2781,R100 ,R116 ,19231.18,1.12,89292,5,10,A -2784,R300 ,R372 ,9476.47,0.55,86519,4,10,A -2785,R300 ,R371 ,17529.66,1.02,86089,4,10,A -2786,R200 ,R265 ,7278.99,0.42,47568,10,10,A -2787,R400 ,R454 ,18302.68,1.07,87777,3,10,A -2790,R400 ,R441 ,27809.78,1.62,86814,3,10,A -2791,R200 ,R258 ,6161.97,0.36,40431,10,10,A -2795,R200 ,R245 ,9597.21,0.56,53312,9,10,A -2796,R300 ,R340 ,16983.81,0.99,87476,4,10,A -2799,R100 ,R116 ,17566.06,1.02,57790,5,10,A -2802,R200 ,R231 ,14610.41,0.85,78679,9,10,A -2803,R300 ,R327 ,10634.74,0.62,86126,6,10,A -2804,R200 ,R212 ,23877.14,1.39,86680,9,10,A -2805,R400 ,R438 ,13600.31,0.79,86209,2,10,A -2810,R200 ,R209 ,22919.47,1.34,87264,9,10,A -2811,R400 ,R459 ,16398.04,0.96,88436,2,10,A -2813,R300 ,R378 ,18224.95,1.06,87120,6,10,A -2815,R300 ,R351 ,11193.33,0.65,86144,4,10,A -2816,R300 ,R369 ,12425.87,0.72,83199,4,10,A -2818,R100 ,R136 ,16262.92,0.95,86314,5,10,A -2820,R100 ,R175 ,16508.87,0.96,85821,8,10,A -2822,R400 ,R429 ,19541.01,1.14,77450,1,10,A -2824,R100 ,R147 ,15836.67,0.92,85871,5,10,A -2829,R200 ,R211 ,16595.32,0.97,83206,9,10,A -2830,R200 ,R233 ,16857.37,0.98,86011,9,10,A -2831,R200 ,R206 ,13699.62,0.8,82986,9,10,A -2840,R400 ,R455 ,20786.26,1.21,84895,2,10,A -2843,R300 ,R349 ,17118.33,1,85989,4,10,A -2844,R400 ,R455 ,15425.05,0.9,83289,2,10,A -2845,R400 ,R411 ,17259.75,1.01,83326,3,10,A -2847,R400 ,R424 ,19538.61,1.14,86329,2,10,A -2848,R300 ,R349 ,13042.3,0.76,87087,4,10,A -2850,R400 ,R423 ,14194.74,0.83,49455,2,10,A -2851,R100 ,R173 ,14938.5,0.87,85416,5,10,A -2853,R400 ,R450 ,13751.56,0.8,86202,2,10,A -2855,R200 ,R231 ,23567.67,1.37,87616,9,10,A -2857,R100 ,R161 ,18571.01,1.08,85375,10,10,A -2860,R100 ,R112 ,14876.32,0.87,79532,5,10,A -2865,R300 ,R304 ,11109.83,0.65,82010,6,10,A -2867,R400 ,R453 ,12625.36,0.74,80000,3,10,A -2868,R300 ,R311 ,8076.43,0.47,81752,6,10,A -2870,R200 ,R257 ,31340,1.83,0,9,10,A -2871,R200 ,R261 ,11190.52,0.65,50000,9,10,A -3200,R100 ,R126 ,2018.34,0.12,12067,5,10,A -3201,R200 ,R210 ,1221.53,0.07,8137,9,10,A -3202,R200 ,R211 ,787.98,0.05,4762,9,10,A -3203,R200 ,R212 ,1577.67,0.09,9712,9,10,A -3204,R100 ,R163 ,1204.56,0.07,7819,5,10,A -3205,R200 ,R201 ,1622.35,0.09,9555,9,10,A -3206,R400 ,R462 ,2106.23,0.12,11683,3,10,A -3207,R100 ,R116 ,1450.92,0.08,12189,5,10,A -3208,R100 ,R116 ,1691.28,0.1,129000,5,10,A -3210,R400 ,R412 ,2075.48,0.12,11362,3,10,A -3211,R400 ,R412 ,1689.44,0.1,7450,3,10,A -3212,R400 ,R462 ,1521.78,0.09,10594,3,10,A -3214,R100 ,R116 ,1771.57,0.1,12560,5,10,A -3216,R200 ,R204 ,1279.47,0.07,11017,9,10,A -3217,R200 ,R204 ,2606.84,0.15,13776,9,10,A -3218,R200 ,R219 ,2980.91,0.17,18948,9,10,A -3219,R100 ,R145 ,1809.48,0.11,11937,5,10,A -3221,R100 ,R116 ,1781.38,0.1,15806,5,10,A -3222,R400 ,R429 ,1653.52,0.1,10403,1,10,A -3223,R400 ,R429 ,1383.63,0.08,9955,1,10,A -3224,R200 ,R233 ,2200.74,0.13,13280,9,10,A -3225,R200 ,R207 ,2690.44,0.16,21625,9,10,A -3226,R400 ,R429 ,1262.9,0.07,8196,1,10,A -3227,R400 ,R462 ,2603.03,0.15,18900,3,10,A -3229,R400 ,R423 ,2800.6,0.16,24167,2,10,A -3230,R400 ,R469 ,1471.29,0.09,10521,2,10,A -3233,R200 ,R202 ,1766.29,0.1,9700,9,10,A -3234,R400 ,R430 ,2123.94,0.12,13923,3,10,A -3235,R400 ,R451 ,4530.29,0.26,29059,2,10,A -3236,R400 ,R455 ,3341.1,0.19,29809,2,10,A -3237,R400 ,R469 ,1859.54,0.11,15806,2,10,A -3238,R300 ,R324 ,1680.59,0.1,11545,4,10,A -3239,R100 ,R101 ,1831.61,0.11,11100,5,10,A -3240,R200 ,R212 ,2975.19,0.17,31988,9,10,A -3241,R300 ,R372 ,1797.86,0.1,7500,4,10,A -3242,R400 ,R448 ,3059.54,0.18,18745,3,10,A -3243,R400 ,R423 ,2207.15,0.13,21600,2,10,A -3244,R100 ,R169 ,2485.25,0.14,16500,5,10,A -3247,R400 ,R421 ,5296.14,0.31,32664,3,10,A -3249,R400 ,R423 ,1605.65,0.09,14249,2,10,A -3250,R300 ,R322 ,903.51,0.05,11000,6,10,A -3251,R200 ,R236 ,2025.36,0.12,14200,9,10,A -3252,R100 ,R141 ,1802.91,0.11,20300,5,10,A -3253,R400 ,R452 ,3975.59,0.23,31000,1,10,A -3254,R100 ,R172 ,1945.21,0.11,9128,5,10,A -3255,R300 ,R372 ,1356.87,0.08,11300,4,10,A -3258,R200 ,R222 ,4202.41,0.25,25046,9,10,A -3259,R400 ,R455 ,4858.49,0.28,31369,2,10,A -3261,R300 ,R375 ,4091.49,0.24,31050,9,10,A -3262,R200 ,R236 ,3376.77,0.2,20225,9,10,A -3263,R400 ,R462 ,2252.46,0.13,20262,3,10,A -3264,R200 ,R210 ,2131.68,0.12,17000,9,10,A -3265,R200 ,R210 ,3463.55,0.2,29965,9,10,A -3267,R200 ,R211 ,1207.94,0.07,13000,9,10,A -3270,R100 ,R141 ,1616.55,0.09,12400,5,10,A -3273,R300 ,R348 ,2686.86,0.16,19200,4,10,A -3274,R200 ,R244 ,3067.18,0.18,25700,9,10,A -3276,R400 ,R423 ,1044.2,0.06,12400,2,10,A -3277,R400 ,R423 ,2645.38,0.15,22496,2,10,A -3279,R100 ,R175 ,1627.21,0.09,12100,8,10,A -3281,R200 ,R258 ,1604.14,0.09,17230,10,10,A -3283,R100 ,R141 ,1513.74,0.09,16700,5,10,A -3284,R400 ,R423 ,1161.45,0.07,11300,2,10,A -3285,R400 ,R462 ,2880.72,0.17,36700,3,10,A -3287,R400 ,R452 ,1718.72,0.1,19800,1,10,A -3290,R400 ,R446 ,4845.36,0.28,43415,2,10,A -3292,R300 ,R352 ,1573.85,0.09,32600,6,10,A -3293,R200 ,R236 ,3814.79,0.22,31536,9,10,A -3295,R400 ,R412 ,1857.09,0.11,0,3,10,A -3296,R400 ,R422 ,4049.03,0.24,33425,2,10,A -3304,R400 ,R429 ,1836.82,0.11,29738,1,10,A -3305,R400 ,R470 ,3247.8,0.19,14200,1,10,A -3306,R400 ,R447 ,3985.04,0.23,31800,1,10,A -3307,R100 ,R116 ,1214.94,0.07,6300,5,10,A -3310,R400 ,R421 ,3038.08,0.18,39623,3,10,A -3315,R200 ,R261 ,2407.47,0.14,16400,9,10,A -3316,R100 ,R178 ,2398.48,0.14,18196,8,10,A -3318,R400 ,R424 ,2678.01,0.16,35000,2,10,A diff --git a/data/input/test_glmnet_ts.csv b/data/input/test_glmnet_ts.csv deleted file mode 100644 index b6c121b..0000000 --- a/data/input/test_glmnet_ts.csv +++ /dev/null @@ -1,105 +0,0 @@ -week,y,x1,x2 -1,14,2,18 -2,12,2,15 -3,14,1,13 -4,11,1,13 -5,15,1,16 -6,17,1,17 -7,16,3,20 -8,124.50,2,1.8 -9,221.10,2,1.5 -10,307.34,2,1.1 -11,384.56,1,1.9 -12,457.30,3,1.4 -13,520.72,2,1.3 -14,578.60,2,1.7 -15,638.64,1,1.3 -16,705.73,2,1.9 -17,776.10,3,1.6 -18,845.89,3,1.1 -19,913.73,1,1.8 -20,983.38,3,1.1 -21,1051.00,2,1.6 -22,1120.12,3,1.7 -23,1185.28,2,1 -24,1254.56,3,1.6 -25,1319.29,1,1.4 -26,1384.44,1,1.4 -27,1451.18,2,1.1 -28,1519.01,2,1.5 -29,1588.22,3,1.2 -30,1657.03,2,2 -31,1722.13,1,1.3 -32,1788.45,2,1 -33,1854.50,1,1.7 -34,1921.87,2,1.4 -35,1987.28,1,1.4 -36,2055.32,2,1.5 -37,2122.60,1,2 -38,2189.50,2,1.1 -39,2259.99,3,1.8 -40,2330.64,3,2 -41,2398.12,2,1.7 -42,2463.98,1,1.9 -43,2529.97,2,1.1 -44,2594.08,1,1.1 -45,2664.22,3,1.8 -46,2731.90,2,1.6 -47,2796.72,1,1.1 -48,2866.71,3,1.5 -49,2935.89,3,1.3 -50,3001.75,1,1.7 -51,3065.93,1,1.1 -52,3135.36,3,1.4 -53,3206.21,3,2 -54,3274.61,3,1.3 -55,3343.67,3,1.5 -56,3413.00,3,1.6 -57,3480.57,2,1.8 -58,3545.81,1,1.8 -59,3609.96,1,1.2 -60,3676.19,2,1 -61,3740.98,1,1.1 -62,3810.10,2,1.9 -63,3880.99,3,1.8 -64,3948.05,1,2 -65,4015.67,2,1.5 -66,4086.20,3,2 -67,4150.30,1,1.2 -68,4214.07,1,1 -69,4280.27,1,1.8 -70,4349.79,3,1.3 -71,4417.47,2,1.5 -72,4483.99,1,1.8 -73,4550.31,1,1.6 -74,4619.66,3,1.3 -75,4687.95,2,1.9 -76,4752.53,1,1.3 -77,4817.71,1,1.3 -78,4883.78,1,1.6 -79,4954.26,3,1.7 -80,5020.31,1,1.7 -81,5090.70,3,1.7 -82,5159.77,3,1.3 -83,5223.75,1,1 -84,5288.34,1,1.2 -85,5353.99,1,1.6 -86,5420.11,1,1.6 -87,5485.58,1,1.3 -88,5555.95,3,1.5 -89,5624.03,2,1.4 -90,5690.95,1,1.9 -91,5757.89,1,2 -92,5822.70,1,1.2 -93,5889.04,2,1 -94,5959.29,3,1.8 -95,6026.81,2,1.5 -96,6096.23,3,1.4 -97,6165.15,3,1.3 -98,6232.78,2,1.7 -99,6297.62,1,1.5 -100,6366.96,3,1.6 -101,6436.54,3,1.7 -102,6506.77,3,2 -103,6576.67,3,1.9 -104,6644.12,3,1 diff --git a/data/input/test_glmnet_ts1.csv b/data/input/test_glmnet_ts1.csv deleted file mode 100644 index e9079fa..0000000 --- a/data/input/test_glmnet_ts1.csv +++ /dev/null @@ -1,105 +0,0 @@ -week,y,x1,x2 -1,3.8,2,2 -2,6.11,2,1.5 -3,6.98,1,1.6 -4,8.59,1,1.9 -5,9.99,1,1.7 -6,11.44,1,2.1 -7,14.02,3,1.4 -8,15.41,2,1.8 -9,16.63,2,1.5 -10,17.79,2,1.1 -11,18.49,1,1.9 -12,20.57,3,1.4 -13,21.31,2,1.3 -14,22.26,2,1.7 -15,22.35,1,1.3 -16,23.65,2,1.9 -17,25.41,3,1.6 -18,26.41,3,1.1 -19,26.12,1,1.8 -20,27.41,3,1.1 -21,27.97,2,1.6 -22,29.15,3,1.7 -23,29.19,2,1 -24,30.33,3,1.6 -25,29.76,1,1.4 -26,29.37,1,1.4 -27,29.97,2,1.1 -28,30.38,2,1.5 -29,31.24,3,1.2 -30,31.66,2,2 -31,30.89,1,1.3 -32,31.17,2,1 -33,31,1,1.7 -34,31.28,2,1.4 -35,30.83,1,1.4 -36,31.33,2,1.5 -37,31.19,1,2 -38,31.25,2,1.1 -39,32.54,3,1.8 -40,33.52,3,2 -41,33.26,2,1.7 -42,32.74,1,1.9 -43,32.87,2,1.1 -44,32.11,1,1.1 -45,33.43,3,1.8 -46,33.47,2,1.6 -47,32.3,1,1.1 -48,33.55,3,1.5 -49,34.33,3,1.3 -50,33.34,1,1.7 -51,32.5,1,1.1 -52,33.81,3,1.4 -53,34.84,3,2 -54,35.14,3,1.3 -55,35.71,3,1.5 -56,36.26,3,1.6 -57,36.11,2,1.8 -58,35.32,1,1.8 -59,34.48,1,1.2 -60,34.66,2,1 -61,33.77,1,1.1 -62,34.29,2,1.9 -63,35.33,3,1.8 -64,34.39,1,2 -65,34.38,2,1.5 -66,35.63,3,2 -67,34.42,1,1.2 -68,33.37,1,1 -69,33.28,1,1.8 -70,34.33,3,1.3 -71,34.26,2,1.5 -72,33.54,1,1.8 -73,33.14,1,1.6 -74,34.2,3,1.3 -75,34.4,2,1.9 -76,33.36,1,1.3 -77,32.84,1,1.3 -78,32.57,1,1.6 -79,33.92,3,1.7 -80,33.16,1,1.7 -81,34.08,3,1.7 -82,34.89,3,1.3 -83,33.45,1,1 -84,32.83,1,1.2 -85,32.76,1,1.6 -86,32.42,1,1.6 -87,31.91,1,1.3 -88,33.14,3,1.5 -89,33.08,2,1.4 -90,32.49,1,1.9 -91,32.37,1,2 -92,31.88,1,1.2 -93,32.11,2,1 -94,33.35,3,1.8 -95,33.17,2,1.5 -96,33.84,3,1.4 -97,34.51,3,1.3 -98,34.34,2,1.7 -99,33.56,1,1.5 -100,34.79,3,1.6 -101,35.67,3,1.7 -102,36.28,3,2 -103,36.91,3,1.9 -104,36.97,3,1 diff --git a/data/input/test_lag_var.csv b/data/input/test_lag_var.csv deleted file mode 100644 index 7a374be..0000000 --- a/data/input/test_lag_var.csv +++ /dev/null @@ -1,13 +0,0 @@ -week,y,lag_6,lag_4,lag_3,lag_2,lag_1,x1,x2 -1,14,,,,,,2,18 -2,12,,,,,14,2,15 -3,14,,,,14,12,1,13 -4,11,,,14,12,14,1,13 -5,15,,14,12,14,11,1,16 -6,17,,12,14,11,15,1,17 -7,16,14,14,11,15,17,3,20 -8,14,12,11,15,17,16,2,18 -9,19,14,15,17,16,14,2,15 -10,19,11,17,16,14,19,2,11 -11,21,15,16,14,19,19,1,19 -12,15,17,14,19,19,21,3,14 diff --git a/data/input/test_timeseries.csv b/data/input/test_timeseries.csv