Merge pull request #1474 from VesnaT/ada_boost_fix

[FIX] Fix AdaBoost widgets and add some tests

Author: astaric

Orange/widgets/classify/owadaboost.py

@@ -26,39 +26,43 @@ class OWAdaBoostClassification(OWBaseLearner):
     learning_rate = Setting(1.)
     algorithm = Setting(0)
 
+    DEFAULT_BASE_ESTIMATOR = TreeLearner()
+
     def add_main_layout(self):
         box = gui.widgetBox(self.controlArea, "Parameters")
-        self.base_estimator = TreeLearner()
-        self.base_label = gui.label(box, self, "Base estimator: " + self.base_estimator.name)
-
-        gui.spin(box, self, "n_estimators", 1, 100, label="Number of estimators:",
-                 alignment=Qt.AlignRight, callback=self.settings_changed)
-        gui.doubleSpin(box, self, "learning_rate", 1e-5, 1.0, 1e-5,
-                       label="Learning rate:", decimals=5, alignment=Qt.AlignRight,
-                       controlWidth=90, callback=self.settings_changed)
+        self.base_estimator = self.DEFAULT_BASE_ESTIMATOR
+        self.base_label = gui.label(
+            box, self, "Base estimator: " + self.base_estimator.name)
+
+        self.n_estimators_spin = gui.spin(
+            box, self, "n_estimators", 1, 100, label="Number of estimators:",
+            alignment=Qt.AlignRight, callback=self.settings_changed)
+        self.learning_rate_spin = gui.doubleSpin(
+            box, self, "learning_rate", 1e-5, 1.0, 1e-5, label="Learning rate:",
+            decimals=5, alignment=Qt.AlignRight, controlWidth=90,
+            callback=self.settings_changed)
         self.add_specific_parameters(box)
 
     def add_specific_parameters(self, box):
-        gui.comboBox(box, self, "algorithm", label="Algorithm:",
-                     orientation=Qt.Horizontal, items=self.losses,
-                     callback=self.settings_changed)
+        self.algorithm_combo = gui.comboBox(
+            box, self, "algorithm", label="Algorithm:", items=self.losses,
+            orientation=Qt.Horizontal, callback=self.settings_changed)
 
     def create_learner(self):
         return self.LEARNER(
             base_estimator=self.base_estimator,
             n_estimators=self.n_estimators,
+            learning_rate=self.learning_rate,
             preprocessors=self.preprocessors,
             algorithm=self.losses[self.algorithm]
         )
 
-    def set_base_learner(self, model):
-        self.base_estimator = model
-        if self.base_estimator:
-            self.base_label.setText("Base estimator: " + self.base_estimator.name)
-            self.apply_button.setDisabled(False)
-        else:
-            self.base_label.setText("No base estimator")
-            self.apply_button.setDisabled(True)
+    def set_base_learner(self, learner):
+        self.base_estimator = learner if learner \
+            else self.DEFAULT_BASE_ESTIMATOR
+        self.base_label.setText("Base estimator: " + self.base_estimator.name)
+        if self.auto_apply:
+            self.apply()
 
     def get_learner_parameters(self):
         return (("Base estimator", self.base_estimator),
@@ -69,6 +73,7 @@ def get_learner_parameters(self):
 if __name__ == "__main__":
     import sys
     from PyQt4.QtGui import QApplication
+
     a = QApplication(sys.argv)
     ow = OWAdaBoostClassification()
     ow.set_data(Table("iris"))

Orange/widgets/classify/tests/test_owadaboostclassification.py

@@ -1,47 +1,52 @@
 # Test methods with long descriptive names can omit docstrings
 # pylint: disable=missing-docstring
-from PyQt4 import QtGui
-
-from Orange.widgets.tests.base import WidgetTest
+from Orange.classification import TreeLearner, KNNLearner
 from Orange.widgets.classify.owadaboost import OWAdaBoostClassification
+from Orange.widgets.tests.base import (WidgetTest, WidgetLearnerTestMixin,
+                                       GuiToParam)
 
