scikit-learn · adrinjalali · Oct 23, 2019 · Oct 21, 2019 · Oct 21, 2019 · Oct 21, 2019
diff --git a/.gitignore b/.gitignore
@@ -130,6 +130,21 @@ sklearn/svm/libsvm.py
 sklearn/svm/libsvm_sparse.py
 sklearn/svm/liblinear.py
 
+sklearn/neighbors/ball_tree.py
+sklearn/neighbors/base.py
+sklearn/neighbors/classification.py
+sklearn/neighbors/dist_metrics.py
+sklearn/neighbors/graph.py
+sklearn/neighbors/kd_tree.py
+sklearn/neighbors/kde.py
+sklearn/neighbors/lof.py
+sklearn/neighbors/nca.py
+sklearn/neighbors/nearest_centroid.py
+sklearn/neighbors/quad_tree.py
+sklearn/neighbors/regression.py
+sklearn/neighbors/typedefs.py
+sklearn/neighbors/unsupervised.py
+
 sklearn/manifold/isomap.py
 sklearn/manifold/locally_linear.py
 sklearn/manifold/mds.py

diff --git a/doc/modules/density.rst b/doc/modules/density.rst
@@ -78,7 +78,7 @@ It's clear how the kernel shape affects the smoothness of the resulting
 distribution.  The scikit-learn kernel density estimator can be used as
 follows:
 
-   >>> from sklearn.neighbors.kde import KernelDensity
+   >>> from sklearn.neighbors import KernelDensity
    >>> import numpy as np
    >>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
    >>> kde = KernelDensity(kernel='gaussian', bandwidth=0.2).fit(X)

diff --git a/doc/modules/neighbors.rst b/doc/modules/neighbors.rst
@@ -469,7 +469,7 @@ and Quadratic Discriminant Analysis (:class:`sklearn.discriminant_analysis.Quadr
 for more complex methods that do not make this assumption. Usage of the default
 :class:`NearestCentroid` is simple:
 
-    >>> from sklearn.neighbors.nearest_centroid import NearestCentroid
+    >>> from sklearn.neighbors import NearestCentroid
     >>> import numpy as np
     >>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
     >>> y = np.array([1, 1, 1, 2, 2, 2])

diff --git a/sklearn/_build_utils/deprecated_modules.py b/sklearn/_build_utils/deprecated_modules.py
@@ -86,6 +86,35 @@
      'set_verbosity_wrap'),
     ('_liblinear', 'sklearn.svm.liblinear', 'sklearn.svm', 'train_wrap'),
 
+    ('_ball_tree', 'sklearn.neighbors.ball_tree', 'sklearn.neighbors',
+     'BallTree'),
+    ('_base', 'sklearn.neighbors.base', 'sklearn.neighbors',
+     'VALID_METRICS'),
+    ('_classification', 'sklearn.neighbors.classification',
+     'sklearn.neighbors', 'KNeighborsClassifier'),
+    ('_dist_metrics', 'sklearn.neighbors.dist_metrics', 'sklearn.neighbors',
+     'DistanceMetric'),
+    ('_graph', 'sklearn.neighbors.graph', 'sklearn.neighbors',
+     'KNeighborsTransformer'),
+    ('_kd_tree', 'sklearn.neighbors.kd_tree', 'sklearn.neighbors',
+     'KDTree'),
+    ('_kde', 'sklearn.neighbors.kde', 'sklearn.neighbors',
+     'KernelDensity'),
+    ('_lof', 'sklearn.neighbors.lof', 'sklearn.neighbors',
+     'LocalOutlierFactor'),
+    ('_nca', 'sklearn.neighbors.nca', 'sklearn.neighbors',
+     'NeighborhoodComponentsAnalysis'),
+    ('_nearest_centroid', 'sklearn.neighbors.nearest_centroid',
+     'sklearn.neighbors', 'NearestCentroid'),
+    ('_quad_tree', 'sklearn.neighbors.quad_tree', 'sklearn.neighbors',
+     'CELL_DTYPE'),
+    ('_regression', 'sklearn.neighbors.regression', 'sklearn.neighbors',
+     'KNeighborsRegressor'),
+    ('_typedefs', 'sklearn.neighbors.typedefs', 'sklearn.neighbors',
+     'DTYPE'),
+    ('_unsupervised', 'sklearn.neighbors.unsupervised', 'sklearn.neighbors',
+     'NearestNeighbors'),
+
     ('_isomap', 'sklearn.manifold.isomap', 'sklearn.manifold', 'Isomap'),
     ('_locally_linear', 'sklearn.manifold.locally_linear', 'sklearn.manifold',
      'LocallyLinearEmbedding'),

