Fix import order in Python files

examples/calibration/1-quickstart/plot_calibration_hypothesis_testing.py

@@ -30,11 +30,8 @@
 from sklearn.utils import check_random_state
 
 from mapie._typing import NDArray
-from mapie.metrics import (
-    cumulative_differences,
-    length_scale,
-    kolmogorov_smirnov_p_value
-)
+from mapie.metrics import (cumulative_differences, kolmogorov_smirnov_p_value,
+                           length_scale)
 
 ####################################################################
 # 1. Create 1-dimensional dataset and scores to test for calibration

examples/calibration/2-advanced-analysis/plot_asymptotic_convergence_of_p_values.py

@@ -40,11 +40,8 @@
 from sklearn.utils import check_random_state
 
 from mapie._typing import NDArray
-from mapie.metrics import (
-    kolmogorov_smirnov_p_value,
-    kuiper_p_value,
-    spiegelhalter_p_value
-)
+from mapie.metrics import (kolmogorov_smirnov_p_value, kuiper_p_value,
+                           spiegelhalter_p_value)
 
 ##############################################################################
 # First we need to generate scores that are perfecty calibrated. To do so,

examples/classification/4-tutorials/plot_main-tutorial-binary-classification.py

@@ -29,16 +29,15 @@
 
 import matplotlib.pyplot as plt
 import numpy as np
+from sklearn.calibration import CalibratedClassifierCV
 from sklearn.model_selection import train_test_split
 from sklearn.naive_bayes import GaussianNB
 
 from mapie._typing import NDArray
 from mapie.classification import MapieClassifier
-from sklearn.calibration import CalibratedClassifierCV
 from mapie.metrics import (classification_coverage_score,
                            classification_mean_width_score)
 
-
 ##############################################################################
 # 1. Conformal Prediction method using the softmax score of the true label
 # ------------------------------------------------------------------------

examples/regression/2-advanced-analysis/plot-coverage-width-based-criterion.py

@@ -19,6 +19,7 @@
 from sklearn.linear_model import LinearRegression, QuantileRegressor
 from sklearn.pipeline import Pipeline
 from sklearn.preprocessing import PolynomialFeatures
+
 from mapie.metrics import (coverage_width_based, regression_coverage_score,
                            regression_mean_width_score)
 from mapie.regression import MapieQuantileRegressor, MapieRegressor

examples/regression/2-advanced-analysis/plot_conditional_coverage.py

@@ -15,19 +15,17 @@
 import warnings
 from typing import Tuple, Union
 
-import pandas as pd
-import numpy as np
 import matplotlib.pyplot as plt
-
+import numpy as np
+import pandas as pd
 from lightgbm import LGBMRegressor
 
 from mapie._typing import NDArray
-from mapie.regression import MapieQuantileRegressor, MapieRegressor
 from mapie.conformity_scores import (GammaConformityScore,
                                      ResidualNormalisedScore)
-from mapie.metrics import (regression_coverage_score_v2,
-                           regression_ssc_score,
-                           hsic, regression_ssc)
+from mapie.metrics import (hsic, regression_coverage_score_v2, regression_ssc,
+                           regression_ssc_score)
+from mapie.regression import MapieQuantileRegressor, MapieRegressor
 from mapie.subsample import Subsample
 
 warnings.filterwarnings("ignore")

examples/regression/2-advanced-analysis/plot_timeseries_enbpi.py

@@ -25,7 +25,6 @@
 """
 
 import warnings
-
 from typing import cast
 
 import numpy as np
@@ -38,8 +37,8 @@
 from mapie._typing import NDArray
 from mapie.metrics import (regression_coverage_score,
                            regression_mean_width_score)
-from mapie.subsample import BlockBootstrap
 from mapie.regression import MapieTimeSeriesRegressor
+from mapie.subsample import BlockBootstrap
 
 warnings.simplefilter("ignore")
 

examples/regression/3-scientific-articles/plot_kim2020_simulations.py

@@ -30,10 +30,10 @@
 """
 from __future__ import annotations
 
+import ssl
 from io import BytesIO
 from typing import Any, Optional, Tuple
 from urllib.request import urlopen
-import ssl
 from zipfile import ZipFile
 
 import matplotlib.pyplot as plt

examples/regression/3-scientific-articles/plot_zaffran2022_comparison.py

@@ -31,24 +31,22 @@
 distribution shift.
 Advances in Neural Information Processing Systems, 34, 1660-1672.
 """
+import datetime
+import pickle
+import ssl
 import warnings
-
 from typing import Tuple
 from urllib.request import urlopen
-import ssl
-import pickle
 
