scikit-learn · ogrisel · Jul 11, 2024 · Jul 8, 2024 · Jul 8, 2024 · Jul 9, 2024
diff --git a/doc/modules/array_api.rst b/doc/modules/array_api.rst
@@ -123,7 +123,9 @@ Metrics
 - :func:`sklearn.metrics.pairwise.additive_chi2_kernel`
 - :func:`sklearn.metrics.pairwise.chi2_kernel`
 - :func:`sklearn.metrics.pairwise.cosine_similarity`
+- :func:`sklearn.metrics.pairwise.euclidean_distances` (see :ref:`device_support_for_float64`)
 - :func:`sklearn.metrics.pairwise.paired_cosine_distances`
+- :func:`sklearn.metrics.pairwise.rbf_kernel` (see :ref:`device_support_for_float64`)
 - :func:`sklearn.metrics.r2_score`
 - :func:`sklearn.metrics.zero_one_loss`
 
@@ -172,6 +174,8 @@ automatically skipped. Therefore it's important to run the tests with the
     pip install array-api-compat  # and other libraries as needed
     pytest -k "array_api" -v
 
+.. _mps_support:
+
 Note on MPS device support
 --------------------------
 
@@ -191,3 +195,17 @@ To enable the MPS support in PyTorch, set the environment variable
 
 At the time of writing all scikit-learn tests should pass, however, the
 computational speed is not necessarily better than with the CPU device.
+
+.. _device_support_for_float64:
+
+Note on device support for ``float64``
+--------------------------------------
+
+Certain operations within scikit-learn will automatically perform operations
+on floating-point values with `float64` precision to prevent overflows and ensure
+correctness (e.g., :func:`metrics.pairwise.euclidean_distances`). However,
+certain combinations of array namespaces and devices, such as `PyTorch on MPS`
+(see :ref:`mps_support`) do not support the `float64` data type. In these cases,
+scikit-learn will revert to using the `float32` data type instead. This can result in
+different behavior (typically numerically unstable results) compared to not using array
+API dispatching or using a device with `float64` support.
diff --git a/doc/whats_new/v1.6.rst b/doc/whats_new/v1.6.rst
@@ -43,7 +43,9 @@ See :ref:`array_api` for more details.
 - :func:`sklearn.metrics.pairwise.additive_chi2_kernel` :pr:`29144` by :user:`Yaroslav Korobko <Tialo>`;
 - :func:`sklearn.metrics.pairwise.chi2_kernel` :pr:`29267` by :user:`Yaroslav Korobko <Tialo>`;
 - :func:`sklearn.metrics.pairwise.cosine_similarity` :pr:`29014` by :user:`Edoardo Abati <EdAbati>`;
-- :func:`sklearn.metrics.pairwise.paired_cosine_distances` :pr:`29112` by :user:`Edoardo Abati <EdAbati>`.
+- :func:`sklearn.metrics.pairwise.euclidean_distances` :pr:`29433` by :user:`Omar Salman <OmarManzoor>`;
+- :func:`sklearn.metrics.pairwise.paired_cosine_distances` :pr:`29112` by :user:`Edoardo Abati <EdAbati>`;
+- :func:`sklearn.metrics.pairwise.rbf_kernel` :pr:`29433` by :user:`Omar Salman <OmarManzoor>`.
 
 **Classes:**
 
@@ -71,7 +73,7 @@ more details.
   :class:`ensemble.StackingRegressor` now support metadata routing and pass
   ``**fit_params`` to the underlying estimators via their `fit` methods.
   :pr:`28701` by :user:`Stefanie Senger <StefanieSenger>`.
-  
+
 - |Feature| :class:`compose.TransformedTargetRegressor` now supports metadata
   routing in its `fit` and `predict` methods and routes the corresponding
   params to the underlying regressor.