diff --git a/sklearn/cluster/tests/test_hierarchical.py b/sklearn/cluster/tests/test_hierarchical.py
@@ -28,7 +28,7 @@
 from sklearn.metrics.pairwise import PAIRED_DISTANCES, cosine_distances,\
     manhattan_distances, pairwise_distances
 from sklearn.metrics.cluster import normalized_mutual_info_score
-from sklearn.neighbors.graph import kneighbors_graph
+from sklearn.neighbors import kneighbors_graph
 from sklearn.cluster._hierarchical_fast import average_merge, max_merge
 from sklearn.utils._fast_dict import IntFloatDict
 from sklearn.utils.testing import assert_array_equal

diff --git a/sklearn/impute/_knn.py b/sklearn/impute/_knn.py
@@ -4,8 +4,8 @@
 from ..utils.validation import FLOAT_DTYPES
 from ..metrics import pairwise_distances
 from ..metrics.pairwise import _NAN_METRICS
-from ..neighbors.base import _get_weights
-from ..neighbors.base import _check_weights
+from ..neighbors._base import _get_weights
+from ..neighbors._base import _check_weights
 from ..utils import check_array
 from ..utils import is_scalar_nan
 from ..utils._mask import _get_mask

diff --git a/sklearn/manifold/_barnes_hut_tsne.pyx b/sklearn/manifold/_barnes_hut_tsne.pyx
@@ -15,7 +15,7 @@ from libc.math cimport sqrt, log
 import numpy as np
 cimport numpy as np
 
-from ..neighbors.quad_tree cimport _QuadTree
+from ..neighbors._quad_tree cimport _QuadTree
 
 cdef char* EMPTY_STRING = ""
 

diff --git a/sklearn/neighbors/__init__.py b/sklearn/neighbors/__init__.py
@@ -3,19 +3,19 @@
 algorithm.
 """
 
-from .ball_tree import BallTree
-from .kd_tree import KDTree
-from .dist_metrics import DistanceMetric
-from .graph import kneighbors_graph, radius_neighbors_graph
-from .graph import KNeighborsTransformer, RadiusNeighborsTransformer
-from .unsupervised import NearestNeighbors
-from .classification import KNeighborsClassifier, RadiusNeighborsClassifier
-from .regression import KNeighborsRegressor, RadiusNeighborsRegressor
-from .nearest_centroid import NearestCentroid
-from .kde import KernelDensity
-from .lof import LocalOutlierFactor
-from .nca import NeighborhoodComponentsAnalysis
-from .base import VALID_METRICS, VALID_METRICS_SPARSE
+from ._ball_tree import BallTree
+from ._kd_tree import KDTree
+from ._dist_metrics import DistanceMetric
+from ._graph import kneighbors_graph, radius_neighbors_graph
+from ._graph import KNeighborsTransformer, RadiusNeighborsTransformer
+from ._unsupervised import NearestNeighbors
+from ._classification import KNeighborsClassifier, RadiusNeighborsClassifier
+from ._regression import KNeighborsRegressor, RadiusNeighborsRegressor
+from ._nearest_centroid import NearestCentroid
+from ._kde import KernelDensity
+from ._lof import LocalOutlierFactor
+from ._nca import NeighborhoodComponentsAnalysis
+from ._base import VALID_METRICS, VALID_METRICS_SPARSE
 
