fix: "UserWarning: X has feature names" when predicting (#53)

Closes #51.

### Summary of Changes

Suppress a warning from `scikit-learn` that is not relevant for us.

See also:
https://stackoverflow.com/questions/69326639/sklearn-warning-valid-feature-names-in-version-1-0

---------

Co-authored-by: lars-reimann <lars-reimann@users.noreply.github.com>

docs/tutorials/machine_learning.ipynb

@@ -115,4 +115,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 0
-}
+}

src/safeds/_util/_util_sklearn.py → src/safeds/ml/_util_sklearn.py

@@ -67,7 +67,7 @@ def predict(model: Any, dataset: Table, target_name: str) -> Table:
     dataset_df = dataset._data
     dataset_df.columns = dataset.schema.get_column_names()
     try:
-        predicted_target_vector = model.predict(dataset_df)
+        predicted_target_vector = model.predict(dataset_df.values)
         result_set = dataset_df.copy(deep=True)
         if target_name in result_set.columns:
             raise ValueError(

src/safeds/ml/classification/_ada_boost.py

@@ -1,7 +1,7 @@
 from typing import Optional
 
 # noinspection PyProtectedMember
-import safeds._util._util_sklearn
+import safeds.ml._util_sklearn
 from safeds.data import SupervisedDataset
 from safeds.data.tabular import Table
 from sklearn.ensemble import AdaBoostClassifier as sk_AdaBoostClassifier
@@ -32,7 +32,7 @@ def fit(self, supervised_dataset: SupervisedDataset) -> None:
         LearningError
             If the supervised dataset contains invalid values or if the training failed.
         """
-        self.target_name = safeds._util._util_sklearn.fit(
+        self.target_name = safeds.ml._util_sklearn.fit(
             self._classification, supervised_dataset
         )
 
@@ -57,7 +57,7 @@ def predict(self, dataset: Table, target_name: Optional[str] = None) -> Table:
         PredictionError
             If prediction with the given dataset failed.
         """
-        return safeds._util._util_sklearn.predict(
+        return safeds.ml._util_sklearn.predict(
             self._classification,
             dataset,
             target_name if target_name is not None else self.target_name,

src/safeds/ml/classification/_decision_tree.py

@@ -1,7 +1,7 @@
 from typing import Optional
 
 # noinspection PyProtectedMember
-import safeds._util._util_sklearn
+import safeds.ml._util_sklearn
 from safeds.data import SupervisedDataset
 from safeds.data.tabular import Table
 from sklearn.tree import DecisionTreeClassifier as sk_DecisionTreeClassifier
@@ -32,7 +32,7 @@ def fit(self, supervised_dataset: SupervisedDataset) -> None:
         LearningError
             If the supervised dataset contains invalid values or if the training failed.
         """
-        self.target_name = safeds._util._util_sklearn.fit(
+        self.target_name = safeds.ml._util_sklearn.fit(
             self._classification, supervised_dataset
         )
 
@@ -57,7 +57,7 @@ def predict(self, dataset: Table, target_name: Optional[str] = None) -> Table:
         PredictionError
             If prediction with the given dataset failed.
         """
-        return safeds._util._util_sklearn.predict(
+        return safeds.ml._util_sklearn.predict(
             self._classification,
             dataset,
             target_name if target_name is not None else self.target_name,

src/safeds/ml/classification/_gradient_boosting_classification.py

@@ -1,7 +1,7 @@
 from typing import Optional
 
 # noinspection PyProtectedMember
-import safeds._util._util_sklearn
+import safeds.ml._util_sklearn
 from safeds.data import SupervisedDataset
 from safeds.data.tabular import Table
 from sklearn.ensemble import GradientBoostingClassifier
@@ -32,7 +32,7 @@ def fit(self, supervised_dataset: SupervisedDataset) -> None:
         LearningError
             If the supervised dataset contains invalid values or if the training failed.
         """
-        self.target_name = safeds._util._util_sklearn.fit(
+        self.target_name = safeds.ml._util_sklearn.fit(
             self._classification, supervised_dataset
         )
 
