Thanks to visit codestin.com
Credit goes to github.com

Skip to content

[MRG] fix test for new default of SVC decision function #8050

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Closed
wants to merge 5 commits into from
Closed

[MRG] fix test for new default of SVC decision function #8050

Changes from all commits
Commits
Show all changes
5 commits
Select commit Hold shift + click to select a range
69dc219
fix test for new default of SVC decision function
amueller Dec 13, 2016
4ce5acd
Merge branch 'master' into ovr_cleanup
agramfort Jun 8, 2017
aef6693
Create estimator_checks.py
agramfort Jun 8, 2017
aa9e7a4
Create estimator_checks.py
agramfort Jun 8, 2017
4152b21
Create estimator_checks.py
agramfort Jun 8, 2017
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
7 changes: 3 additions & 4 deletions sklearn/utils/estimator_checks.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -80,7 +80,7 @@ def _yield_non_meta_checks(name, Estimator):
yield check_estimators_empty_data_messages

if name not in CROSS_DECOMPOSITION + ['SpectralEmbedding']:
# SpectralEmbedding is non-deterministic,
# SpectralEmbedding non-deterministic,
# see issue #4236
# cross-decomposition's "transform" returns X and Y
yield check_pipeline_consistency
Expand Down Expand Up @@ -1109,12 +1109,11 @@ def check_classifiers_train(name, Classifier):
try:
# decision_function agrees with predict
decision = classifier.decision_function(X)
if n_classes is 2:
if n_classes == 2:
assert_equal(decision.shape, (n_samples,))
dec_pred = (decision.ravel() > 0).astype(np.int)
assert_array_equal(dec_pred, y_pred)
if (n_classes is 3 and not isinstance(classifier, BaseLibSVM)):
# 1on1 of LibSVM works differently
if n_classes == 3:
assert_equal(decision.shape, (n_samples, n_classes))
assert_array_equal(np.argmax(decision, axis=1), y_pred)

Expand Down