 __all__ = ['BallTree',
            'DistanceMetric',

diff --git a/sklearn/neighbors/ball_tree.pyx → sklearn/neighbors/_ball_tree.pyx b/sklearn/neighbors/ball_tree.pyx → sklearn/neighbors/_ball_tree.pyx
@@ -22,7 +22,7 @@ VALID_METRICS = ['EuclideanDistance', 'SEuclideanDistance',
                  'PyFuncDistance', 'HaversineDistance']
 
 
-include "binary_tree.pxi"
+include "_binary_tree.pxi"
 
 # Inherit BallTree from BinaryTree
 cdef class BallTree(BinaryTree):

diff --git a/sklearn/neighbors/base.py → sklearn/neighbors/_base.py b/sklearn/neighbors/base.py → sklearn/neighbors/_base.py
@@ -18,8 +18,8 @@
 import joblib
 from joblib import Parallel, delayed, effective_n_jobs
 
-from .ball_tree import BallTree
-from .kd_tree import KDTree
+from ._ball_tree import BallTree
+from ._kd_tree import KDTree
 from ..base import BaseEstimator, MultiOutputMixin
 from ..metrics import pairwise_distances_chunked
 from ..metrics.pairwise import PAIRWISE_DISTANCE_FUNCTIONS

diff --git a/sklearn/neighbors/binary_tree.pxi → sklearn/neighbors/_binary_tree.pxi b/sklearn/neighbors/binary_tree.pxi → sklearn/neighbors/_binary_tree.pxi
@@ -153,11 +153,11 @@ import numpy as np
 import warnings
 from ..utils import check_array
 
-from .typedefs cimport DTYPE_t, ITYPE_t, DITYPE_t
-from .typedefs import DTYPE, ITYPE
+from ._typedefs cimport DTYPE_t, ITYPE_t, DITYPE_t
+from ._typedefs import DTYPE, ITYPE
 
-from .dist_metrics cimport (DistanceMetric, euclidean_dist, euclidean_rdist,
-                           euclidean_dist_to_rdist, euclidean_rdist_to_dist)
+from ._dist_metrics cimport (DistanceMetric, euclidean_dist, euclidean_rdist,
+                             euclidean_dist_to_rdist, euclidean_rdist_to_dist)
 
 cdef extern from "numpy/arrayobject.h":
     void PyArray_ENABLEFLAGS(np.ndarray arr, int flags)
@@ -1002,7 +1002,7 @@ def newObj(obj):
 
 ######################################################################
 # define the reverse mapping of VALID_METRICS
-from .dist_metrics import get_valid_metric_ids
+from ._dist_metrics import get_valid_metric_ids
 VALID_METRIC_IDS = get_valid_metric_ids(VALID_METRICS)
 
 

diff --git a/sklearn/neighbors/classification.py → sklearn/neighbors/_classification.py b/sklearn/neighbors/classification.py → sklearn/neighbors/_classification.py
@@ -14,7 +14,7 @@
 from ..utils.validation import _is_arraylike, _num_samples
 
 import warnings
-from .base import \
+from ._base import \
     _check_weights, _get_weights, \
     NeighborsBase, KNeighborsMixin,\
     RadiusNeighborsMixin, SupervisedIntegerMixin

diff --git a/sklearn/neighbors/dist_metrics.pxd → sklearn/neighbors/_dist_metrics.pxd b/sklearn/neighbors/dist_metrics.pxd → sklearn/neighbors/_dist_metrics.pxd
@@ -7,8 +7,8 @@ cimport cython
 cimport numpy as np
 from libc.math cimport fabs, sqrt, exp, cos, pow
 