deleted file mode 100644 index 8d3f020..0000000 --- a/data/input/test_timeseries.csv +++ /dev/null @@ -1,105 +0,0 @@ -week,y,x1,x2 -1,14,2,18 -2,12,2,15 -3,14,1,13 -4,11,1,13 -5,15,1,16 -6,17,1,17 -7,16,3,20 -8,14,2,18 -9,19,2,15 -10,19,2,11 -11,21,1,19 -12,15,3,14 -13,21,2,13 -14,22,2,17 -15,25,1,13 -16,19,2,19 -17,23,3,16 -18,27,3,11 -19,31,1,18 -20,21,3,11 -21,25,2,16 -22,30,3,17 -23,36,2,10 -24,23,3,16 -25,31,1,14 -26,32,1,14 -27,39,2,11 -28,27,2,15 -29,33,3,12 -30,34,2,20 -31,41,1,13 -32,30,2,10 -33,36,1,17 -34,39,2,14 -35,44,1,14 -36,34,2,15 -37,41,1,20 -38,41,2,11 -39,50,3,18 -40,40,3,20 -41,46,2,17 -42,48,1,19 -43,56,2,11 -44,45,1,11 -45,51,3,18 -46,52,2,16 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-mllib/lib/cluster.py 103 0 100% -mllib/lib/glmnet_ts.py 113 1 99% 88 -mllib/lib/model.py 44 0 100% ------------------------------------------------------- -TOTAL 274 1 99% +Name Stmts Miss Cover Missing +----------------------------------------------------------------------------------------- +/media/ph33r/Data/Project/mllib/Git/mllib/__init__.py 7 0 100% +/media/ph33r/Data/Project/mllib/Git/mllib/lib/__init__.py 7 0 100% +/media/ph33r/Data/Project/mllib/Git/mllib/lib/cluster.py 103 0 100% +/media/ph33r/Data/Project/mllib/Git/mllib/lib/model.py 44 0 100% +----------------------------------------------------------------------------------------- +TOTAL 161 0 100% diff --git a/mllib/lib/glmnet_ts.py b/mllib/lib/glmnet_ts.py deleted file mode 100644 index 9a1ddd3..0000000 --- a/mllib/lib/glmnet_ts.py +++ /dev/null @@ -1,302 +0,0 @@ -""" -Module for commonly used machine learning modelling algorithms. - -**Available routines:** - -- udf ``create_lag_vars``: Create lag variables for time series data. -- class ``GLMNet``: Builds GLMnet model using cross validation. - -Credits -------- -:: - - Authors: - - Madhu - - Diptesh - - Date: Sep 16, 2021 -""" - -# pylint: disable=invalid-name -# pylint: disable=R0902,R0903,R0913,R0914,C0413 - -from typing import List, Dict - -import re -import sys -from inspect import getsourcefile -from os.path import abspath - -import pandas as pd -import numpy as np - -from sklearn.linear_model import ElasticNetCV -from sklearn.model_selection import TimeSeriesSplit as ts_split - -path = abspath(getsourcefile(lambda: 0)) -path = re.sub(r"(.+\/)(.+.py)", "\\1", path) -sys.path.insert(0, path) - -import metrics # noqa: F841 - -# ============================================================================= -# --- DO NOT CHANGE ANYTHING FROM HERE -# ============================================================================= - - -def create_lag_vars(df: pd.DataFrame, - y_var: List[str], - x_var: List[str], - lst_lag: List[int] = None, - n_interval: str = None) -> pd.DataFrame: - """Create lag variables for time series data. - - Parameters - ---------- - df : pd.DataFrame - - Pandas dataframe containing `y_var`, `x_var` and `n_interval` - (if provided). - - y_var : List[str] - - Dependant variable. - - x_var : List[str] - - Independant variables. - - lst_lag : List[int] - - Lag variables list (the default is None) - - n_interval : str, optional - - Column name of the time interval variable (the default is None). - - Returns - ------- - pd.DataFrame - - Pandas dataframe containing `y_var`, lag variables (`lag_xx`) and - `x_var`. - - """ - if n_interval is None: - df = df.reset_index(drop=True) - elif len(df) != (df[n_interval].max() - df[n_interval].min() + 1): - sys.exit("Missing/duplicate time instance found in input data") - else: - df = df.sort_values(by=n_interval) - df = df.reset_index(drop=True) - y_lag = df[y_var].copy(deep=True) - time_int = len(y_lag) - if lst_lag is None: - lst_lag = [] - while time_int > 8: - time_int = int(np.floor(time_int/2)) - lst_lag.extend([time_int]) - lst_lag.extend([4, 3, 2, 1]) - for lag in lst_lag: - y_lag.loc[:, "lag_" + str(lag)] = y_lag["y"].shift(lag) - y_lag = y_lag.join(df[x_var]) - if n_interval: - y_lag = y_lag.join(df[n_interval]) - y_lag = y_lag.set_index(n_interval) - op = y_lag.dropna().reset_index(drop=True) - return lst_lag, op - - -class GLMNet_ts(): - """GLMNet time series module. - - Objective: - - Build - `GLMNet `_ - model using optimal alpha and lambda - - Parameters - ---------- - df : pd.DataFrame - - Pandas dataframe containing `y_var` and `x_var` variables. - - y_var : List[str] - - Dependant variable. - - x_var : List[str] - - Independant variables. - - lst_lag : List[int] - - Lag variables list (the default is None) - - n_interval : str, optional - - Column name of the time interval variable (the default is None). - - - param : Dict, optional - - GLMNet parameters (the default is None). - In case of None, the parameters will default to:: - - seed: 1 - a_inc: 0.05 - test_perc: 0.25 - n_jobs: -1 - k_fold: 10 - - """ - - def __init__(self, - df: pd.DataFrame, - y_var: List[str], - x_var: List[str], - lst_lag: List[int] = None, - n_interval: str = None, - param: Dict = None): - """Initialize variables for module ``GLMNet``.""" - if n_interval is None: - self.df = df[y_var + x_var] - else: - self.df = df[y_var + x_var + [n_interval]] - self.y_var = y_var - self.x_var = x_var - self.lst_lag = lst_lag - self.n_interval = n_interval - self.model_summary = None - self.max_epoch = None - if param is None: - param = {"seed": 1, - "a_inc": 0.05, - "test_perc": 0.25, - "n_jobs": -1, - "k_fold": 10} - self.param = param - self.param["l1_range"] = list(np.round(np.arange(self.param["a_inc"], - 1.01, - self.param["a_inc"]), - 2)) - self._fit() - self._compute_metrics() - - def _fit(self) -> None: - """Fit the best GLMNet time series model.""" - if self.n_interval is None: - self.max_epoch = len(self.df) - 1 - else: - self.max_epoch = self.df[self.n_interval].max() - self.lag_var, df_ip = create_lag_vars(self.df, - self.y_var, - self.x_var, - self.lst_lag, - self.n_interval) - x_var = list(df_ip.columns) - x_var.remove(self.y_var[0]) - df_train = df_ip.iloc[0:int(len(df_ip) * (1-self.param["test_perc"]))] - df_test = df_ip.iloc[int(len(df_ip) * (1-self.param["test_perc"])):] - train_x = df_train[x_var] - train_y = df_train[self.y_var] - test_x = df_test[x_var] - test_y = df_test[self.y_var] - self.param["k_fold"] = ts_split(n_splits=self.param["k_fold"]) - self.param["k_fold"] = self.param["k_fold"].split(X=train_y) - mod = ElasticNetCV(l1_ratio=self.param["l1_range"], - fit_intercept=True, - alphas=[1e-5, 1e-4, 1e-3, 1e-2, 1e-1, - 1.0, 10.0, 