[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

econml/_cate_estimator.py

@@ -10,10 +10,10 @@
 from copy import deepcopy
 from warnings import warn
 from .inference import BootstrapInference
-from .utilities import (tensordot, ndim, reshape, shape, parse_final_model_params, get_feature_names_or_default,
-                        inverse_onehot, Summary, get_input_columns, check_input_arrays, jacify_featurizer)
+from .utilities import (ndim, shape, parse_final_model_params, get_feature_names_or_default,
+                        inverse_onehot, Summary, get_input_columns, check_input_arrays)
 from .inference import StatsModelsInference, StatsModelsInferenceDiscrete, LinearModelFinalInference, \
-    LinearModelFinalInferenceDiscrete, NormalInferenceResults, GenericSingleTreatmentModelFinalInference, \
+    LinearModelFinalInferenceDiscrete, GenericSingleTreatmentModelFinalInference, \
     GenericModelFinalInferenceDiscrete
 from ._shap import _shap_explain_cme, _shap_explain_joint_linear_model_cate
 from .dowhy import DoWhyWrapper

econml/_ensemble/_ensemble.py

@@ -13,7 +13,6 @@
 import numpy as np
 from abc import ABCMeta, abstractmethod
 from sklearn.base import BaseEstimator, clone
-from sklearn.utils import check_random_state
 from joblib import effective_n_jobs
 from packaging.version import parse
 import sklearn

econml/_ortho_learner.py

@@ -24,28 +24,22 @@ class in this module implements the general logic in a very versatile way
 
 """
 
-import copy
 from collections import namedtuple
-from warnings import warn
 from abc import abstractmethod
 from typing import List, Union
-import inspect
-from collections import defaultdict
-import re
 
 import numpy as np
 from sklearn.base import clone
 from sklearn.model_selection import KFold, StratifiedKFold, GroupKFold, StratifiedGroupKFold, check_cv
-from sklearn.preprocessing import (FunctionTransformer, LabelEncoder,
-                                   OneHotEncoder)
+from sklearn.preprocessing import (LabelEncoder)
 from sklearn.utils import check_random_state
 
 from ._cate_estimator import (BaseCateEstimator, LinearCateEstimator,
                               TreatmentExpansionMixin)
 from .inference import BootstrapInference
-from .utilities import (_deprecate_positional, check_input_arrays,
-                        cross_product, filter_none_kwargs, one_hot_encoder, strata_from_discrete_arrays,
-                        inverse_onehot, jacify_featurizer, ndim, reshape, shape, transpose)
+from .utilities import (check_input_arrays,
+                        filter_none_kwargs, one_hot_encoder, strata_from_discrete_arrays,
+                        jacify_featurizer, reshape, shape)
 from .sklearn_extensions.model_selection import ModelSelector
 
 try:

econml/cate_interpreter/_interpreters.py

@@ -4,8 +4,7 @@
 import abc
 import numbers
 import numpy as np
-import sklearn
-from sklearn.tree import DecisionTreeRegressor, DecisionTreeClassifier
+from sklearn.tree import DecisionTreeRegressor
 from sklearn.utils import check_array
 from ..policy import PolicyTree
 from .._tree_exporter import (_SingleTreeExporterMixin,

econml/data/dynamic_panel_dgp.py

@@ -372,7 +372,7 @@ class SemiSynthetic:
 
     def create_instance(self):
         # get new covariance matrix
-        self.cov_new = joblib.load(os.path.join(dir, f"input_dynamicdgp/cov_new.jbl"))
+        self.cov_new = joblib.load(os.path.join(dir, "input_dynamicdgp/cov_new.jbl"))
 
         # get coefs
         self.index = ["proxy1", "proxy2", "proxy3", "proxy4",

econml/dml/_rlearner.py

@@ -27,13 +27,9 @@
 
 from abc import abstractmethod
 import numpy as np
-import copy
-from warnings import warn
 
 from ..sklearn_extensions.model_selection import ModelSelector
-from ..utilities import (shape, reshape, ndim, hstack, filter_none_kwargs, _deprecate_positional)
-from sklearn.linear_model import LinearRegression
-from sklearn.base import clone
+from ..utilities import (filter_none_kwargs)
 from .._ortho_learner import _OrthoLearner
 