-from .typedefs cimport DTYPE_t, ITYPE_t, DITYPE_t
-from .typedefs import DTYPE, ITYPE
+from ._typedefs cimport DTYPE_t, ITYPE_t, DITYPE_t
+from ._typedefs import DTYPE, ITYPE
 
 ######################################################################
 # Inline distance functions

diff --git a/sklearn/neighbors/dist_metrics.pyx → sklearn/neighbors/_dist_metrics.pyx b/sklearn/neighbors/dist_metrics.pyx → sklearn/neighbors/_dist_metrics.pyx
@@ -48,8 +48,8 @@ cdef inline np.ndarray _buffer_to_ndarray(DTYPE_t* x, np.npy_intp n):
 from libc.math cimport fabs, sqrt, exp, pow, cos, sin, asin
 cdef DTYPE_t INF = np.inf
 
-from .typedefs cimport DTYPE_t, ITYPE_t, DITYPE_t, DTYPECODE
-from .typedefs import DTYPE, ITYPE
+from ._typedefs cimport DTYPE_t, ITYPE_t, DITYPE_t, DTYPECODE
+from ._typedefs import DTYPE, ITYPE
 
 
 ######################################################################

diff --git a/sklearn/neighbors/graph.py → sklearn/neighbors/_graph.py b/sklearn/neighbors/graph.py → sklearn/neighbors/_graph.py
@@ -4,10 +4,10 @@
 #         Tom Dupre la Tour
 #
 # License: BSD 3 clause (C) INRIA, University of Amsterdam
-from .base import KNeighborsMixin, RadiusNeighborsMixin
-from .base import NeighborsBase
-from .base import UnsupervisedMixin
-from .unsupervised import NearestNeighbors
+from ._base import KNeighborsMixin, RadiusNeighborsMixin
+from ._base import NeighborsBase
+from ._base import UnsupervisedMixin
+from ._unsupervised import NearestNeighbors
 from ..base import TransformerMixin
 from ..utils.validation import check_is_fitted
 

diff --git a/sklearn/neighbors/kd_tree.pyx → sklearn/neighbors/_kd_tree.pyx b/sklearn/neighbors/kd_tree.pyx → sklearn/neighbors/_kd_tree.pyx
@@ -15,7 +15,7 @@ VALID_METRICS = ['EuclideanDistance', 'ManhattanDistance',
                  'ChebyshevDistance', 'MinkowskiDistance']
 
 
-include "binary_tree.pxi"
+include "_binary_tree.pxi"
 
 # Inherit KDTree from BinaryTree
 cdef class KDTree(BinaryTree):

diff --git a/sklearn/neighbors/kde.py → sklearn/neighbors/_kde.py b/sklearn/neighbors/kde.py → sklearn/neighbors/_kde.py
@@ -10,8 +10,8 @@
 from ..utils import check_array, check_random_state, check_consistent_length
 
 from ..utils.extmath import row_norms
-from .ball_tree import BallTree, DTYPE
-from .kd_tree import KDTree
+from ._ball_tree import BallTree, DTYPE
+from ._kd_tree import KDTree
 
 
 VALID_KERNELS = ['gaussian', 'tophat', 'epanechnikov', 'exponential', 'linear',

diff --git a/sklearn/neighbors/lof.py → sklearn/neighbors/_lof.py b/sklearn/neighbors/lof.py → sklearn/neighbors/_lof.py
@@ -5,9 +5,9 @@
 import numpy as np
 import warnings
 
-from .base import NeighborsBase
-from .base import KNeighborsMixin
-from .base import UnsupervisedMixin
+from ._base import NeighborsBase
+from ._base import KNeighborsMixin
+from ._base import UnsupervisedMixin
 from ..base import OutlierMixin
 
 from ..utils.validation import check_is_fitted

diff --git a/sklearn/neighbors/nca.py → sklearn/neighbors/_nca.py b/sklearn/neighbors/nca.py → sklearn/neighbors/_nca.py
@@ -127,7 +127,7 @@ class NeighborhoodComponentsAnalysis(TransformerMixin, BaseEstimator):
 
