From 903d250b1348de025f00df6d48ca38b45e50527e Mon Sep 17 00:00:00 2001
From: Edoardo Abati <29585319+EdAbati@users.noreply.github.com>
Date: Fri, 18 Aug 2023 16:59:57 +0200
Subject: [PATCH 1/8] added first draft of MaxAbsScaler.partial_fit with Array
 API

---
 sklearn/preprocessing/_data.py           | 18 ++++++++++------
 sklearn/preprocessing/tests/test_data.py | 27 ++++++++++++++++++++++++
 2 files changed, 38 insertions(+), 7 deletions(-)

diff --git a/sklearn/preprocessing/_data.py b/sklearn/preprocessing/_data.py
index b51f2af2c3d17..ebb43f7cca641 100644
--- a/sklearn/preprocessing/_data.py
+++ b/sklearn/preprocessing/_data.py
@@ -22,7 +22,8 @@
     TransformerMixin,
     _fit_context,
 )
-from ..utils import check_array
+from ..utils import _array_api, check_array
+from ..utils._array_api import get_namespace
 from ..utils._param_validation import Interval, Options, StrOptions, validate_params
 from ..utils.extmath import _incremental_mean_and_var, row_norms
 from ..utils.sparsefuncs import (
@@ -103,16 +104,17 @@ def _handle_zeros_in_scale(scale, copy=True, constant_mask=None):
         if scale == 0.0:
             scale = 1.0
         return scale
-    elif isinstance(scale, np.ndarray):
+    else:
+        xp, _ = get_namespace(scale)
         if constant_mask is None:
             # Detect near constant values to avoid dividing by a very small
             # value that could lead to surprising results and numerical
             # stability issues.
-            constant_mask = scale < 10 * np.finfo(scale.dtype).eps
+            constant_mask = scale < 10 * xp.finfo(scale.dtype).eps
 
         if copy:
             # New array to avoid side-effects
-            scale = scale.copy()
+            scale = xp.asarray(scale, copy=True)
         scale[constant_mask] = 1.0
         return scale
 
@@ -1203,12 +1205,14 @@ def partial_fit(self, X, y=None):
         self : object
             Fitted scaler.
         """
+        xp, _ = get_namespace(X)
+
         first_pass = not hasattr(self, "n_samples_seen_")
         X = self._validate_data(
             X,
             reset=first_pass,
             accept_sparse=("csr", "csc"),
-            dtype=FLOAT_DTYPES,
+            dtype=_array_api.supported_float_dtypes(xp),
             force_all_finite="allow-nan",
         )
 
@@ -1216,12 +1220,12 @@ def partial_fit(self, X, y=None):
             mins, maxs = min_max_axis(X, axis=0, ignore_nan=True)
             max_abs = np.maximum(np.abs(mins), np.abs(maxs))
         else:
-            max_abs = np.nanmax(np.abs(X), axis=0)
+            max_abs = _array_api._nanmax(xp.abs(X), axis=0)
 
         if first_pass:
             self.n_samples_seen_ = X.shape[0]
         else:
-            max_abs = np.maximum(self.max_abs_, max_abs)
+            max_abs = xp.maximum(self.max_abs_, max_abs)
             self.n_samples_seen_ += X.shape[0]
 
         self.max_abs_ = max_abs
diff --git a/sklearn/preprocessing/tests/test_data.py b/sklearn/preprocessing/tests/test_data.py
index 189d8875bc8f2..ecee9ff3788c7 100644
--- a/sklearn/preprocessing/tests/test_data.py
+++ b/sklearn/preprocessing/tests/test_data.py
@@ -41,6 +41,9 @@
 from sklearn.preprocessing._data import BOUNDS_THRESHOLD, _handle_zeros_in_scale
 from sklearn.svm import SVR
 from sklearn.utils import gen_batches, shuffle
+from sklearn.utils._array_api import (
+    yield_namespace_device_dtype_combinations,
+)
 from sklearn.utils._testing import (
     _convert_container,
     assert_allclose,
@@ -51,6 +54,10 @@
     assert_array_less,
     skip_if_32bit,
 )
+from sklearn.utils.estimator_checks import (
+    _get_check_estimator_ids,
+    check_array_api_input_and_values,
+)
 from sklearn.utils.sparsefuncs import mean_variance_axis
 
 iris = datasets.load_iris()
@@ -1640,6 +1647,26 @@ def test_robust_scaler_unit_variance():
     assert X_trans.std() == pytest.approx(1, abs=1e-2)
 
