scikit-learn · betatim · May 16, 2024 · Sep 15, 2023 · Sep 15, 2023 · Sep 15, 2023
diff --git a/doc/whats_new/v1.6.rst b/doc/whats_new/v1.6.rst
@@ -39,7 +39,8 @@ See :ref:`array_api` for more details.
 
 **Classes:**
 
--
+- :class:`preprocessing.LabelEncoder` now supports Array API compatible inputs.
+  :pr:`27381` by :user:`Omar Salman <OmarManzoor>`.
 
 Metadata Routing
 ----------------

diff --git a/sklearn/preprocessing/_label.py b/sklearn/preprocessing/_label.py
@@ -17,6 +17,7 @@
 
 from ..base import BaseEstimator, TransformerMixin, _fit_context
 from ..utils import column_or_1d
+from ..utils._array_api import _setdiff1d, device, get_namespace
 from ..utils._encode import _encode, _unique
 from ..utils._param_validation import Interval, validate_params
 from ..utils.multiclass import type_of_target, unique_labels
@@ -129,10 +130,11 @@ def transform(self, y):
             Labels as normalized encodings.
         """
         check_is_fitted(self)
+        xp, _ = get_namespace(y)
         y = column_or_1d(y, dtype=self.classes_.dtype, warn=True)
         # transform of empty array is empty array
         if _num_samples(y) == 0:
-            return np.array([])
+            return xp.asarray([])
 
         return _encode(y, uniques=self.classes_)
 
@@ -141,7 +143,7 @@ def inverse_transform(self, y):
 
         Parameters
         ----------
-        y : ndarray of shape (n_samples,)
+        y : array-like of shape (n_samples,)
             Target values.
 
         Returns
@@ -150,19 +152,24 @@ def inverse_transform(self, y):
             Original encoding.
         """
         check_is_fitted(self)
+        xp, _ = get_namespace(y)
         y = column_or_1d(y, warn=True)
         # inverse transform of empty array is empty array
         if _num_samples(y) == 0:
-            return np.array([])
+            return xp.asarray([])
 
-        diff = np.setdiff1d(y, np.arange(len(self.classes_)))
-        if len(diff):
+        diff = _setdiff1d(
+            ar1=y,
+            ar2=xp.arange(self.classes_.shape[0], device=device(y)),
+            xp=xp,
+        )
+        if diff.shape[0]:
             raise ValueError("y contains previously unseen labels: %s" % str(diff))
-        y = np.asarray(y)
-        return self.classes_[y]
+        y = xp.asarray(y)
+        return xp.take(self.classes_, y, axis=0)
 
     def _more_tags(self):
-        return {"X_types": ["1dlabels"]}
+        return {"X_types": ["1dlabels"], "array_api_support": True}
 
 
 class LabelBinarizer(TransformerMixin, BaseEstimator, auto_wrap_output_keys=None):

diff --git a/sklearn/preprocessing/tests/test_label.py b/sklearn/preprocessing/tests/test_label.py
@@ -2,7 +2,7 @@
 import pytest
 from scipy.sparse import issparse
 
-from sklearn import datasets
+from sklearn import config_context, datasets
 from sklearn.preprocessing._label import (
     LabelBinarizer,
     LabelEncoder,
@@ -11,7 +11,16 @@
     _inverse_binarize_thresholding,
     label_binarize,
 )
-from sklearn.utils._testing import assert_array_equal, ignore_warnings
+from sklearn.utils._array_api import (
+    _convert_to_numpy,
+    get_namespace,
+    yield_namespace_device_dtype_combinations,
+)
+from sklearn.utils._testing import (
+    _array_api_for_tests,
+    assert_array_equal,
+    ignore_warnings,
+)
 from sklearn.utils.fixes import (
     COO_CONTAINERS,
     CSC_CONTAINERS,
@@ -697,3 +706,42 @@ def test_label_encoders_do_not_have_set_output(encoder):
     y_encoded_with_kwarg = encoder.fit_transform(y=["a", "b", "c"])
     y_encoded_positional = encoder.fit_transform(["a", "b", "c"])
     assert_array_equal(y_encoded_with_kwarg, y_encoded_positional)
+
+
+@pytest.mark.parametrize(
+    "array_namespace, device, dtype", yield_namespace_device_dtype_combinations()
+)
+@pytest.mark.parametrize(
+    "y",
+    [
+        np.array([2, 1, 3, 1, 3]),
+        np.array([1, 1, 4, 5, -1, 0]),
+        np.array([3, 5, 9, 5, 9, 3]),
+    ],
+)
+def test_label_encoder_array_api_compliance(y, array_namespace, device, dtype):
+    xp = _array_api_for_tests(array_namespace, device)
+    xp_y = xp.asarray(y, device=device)
+    with config_context(array_api_dispatch=True):
+        xp_label = LabelEncoder()
+        np_label = LabelEncoder()
+        xp_label = xp_label.fit(xp_y)
+        xp_transformed = xp_label.transform(xp_y)
+        xp_inv_transformed = xp_label.inverse_transform(xp_transformed)
+        np_label = np_label.fit(y)
+        np_transformed = np_label.transform(y)
+        assert get_namespace(xp_transformed)[0].__name__ == xp.__name__
+        assert get_namespace(xp_inv_transformed)[0].__name__ == xp.__name__
+        assert get_namespace(xp_label.classes_)[0].__name__ == xp.__name__
+        assert_array_equal(_convert_to_numpy(xp_transformed, xp), np_transformed)
+        assert_array_equal(_convert_to_numpy(xp_inv_transformed, xp), y)
+        assert_array_equal(_convert_to_numpy(xp_label.classes_, xp), np_label.classes_)
+
+        xp_label = LabelEncoder()
+        np_label = LabelEncoder()
+        xp_transformed = xp_label.fit_transform(xp_y)
+        np_transformed = np_label.fit_transform(y)
+        assert get_namespace(xp_transformed)[0].__name__ == xp.__name__
+        assert get_namespace(xp_label.classes_)[0].__name__ == xp.__name__
+        assert_array_equal(_convert_to_numpy(xp_transformed, xp), np_transformed)
+        assert_array_equal(_convert_to_numpy(xp_label.classes_, xp), np_label.classes_)
diff --git a/sklearn/utils/_array_api.py b/sklearn/utils/_array_api.py
@@ -406,6 +406,11 @@ def unique_counts(self, x):
     def unique_values(self, x):
         return numpy.unique(x)
 
