test: output of predict should include entire input

Safe-DS · Mar 29, 2023 · 6c0de34 · 6c0de34
1 parent 9e7b7fe
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Showing 15 changed files with 199 additions and 118 deletions.
diff --git a/tests/safeds/ml/classification/test_ada_boost.py b/tests/safeds/ml/classification/test_ada_boost.py
@@ -50,6 +50,12 @@ def test_should_include_features_of_prediction_input(self, classifier: Classifie
         prediction = fitted_classifier.predict(valid_data.features)
         assert prediction.features == valid_data.features
 
+    def test_should_include_complete_prediction_input(self, classifier: Classifier, valid_data: TaggedTable) -> None:
+        fitted_regressor = classifier.fit(valid_data)
+        prediction = fitted_regressor.predict(valid_data.drop_columns(["target"]))
+        target_name = prediction.target.name
+        assert prediction.drop_columns([target_name]) == valid_data.drop_columns([target_name])
+
     def test_should_set_correct_target_name(self, classifier: Classifier, valid_data: TaggedTable) -> None:
         fitted_classifier = classifier.fit(valid_data)
         prediction = fitted_classifier.predict(valid_data.features)

diff --git a/tests/safeds/ml/classification/test_decision_tree.py b/tests/safeds/ml/classification/test_decision_tree.py
@@ -50,6 +50,12 @@ def test_should_include_features_of_prediction_input(self, classifier: Classifie
         prediction = fitted_classifier.predict(valid_data.features)
         assert prediction.features == valid_data.features
 
+    def test_should_include_complete_prediction_input(self, classifier: Classifier, valid_data: TaggedTable) -> None:
+        fitted_regressor = classifier.fit(valid_data)
+        prediction = fitted_regressor.predict(valid_data.drop_columns(["target"]))
+        target_name = prediction.target.name
+        assert prediction.drop_columns([target_name]) == valid_data.drop_columns([target_name])
+
     def test_should_set_correct_target_name(self, classifier: Classifier, valid_data: TaggedTable) -> None:
         fitted_classifier = classifier.fit(valid_data)
         prediction = fitted_classifier.predict(valid_data.features)

diff --git a/tests/safeds/ml/classification/test_gradient_boosting.py b/tests/safeds/ml/classification/test_gradient_boosting.py
@@ -50,6 +50,12 @@ def test_should_include_features_of_prediction_input(self, classifier: Classifie
         prediction = fitted_classifier.predict(valid_data.features)
         assert prediction.features == valid_data.features
 
+    def test_should_include_complete_prediction_input(self, classifier: Classifier, valid_data: TaggedTable) -> None:
+        fitted_regressor = classifier.fit(valid_data)
+        prediction = fitted_regressor.predict(valid_data.drop_columns(["target"]))
+        target_name = prediction.target.name
+        assert prediction.drop_columns([target_name]) == valid_data.drop_columns([target_name])
+
     def test_should_set_correct_target_name(self, classifier: Classifier, valid_data: TaggedTable) -> None:
         fitted_classifier = classifier.fit(valid_data)
         prediction = fitted_classifier.predict(valid_data.features)

diff --git a/tests/safeds/ml/classification/test_k_nearest_neighbors.py b/tests/safeds/ml/classification/test_k_nearest_neighbors.py
@@ -3,7 +3,6 @@
 from safeds.data.tabular.containers import Table, TaggedTable, Column
 from safeds.exceptions import LearningError, PredictionError
 from safeds.ml.classification import Classifier, KNearestNeighbors
-from tests.helpers import resolve_resource_path
 
 
 @pytest.fixture()
@@ -51,6 +50,12 @@ def test_should_include_features_of_prediction_input(self, classifier: Classifie
         prediction = fitted_classifier.predict(valid_data.features)
         assert prediction.features == valid_data.features
 
+    def test_should_include_complete_prediction_input(self, classifier: Classifier, valid_data: TaggedTable) -> None:
+        fitted_regressor = classifier.fit(valid_data)
+        prediction = fitted_regressor.predict(valid_data.drop_columns(["target"]))
+        target_name = prediction.target.name
+        assert prediction.drop_columns([target_name]) == valid_data.drop_columns([target_name])
+
     def test_should_set_correct_target_name(self, classifier: Classifier, valid_data: TaggedTable) -> None:
         fitted_classifier = classifier.fit(valid_data)
         prediction = fitted_classifier.predict(valid_data.features)

diff --git a/tests/safeds/ml/classification/test_logistic_regression.py b/tests/safeds/ml/classification/test_logistic_regression.py
@@ -50,6 +50,12 @@ def test_should_include_features_of_prediction_input(self, classifier: Classifie
         prediction = fitted_classifier.predict(valid_data.features)
         assert prediction.features == valid_data.features
 