diff --git a/sklearn/decomposition/_base.py b/sklearn/decomposition/_base.py
@@ -9,7 +9,7 @@
 from scipy import linalg
 
 from ..base import BaseEstimator, ClassNamePrefixFeaturesOutMixin, TransformerMixin
-from ..utils._array_api import _add_to_diagonal, device, get_namespace
+from ..utils._array_api import _fill_or_add_to_diagonal, device, get_namespace
 from ..utils.validation import check_is_fitted
 
 
@@ -47,7 +47,7 @@ def get_covariance(self):
             xp.asarray(0.0, device=device(exp_var)),
         )
         cov = (components_.T * exp_var_diff) @ components_
-        _add_to_diagonal(cov, self.noise_variance_, xp)
+        _fill_or_add_to_diagonal(cov, self.noise_variance_, xp)
         return cov
 
     def get_precision(self):
@@ -89,10 +89,10 @@ def get_precision(self):
             xp.asarray(0.0, device=device(exp_var)),
         )
         precision = components_ @ components_.T / self.noise_variance_
-        _add_to_diagonal(precision, 1.0 / exp_var_diff, xp)
+        _fill_or_add_to_diagonal(precision, 1.0 / exp_var_diff, xp)
         precision = components_.T @ linalg_inv(precision) @ components_
         precision /= -(self.noise_variance_**2)
-        _add_to_diagonal(precision, 1.0 / self.noise_variance_, xp)
+        _fill_or_add_to_diagonal(precision, 1.0 / self.noise_variance_, xp)
         return precision
 
     @abstractmethod

diff --git a/sklearn/metrics/pairwise.py b/sklearn/metrics/pairwise.py
@@ -22,9 +22,13 @@
     gen_even_slices,
 )
 from ..utils._array_api import (
+    _fill_or_add_to_diagonal,
     _find_matching_floating_dtype,
     _is_numpy_namespace,
+    _max_precision_float_dtype,
+    _modify_in_place_if_numpy,
     get_namespace,
+    get_namespace_and_device,
 )
 from ..utils._chunking import get_chunk_n_rows
 from ..utils._mask import _get_mask
@@ -335,13 +339,14 @@ def euclidean_distances(
     array([[1.        ],
            [1.41421356]])
     """
+    xp, _ = get_namespace(X, Y)
     X, Y = check_pairwise_arrays(X, Y)
 
     if X_norm_squared is not None:
         X_norm_squared = check_array(X_norm_squared, ensure_2d=False)
         original_shape = X_norm_squared.shape
         if X_norm_squared.shape == (X.shape[0],):
-            X_norm_squared = X_norm_squared.reshape(-1, 1)
+            X_norm_squared = xp.reshape(X_norm_squared, (-1, 1))
         if X_norm_squared.shape == (1, X.shape[0]):
             X_norm_squared = X_norm_squared.T
         if X_norm_squared.shape != (X.shape[0], 1):
@@ -354,7 +359,7 @@ def euclidean_distances(
         Y_norm_squared = check_array(Y_norm_squared, ensure_2d=False)
         original_shape = Y_norm_squared.shape
         if Y_norm_squared.shape == (Y.shape[0],):
-            Y_norm_squared = Y_norm_squared.reshape(1, -1)
+            Y_norm_squared = xp.reshape(Y_norm_squared, (1, -1))
         if Y_norm_squared.shape == (Y.shape[0], 1):
             Y_norm_squared = Y_norm_squared.T
         if Y_norm_squared.shape != (1, Y.shape[0]):
@@ -375,24 +380,30 @@ def _euclidean_distances(X, Y, X_norm_squared=None, Y_norm_squared=None, squared
     float32, norms needs to be recomputed on upcast chunks.
     TODO: use a float64 accumulator in row_norms to avoid the latter.
     """
-    if X_norm_squared is not None and X_norm_squared.dtype != np.float32:
-        XX = X_norm_squared.reshape(-1, 1)
-    elif X.dtype != np.float32:
-        XX = row_norms(X, squared=True)[:, np.newaxis]
+    xp, _, device_ = get_namespace_and_device(X, Y)
+    if X_norm_squared is not None and X_norm_squared.dtype != xp.float32:
+        XX = xp.reshape(X_norm_squared, (-1, 1))
+    elif X.dtype != xp.float32:
+        XX = row_norms(X, squared=True)[:, None]
     else:
         XX = None
 
