8000 Fix imports in pip3 ubuntu by suffixing affected files (#15891) · panpiort8/scikit-learn@281e36e · GitHub
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thomasjpfanPan Jan
thomasjpfan
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Pan Jan
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Fix imports in pip3 ubuntu by suffixing affected files (scikit-learn#15891)
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‎sklearn/_build_utils/deprecated_modules.py

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Original file line numberDiff line numberDiff line change
@@ -47,9 +47,9 @@
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'SpectralBiclustering'),
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('_birch', 'sklearn.cluster.birch', 'sklearn.cluster', 'Birch'),
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('_dbscan', 'sklearn.cluster.dbscan_', 'sklearn.cluster', 'DBSCAN'),
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('_hierarchical', 'sklearn.cluster.hierarchical', 'sklearn.cluster',
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('_agglomerative', 'sklearn.cluster.hierarchical', 'sklearn.cluster',
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'FeatureAgglomeration'),
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('_k_means', 'sklearn.cluster.k_means_', 'sklearn.cluster', 'KMeans'),
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('_kmeans', 'sklearn.cluster.k_means_', 'sklearn.cluster', 'KMeans'),
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('_mean_shift', 'sklearn.cluster.mean_shift_', 'sklearn.cluster',
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'MeanShift'),
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('_optics', 'sklearn.cluster.optics_', 'sklearn.cluster', 'OPTICS'),
@@ -101,7 +101,7 @@
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('_kernel_pca', 'sklearn.decomposition.kernel_pca',
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'sklearn.decomposition', 'KernelPCA'),
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('_nmf', 'sklearn.decomposition.nmf', 'sklearn.decomposition', 'NMF'),
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('_online_lda', 'sklearn.decomposition.online_lda',
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('_lda', 'sklearn.decomposition.online_lda',
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'sklearn.decomposition', 'LatentDirichletAllocation'),
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('_online_lda_fast', 'sklearn.decomposition.online_lda_fast',
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'sklearn.decomposition', 'mean_change'),
@@ -140,7 +140,7 @@
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('_dict_vectorizer', 'sklearn.feature_extraction.dict_vectorizer',
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'sklearn.feature_extraction', 'DictVectorizer'),
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('_hashing', 'sklearn.feature_extraction.hashing',
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('_hash', 'sklearn.feature_extraction.hashing',
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'sklearn.feature_extraction', 'FeatureHasher'),
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('_stop_words', 'sklearn.feature_extraction.stop_words',
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'sklearn.feature_extraction.text', 'ENGLISH_STOP_WORDS'),

‎sklearn/cluster/__init__.py

Lines changed: 3 additions & 3 deletions
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@@ -7,9 +7,9 @@
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from ._mean_shift import (mean_shift, MeanShift,
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estimate_bandwidth, get_bin_seeds)
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from ._affinity_propagation import affinity_propagation, AffinityPropagation
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from ._hierarchical import (ward_tree, AgglomerativeClustering, linkage_tree,
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FeatureAgglomeration)
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from ._k_means import k_means, KMeans, MiniBatchKMeans
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from ._agglomerative import (ward_tree, AgglomerativeClustering,
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linkage_tree, FeatureAgglomeration)
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from ._kmeans import k_means, KMeans, MiniBatchKMeans
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from ._dbscan import dbscan, DBSCAN
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from ._optics import (OPTICS, cluster_optics_dbscan, compute_optics_graph,
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cluster_optics_xi)

‎sklearn/cluster/_hierarchical.py renamed to ‎sklearn/cluster/_agglomerative.py

Lines changed: 3 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -25,7 +25,7 @@
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from ._feature_agglomeration import AgglomerationTransform
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from ..utils._fast_dict import IntFloatDict
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from ..utils.fixes import _astype_copy_false
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from ..utils import deprecated
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###############################################################################
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# For non fully-connected graphs
@@ -249,8 +249,8 @@ def ward_tree(X, connectivity=None, n_clusters=None, return_distance=False):
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else:
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if n_clusters > n_samples:
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raise ValueError('Cannot provide more clusters than samples. '
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'%i n_clusters was asked, and there are %i samples.'
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% (n_clusters, n_samples))
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'%i n_clusters was asked, and there are %i '
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'samples.' % (n_clusters, n_samples))
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n_nodes = 2 * n_samples - n_clusters
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# create inertia matrix

‎sklearn/cluster/_k_means.py renamed to ‎sklearn/cluster/_kmeans.py

File renamed without changes.