 

econml/dml/causal_forest.py

@@ -4,20 +4,15 @@
 from warnings import warn
 
 import numpy as np
-from sklearn.linear_model import LogisticRegressionCV
-from sklearn.base import clone, BaseEstimator
-from sklearn.preprocessing import FunctionTransformer
-from sklearn.pipeline import Pipeline
+from sklearn.base import clone
 from sklearn.model_selection import train_test_split
 from itertools import product
 from .dml import _BaseDML
 from .dml import _make_first_stage_selector
-from ..sklearn_extensions.linear_model import WeightedLassoCVWrapper
-from ..sklearn_extensions.model_selection import WeightedStratifiedKFold
 from ..inference import NormalInferenceResults
 from ..inference._inference import Inference
-from ..utilities import (add_intercept, shape, check_inputs, check_input_arrays,
-                         _deprecate_positional, cross_product, Summary)
+from ..utilities import (shape, check_input_arrays,
+                         cross_product, Summary)
 from ..grf import CausalForest, MultiOutputGRF
 from .._cate_estimator import LinearCateEstimator
 from .._shap import _shap_explain_multitask_model_cate

econml/dml/dml.py

@@ -5,36 +5,25 @@
 
 import numpy as np
 from sklearn.base import TransformerMixin, clone
-from sklearn.exceptions import NotFittedError
-from sklearn.linear_model import (ElasticNetCV, LassoCV, LogisticRegressionCV)
-from sklearn.ensemble import RandomForestRegressor
-from sklearn.model_selection import KFold, StratifiedKFold, check_cv
+from sklearn.linear_model import (ElasticNetCV)
 from sklearn.pipeline import Pipeline
-from sklearn.preprocessing import (FunctionTransformer, LabelEncoder,
-                                   OneHotEncoder)
+from sklearn.preprocessing import (FunctionTransformer)
 from sklearn.utils import check_random_state
-import copy
 
 from .._ortho_learner import _OrthoLearner
 from ._rlearner import _RLearner
 from .._cate_estimator import (DebiasedLassoCateEstimatorMixin,
-                               ForestModelFinalCateEstimatorMixin,
                                LinearModelFinalCateEstimatorMixin,
                                StatsModelsCateEstimatorMixin,
                                LinearCateEstimator)
-from ..inference import StatsModelsInference, GenericSingleTreatmentModelFinalInference
+from ..inference import GenericSingleTreatmentModelFinalInference
 from ..sklearn_extensions.linear_model import (MultiOutputDebiasedLasso,
-                                               StatsModelsLinearRegression,
-                                               WeightedLassoCVWrapper)
-from ..sklearn_extensions.model_selection import WeightedStratifiedKFold
-from ..utilities import (_deprecate_positional, add_intercept,
+                                               StatsModelsLinearRegression)
+from ..utilities import (add_intercept,
                          broadcast_unit_treatments, check_high_dimensional,
-                         cross_product, deprecated,
-                         hstack, inverse_onehot, ndim, reshape,
-                         reshape_treatmentwise_effects, shape, transpose,
-                         get_feature_names_or_default, filter_none_kwargs)
+                         cross_product, hstack, inverse_onehot, reshape_treatmentwise_effects, shape, get_feature_names_or_default, filter_none_kwargs)
 from .._shap import _shap_explain_model_cate
-from ..sklearn_extensions.model_selection import get_selector, ModelSelector, SingleModelSelector
+from ..sklearn_extensions.model_selection import get_selector, SingleModelSelector
 
 
 def _combine(X, W, n_samples):

econml/dr/_drlearner.py

@@ -36,25 +36,20 @@
 """
 
