def test_zero_estimator_reg(): # Test if ZeroEstimator works for regression. est = GradientBoostingRegressor(n_estimators=20, max_depth=1, random_state=1, init=ZeroEstimator()) est.fit(boston.data, boston.target) y_pred = est.predict(boston.data) mse = mean_squared_error(boston.target, y_pred) assert_almost_equal(mse, 33.0, decimal=0) est = GradientBoostingRegressor(n_estimators=20, max_depth=1, random_state=1, init='zero') est.fit(boston.data, boston.target) y_pred = est.predict(boston.data) mse = mean_squared_error(boston.target, y_pred) assert_almost_equal(mse, 33.0, decimal=0) est = GradientBoostingRegressor(n_estimators=20, max_depth=1, random_state=1, init='foobar') assert_raises(ValueError, est.fit, boston.data, boston.target)
def test_zero_estimator_clf(): # Test if ZeroEstimator works for classification. X = iris.data y = np.array(iris.target) est = GradientBoostingClassifier(n_estimators=20, max_depth=1, random_state=1, init=ZeroEstimator()) est.fit(X, y) assert_greater(est.score(X, y), 0.96) est = GradientBoostingClassifier(n_estimators=20, max_depth=1, random_state=1, init='zero') est.fit(X, y) assert_greater(est.score(X, y), 0.96) # binary clf mask = y != 0 y[mask] = 1 y[~mask] = 0 est = GradientBoostingClassifier(n_estimators=20, max_depth=1, random_state=1, init='zero') est.fit(X, y) assert_greater(est.score(X, y), 0.96) est = GradientBoostingClassifier(n_estimators=20, max_depth=1, random_state=1, init='foobar') assert_raises(ValueError, est.fit, X, y)
def init_estimator(self): return ZeroEstimator()
def init_estimator(self): # pragma: no cover return ZeroEstimator()