MAINT: create private modules when they should (#452)

closes #422

Author: glemaitre

imblearn/combine/__init__.py

@@ -2,7 +2,7 @@
 over-sampling and under-sampling.
 """
 
-from .smote_enn import SMOTEENN
-from .smote_tomek import SMOTETomek
+from ._smote_enn import SMOTEENN
+from ._smote_tomek import SMOTETomek
 
 __all__ = ['SMOTEENN', 'SMOTETomek']

imblearn/combine/smote_enn.py renamed to imblearn/combine/_smote_enn.py

@@ -7,7 +7,6 @@
 from __future__ import division
 
 import logging
-import warnings
 
 from sklearn.base import clone
 from sklearn.utils import check_X_y

imblearn/combine/smote_tomek.py renamed to imblearn/combine/_smote_tomek.py

@@ -8,7 +8,6 @@
 from __future__ import division
 
 import logging
-import warnings
 
 from sklearn.base import clone
 from sklearn.utils import check_X_y

imblearn/datasets/__init__.py

@@ -3,8 +3,8 @@
 imbalanced data.
 """
 
-from .imbalance import make_imbalance
+from ._imbalance import make_imbalance
 
-from .zenodo import fetch_datasets
+from ._zenodo import fetch_datasets
 
 __all__ = ['make_imbalance', 'fetch_datasets']

imblearn/datasets/imbalance.py renamed to imblearn/datasets/_imbalance.py

@@ -11,7 +11,7 @@
 
 from sklearn.utils import check_X_y
 
-from ..under_sampling.prototype_selection import RandomUnderSampler
+from ..under_sampling import RandomUnderSampler
 from ..utils import check_sampling_strategy
 
 LOGGER = logging.getLogger(__name__)

imblearn/datasets/zenodo.py renamed to imblearn/datasets/_zenodo.py

@@ -3,9 +3,9 @@
 under-sampled subsets combined inside an ensemble.
 """
 
-from .easy_ensemble import EasyEnsemble
-from .balance_cascade import BalanceCascade
+from ._easy_ensemble import EasyEnsemble
+from ._balance_cascade import BalanceCascade
 
-from .classifier import BalancedBaggingClassifier
+from ._classifier import BalancedBaggingClassifier
 
 __all__ = ['EasyEnsemble', 'BalancedBaggingClassifier', 'BalanceCascade']

imblearn/ensemble/__init__.py

@@ -3,12 +3,12 @@
 metrics and pairwise metrics and distance computations.
 """
 
-from .classification import sensitivity_specificity_support
-from .classification import sensitivity_score
-from .classification import specificity_score
-from .classification import geometric_mean_score
-from .classification import make_index_balanced_accuracy
-from .classification import classification_report_imbalanced
+from ._classification import sensitivity_specificity_support
+from ._classification import sensitivity_score
+from ._classification import specificity_score
+from ._classification import geometric_mean_score
+from ._classification import make_index_balanced_accuracy
+from ._classification import classification_report_imbalanced
 
 __all__ = [
     'sensitivity_specificity_support', 'sensitivity_score',

imblearn/ensemble/balance_cascade.py renamed to imblearn/ensemble/_balance_cascade.py

@@ -3,11 +3,11 @@
 perform over-sampling.
 """
 
-from .adasyn import ADASYN
-from .random_over_sampler import RandomOverSampler
-from .smote import SMOTE
-from .smote import BorderlineSMOTE
-from .smote import SVMSMOTE
+from ._adasyn import ADASYN
+from ._random_over_sampler import RandomOverSampler
+from ._smote import SMOTE
+from ._smote import BorderlineSMOTE
+from ._smote import SVMSMOTE
 
 __all__ = ['ADASYN', 'RandomOverSampler',
            'SMOTE', 'BorderlineSMOTE', 'SVMSMOTE']

imblearn/ensemble/classifier.py renamed to imblearn/ensemble/_classifier.py

@@ -3,18 +3,18 @@
 a dataset.
 """
 
-from .prototype_generation import ClusterCentroids
+from ._prototype_generation import ClusterCentroids
 
