small update

Safe-DS · Aug 19, 2024 · 7f23c69 · 7f23c69
1 parent 69aa8e6
commit 7f23c69
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diff --git a/src/safeds/ml/nn/_model.py b/src/safeds/ml/nn/_model.py
@@ -1189,7 +1189,8 @@ def _get_best_cnn_model_table(
         #Set positive class to index of positive column, to be able to calculate Classification Metrics
         if positive_class is not None:
             for column_index in range(labels.column_count):
-                if labels.column_names[column_index] == positive_class:
+                #TODO Find out if a column is the positive class, maybe by the column name?
+                #if labels.column_names[column_index] == positive_class:
                     positive_class = labels.column_names[column_index]
                     break
 

diff --git a/tests/safeds/ml/nn/test_model.py b/tests/safeds/ml/nn/test_model.py
@@ -208,6 +208,19 @@ def test_should_catch_invalid_fit_data(self, device: Device, table: TabularDatas
             ):
                 model.fit(table)
 
+        def test_should_raise_when_time_series_classification_with_continuous_data(self, device: Device) -> None:
+            configure_test_with_device(device)
+            data = Table.from_dict({"a": [1, 2, 3], "b": [1, 2, 3], "c": [0, 1, 0]}).to_time_series_dataset("c",1,continuous=True)
+            model = NeuralNetworkClassifier(
+                InputConversionTimeSeries(),
+                [ForwardLayer(neuron_count=4), LSTMLayer(neuron_count=1)],
+            )
+            with pytest.raises(
+                NotImplementedError,
+                match="Continuous Predictions are currently not supported for Time Series Classification."
+            ):
+                model.fit(data)
+
         def test_should_raise_if_fit_doesnt_batch_callback(self, device: Device) -> None:
             configure_test_with_device(device)
             model = NeuralNetworkClassifier(
@@ -312,6 +325,19 @@ def test_should_raise_when_fitting_by_exhaustive_search_without_choice(self, dev
                     "accuracy",
                 )
 
+        def test_should_raise_when_time_series_classification_with_continuous_data(self, device: Device) -> None:
+            configure_test_with_device(device)
+            data = Table.from_dict({"a": [1, 2, 3], "b": [1, 2, 3], "c": [0, 1, 0]}).to_time_series_dataset("c",1,continuous=True)
+            model = NeuralNetworkClassifier(
+                InputConversionTimeSeries(),
+                [ForwardLayer(neuron_count=Choice(2, 4)), LSTMLayer(neuron_count=1)],
+            )
+            with pytest.raises(
+                NotImplementedError,
+                match="Continuous Predictions are currently not supported for Time Series Classification."
+            ):
+                model.fit_by_exhaustive_search(data, "accuracy")
+
         @pytest.mark.parametrize(
             ("metric", "positive_class"),
             [