‎sklearn/cluster/_spectral.py

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@@ -15,7 +15,7 @@
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from ..metrics.pairwise import pairwise_kernels
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from ..neighbors import kneighbors_graph, NearestNeighbors
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from ..manifold import spectral_embedding
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from ._k_means import k_means
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from ._kmeans import k_means
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2020

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def discretize(vectors, copy=True, max_svd_restarts=30, n_iter_max=20,

‎sklearn/cluster/tests/test_hierarchical.py

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@@ -22,8 +22,9 @@
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from sklearn.cluster import ward_tree
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from sklearn.cluster import AgglomerativeClustering, FeatureAgglomeration
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from sklearn.cluster._hierarchical import (_hc_cut, _TREE_BUILDERS,
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linkage_tree, _fix_connectivity)
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from sklearn.cluster._agglomerative import (_hc_cut, _TREE_BUILDERS,
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linkage_tree,
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_fix_connectivity)
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from sklearn.feature_extraction.image import grid_to_graph
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from sklearn.metrics.pairwise import PAIRED_DISTANCES, cosine_distances,\
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manhattan_distances, pairwise_distances

‎sklearn/cluster/tests/test_k_means.py

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@@ -22,8 +22,8 @@
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from sklearn.metrics.cluster import v_measure_score
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from sklearn.cluster import KMeans, k_means
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from sklearn.cluster import MiniBatchKMeans
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from sklearn.cluster._k_means import _labels_inertia
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from sklearn.cluster._k_means import _mini_batch_step
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from sklearn.cluster._kmeans import _labels_inertia
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from sklearn.cluster._kmeans import _mini_batch_step
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from sklearn.datasets import make_blobs
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from io import StringIO
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from sklearn.metrics.cluster import homogeneity_score
@@ -734,7 +734,7 @@ def test_k_means_function():
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def test_x_squared_norms_init_centroids():
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# Test that x_squared_norms can be None in _init_centroids
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from sklearn.cluster._k_means import _init_centroids
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from sklearn.cluster._kmeans import _init_centroids
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X_norms = np.sum(X**2, axis=1)
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precompute = _init_centroids(
@@ -921,7 +921,7 @@ def test_sample_weight_length():
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def test_check_normalize_sample_weight():
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from sklearn.cluster._k_means import _check_normalize_sample_weight
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from sklearn.cluster._kmeans import _check_normalize_sample_weight
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sample_weight = None
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checked_sample_weight = _check_normalize_sample_weight(sample_weight, X)
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assert _num_samples(X) == _num_samples(checked_sample_weight)

‎sklearn/decomposition/__init__.py

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@@ -16,7 +16,7 @@
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MiniBatchDictionaryLearning, SparseCoder)
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from ._factor_analysis import FactorAnalysis
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from ..utils.extmath import randomized_svd
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from ._online_lda import LatentDirichletAllocation
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from ._lda import LatentDirichletAllocation
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__all__ = ['DictionaryLearning',
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'FastICA',

‎sklearn/decomposition/_online_lda.p 9738 y renamed to ‎sklearn/decomposition/_lda.py

File renamed without changes.

‎sklearn/decomposition/tests/test_online_lda.py

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@@ -8,8 +8,8 @@
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import pytest
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from sklearn.decomposition import LatentDirichletAllocation
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from sklearn.decomposition._online_lda import (_dirichlet_expectation_1d,
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_dirichlet_expectation_2d)
11+
from sklearn.decomposition._lda import (_dirichlet_expectation_1d,
12+
_dirichlet_expectation_2d)
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from sklearn.utils._testing import assert_allclose
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from sklearn.utils._testing import assert_array_almost_equal

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