 
+@pytest.mark.parametrize(
+    "array_namespace, device, dtype", yield_namespace_device_dtype_combinations()
+)
+@pytest.mark.parametrize(
+    "check",
+    [check_array_api_input_and_values],
+    ids=_get_check_estimator_ids,
+)
+@pytest.mark.parametrize(
+    "estimator",
+    [MinMaxScaler()],
+    ids=_get_check_estimator_ids,
+)
+def test_maxabscaler_array_api_compliance(
+    estimator, check, array_namespace, device, dtype
+):
+    name = estimator.__class__.__name__
+    check(name, estimator, array_namespace, device=device, dtype=dtype)
+
+
 def test_maxabs_scaler_zero_variance_features():
     # Check MaxAbsScaler on toy data with zero variance features
     X = [[0.0, 1.0, +0.5], [0.0, 1.0, -0.3], [0.0, 1.0, +1.5], [0.0, 0.0, +0.0]]

From a8f45588e67753d1a80bb1eb11fc62f58860322b Mon Sep 17 00:00:00 2001
From: Edoardo Abati <29585319+EdAbati@users.noreply.github.com>
Date: Sat, 19 Aug 2023 11:14:03 +0200
Subject: [PATCH 2/8] fixed test estimator

---
 sklearn/preprocessing/tests/test_data.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sklearn/preprocessing/tests/test_data.py b/sklearn/preprocessing/tests/test_data.py
index 90b03c5b860e2..e76ff7156a9ed 100644
--- a/sklearn/preprocessing/tests/test_data.py
+++ b/sklearn/preprocessing/tests/test_data.py
@@ -1677,7 +1677,7 @@ def test_robust_scaler_unit_variance():
 )
 @pytest.mark.parametrize(
     "estimator",
-    [MinMaxScaler()],
+    [MaxAbsScaler()],
     ids=_get_check_estimator_ids,
 )
 def test_maxabscaler_array_api_compliance(

From 406f332c22f95caade0757e86d9b342af5c11ca3 Mon Sep 17 00:00:00 2001
From: Edoardo Abati <29585319+EdAbati@users.noreply.github.com>
Date: Sat, 19 Aug 2023 11:16:14 +0200
Subject: [PATCH 3/8] fixed nanmin and nanmax so that +/-inf arrays are sent to
 the correct device

---
 sklearn/utils/_array_api.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/sklearn/utils/_array_api.py b/sklearn/utils/_array_api.py
index 2bc49117792c9..0fb5a0e264aa8 100644
--- a/sklearn/utils/_array_api.py
+++ b/sklearn/utils/_array_api.py
@@ -495,7 +495,7 @@ def _nanmin(X, axis=None):
 
     else:
         mask = xp.isnan(X)
-        X = xp.min(xp.where(mask, xp.asarray(+xp.inf), X), axis=axis)
+        X = xp.min(xp.where(mask, xp.asarray(+xp.inf, device=device(X)), X), axis=axis)
         # Replace Infs from all NaN slices with NaN again
         mask = xp.all(mask, axis=axis)
         if xp.any(mask):
@@ -512,7 +512,7 @@ def _nanmax(X, axis=None):
 
     else:
         mask = xp.isnan(X)
-        X = xp.max(xp.where(mask, xp.asarray(-xp.inf), X), axis=axis)
+        X = xp.max(xp.where(mask, xp.asarray(-xp.inf, device=device(X)), X), axis=axis)
         # Replace Infs from all NaN slices with NaN again
         mask = xp.all(mask, axis=axis)
         if xp.any(mask):

From 20bf65046e8747ec10ad937bde38133f16d61c1d Mon Sep 17 00:00:00 2001
From: Edoardo Abati <29585319+EdAbati@users.noreply.github.com>
Date: Sat, 19 Aug 2023 11:16:43 +0200
Subject: [PATCH 4/8] implemented transform and inverse_transform with the
 array api

