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MAINT Make files private for sklearn.metrics and sklearn.metrics.cluster (#15306)
1 parent f86e852 commit e947689

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lines changed

‎.gitignore

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@@ -130,6 +130,18 @@ sklearn/svm/libsvm.py
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sklearn/svm/libsvm_sparse.py
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sklearn/svm/liblinear.py
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sklearn/metrics/cluster/bicluster.py
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sklearn/metrics/cluster/supervised.py
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sklearn/metrics/cluster/unsupervised.py
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sklearn/metrics/cluster/expected_mutual_info_fast.py
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sklearn/metrics/base.py
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sklearn/metrics/classification.py
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sklearn/metrics/regression.py
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sklearn/metrics/ranking.py
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sklearn/metrics/pairwise_fast.py
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sklearn/metrics/scorer.py
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sklearn/inspection/partial_dependence.py
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sklearn/inspection/permutation_importance.py
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‎sklearn/_build_utils/deprecated_modules.py

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'set_verbosity_wrap'),
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('_liblinear', 'sklearn.svm.liblinear', 'sklearn.svm', 'train_wrap'),
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('_bicluster', 'sklearn.metrics.cluster.bicluster',
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'sklearn.metrics.cluster', 'consensus_score'),
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('_supervised', 'sklearn.metrics.cluster.supervised',
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'sklearn.metrics.cluster', 'entropy'),
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('_unsupervised', 'sklearn.metrics.cluster.unsupervised',
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'sklearn.metrics.cluster', 'silhouette_score'),
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('_expected_mutual_info_fast',
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'sklearn.metrics.cluster.expected_mutual_info_fast',
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'sklearn.metrics.cluster', 'expected_mutual_information'),
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('_base', 'sklearn.metrics.base', 'sklearn.metrics', 'combinations'),
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('_classification', 'sklearn.metrics.classification', 'sklearn.metrics',
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'accuracy_score'),
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('_regression', 'sklearn.metrics.regression', 'sklearn.metrics',
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'max_error'),
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('_ranking', 'sklearn.metrics.ranking', 'sklearn.metrics', 'roc_curve'),
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('_pairwise_fast', 'sklearn.metrics.pairwise_fast', 'sklearn.metrics',
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'np'),
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('_scorer', 'sklearn.metrics.scorer', 'sklearn.metrics', 'get_scorer'),
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('_partial_dependence', 'sklearn.inspection.partial_dependence',
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'sklearn.inspection', 'partial_dependence'),
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('_permutation_importance', 'sklearn.inspection.permutation_importance',

‎sklearn/base.py

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@@ -404,7 +404,7 @@ def score(self, X, y, sample_weight=None):
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"""
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from .metrics import r2_score
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from .metrics.regression import _check_reg_targets
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from .metrics._regression import _check_reg_targets
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y_pred = self.predict(X)
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# XXX: Remove the check in 0.23
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y_type, _, _, _ = _check_reg_targets(y, y_pred, None)

‎sklearn/linear_model/ridge.py

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from ..utils.validation import _check_sample_weight
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from ..preprocessing import LabelBinarizer
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from ..model_selection import GridSearchCV
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from ..metrics.scorer import check_scoring
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from ..metrics import check_scoring
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from ..exceptions import ConvergenceWarning
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from ..utils.sparsefuncs import mean_variance_axis
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‎sklearn/metrics/__init__.py

