def test_notfitted():
eclf = VotingClassifier(estimators=[('lr1', LogisticRegression()),
('lr2', LogisticRegression())],
voting='soft')
ereg = VotingRegressor([('dr', DummyRegressor())])
msg = ("This %s instance is not fitted yet. Call \'fit\'"
" with appropriate arguments before using this estimator.")
with pytest.raises(NotFittedError, match=msg % 'VotingClassifier'):
eclf.predict(X)
with pytest.raises(NotFittedError, match=msg % 'VotingClassifier'):
eclf.predict_proba(X)
with pytest.raises(NotFittedError, match=msg % 'VotingClassifier'):
eclf.transform(X)
with pytest.raises(NotFittedError, match=msg % 'VotingRegressor'):
ereg.predict(X_r)
with pytest.raises(NotFittedError, match=msg % 'VotingRegressor'):
ereg.transform(X_r)
def test_notfitted():
eclf = VotingClassifier(
estimators=[("lr1", LogisticRegression()),
("lr2", LogisticRegression())],
voting="soft",
)
ereg = VotingRegressor([("dr", DummyRegressor())])
msg = ("This %s instance is not fitted yet. Call 'fit'"
" with appropriate arguments before using this estimator.")
with pytest.raises(NotFittedError, match=msg % "VotingClassifier"):
eclf.predict(X)
with pytest.raises(NotFittedError, match=msg % "VotingClassifier"):
eclf.predict_proba(X)
with pytest.raises(NotFittedError, match=msg % "VotingClassifier"):
eclf.transform(X)
with pytest.raises(NotFittedError, match=msg % "VotingRegressor"):
ereg.predict(X_r)
with pytest.raises(NotFittedError, match=msg % "VotingRegressor"):
ereg.transform(X_r)