 from warnings import warn
-from copy import deepcopy
 
 import numpy as np
 from sklearn.base import clone
-from sklearn.linear_model import (LassoCV, LinearRegression,
-                                  LogisticRegressionCV)
-from sklearn.ensemble import RandomForestRegressor
 
 
 from .._ortho_learner import _OrthoLearner
-from .._cate_estimator import (DebiasedLassoCateEstimatorDiscreteMixin, BaseCateEstimator,
-                               ForestModelFinalCateEstimatorDiscreteMixin,
+from .._cate_estimator import (DebiasedLassoCateEstimatorDiscreteMixin, ForestModelFinalCateEstimatorDiscreteMixin,
                                StatsModelsCateEstimatorDiscreteMixin, LinearCateEstimator)
 from ..inference import GenericModelFinalInferenceDiscrete
 from ..grf import RegressionForest
 from ..sklearn_extensions.linear_model import (
-    DebiasedLasso, StatsModelsLinearRegression, WeightedLassoCVWrapper)
+    DebiasedLasso, StatsModelsLinearRegression)
 from ..sklearn_extensions.model_selection import ModelSelector, SingleModelSelector, get_selector
-from ..utilities import (_deprecate_positional, check_high_dimensional,
+from ..utilities import (check_high_dimensional,
                          filter_none_kwargs, inverse_onehot, get_feature_names_or_default)
 from .._shap import _shap_explain_multitask_model_cate, _shap_explain_model_cate
 

econml/federated_learning.py

@@ -1,7 +1,6 @@
 # Copyright (c) PyWhy contributors. All rights reserved.
 # Licensed under the MIT License.
 
-import numpy as np
 from sklearn import clone
 
 from econml.utilities import check_input_arrays

econml/grf/_base_grf.py

@@ -10,19 +10,19 @@
 # All rights reserved.
 
 import numbers
-from warnings import catch_warnings, simplefilter, warn
+from warnings import warn
 from abc import ABCMeta, abstractmethod
 import numpy as np
 import threading
 from .._ensemble import (BaseEnsemble, _partition_estimators, _get_n_samples_subsample,
-                         _accumulate_prediction, _accumulate_prediction_var, _accumulate_prediction_and_var,
+                         _accumulate_prediction, _accumulate_prediction_and_var,
                          _accumulate_oob_preds)
-from ..utilities import check_inputs, cross_product
+from ..utilities import check_inputs
 from ..tree._tree import DTYPE, DOUBLE
 from ._base_grftree import GRFTree
 from joblib import Parallel, delayed
 from scipy.sparse import hstack as sparse_hstack
-from sklearn.utils import check_random_state, compute_sample_weight
+from sklearn.utils import check_random_state
 from sklearn.utils.validation import _check_sample_weight, check_is_fitted
 from sklearn.utils import check_X_y
 import scipy.stats

econml/grf/_base_grftree.py

@@ -12,11 +12,8 @@
 import numpy as np
 from ._criterion import LinearMomentGRFCriterionMSE, LinearMomentGRFCriterion
 from ..tree import BaseTree
-from sklearn.model_selection import train_test_split
-from sklearn.utils import check_array
 from sklearn.utils import check_random_state
 from sklearn.utils.validation import check_is_fitted
-import copy
 
 # =============================================================================
 # Types and constants

econml/grf/classes.py

@@ -2,7 +2,6 @@
 # Licensed under the MIT License.
 
 import numpy as np
-from warnings import warn
 from ..utilities import cross_product
 from ._base_grf import BaseGRF
 from ..utilities import check_inputs

econml/inference/_bootstrap.py

@@ -6,8 +6,6 @@
 from joblib import Parallel, delayed
 from sklearn.base import clone
 from scipy.stats import norm
-from collections import OrderedDict
-import pandas as pd
 
 
 class BootstrapEstimator:

econml/inference/_inference.py

@@ -9,14 +9,11 @@
 import pandas as pd
 import scipy
 from scipy.stats import norm
-from statsmodels.iolib.table import SimpleTable
 
 from ._bootstrap import BootstrapEstimator
-from ..sklearn_extensions.linear_model import StatsModelsLinearRegression
 from ..utilities import (Summary, _safe_norm_ppf, broadcast_unit_treatments,
                          cross_product, inverse_onehot, ndim,
-                         parse_final_model_params, jacify_featurizer,
-                         reshape_treatmentwise_effects, shape, filter_none_kwargs)
+                         parse_final_model_params, reshape_treatmentwise_effects, shape, filter_none_kwargs)
 