+    def test_should_include_complete_prediction_input(self, classifier: Classifier, valid_data: TaggedTable) -> None:
+        fitted_regressor = classifier.fit(valid_data)
+        prediction = fitted_regressor.predict(valid_data.drop_columns(["target"]))
+        target_name = prediction.target.name
+        assert prediction.drop_columns([target_name]) == valid_data.drop_columns([target_name])
+
     def test_should_set_correct_target_name(self, classifier: Classifier, valid_data: TaggedTable) -> None:
         fitted_classifier = classifier.fit(valid_data)
         prediction = fitted_classifier.predict(valid_data.features)

diff --git a/tests/safeds/ml/classification/test_random_forest.py b/tests/safeds/ml/classification/test_random_forest.py
@@ -50,6 +50,12 @@ def test_should_include_features_of_prediction_input(self, classifier: Classifie
         prediction = fitted_classifier.predict(valid_data.features)
         assert prediction.features == valid_data.features
 
+    def test_should_include_complete_prediction_input(self, classifier: Classifier, valid_data: TaggedTable) -> None:
+        fitted_regressor = classifier.fit(valid_data)
+        prediction = fitted_regressor.predict(valid_data.drop_columns(["target"]))
+        target_name = prediction.target.name
+        assert prediction.drop_columns([target_name]) == valid_data.drop_columns([target_name])
+
     def test_should_set_correct_target_name(self, classifier: Classifier, valid_data: TaggedTable) -> None:
         fitted_classifier = classifier.fit(valid_data)
         prediction = fitted_classifier.predict(valid_data.features)

diff --git a/tests/safeds/ml/regression/test_ada_boost.py b/tests/safeds/ml/regression/test_ada_boost.py
@@ -35,41 +35,39 @@ def invalid_data() -> TaggedTable:
 
 
 class TestFit:
-    def test_should_succeed_on_valid_data(self, regressor: Regressor, valid_data: TaggedTable) -> None:
-        regressor.fit(valid_data)
+    def test_should_succeed_on_valid_data(self, classifier, valid_data: TaggedTable) -> None:
+        classifier.fit(valid_data)
         assert True  # This asserts that the fit method succeeds
 
-    def test_should_raise_on_invalid_data(self, regressor: Regressor, invalid_data: TaggedTable) -> None:
+    def test_should_raise_on_invalid_data(self, classifier, invalid_data: TaggedTable) -> None:
         with pytest.raises(LearningError):
-            regressor.fit(invalid_data)
+            classifier.fit(invalid_data)
 
 
 class TestPredict:
-    def test_should_include_features_of_prediction_input(self, regressor: Regressor, valid_data: TaggedTable) -> None:
-        fitted_regressor = regressor.fit(valid_data)
+    def test_should_include_features_of_prediction_input(self, classifier, valid_data: TaggedTable) -> None:
+        fitted_regressor = classifier.fit(valid_data)
         prediction = fitted_regressor.predict(valid_data.features)
         assert prediction.features == valid_data.features
 
-    def test_should_set_correct_target_name(self, regressor: Regressor, valid_data: TaggedTable) -> None:
-        fitted_regressor = regressor.fit(valid_data)
+    def test_should_include_complete_prediction_input(self, classifier, valid_data: TaggedTable) -> None:
+        fitted_regressor = classifier.fit(valid_data)
+        prediction = fitted_regressor.predict(valid_data.drop_columns(["target"]))
+        target_name = prediction.target.name
+        assert prediction.drop_columns([target_name]) == valid_data.drop_columns([target_name])
+
+    def test_should_set_correct_target_name(self, classifier, valid_data: TaggedTable) -> None:
+        fitted_regressor = classifier.fit(valid_data)
         prediction = fitted_regressor.predict(valid_data.features)
         assert prediction.target.name == "target"
 
