def test_gb_regression(n_samples=1000):
X, _ = generate_sample(n_samples, 10, distance=0.6)
y = numpy.tanh(X.sum(axis=1))
clf = UGradientBoostingRegressor(loss=MSELossFunction())
clf.fit(X, y)
y_pred = clf.predict(X)
zeromse = 0.5 * mean_squared_error(y, y * 0.)
assert mean_squared_error(y, y_pred) < zeromse, 'something wrong with regression quality'
def test_gb_regression(n_samples=1000):
X, _ = generate_sample(n_samples, 10, distance=0.6)
y = numpy.tanh(X.sum(axis=1))
clf = UGradientBoostingRegressor(loss=MSELossFunction())
clf.fit(X, y)
y_pred = clf.predict(X)
zeromse = 0.5 * mean_squared_error(y, y * 0.)
assert mean_squared_error(y, y_pred) < zeromse, 'something wrong with regression quality'
def test_constant_fitting(n_samples=1000, n_features=5):
"""
Testing if initial constant fitted properly
"""
X, y = generate_sample(n_samples=n_samples, n_features=n_features)
y = y.astype(numpy.float) + 1000.
for loss in [MSELossFunction(), losses.MAELossFunction()]:
gb = UGradientBoostingRegressor(loss=loss, n_estimators=10)
gb.fit(X, y)
p = gb.predict(X)
assert mean_squared_error(p, y) < 0.5
def test_constant_fitting(n_samples=1000, n_features=5):
"""
Testing if initial constant fitted properly
"""
X, y = generate_sample(n_samples=n_samples, n_features=n_features)
y = y.astype(numpy.float) + 1000.
for loss in [MSELossFunction(), losses.MAELossFunction()]:
gb = UGradientBoostingRegressor(loss=loss, n_estimators=10)
gb.fit(X, y)
p = gb.predict(X)
assert mean_squared_error(p, y) < 0.5
