scikit-learn · lorentzenchr · Sep 5, 2022 · May 22, 2022 · May 22, 2022 · May 23, 2022
diff --git a/doc/whats_new/v1.2.rst b/doc/whats_new/v1.2.rst
@@ -232,6 +232,10 @@ Changelog
   of a binary classification problem. :pr:`22518` by
   :user:`Arturo Amor <ArturoAmorQ>`.
 
+- |Fix| Allows `csr_matrix` as input for parameter: `y_true` of
+   the :func:`metrics.label_ranking_average_precision_score` metric.
+   :pr:`23442` by :user:`Sean Atukorala <ShehanAT>`
+
 - |Fix| :func:`metrics.ndcg_score` will now trigger a warning when the `y_true`
   value contains a negative value. Users may still use negative values, but the
   result may not be between 0 and 1. Starting in v1.4, passing in negative

diff --git a/sklearn/metrics/_ranking.py b/sklearn/metrics/_ranking.py
@@ -23,7 +23,7 @@
 from functools import partial
 
 import numpy as np
-from scipy.sparse import csr_matrix
+from scipy.sparse import csr_matrix, issparse
 from scipy.stats import rankdata
 
 from ..utils import assert_all_finite
@@ -1071,7 +1071,7 @@ def label_ranking_average_precision_score(y_true, y_score, *, sample_weight=None
     0.416...
     """
     check_consistent_length(y_true, y_score, sample_weight)
-    y_true = check_array(y_true, ensure_2d=False)
+    y_true = check_array(y_true, ensure_2d=False, accept_sparse="csr")
     y_score = check_array(y_score, ensure_2d=False)
 
     if y_true.shape != y_score.shape:
@@ -1084,7 +1084,9 @@ def label_ranking_average_precision_score(y_true, y_score, *, sample_weight=None
     ):
         raise ValueError("{0} format is not supported".format(y_type))
 
-    y_true = csr_matrix(y_true)
+    if not issparse(y_true):
+        y_true = csr_matrix(y_true)
+
     y_score = -y_score
 
     n_samples, n_labels = y_true.shape

diff --git a/sklearn/metrics/tests/test_ranking.py b/sklearn/metrics/tests/test_ranking.py
@@ -2042,3 +2042,12 @@ def test_top_k_accuracy_score_warning(y_true, k):
 def test_top_k_accuracy_score_error(y_true, y_score, labels, msg):
     with pytest.raises(ValueError, match=msg):
         top_k_accuracy_score(y_true, y_score, k=2, labels=labels)
+
+
+def test_label_ranking_avg_precision_score_should_allow_csr_matrix_for_y_true_input():
+    # Test that label_ranking_avg_precision_score accept sparse y_true.
+    # Non-regression test for #22575
+    y_true = csr_matrix([[1, 0, 0], [0, 0, 1]])
+    y_score = np.array([[0.5, 0.9, 0.6], [0, 0, 1]])
+    result = label_ranking_average_precision_score(y_true, y_score)
+    assert result == pytest.approx(2 / 3)