 """Options for performing inference in estimators."""
 

econml/iv/dml/_dml.py

@@ -15,20 +15,17 @@
 
 import numpy as np
 from sklearn.base import clone
-from sklearn.linear_model import LinearRegression, LogisticRegressionCV
 from sklearn.pipeline import Pipeline
 from sklearn.preprocessing import FunctionTransformer
 from itertools import product
 
 from ..._ortho_learner import _OrthoLearner
-from ..._cate_estimator import LinearModelFinalCateEstimatorMixin, StatsModelsCateEstimatorMixin, LinearCateEstimator
-from ...inference import StatsModelsInference, GenericSingleTreatmentModelFinalInference
-from ...sklearn_extensions.linear_model import StatsModels2SLS, StatsModelsLinearRegression, WeightedLassoCVWrapper
-from ...sklearn_extensions.model_selection import (ModelSelector, SingleModelSelector,
-                                                   WeightedStratifiedKFold, get_selector)
-from ...utilities import (_deprecate_positional, get_feature_names_or_default, filter_none_kwargs, add_intercept,
+from ..._cate_estimator import LinearModelFinalCateEstimatorMixin, LinearCateEstimator
+from ...sklearn_extensions.linear_model import StatsModels2SLS, StatsModelsLinearRegression
+from ...sklearn_extensions.model_selection import (ModelSelector, SingleModelSelector)
+from ...utilities import (get_feature_names_or_default, filter_none_kwargs, add_intercept,
                           cross_product, broadcast_unit_treatments, reshape_treatmentwise_effects, shape,
-                          parse_final_model_params, deprecated, Summary)
+                          parse_final_model_params, Summary)
 from ...dml.dml import _make_first_stage_selector, _FinalWrapper
 from ...dml._rlearner import _ModelFinal
 from ..._shap import _shap_explain_joint_linear_model_cate, _shap_explain_model_cate

econml/iv/dr/_dr.py

@@ -15,7 +15,6 @@
 
 import numpy as np
 from sklearn.base import clone
-from sklearn.linear_model import LinearRegression, LogisticRegressionCV
 from sklearn.pipeline import Pipeline
 from sklearn.preprocessing import FunctionTransformer
 from sklearn.dummy import DummyClassifier
@@ -25,13 +24,12 @@
 from ..._cate_estimator import (StatsModelsCateEstimatorMixin, DebiasedLassoCateEstimatorMixin,
                                 ForestModelFinalCateEstimatorMixin, GenericSingleTreatmentModelFinalInference,
                                 LinearCateEstimator)
-from ...inference import StatsModelsInference
-from ...sklearn_extensions.linear_model import StatsModelsLinearRegression, DebiasedLasso, WeightedLassoCVWrapper
-from ...sklearn_extensions.model_selection import ModelSelector, SingleModelSelector, WeightedStratifiedKFold
-from ...utilities import (_deprecate_positional, add_intercept, filter_none_kwargs,
+from ...sklearn_extensions.linear_model import StatsModelsLinearRegression, DebiasedLasso
+from ...sklearn_extensions.model_selection import ModelSelector, SingleModelSelector
+from ...utilities import (add_intercept, filter_none_kwargs,
                           inverse_onehot, get_feature_names_or_default, check_high_dimensional, check_input_arrays)
 from ...grf import RegressionForest
-from ...dml.dml import _make_first_stage_selector, _FinalWrapper
+from ...dml.dml import _make_first_stage_selector
 from ...iv.dml import NonParamDMLIV
 from ..._shap import _shap_explain_model_cate
 

econml/iv/nnet/_deepiv.py

@@ -5,7 +5,7 @@
 
 import numpy as np
 from ..._cate_estimator import BaseCateEstimator
-from ...utilities import check_input_arrays, _deprecate_positional, deprecated, MissingModule
+from ...utilities import check_input_arrays, MissingModule
 try:
     import keras
     from keras import backend as K

econml/iv/sieve/_tsls.py

@@ -4,12 +4,11 @@
 """Provides a non-parametric two-stage least squares instrumental variable estimator."""
 
