Merge pull request #7123 from janezd/predictions-no-literals

[FIX] Predictions: allow loading of workflows saved in a localized version (target class setting fix)

Author: markotoplak

Orange/widgets/evaluate/owpredictions.py

@@ -67,7 +67,7 @@ class OWPredictions(OWWidget):
     description = "Display predictions of models for an input dataset."
     keywords = "predictions"
 
-    settings_version = 2
+    settings_version = 3
 
     want_control_area = False
 
@@ -105,13 +105,13 @@ class Error(OWWidget.Error):
                      "Show probabilities for classes in data that are also\n"
                      "known to the model"
                      ]
+    TARGET_AVERAGE = "(Average over classes)"
 
     NO_PROBS, DATA_PROBS, MODEL_PROBS, BOTH_PROBS = range(4)
     shown_probs = settings.ContextSetting(NO_PROBS)
     selection = settings.Setting([], schema_only=True)
     show_scores = settings.Setting(True)
-    TARGET_AVERAGE = "(Average over classes)"
-    target_class = settings.ContextSetting(TARGET_AVERAGE)
+    target_class = settings.ContextSetting("")
     show_probability_errors = settings.ContextSetting(True)
     show_reg_errors = settings.ContextSetting(DIFF_ERROR)
 
@@ -174,7 +174,8 @@ def __init__(self):
             gui.widgetLabel(scoreopts, "Target class:"),
             gui.comboBox(
             scoreopts, self, "target_class", items=[], contentsLength=30,
-            sendSelectedValue=True, callback=self._on_target_changed)
+            sendSelectedValue=True, callback=self._on_target_changed,
+            emptyString=self.TARGET_AVERAGE)
         ]
         gui.rubber(scoreopts)
 
@@ -311,7 +312,7 @@ def _set_target_combos(self):
             for i, tip in enumerate(self.PROB_TOOLTIPS):
                 prob_combo.setItemData(i, tip, Qt.ToolTipRole)
             self.shown_probs = self.DATA_PROBS
-            self.target_class = self.TARGET_AVERAGE
+            self.target_class = ""
         else:
             self.shown_probs = self.NO_PROBS
             model = prob_combo.model()
@@ -433,7 +434,7 @@ def _call_predictors(self):
 
     def _update_scores(self):
         model = self.score_table.model
-        if self.is_discrete_class and self.target_class != self.TARGET_AVERAGE:
+        if self.is_discrete_class and self.target_class:
             target = self.class_var.values.index(self.target_class)
         else:
             target = None
@@ -971,7 +972,8 @@ def merge_data_with_predictions():
 
             self.report_name("Scores")
             if self.is_discrete_class:
-                self.report_items([("Target class", self.target_class)])
+                self.report_items([("Target class",
+                                    self.target_class or self.TARGET_AVERAGE)])
             self.report_table(self.score_table.view)
 
     def resizeEvent(self, event):
@@ -988,6 +990,19 @@ def migrate_settings(cls, settings, version):
             if "score_table" in settings:
                 ScoreTable.migrate_to_show_scores_hints(settings["score_table"])
 
+    @classmethod
+    def migrate_context(cls, context, version):
+        if version < 3:
+            target_class = context.values.get("target_class")
+            # The second condition is a workaround if the workflow is opened
+            # in a wrong language. Assuming that contexts (and other code) works
+            # target_class will always appear among values, and if it doesn't,
+            # it must be because of averaging.
+            # The first condition is also covered by the second, but let it
+            # be there for clarity.
+            if target_class == cls.TARGET_AVERAGE \
+                    or target_class not in context.classes:
+                context.values["target_class"] = ""
 
 class ItemDelegate(TableDataDelegate):
     def initStyleOption(self, option, index):

Orange/widgets/evaluate/tests/test_owpredictions.py

@@ -13,6 +13,8 @@
 from AnyQt.QtCore import QItemSelectionModel, QItemSelection, Qt, QRect
 from AnyQt.QtWidgets import QToolTip
 
+from orangewidget.settings import Context
+
 from Orange.base import Model
 from Orange.classification import LogisticRegressionLearner, NaiveBayesLearner
 from Orange.classification.majority import ConstantModel, MajorityLearner
@@ -1288,6 +1290,53 @@ def test_migrate_shown_scores(self):
         self.widget.migrate_settings(settings, 1)
         self.assertTrue(settings["score_table"]["show_score_hints"]["Sensitivity"])
 
+    def test_migrate_context_2_3(self):
+        settings = {
+            'controlAreaVisible': True, 'selection': [],
+            'show_scores': True,
+            'score_table': {
+                'show_score_hints': {
+                    'Model_': True, 'Train_': False, 'Test_': False,
+                    'CA': True, 'PrecisionRecallFSupport': True,
+                    'TargetScore': True, 'Precision': True,
+                    'Recall': True, 'F1': True, 'AUC': True,
+                    'LogLoss': False, 'Specificity': False,
+                    'MatthewsCorrCoefficient': True, 'MSE': True,
+                    'RMSE': True, 'MAE': True, 'MAPE': True, 'R2': True,
+                    'CVRMSE': False, 'ClusteringScore': True,
+                    'Silhouette': True, 'AdjustedMutualInfoScore': True}},
+            'context_settings': [
+                Context(
+                    classes=('Iris-setosa',
+                             'Iris-versicolor',
+                             'Iris-virginica'),
+                    values={'show_probability_errors': True,
+                            'show_reg_errors': 1,
+                            'shown_probs': 1,
+                            'score_table': {},
+                            'target_class': '(Average over classes)',
+                            '__version__': 2})],
+            '__version__': 2,
+        }
+
+        widget = self.create_widget(OWPredictions, stored_settings=settings)
+        self.send_signal(widget.Inputs.data, self.iris)
+        self.assertEqual(widget.target_class, "")
+
+        settings["context_settings"][0].values["target_class"] = "Iris-versicolor"
+        settings["__version__"] = 2
+        widget = self.create_widget(OWPredictions, stored_settings=settings)
+        self.send_signal(widget.Inputs.data, self.iris)
+        self.assertEqual(widget.target_class, "Iris-versicolor")
+
+        # Test fallback for older workflows opened in wrong language
+        settings["context_settings"][0].values["target_class"] = "Povprečje"
+        settings["__version__"] = 2
+        widget = self.create_widget(OWPredictions, stored_settings=settings)
+        self.send_signal(widget.Inputs.data, self.iris)
+        self.assertEqual(widget.target_class, "")
+
+
     def test_output_error_reg(self):
         data = self.housing
         lin_reg = LinearRegressionLearner()

i18n/si/msgs.jaml

@@ -9363,6 +9363,8 @@ widgets/evaluate/owpredictions.py:
             Target class: Ciljni razred
         def `migrate_settings`:
             score_table: false
+        def `migrate_context`:
+            target_class: false
     class `ClassificationItemDelegate`:
         def `__init__`:
             ' : ': false