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"""
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from .ranking import auc
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from .ranking import average_precision_score
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from .ranking import coverage_error
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from .ranking import dcg_score
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from .ranking import label_ranking_average_precision_score
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from .ranking import label_ranking_loss
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from .ranking import ndcg_score
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from .ranking import precision_recall_curve
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from .ranking import roc_auc_score
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from .ranking import roc_curve
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from ._ranking import auc
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from ._ranking import average_precision_score
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from ._ranking import coverage_error
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from ._ranking import dcg_score
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from ._ranking import label_ranking_average_precision_score
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from ._ranking import label_ranking_loss
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from ._ranking import ndcg_score
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from ._ranking import precision_recall_curve
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from ._ranking import roc_auc_score
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from ._ranking import roc_curve
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from .classification import accuracy_score
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from .classification import balanced_accuracy_score
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from .classification import classification_report
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from .classification import cohen_kappa_score
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from .classification import confusion_matrix
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from .classification import f1_score
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from .classification import fbeta_score
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from .classification import hamming_loss
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from .classification import hinge_loss
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from .classification import jaccard_similarity_score
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from .classification import jaccard_score
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from .classification import log_loss
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from .classification import matthews_corrcoef
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from .classification import precision_recall_fscore_support
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from .classification import precision_score
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from .classification import recall_score
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from .classification import zero_one_loss
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from .classification import brier_score_loss
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from .classification import multilabel_confusion_matrix
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from ._classification import accuracy_score
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from ._classification import balanced_accuracy_score
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from ._classification import classification_report
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from ._classification import cohen_kappa_score
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from ._classification import confusion_matrix
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from ._classification import f1_score
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from ._classification import fbeta_score
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from ._classification import hamming_loss
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from ._classification import hinge_loss
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from ._classification import jaccard_similarity_score
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from ._classification import jaccard_score
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from ._classification import log_loss
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from ._classification import matthews_corrcoef
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from ._classification import precision_recall_fscore_support
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from ._classification import precision_score
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from ._classification import recall_score
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from ._classification import zero_one_loss
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from ._classification import brier_score_loss
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from ._classification import multilabel_confusion_matrix
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from . import cluster
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from .cluster import adjusted_mutual_info_score
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from .pairwise import pairwise_kernels
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from .pairwise import pairwise_distances_chunked
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from .regression import explained_variance_score
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from .regression import max_error
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from .regression import mean_absolute_error
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from .regression import mean_squared_error
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from .regression import mean_squared_log_error
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from .regression import median_absolute_error
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from .regression import r2_score
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from .regression import mean_tweedie_deviance
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from .regression import mean_poisson_deviance
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from .regression import mean_gamma_deviance
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from ._regression import explained_variance_score
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from ._regression import max_error
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from ._regression import mean_absolute_error
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from ._regression import mean_squared_error
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from ._regression import mean_squared_log_error
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from ._regression import median_absolute_error
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from ._regression import r2_score
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from ._regression import mean_tweedie_deviance
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from ._regression import mean_poisson_deviance
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from ._regression import mean_gamma_deviance
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from .scorer import check_scoring
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from .scorer import make_scorer
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from .scorer import SCORERS
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from .scorer import get_scorer
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from ._scorer import check_scoring
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from ._scorer import make_scorer
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from ._scorer import SCORERS
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from ._scorer import get_scorer
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from ._plot.roc_curve import plot_roc_curve
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from ._plot.roc_curve import RocCurveDisplay

‎sklearn/metrics/base.py renamed to ‎sklearn/metrics/_base.py

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‎sklearn/metrics/classification.py renamed to ‎sklearn/metrics/_classification.py

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‎sklearn/metrics/pairwise_fast.pyx renamed to ‎sklearn/metrics/_pairwise_fast.pyx

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‎sklearn/metrics/ranking.py renamed to ‎sklearn/metrics/_ranking.py

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from ..preprocessing import label_binarize
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from ..preprocessing._label import _encode
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from .base import _average_binary_score, _average_multiclass_ovo_score
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from ._base import _average_binary_score, _average_multiclass_ovo_score
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def auc(x, y):

‎sklearn/metrics/regression.py renamed to ‎sklearn/metrics/_regression.py

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‎sklearn/metrics/scorer.py renamed to ‎sklearn/metrics/_scorer.py

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@@ -406,7 +406,7 @@ def check_scoring(estimator, scoring=None, allow_none=False):
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module = getattr(scoring, '__module__', None)
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if hasattr(module, 'startswith') and \
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module.startswith('sklearn.metrics.') and \
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not module.startswith('sklearn.metrics.scorer') and \
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not module.startswith('sklearn.metrics._scorer') and \
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not module.startswith('sklearn.metrics.tests.'):
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raise ValueError('scoring value %r looks like it is a metric '
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'function rather than a scorer. A scorer should '

