MAINT Parameters validation for sklearn.preprocessing.normalize (#26069)

jiawei-zhang-a · jeremiedbb · web-flow · commit fb526710b6ff · 2023-04-04T13:50:01.000Z
Co-authored-by: jeremiedbb &lt;jeremiedbb@yahoo.fr&gt;
diff --git a/sklearn/preprocessing/_data.py b/sklearn/preprocessing/_data.py
@@ -1777,6 +1777,15 @@ def robust_scale(
     return X
 
 
+@validate_params(
+    {
+        "X": ["array-like", "sparse matrix"],
+        "norm": [StrOptions({"l1", "l2", "max"})],
+        "axis": [Options(Integral, {0, 1})],
+        "copy": ["boolean"],
+        "return_norm": ["boolean"],
+    }
+)
 def normalize(X, norm="l2", *, axis=1, copy=True, return_norm=False):
     """Scale input vectors individually to unit norm (vector length).
 
@@ -1826,15 +1835,10 @@ def normalize(X, norm="l2", *, axis=1, copy=True, return_norm=False):
     see :ref:`examples/preprocessing/plot_all_scaling.py
     <sphx_glr_auto_examples_preprocessing_plot_all_scaling.py>`.
     """
-    if norm not in ("l1", "l2", "max"):
-        raise ValueError("'%s' is not a supported norm" % norm)
-
     if axis == 0:
         sparse_format = "csc"
-    elif axis == 1:
+    else:  # axis == 1:
         sparse_format = "csr"
-    else:
-        raise ValueError("'%d' is not a supported axis" % axis)
 
     X = check_array(
         X,
diff --git a/sklearn/preprocessing/tests/test_data.py b/sklearn/preprocessing/tests/test_data.py
@@ -1988,10 +1988,6 @@ def test_normalize():
     # Only tests functionality not used by the tests for Normalizer.
     X = np.random.RandomState(37).randn(3, 2)
     assert_array_equal(normalize(X, copy=False), normalize(X.T, axis=0, copy=False).T)
-    with pytest.raises(ValueError):
-        normalize([[0]], axis=2)
-    with pytest.raises(ValueError):
-        normalize([[0]], norm="l3")
 
     rs = np.random.RandomState(0)
     X_dense = rs.randn(10, 5)
diff --git a/sklearn/tests/test_public_functions.py b/sklearn/tests/test_public_functions.py
@@ -228,6 +228,7 @@ def _check_function_param_validation(
     "sklearn.preprocessing.binarize",
     "sklearn.preprocessing.label_binarize",
     "sklearn.preprocessing.maxabs_scale",
+    "sklearn.preprocessing.normalize",
     "sklearn.preprocessing.scale",
     "sklearn.random_projection.johnson_lindenstrauss_min_dim",
     "sklearn.svm.l1_min_c",
