8000 FIX improve error message for large sparse matrix input in LogisticRe… · scikit-learn/scikit-learn@762eca5 · GitHub
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TomDLTTomDLT
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FIX improve error message for large sparse matrix input in LogisticRegression (#21093)
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-3
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‎doc/whats_new/v1.1.rst

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@@ -38,6 +38,13 @@ Changelog
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:pr:`123456` by :user:`Joe Bloggs <joeongithub>`.
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where 123456 is the *pull request* number, not the issue number.
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:mod:`sklearn.linear_model`
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...........................
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- |Fix| :class:`linear_model.LogisticRegression` now raises a better error
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message when the solver does not support sparse matrices with int64 indices.
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:pr:`21093` by `Tom Dupre la Tour`_.
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:mod:`sklearn.utils`
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....................
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‎sklearn/linear_model/_logistic.py

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@@ -663,7 +663,7 @@ def _logistic_regression_path(
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X,
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accept_sparse="csr",
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dtype=np.float64,
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accept_large_sparse=solver != "liblinear",
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accept_large_sparse=solver not in ["liblinear", "sag", "saga"],
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)
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y = check_array(y, ensure_2d=False, dtype=None)
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check_consistent_length(X, y)
@@ -1511,7 +1511,7 @@ def fit(self, X, y, sample_weight=None):
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accept_sparse="csr",
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dtype=_dtype,
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order="C",
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accept_large_sparse=solver != "liblinear",
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accept_large_sparse=solver not in ["liblinear", "sag", "saga"],
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)
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check_classification_targets(y)
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self.classes_ = np.unique(y)
@@ -2080,7 +2080,7 @@ def fit(self, X, y, sample_weight=None):
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accept_sparse="csr",
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dtype=np.float64,
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order="C",
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accept_large_sparse=solver != "liblinear",
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accept_large_sparse=solver not in ["liblinear", "sag", "saga"],
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)
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check_classification_targets(y)
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‎sklearn/linear_model/tests/test_logistic.py

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@@ -2237,3 +2237,22 @@ def test_sample_weight_not_modified(multi_class, class_weight):
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)
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clf.fit(X, y, sample_weight=W)
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assert_allclose(expected, W)
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@pytest.mark.parametrize("solver", ["liblinear", "lbfgs", "newton-cg", "sag", "saga"])
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def test_large_sparse_matrix(solver):
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# Solvers either accept large sparse matrices, or raise helpful error.
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# Non-regression test for pull-request #21093.
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# generate sparse matrix with int64 indices
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X = sp.rand(20, 10, format="csr")
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for attr in ["indices", "indptr"]:
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setattr(X, attr, getattr(X, attr).astype("int64"))
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y = np.random.randint(2, size=X.shape[0])
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if solver in ["liblinear", "sag", "saga"]:
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msg = "Only sparse matrices with 32-bit integer indices"
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with pytest.raises(ValueError, match=msg):
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LogisticRegression(solver=solver).fit(X, y)
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else:
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LogisticRegression(solver=solver).fit(X, y)

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