‎sklearn/metrics/cluster/__init__.py

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- supervised, which uses a ground truth class values for each sample.
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- unsupervised, which does not and measures the 'quality' of the model itself.
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"""
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from .supervised import adjusted_mutual_info_score
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from .supervised import normalized_mutual_info_score
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from .supervised import adjusted_rand_score
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from .supervised import completeness_score
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from .supervised import contingency_matrix
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from .supervised import expected_mutual_information
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from .supervised import homogeneity_completeness_v_measure
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from .supervised import homogeneity_score
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from .supervised import mutual_info_score
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from .supervised import v_measure_score
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from .supervised import fowlkes_mallows_score
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from .supervised import entropy
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from .unsupervised import silhouette_samples
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from .unsupervised import silhouette_score
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from .unsupervised import calinski_harabasz_score
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from .unsupervised import calinski_harabaz_score
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from .unsupervised import davies_bouldin_score
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from .bicluster import consensus_score
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from ._supervised import adjusted_mutual_info_score
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from ._supervised import normalized_mutual_info_score
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from ._supervised import adjusted_rand_score
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from ._supervised import completeness_score
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from ._supervised import contingency_matrix
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from ._supervised import expected_mutual_information
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from ._supervised import homogeneity_completeness_v_measure
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from ._supervised import homogeneity_score
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from ._supervised import mutual_info_score
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from ._supervised import v_measure_score
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from ._supervised import fowlkes_mallows_score
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from ._supervised import entropy
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from ._unsupervised import silhouette_samples
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from ._unsupervised import silhouette_score
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from ._unsupervised import calinski_harabasz_score
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from ._unsupervised import calinski_harabaz_score
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from ._unsupervised import davies_bouldin_score
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from ._bicluster import consensus_score
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__all__ = ["adjusted_mutual_info_score", "normalized_mutual_info_score",
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"adjusted_rand_score", "completeness_score", "contingency_matrix",

‎sklearn/metrics/cluster/bicluster.py renamed to ‎sklearn/metrics/cluster/_bicluster.py

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‎sklearn/metrics/cluster/expected_mutual_info_fast.pyx renamed to ‎sklearn/metrics/cluster/_expected_mutual_info_fast.pyx

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‎sklearn/metrics/cluster/supervised.py renamed to ‎sklearn/metrics/cluster/_supervised.py

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import numpy as np
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from scipy import sparse as sp
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from .expected_mutual_info_fast import expected_mutual_information
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from ._expected_mutual_info_fast import expected_mutual_information
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from ...utils.validation import check_array, check_consistent_length
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from ...utils.fixes import comb, _astype_copy_false
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‎sklearn/metrics/cluster/unsupervised.py renamed to ‎sklearn/metrics/cluster/_unsupervised.py

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‎sklearn/metrics/cluster/setup.py

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libraries = []
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if os.name == 'posix':
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libraries.append('m')
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config.add_extension("expected_mutual_info_fast",
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sources=["expected_mutual_info_fast.pyx"],
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config.add_extension("_expected_mutual_info_fast",
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sources=["_expected_mutual_info_fast.pyx"],
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include_dirs=[numpy.get_include()],
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libraries=libraries)
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‎sklearn/metrics/cluster/tests/test_bicluster.py

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from sklearn.utils.testing import assert_almost_equal
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from sklearn.metrics.cluster.bicluster import _jaccard
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from sklearn.metrics.cluster._bicluster import _jaccard
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from sklearn.metrics import consensus_score
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‎sklearn/metrics/cluster/tests/test_supervised.py

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from sklearn.metrics.cluster import mutual_info_score
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from sklearn.metrics.cluster import normalized_mutual_info_score
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from sklearn.metrics.cluster import v_measure_score
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from sklearn.metrics.cluster.supervised import _generalized_average
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from sklearn.metrics.cluster._supervised import _generalized_average
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from sklearn.utils import assert_all_finite
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from sklearn.utils.testing import (