-from .prototype_selection import RandomUnderSampler
-from .prototype_selection import TomekLinks
-from .prototype_selection import NearMiss
-from .prototype_selection import CondensedNearestNeighbour
-from .prototype_selection import OneSidedSelection
-from .prototype_selection import NeighbourhoodCleaningRule
-from .prototype_selection import EditedNearestNeighbours
-from .prototype_selection import RepeatedEditedNearestNeighbours
-from .prototype_selection import AllKNN
-from .prototype_selection import InstanceHardnessThreshold
+from ._prototype_selection import RandomUnderSampler
+from ._prototype_selection import TomekLinks
+from ._prototype_selection import NearMiss
+from ._prototype_selection import CondensedNearestNeighbour
+from ._prototype_selection import OneSidedSelection
+from ._prototype_selection import NeighbourhoodCleaningRule
+from ._prototype_selection import EditedNearestNeighbours
+from ._prototype_selection import RepeatedEditedNearestNeighbours
+from ._prototype_selection import AllKNN
+from ._prototype_selection import InstanceHardnessThreshold
 
 __all__ = [
     'ClusterCentroids', 'RandomUnderSampler', 'InstanceHardnessThreshold',

imblearn/ensemble/easy_ensemble.py renamed to imblearn/ensemble/_easy_ensemble.py

@@ -3,6 +3,6 @@
 methods that generate new samples in order to balance the dataset.
 """
 
-from .cluster_centroids import ClusterCentroids
+from ._cluster_centroids import ClusterCentroids
 
 __all__ = ['ClusterCentroids']

imblearn/metrics/__init__.py

@@ -0,0 +1,22 @@
+"""
+The :mod:`imblearn.under_sampling.prototype_selection` submodule contains
+methods that select samples in order to balance the dataset.
+"""
+
+from ._random_under_sampler import RandomUnderSampler
+from ._tomek_links import TomekLinks
+from ._nearmiss import NearMiss
+from ._condensed_nearest_neighbour import CondensedNearestNeighbour
+from ._one_sided_selection import OneSidedSelection
+from ._neighbourhood_cleaning_rule import NeighbourhoodCleaningRule
+from ._edited_nearest_neighbours import EditedNearestNeighbours
+from ._edited_nearest_neighbours import RepeatedEditedNearestNeighbours
+from ._edited_nearest_neighbours import AllKNN
+from ._instance_hardness_threshold import InstanceHardnessThreshold
+
+__all__ = [
+    'RandomUnderSampler', 'InstanceHardnessThreshold', 'NearMiss',
+    'TomekLinks', 'EditedNearestNeighbours', 'RepeatedEditedNearestNeighbours',
+    'AllKNN', 'OneSidedSelection', 'CondensedNearestNeighbour',
+    'NeighbourhoodCleaningRule'
+]

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

@@ -14,7 +14,7 @@
 from sklearn.utils import safe_indexing
 
 from ..base import BaseCleaningSampler
-from .edited_nearest_neighbours import EditedNearestNeighbours
+from ._edited_nearest_neighbours import EditedNearestNeighbours
 from ...utils import check_neighbors_object
 from ...utils import Substitution
 from ...utils.deprecation import deprecate_parameter

imblearn/over_sampling/__init__.py

@@ -15,7 +15,7 @@
 from sklearn.utils import check_random_state, safe_indexing
 
 from ..base import BaseCleaningSampler
-from .tomek_links import TomekLinks
+from ._tomek_links import TomekLinks
 from ...utils import Substitution
 from ...utils._docstring import _random_state_docstring
 

imblearn/over_sampling/adasyn.py renamed to imblearn/over_sampling/_adasyn.py

@@ -4,11 +4,11 @@
 
 from ._docstring import Substitution
 
-from .validation import check_neighbors_object
-from .validation import check_target_type
-from .validation import hash_X_y
-from .validation import check_ratio
-from .validation import check_sampling_strategy
+from ._validation import check_neighbors_object
+from ._validation import check_target_type
+from ._validation import hash_X_y
+from ._validation import check_ratio
+from ._validation import check_sampling_strategy
 
 __all__ = [
     'Substitution', 'check_neighbors_object', 'check_target_type', 'hash_X_y',