388 lines
13 KiB
Python
388 lines
13 KiB
Python
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import numpy as np
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import pytest
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from sklearn import datasets
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from sklearn.base import BaseEstimator, TransformerMixin, clone
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from sklearn.compose import TransformedTargetRegressor
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from sklearn.dummy import DummyRegressor
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from sklearn.linear_model import LinearRegression, OrthogonalMatchingPursuit
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from sklearn.pipeline import Pipeline
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from sklearn.preprocessing import FunctionTransformer, StandardScaler
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from sklearn.utils._testing import assert_allclose, assert_no_warnings
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friedman = datasets.make_friedman1(random_state=0)
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def test_transform_target_regressor_error():
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X, y = friedman
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# provide a transformer and functions at the same time
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regr = TransformedTargetRegressor(
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regressor=LinearRegression(),
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transformer=StandardScaler(),
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func=np.exp,
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inverse_func=np.log,
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)
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with pytest.raises(
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ValueError,
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match="'transformer' and functions 'func'/'inverse_func' cannot both be set.",
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):
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regr.fit(X, y)
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# fit with sample_weight with a regressor which does not support it
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sample_weight = np.ones((y.shape[0],))
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regr = TransformedTargetRegressor(
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regressor=OrthogonalMatchingPursuit(), transformer=StandardScaler()
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)
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with pytest.raises(
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TypeError,
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match=r"fit\(\) got an unexpected " "keyword argument 'sample_weight'",
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):
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regr.fit(X, y, sample_weight=sample_weight)
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# func is given but inverse_func is not
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regr = TransformedTargetRegressor(func=np.exp)
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with pytest.raises(
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ValueError,
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match="When 'func' is provided, 'inverse_func' must also be provided",
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):
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regr.fit(X, y)
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def test_transform_target_regressor_invertible():
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X, y = friedman
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regr = TransformedTargetRegressor(
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regressor=LinearRegression(),
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func=np.sqrt,
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inverse_func=np.log,
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check_inverse=True,
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)
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with pytest.warns(
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UserWarning,
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match=(
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"The provided functions or"
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" transformer are not strictly inverse of each other."
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),
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):
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regr.fit(X, y)
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regr = TransformedTargetRegressor(
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regressor=LinearRegression(), func=np.sqrt, inverse_func=np.log
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)
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regr.set_params(check_inverse=False)
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assert_no_warnings(regr.fit, X, y)
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def _check_standard_scaled(y, y_pred):
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y_mean = np.mean(y, axis=0)
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y_std = np.std(y, axis=0)
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assert_allclose((y - y_mean) / y_std, y_pred)
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def _check_shifted_by_one(y, y_pred):
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assert_allclose(y + 1, y_pred)
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def test_transform_target_regressor_functions():
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X, y = friedman
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regr = TransformedTargetRegressor(
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regressor=LinearRegression(), func=np.log, inverse_func=np.exp
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)
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y_pred = regr.fit(X, y).predict(X)
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# check the transformer output
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y_tran = regr.transformer_.transform(y.reshape(-1, 1)).squeeze()
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assert_allclose(np.log(y), y_tran)
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assert_allclose(
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y, regr.transformer_.inverse_transform(y_tran.reshape(-1, 1)).squeeze()
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)
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assert y.shape == y_pred.shape
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assert_allclose(y_pred, regr.inverse_func(regr.regressor_.predict(X)))
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# check the regressor output
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lr = LinearRegression().fit(X, regr.func(y))
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assert_allclose(regr.regressor_.coef_.ravel(), lr.coef_.ravel())
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def test_transform_target_regressor_functions_multioutput():
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X = friedman[0]
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y = np.vstack((friedman[1], friedman[1] ** 2 + 1)).T
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regr = TransformedTargetRegressor(
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regressor=LinearRegression(), func=np.log, inverse_func=np.exp
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)
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y_pred = regr.fit(X, y).predict(X)
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# check the transformer output
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y_tran = regr.transformer_.transform(y)
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assert_allclose(np.log(y), y_tran)
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assert_allclose(y, regr.transformer_.inverse_transform(y_tran))
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assert y.shape == y_pred.shape
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assert_allclose(y_pred, regr.inverse_func(regr.regressor_.predict(X)))
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# check the regressor output
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lr = LinearRegression().fit(X, regr.func(y))
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assert_allclose(regr.regressor_.coef_.ravel(), lr.coef_.ravel())
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@pytest.mark.parametrize(
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"X,y", [friedman, (friedman[0], np.vstack((friedman[1], friedman[1] ** 2 + 1)).T)]
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)
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def test_transform_target_regressor_1d_transformer(X, y):
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# All transformer in scikit-learn expect 2D data. FunctionTransformer with
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# validate=False lift this constraint without checking that the input is a
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# 2D vector. We check the consistency of the data shape using a 1D and 2D y
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# array.
