[MRG] ENH: Make StackingRegressor support Multioutput #27704
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@OmarManzoor would you maybe have time to have a look here? |
OmarManzoor
reviewed
Apr 18, 2024
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Thank you for the PR @hmasdev. A few comments. I think we will also need a changelog entry for this.
OmarManzoor
reviewed
Apr 25, 2024
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| # NOTE: In this case the estimator can predict almost exactly the target | ||
| assert_allclose( | ||
| y_pred, | ||
| # NOTE: when the target is 2D but with a single output, | ||
| # the predictions are 1D because of column_or_1d |
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| # NOTE: In this case the estimator can predict almost exactly the target | |
| assert_allclose( | |
| y_pred, | |
| # NOTE: when the target is 2D but with a single output, | |
| # the predictions are 1D because of column_or_1d | |
| # NOTE: In this case the estimator can predict almost exactly the target. | |
| # When the target is 2D but with a single output the predictions are 1D | |
| # because of column_or_1d | |
| assert_allclose( | |
| y_pred, |
| rtol=acceptable_relative_tolerance, | ||
| atol=acceptable_aboslute_tolerance, | ||
| ) | ||
| # transform |
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| # transform |
| ) | ||
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| reg.fit(X_train, y_train) | ||
| # predict |
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| # predict |
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| def test_stacking_regressor_multioutput_with_passthrough(): | ||
| """Check that a stacking regressor with multioutput works""" |
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| """Check that a stacking regressor with multioutput works""" | |
| """Check that a stacking regressor with passthrough works with multioutput""" |
| rtol=acceptable_relative_tolerance, | ||
| atol=acceptable_aboslute_tolerance, | ||
| ) | ||
| # transform |
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| # transform |
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| def test_stacking_regressor_multioutput(): | ||
| """Check that a stacking regressor with multioutput works""" |
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| """Check that a stacking regressor with multioutput works""" | |
| """Check that a stacking regressor works with multioutput""" |
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@OmarManzoor
Actually, I don't have a good idea yet on how to handle an estimator that does not support multiple outputs when it is used in a multi-output problem. Do you know such an API? Python 3.10.13 (main, Feb 22 2024, 10:50:12) [GCC 11.4.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> from sklearn.ensemble import StackingRegressor
>>> from sklearn.svm import SVR
>>> from sklearn.linear_model import LinearRegression
>>> lr = LinearRegression()
>>> svr = SVR()
>>> model = StackingRegressor(estimators=[('lr', lr), ('svr', svr)])
>>> import numpy as np
>>> X = np.random.randn(10, 2)
>>> Y = X ** 2
>>> model.fit(X, Y)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/root/workspace/scikit-learn/sklearn/ensemble/_stacking.py", line 973, in fit
return super().fit(X, y, sample_weight)
File "/root/workspace/scikit-learn/sklearn/base.py", line 1473, in wrapper
return fit_method(estimator, *args, **kwargs)
File "/root/workspace/scikit-learn/sklearn/ensemble/_stacking.py", line 224, in fit
self.estimators_ = Parallel(n_jobs=self.n_jobs)(
File "/root/workspace/scikit-learn/sklearn/utils/parallel.py", line 67, in __call__
return super().__call__(iterable_with_config)
File "/root/workspace/scikit-learn/sklearn-env/lib/python3.10/site-packages/joblib/parallel.py", line 1918, in __call__
return output if self.return_generator else list(output)
File "/root/workspace/scikit-learn/sklearn-env/lib/python3.10/site-packages/joblib/parallel.py", line 1847, in _get_sequential_output
res = func(*args, **kwargs)
File "/root/workspace/scikit-learn/sklearn/utils/parallel.py", line 129, in __call__
return self.function(*args, **kwargs)
File "/root/workspace/scikit-learn/sklearn/ensemble/_base.py", line 40, in _fit_single_estimator
estimator.fit(X, y, **fit_params)
File "/root/workspace/scikit-learn/sklearn/base.py", line 1473, in wrapper
return fit_method(estimator, *args, **kwargs)
File "/root/workspace/scikit-learn/sklearn/svm/_base.py", line 190, in fit
X, y = self._validate_data(
File "/root/workspace/scikit-learn/sklearn/base.py", line 650, in _validate_data
X, y = check_X_y(X, y, **check_params)
File "/root/workspace/scikit-learn/sklearn/utils/validation.py", line 1282, in check_X_y
y = _check_y(y, multi_output=multi_output, y_numeric=y_numeric, estimator=estimator)
File "/root/workspace/scikit-learn/sklearn/utils/validation.py", line 1303, in _check_y
y = column_or_1d(y, warn=True)
File "/root/workspace/scikit-learn/sklearn/utils/validation.py", line 1370, in column_or_1d
raise ValueError(
ValueError: y should be a 1d array, got an array of shape (10, 2) instead. |
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Note that >>> import numpy as np
>>> from sklearn.linear_model import LogisticRegression
>>> from sklearn.ensemble import StackingClassifier
>>> from sklearn.multioutput import MultiOutputClassifier
>>> X = np.random.randn(10, 2)
>>> Y = X > 0 # multilabel classification
>>> model = StackingClassifier(estimators=[('lr', MultiOutputClassifier(LogisticRegression(C=1e3))), ('lr2', MultiOutputClassifier(LogisticRegression(C=1e3)))], final_estimator=MultiOutputClassifier(LogisticRegression(C=1e3)))
>>> model.fit(X, Y)
StackingClassifier(estimators=[('lr',
MultiOutputClassifier(estimator=LogisticRegression(C=1000.0))),
('lr2',
MultiOutputClassifier(estimator=LogisticRegression(C=1000.0)))],
final_estimator=MultiOutputClassifier(estimator=LogisticRegression(C=1000.0)))
>>> model.predict(X)[:3]
array([[ True, True],
[False, True],
[False, False]])
>>> model.predict_proba(X)[:3]
array([[1.55247883e-03, 7.05602027e-04],
[9.99983741e-01, 2.31839536e-03],
[9.99873471e-01, 9.99473360e-01]])
>>> Z = np.random.choice(range(3), size=X.shape) # multiclass-multioutput classification
>>> model.fit(X, Z)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/root/workspace/scikit-learn/sklearn/ensemble/_stacking.py", line 669, in fit
self._label_encoder = LabelEncoder().fit(y)
File "/root/workspace/scikit-learn/sklearn/preprocessing/_label.py", line 97, in fit
y = column_or_1d(y, warn=True)
File "/root/workspace/scikit-learn/sklearn/utils/validation.py", line 1370, in column_or_1d
raise ValueError(
ValueError: y should be a 1d array, got an array of shape (10, 2) instead.Ref. https://scikit-learn.org/stable/modules/multiclass.html |
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Reference Issues/PRs
Related to #25597
Similar to #8547
Similar to #19223
What does this implement/fix? Explain your changes.
Any other comments?
I am concerned the followings: