FIX: make LinearRegression perfectly consistent across sparse or dense#13279
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GaelVaroquaux merged 3 commits intoscikit-learn:masterfrom Feb 27, 2019
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| clf_dense.fit(X, y) | ||
| clf_sparse.fit(Xcsr, y) | ||
| assert_almost_equal(clf_dense.intercept_, clf_sparse.intercept_) | ||
| assert_array_almost_equal(clf_dense.coef_, clf_sparse.coef_) |
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| assert_array_almost_equal(clf_dense.coef_, clf_sparse.coef_) | |
| assert_allclose(clf_dense.coef_, clf_sparse.coef_) |
| clf_sparse = LinearRegression(**params) | ||
| clf_dense.fit(X, y) | ||
| clf_sparse.fit(Xcsr, y) | ||
| assert_almost_equal(clf_dense.intercept_, clf_sparse.intercept_) |
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| assert_almost_equal(clf_dense.intercept_, clf_sparse.intercept_) | |
| assert clf_dense.intercept_ == pytest.approx(clf_sparse.intercept_) |
glemaitre
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jnothman
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Feb 26, 2019
doc/whats_new/v0.21.rst
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| :issue:`12972` by :user:`Lucio Fernandez-Arjona <luk-f-a>` | ||
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| - |Fix| Fixed a bug in :class:`linear_model.LinearRegression` that | ||
| was not returning the same coeffecient and intercepts with |
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I think this is missing mention of sparse/dense
sklearn/linear_model/base.py
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| def matvec(b): | ||
| return X.dot(b) - b.dot(X_offset_scale) | ||
| def rmatvec(b): | ||
| return X.T.dot(b) - (X_offset_scale) * np.sum(b) |
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| X_centered = sparse.linalg.LinearOperator(shape=X.shape, | ||
| matvec=matvec, | ||
| rmatvec=rmatvec) |
GaelVaroquaux
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Beautiful solution. +1 for merge.
Merging.
wdevazelhes
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Feb 27, 2019
…e the fit_intercept=False that should not be needed since scikit-learn#13279 is merged
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When 0-indented you require two blank lines, but elsewhere one.
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xhluca
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Apr 28, 2019
scikit-learn#13279) * FIX : make LinearRegression perfectly consistent across sparse or dense * comments * review
xhluca
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… or dense (scikit-learn#13279)" This reverts commit 41f106c.
xhluca
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Apr 28, 2019
… or dense (scikit-learn#13279)" This reverts commit 41f106c.
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Jul 12, 2019
scikit-learn#13279) * FIX : make LinearRegression perfectly consistent across sparse or dense * comments * review
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due to non centering of X when sparse, LinearRegression has never been 100% the same as the dense solver. This now fixes this.
cc @amueller