Fix incorrect test set values in leave_k_out splits with sparse user rows

implicit/evaluation.pyx

@@ -205,8 +205,13 @@ cpdef leave_k_out_split(
     candidate_items = items[full_candidate_mask]
     candidate_data = data[full_candidate_mask]
 
+    # reindex candidate_user indices so they are properly formatted for the
+    # calculations in _take_tails
+    xsorted = np.argsort(unique_candidate_users)
+    reindexed_candidate_users = np.searchsorted(unique_candidate_users[xsorted], candidate_users)
+
     test_idx, train_idx = _take_tails(
-        candidate_users, K, shuffled=True, return_complement=True
+        reindexed_candidate_users, K, shuffled=True, return_complement=True
     )
 
     # get all remaining remaining candidate user-item pairs, and prepare to append to

tests/evaluation_test.py

@@ -15,7 +15,22 @@ def _get_sample_matrix():
 
 
 def _get_matrix():
-    mat = random(100, 100, density=0.5, format="csr", dtype=np.float32)
+    mat = random(100, 100, density=0.1, format="csr", dtype=np.float32)
+    return mat.tocoo()
+
+
+def _get_fixed_matrix():
+    mat = csr_matrix(
+        [
+            [1, 0, 0, 0],
+            [3, 2, 1, 0],
+            [1, 0, 0, 0],
+            [0, 1, 0, 0],
+            [0, 0, 1, 0],
+            [0, 1, 1, 1],
+            [0, 0, 1, 0],
+        ]
+    )
     return mat.tocoo()
 
 
@@ -48,6 +63,10 @@ def test_leave_k_out_outputs_produce_input():
     train, test = leave_k_out_split(mat, K=1)
     assert ((train + test) - mat).nnz == 0
 
+    mat = _get_fixed_matrix()
+    train, test = leave_k_out_split(mat, K=1)
+    assert ((train + test) - mat).nnz == 0
+
 
 def test_leave_k_split_is_reservable():
     """