100.0], - normalize=False, - cv=self.param["k_fold"], - n_jobs=self.param["n_jobs"], - random_state=self.param["seed"]) - mod.fit(train_x, train_y.values.ravel()) - opt = {"alpha": mod.l1_ratio_, - "lambda": mod.alpha_, - "intercept": mod.intercept_, - "coef": mod.coef_, - "train_v": mod.score(train_x, train_y), - "test_v": mod.score(test_x, test_y)} - self.model = mod - self.opt = opt - - def _compute_metrics(self): - """Compute commonly used metrics to evaluate the model.""" - y = self.df[self.y_var].iloc[max(self.lst_lag): - len(self.df), 0].values.tolist() - if self.n_interval is None: - y_hat = list(self.predict(self.df[self.x_var][max(self.lst_lag): - len(self.df)])["y"] - .values) - else: - y_hat = list(self.predict(self.df[self.x_var - + [self.n_interval]] - [max(self.lst_lag):len(self.df)])["y"] - .values) - model_summary = {"rsq": np.round(metrics.rsq(y, y_hat), 3), - "mae": np.round(metrics.mae(y, y_hat), 3), - "mape": np.round(metrics.mape(y, y_hat), 3), - "rmse": np.round(metrics.rmse(y, y_hat), 3)} - model_summary["mse"] = np.round(model_summary["rmse"] ** 2, 3) - self.model_summary = model_summary - - def predict(self, df_predict: pd.DataFrame) -> pd.DataFrame: - """Predict y_var/target variable. - - Parameters - ---------- - df_predict : pd.DataFrame - - Pandas dataframe containing `x_var`, 'n_interval' (optional) - - Returns - ------- - pd.DataFrame - - Pandas dataframe containing predicted `y_var` and `x_var`. - - """ - if self.n_interval is None: - df_predict = df_predict[self.x_var] - else: - df_predict = df_predict[self.x_var + [self.n_interval]] - if self.n_interval is None: - df_ip = self.df - df_predict = df_predict.reset_index(drop=True) - df_predict = \ - df_predict.set_index(df_predict.index+self.max_epoch+1) - elif len(df_predict) != (df_predict[self.n_interval].max() - - df_predict[self.n_interval].min() + 1)\ - or df_predict[self.n_interval].min()\ - > self.max_epoch + 1: - sys.exit("Missing time instance found in input data") - else: - df_ip = self.df[self.df[self.n_interval] - <= df_predict[self.n_interval].min()] - df_predict = df_predict.sort_values(by=self.n_interval) - df_predict = df_predict.set_index(self.n_interval) - df_predict = df_predict[self.x_var] - df_predict["y"] = -1 - for i in range(0, len(df_predict)): - df_pred = pd.DataFrame(df_predict.iloc[i]) - df_pred = df_pred.T - period_val = df_pred.index - df_pred = df_pred[self.x_var].reset_index(drop=True) - df_pred_x = pd.DataFrame( - {"lag_" + str(self.lst_lag[0]): - df_ip.iloc[len(df_ip) - self.lst_lag[0]][self.y_var]}) - for j in range(1, len(self.lst_lag)): - df_tmp = pd.DataFrame( - {"lag_"+str(self.lst_lag[j]): - df_ip.iloc[len(df_ip)-self.lst_lag[j]][self.y_var]}) - df_pred_x = df_pred_x.join(df_tmp) - df_pred_x = df_pred_x.reset_index(drop=True) - df_pred_x = df_pred_x.join(df_pred) - y_hat = self.model.predict(df_pred_x) - df_tmp = pd.DataFrame() - df_tmp['y'] = y_hat - df_ip = df_ip.append(df_tmp).reset_index(drop=True) - df_predict.loc[period_val, "y"] = y_hat - return df_predict diff --git a/requirements.txt b/requirements.txt index 25c2389..ef333fe 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,4 @@ -pytest==5.3.5 -Cython==0.29.15 numpy==1.19.5 pandas==1.1.3 +Cython==0.29.15 scikit_learn==1.0 diff --git a/tests/test_glmnet_ts.py b/tests/test_glmnet_ts.py deleted file mode 100644 index d09e0c3..0000000 --- a/tests/test_glmnet_ts.py +++ /dev/null @@ -1,203 +0,0 @@ -""" -Test suite module for ``glmnet_ts``. - -Credits -------- -:: - - Authors: - - Madhu - - Diptesh - - Date: Sep 24, 2021 -""" - -# pylint: disable=invalid-name -# pylint: disable=wrong-import-position - -import unittest -import warnings -import re -import sys - -from inspect import getsourcefile -from os.path import abspath - -import pandas as pd -import numpy as np -import pytest - -# Set base path -path = abspath(getsourcefile(lambda: 0)) -path = re.sub(r"(.+)(\/tests.*)", "\\1", path) - -sys.path.insert(0, path) - -from mllib.lib.glmnet_ts import create_lag_vars # noqa: F841 -from mllib.lib.glmnet_ts import GLMNet_ts # noqa: F841 - -# ============================================================================= -# --- DO NOT CHANGE ANYTHING FROM HERE -# ============================================================================= - -path = path + "/data/input/" - -# ============================================================================= -# --- User defined functions -# ============================================================================= - - -def ignore_warnings(test_func): - """Suppress deprecation warnings.""" - - def do_test(self, *args, **kwargs): - with warnings.catch_warnings(): - warnings.simplefilter("ignore") - test_func(self, *args, **kwargs) - return do_test - - -class TestCreateLagVars(unittest.TestCase): - """Test suite for UDF ``create_lag_vars``.""" - - def setUp(self): - """Set up for UDF ``create_lag_vars``.""" - - def test_no_interval_specified(self): - """Lag vars: Test when no interval is specified.""" - df_ip = pd.read_csv(path + "test_lag_var.csv") - lst_lag, df_op = create_lag_vars(df=df_ip, - y_var=["y"], - x_var=["x1", "x2"]) - exp_op = df_ip[list(df_ip.columns[1:])].dropna().reset_index(drop=True) - self.assertEqual(df_op.equals(exp_op), True) - self.assertEqual([6, 4, 3, 2, 1], lst_lag) - - def test_interval_specified(self): - """Lag vars: Test when interval is specified.""" - df_ip = pd.read_csv(path + "test_lag_var.csv") - lst_lag, df_op = create_lag_vars(df=df_ip, - y_var=["y"], - x_var=["x1", "x2"], - n_interval="week") - exp_op = df_ip[list(df_ip.columns[1:])].dropna().reset_index(drop=True) - self.assertEqual(df_op.equals(exp_op), True) - self.assertEqual([6, 4, 3, 2, 1], lst_lag) - - def test_lag_vars_specified(self): - """Lag vars: Test when lags are specified.""" - df_ip = pd.read_csv(path + "test_lag_var.csv") - lst_lag, df_op = create_lag_vars(df=df_ip, - y_var=["y"], - x_var=["x1", "x2"], - lst_lag=[3, 2, 1]) - exp_op = df_ip.iloc[:, [1, 4, 5, 6, 7, 8]]\ - .dropna().reset_index(drop=True) - self.assertEqual(df_op.equals(exp_op), True) - self.assertEqual([3, 2, 1], lst_lag) - - -class TestGLMNet_ts(unittest.TestCase): - """Test suite for module ``GLMNet_ts``.""" - - def setUp(self): - """Set up for module ``GLMNet_ts``.""" - - def test_known_equation(self): - """GLMNet_ts: Test a known equation with/without n_interval.""" - df_ip = pd.read_csv(path + "test_glmnet_ts1.csv") - df_train_ip = df_ip.iloc[0:len(df_ip)] - mod = GLMNet_ts(df=df_train_ip, - y_var=["y"], - x_var=["x1", "x2"], - lst_lag=[3, 1]) - op = mod.opt - self.assertTrue(0.5 <= np.round(op.get('intercept'), 0) <= 1.5) - self.assertTrue(0.15 <= np.round(op.get('coef')[0], 2) <= 0.25) - self.assertTrue(0.65 <= np.round(op.get('coef')[1], 2) <= 0.75) - self.assertTrue(0.75 <= np.round(op.get('coef')[2], 2) <= 0.85) - self.assertTrue(0.45 <= np.round(op.get('coef')[3], 2) <= 0.55) - mod = GLMNet_ts(df=df_train_ip, - y_var=["y"], - x_var=["x1", "x2"], - lst_lag=[3, 1], - n_interval="week") - op = mod.opt - self.assertTrue(0.5 <= np.round(op.get('intercept'), 0) <= 1.5) - self.assertTrue(0.15 <= np.round(op.get('coef')[0], 2) <= 0.25) - self.assertTrue(0.65 <= np.round(op.get('coef')[1], 2) <= 0.75) - self.assertTrue(0.75 <= np.round(op.get('coef')[2], 2) <= 0.85) - self.assertTrue(0.45 <= np.round(op.get('coef')[3], 2) <= 0.55) - - def test_predict_target_variable(self): - """GLMNet_ts: Test predictor with/without n_interval.""" - df_ip = pd.read_csv(path + "test_glmnet_ts1.csv") - # without n_interval - df_train_ip = df_ip.iloc[0:95] - mod = GLMNet_ts(df=df_train_ip, - y_var=["y"], - x_var=["x1", "x2"], - lst_lag=[3, 1]) - op = mod.opt - df_predict = df_ip.iloc[95:len(df_ip)] - y_pred = mod.predict(df_predict) - y_pred = np.round(np.array(y_pred["y"]), 1) - df_exp = df_ip.copy(deep=True) - df_exp['lag_3'] = df_exp["y"].shift(3) - df_exp['lag_1'] = df_exp["y"].shift(1) - df_exp = df_exp[["lag_3", "lag_1", "x1", "x2"]] - df_exp = df_exp.iloc[95:len(df_ip)] - df_exp["y"] = op.get('intercept')\ - + op.get('coef')[0] * df_exp["lag_3"]\ - + op.get('coef')[1] * df_exp["lag_1"]\ - + op.get('coef')[2] * df_exp["x1"]\ - + op.get('coef')[3] * df_exp["x2"] - y_exp = np.round(np.array(df_exp["y"]), 1) - for i, j in zip(y_pred, y_exp): - self.assertTrue(j - 0.1 <= i <= j + 0.1) - # with n_interval - mod = GLMNet_ts(df=df_train_ip, - y_var=["y"], - x_var=["x1", "x2"], - lst_lag=[3, 1], - n_interval="week") - op = mod.opt - df_predict = df_ip.iloc[95:len(df_ip)] - y_pred = mod.predict(df_predict) - y_pred = np.round(np.array(y_pred["y"]), 1) - df_exp = df_ip.copy(deep=True) - df_exp['lag_3'] = df_exp["y"].shift(3) - df_exp['lag_1'] = df_exp["y"].shift(1) - df_exp = df_exp[["lag_3", "lag_1", "x1", "x2"]] - df_exp = df_exp.iloc[95:len(df_ip)] - df_exp["y"] = op.get('intercept')\ - + op.get('coef')[0] * df_exp["lag_3"]\ - + op.get('coef')[1] * df_exp["lag_1"]\ - + op.get('coef')[2] * df_exp["x1"]\ - + op.get('coef')[3] * df_exp["x2"] - y_exp = np.round(np.array(df_exp["y"]), 1) - for i, j in zip(y_pred, y_exp): - self.assertTrue(j - 0.1 <= i <= j + 0.1) - - @staticmethod - def test_for_exit(): - """GLMNet_ts: Test for missing time instance.""" - df_ip = pd.read_csv(path + "test_glmnet_ts1.csv") - # without n_interval - df_train_ip = df_ip.iloc[0:95] - mod = GLMNet_ts(df=df_train_ip, - y_var=["y"], - x_var=["x1", "x2"], - lst_lag=[3, 1], - n_interval="week") - df_predict = df_ip.iloc[96:len(df_ip)] - with pytest.raises(SystemExit): - df_predict = mod.predict(df_predict) - - -# ============================================================================= -# --- Main -# ============================================================================= - -if __name__ == '__main__': - unittest.main()