Predictions: Output annotated table

Orange/widgets/evaluate/owpredictions.py

@@ -31,6 +31,7 @@
 from Orange.widgets.utils.widgetpreview import WidgetPreview
 from Orange.widgets.widget import OWWidget, Msg, Input, Output, MultiInput
 from Orange.widgets.utils.itemmodels import TableModel
+from Orange.widgets.utils.annotated_data import lazy_annotated_table
 from Orange.widgets.utils.sql import check_sql_input
 from Orange.widgets.utils.state_summary import format_summary_details
 from Orange.widgets.utils.colorpalettes import LimitedDiscretePalette
@@ -72,7 +73,8 @@ class Inputs:
         predictors = MultiInput("Predictors", Model, filter_none=True)
 
     class Outputs:
-        predictions = Output("Predictions", Orange.data.Table)
+        predictions = Output("Predictions", Orange.data.Table, default=True)
+        annotated = Output("Annotated Predictions", Orange.data.Table)
         evaluation_results = Output("Evaluation Results", Results)
 
     class Warning(OWWidget.Warning):
@@ -815,6 +817,7 @@ def _commit_evaluation_results(self):
     def _commit_predictions(self):
         if not self.data:
             self.Outputs.predictions.send(None)
+            self.Outputs.annotated.send(None)
             return
 
         newmetas = []
@@ -855,12 +858,17 @@ def _commit_predictions(self):
             # Reorder rows as they are ordered in view
             shown_rows = datamodel.mapFromSourceRows(rows)
             rows = rows[numpy.argsort(shown_rows)]
-            predictions = predictions[rows]
-        elif datamodel.sortColumn() >= 0 \
-                or predmodel is not None and predmodel.sortColumn() > 0:
-            # No selection: output all, but in the shown order
-            predictions = predictions[datamodel.mapToSourceRows(...)]
-        self.Outputs.predictions.send(predictions)
+            selected = predictions[rows]
+            annotated_data = lazy_annotated_table(predictions, rows)
+        else:
+            if datamodel.sortColumn() >= 0 \
+                    or predmodel is not None and predmodel.sortColumn() > 0:
+                predictions = predictions[datamodel.mapToSourceRows(...)]
+            selected = predictions
+            annotated_data = predictions
+        self.Outputs.predictions.send(selected)
+        self.Outputs.annotated.send(annotated_data)
+
 
     def _add_classification_out_columns(self, slot, newmetas, newcolumns, index):
         pred = slot.predictor

Orange/widgets/evaluate/tests/test_owpredictions.py

@@ -36,6 +36,7 @@
 from Orange.evaluation import Results
 from Orange.widgets.tests.utils import excepthook_catch, \
     possible_duplicate_table, simulate
+from Orange.widgets.utils.annotated_data import ANNOTATED_DATA_FEATURE_NAME
 from Orange.widgets.utils.colorpalettes import LimitedDiscretePalette
 
 
@@ -515,6 +516,51 @@ def test_select(self):
                for index in self.widget.dataview.selectionModel().selectedIndexes()}
         self.assertEqual(sel, {(1, col) for col in range(5)})
 
+    def test_selection_output(self):
+        log_reg_iris = LogisticRegressionLearner()(self.iris)
+        self.send_signal(self.widget.Inputs.predictors, log_reg_iris)
+        self.send_signal(self.widget.Inputs.data, self.iris)
+
+        selmodel = self.widget.dataview.selectionModel()
+        pred_model = self.widget.predictionsview.model()
+
+        selmodel.select(self.widget.dataview.model().index(1, 0), QItemSelectionModel.Select)
+        selmodel.select(self.widget.dataview.model().index(3, 0), QItemSelectionModel.Select)
+        output = self.get_output(self.widget.Outputs.predictions)
+        self.assertEqual(len(output), 2)
+        self.assertEqual(output[0], self.iris[1])
+        self.assertEqual(output[1], self.iris[3])
+        output = self.get_output(self.widget.Outputs.annotated)
+        self.assertEqual(len(output), len(self.iris))
+        col = output.get_column(ANNOTATED_DATA_FEATURE_NAME)
+        self.assertEqual(np.sum(col), 2)
+        self.assertEqual(col[1], 1)
+        self.assertEqual(col[3], 1)
+
+        pred_model.sort(0)
+        output = self.get_output(self.widget.Outputs.predictions)
+        self.assertEqual(len(output), 2)
+        self.assertEqual(output[0], self.iris[1])
+        self.assertEqual(output[1], self.iris[3])
+        output = self.get_output(self.widget.Outputs.annotated)
+        self.assertEqual(len(output), len(self.iris))
+        col = output.get_column(ANNOTATED_DATA_FEATURE_NAME)
+        self.assertEqual(np.sum(col), 2)
+        self.assertEqual(col[1], 1)
+        self.assertEqual(col[3], 1)
+
+        pred_model.sort(0, Qt.DescendingOrder)
+        output = self.get_output(self.widget.Outputs.predictions)
+        self.assertEqual(len(output), 2)
+        self.assertEqual(output[0], self.iris[3])
+        self.assertEqual(output[1], self.iris[1])
+        output = self.get_output(self.widget.Outputs.annotated)
+        self.assertEqual(len(output), len(self.iris))
+        col = output.get_column(ANNOTATED_DATA_FEATURE_NAME)
+        self.assertEqual(np.sum(col), 2)
+        self.assertEqual(col[1], 1)
+        self.assertEqual(col[3], 1)
+
     def test_select_data_first(self):
         log_reg_iris = LogisticRegressionLearner()(self.iris)
         self.send_signal(self.widget.Inputs.data, self.iris)