Merge pull request #20 from VesnaT/add_tests

[FIX] Improve coverage

orangecontrib/explain/widgets/owexplainfeaturebase.py

@@ -397,6 +397,7 @@ class Error(OWWidget.Error):
     class Information(OWWidget.Information):
         data_sampled = Msg("Data has been sampled.")
 
+    settingsHandler = NotImplemented
     n_attributes = Setting(10)
     zoom_level = Setting(0)
     selection = Setting((), schema_only=True)
@@ -502,11 +503,6 @@ def _clear_scene(self):
 
     def update_scene(self):
         self._clear_scene()
-        if self.results is not None:
-            values = np.mean(self.results.x, axis=0)
-            indices = np.argsort(values)[::-1]
-            names = [self.results.names[i] for i in indices]
-            self.setup_plot(values[indices], names)
 
     def setup_plot(self, values: np.ndarray, names: List[str], *plot_args):
         width = int(self.view.viewport().rect().width())
@@ -622,6 +618,7 @@ def run(data: Table, model: Model, *, state: TaskState) -> BaseResults:
 
 if __name__ == "__main__":  # pragma: no cover
     from Orange.classification import RandomForestLearner
+    from Orange.widgets.settings import DomainContextHandler
     from Orange.widgets.utils.widgetpreview import WidgetPreview
 
 
@@ -676,6 +673,7 @@ def select_from_settings(self, *_):
     class Widget(OWExplainFeatureBase):
         name = "Explain"
         PLOT_CLASS = Plot
+        settingsHandler = DomainContextHandler()
 
         def update_selection(self, *_):
             pass
@@ -686,6 +684,14 @@ def get_selected_data(self):
         def get_scores_table(self):
             return None
 
+        def update_scene(self):
+            super().update_scene()
+            if self.results is not None:
+                values = np.mean(self.results.x, axis=0)
+                indices = np.argsort(values)[::-1]
+                names = [self.results.names[i] for i in indices]
+                self.setup_plot(values[indices], names)
+
         @staticmethod
         def run(data, model, state):
             if not data or not model:

orangecontrib/explain/widgets/owexplainmodel.py

@@ -332,7 +332,7 @@ def _set_items(self, x: np.ndarray, labels: List[str], colors: np.ndarray):
                 item.selection_changed.connect(self.select)
                 self._items.append(item)
                 self._layout.addItem(item, i, FeaturesPlot.ITEM_COLUMN)
-                if i == MAX_N_ITEMS:
+                if i == MAX_N_ITEMS - 1:
                     break
 
 
@@ -399,7 +399,7 @@ def setup_controls(self):
 
     # Plot setup
     def update_scene(self):
-        self._clear_scene()
+        super().update_scene()
         if self.results is not None:
             assert isinstance(self.results.x, list)
             x = self.results.x[self.target_index]

orangecontrib/explain/widgets/owpermutationimportance.py

@@ -122,7 +122,7 @@ def _set_items(self, x: np.ndarray, labels: List[str], std: np.ndarray,
             item.set_data(x[i], std[i])
             self._items.append(item)
             self._layout.addItem(item, i, FeaturesPlot.ITEM_COLUMN)
-            if i == MAX_N_ITEMS:
+            if i == MAX_N_ITEMS - 1:
                 break
         self._bottom_axis.setLabel(x_label)
 
@@ -290,7 +290,7 @@ def get_runner_parameters(self) -> Tuple[Optional[Table], Optional[Model],
 
     # Plot setup
     def update_scene(self):
-        self._clear_scene()
+        super().update_scene()
         if self.results is not None:
             importance = self.results.x
             mean = np.mean(importance, axis=1)

orangecontrib/explain/widgets/tests/test_owpermutationimportance.py

@@ -205,6 +205,24 @@ def test_n_repeats(self, mocked_func: Mock):
         self.wait_until_finished()
         self.assertEqual(mocked_func.call_args[0][3], 3)
 
+    @patch("orangecontrib.explain.widgets.owpermutationimportance."
+           "MAX_N_ITEMS", 3)
+    def test_n_attributes(self):
+        self.widget.controls.n_attributes.setValue(3)
+        self.send_signal(self.widget.Inputs.data, self.iris)
+        self.send_signal(self.widget.Inputs.model, self.rf_cls)
+        self.wait_until_finished()
+        domain = self.iris.domain
+        domain = Domain(domain.attributes[:3], domain.class_vars)
+        self.assertDomainInPlot(self.widget.plot, domain)
+
+    def test_zoom_level(self):
+        self.send_signal(self.widget.Inputs.data, self.iris)
+        self.send_signal(self.widget.Inputs.model, self.rf_cls)
+        self.wait_until_finished()
+        self.widget.controls.zoom_level.setValue(10)
+        self.assertDomainInPlot(self.widget.plot, self.iris.domain)
+
     def test_plot(self):
         self.send_signal(self.widget.Inputs.data, self.iris)
         self.wait_until_finished()