-    def test_should_include_other_columns_of_prediction_input(self, regressor: Regressor, valid_data: TaggedTable) -> None:
-        fitted_regressor = regressor.fit(valid_data)
-        prediction = fitted_regressor.predict(valid_data.drop_columns(["target"]))
-        other_column_names = list(
-            set(valid_data.get_column_names()) - set(valid_data.features.get_column_names()) - {valid_data.target.name}
-        )
-        assert prediction.keep_only_columns(other_column_names) == valid_data.keep_only_columns(other_column_names)
-
-    def test_should_raise_when_not_fitted(self, regressor: Regressor, valid_data: TaggedTable) -> None:
+    def test_should_raise_when_not_fitted(self, classifier, valid_data: TaggedTable) -> None:
         with pytest.raises(PredictionError):
-            regressor.predict(valid_data.features)
+            classifier.predict(valid_data.features)
 
     def test_should_raise_on_invalid_data(
-        self, regressor: Regressor, valid_data: TaggedTable, invalid_data: TaggedTable
+        self, classifier, valid_data: TaggedTable, invalid_data: TaggedTable
     ) -> None:
-        fitted_regressor = regressor.fit(valid_data)
+        fitted_regressor = classifier.fit(valid_data)
         with pytest.raises(PredictionError):
             fitted_regressor.predict(invalid_data.features)
diff --git a/tests/safeds/ml/regression/test_decision_tree.py b/tests/safeds/ml/regression/test_decision_tree.py
@@ -35,33 +35,39 @@ def invalid_data() -> TaggedTable:
 
 
 class TestFit:
-    def test_should_succeed_on_valid_data(self, regressor: Regressor, valid_data: TaggedTable) -> None:
-        regressor.fit(valid_data)
+    def test_should_succeed_on_valid_data(self, classifier, valid_data: TaggedTable) -> None:
+        classifier.fit(valid_data)
         assert True  # This asserts that the fit method succeeds
 
-    def test_should_raise_on_invalid_data(self, regressor: Regressor, invalid_data: TaggedTable) -> None:
+    def test_should_raise_on_invalid_data(self, classifier, invalid_data: TaggedTable) -> None:
         with pytest.raises(LearningError):
-            regressor.fit(invalid_data)
+            classifier.fit(invalid_data)
 
 
 class TestPredict:
-    def test_should_include_features_of_prediction_input(self, regressor: Regressor, valid_data: TaggedTable) -> None:
-        fitted_regressor = regressor.fit(valid_data)
+    def test_should_include_features_of_prediction_input(self, classifier, valid_data: TaggedTable) -> None:
+        fitted_regressor = classifier.fit(valid_data)
         prediction = fitted_regressor.predict(valid_data.features)
         assert prediction.features == valid_data.features
 
-    def test_should_set_correct_target_name(self, regressor: Regressor, valid_data: TaggedTable) -> None:
-        fitted_regressor = regressor.fit(valid_data)
+    def test_should_include_complete_prediction_input(self, classifier, valid_data: TaggedTable) -> None:
+        fitted_regressor = classifier.fit(valid_data)
+        prediction = fitted_regressor.predict(valid_data.drop_columns(["target"]))
+        target_name = prediction.target.name
+        assert prediction.drop_columns([target_name]) == valid_data.drop_columns([target_name])
+
+    def test_should_set_correct_target_name(self, classifier, valid_data: TaggedTable) -> None:
+        fitted_regressor = classifier.fit(valid_data)
         prediction = fitted_regressor.predict(valid_data.features)
         assert prediction.target.name == "target"
 
-    def test_should_raise_when_not_fitted(self, regressor: Regressor, valid_data: TaggedTable) -> None:
+    def test_should_raise_when_not_fitted(self, classifier, valid_data: TaggedTable) -> None:
         with pytest.raises(PredictionError):
-            regressor.predict(valid_data.features)
+            classifier.predict(valid_data.features)
 
     def test_should_raise_on_invalid_data(
-        self, regressor: Regressor, valid_data: TaggedTable, invalid_data: TaggedTable
+        self, classifier, valid_data: TaggedTable, invalid_data: TaggedTable
     ) -> None:
-        fitted_regressor = regressor.fit(valid_data)
+        fitted_regressor = classifier.fit(valid_data)
         with pytest.raises(PredictionError):
             fitted_regressor.predict(invalid_data.features)
diff --git a/tests/safeds/ml/regression/test_elastic_net.py b/tests/safeds/ml/regression/test_elastic_net.py
@@ -35,33 +35,39 @@ def invalid_data() -> TaggedTable:
 
 
 class TestFit:
-    def test_should_succeed_on_valid_data(self, regressor: Regressor, valid_data: TaggedTable) -> None:
-        regressor.fit(valid_data)
+    def test_should_succeed_on_valid_data(self, classifier, valid_data: TaggedTable) -> None:
+        classifier.fit(valid_data)
         assert True  # This asserts that the fit method succeeds
 
