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Merged
merged 11 commits into from
Nov 20, 2020
Merged

ENH Adds n_feature_in_ checking to cluster #18727

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a501ca1
CI Check review_request_removed
thomasjpfan Oct 9, 2020
0fb69d4
ENH Checks n_features_in_ in cluster module
thomasjpfan Oct 14, 2020
f48580a
Merge remote-tracking branch 'upstream/master' into master
thomasjpfan Oct 22, 2020
357f268
CI Fixes CI sync
thomasjpfan Oct 28, 2020
8360cfb
Merge remote-tracking branch 'upstream/master' into master
thomasjpfan Oct 28, 2020
fa74c44
Merge branch 'fix_ci_sync' into master
thomasjpfan Oct 28, 2020
4b8f634
Merge remote-tracking branch 'origin/master' into pr_branch
invalid-email-address Oct 28, 2020
0bc1f28
Merge remote-tracking branch 'upstream/master' into n_features_cluster
thomasjpfan Nov 2, 2020
ce54d6c
REV Reduces diff
thomasjpfan Nov 2, 2020
5596764
STY Formatting
thomasjpfan Nov 2, 2020
86bd36c
ENH Uses context manager to avoid finite check
thomasjpfan Nov 2, 2020
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8 changes: 5 additions & 3 deletions sklearn/cluster/_affinity_propagation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,11 +10,12 @@

from ..exceptions import ConvergenceWarning
from ..base import BaseEstimator, ClusterMixin
from ..utils import as_float_array, check_array, check_random_state
from ..utils import as_float_array, check_random_state
from ..utils.deprecation import deprecated
from ..utils.validation import check_is_fitted, _deprecate_positional_args
from ..metrics import euclidean_distances
from ..metrics import pairwise_distances_argmin
from .._config import config_context


def _equal_similarities_and_preferences(S, preference):
Expand Down Expand Up @@ -446,13 +447,14 @@ def predict(self, X):
Cluster labels.
"""
check_is_fitted(self)
X = check_array(X)
X = self._validate_data(X, reset=False)
if not hasattr(self, "cluster_centers_"):
raise ValueError("Predict method is not supported when "
"affinity='precomputed'.")

if self.cluster_centers_.shape[0] > 0:
return pairwise_distances_argmin(X, self.cluster_centers_)
with config_context(assume_finite=True):
return pairwise_distances_argmin(X, self.cluster_centers_)
Comment on lines +456 to +457
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As a quick benchmark:

from sklearn.cluster import AffinityPropagation
from sklearn.datasets import make_classification

X, _ = make_classification(n_features=10_000, n_samples=5_000, random_state=42)
aff_prop = AffinityPropagation(random_state=42)
aff_prop.fit(X)

# this PR
%timeit aff_prop.predict(X)
# 182 ms ± 2.23 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

# master
%timeit aff_prop.predict(X)
# 254 ms ± 5.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

else:
warnings.warn("This model does not have any cluster centers "
"because affinity propagation did not converge. "
Expand Down
20 changes: 11 additions & 9 deletions sklearn/cluster/_birch.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,11 +12,11 @@
from ..metrics import pairwise_distances_argmin
from ..metrics.pairwise import euclidean_distances
from ..base import TransformerMixin, ClusterMixin, BaseEstimator
from ..utils import check_array
from ..utils.extmath import row_norms
from ..utils.validation import check_is_fitted, _deprecate_positional_args
from ..exceptions import ConvergenceWarning
from . import AgglomerativeClustering
from .._config import config_context


def _iterate_sparse_X(X):
Expand Down Expand Up @@ -585,14 +585,14 @@ def predict(self, X):
labels : ndarray of shape(n_samples,)
Labelled data.
"""
X = check_array(X, accept_sparse='csr')
self._check_fit(X)
check_is_fitted(self)
X = self._validate_data(X, accept_sparse='csr', reset=False)
kwargs = {'Y_norm_squared': self._subcluster_norms}
return self.subcluster_labels_[
pairwise_distances_argmin(X,
self.subcluster_centers_,
metric_kwargs=kwargs)
]

with config_context(assume_finite=True):
argmin = pairwise_distances_argmin(X, self.subcluster_centers_,
metric_kwargs=kwargs)
return self.subcluster_labels_[argmin]

def transform(self, X):
"""
Expand All @@ -612,7 +612,9 @@ def transform(self, X):
Transformed data.
"""
check_is_fitted(self)
return euclidean_distances(X, self.subcluster_centers_)
self._validate_data(X, accept_sparse='csr', reset=False)
with config_context(assume_finite=True):
return euclidean_distances(X, self.subcluster_centers_)

def _global_clustering(self, X=None):
"""
Expand Down
6 changes: 1 addition & 5 deletions sklearn/cluster/_feature_agglomeration.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,7 +8,6 @@
import numpy as np

from ..base import TransformerMixin
from ..utils import check_array
from ..utils.validation import check_is_fitted
from scipy.sparse import issparse

Expand Down Expand Up @@ -38,10 +37,7 @@ def transform(self, X):
"""
check_is_fitted(self)

X = check_array(X)
if len(self.labels_) != X.shape[1]:
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We're assuming that the invariance of len(self.labels_) == X_train.shape[1] is enforced elsewhere, right? I guess this was only a workaround for not having n_features_in_ anyway.

raise ValueError("X has a different number of features than "
"during fitting.")
X = self._validate_data(X, reset=False)
if self.pooling_func == np.mean and not issparse(X):
size = np.bincount(self.labels_)
n_samples = X.shape[0]
Expand Down
12 changes: 3 additions & 9 deletions sklearn/cluster/_kmeans.py
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Expand Up @@ -854,15 +854,9 @@ def _validate_center_shape(self, X, centers):
f"match the number of features of the data {X.shape[1]}.")

def _check_test_data(self, X):
X = check_array(X, accept_sparse='csr', dtype=[np.float64, np.float32],
order='C', accept_large_sparse=False)
n_samples, n_features = X.shape
expected_n_features = self.cluster_centers_.shape[1]
if not n_features == expected_n_features:
raise ValueError(
f"Incorrect number of features. Got {n_features} features, "
f"expected {expected_n_features}.")

X = self._validate_data(X, accept_sparse='csr', reset=False,
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do we still need the function now? but fine with me.

dtype=[np.float64, np.float32],
order='C', accept_large_sparse=False)
return X

def _check_mkl_vcomp(self, X, n_samples):
Expand Down
6 changes: 4 additions & 2 deletions sklearn/cluster/_mean_shift.py
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Expand Up @@ -25,6 +25,7 @@
from ..base import BaseEstimator, ClusterMixin
from ..neighbors import NearestNeighbors
from ..metrics.pairwise import pairwise_distances_argmin
from .._config import config_context


@_deprecate_positional_args
Expand Down Expand Up @@ -462,5 +463,6 @@ def predict(self, X):
Index of the cluster each sample belongs to.
"""
check_is_fitted(self)

return pairwise_distances_argmin(X, self.cluster_centers_)
X = self._validate_data(X, reset=False)
with config_context(assume_finite=True):
return pairwise_distances_argmin(X, self.cluster_centers_)
1 change: 0 additions & 1 deletion sklearn/tests/test_common.py
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Expand Up @@ -336,7 +336,6 @@ def test_search_cv(estimator, check, request):
# check_classifiers_train would need to be updated with the error message
N_FEATURES_IN_AFTER_FIT_MODULES_TO_IGNORE = {
'calibration',
'cluster',
'compose',
'covariance',
'cross_decomposition',
Expand Down