Merge pull request #1476 from VesnaT/tree_test

[ENH] TestWidgetTree: Cls and reg tree widgets test upgrade

Author: astaric

Orange/widgets/classify/owclassificationtree.py

@@ -26,22 +26,25 @@ class OWClassificationTree(OWBaseLearner):
     scores = (("Entropy", "entropy"), ("Gini Index", "gini"))
 
     def add_main_layout(self):
-        gui.comboBox(self.controlArea, self, "attribute_score",
-                     box='Feature Selection',
-                     items=[name for name, _ in self.scores],
-                     callback=self.settings_changed)
+        self.score_combo = gui.comboBox(
+            self.controlArea, self, "attribute_score", box='Feature Selection',
+            items=[name for name, _ in self.scores],
+            callback=self.settings_changed)
 
         box = gui.vBox(self.controlArea, 'Pruning')
-        gui.spin(box, self, "min_leaf", 1, 1000,
-                 label="Min. instances in leaves: ", checked="limit_min_leaf",
-                 callback=self.settings_changed)
-        gui.spin(box, self, "min_internal", 1, 1000,
-                 label="Stop splitting nodes with less instances than: ",
-                 checked="limit_min_internal",
-                 callback=self.settings_changed)
-        gui.spin(box, self, "max_depth", 1, 1000,
-                 label="Limit the depth to: ", checked="limit_depth",
-                 callback=self.settings_changed)
+        self.min_leaf_spin = gui.spin(
+            box, self, "min_leaf", 1, 1000, label="Min. instances in leaves: ",
+            checked="limit_min_leaf", callback=self.settings_changed,
+            checkCallback=self.settings_changed)
+        self.min_internal_spin = gui.spin(
+            box, self, "min_internal", 1, 1000,
+            label="Stop splitting nodes with less instances than: ",
+            checked="limit_min_internal", callback=self.settings_changed,
+            checkCallback=self.settings_changed)
+        self.max_depth_spin = gui.spin(
+            box, self, "max_depth", 1, 1000, label="Limit the depth to: ",
+            checked="limit_depth", callback=self.settings_changed,
+            checkCallback=self.settings_changed)
 
     def create_learner(self):
         return self.LEARNER(

Orange/widgets/classify/tests/test_owclassificationtree.py

@@ -1,11 +1,48 @@
 # Test methods with long descriptive names can omit docstrings
 # pylint: disable=missing-docstring
 from Orange.widgets.classify.owclassificationtree import OWClassificationTree
-from Orange.widgets.tests.base import WidgetTest, WidgetLearnerTestMixin
+from Orange.widgets.tests.base import (WidgetTest, WidgetLearnerTestMixin,
+                                       GuiToParam)
 
 
 class TestOWClassificationTree(WidgetTest, WidgetLearnerTestMixin):
     def setUp(self):
         self.widget = self.create_widget(OWClassificationTree,
                                          stored_settings={"auto_apply": False})
         self.init()
+
+        def combo_set_value(i, x):
+            x.activated.emit(i)
+            x.setCurrentIndex(i)
+
+        scores = [score[1] for score in self.widget.scores]
+        md_spin = self.widget.max_depth_spin[1]
+        mi_spin = self.widget.min_internal_spin[1]
+        ml_spin = self.widget.min_leaf_spin[1]
+        md_min_max = [md_spin.minimum(), md_spin.maximum()]
+        mi_min_max = [mi_spin.minimum(), mi_spin.maximum()]
+        ml_min_max = [ml_spin.minimum(), ml_spin.maximum()]
+        self.gui_to_params = [
+            GuiToParam('criterion', self.widget.score_combo,
+                       lambda x: scores[x.currentIndex()],
+                       combo_set_value, scores, list(range(len(scores)))),
+            GuiToParam('max_depth', md_spin, lambda x: x.value(),
+                       lambda i, x: x.setValue(i), md_min_max, md_min_max),
+            GuiToParam('min_samples_split', mi_spin, lambda x: x.value(),
+                       lambda i, x: x.setValue(i), mi_min_max, mi_min_max),
+            GuiToParam('min_samples_leaf', ml_spin, lambda x: x.value(),
+                       lambda i, x: x.setValue(i), ml_min_max, ml_min_max)]
+
+    def test_parameters_unchecked(self):
+        """Check learner and model for various values of all parameters
+        when pruning parameters are not checked
+        """
+        self.widget.max_depth_spin[0].setCheckState(False)
+        self.widget.min_internal_spin[0].setCheckState(False)
+        self.widget.min_leaf_spin[0].setCheckState(False)
+        for i, val in ((1, None), (2, 2), (3, 1)):
+            el = self.gui_to_params[i]
+            self.gui_to_params[i] = GuiToParam(
+                el.name, el.gui_el, lambda x, value=val: value,
+                el.set, [val], el.set_values[0:1])
+        self.test_parameters()

Orange/widgets/regression/tests/test_owregressiontree.py

@@ -1,11 +1,48 @@
 # Test methods with long descriptive names can omit docstrings
 # pylint: disable=missing-docstring
 from Orange.widgets.regression.owregressiontree import OWRegressionTree
-from Orange.widgets.tests.base import WidgetTest, WidgetLearnerTestMixin
+from Orange.widgets.tests.base import (WidgetTest, WidgetLearnerTestMixin,
+                                       GuiToParam)
 
 
 class TestOWRegressionTree(WidgetTest, WidgetLearnerTestMixin):
     def setUp(self):
         self.widget = self.create_widget(OWRegressionTree,
                                          stored_settings={"auto_apply": False})
         self.init()
+
+        def combo_set_value(i, x):
+            x.activated.emit(i)
+            x.setCurrentIndex(i)
+
+        scores = [score[1] for score in self.widget.scores]
+        md_spin = self.widget.max_depth_spin[1]
+        mi_spin = self.widget.min_internal_spin[1]
+        ml_spin = self.widget.min_leaf_spin[1]
+        md_min_max = [md_spin.minimum(), md_spin.maximum()]
+        mi_min_max = [mi_spin.minimum(), mi_spin.maximum()]
+        ml_min_max = [ml_spin.minimum(), ml_spin.maximum()]
+        self.gui_to_params = [
+            GuiToParam('criterion', self.widget.score_combo,
+                       lambda x: scores[x.currentIndex()],
+                       combo_set_value, scores, list(range(len(scores)))),
+            GuiToParam('max_depth', md_spin, lambda x: x.value(),
+                       lambda i, x: x.setValue(i), md_min_max, md_min_max),
+            GuiToParam('min_samples_split', mi_spin, lambda x: x.value(),
+                       lambda i, x: x.setValue(i), mi_min_max, mi_min_max),
+            GuiToParam('min_samples_leaf', ml_spin, lambda x: x.value(),
+                       lambda i, x: x.setValue(i), ml_min_max, ml_min_max)]
+
+    def test_parameters_unchecked(self):
+        """Check learner and model for various values of all parameters
+        when pruning parameters are not checked
+        """
+        self.widget.max_depth_spin[0].setCheckState(False)
+        self.widget.min_internal_spin[0].setCheckState(False)
+        self.widget.min_leaf_spin[0].setCheckState(False)
+        for i, val in ((1, None), (2, 2), (3, 1)):
+            el = self.gui_to_params[i]
+            self.gui_to_params[i] = GuiToParam(
+                el.name, el.gui_el, lambda x, value=val: value,
+                el.set, [val], el.set_values[0:1])
+        self.test_parameters()