     Examples
     --------
-    >>> from sklearn.neighbors.nca import NeighborhoodComponentsAnalysis
+    >>> from sklearn.neighbors import NeighborhoodComponentsAnalysis
     >>> from sklearn.neighbors import KNeighborsClassifier
     >>> from sklearn.datasets import load_iris
     >>> from sklearn.model_selection import train_test_split

diff --git a/sklearn/neighbors/nearest_centroid.py → sklearn/neighbors/_nearest_centroid.py b/sklearn/neighbors/nearest_centroid.py → sklearn/neighbors/_nearest_centroid.py
@@ -54,7 +54,7 @@ class NearestCentroid(ClassifierMixin, BaseEstimator):
 
     Examples
     --------
-    >>> from sklearn.neighbors.nearest_centroid import NearestCentroid
+    >>> from sklearn.neighbors import NearestCentroid
     >>> import numpy as np
     >>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
     >>> y = np.array([1, 1, 1, 2, 2, 2])

diff --git a/sklearn/neighbors/quad_tree.pxd → sklearn/neighbors/_quad_tree.pxd b/sklearn/neighbors/quad_tree.pxd → sklearn/neighbors/_quad_tree.pxd
diff --git a/sklearn/neighbors/quad_tree.pyx → sklearn/neighbors/_quad_tree.pyx b/sklearn/neighbors/quad_tree.pyx → sklearn/neighbors/_quad_tree.pyx
diff --git a/sklearn/neighbors/regression.py → sklearn/neighbors/_regression.py b/sklearn/neighbors/regression.py → sklearn/neighbors/_regression.py
@@ -14,8 +14,8 @@
 
 import numpy as np
 
-from .base import _get_weights, _check_weights, NeighborsBase, KNeighborsMixin
-from .base import RadiusNeighborsMixin, SupervisedFloatMixin
+from ._base import _get_weights, _check_weights, NeighborsBase, KNeighborsMixin
+from ._base import RadiusNeighborsMixin, SupervisedFloatMixin
 from ..base import RegressorMixin
 from ..utils import check_array
 

diff --git a/sklearn/neighbors/typedefs.pxd → sklearn/neighbors/_typedefs.pxd b/sklearn/neighbors/typedefs.pxd → sklearn/neighbors/_typedefs.pxd
diff --git a/sklearn/neighbors/typedefs.pyx → sklearn/neighbors/_typedefs.pyx b/sklearn/neighbors/typedefs.pyx → sklearn/neighbors/_typedefs.pyx
diff --git a/sklearn/neighbors/unsupervised.py → sklearn/neighbors/_unsupervised.py b/sklearn/neighbors/unsupervised.py → sklearn/neighbors/_unsupervised.py
@@ -1,8 +1,8 @@
 """Unsupervised nearest neighbors learner"""
-from .base import NeighborsBase
-from .base import KNeighborsMixin
-from .base import RadiusNeighborsMixin
-from .base import UnsupervisedMixin
+from ._base import NeighborsBase
+from ._base import KNeighborsMixin
+from ._base import RadiusNeighborsMixin
+from ._base import UnsupervisedMixin
 
 
 class NearestNeighbors(NeighborsBase, KNeighborsMixin,

diff --git a/sklearn/neighbors/setup.py b/sklearn/neighbors/setup.py
@@ -10,29 +10,29 @@ def configuration(parent_package='', top_path=None):
     if os.name == 'posix':
         libraries.append('m')
 
-    config.add_extension('ball_tree',
-                         sources=['ball_tree.pyx'],
+    config.add_extension('_ball_tree',
+                         sources=['_ball_tree.pyx'],
                          include_dirs=[numpy.get_include()],
                          libraries=libraries)
 