-import datetime
 import numpy as np
 import pandas as pd
 from matplotlib import pylab as plt
-
 from sklearn.ensemble import RandomForestRegressor
 from sklearn.model_selection import PredefinedSplit
-from mapie.conformity_scores import AbsoluteConformityScore
-from mapie.time_series_regression import MapieTimeSeriesRegressor
 
 from mapie._typing import NDArray
+from mapie.conformity_scores import AbsoluteConformityScore
+from mapie.time_series_regression import MapieTimeSeriesRegressor
 
 warnings.simplefilter("ignore")
 

examples/regression/4-tutorials/plot_ResidualNormalisedScore_tutorial.py

@@ -9,15 +9,14 @@
 """
 import warnings
 
-import numpy as np
 import matplotlib.pyplot as plt
+import numpy as np
 from matplotlib.ticker import FormatStrFormatter
 from numpy.typing import ArrayLike
-
 from sklearn.datasets import fetch_california_housing
-from sklearn.model_selection import train_test_split
 from sklearn.ensemble import RandomForestRegressor
 from sklearn.linear_model import LinearRegression
+from sklearn.model_selection import train_test_split
 
 from mapie.conformity_scores import ResidualNormalisedScore
 from mapie.metrics import regression_coverage_score_v2, regression_ssc_score

examples/regression/4-tutorials/plot_ts-tutorial.py

@@ -69,11 +69,10 @@ class that block bootstraps the training set.
 from sklearn.ensemble import RandomForestRegressor
 from sklearn.model_selection import RandomizedSearchCV, TimeSeriesSplit
 
-from mapie.metrics import (regression_coverage_score,
-                           regression_mean_width_score,
-                           coverage_width_based)
-from mapie.subsample import BlockBootstrap
+from mapie.metrics import (coverage_width_based, regression_coverage_score,
+                           regression_mean_width_score)
 from mapie.regression import MapieTimeSeriesRegressor
+from mapie.subsample import BlockBootstrap
 
 warnings.simplefilter("ignore")
 

mapie/conformity_scores/conformity_scores.py

@@ -1,7 +1,7 @@
 from abc import ABCMeta, abstractmethod
+from typing import Tuple
 
 import numpy as np
-from typing import Tuple
 
 from mapie._compatibility import np_nanquantile
 from mapie._typing import ArrayLike, NDArray

mapie/conformity_scores/residual_conformity_scores.py

@@ -1,18 +1,16 @@
 import warnings
-from typing import Optional, Union, Tuple
+from typing import Optional, Tuple, Union
 
 import numpy as np
 from sklearn.base import RegressorMixin, clone
 from sklearn.linear_model import LinearRegression
 from sklearn.model_selection import train_test_split
 from sklearn.pipeline import Pipeline
-from sklearn.utils.validation import (check_is_fitted,
-                                      check_random_state,
+from sklearn.utils.validation import (check_is_fitted, check_random_state,
                                       indexable)
 
 from mapie._machine_precision import EPSILON
 from mapie._typing import ArrayLike, NDArray
-
 from mapie.conformity_scores import ConformityScore
 
 

mapie/control_risk/crc_rcps.py

@@ -1,7 +1,7 @@
-from numpy.typing import NDArray
 from typing import Optional, Tuple
 
 import numpy as np
+from numpy.typing import NDArray
 
 
 def get_r_hat_plus(

mapie/control_risk/ltt.py

@@ -1,10 +1,10 @@
 import warnings
-
-from typing import Tuple, List, Optional, Any
-from mapie._typing import NDArray, ArrayLike
+from typing import Any, List, Optional, Tuple
 
 import numpy as np
 
+from mapie._typing import ArrayLike, NDArray
+
 from .p_values import compute_hoeffdding_bentkus_p_value
 
 

mapie/control_risk/p_values.py

@@ -1,11 +1,11 @@
 from typing import Union, cast
 
-from mapie._typing import NDArray
-from mapie.utils import check_alpha
-
 import numpy as np
 from scipy.stats import binom
 
+from mapie._typing import NDArray
+from mapie.utils import check_alpha
+
 
 def compute_hoeffdding_bentkus_p_value(
     r_hat: NDArray,

mapie/control_risk/risks.py

@@ -1,10 +1,10 @@
 from typing import cast
 
-from mapie._typing import NDArray
-
 import numpy as np
 from sklearn.utils.validation import column_or_1d
 
+from mapie._typing import NDArray
+
 
 def compute_risk_recall(
     lambdas: NDArray,

mapie/estimator/estimator.py

@@ -7,13 +7,13 @@
 from sklearn.base import RegressorMixin, clone
 from sklearn.model_selection import BaseCrossValidator
 from sklearn.utils import _safe_indexing
-from sklearn.utils.validation import (_num_samples, check_is_fitted)
+from sklearn.utils.validation import _num_samples, check_is_fitted
 
 from mapie._typing import ArrayLike, NDArray
 from mapie.aggregation_functions import aggregate_all, phi2D
+from mapie.estimator.interface import EnsembleEstimator
 from mapie.utils import (check_nan_in_aposteriori_prediction, check_no_agg_cv,
                          fit_estimator)
-from mapie.estimator.interface import EnsembleEstimator
 
 
 class EnsembleRegressor(EnsembleEstimator):

mapie/estimator/interface.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
-from abc import ABCMeta, abstractmethod
 