 import numpy as np
-from copy import deepcopy
 from sklearn import clone
 from sklearn.linear_model import LinearRegression
 from ...utilities import (shape, transpose, reshape, cross_product, ndim, size,
-                          _deprecate_positional, check_input_arrays)
-from ..._cate_estimator import BaseCateEstimator, LinearCateEstimator
+                          check_input_arrays)
+from ..._cate_estimator import BaseCateEstimator
 from numpy.polynomial.hermite_e import hermeval
 from sklearn.base import TransformerMixin
 from sklearn.preprocessing import PolynomialFeatures

econml/metalearners/_metalearners.py

@@ -9,15 +9,12 @@
 """
 
 import numpy as np
-import warnings
 from .._cate_estimator import BaseCateEstimator, LinearCateEstimator, TreatmentExpansionMixin
 from sklearn import clone
 from sklearn.linear_model import LogisticRegression
 from sklearn.pipeline import Pipeline
-from sklearn.utils import check_array, check_X_y
-from sklearn.preprocessing import OneHotEncoder, FunctionTransformer
-from ..utilities import (check_inputs, check_models, broadcast_unit_treatments, reshape_treatmentwise_effects,
-                         one_hot_encoder, inverse_onehot, transpose, _deprecate_positional)
+from sklearn.utils import check_array
+from ..utilities import (check_inputs, check_models, broadcast_unit_treatments, one_hot_encoder, inverse_onehot, transpose)
 from .._shap import _shap_explain_model_cate
 
 

econml/orf/_ortho_forest.py

@@ -20,28 +20,24 @@
 For more details on these methods, see our paper [Oprescu2019]_.
 """
 
-import abc
-import inspect
 import numpy as np
 import warnings
 from joblib import Parallel, delayed
 from sklearn import clone
 from scipy.stats import norm
 from sklearn.exceptions import NotFittedError
-from sklearn.linear_model import LassoCV, Lasso, LinearRegression, LogisticRegression, \
-    LogisticRegressionCV, ElasticNet
+from sklearn.linear_model import LinearRegression, LogisticRegression, \
+    LogisticRegressionCV
 from sklearn.model_selection import KFold, StratifiedKFold
 from sklearn.pipeline import Pipeline
-from sklearn.preprocessing import OneHotEncoder, LabelEncoder, PolynomialFeatures, FunctionTransformer
 from sklearn.utils import check_random_state, check_array, column_or_1d
 from ..sklearn_extensions.linear_model import WeightedLassoCVWrapper
 from .._cate_estimator import BaseCateEstimator, LinearCateEstimator, TreatmentExpansionMixin
 from ._causal_tree import CausalTree
 from ..inference import NormalInferenceResults
 from ..inference._inference import Inference
-from ..utilities import (one_hot_encoder, reshape, reshape_Y_T, MAX_RAND_SEED, check_inputs, _deprecate_positional,
-                         cross_product, inverse_onehot, check_input_arrays, jacify_featurizer,
-                         _RegressionWrapper, deprecated, ndim)
+from ..utilities import (one_hot_encoder, reshape_Y_T, MAX_RAND_SEED, check_inputs, cross_product, check_input_arrays, jacify_featurizer,
+                         _RegressionWrapper, ndim)
 from sklearn.model_selection import check_cv
 # TODO: consider working around relying on sklearn implementation details
 from ..sklearn_extensions.model_selection import _cross_val_predict