-    config.add_extension('kd_tree',
-                         sources=['kd_tree.pyx'],
+    config.add_extension('_kd_tree',
+                         sources=['_kd_tree.pyx'],
                          include_dirs=[numpy.get_include()],
                          libraries=libraries)
 
-    config.add_extension('dist_metrics',
-                         sources=['dist_metrics.pyx'],
+    config.add_extension('_dist_metrics',
+                         sources=['_dist_metrics.pyx'],
                          include_dirs=[numpy.get_include(),
                                        os.path.join(numpy.get_include(),
                                                     'numpy')],
                          libraries=libraries)
 
-    config.add_extension('typedefs',
-                         sources=['typedefs.pyx'],
+    config.add_extension('_typedefs',
+                         sources=['_typedefs.pyx'],
                          include_dirs=[numpy.get_include()],
                          libraries=libraries)
-    config.add_extension("quad_tree",
-                         sources=["quad_tree.pyx"],
+    config.add_extension("_quad_tree",
+                         sources=["_quad_tree.pyx"],
                          include_dirs=[numpy.get_include()],
                          libraries=libraries)
 

diff --git a/sklearn/neighbors/tests/test_ball_tree.py b/sklearn/neighbors/tests/test_ball_tree.py
@@ -3,10 +3,10 @@
 import numpy as np
 import pytest
 from numpy.testing import assert_array_almost_equal
-from sklearn.neighbors.ball_tree import (BallTree, NeighborsHeap,
-                                         simultaneous_sort, kernel_norm,
-                                         nodeheap_sort, DTYPE, ITYPE)
-from sklearn.neighbors.dist_metrics import DistanceMetric
+from sklearn.neighbors._ball_tree import (BallTree, NeighborsHeap,
+                                          simultaneous_sort, kernel_norm,
+                                          nodeheap_sort, DTYPE, ITYPE)
+from sklearn.neighbors import DistanceMetric
 from sklearn.utils import check_random_state
 from sklearn.utils.testing import assert_allclose
 

diff --git a/sklearn/neighbors/tests/test_dist_metrics.py b/sklearn/neighbors/tests/test_dist_metrics.py
@@ -9,7 +9,7 @@
 from distutils.version import LooseVersion
 from scipy import __version__ as scipy_version
 from scipy.spatial.distance import cdist
-from sklearn.neighbors.dist_metrics import DistanceMetric
+from sklearn.neighbors import DistanceMetric
 from sklearn.neighbors import BallTree
 from sklearn.utils import check_random_state
 from sklearn.utils.testing import assert_raises_regex

diff --git a/sklearn/neighbors/tests/test_graph.py b/sklearn/neighbors/tests/test_graph.py
@@ -2,7 +2,7 @@
 
 from sklearn.metrics import euclidean_distances
 from sklearn.neighbors import KNeighborsTransformer, RadiusNeighborsTransformer
-from sklearn.neighbors.base import _is_sorted_by_data
+from sklearn.neighbors._base import _is_sorted_by_data
 
 
 def test_transformer_result():

diff --git a/sklearn/neighbors/tests/test_kd_tree.py b/sklearn/neighbors/tests/test_kd_tree.py
@@ -3,10 +3,10 @@
 
 import pytest
 
-from sklearn.neighbors.kd_tree import (KDTree, NeighborsHeap,
-                                       simultaneous_sort, kernel_norm,
-                                       nodeheap_sort, DTYPE, ITYPE)
-from sklearn.neighbors.dist_metrics import DistanceMetric
+from sklearn.neighbors._kd_tree import (KDTree, NeighborsHeap,
+                                        simultaneous_sort, kernel_norm,
+                                        nodeheap_sort, DTYPE, ITYPE)
+from sklearn.neighbors import DistanceMetric
 from sklearn.utils import check_random_state
 from sklearn.utils.testing import assert_allclose