Gui: Fixups according to improved sparsity

Orange/widgets/data/tests/test_owfile.py

@@ -308,8 +308,7 @@ def test_no_specified_reader(self):
         self.assertTrue(self.widget.Error.missing_reader.is_shown())
 
     def test_domain_edit_on_sparse_data(self):
-        iris = Table("iris")
-        iris.X = sp.csr_matrix(iris.X)
+        iris = Table("iris").to_sparse()
 
         f = tempfile.NamedTemporaryFile(suffix='.pickle', delete=False)
         pickle.dump(iris, f)

Orange/widgets/data/tests/test_owmergedata.py

@@ -435,8 +435,7 @@ def test_sparse(self):
         """
         data = Table("iris")[::25]
         data_ed_dense = Table("titanic")[::300]
-        data_ed_sparse = Table("titanic")[::300]
-        data_ed_sparse.X = sp.csr_matrix(data_ed_sparse.X)
+        data_ed_sparse = Table("titanic")[::300].to_sparse()
         self.send_signal("Data", data)
 
         self.send_signal("Extra Data", data_ed_dense)

Orange/widgets/model/tests/test_tree.py

@@ -1,6 +1,5 @@
 # pylint: disable=protected-access
 import numpy as np
-import scipy.sparse as sp
 
 from Orange.base import Model
 from Orange.data import Table
@@ -49,8 +48,7 @@ def test_sparse_data_classification(self):
         table1 = Table("iris")
         self.send_signal("Data", table1)
         model_dense = self.get_output("Model")
-        table2 = Table("iris")
-        table2.X = sp.csr_matrix(table2.X)
+        table2 = Table("iris").to_sparse()
         self.send_signal("Data", table2)
         model_sparse = self.get_output("Model")
         self.assertTrue(np.array_equal(model_dense._code, model_sparse._code))
@@ -64,8 +62,7 @@ def test_sparse_data_regression(self):
         table1 = Table("housing")
         self.send_signal("Data", table1)
         model_dense = self.get_output("Model")
-        table2 = Table("housing")
-        table2.X = sp.csr_matrix(table2.X)
+        table2 = Table("housing").to_sparse()
         self.send_signal("Data", table2)
         model_sparse = self.get_output("Model")
         self.assertTrue(np.array_equal(model_dense._code, model_sparse._code))

Orange/widgets/unsupervised/tests/test_owmanifoldlearning.py

@@ -66,8 +66,7 @@ def _compare_tables(self, _output, n_components):
         np.testing.assert_array_equal(self.iris.metas, _output.metas)
 
     def test_sparse_data(self):
-        data = Table("iris")
-        data.X = sparse.csr_matrix(data.X)
+        data = Table("iris").to_sparse()
         self.assertTrue(sparse.issparse(data.X))
         self.widget.manifold_method_index = 2
         self.send_signal(self.widget.Inputs.data, data)

Orange/widgets/visualize/owheatmap.py

@@ -663,8 +663,7 @@ def set_dataset(self, data=None):
 
         if data is not None and sp.issparse(data.X):
             try:
-                data = data.copy()
-                data.X = data.X.toarray()
+                data = data.to_dense()
             except MemoryError:
                 data = None
                 self.Error.not_enough_memory()

Orange/widgets/visualize/owscatterplotgraph.py

@@ -623,13 +623,7 @@ def sparse_to_dense(self):
         domain = data.domain
         all_attrs = domain.variables + domain.metas
         attrs = list(set(all_attrs) & attrs)
-        selected_data = data[:, attrs]
-        if sp.issparse(selected_data.X):
-            selected_data.X = selected_data.X.toarray()
-        if sp.issparse(selected_data.Y):
-            selected_data.Y = selected_data.Y.toarray()
-        if sp.issparse(selected_data.metas):
-            selected_data.metas = selected_data.metas.toarray()
+        selected_data = data[:, attrs].to_dense()
         return selected_data
 
     def _clear_plot_widget(self):

Orange/widgets/visualize/owsieve.py

@@ -209,7 +209,7 @@ def discretizer(data):
                 discretize = Discretize(
                     method=EqualFreq(n=4), remove_const=False,
                     discretize_classes=True, discretize_metas=True)
-                return discretize(data)
+                return discretize(data).to_dense()
             return data
 
         if not data.is_sparse() and not init:
@@ -219,7 +219,6 @@ def discretizer(data):
                      self.attr_y}
             new_domain = data.domain.select_columns(attrs)
             data = Table.from_table(new_domain, data)
-            data.X = data.X.toarray()
         return discretizer(data)
 
     @Inputs.features

Orange/widgets/visualize/tests/test_owscatterplot.py

@@ -317,11 +317,8 @@ def test_sparse(self):
         GH-2152
         GH-2157
         """
-        table = Table("iris")
-        table.X = sp.csr_matrix(table.X)
-        self.assertTrue(sp.issparse(table.X))
-        table.Y = sp.csr_matrix(table._Y)  # pylint: disable=protected-access
-        self.assertTrue(sp.issparse(table.Y))
+        table = Table("iris").to_sparse(sparse_attributes=True,
+                                        sparse_class=True)
         self.send_signal(self.widget.Inputs.data, table)
         self.widget.set_subset_data(table[:30])
         data = self.get_output("Data")

Orange/widgets/visualize/tests/test_owsieve.py

@@ -3,7 +3,6 @@
 from math import isnan
 from unittest.mock import patch
 import numpy as np
-import scipy.sparse as sp
 
 from AnyQt.QtCore import QEvent, QPoint, Qt
 from AnyQt.QtGui import QMouseEvent
@@ -100,7 +99,7 @@ def test_sparse_data(self):
         output = self.get_output("Data")
         self.assertFalse(output.is_sparse())
 
-        table.X = sp.csr_matrix(table.X)
+        table = table.to_sparse()
         self.send_signal(self.widget.Inputs.data, table)
         self.assertEqual(len(self.widget.discrete_data.domain), 2)
         output = self.get_output("Data")