style: apply automated linter fixes

Safe-DS · Mar 30, 2023 · eee1d9e · eee1d9e
1 parent a624465
commit eee1d9e
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Showing 2 changed files with 27 additions and 34 deletions.
diff --git a/tests/safeds/ml/classification/test_classifier.py b/tests/safeds/ml/classification/test_classifier.py
@@ -3,11 +3,17 @@
 import pandas as pd
 import pytest
 from _pytest.fixtures import FixtureRequest
-
 from safeds.data.tabular.containers import Column, Table, TaggedTable
 from safeds.exceptions import LearningError, PredictionError
-from safeds.ml.classification import Classifier, AdaBoost, DecisionTree, GradientBoosting, KNearestNeighbors, \
-    LogisticRegression, RandomForest
+from safeds.ml.classification import (
+    AdaBoost,
+    Classifier,
+    DecisionTree,
+    GradientBoosting,
+    KNearestNeighbors,
+    LogisticRegression,
+    RandomForest,
+)
 
 
 def classifiers() -> list[Classifier]:
@@ -23,14 +29,7 @@ def classifiers() -> list[Classifier]:
         The list of classifiers to test.
     """
 
-    return [
-        AdaBoost(),
-        DecisionTree(),
-        GradientBoosting(),
-        KNearestNeighbors(2),
-        LogisticRegression(),
-        RandomForest()
-    ]
+    return [AdaBoost(), DecisionTree(), GradientBoosting(), KNearestNeighbors(2), LogisticRegression(), RandomForest()]
 
 
 @pytest.fixture()
@@ -57,11 +56,7 @@ def invalid_data() -> TaggedTable:
     ).tag_columns(target_name="target", feature_names=["feat1", "feat2"])
 
 
-@pytest.mark.parametrize(
-    "classifier",
-    classifiers(),
-    ids=lambda x: x.__class__.__name__
-)
+@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)
@@ -78,11 +73,7 @@ def test_should_raise_on_invalid_data(self, classifier: Classifier, invalid_data
             classifier.fit(invalid_data)
 
 
-@pytest.mark.parametrize(
-    "classifier",
-    classifiers(),
-    ids=lambda x: x.__class__.__name__
-)
+@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)

diff --git a/tests/safeds/ml/regression/test_regressor.py b/tests/safeds/ml/regression/test_regressor.py
@@ -3,11 +3,21 @@
 import pandas as pd
 import pytest
 from _pytest.fixtures import FixtureRequest
-
 from safeds.data.tabular.containers import Column, Table, TaggedTable
 from safeds.exceptions import ColumnLengthMismatchError, LearningError, PredictionError
-from safeds.ml.regression import Regressor, AdaBoost, DecisionTree, ElasticNetRegression, GradientBoosting, \
-    KNearestNeighbors, LassoRegression, LinearRegression, RandomForest, RidgeRegression
+from safeds.ml.regression import (
+    AdaBoost,
+    DecisionTree,
+    ElasticNetRegression,
+    GradientBoosting,
+    KNearestNeighbors,
+    LassoRegression,
+    LinearRegression,
+    RandomForest,
+    Regressor,
+    RidgeRegression,
+)
+
 # noinspection PyProtectedMember
 from safeds.ml.regression._regressor import _check_metrics_preconditions
 
@@ -62,11 +72,7 @@ def invalid_data() -> TaggedTable:
     ).tag_columns(target_name="target", feature_names=["feat1", "feat2"])
 
 
-@pytest.mark.parametrize(
-    "regressor",
-    regressors(),
-    ids=lambda x: x.__class__.__name__
-)
+@pytest.mark.parametrize("regressor", regressors(), ids=lambda x: x.__class__.__name__)
 class TestFit:
     def test_should_succeed_on_valid_data(self, regressor: Regressor, valid_data: TaggedTable) -> None:
         regressor.fit(valid_data)
@@ -83,11 +89,7 @@ def test_should_raise_on_invalid_data(self, regressor: Regressor, invalid_data:
             regressor.fit(invalid_data)
 
 
-@pytest.mark.parametrize(
-    "regressor",
-    regressors(),
-    ids=lambda x: x.__class__.__name__
-)
+@pytest.mark.parametrize("regressor", regressors(), ids=lambda x: x.__class__.__name__)
 class TestPredict:
     def test_should_include_features_of_input_table(self, regressor: Regressor, valid_data: TaggedTable) -> None:
         fitted_regressor = regressor.fit(valid_data)