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[MRG] Add check for n_components in pca #10042

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Merged
merged 6 commits into from
Nov 2, 2017
Merged

[MRG] Add check for n_components in pca #10042

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8bf05bd
Add check for n_components in pca
CoderPat Oct 29, 2017
c8fab7e
Add more consistency to checks and more tests
CoderPat Oct 30, 2017
23df5f7
Fix mistake in identation
CoderPat Oct 30, 2017
11824dc
Fix typo and bug in solver selection in test
CoderPat Oct 30, 2017
3b26974
Add more consistency to type checking
CoderPat Oct 30, 2017
d1be530
fix type checking for compatibilty with python2 long
CoderPat Oct 30, 2017
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11 changes: 11 additions & 0 deletions sklearn/decomposition/pca.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,6 +11,7 @@
# License: BSD 3 clause

from math import log, sqrt
import numbers

import numpy as np
from scipy import linalg
Expand Down Expand Up @@ -417,6 +418,12 @@ def _fit_full(self, X, n_components):
"min(n_samples, n_features)=%r with "
"svd_solver='full'"
% (n_components, min(n_samples, n_features)))
elif n_components >= 1:
if not isinstance(n_components, (numbers.Integral, np.integer)):
raise ValueError("n_components=%r must be of type int "
"when greater than or equal to 1, "
"was of type=%r"
% (n_components, type(n_components)))

# Center data
self.mean_ = np.mean(X, axis=0)
Expand Down Expand Up @@ -477,6 +484,10 @@ def _fit_truncated(self, X, n_components, svd_solver):
"svd_solver='%s'"
% (n_components, min(n_samples, n_features),
svd_solver))
elif not isinstance(n_components, (numbers.Integral, np.integer)):
raise ValueError("n_components=%r must be of type int "
"when greater than or equal to 1, was of type=%r"
% (n_components, type(n_components)))
elif svd_solver == 'arpack' and n_components == min(n_samples,
n_features):
raise ValueError("n_components=%r must be strictly less than "
Expand Down
10 changes: 10 additions & 0 deletions sklearn/decomposition/tests/test_pca.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9,6 +9,7 @@
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_raises_regex
from sklearn.utils.testing import assert_raise_message
from sklearn.utils.testing import assert_no_warnings
from sklearn.utils.testing import assert_warns_message
from sklearn.utils.testing import ignore_warnings
Expand Down Expand Up @@ -389,6 +390,15 @@ def test_pca_validation():
PCA(n_components, svd_solver=solver)
.fit, data)

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When constructing test in PCA, it is common to use for solver in solver_list: to loop through all the possible svd_solvers. There's already a loop above your code so it might be better to move your test into the loop to make sure that the code works for all svd_solvers.

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Ye, I will do it since @jnothman also suggested it

n_components = 1.0
type_ncom = type(n_components)
assert_raise_message(ValueError,
"n_components={} must be of type int "
"when greater than or equal to 1, was of type={}"
.format(n_components, type_ncom),
PCA(n_components, svd_solver=solver).fit, data)



def test_n_components_none():
# Ensures that n_components == None is handled correctly
Expand Down