+from abc import ABCMeta, abstractmethod
 from typing import Optional, Tuple, Union
 
 from sklearn.base import RegressorMixin

mapie/metrics.py

@@ -1,24 +1,18 @@
-from typing import Optional, cast, Union, Tuple
+from typing import Optional, Tuple, Union, cast
 
-import scipy
 import numpy as np
+import scipy
 from sklearn.utils import check_random_state
 from sklearn.utils.validation import check_array, column_or_1d
 
+from ._machine_precision import EPSILON
 from ._typing import ArrayLike, NDArray
-from .utils import (calc_bins, check_alpha,
+from .utils import (calc_bins, check_alpha, check_array_inf, check_array_nan,
                     check_array_shape_classification,
-                    check_array_shape_regression,
-                    check_array_inf,
-                    check_array_nan,
-                    check_arrays_length,
-                    check_binary_zero_one,
-                    check_lower_upper_bounds,
-                    check_nb_intervals_sizes,
-                    check_nb_sets_sizes,
-                    check_number_bins,
-                    check_split_strategy)
-from ._machine_precision import EPSILON
+                    check_array_shape_regression, check_arrays_length,
+                    check_binary_zero_one, check_lower_upper_bounds,
+                    check_nb_intervals_sizes, check_nb_sets_sizes,
+                    check_number_bins, check_split_strategy)
 
 
 def regression_coverage_score(

mapie/multi_label_classification.py

@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 import warnings
+from itertools import chain
 from typing import Iterable, Optional, Sequence, Tuple, Union, cast
 
 import numpy as np
@@ -14,14 +15,10 @@
                                       indexable)
 
 from ._typing import ArrayLike, NDArray
-from .utils import check_alpha, check_n_jobs, check_verbose
-
-from itertools import chain
-
+from .control_risk.crc_rcps import find_lambda_star, get_r_hat_plus
 from .control_risk.ltt import find_lambda_control_star, ltt_procedure
 from .control_risk.risks import compute_risk_precision, compute_risk_recall
-from .control_risk.crc_rcps import get_r_hat_plus
-from .control_risk.crc_rcps import find_lambda_star
+from .utils import check_alpha, check_n_jobs, check_verbose
 
 
 class MapieMultiLabelClassifier(BaseEstimator, ClassifierMixin):

mapie/regression/__init__.py

@@ -1,5 +1,5 @@
-from .regression import MapieRegressor
 from .quantile_regression import MapieQuantileRegressor
+from .regression import MapieRegressor
 from .time_series_regression import MapieTimeSeriesRegressor
 
 __all__ = [

mapie/regression/regression.py

@@ -1,21 +1,18 @@
 from __future__ import annotations
 
-from typing import Iterable, Optional, Tuple, Union, cast
-
 import warnings
+from typing import Iterable, Optional, Tuple, Union, cast
 
 import numpy as np
 from sklearn.base import BaseEstimator, RegressorMixin
 from sklearn.linear_model import LinearRegression
 from sklearn.model_selection import BaseCrossValidator
 from sklearn.pipeline import Pipeline
 from sklearn.utils import check_random_state
-from sklearn.utils.validation import (_check_y, check_is_fitted,
-                                      indexable)
+from sklearn.utils.validation import _check_y, check_is_fitted, indexable
 
 from mapie._typing import ArrayLike, NDArray
-from mapie.conformity_scores import (ConformityScore,
-                                     ResidualNormalisedScore)
+from mapie.conformity_scores import ConformityScore, ResidualNormalisedScore
 from mapie.estimator.estimator import EnsembleRegressor
 from mapie.utils import (check_alpha, check_alpha_and_n_samples,
                          check_conformity_score, check_cv,

mapie/regression/time_series_regression.py

@@ -1,8 +1,7 @@
 from __future__ import annotations
 
-from typing import Iterable, Optional, Tuple, Union, cast
-
 import warnings
+from typing import Iterable, Optional, Tuple, Union, cast
 
 import numpy as np
 from sklearn.base import RegressorMixin
@@ -12,7 +11,7 @@
 from mapie._typing import ArrayLike, NDArray
 from mapie.conformity_scores import ConformityScore
 from mapie.regression import MapieRegressor
-from mapie.utils import (check_alpha, check_gamma)
+from mapie.utils import check_alpha, check_gamma
 
 
 class MapieTimeSeriesRegressor(MapieRegressor):