OwLouvain: Properly compare new data with old without warnings

Orange/statistics/util.py

@@ -12,6 +12,21 @@
 import scipy.stats.stats
 
 
+def sparse_array_equal(x1, x2):
+    """Check if two sparse arrays are equal."""
+    assert sp.issparse(x1)
+    assert sp.issparse(x2)
+
+    return x1.shape == x2.shape and (x1 != x2).nnz == 0
+
+
+def array_equal(x1, x2):
+    """Equivalent of np.array_equal that properly handles sparse matrices."""
+    if sp.issparse(x1) and sp.issparse(x2):
+        return sparse_array_equal(x1, x2)
+    return np.array_equal(x1, x2)
+
+
 def _count_nans_per_row_sparse(X, weights, dtype=None):
     """ Count the number of nans (undefined) values per row. """
     if weights is not None:

Orange/widgets/unsupervised/owlouvainclustering.py

@@ -19,6 +19,7 @@
 from Orange.data.util import get_unique_names
 from Orange import preprocess
 from Orange.projection import PCA
+from Orange.statistics import util as ut
 from Orange.widgets import widget, gui, report
 from Orange.widgets.settings import DomainContextHandler, ContextSetting, \
     Setting
@@ -407,8 +408,7 @@ def set_data(self, data):
         # Make sure to properly enable/disable slider based on `apply_pca` setting
         self.controls.pca_components.setEnabled(self.apply_pca)
 
-        # If X hasn't changed, there's no reason to recompute clusters
-        if prev_data and self.data and np.array_equal(self.data.X, prev_data.X):
+        if prev_data and self.data and ut.array_equal(prev_data, self.data):
             if self.auto_commit:
                 self._send_data()
             return