     if Y is X:
         YY = None if XX is None else XX.T
     else:
-        if Y_norm_squared is not None and Y_norm_squared.dtype != np.float32:
-            YY = Y_norm_squared.reshape(1, -1)
-        elif Y.dtype != np.float32:
-            YY = row_norms(Y, squared=True)[np.newaxis, :]
+        if Y_norm_squared is not None and Y_norm_squared.dtype != xp.float32:
+            YY = xp.reshape(Y_norm_squared, (1, -1))
+        elif Y.dtype != xp.float32:
+            YY = row_norms(Y, squared=True)[None, :]
         else:
             YY = None
 
-    if X.dtype == np.float32 or Y.dtype == np.float32:
+    if _max_precision_float_dtype(xp=xp, device=device_) == xp.float32:
+        # special case for mps devices which don't support float64.
+        X_r = X[:, None]
+        Y_r = Y[None, :]
+        distances = xp.sum((X_r - Y_r) ** 2, axis=2)
+    elif X.dtype == xp.float32 or Y.dtype == xp.float32:
         # To minimize precision issues with float32, we compute the distance
         # matrix on chunks of X and Y upcast to float64
         distances = _euclidean_distances_upcast(X, XX, Y, YY)
@@ -401,14 +412,22 @@ def _euclidean_distances(X, Y, X_norm_squared=None, Y_norm_squared=None, squared
         distances = -2 * safe_sparse_dot(X, Y.T, dense_output=True)
         distances += XX
         distances += YY
-    np.maximum(distances, 0, out=distances)
+
+    xp_zero = xp.asarray(0, device=device_, dtype=distances.dtype)
+    distances = _modify_in_place_if_numpy(
+        xp, xp.maximum, distances, xp_zero, out=distances
+    )
 
     # Ensure that distances between vectors and themselves are set to 0.0.
     # This may not be the case due to floating point rounding errors.
     if X is Y:
-        np.fill_diagonal(distances, 0)
+        _fill_or_add_to_diagonal(distances, 0, xp=xp, add_value=False)
+
+    if squared:
+        return distances
 
-    return distances if squared else np.sqrt(distances, out=distances)
+    distances = _modify_in_place_if_numpy(xp, xp.sqrt, distances, out=distances)
+    return distances
 
 
 @validate_params(
@@ -552,15 +571,20 @@ def _euclidean_distances_upcast(X, XX=None, Y=None, YY=None, batch_size=None):
     X and Y are upcast to float64 by chunks, which size is chosen to limit
     memory increase by approximately 10% (at least 10MiB).
     """
+    xp, _, device_ = get_namespace_and_device(X, Y)
     n_samples_X = X.shape[0]
     n_samples_Y = Y.shape[0]
     n_features = X.shape[1]
 
-    distances = np.empty((n_samples_X, n_samples_Y), dtype=np.float32)
+    distances = xp.empty((n_samples_X, n_samples_Y), dtype=xp.float32, device=device_)
 
     if batch_size is None:
-        x_density = X.nnz / np.prod(X.shape) if issparse(X) else 1
-        y_density = Y.nnz / np.prod(Y.shape) if issparse(Y) else 1
+        x_density = (
+            X.nnz / np.prod(X.shape) if issparse(X) else xp.asarray(1, device=device_)
+        )
+        y_density = (
+            Y.nnz / np.prod(Y.shape) if issparse(Y) else xp.asarray(1, device=device_)
+        )
 
