test: improve ml tests (#129)

Closes #116.
Closes #117.

### Summary of Changes

* Reduce code duplication
* Test that `fit` doesn't change the input table
* Test that `predict` doesn't change the input table

---------

Co-authored-by: lars-reimann <[email protected]>

tests/safeds/ml/classification/test_classifier.py

@@ -1,8 +1,112 @@
 from __future__ import annotations
 
 import pandas as pd
+import pytest
+from _pytest.fixtures import FixtureRequest
 from safeds.data.tabular.containers import Column, Table, TaggedTable
-from safeds.ml.classification import Classifier
+from safeds.exceptions import LearningError, PredictionError
+from safeds.ml.classification import (
+    AdaBoost,
+    Classifier,
+    DecisionTree,
+    GradientBoosting,
+    KNearestNeighbors,
+    LogisticRegression,
+    RandomForest,
+)
+
+
+def classifiers() -> list[Classifier]:
+    """
+    Returns the list of classifiers to test.
+
+    After you implemented a new classifier, add it to this list to ensure its `fit` and `predict` method work as
+    expected. Place tests of methods that are specific to your classifier in a separate test file.
+
+    Returns
+    -------
+    classifiers : list[Classifier]
+        The list of classifiers to test.
+    """
+
+    return [AdaBoost(), DecisionTree(), GradientBoosting(), KNearestNeighbors(2), LogisticRegression(), RandomForest()]
+
+
+@pytest.fixture()
+def valid_data() -> TaggedTable:
+    return Table.from_columns(
+        [
+            Column("id", [1, 4]),
+            Column("feat1", [2, 5]),
+            Column("feat2", [3, 6]),
+            Column("target", [0, 1]),
+        ]
+    ).tag_columns(target_name="target", feature_names=["feat1", "feat2"])
+
+
+@pytest.fixture()
+def invalid_data() -> TaggedTable:
+    return Table.from_columns(
+        [
+            Column("id", [1, 4]),
+            Column("feat1", ["a", 5]),
+            Column("feat2", [3, 6]),
+            Column("target", [0, 1]),
+        ]
+    ).tag_columns(target_name="target", feature_names=["feat1", "feat2"])
+
+
+@pytest.mark.parametrize("classifier", classifiers(), ids=lambda x: x.__class__.__name__)
+class TestFit:
+    def test_should_succeed_on_valid_data(self, classifier: Classifier, valid_data: TaggedTable) -> None:
+        classifier.fit(valid_data)
+        assert True  # This asserts that the fit method succeeds
+
+    def test_should_not_change_input_table(self, classifier: Classifier, request: FixtureRequest) -> None:
+        valid_data = request.getfixturevalue("valid_data")
+        valid_data_copy = request.getfixturevalue("valid_data")
+        classifier.fit(valid_data)
+        assert valid_data == valid_data_copy
+
+    def test_should_raise_on_invalid_data(self, classifier: Classifier, invalid_data: TaggedTable) -> None:
+        with pytest.raises(LearningError):
+            classifier.fit(invalid_data)
+
+
+@pytest.mark.parametrize("classifier", classifiers(), ids=lambda x: x.__class__.__name__)
+class TestPredict:
+    def test_should_include_features_of_input_table(self, classifier: Classifier, valid_data: TaggedTable) -> None:
+        fitted_classifier = classifier.fit(valid_data)
+        prediction = fitted_classifier.predict(valid_data.features)
+        assert prediction.features == valid_data.features
+
+    def test_should_include_complete_input_table(self, classifier: Classifier, valid_data: TaggedTable) -> None:
+        fitted_regressor = classifier.fit(valid_data)
+        prediction = fitted_regressor.predict(valid_data.remove_columns(["target"]))
+        assert prediction.remove_columns(["target"]) == valid_data.remove_columns(["target"])
+
+    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)
+        assert prediction.target.name == "target"
+
+    def test_should_not_change_input_table(self, classifier: Classifier, request: FixtureRequest) -> None:
+        valid_data = request.getfixturevalue("valid_data")
+        valid_data_copy = request.getfixturevalue("valid_data")
+        fitted_classifier = classifier.fit(valid_data)
+        fitted_classifier.predict(valid_data.features)
+        assert valid_data == valid_data_copy
+
+    def test_should_raise_when_not_fitted(self, classifier: Classifier, valid_data: TaggedTable) -> None:
+        with pytest.raises(PredictionError):
+            classifier.predict(valid_data.features)
+
+    def test_should_raise_on_invalid_data(
+        self, classifier: Classifier, valid_data: TaggedTable, invalid_data: TaggedTable
+    ) -> None:
+        fitted_classifier = classifier.fit(valid_data)
+        with pytest.raises(PredictionError):
+            fitted_classifier.predict(invalid_data.features)
 
 
 class DummyClassifier(Classifier):