def test_grid_search_failing_classifier():
X, y = make_classification(n_samples=20, n_features=10, random_state=0)
clf = FailingClassifier()
# refit=False because we want to test the behaviour of the grid search part
gs = dcv.GridSearchCV(clf, [{'parameter': [0, 1, 2]}], scoring='accuracy',
refit=False, error_score=0.0)
with pytest.warns(FitFailedWarning):
gs.fit(X, y)
n_candidates = len(gs.cv_results_['params'])
# Ensure that grid scores were set to zero as required for those fits
# that are expected to fail.
def get_cand_scores(i):
return np.array(list(gs.cv_results_['split%d_test_score' % s][i]
for s in range(gs.n_splits_)))
assert all((np.all(get_cand_scores(cand_i) == 0.0)
for cand_i in range(n_candidates)
if gs.cv_results_['param_parameter'][cand_i] ==
FailingClassifier.FAILING_PARAMETER))
gs = dcv.GridSearchCV(clf, [{'parameter': [0, 1, 2]}], scoring='accuracy',
refit=False, error_score=float('nan'))
with pytest.warns(FitFailedWarning):
gs.fit(X, y)
n_candidates = len(gs.cv_results_['params'])
assert all(np.all(np.isnan(get_cand_scores(cand_i)))
for cand_i in range(n_candidates)
if gs.cv_results_['param_parameter'][cand_i] ==
FailingClassifier.FAILING_PARAMETER)
def test_grid_search_failing_classifier_raise():
X, y = make_classification(n_samples=20, n_features=10, random_state=0)
clf = FailingClassifier()
# refit=False because we want to test the behaviour of the grid search part
gs = dcv.GridSearchCV(clf, [{'parameter': [0, 1, 2]}], scoring='accuracy',
refit=False, error_score='raise')
# FailingClassifier issues a ValueError so this is what we look for.
with pytest.raises(ValueError):
gs.fit(X, y)
def test_pipeline_fit_failure():
X, y = make_classification(n_samples=100, n_features=10, random_state=0)
pipe = Pipeline([('bad', FailingClassifier()), ('good1', MockClassifier()),
('good2', MockClassifier())])
grid = {'bad__parameter': [0, 1, 2]}
gs = dcv.GridSearchCV(pipe, grid, refit=False)
# Check that failure raises if error_score is `'raise'`
with pytest.raises(ValueError):
gs.fit(X, y)
# Check that grid scores were set to error_score on failure
gs.error_score = float('nan')
with pytest.warns(FitFailedWarning):
gs.fit(X, y)
check_scores_all_nan(gs, 'bad__parameter')
def test_feature_union_fit_failure_multiple_metrics():
scoring = {"score_1": _passthrough_scorer, "score_2": _passthrough_scorer}
X, y = make_classification(n_samples=100, n_features=10, random_state=0)
pipe = Pipeline([('union',
FeatureUnion([('good', MockClassifier()),
('bad', FailingClassifier())],
transformer_weights={'bad': 0.5})),
('clf', MockClassifier())])
grid = {'union__bad__parameter': [0, 1, 2]}
gs = dcv.GridSearchCV(pipe, grid, refit=False, scoring=scoring)
# Check that failure raises if error_score is `'raise'`
with pytest.raises(ValueError):
gs.fit(X, y)
# Check that grid scores were set to error_score on failure
gs.error_score = float('nan')
with pytest.warns(FitFailedWarning):
gs.fit(X, y)
for key in scoring:
check_scores_all_nan(gs, 'union__bad__parameter', score_key=key)