 
-
-class TestOWAdaBoostClassification(WidgetTest):
-
+class TestOWAdaBoostClassification(WidgetTest, WidgetLearnerTestMixin):
     def setUp(self):
-        self.widget = self.create_widget(OWAdaBoostClassification)
-        self.spinners = []
-        self.spinners.append(self.widget.findChildren(QtGui.QSpinBox)[0])
-        self.spinners.append(self.widget.findChildren(QtGui.QDoubleSpinBox)[0])
-        self.combobox_algorithm = self.widget.findChildren(QtGui.QComboBox)[0]
-
-    def test_visible_boxes(self):
-        """ Check if boxes are visible """
-        self.assertEqual(self.spinners[0].isHidden(), False)
-        self.assertEqual(self.spinners[1].isHidden(), False)
-        self.assertEqual(self.combobox_algorithm.isHidden(), False)
-
-    def test_parameters_on_output(self):
-        """ Check right paramaters  on output """
-        self.widget.apply()
-        learner_params = self.widget.learner.params
-        self.assertEqual(learner_params.get("n_estimators"), self.spinners[0].value())
-        self.assertEqual(learner_params.get("learning_rate"), self.spinners[1].value())
-        self.assertEqual(learner_params.get('algorithm'), self.combobox_algorithm.currentText())
-
-
-    def test_output_algorithm(self):
-        """ Check if right learning algorithm is on output when we change algorithm """
-        for index, algorithmName in enumerate(self.widget.losses):
-            self.combobox_algorithm.setCurrentIndex(index)
-            self.combobox_algorithm.activated.emit(index)
-            self.assertEqual(self.combobox_algorithm.currentText(), algorithmName)
-            self.widget.apply()
-            self.assertEqual(self.widget.learner.params.get("algorithm").capitalize(),
-                             self.combobox_algorithm.currentText().capitalize())
-
-    def test_learner_on_output(self):
-        """ Check if learner is on output after create widget  and apply """
-        self.widget.apply()
-        self.assertNotEqual(self.widget.learner, None)
+        self.widget = self.create_widget(OWAdaBoostClassification,
+                                         stored_settings={"auto_apply": False})
+        self.init()
+
+        def combo_set_value(i, x):
+            x.activated.emit(i)
+            x.setCurrentIndex(i)
+
+        losses = self.widget.losses
+        nest_spin = self.widget.n_estimators_spin
+        nest_min_max = [nest_spin.minimum(), nest_spin.maximum()]
+        rate_spin = self.widget.learning_rate_spin
+        rate_min_max = [rate_spin.minimum(), rate_spin.maximum()]
+        self.gui_to_params = [
+            GuiToParam('algorithm', self.widget.algorithm_combo,
+                       lambda x: x.currentText(),
+                       combo_set_value, losses, list(range(len(losses)))),
+            GuiToParam('learning_rate', rate_spin, lambda x: x.value(),
+                       lambda i, x: x.setValue(i), rate_min_max, rate_min_max),
+            GuiToParam('n_estimators', nest_spin, lambda x: x.value(),
+                       lambda i, x: x.setValue(i), nest_min_max, nest_min_max)]
+
+    def test_input_learner(self):
+        """Check if base learner properly changes with learner on the input"""
+        max_depth = 2
+        default_base_est = self.widget.base_estimator
+        self.assertIsInstance(default_base_est, TreeLearner)
+        self.assertIsNone(default_base_est.params.get("max_depth"))
+        self.send_signal("Learner", TreeLearner(max_depth=max_depth))
+        self.assertEqual(self.widget.base_estimator.params.get("max_depth"),
+                         max_depth)
+        self.widget.apply_button.button.click()
+        output_base_est = self.get_output("Learner").params.get("base_estimator")
+        self.assertEqual(output_base_est.max_depth, max_depth)
+
+    def test_input_learner_disconnect(self):
+        """Check base learner after disconnecting learner on the input"""
+        self.send_signal("Learner", KNNLearner())
+        self.assertIsInstance(self.widget.base_estimator, KNNLearner)
+        self.send_signal("Learner", None)
+        self.assertEqual(self.widget.base_estimator,
+                         self.widget.DEFAULT_BASE_ESTIMATOR)

Orange/widgets/regression/owadaboostregression.py

@@ -1,6 +1,7 @@
 from PyQt4.QtCore import Qt
 
 from Orange.regression.base_regression import LearnerRegression
+from Orange.regression import TreeRegressionLearner
 from Orange.data import Table
 from Orange.ensembles import SklAdaBoostRegressionLearner
 from Orange.widgets import gui
@@ -22,15 +23,18 @@ class OWAdaBoostRegression(owadaboost.OWAdaBoostClassification):
     losses = ["Linear", "Square", "Exponential"]
     loss = Setting(0)
 
+    DEFAULT_BASE_ESTIMATOR = TreeRegressionLearner()
+
     def add_specific_parameters(self, box):
-        gui.comboBox(box, self, "loss", label="Loss:",
-                     orientation=Qt.Horizontal, items=self.losses,
-                     callback=self.settings_changed)
+        self.loss_combo = gui.comboBox(
+            box, self, "loss", label="Loss:", orientation=Qt.Horizontal,
+            items=self.losses, callback=self.settings_changed)
 
     def create_learner(self):
         return self.LEARNER(
             base_estimator=self.base_estimator,
             n_estimators=self.n_estimators,
+            learning_rate=self.learning_rate,
             preprocessors=self.preprocessors,
             loss=self.losses[self.loss].lower()
         )
@@ -44,6 +48,7 @@ def get_learner_parameters(self):
 if __name__ == "__main__":
     import sys
     from PyQt4.QtGui import QApplication
+
     a = QApplication(sys.argv)
     ow = OWAdaBoostRegression()
     ow.set_data(Table("housing"))

