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)