---
 sklearn/preprocessing/_data.py | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/sklearn/preprocessing/_data.py b/sklearn/preprocessing/_data.py
index d4ef1df569972..ba8627935f28d 100644
--- a/sklearn/preprocessing/_data.py
+++ b/sklearn/preprocessing/_data.py
@@ -1256,12 +1256,15 @@ def transform(self, X):
             Transformed array.
         """
         check_is_fitted(self)
+
+        xp, _ = get_namespace(X)
+
         X = self._validate_data(
             X,
             accept_sparse=("csr", "csc"),
             copy=self.copy,
             reset=False,
-            dtype=FLOAT_DTYPES,
+            dtype=_array_api.supported_float_dtypes(xp),
             force_all_finite="allow-nan",
         )
 
@@ -1285,11 +1288,14 @@ def inverse_transform(self, X):
             Transformed array.
         """
         check_is_fitted(self)
+
+        xp, _ = get_namespace(X)
+
         X = check_array(
             X,
             accept_sparse=("csr", "csc"),
             copy=self.copy,
-            dtype=FLOAT_DTYPES,
+            dtype=_array_api.supported_float_dtypes(xp),
             force_all_finite="allow-nan",
         )
 

From 9daaa49f938b682b515fb30b7855a34acfac4124 Mon Sep 17 00:00:00 2001
From: Edoardo Abati <29585319+EdAbati@users.noreply.github.com>
Date: Sat, 19 Aug 2023 11:43:53 +0200
Subject: [PATCH 5/8] updated docs

---
 doc/modules/array_api.rst | 1 +
 doc/whats_new/v1.4.rst    | 4 ++--
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/doc/modules/array_api.rst b/doc/modules/array_api.rst
index 7effd3ee6eb43..b1f1f332fb057 100644
--- a/doc/modules/array_api.rst
+++ b/doc/modules/array_api.rst
@@ -97,6 +97,7 @@ Estimators
   `svd_solver="randomized"` and `power_iteration_normalizer="QR"`)
 - :class:`discriminant_analysis.LinearDiscriminantAnalysis` (with `solver="svd"`)
 - :class:`preprocessing.MinMaxScaler`
+- :class:`preprocessing.MaxAbsScaler`
 
 Tools
 -----
diff --git a/doc/whats_new/v1.4.rst b/doc/whats_new/v1.4.rst
index bfa490ff5e821..02eb1bfbb1db4 100644
--- a/doc/whats_new/v1.4.rst
+++ b/doc/whats_new/v1.4.rst
@@ -200,11 +200,11 @@ Changelog
   when `sparse_output=True` and the output is configured to be pandas.
   :pr:`26931` by `Thomas Fan`_.
 
-- |MajorFeature| :class:`preprocessing.MinMaxScaler` now
+- |MajorFeature| :class:`preprocessing.MinMaxScaler` and :class:`preprocessing.MaxAbsScaler` now
   supports 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`_.
+  :ref:`array_api` for more details. :pr:`26243` by `Tim Head`_ and :pr:`27110` by :user:`Edoardo Abati <EdAbati>`.
 
 :mod:`sklearn.tree`
 ...................

From 6ea0834e128e34cf60c07f84a95444c1d37a181a Mon Sep 17 00:00:00 2001
From: Edoardo Abati <29585319+EdAbati@users.noreply.github.com>
Date: Mon, 21 Aug 2023 12:32:57 +0200
Subject: [PATCH 6/8] merged the array_api test functions

---
 sklearn/preprocessing/tests/test_data.py | 26 ++----------------------
 1 file changed, 2 insertions(+), 24 deletions(-)

diff --git a/sklearn/preprocessing/tests/test_data.py b/sklearn/preprocessing/tests/test_data.py
index e76ff7156a9ed..c395d4d21b378 100644
--- a/sklearn/preprocessing/tests/test_data.py
+++ b/sklearn/preprocessing/tests/test_data.py
@@ -701,12 +701,10 @@ def test_standard_check_array_of_inverse_transform():
 )
 @pytest.mark.parametrize(
     "estimator",
-    [MinMaxScaler()],
+    [MinMaxScaler(), MaxAbsScaler()],
     ids=_get_check_estimator_ids,
 )
-def test_minmaxscaler_array_api_compliance(
-    estimator, check, array_namespace, device, dtype
-):
+def test_scaler_array_api_compliance(estimator, check, array_namespace, device, dtype):
     name = estimator.__class__.__name__
     check(name, estimator, array_namespace, device=device, dtype=dtype)
 
