8000 MAINT Parameters validation for sklearn.preprocessing.normalize (#26069) · thomasjpfan/scikit-learn@fb52671 · GitHub
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MAINT Parameters validation for sklearn.preprocessing.normalize (scikit-learn#26069)
Co-authored-by: jeremiedbb <jeremiedbb@yahoo.fr>
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‎sklearn/preprocessing/_data.py

Lines changed: 10 additions & 6 deletions
Original file line numberDiff line numberDiff line change
@@ -1777,6 +1777,15 @@ def robust_scale(
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return X
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@validate_params(
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{
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"X": ["array-like", "sparse matrix"],
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"norm": [StrOptions({"l1", "l2", "max"})],
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"axis": [Options(Integral, {0, 1})],
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"copy": ["boolean"],
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"return_norm": ["boolean"],
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}
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)
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def normalize(X, norm="l2", *, axis=1, copy=True, return_norm=False):
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"""Scale input vectors individually to unit norm (vector length).
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@@ -1826,15 +1835,10 @@ def normalize(X, norm="l2", *, axis=1, copy=True, return_norm=False):
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see :ref:`examples/preprocessing/plot_all_scaling.py
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<sphx_glr_auto_examples_preprocessing_plot_all_scaling.py>`.
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"""
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if norm not in ("l1", "l2", "max"):
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raise ValueError("'%s' is not a supported norm" % norm)
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if axis == 0:
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sparse_format = "csc"
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elif axis == 1:
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else: # axis == 1:
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sparse_format = "csr"
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else:
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raise ValueError("'%d' is not a supported axis" % axis)
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X = check_array(
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X,

‎sklearn/preprocessing/tests/test_data.py

Lines changed: 0 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -1988,10 +1988,6 @@ def test_normalize():
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# Only tests functionality not used by the tests for Normalizer.
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X = np.random.RandomState(37).randn(3, 2)
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assert_array_equal(normalize(X, copy=False), normalize(X.T, axis=0, copy=False).T)
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with pytest.raises(ValueError):
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normalize([[0]], axis=2)
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with pytest.raises(ValueError):
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normalize([[0]], norm="l3")
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rs = np.random.RandomState(0)
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X_dense = rs.randn(10, 5)

‎sklearn/tests/test_public_functions.py

Lines changed: 1 addition & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -228,6 +228,7 @@ def _check_function_param_validation(
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"sklearn.preprocessing.binarize",
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"sklearn.preprocessing.label_binarize",
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"sklearn.preprocessing.maxabs_scale",
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"sklearn.preprocessing.normalize",
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"sklearn.preprocessing.scale",
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"sklearn.random_projection.johnson_lindenstrauss_min_dim",
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"sklearn.svm.l1_min_c",

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