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()