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transformer = FunctionTransformer(
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func=lambda x: x + 1, inverse_func=lambda x: x - 1
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)
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regr = TransformedTargetRegressor(
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regressor=LinearRegression(), transformer=transformer
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)
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y_pred = regr.fit(X, y).predict(X)
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assert y.shape == y_pred.shape
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# consistency forward transform
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y_tran = regr.transformer_.transform(y)
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_check_shifted_by_one(y, y_tran)
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assert y.shape == y_pred.shape
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# consistency inverse transform
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assert_allclose(y, regr.transformer_.inverse_transform(y_tran).squeeze())
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# consistency of the regressor
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lr = LinearRegression()
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transformer2 = clone(transformer)
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lr.fit(X, transformer2.fit_transform(y))
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y_lr_pred = lr.predict(X)
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assert_allclose(y_pred, transformer2.inverse_transform(y_lr_pred))
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assert_allclose(regr.regressor_.coef_, lr.coef_)
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@pytest.mark.parametrize(
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"X,y", [friedman, (friedman[0], np.vstack((friedman[1], friedman[1] ** 2 + 1)).T)]
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)
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def test_transform_target_regressor_2d_transformer(X, y):
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# Check consistency with transformer accepting only 2D array and a 1D/2D y
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# array.
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transformer = StandardScaler()
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regr = TransformedTargetRegressor(
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regressor=LinearRegression(), transformer=transformer
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)
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y_pred = regr.fit(X, y).predict(X)
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assert y.shape == y_pred.shape
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# consistency forward transform
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if y.ndim == 1: # create a 2D array and squeeze results
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y_tran = regr.transformer_.transform(y.reshape(-1, 1))
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else:
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y_tran = regr.transformer_.transform(y)
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_check_standard_scaled(y, y_tran.squeeze())
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assert y.shape == y_pred.shape
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# consistency inverse transform
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assert_allclose(y, regr.transformer_.inverse_transform(y_tran).squeeze())
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# consistency of the regressor
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lr = LinearRegression()
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transformer2 = clone(transformer)
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if y.ndim == 1: # create a 2D array and squeeze results
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lr.fit(X, transformer2.fit_transform(y.reshape(-1, 1)).squeeze())
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y_lr_pred = lr.predict(X).reshape(-1, 1)
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y_pred2 = transformer2.inverse_transform(y_lr_pred).squeeze()
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else:
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lr.fit(X, transformer2.fit_transform(y))
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y_lr_pred = lr.predict(X)
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y_pred2 = transformer2.inverse_transform(y_lr_pred)
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assert_allclose(y_pred, y_pred2)
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assert_allclose(regr.regressor_.coef_, lr.coef_)
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def test_transform_target_regressor_2d_transformer_multioutput():
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# Check consistency with transformer accepting only 2D array and a 2D y
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# array.
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X = friedman[0]
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y = np.vstack((friedman[1], friedman[1] ** 2 + 1)).T
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transformer = StandardScaler()
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regr = TransformedTargetRegressor(
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regressor=LinearRegression(), transformer=transformer
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)
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y_pred = regr.fit(X, y).predict(X)
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assert y.shape == y_pred.shape
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# consistency forward transform
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y_tran = regr.transformer_.transform(y)
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_check_standard_scaled(y, y_tran)
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assert y.shape == y_pred.shape
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# consistency inverse transform
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assert_allclose(y, regr.transformer_.inverse_transform(y_tran).squeeze())
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# consistency of the regressor
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lr = LinearRegression()
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transformer2 = clone(transformer)
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lr.fit(X, transformer2.fit_transform(y))
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y_lr_pred = lr.predict(X)
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assert_allclose(y_pred, transformer2.inverse_transform(y_lr_pred))
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assert_allclose(regr.regressor_.coef_, lr.coef_)
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def test_transform_target_regressor_3d_target():
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# Non-regression test for:
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# https://github.com/scikit-learn/scikit-learn/issues/18866
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# Check with a 3D target with a transformer that reshapes the target
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X = friedman[0]
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y = np.tile(friedman[1].reshape(-1, 1, 1), [1, 3, 2])
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def flatten_data(data):
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return data.reshape(data.shape[0], -1)
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def unflatten_data(data):
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return data.reshape(data.shape[0], -1, 2)
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transformer = FunctionTransformer(func=flatten_data, inverse_func=unflatten_data)
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regr = TransformedTargetRegressor(
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regressor=LinearRegression(), transformer=transformer
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)
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y_pred = regr.fit(X, y).predict(X)
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assert y.shape == y_pred.shape
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def test_transform_target_regressor_multi_to_single():
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X = friedman[0]
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y = np.transpose([friedman[1], (friedman[1] ** 2 + 1)])
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def func(y):
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out = np.sqrt(y[:, 0] ** 2 + y[:, 1] ** 2)
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return out[:, np.newaxis]
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def inverse_func(y):
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return y
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tt = TransformedTargetRegressor(
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func=func, inverse_func=inverse_func, check_inverse=False
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)
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tt.fit(X, y)
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y_pred_2d_func = tt.predict(X)
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assert y_pred_2d_func.shape == (100, 1)
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# force that the function only return a 1D array
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def func(y):
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return np.sqrt(y[:, 0] ** 2 + y[:, 1] ** 2)
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tt = TransformedTargetRegressor(
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func=func, inverse_func=inverse_func, check_inverse=False
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)
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tt.fit(X, y)
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y_pred_1d_func = tt.predict(X)
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assert y_pred_1d_func.shape == (100, 1)
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assert_allclose(y_pred_1d_func, y_pred_2d_func)
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class DummyCheckerArrayTransformer(TransformerMixin, BaseEstimator):
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def fit(self, X, y=None):
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assert isinstance(X, np.ndarray)
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return self
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def transform(self, X):
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assert isinstance(X, np.ndarray)
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return X
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def inverse_transform(self, X):
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assert isinstance(X, np.ndarray)
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return X
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class DummyCheckerListRegressor(DummyRegressor):
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def fit(self, X, y, sample_weight=None):
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assert isinstance(X, list)
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return super().fit(X, y, sample_weight)
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def predict(self, X):
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assert isinstance(X, list)
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return super().predict(X)
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def test_transform_target_regressor_ensure_y_array():
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# check that the target ``y`` passed to the transformer will always be a
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# numpy array. Similarly, if ``X`` is passed as a list, we check that the
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# predictor receive as it is.
