Statistics.tests: Inject explicit zeros into dense_sparse decorator

Author: pavlin-policar

Orange/statistics/util.py

@@ -131,10 +131,11 @@ def bincount(x, weights=None, max_val=None, minlength=None):
         bc = np.bincount(
             x.astype(np.int32, copy=False), weights=weights, minlength=minlength
         ).astype(float)
-        # Since `csr_matrix.values` only contain non-zero values, we must count
-        # those separately and set the appropriate bin
+        # Since `csr_matrix.values` only contain non-zero values or explicit
+        # zeros, we must count implicit zeros separately and add them to the
+        # explicit ones found before
         if sp.issparse(x_original):
-            bc[0] = zero_weights
+            bc[0] += zero_weights
 
     return bc, nans
 

Orange/tests/test_statistics.py

@@ -1,6 +1,6 @@
 import unittest
 import warnings
-from functools import wraps
+from functools import wraps, partial
 from itertools import chain
 
 import numpy as np
@@ -16,9 +16,21 @@ def dense_sparse(test_case):
     """Run a single test case on both dense and sparse data."""
     @wraps(test_case)
     def _wrapper(self):
+
+        def sparse_with_explicit_zero(x, array):
+            """Inject one explicit zero into a sparse array."""
+            np_array, sp_array = np.atleast_2d(x), array(x)
+            assert issparse(sp_array), 'Can not inject explicit zero into non-sparse matrix'
+
+            zero_indices = np.argwhere(np_array == 0)
+            if zero_indices.size:
+                sp_array[tuple(zero_indices[0])] = 0
+
+            return sp_array
+
         test_case(self, lambda x: np.array(x))
-        test_case(self, lambda x: csr_matrix(x))
-        test_case(self, lambda x: csc_matrix(x))
+        test_case(self, partial(sparse_with_explicit_zero, array=csr_matrix))
+        test_case(self, partial(sparse_with_explicit_zero, array=csc_matrix))
 
     return _wrapper