def test_feature_importance(X_y_binary):
X, y = X_y_binary
sk_clf = SKElasticNetClassifier(loss="log",
penalty="elasticnet",
alpha=0.5,
l1_ratio=0.5,
n_jobs=1,
random_state=0)
sk_clf.fit(X, y)
clf = ElasticNetClassifier(n_jobs=1)
clf.fit(X, y)
np.testing.assert_almost_equal(sk_clf.coef_.flatten(), clf.feature_importance, decimal=5)
def test_feature_importance_multi(X_y_multi):
X, y = X_y_multi
sk_clf = SKElasticNetClassifier(loss="log",
penalty="elasticnet",
alpha=0.5,
l1_ratio=0.5,
n_jobs=1,
random_state=0)
sk_clf.fit(X, y)
clf = ElasticNetClassifier(n_jobs=1)
clf.fit(X, y)
sk_features = np.linalg.norm(sk_clf.coef_, axis=0, ord=2)
np.testing.assert_almost_equal(sk_features, clf.feature_importance, decimal=5)
def test_overwrite_loss_parameter_in_kwargs():
with pytest.warns(expected_warning=UserWarning) as warnings:
en = ElasticNetClassifier(loss="hinge")
assert len(warnings) == 1
# check that the message matches
assert warnings[0].message.args[0] == ("Parameter loss is being set to 'log' so that ElasticNetClassifier can predict probabilities"
". Originally received 'hinge'.")
assert en.parameters['loss'] == 'log'
def test_fit_predict_multi(X_y_multi):
X, y = X_y_multi
sk_clf = SKElasticNetClassifier(loss="log",
penalty="elasticnet",
alpha=0.5,
l1_ratio=0.5,
n_jobs=-1,
random_state=0)
sk_clf.fit(X, y)
y_pred_sk = sk_clf.predict(X)
y_pred_proba_sk = sk_clf.predict_proba(X)
clf = ElasticNetClassifier()
fitted = clf.fit(X, y)
assert isinstance(fitted, ElasticNetClassifier)
y_pred = clf.predict(X)
y_pred_proba = clf.predict_proba(X)
np.testing.assert_almost_equal(y_pred_sk, y_pred.to_series().values, decimal=5)
np.testing.assert_almost_equal(y_pred_proba_sk, y_pred_proba.to_dataframe().values, decimal=5)
def test_fit_predict_binary(X_y_binary):
X, y = X_y_binary
sk_clf = SKElasticNetClassifier(loss="log",
penalty="elasticnet",
alpha=0.5,
l1_ratio=0.5,
n_jobs=-1,
random_state=0)
sk_clf.fit(X, y)
y_pred_sk = sk_clf.predict(X)
y_pred_proba_sk = sk_clf.predict_proba(X)
clf = ElasticNetClassifier()
clf.fit(X, y)
y_pred = clf.predict(X)
y_pred_proba = clf.predict_proba(X)
np.testing.assert_almost_equal(y_pred, y_pred_sk, decimal=5)
np.testing.assert_almost_equal(y_pred_proba, y_pred_proba_sk, decimal=5)