TestAndScore: Fix data errors

Author: PrimozGodec

Orange/widgets/evaluate/owtestandscore.py

@@ -32,6 +32,7 @@
 from Orange.evaluation import Results
 from Orange.preprocess.preprocess import Preprocess
 import Orange.regression
+from Orange.statistics.util import unique
 from Orange.widgets import gui, settings, widget
 from Orange.widgets.evaluate.utils import \
     usable_scorers, ScoreTable, learner_name, scorer_caller
@@ -189,17 +190,14 @@ class Outputs:
     class Error(OWWidget.Error):
         train_data_empty = Msg("Train dataset is empty.")
         test_data_empty = Msg("Test dataset is empty.")
-        class_required = Msg("Train data input requires a target variable.")
-        too_many_classes = Msg("Too many target variables.")
         class_required_test = Msg("Test data input requires a target variable.")
         too_many_folds = Msg("Number of folds exceeds the data size")
         class_inconsistent = Msg("Test and train datasets "
                                  "have different target variables.")
         memory_error = Msg("Not enough memory.")
-        no_class_values = Msg("Target variable has no values.")
-        only_one_class_var_value = Msg("Target variable has only one value.")
         test_data_incompatible = Msg(
             "Test data may be incompatible with train data.")
+        data_error = Msg("{}")
 
     class Warning(OWWidget.Warning):
         missing_data = \
@@ -383,25 +381,29 @@ def set_train_data(self, data):
         self.cancel()
         self.Information.data_sampled.clear()
         self.Error.train_data_empty.clear()
-        self.Error.class_required.clear()
-        self.Error.too_many_classes.clear()
-        self.Error.no_class_values.clear()
-        self.Error.only_one_class_var_value.clear()
+        self.Error.data_error.clear()
+
         if data is not None and not data:
             self.Error.train_data_empty()
             data = None
         if data:
-            conds = [not data.domain.class_vars,
-                     len(data.domain.class_vars) > 1,
-                     np.isnan(data.Y).all(),
-                     data.domain.has_discrete_class and len(data.domain.class_var.values) == 1]
-            errors = [self.Error.class_required,
-                      self.Error.too_many_classes,
-                      self.Error.no_class_values,
-                      self.Error.only_one_class_var_value]
-            for cond, error in zip(conds, errors):
+            data_errors = [
+                (
+                    "Train data input requires a target variable.",
+                    not data.domain.class_vars
+                ),
+                ("Too many target variables.", len(data.domain.class_vars) > 1),
+                ("Target variable has no values.", np.isnan(data.Y).all()),
+                (
+                    "Target variable has only one value.",
+                    data.domain.has_discrete_class and len(unique(data.Y)) < 2
+                ),
+                ("Data has no features to learn from.", data.X.shape[1] == 0),
+            ]
+
+            for error_msg, cond in data_errors:
                 if cond:
-                    error()
+                    self.Error.data_error(error_msg)
                     data = None
                     break
 

Orange/widgets/evaluate/tests/test_owtestandscore.py

@@ -166,10 +166,10 @@ def test_one_class_value(self):
                 "yyyy"))
         )
         self.widget.n_folds = 0
-        self.assertFalse(self.widget.Error.only_one_class_var_value.is_shown())
+        self.assertFalse(self.widget.Error.data_error.is_shown())
         self.send_signal("Data", table)
         self.send_signal("Learner", MajorityLearner(), 0, wait=1000)
-        self.assertTrue(self.widget.Error.only_one_class_var_value.is_shown())
+        self.assertTrue(self.widget.Error.data_error.is_shown())
 
     def test_nan_class(self):
         """
@@ -178,12 +178,14 @@ def test_nan_class(self):
         """
         def assertErrorShown(data, is_shown):
             self.send_signal("Data", data)
-            self.assertEqual(is_shown, self.widget.Error.no_class_values.is_shown())
+            self.assertEqual(is_shown, self.widget.Error.data_error.is_shown())
 
         data = Table("iris")[::30]
         data.Y[:] = np.nan
 
-        for data, is_shown in zip([None, data, Table("iris")[:30]], [False, True, False]):
+        for data, is_shown in zip(
+                [None, data, Table("iris")[:30]], [False, True, True]
+        ):
             assertErrorShown(data, is_shown)
 
     def test_addon_scorers(self):
@@ -319,15 +321,19 @@ def _test_scores(self, train_data, test_data, learner, sampling, n_folds):
         self.send_signal(self.widget.Inputs.learner, learner, 0, wait=5000)
         return self._retrieve_scores()
 
-    def test_scores_constant_all_same(self):
+    def test_scores_constant(self):
         table = Table.from_list(
             self.scores_domain,
-            list(zip(*self.scores_table_values + [list("yyyy")]))
+            list(zip(*self.scores_table_values + [list("yyyn")]))
         )
 
-        self.assertTupleEqual(self._test_scores(
-            table, table, ConstantLearner(), OWTestAndScore.TestOnTest, None),
-                              (None, 1, 1, 1, 1))
+        self.assertTupleEqual(
+            self._test_scores(
+                table, table[:3], ConstantLearner(),
+                OWTestAndScore.TestOnTest, None
+            ),
+            (None, 1, 1, 1, 1)
+        )
 
     def test_scores_log_reg_overfitted(self):
         table = Table.from_list(