-    def test_should_raise_on_invalid_data(self, regressor: Regressor, invalid_data: TaggedTable) -> None:
+    def test_should_raise_on_invalid_data(self, classifier, invalid_data: TaggedTable) -> None:
         with pytest.raises(LearningError):
-            regressor.fit(invalid_data)
+            classifier.fit(invalid_data)
 
 
 class TestPredict:
-    def test_should_include_features_of_prediction_input(self, regressor: Regressor, valid_data: TaggedTable) -> None:
-        fitted_regressor = regressor.fit(valid_data)
+    def test_should_include_features_of_prediction_input(self, classifier, valid_data: TaggedTable) -> None:
+        fitted_regressor = classifier.fit(valid_data)
         prediction = fitted_regressor.predict(valid_data.features)
         assert prediction.features == valid_data.features
 
-    def test_should_set_correct_target_name(self, regressor: Regressor, valid_data: TaggedTable) -> None:
-        fitted_regressor = regressor.fit(valid_data)
+    def test_should_include_complete_prediction_input(self, classifier, valid_data: TaggedTable) -> None:
+        fitted_regressor = classifier.fit(valid_data)
+        prediction = fitted_regressor.predict(valid_data.drop_columns(["target"]))
+        target_name = prediction.target.name
+        assert prediction.drop_columns([target_name]) == valid_data.drop_columns([target_name])
+
+    def test_should_set_correct_target_name(self, classifier, valid_data: TaggedTable) -> None:
+        fitted_regressor = classifier.fit(valid_data)
         prediction = fitted_regressor.predict(valid_data.features)
         assert prediction.target.name == "target"
 
-    def test_should_raise_when_not_fitted(self, regressor: Regressor, valid_data: TaggedTable) -> None:
+    def test_should_raise_when_not_fitted(self, classifier, valid_data: TaggedTable) -> None:
         with pytest.raises(PredictionError):
-            regressor.predict(valid_data.features)
+            classifier.predict(valid_data.features)
 
     def test_should_raise_on_invalid_data(
-        self, regressor: Regressor, valid_data: TaggedTable, invalid_data: TaggedTable
+        self, classifier, valid_data: TaggedTable, invalid_data: TaggedTable
     ) -> None:
-        fitted_regressor = regressor.fit(valid_data)
+        fitted_regressor = classifier.fit(valid_data)
         with pytest.raises(PredictionError):
             fitted_regressor.predict(invalid_data.features)
diff --git a/tests/safeds/ml/regression/test_gradient_boosting.py b/tests/safeds/ml/regression/test_gradient_boosting.py
@@ -6,7 +6,7 @@
 
 
 @pytest.fixture()
-def regressor() -> Regressor:
+def classifier() -> Regressor:
     return GradientBoosting()
 
 
@@ -35,33 +35,39 @@ def invalid_data() -> TaggedTable:
 
 
 class TestFit:
-    def test_should_succeed_on_valid_data(self, regressor: Regressor, valid_data: TaggedTable) -> None:
-        regressor.fit(valid_data)
+    def test_should_succeed_on_valid_data(self, classifier, valid_data: TaggedTable) -> None:
+        classifier.fit(valid_data)
         assert True  # This asserts that the fit method succeeds
 
-    def test_should_raise_on_invalid_data(self, regressor: Regressor, invalid_data: TaggedTable) -> None:
+    def test_should_raise_on_invalid_data(self, classifier, invalid_data: TaggedTable) -> None:
         with pytest.raises(LearningError):
-            regressor.fit(invalid_data)
+            classifier.fit(invalid_data)
 
 
 class TestPredict:
-    def test_should_include_features_of_prediction_input(self, regressor: Regressor, valid_data: TaggedTable) -> None:
-        fitted_regressor = regressor.fit(valid_data)
+    def test_should_include_features_of_prediction_input(self, classifier, valid_data: TaggedTable) -> None:
+        fitted_regressor = classifier.fit(valid_data)
         prediction = fitted_regressor.predict(valid_data.features)
         assert prediction.features == valid_data.features
 