Orange/widgets/regression/tests/test_owadaboostregression.py

@@ -0,0 +1,52 @@
+# Test methods with long descriptive names can omit docstrings
+# pylint: disable=missing-docstring
+from Orange.regression import TreeRegressionLearner, KNNRegressionLearner
+from Orange.widgets.regression.owadaboostregression import OWAdaBoostRegression
+from Orange.widgets.tests.base import (WidgetTest, WidgetLearnerTestMixin,
+                                       GuiToParam)
+
+
+class TestOWAdaBoostRegression(WidgetTest, WidgetLearnerTestMixin):
+    def setUp(self):
+        self.widget = self.create_widget(OWAdaBoostRegression,
+                                         stored_settings={"auto_apply": False})
+        self.init()
+
+        def combo_set_value(i, x):
+            x.activated.emit(i)
+            x.setCurrentIndex(i)
+
+        losses = [loss.lower() for loss in self.widget.losses]
+        nest_spin = self.widget.n_estimators_spin
+        nest_min_max = [nest_spin.minimum(), nest_spin.maximum()]
+        rate_spin = self.widget.learning_rate_spin
+        rate_min_max = [rate_spin.minimum(), rate_spin.maximum()]
+        self.gui_to_params = [
+            GuiToParam('loss', self.widget.loss_combo,
+                       lambda x: x.currentText().lower(),
+                       combo_set_value, losses, list(range(len(losses)))),
+            GuiToParam('learning_rate', rate_spin, lambda x: x.value(),
+                       lambda i, x: x.setValue(i), rate_min_max, rate_min_max),
+            GuiToParam('n_estimators', nest_spin, lambda x: x.value(),
+                       lambda i, x: x.setValue(i), nest_min_max, nest_min_max)]
+
+    def test_input_learner(self):
+        """Check if base learner properly changes with learner on the input"""
+        max_depth = 2
+        default_base_est = self.widget.base_estimator
+        self.assertIsInstance(default_base_est, TreeRegressionLearner)
+        self.assertIsNone(default_base_est.params.get("max_depth"))
+        self.send_signal("Learner", TreeRegressionLearner(max_depth=max_depth))
+        self.assertEqual(self.widget.base_estimator.params.get("max_depth"),
+                         max_depth)
+        self.widget.apply_button.button.click()
+        output_base_est = self.get_output("Learner").params.get("base_estimator")
+        self.assertEqual(output_base_est.max_depth, max_depth)
+
+    def test_input_learner_disconnect(self):
+        """Check base learner after disconnecting learner on the input"""
+        self.send_signal("Learner", KNNRegressionLearner())
+        self.assertIsInstance(self.widget.base_estimator, KNNRegressionLearner)
+        self.send_signal("Learner", None)
+        self.assertEqual(self.widget.base_estimator,
+                         self.widget.DEFAULT_BASE_ESTIMATOR)

Orange/widgets/tests/base.py

@@ -1,6 +1,5 @@
 import unittest
 from collections import namedtuple
-from PyQt4 import QtGui
 
 from PyQt4.QtGui import QApplication
 import sip
@@ -259,18 +258,17 @@ def test_output_model(self):
     def test_output_learner_name(self):
         """Check if learner's name properly changes"""
         new_name = "Learner Name"
-        name_line_edit = self.widget.findChildren(QtGui.QLineEdit)[0]
         self.widget.apply_button.button.click()
-        self.assertEqual(self.widget.learner.name, name_line_edit.text())
-        name_line_edit.setText(new_name)
+        self.assertEqual(self.widget.learner.name,
+                         self.widget.name_line_edit.text())
+        self.widget.name_line_edit.setText(new_name)
         self.widget.apply_button.button.click()
         self.assertEqual(self.get_output("Learner").name, new_name)
 
     def test_output_model_name(self):
         """Check if model's name properly changes"""
         new_name = "Model Name"
-        name_line_edit = self.widget.findChildren(QtGui.QLineEdit)[0]
-        name_line_edit.setText(new_name)
+        self.widget.name_line_edit.setText(new_name)
         self.send_signal("Data", self.data)
         self.widget.apply_button.button.click()
         self.assertEqual(self.get_output(self.model_name).name, new_name)
@@ -294,3 +292,11 @@ def test_parameters(self):
                 param = self.widget.learner.params.get(element.name)
                 self.assertEqual(param, element.get(element.gui_el))
                 self.assertEqual(param, val)
+                param = self.get_output("Learner").params.get(element.name)
+                self.assertEqual(param, val)
+                model = self.get_output(self.model_name)
+                if model is not None:
+                    self.assertEqual(model.params.get(element.name), val)
+                else:
+                    self.assertIn(self.widget.DATA_ERROR_ID,
+                                  self.widget.widgetState.get("Error"))

Orange/widgets/utils/owlearnerwidget.py

@@ -225,10 +225,10 @@ def add_main_layout(self):
         pass
 
     def add_learner_name_widget(self):
-        gui.lineEdit(self.controlArea, self, 'learner_name', box='Name',
-                     tooltip='The name will identify this model in other widgets',
-                     orientation=Qt.Horizontal,
-                     callback=lambda: self.apply())
+        self.name_line_edit = gui.lineEdit(
+            self.controlArea, self, 'learner_name', box='Name',
+            tooltip='The name will identify this model in other widgets',
+            orientation=Qt.Horizontal, callback=lambda: self.apply())
 
     def add_bottom_buttons(self):
         box = gui.hBox(self.controlArea, True)