@@ -1667,26 +1665,6 @@ def test_robust_scaler_unit_variance():
     assert X_trans.std() == pytest.approx(1, abs=1e-2)
 
 
-@pytest.mark.parametrize(
-    "array_namespace, device, dtype", yield_namespace_device_dtype_combinations()
-)
-@pytest.mark.parametrize(
-    "check",
-    [check_array_api_input_and_values],
-    ids=_get_check_estimator_ids,
-)
-@pytest.mark.parametrize(
-    "estimator",
-    [MaxAbsScaler()],
-    ids=_get_check_estimator_ids,
-)
-def test_maxabscaler_array_api_compliance(
-    estimator, check, array_namespace, device, dtype
-):
-    name = estimator.__class__.__name__
-    check(name, estimator, array_namespace, device=device, dtype=dtype)
-
-
 def test_maxabs_scaler_zero_variance_features():
     # Check MaxAbsScaler on toy data with zero variance features
     X = [[0.0, 1.0, +0.5], [0.0, 1.0, -0.3], [0.0, 1.0, +1.5], [0.0, 0.0, +0.0]]

From 81fa4f24aee89053ea5d2a6579f42a6f6e28c9bd Mon Sep 17 00:00:00 2001
From: Edoardo Abati <29585319+EdAbati@users.noreply.github.com>
Date: Mon, 21 Aug 2023 12:34:03 +0200
Subject: [PATCH 7/8] removed extra space

---
 doc/whats_new/v1.4.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/whats_new/v1.4.rst b/doc/whats_new/v1.4.rst
index 0fc598c65f62d..351e155672098 100644
--- a/doc/whats_new/v1.4.rst
+++ b/doc/whats_new/v1.4.rst
@@ -108,7 +108,7 @@ Changelog
   `full` and `randomized` solvers (with QR power iterations). See
   :ref:`array_api` for more details.
   :pr:`26315` and :pr:`27098` by :user:`Mateusz Sokół <mtsokol>`,
-  :user:`Olivier Grisel <ogrisel>` and :user:` Edoardo Abati <EdAbati>`.
+  :user:`Olivier Grisel <ogrisel>` and :user:`Edoardo Abati <EdAbati>`.
 
 - |Enhancement| :func:`decomposition.non_negative_factorization`, :class:`decomposition.NMF`,
   and :class:`decomposition.MiniBatchNMF` now support :class:`scipy.sparse.sparray`

From 3fb8d5a5fc60c2abdfef07963dc6c76ad6ceaf60 Mon Sep 17 00:00:00 2001
From: Edoardo Abati <29585319+EdAbati@users.noreply.github.com>
Date: Tue, 22 Aug 2023 18:55:57 +0200
Subject: [PATCH 8/8] scalers in alphabetical order

---
 doc/modules/array_api.rst                | 2 +-
 sklearn/preprocessing/tests/test_data.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/doc/modules/array_api.rst b/doc/modules/array_api.rst
index b1f1f332fb057..161307dfddcf4 100644
--- a/doc/modules/array_api.rst
+++ b/doc/modules/array_api.rst
@@ -96,8 +96,8 @@ Estimators
 - :class:`decomposition.PCA` (with `svd_solver="full"`,
   `svd_solver="randomized"` and `power_iteration_normalizer="QR"`)
 - :class:`discriminant_analysis.LinearDiscriminantAnalysis` (with `solver="svd"`)
-- :class:`preprocessing.MinMaxScaler`
 - :class:`preprocessing.MaxAbsScaler`
+- :class:`preprocessing.MinMaxScaler`
 
 Tools
 -----
diff --git a/sklearn/preprocessing/tests/test_data.py b/sklearn/preprocessing/tests/test_data.py
index c395d4d21b378..d61996b76a25c 100644
--- a/sklearn/preprocessing/tests/test_data.py
+++ b/sklearn/preprocessing/tests/test_data.py
@@ -701,7 +701,7 @@ def test_standard_check_array_of_inverse_transform():
 )
 @pytest.mark.parametrize(
     "estimator",
-    [MinMaxScaler(), MaxAbsScaler()],
+    [MaxAbsScaler(), MinMaxScaler()],
     ids=_get_check_estimator_ids,
 )
 def test_scaler_array_api_compliance(estimator, check, array_namespace, device, dtype):