def test_transform_target_regressor_functions_multioutput():
X = friedman[0]
y = np.vstack((friedman[1], friedman[1] ** 2 + 1)).T
regr = TransformedTargetRegressor(regressor=LinearRegression(),
func=np.log, inverse_func=np.exp)
y_pred = regr.fit(X, y).predict(X)
# check the transformer output
y_tran = regr.transformer_.transform(y)
assert_allclose(np.log(y), y_tran)
assert_allclose(y, regr.transformer_.inverse_transform(y_tran))
assert y.shape == y_pred.shape
assert_allclose(y_pred, regr.inverse_func(regr.regressor_.predict(X)))
# check the regressor output
lr = LinearRegression().fit(X, regr.func(y))
assert_allclose(regr.regressor_.coef_.ravel(), lr.coef_.ravel())
def test_transform_target_regressor_functions():
X, y = friedman
regr = TransformedTargetRegressor(regressor=LinearRegression(),
func=np.log, inverse_func=np.exp)
y_pred = regr.fit(X, y).predict(X)
# check the transformer output
y_tran = regr.transformer_.transform(y.reshape(-1, 1)).squeeze()
assert_allclose(np.log(y), y_tran)
assert_allclose(y, regr.transformer_.inverse_transform(
y_tran.reshape(-1, 1)).squeeze())
assert y.shape == y_pred.shape
assert_allclose(y_pred, regr.inverse_func(regr.regressor_.predict(X)))
# check the regressor output
lr = LinearRegression().fit(X, regr.func(y))
assert_allclose(regr.regressor_.coef_.ravel(), lr.coef_.ravel())
def test_transform_target_regressor_functions_multioutput():
X = friedman[0]
y = np.vstack((friedman[1], friedman[1]**2 + 1)).T
regr = TransformedTargetRegressor(regressor=LinearRegression(),
func=np.log,
inverse_func=np.exp)
y_pred = regr.fit(X, y).predict(X)
# check the transformer output
y_tran = regr.transformer_.transform(y)
assert_allclose(np.log(y), y_tran)
assert_allclose(y, regr.transformer_.inverse_transform(y_tran))
assert y.shape == y_pred.shape
assert_allclose(y_pred, regr.inverse_func(regr.regressor_.predict(X)))
# check the regressor output
lr = LinearRegression().fit(X, regr.func(y))
assert_allclose(regr.regressor_.coef_.ravel(), lr.coef_.ravel())
def test_transform_target_regressor_functions():
X, y = friedman
regr = TransformedTargetRegressor(regressor=LinearRegression(),
func=np.log,
inverse_func=np.exp)
y_pred = regr.fit(X, y).predict(X)
# check the transformer output
y_tran = regr.transformer_.transform(y.reshape(-1, 1)).squeeze()
assert_allclose(np.log(y), y_tran)
assert_allclose(
y,
regr.transformer_.inverse_transform(y_tran.reshape(-1, 1)).squeeze())
assert y.shape == y_pred.shape
assert_allclose(y_pred, regr.inverse_func(regr.regressor_.predict(X)))
# check the regressor output
lr = LinearRegression().fit(X, regr.func(y))
assert_allclose(regr.regressor_.coef_.ravel(), lr.coef_.ravel())