‎sklearn/metrics/pairwise.py

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from ..preprocessing import normalize
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from ..utils._mask import _get_mask
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from .pairwise_fast import _chi2_kernel_fast, _sparse_manhattan
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from ._pairwise_fast import _chi2_kernel_fast, _sparse_manhattan
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from ..exceptions import DataConversionWarning
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‎sklearn/metrics/setup.py

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config.add_subpackage('_plot.tests')
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config.add_subpackage('cluster')
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config.add_extension("pairwise_fast",
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sources=["pairwise_fast.pyx"],
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config.add_extension("_pairwise_fast",
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sources=["_pairwise_fast.pyx"],
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libraries=libraries)
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config.add_subpackage('tests')

‎sklearn/metrics/tests/test_classification.py

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from sklearn.metrics import brier_score_loss
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from sklearn.metrics import multilabel_confusion_matrix
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from sklearn.metrics.classification import _check_targets
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from sklearn.metrics._classification import _check_targets
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from sklearn.exceptions import UndefinedMetricWarning
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from scipy.spatial.distance import hamming as sp_hamming

‎sklearn/metrics/tests/test_common.py

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from sklearn.metrics import ndcg_score
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from sklearn.metrics import dcg_score
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from sklearn.metrics.base i F438 mport _average_binary_score
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from sklearn.metrics._base import _average_binary_score
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# Note toward developers about metric testing

‎sklearn/metrics/tests/test_ranking.py

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from sklearn.metrics import label_ranking_loss
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from sklearn.metrics import roc_auc_score
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from sklearn.metrics import roc_curve
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from sklearn.metrics.ranking import _ndcg_sample_scores, _dcg_sample_scores
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from sklearn.metrics.ranking import ndcg_score, dcg_score
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from sklearn.metrics._ranking import _ndcg_sample_scores, _dcg_sample_scores
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from sklearn.metrics import ndcg_score, dcg_score
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from sklearn.exceptions import UndefinedMetricWarning
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‎sklearn/metrics/tests/test_regression.py

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from sklearn.metrics import r2_score
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from sklearn.metrics import mean_tweedie_deviance
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from sklearn.metrics.regression import _check_reg_targets
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from sklearn.metrics._regression import _check_reg_targets
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from ...exceptions import UndefinedMetricWarning
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‎sklearn/metrics/tests/test_score_objects.py

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log_loss, precision_score, recall_score,
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jaccard_score)
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from sklearn.metrics import cluster as cluster_module
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from sklearn.metrics.scorer import (check_scoring, _PredictScorer,
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_passthrough_scorer, _MultimetricScorer)
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from sklearn.metrics import check_scoring
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from sklearn.metrics._scorer import (_PredictScorer, _passthrough_scorer,
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_MultimetricScorer,
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_check_multimetric_scoring)
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from sklearn.metrics import accuracy_score
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from sklearn.metrics.scorer import _check_multimetric_scoring
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from sklearn.metrics import make_scorer, get_scorer, SCORERS
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from sklearn.neighbors import KNeighborsClassifier
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from sklearn.svm import LinearSVC

‎sklearn/model_selection/_search.py

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from ..utils.random import sample_without_replacement
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from ..utils.validation import indexable, check_is_fitted
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from ..utils.metaestimators import if_delegate_has_method
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from ..metrics.scorer import _check_multimetric_scoring
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from ..metrics.scorer import check_scoring
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from ..metrics._scorer import _check_multimetric_scoring
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from ..metrics import check_scoring
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__all__ = ['GridSearchCV', 'ParameterGrid', 'fit_grid_point',

‎sklearn/model_selection/_validation.py

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_message_with_time)
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from ..utils.validation import _is_arraylike, _num_samples
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from ..utils.metaestimators import _safe_split
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from ..metrics.scorer import (check_scoring, _check_multimetric_scoring,
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_MultimetricScorer)
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from ..metrics import check_scoring
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from ..metrics._scorer import _check_multimetric_scoring, _MultimetricScorer
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from ..exceptions import FitFailedWarning
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from ._split import check_cv
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from ..preprocessing import LabelEncoder

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