def _test_ridge_loo(filter_):
# test that can work with both dense or sparse matrices
n_samples = X_diabetes.shape[0]
ret = []
fit_intercept = filter_ == DENSE_FILTER
ridge_gcv = _RidgeGCV(fit_intercept=fit_intercept)
# check best alpha
ridge_gcv.fit(filter_(X_diabetes), y_diabetes)
alpha_ = ridge_gcv.alpha_
ret.append(alpha_)
# check that we get same best alpha with custom loss_func
f = ignore_warnings
scoring = make_scorer(mean_squared_error, greater_is_better=False)
ridge_gcv2 = RidgeCV(fit_intercept=False, scoring=scoring)
f(ridge_gcv2.fit)(filter_(X_diabetes), y_diabetes)
assert ridge_gcv2.alpha_ == pytest.approx(alpha_)
# check that we get same best alpha with custom score_func
func = lambda x, y: -mean_squared_error(x, y)
scoring = make_scorer(func)
ridge_gcv3 = RidgeCV(fit_intercept=False, scoring=scoring)
f(ridge_gcv3.fit)(filter_(X_diabetes), y_diabetes)
assert ridge_gcv3.alpha_ == pytest.approx(alpha_)
# check that we get same best alpha with a scorer
scorer = get_scorer('neg_mean_squared_error')
ridge_gcv4 = RidgeCV(fit_intercept=False, scoring=scorer)
ridge_gcv4.fit(filter_(X_diabetes), y_diabetes)
assert ridge_gcv4.alpha_ == pytest.approx(alpha_)
# check that we get same best alpha with sample weights
if filter_ == DENSE_FILTER:
ridge_gcv.fit(filter_(X_diabetes),
y_diabetes,
sample_weight=np.ones(n_samples))
assert ridge_gcv.alpha_ == pytest.approx(alpha_)
# simulate several responses
Y = np.vstack((y_diabetes, y_diabetes)).T
ridge_gcv.fit(filter_(X_diabetes), Y)
Y_pred = ridge_gcv.predict(filter_(X_diabetes))
ridge_gcv.fit(filter_(X_diabetes), y_diabetes)
y_pred = ridge_gcv.predict(filter_(X_diabetes))
assert_allclose(np.vstack((y_pred, y_pred)).T, Y_pred, rtol=1e-5)
return ret
def test_check_scoring_and_check_multimetric_scoring():
check_scoring_validator_for_single_metric_usecases(check_scoring)
# To make sure the check_scoring is correctly applied to the constituent
# scorers
check_scoring_validator_for_single_metric_usecases(
check_multimetric_scoring_single_metric_wrapper)
# For multiple metric use cases
# Make sure it works for the valid cases
for scoring in (('accuracy',), ['precision'],
{'acc': 'accuracy', 'precision': 'precision'},
('accuracy', 'precision'), ['precision', 'accuracy'],
{'accuracy': make_scorer(accuracy_score),
'precision': make_scorer(precision_score)}):
estimator = LinearSVC(random_state=0)
estimator.fit([[1], [2], [3]], [1, 1, 0])
scorers, is_multi = _check_multimetric_scoring(estimator, scoring)
assert is_multi
assert isinstance(scorers, dict)
assert sorted(scorers.keys()) == sorted(list(scoring))
assert all([isinstance(scorer, _PredictScorer)
for scorer in list(scorers.values())])
if 'acc' in scoring:
assert_almost_equal(scorers['acc'](
estimator, [[1], [2], [3]], [1, 0, 0]), 2. / 3.)
if 'accuracy' in scoring:
assert_almost_equal(scorers['accuracy'](
estimator, [[1], [2], [3]], [1, 0, 0]), 2. / 3.)
if 'precision' in scoring:
assert_almost_equal(scorers['precision'](
estimator, [[1], [2], [3]], [1, 0, 0]), 0.5)
estimator = EstimatorWithFitAndPredict()
estimator.fit([[1]], [1])
# Make sure it raises errors when scoring parameter is not valid.
# More weird corner cases are tested at test_validation.py
error_message_regexp = ".*must be unique strings.*"
for scoring in ((make_scorer(precision_score), # Tuple of callables
make_scorer(accuracy_score)), [5],
(make_scorer(precision_score),), (), ('f1', 'f1')):
with pytest.raises(ValueError, match=error_message_regexp):
_check_multimetric_scoring(estimator, scoring=scoring)
def test_raises_on_score_list():
# Test that when a list of scores is returned, we raise proper errors.
X, y = make_blobs(random_state=0)
f1_scorer_no_average = make_scorer(f1_score, average=None)
clf = DecisionTreeClassifier()
with pytest.raises(ValueError):
cross_val_score(clf, X, y, scoring=f1_scorer_no_average)
grid_search = GridSearchCV(clf, scoring=f1_scorer_no_average,
param_grid={'max_depth': [1, 2]})
with pytest.raises(ValueError):
grid_search.fit(X, y)
def test_multiclass_roc_proba_scorer_label():
scorer = make_scorer(roc_auc_score, multi_class='ovo',
labels=[0, 1, 2], needs_proba=True)
X, y = make_classification(n_classes=3, n_informative=3, n_samples=20,
random_state=0)
lr = LogisticRegression(multi_class="multinomial").fit(X, y)
y_proba = lr.predict_proba(X)
y_binary = y == 0
expected_score = roc_auc_score(y_binary, y_proba,
multi_class='ovo',
labels=[0, 1, 2])
assert scorer(lr, X, y_binary) == pytest.approx(expected_score)
def test_classification_scores():
