def test_multi_target_sample_weight_partial_fit():
# weighted regressor
X = [[1, 2, 3], [4, 5, 6]]
y = [[3.141, 2.718], [2.718, 3.141]]
w = [2., 1.]
rgr_w = MultiOutputRegressor(SGDRegressor(random_state=0, max_iter=5))
rgr_w.partial_fit(X, y, w)
# weighted with different weights
w = [2., 2.]
rgr = MultiOutputRegressor(SGDRegressor(random_state=0, max_iter=5))
rgr.partial_fit(X, y, w)
assert rgr.predict(X)[0][0] != rgr_w.predict(X)[0][0]
def test_multi_target_sample_weights():
# weighted regressor
Xw = [[1, 2, 3], [4, 5, 6]]
yw = [[3.141, 2.718], [2.718, 3.141]]
w = [2., 1.]
rgr_w = MultiOutputRegressor(GradientBoostingRegressor(random_state=0))
rgr_w.fit(Xw, yw, w)
# unweighted, but with repeated samples
X = [[1, 2, 3], [1, 2, 3], [4, 5, 6]]
y = [[3.141, 2.718], [3.141, 2.718], [2.718, 3.141]]
rgr = MultiOutputRegressor(GradientBoostingRegressor(random_state=0))
rgr.fit(X, y)
X_test = [[1.5, 2.5, 3.5], [3.5, 4.5, 5.5]]
assert_almost_equal(rgr.predict(X_test), rgr_w.predict(X_test))
def test_multi_target_sparse_regression():
X, y = datasets.make_regression(n_targets=3)
X_train, y_train = X[:50], y[:50]
X_test = X[50:]
for sparse in [
sp.csr_matrix, sp.csc_matrix, sp.coo_matrix, sp.dok_matrix,
sp.lil_matrix
]:
rgr = MultiOutputRegressor(Lasso(random_state=0))
rgr_sparse = MultiOutputRegressor(Lasso(random_state=0))
rgr.fit(X_train, y_train)
rgr_sparse.fit(sparse(X_train), y_train)
assert_almost_equal(rgr.predict(X_test),
rgr_sparse.predict(sparse(X_test)))
def test_multi_target_regression_partial_fit():
X, y = datasets.make_regression(n_targets=3)
X_train, y_train = X[:50], y[:50]
X_test, y_test = X[50:], y[50:]
references = np.zeros_like(y_test)
half_index = 25
for n in range(3):
sgr = SGDRegressor(random_state=0, max_iter=5)
sgr.partial_fit(X_train[:half_index], y_train[:half_index, n])
sgr.partial_fit(X_train[half_index:], y_train[half_index:, n])
references[:, n] = sgr.predict(X_test)
sgr = MultiOutputRegressor(SGDRegressor(random_state=0, max_iter=5))
sgr.partial_fit(X_train[:half_index], y_train[:half_index])
sgr.partial_fit(X_train[half_index:], y_train[half_index:])
y_pred = sgr.predict(X_test)
assert_almost_equal(references, y_pred)
assert not hasattr(MultiOutputRegressor(Lasso), 'partial_fit')
def test_multi_target_regression():
X, y = datasets.make_regression(n_targets=3)
X_train, y_train = X[:50], y[:50]
X_test, y_test = X[50:], y[50:]
references = np.zeros_like(y_test)
for n in range(3):
rgr = GradientBoostingRegressor(random_state=0)
rgr.fit(X_train, y_train[:, n])
references[:, n] = rgr.predict(X_test)
rgr = MultiOutputRegressor(GradientBoostingRegressor(random_state=0))
rgr.fit(X_train, y_train)
y_pred = rgr.predict(X_test)
assert_almost_equal(references, y_pred)
def test_multi_target_sample_weights_api():
X = [[1, 2, 3], [4, 5, 6]]
y = [[3.141, 2.718], [2.718, 3.141]]
w = [0.8, 0.6]
rgr = MultiOutputRegressor(Lasso())
assert_raises_regex(ValueError, "does not support sample weights", rgr.fit,
X, y, w)
# no exception should be raised if the base estimator supports weights
rgr = MultiOutputRegressor(GradientBoostingRegressor(random_state=0))
rgr.fit(X, y, w)
def test_multi_target_regression_one_target():
# Test multi target regression raises
X, y = datasets.make_regression(n_targets=1)
rgr = MultiOutputRegressor(GradientBoostingRegressor(random_state=0))
assert_raises(ValueError, rgr.fit, X, y)
# Create a random dataset
rng = np.random.RandomState(1)
X = np.sort(200 * rng.rand(600, 1) - 100, axis=0)
y = np.array([np.pi * np.sin(X).ravel(), np.pi * np.cos(X).ravel()]).T
y += (0.5 - rng.rand(*y.shape))
X_train, X_test, y_train, y_test = train_test_split(X,
y,
train_size=400,
test_size=200,
random_state=4)
max_depth = 30
regr_multirf = MultiOutputRegressor(
RandomForestRegressor(n_estimators=100,
max_depth=max_depth,
random_state=0))
regr_multirf.fit(X_train, y_train)
regr_rf = RandomForestRegressor(n_estimators=100,
max_depth=max_depth,
random_state=2)
regr_rf.fit(X_train, y_train)
# Predict on new data
y_multirf = regr_multirf.predict(X_test)
y_rf = regr_rf.predict(X_test)
# Plot the results
plt.figure()
s = 50