FIX Allow sparse input data for OutputCodeClassifier (scikit-learn#17233

)

Co-authored-by: Guillaume Lemaitre <[email protected]>

doc/whats_new/v0.24.rst

@@ -113,6 +113,15 @@ Changelog
   :pr:`16530` by :user:`Shuhua Fan <jim0421>`.
 
 
+:mod:`sklearn.multiclass`
+.........................
+
+- |Fix| A fix to allow :class:`multiclass.OutputCodeClassifier` to accept
+  sparse input data in its `fit` and `predict` methods. The check for
+  validity of the input is now delegated to the base estimator.
+  :pr:`17233` by :user:`Zolisa Bleki <zoj613>`.
+
+
 Code and Documentation Contributors
 -----------------------------------
 

sklearn/multiclass.py

@@ -802,7 +802,7 @@ def fit(self, X, y):
         -------
         self
         """
-        X, y = self._validate_data(X, y)
+        X, y = self._validate_data(X, y, accept_sparse=True)
         if self.code_size <= 0:
             raise ValueError("code_size should be greater than 0, got {0}"
                              "".format(self.code_size))
@@ -850,7 +850,7 @@ def predict(self, X):
             Predicted multi-class targets.
         """
         check_is_fitted(self)
-        X = check_array(X)
+        X = check_array(X, accept_sparse=True)
         Y = np.array([_predict_binary(e, X) for e in self.estimators_]).T
         pred = euclidean_distances(Y, self.code_book_).argmin(axis=1)
         return self.classes_[pred]

sklearn/tests/test_multiclass.py

@@ -1,5 +1,6 @@
 import numpy as np
 import scipy.sparse as sp
+import pytest
 
 from re import escape
 
@@ -9,11 +10,13 @@
 from sklearn.utils._testing import assert_warns
 from sklearn.utils._testing import assert_raise_message
 from sklearn.utils._testing import assert_raises_regexp
+from sklearn.utils._mocking import CheckingClassifier
 from sklearn.multiclass import OneVsRestClassifier
 from sklearn.multiclass import OneVsOneClassifier
 from sklearn.multiclass import OutputCodeClassifier
 from sklearn.utils.multiclass import (check_classification_targets,
                                       type_of_target)
+from sklearn.utils import check_array
 from sklearn.utils import shuffle
 
 from sklearn.metrics import precision_score
@@ -705,6 +708,32 @@ def test_ecoc_float_y():
                          " got -1", ovo.fit, X, y)
 
 
+def test_ecoc_delegate_sparse_base_estimator():
+    # Non-regression test for
+    # https://github.com/scikit-learn/scikit-learn/issues/17218
+    X, y = iris.data, iris.target
+    X_sp = sp.csc_matrix(X)
+
+    # create an estimator that does not support sparse input
+    base_estimator = CheckingClassifier(
+        check_X=check_array,
+        check_X_params={"ensure_2d": True, "accept_sparse": False},
+    )
+    ecoc = OutputCodeClassifier(base_estimator, random_state=0)
+
+    with pytest.raises(TypeError, match="A sparse matrix was passed"):
+        ecoc.fit(X_sp, y)
+
+    ecoc.fit(X, y)
+    with pytest.raises(TypeError, match="A sparse matrix was passed"):
+        ecoc.predict(X_sp)
+
+    # smoke test to check when sparse input should be supported
+    ecoc = OutputCodeClassifier(LinearSVC(random_state=0))
+    ecoc.fit(X_sp, y).predict(X_sp)
+    assert len(ecoc.estimators_) == 4
+
+
 def test_pairwise_indices():
     clf_precomputed = svm.SVC(kernel='precomputed')
     X, y = iris.data, iris.target