# Test classification scorers.
X, y = make_blobs(random_state=0, centers=2)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
clf = LinearSVC(random_state=0)
clf.fit(X_train, y_train)
for prefix, metric in [('f1', f1_score), ('precision', precision_score),
('recall', recall_score),
('jaccard', jaccard_score)]:
score1 = get_scorer('%s_weighted' % prefix)(clf, X_test, y_test)
score2 = metric(y_test, clf.predict(X_test), pos_label=None,
average='weighted')
assert_almost_equal(score1, score2)
score1 = get_scorer('%s_macro' % prefix)(clf, X_test, y_test)
score2 = metric(y_test, clf.predict(X_test), pos_label=None,
average='macro')
assert_almost_equal(score1, score2)
score1 = get_scorer('%s_micro' % prefix)(clf, X_test, y_test)
score2 = metric(y_test, clf.predict(X_test), pos_label=None,
average='micro')
assert_almost_equal(score1, score2)
score1 = get_scorer('%s' % prefix)(clf, X_test, y_test)
score2 = metric(y_test, clf.predict(X_test), pos_label=1)
assert_almost_equal(score1, score2)
# test fbeta score that takes an argument
scorer = make_scorer(fbeta_score, beta=2)
score1 = scorer(clf, X_test, y_test)
score2 = fbeta_score(y_test, clf.predict(X_test), beta=2)
assert_almost_equal(score1, score2)
# test that custom scorer can be pickled
unpickled_scorer = pickle.loads(pickle.dumps(scorer))
score3 = unpickled_scorer(clf, X_test, y_test)
assert_almost_equal(score1, score3)
# smoke test the repr:
repr(fbeta_score)
def test_make_scorer():
# Sanity check on the make_scorer factory function.
f = lambda *args: 0
with pytest.raises(ValueError):
make_scorer(f, needs_threshold=True, needs_proba=True)
def test_rfecv():
generator = check_random_state(0)
iris = load_iris()
X = np.c_[iris.data, generator.normal(size=(len(iris.data), 6))]
y = list(iris.target) # regression test: list should be supported
# Test using the score function
rfecv = RFECV(estimator=SVC(kernel="linear"), step=1)
rfecv.fit(X, y)
# non-regression test for missing worst feature:
assert len(rfecv.grid_scores_) == X.shape[1]
assert len(rfecv.ranking_) == X.shape[1]
X_r = rfecv.transform(X)
# All the noisy variable were filtered out
assert_array_equal(X_r, iris.data)
# same in sparse
rfecv_sparse = RFECV(estimator=SVC(kernel="linear"), step=1)
X_sparse = sparse.csr_matrix(X)
rfecv_sparse.fit(X_sparse, y)
X_r_sparse = rfecv_sparse.transform(X_sparse)
assert_array_equal(X_r_sparse.toarray(), iris.data)
# Test using a customized loss function
scoring = make_scorer(zero_one_loss, greater_is_better=False)
rfecv = RFECV(estimator=SVC(kernel="linear"), step=1, scoring=scoring)
ignore_warnings(rfecv.fit)(X, y)
X_r = rfecv.transform(X)
assert_array_equal(X_r, iris.data)
# Test using a scorer
scorer = get_scorer('accuracy')
rfecv = RFECV(estimator=SVC(kernel="linear"), step=1, scoring=scorer)
rfecv.fit(X, y)
X_r = rfecv.transform(X)
assert_array_equal(X_r, iris.data)
# Test fix on grid_scores
def test_scorer(estimator, X, y):
return 1.0
rfecv = RFECV(estimator=SVC(kernel="linear"), step=1, scoring=test_scorer)
rfecv.fit(X, y)
assert_array_equal(rfecv.grid_scores_, np.ones(len(rfecv.grid_scores_)))
# In the event of cross validation score ties, the expected behavior of
# RFECV is to return the FEWEST features that maximize the CV score.
# Because test_scorer always returns 1.0 in this example, RFECV should
# reduce the dimensionality to a single feature (i.e. n_features_ = 1)
assert rfecv.n_features_ == 1
# Same as the first two tests, but with step=2
rfecv = RFECV(estimator=SVC(kernel="linear"), step=2)
rfecv.fit(X, y)
assert len(rfecv.grid_scores_) == 6
assert len(rfecv.ranking_) == X.shape[1]
X_r = rfecv.transform(X)
assert_array_equal(X_r, iris.data)
rfecv_sparse = RFECV(estimator=SVC(kernel="linear"), step=2)
X_sparse = sparse.csr_matrix(X)
rfecv_sparse.fit(X_sparse, y)
X_r_sparse = rfecv_sparse.transform(X_sparse)
assert_array_equal(X_r_sparse.toarray(), iris.data)
# Verifying that steps < 1 don't blow up.
rfecv_sparse = RFECV(estimator=SVC(kernel="linear"), step=.2)
X_sparse = sparse.csr_matrix(X)
rfecv_sparse.fit(X_sparse, y)
X_r_sparse = rfecv_sparse.transform(X_sparse)
assert_array_equal(X_r_sparse.toarray(), iris.data)