         # Allow 10% more memory than X, Y and the distance matrix take (at
         # least 10MiB)
@@ -580,15 +604,15 @@ def _euclidean_distances_upcast(X, XX=None, Y=None, YY=None, batch_size=None):
         # Hence x² + (xd+yd)kx = M, where x=batch_size, k=n_features, M=maxmem
         #                                 xd=x_density and yd=y_density
         tmp = (x_density + y_density) * n_features
-        batch_size = (-tmp + np.sqrt(tmp**2 + 4 * maxmem)) / 2
+        batch_size = (-tmp + xp.sqrt(tmp**2 + 4 * maxmem)) / 2
         batch_size = max(int(batch_size), 1)
 
     x_batches = gen_batches(n_samples_X, batch_size)
 
     for i, x_slice in enumerate(x_batches):
-        X_chunk = X[x_slice].astype(np.float64)
+        X_chunk = xp.astype(X[x_slice], xp.float64)
         if XX is None:
-            XX_chunk = row_norms(X_chunk, squared=True)[:, np.newaxis]
+            XX_chunk = row_norms(X_chunk, squared=True)[:, None]
         else:
             XX_chunk = XX[x_slice]
 
@@ -601,17 +625,17 @@ def _euclidean_distances_upcast(X, XX=None, Y=None, YY=None, batch_size=None):
                 d = distances[y_slice, x_slice].T
 
             else:
-                Y_chunk = Y[y_slice].astype(np.float64)
+                Y_chunk = xp.astype(Y[y_slice], xp.float64)
                 if YY is None:
-                    YY_chunk = row_norms(Y_chunk, squared=True)[np.newaxis, :]
+                    YY_chunk = row_norms(Y_chunk, squared=True)[None, :]
                 else:
                     YY_chunk = YY[:, y_slice]
 
                 d = -2 * safe_sparse_dot(X_chunk, Y_chunk.T, dense_output=True)
                 d += XX_chunk
                 d += YY_chunk
 
-            distances[x_slice, y_slice] = d.astype(np.float32, copy=False)
+            distances[x_slice, y_slice] = xp.astype(d, xp.float32, copy=False)
 
     return distances
 
@@ -1549,13 +1573,15 @@ def rbf_kernel(X, Y=None, gamma=None):
     array([[0.71..., 0.51...],
            [0.51..., 0.71...]])
     """
+    xp, _ = get_namespace(X, Y)
     X, Y = check_pairwise_arrays(X, Y)
     if gamma is None:
         gamma = 1.0 / X.shape[1]
 
     K = euclidean_distances(X, Y, squared=True)
     K *= -gamma
-    np.exp(K, K)  # exponentiate K in-place
+    # exponentiate K in-place when using numpy
+    K = _modify_in_place_if_numpy(xp, xp.exp, K, out=K)
     return K
 
 

diff --git a/sklearn/metrics/tests/test_common.py b/sklearn/metrics/tests/test_common.py
@@ -55,7 +55,9 @@
     additive_chi2_kernel,
     chi2_kernel,
     cosine_similarity,
+    euclidean_distances,
     paired_cosine_distances,
+    rbf_kernel,
 )
 from sklearn.preprocessing import LabelBinarizer
 from sklearn.utils import shuffle
@@ -2014,6 +2016,8 @@ def check_array_api_metric_pairwise(metric, array_namespace, device, dtype_name)
     mean_gamma_deviance: [check_array_api_regression_metric],
     max_error: [check_array_api_regression_metric],
     chi2_kernel: [check_array_api_metric_pairwise],
+    euclidean_distances: [check_array_api_metric_pairwise],
+    rbf_kernel: [check_array_api_metric_pairwise],
 }
 
 

diff --git a/sklearn/utils/_array_api.py b/sklearn/utils/_array_api.py
@@ -302,6 +302,12 @@ def __eq__(self, other):
     def isdtype(self, dtype, kind):
         return isdtype(dtype, kind, xp=self._namespace)
 
+    def maximum(self, x1, x2):
+        x1_np = _convert_to_numpy(x1, xp=self._namespace)
+        x2_np = _convert_to_numpy(x2, xp=self._namespace)
+        x_max = numpy.maximum(x1_np, x2_np)
+        return self._namespace.asarray(x_max, device=device(x1, x2))
+
 
 def _check_device_cpu(device):  # noqa
     if device not in {"cpu", None}:
@@ -566,7 +572,28 @@ def get_namespace(*arrays, remove_none=True, remove_types=(str,), xp=None):
 
 
 def get_namespace_and_device(*array_list, remove_none=True, remove_types=(str,)):
-    """Combination into one single function of `get_namespace` and `device`."""
+    """Combination into one single function of `get_namespace` and `device`.
+
+    Parameters
+    ----------
+    *array_list : array objects
+        Array objects.
+    remove_none : bool, default=True
+        Whether to ignore None objects passed in arrays.
+    remove_types : tuple or list, default=(str,)
+        Types to ignore in the arrays.
+
+    Returns
+    -------
+    namespace : module
+        Namespace shared by array objects. If any of the `arrays` are not arrays,
+        the namespace defaults to NumPy.
+    is_array_api_compliant : bool
+        True if the arrays are containers that implement the Array API spec.
+        Always False when array_api_dispatch=False.
+    device : device
+        `device` object (see the "Device Support" section of the array API spec).
+    """
     array_list = _remove_non_arrays(
         *array_list, remove_none=remove_none, remove_types=remove_types
     )
@@ -592,21 +619,34 @@ def _expit(X, xp=None):
     return 1.0 / (1.0 + xp.exp(-X))
 
 
-def _add_to_diagonal(array, value, xp):
-    # Workaround for the lack of support for xp.reshape(a, shape, copy=False) in
-    # numpy.array_api: https://github.com/numpy/numpy/issues/23410
-    value = xp.asarray(value, dtype=array.dtype)
-    if _is_numpy_namespace(xp):
-        array_np = numpy.asarray(array)
-        array_np.flat[:: array.shape[0] + 1] += value
-        return xp.asarray(array_np)
-    elif value.ndim == 1:
-        for i in range(array.shape[0]):
-            array[i, i] += value[i]
+def _fill_or_add_to_diagonal(array, value, xp, add_value=True, wrap=False):
+    """Implementation to facilitate adding or assigning specified values to the
+    diagonal of a 2-d array.
+
+    If ``add_value`` is `True` then the values will be added to the diagonal
+    elements otherwise the values will be assigned to the diagonal elements.
+    By default, ``add_value`` is set to `True. This is currently only
+    supported for 2-d arrays.
+
+    The implementation is taken from the `numpy.fill_diagonal` function:
+    https://github.com/numpy/numpy/blob/v2.0.0/numpy/lib/_index_tricks_impl.py#L799-L929
+    """
+    if array.ndim != 2:
+        raise ValueError("array should be 2-d")
+
+    value = xp.asarray(value, dtype=array.dtype, device=device(array))
+    end = None
+    # Explicit, fast formula for the common case.  For 2-d arrays, we
+    # accept rectangular ones.
+    step = array.shape[1] + 1
+    if not wrap:
+        end = array.shape[1] * array.shape[1]
+
+    array_flat = xp.reshape(array, (-1,))
+    if add_value:
+        array_flat[:end:step] += value
     else:
-        # scalar value
-        for i in range(array.shape[0]):
-            array[i, i] += value
+        array_flat[:end:step] = value
 
 
 def _max_precision_float_dtype(xp, device):
@@ -1000,3 +1040,11 @@ def _count_nonzero(X, xp, device, axis=None, sample_weight=None):
 
     zero_scalar = xp.asarray(0, device=device, dtype=weights.dtype)
     return xp.sum(xp.where(X != 0, weights, zero_scalar), axis=axis)
+
+
+def _modify_in_place_if_numpy(xp, func, *args, out=None, **kwargs):
+    if _is_numpy_namespace(xp):
+        func(*args, out=out, **kwargs)
+    else:
+        out = func(*args, **kwargs)
+    return out