-    def test_should_set_correct_target_name(self, regressor: Regressor, valid_data: TaggedTable) -> None:
-        fitted_regressor = regressor.fit(valid_data)
+    def test_should_include_complete_prediction_input(self, classifier, valid_data: TaggedTable) -> None:
+        fitted_regressor = classifier.fit(valid_data)
+        prediction = fitted_regressor.predict(valid_data.drop_columns(["target"]))
+        target_name = prediction.target.name
+        assert prediction.drop_columns([target_name]) == valid_data.drop_columns([target_name])
+
+    def test_should_set_correct_target_name(self, classifier, valid_data: TaggedTable) -> None:
+        fitted_regressor = classifier.fit(valid_data)
         prediction = fitted_regressor.predict(valid_data.features)
         assert prediction.target.name == "target"
 
-    def test_should_raise_when_not_fitted(self, regressor: Regressor, valid_data: TaggedTable) -> None:
+    def test_should_raise_when_not_fitted(self, classifier, valid_data: TaggedTable) -> None:
         with pytest.raises(PredictionError):
-            regressor.predict(valid_data.features)
+            classifier.predict(valid_data.features)
 
     def test_should_raise_on_invalid_data(
-        self, regressor: Regressor, valid_data: TaggedTable, invalid_data: TaggedTable
+        self, classifier, valid_data: TaggedTable, invalid_data: TaggedTable
     ) -> None:
-        fitted_regressor = regressor.fit(valid_data)
+        fitted_regressor = classifier.fit(valid_data)
         with pytest.raises(PredictionError):
             fitted_regressor.predict(invalid_data.features)
diff --git a/tests/safeds/ml/regression/test_k_nearest_neighbors.py b/tests/safeds/ml/regression/test_k_nearest_neighbors.py
@@ -35,33 +35,39 @@ def invalid_data() -> TaggedTable:
 
 
 class TestFit:
-    def test_should_succeed_on_valid_data(self, regressor: Regressor, valid_data: TaggedTable) -> None:
-        regressor.fit(valid_data)
+    def test_should_succeed_on_valid_data(self, classifier, valid_data: TaggedTable) -> None:
+        classifier.fit(valid_data)
         assert True  # This asserts that the fit method succeeds
 
-    def test_should_raise_on_invalid_data(self, regressor: Regressor, invalid_data: TaggedTable) -> None:
+    def test_should_raise_on_invalid_data(self, classifier, invalid_data: TaggedTable) -> None:
         with pytest.raises(LearningError):
-            regressor.fit(invalid_data)
+            classifier.fit(invalid_data)
 
 
 class TestPredict:
-    def test_should_include_features_of_prediction_input(self, regressor: Regressor, valid_data: TaggedTable) -> None:
-        fitted_regressor = regressor.fit(valid_data)
+    def test_should_include_features_of_prediction_input(self, classifier, valid_data: TaggedTable) -> None:
+        fitted_regressor = classifier.fit(valid_data)
         prediction = fitted_regressor.predict(valid_data.features)
         assert prediction.features == valid_data.features
 
-    def test_should_set_correct_target_name(self, regressor: Regressor, valid_data: TaggedTable) -> None:
-        fitted_regressor = regressor.fit(valid_data)
+    def test_should_include_complete_prediction_input(self, classifier, valid_data: TaggedTable) -> None:
+        fitted_regressor = classifier.fit(valid_data)
+        prediction = fitted_regressor.predict(valid_data.drop_columns(["target"]))
+        target_name = prediction.target.name
+        assert prediction.drop_columns([target_name]) == valid_data.drop_columns([target_name])
+
+    def test_should_set_correct_target_name(self, classifier, valid_data: TaggedTable) -> None:
+        fitted_regressor = classifier.fit(valid_data)
         prediction = fitted_regressor.predict(valid_data.features)
         assert prediction.target.name == "target"
 
-    def test_should_raise_when_not_fitted(self, regressor: Regressor, valid_data: TaggedTable) -> None:
+    def test_should_raise_when_not_fitted(self, classifier, valid_data: TaggedTable) -> None:
         with pytest.raises(PredictionError):
-            regressor.predict(valid_data.features)
+            classifier.predict(valid_data.features)
 
     def test_should_raise_on_invalid_data(
-        self, regressor: Regressor, valid_data: TaggedTable, invalid_data: TaggedTable
+        self, classifier, valid_data: TaggedTable, invalid_data: TaggedTable
     ) -> None:
-        fitted_regressor = regressor.fit(valid_data)
+        fitted_regressor = classifier.fit(valid_data)
         with pytest.raises(PredictionError):
             fitted_regressor.predict(invalid_data.features)