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X, y = friedman
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tt = TransformedTargetRegressor(
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transformer=DummyCheckerArrayTransformer(),
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regressor=DummyCheckerListRegressor(),
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check_inverse=False,
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)
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tt.fit(X.tolist(), y.tolist())
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tt.predict(X.tolist())
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with pytest.raises(AssertionError):
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tt.fit(X, y.tolist())
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with pytest.raises(AssertionError):
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tt.predict(X)
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class DummyTransformer(TransformerMixin, BaseEstimator):
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"""Dummy transformer which count how many time fit was called."""
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def __init__(self, fit_counter=0):
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self.fit_counter = fit_counter
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def fit(self, X, y=None):
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self.fit_counter += 1
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return self
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def transform(self, X):
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return X
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def inverse_transform(self, X):
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return X
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@pytest.mark.parametrize("check_inverse", [False, True])
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def test_transform_target_regressor_count_fit(check_inverse):
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# regression test for gh-issue #11618
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# check that we only call a single time fit for the transformer
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X, y = friedman
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ttr = TransformedTargetRegressor(
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transformer=DummyTransformer(), check_inverse=check_inverse
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)
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ttr.fit(X, y)
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assert ttr.transformer_.fit_counter == 1
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class DummyRegressorWithExtraFitParams(DummyRegressor):
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def fit(self, X, y, sample_weight=None, check_input=True):
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# on the test below we force this to false, we make sure this is
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# actually passed to the regressor
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assert not check_input
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return super().fit(X, y, sample_weight)
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def test_transform_target_regressor_pass_fit_parameters():
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X, y = friedman
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regr = TransformedTargetRegressor(
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regressor=DummyRegressorWithExtraFitParams(), transformer=DummyTransformer()
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)
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regr.fit(X, y, check_input=False)
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assert regr.transformer_.fit_counter == 1
|
||
|
|
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||
|
|
|
||
|
|
def test_transform_target_regressor_route_pipeline():
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||
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|
X, y = friedman
|
||
|
|
|
||
|
|
regr = TransformedTargetRegressor(
|
||
|
|
regressor=DummyRegressorWithExtraFitParams(), transformer=DummyTransformer()
|
||
|
|
)
|
||
|
|
estimators = [("normalize", StandardScaler()), ("est", regr)]
|
||
|
|
|
||
|
|
pip = Pipeline(estimators)
|
||
|
|
pip.fit(X, y, **{"est__check_input": False})
|
||
|
|
|
||
|
|
assert regr.transformer_.fit_counter == 1
|
||
|
|
|
||
|
|
|
||
|
|
class DummyRegressorWithExtraPredictParams(DummyRegressor):
|
||
|
|
def predict(self, X, check_input=True):
|
||
|
|
# In the test below we make sure that the check input parameter is
|
||
|
|
# passed as false
|
||
|
|
self.predict_called = True
|
||
|
|
assert not check_input
|
||
|
|
return super().predict(X)
|
||
|
|
|
||
|
|
|
||
|
|
def test_transform_target_regressor_pass_extra_predict_parameters():
|
||
|
|
# Checks that predict kwargs are passed to regressor.
|
||
|
|
X, y = friedman
|
||
|
|
regr = TransformedTargetRegressor(
|
||
|
|
regressor=DummyRegressorWithExtraPredictParams(), transformer=DummyTransformer()
|
||
|
|
)
|
||
|
|
|
||
|
|
regr.fit(X, y)
|
||
|
|
regr.predict(X, check_input=False)
|
||
|
|
assert regr.regressor_.predict_called
|