scikit-learn · ogrisel · Nov 14, 2023 · Oct 9, 2023 · Oct 10, 2023 · Oct 10, 2023
diff --git a/doc/modules/array_api.rst b/doc/modules/array_api.rst
@@ -99,6 +99,7 @@ Estimators
 - :class:`preprocessing.KernelCenterer`
 - :class:`preprocessing.MaxAbsScaler`
 - :class:`preprocessing.MinMaxScaler`
+- :class:`preprocessing.Normalizer`
 
 Metrics
 -------

diff --git a/doc/whats_new/v1.4.rst b/doc/whats_new/v1.4.rst
@@ -410,14 +410,16 @@ Changelog
 :mod:`sklearn.preprocessing`
 ............................
 
-- |MajorFeature| :class:`preprocessing.MinMaxScaler`, :class:`preprocessing.MaxAbsScaler`
-  and :class:`preprocessing.KernelCenterer` now
-  support the `Array API <https://data-apis.org/array-api/latest/>`_. Array API
+- |MajorFeature| The following classes now support the
+  `Array API <https://data-apis.org/array-api/latest/>`_. Array API
   support is considered experimental and might evolve without being subject to
   our usual rolling deprecation cycle policy. See
   :ref:`array_api` for more details.
-  :pr:`26243` by `Tim Head`_, :pr:`27110` by :user:`Edoardo Abati <EdAbati>` and
-  :pr:`27556` by :user:`Edoardo Abati <EdAbati>`.
+
+  - :class:`preprocessing.MinMaxScaler` :pr:`26243` by `Tim Head`_
+  - :class:`preprocessing.MaxAbsScaler` :pr:`27110` by :user:`Edoardo Abati <EdAbati>`
+  - :class:`preprocessing.KernelCenterer` :pr:`27556` by :user:`Edoardo Abati <EdAbati>`
+  - :class:`preprocessing.Normalizer` :pr:`27558` by :user:`Edoardo Abati <EdAbati>`
 
 - |Efficiency| :class:`preprocessing.OrdinalEncoder` avoids calculating
   missing indices twice to improve efficiency.

diff --git a/sklearn/preprocessing/_data.py b/sklearn/preprocessing/_data.py
@@ -1858,12 +1858,14 @@ def normalize(X, norm="l2", *, axis=1, copy=True, return_norm=False):
     else:  # axis == 1:
         sparse_format = "csr"
 
+    xp, _ = get_namespace(X)
+
     X = check_array(
         X,
         accept_sparse=sparse_format,
         copy=copy,
         estimator="the normalize function",
-        dtype=FLOAT_DTYPES,
+        dtype=_array_api.supported_float_dtypes(xp),
     )
     if axis == 0:
         X = X.T
@@ -1887,13 +1889,13 @@ def normalize(X, norm="l2", *, axis=1, copy=True, return_norm=False):
             X.data[mask] /= norms_elementwise[mask]
     else:
         if norm == "l1":
-            norms = np.abs(X).sum(axis=1)
+            norms = xp.sum(xp.abs(X), axis=1)
         elif norm == "l2":
             norms = row_norms(X)
         elif norm == "max":
-            norms = np.max(abs(X), axis=1)
+            norms = xp.max(xp.abs(X), axis=1)
         norms = _handle_zeros_in_scale(norms, copy=False)
-        X /= norms[:, np.newaxis]
+        X /= norms[:, None]
 
     if axis == 0:
         X = X.T
@@ -2031,7 +2033,7 @@ def transform(self, X, copy=None):
         return normalize(X, norm=self.norm, axis=1, copy=copy)
 
     def _more_tags(self):
-        return {"stateless": True}
+        return {"stateless": True, "array_api_support": True}
 
 
 @validate_params(

diff --git a/sklearn/preprocessing/tests/test_data.py b/sklearn/preprocessing/tests/test_data.py
@@ -691,7 +691,14 @@ def test_standard_check_array_of_inverse_transform():
 )
 @pytest.mark.parametrize(
     "estimator",
-    [MaxAbsScaler(), MinMaxScaler(), KernelCenterer()],
+    [
+        MaxAbsScaler(),
+        MinMaxScaler(),
+        KernelCenterer(),
+        Normalizer(norm="l1"),
+        Normalizer(norm="l2"),
+        Normalizer(norm="max"),
+    ],
     ids=_get_check_estimator_ids,
 )
 def test_scaler_array_api_compliance(estimator, check, array_namespace, device, dtype):

diff --git a/sklearn/utils/extmath.py b/sklearn/utils/extmath.py
@@ -78,11 +78,18 @@ def row_norms(X, squared=False):
     if sparse.issparse(X):
         X = X.tocsr()
         norms = csr_row_norms(X)
+        if not squared:
+            norms = np.sqrt(norms)
     else:
-        norms = np.einsum("ij,ij->i", X, X)
-
-    if not squared:
-        np.sqrt(norms, norms)
+        xp, _ = get_namespace(X)
+        if _is_numpy_namespace(xp):
+            X = np.asarray(X)
+            norms = np.einsum("ij,ij->i", X, X)
+            norms = xp.asarray(norms)
+        else:
+            norms = xp.sum(xp.multiply(X, X), axis=1)
+        if not squared:
+            norms = xp.sqrt(norms)
     return norms