+    def unique_all(self, x):
+        return numpy.unique(
+            x, return_index=True, return_inverse=True, return_counts=True
+        )
+
     def concat(self, arrays, *, axis=None):
         return numpy.concatenate(arrays, axis=axis)
 
@@ -839,3 +844,121 @@ def indexing_dtype(xp):
     # TODO: once sufficiently adopted, we might want to instead rely on the
     # newer inspection API: https://github.com/data-apis/array-api/issues/640
     return xp.asarray(0).dtype
+
+
+def _searchsorted(xp, a, v, *, side="left", sorter=None):
+    # Temporary workaround needed as long as searchsorted is not widely
+    # adopted by implementers of the Array API spec. This is a quite
+    # recent addition to the spec:
+    # https://data-apis.org/array-api/latest/API_specification/generated/array_api.searchsorted.html # noqa
+    if hasattr(xp, "searchsorted"):
+        return xp.searchsorted(a, v, side=side, sorter=sorter)
+
+    a_np = _convert_to_numpy(a, xp=xp)
+    v_np = _convert_to_numpy(v, xp=xp)
+    indices = numpy.searchsorted(a_np, v_np, side=side, sorter=sorter)
+    return xp.asarray(indices, device=device(a))
+
+
+def _setdiff1d(ar1, ar2, xp, assume_unique=False):
+    """Find the set difference of two arrays.
+
+    Return the unique values in `ar1` that are not in `ar2`.
+    """
+    if _is_numpy_namespace(xp):
+        return xp.asarray(
+            numpy.setdiff1d(
+                ar1=ar1,
+                ar2=ar2,
+                assume_unique=assume_unique,
+            )
+        )
+
+    if assume_unique:
+        ar1 = xp.reshape(ar1, (-1,))
+    else:
+        ar1 = xp.unique_values(ar1)
+        ar2 = xp.unique_values(ar2)
+    return ar1[_in1d(ar1=ar1, ar2=ar2, xp=xp, assume_unique=True, invert=True)]
+
+
+def _isin(element, test_elements, xp, assume_unique=False, invert=False):
+    """Calculates ``element in test_elements``, broadcasting over `element`
+    only.
+
+    Returns a boolean array of the same shape as `element` that is True
+    where an element of `element` is in `test_elements` and False otherwise.
+    """
+    if _is_numpy_namespace(xp):
+        return xp.asarray(
+            numpy.isin(
+                element=element,
+                test_elements=test_elements,
+                assume_unique=assume_unique,
+                invert=invert,
+            )
+        )
+
+    original_element_shape = element.shape
+    element = xp.reshape(element, (-1,))
+    test_elements = xp.reshape(test_elements, (-1,))
+    return xp.reshape(
+        _in1d(
+            ar1=element,
+            ar2=test_elements,
+            xp=xp,
+            assume_unique=assume_unique,
+            invert=invert,
+        ),
+        original_element_shape,
+    )
+
+
+# Note: This is a helper for the functions `_isin` and
+# `_setdiff1d`. It is not meant to be called directly.
+def _in1d(ar1, ar2, xp, assume_unique=False, invert=False):
+    """Checks whether each element of an array is also present in a
+    second array.
+
+    Returns a boolean array the same length as `ar1` that is True
+    where an element of `ar1` is in `ar2` and False otherwise.
+
+    This function has been adapted using the original implementation
+    present in numpy:
+    https://github.com/numpy/numpy/blob/v1.26.0/numpy/lib/arraysetops.py#L524-L758
+    """
+    xp, _ = get_namespace(ar1, ar2, xp=xp)
+
+    # This code is run to make the code significantly faster
+    if ar2.shape[0] < 10 * ar1.shape[0] ** 0.145:
+        if invert:
+            mask = xp.ones(ar1.shape[0], dtype=xp.bool, device=device(ar1))
+            for a in ar2:
+                mask &= ar1 != a
+        else:
+            mask = xp.zeros(ar1.shape[0], dtype=xp.bool, device=device(ar1))
+            for a in ar2:
+                mask |= ar1 == a
+        return mask
+
+    if not assume_unique:
+        ar1, rev_idx = xp.unique_inverse(ar1)
+        ar2 = xp.unique_values(ar2)
+
+    ar = xp.concat((ar1, ar2))
+    device_ = device(ar)
+    # We need this to be a stable sort.
+    order = xp.argsort(ar, stable=True)
+    reverse_order = xp.argsort(order, stable=True)
+    sar = xp.take(ar, order, axis=0)
+    if invert:
+        bool_ar = sar[1:] != sar[:-1]
+    else:
+        bool_ar = sar[1:] == sar[:-1]
+    flag = xp.concat((bool_ar, xp.asarray([invert], device=device_)))
+    ret = xp.take(flag, reverse_order, axis=0)
+
+    if assume_unique:
+        return ret[: ar1.shape[0]]
+    else:
+        return xp.take(ret, rev_idx, axis=0)