@@ -58,7 +58,7 @@ def predict(self, dataset: Table, target_name: Optional[str] = None) -> Table:
         PredictionError
             If prediction with the given dataset failed.
         """
-        return safeds._util._util_sklearn.predict(
+        return safeds.ml._util_sklearn.predict(
             self._classification,
             dataset,
             target_name if target_name is not None else self.target_name,

src/safeds/ml/classification/_k_nearest_neighbors.py

@@ -1,7 +1,7 @@
 from typing import Optional
 
 # noinspection PyProtectedMember
-import safeds._util._util_sklearn
+import safeds.ml._util_sklearn
 from safeds.data import SupervisedDataset
 from safeds.data.tabular import Table
 from sklearn.neighbors import KNeighborsClassifier
@@ -36,7 +36,7 @@ def fit(self, supervised_dataset: SupervisedDataset) -> None:
         LearningError
             If the supervised dataset contains invalid values or if the training failed.
         """
-        self.target_name = safeds._util._util_sklearn.fit(
+        self.target_name = safeds.ml._util_sklearn.fit(
             self._classification, supervised_dataset
         )
 
@@ -61,7 +61,7 @@ def predict(self, dataset: Table, target_name: Optional[str] = None) -> Table:
         PredictionError
             If prediction with the given dataset failed.
         """
-        return safeds._util._util_sklearn.predict(
+        return safeds.ml._util_sklearn.predict(
             self._classification,
             dataset,
             target_name if target_name is not None else self.target_name,

src/safeds/ml/classification/_logistic_regression.py

@@ -1,7 +1,7 @@
 from typing import Optional
 
 # noinspection PyProtectedMember
-import safeds._util._util_sklearn
+import safeds.ml._util_sklearn
 from safeds.data import SupervisedDataset
 from safeds.data.tabular import Table
 from sklearn.linear_model import LogisticRegression as sk_LogisticRegression
@@ -32,7 +32,7 @@ def fit(self, supervised_dataset: SupervisedDataset) -> None:
         LearningError
             If the supervised dataset contains invalid values or if the training failed.
         """
-        self.target_name = safeds._util._util_sklearn.fit(
+        self.target_name = safeds.ml._util_sklearn.fit(
             self._classification, supervised_dataset
         )
 
@@ -57,7 +57,7 @@ def predict(self, dataset: Table, target_name: Optional[str] = None) -> Table:
         PredictionError
             If prediction with the given dataset failed.
         """
-        return safeds._util._util_sklearn.predict(
+        return safeds.ml._util_sklearn.predict(
             self._classification,
             dataset,
             target_name if target_name is not None else self.target_name,

src/safeds/ml/classification/_random_forest.py

@@ -1,7 +1,7 @@
 from typing import Optional
 
 # noinspection PyProtectedMember
-import safeds._util._util_sklearn
+import safeds.ml._util_sklearn
 from safeds.data import SupervisedDataset
 from safeds.data.tabular import Table
 from sklearn.ensemble import RandomForestClassifier
@@ -31,7 +31,7 @@ def fit(self, supervised_dataset: SupervisedDataset) -> None:
         LearningError
             If the supervised dataset contains invalid values or if the training failed.
         """
-        self.target_name = safeds._util._util_sklearn.fit(
+        self.target_name = safeds.ml._util_sklearn.fit(
             self._classification, supervised_dataset
         )
 
@@ -56,7 +56,7 @@ def predict(self, dataset: Table, target_name: Optional[str] = None) -> Table:
         PredictionError
             If prediction with the given dataset failed.
         """
-        return safeds._util._util_sklearn.predict(
+        return safeds.ml._util_sklearn.predict(
             self._classification,
             dataset,
             target_name if target_name is not None else self.target_name,

src/safeds/ml/regression/_ada_boost.py

@@ -1,7 +1,7 @@
 from typing import Optional
 
 # noinspection PyProtectedMember
-import safeds._util._util_sklearn
+import safeds.ml._util_sklearn
 from safeds.data import SupervisedDataset
 from safeds.data.tabular import Table
 from sklearn.ensemble import AdaBoostRegressor as sk_AdaBoostRegressor
@@ -32,7 +32,7 @@ def fit(self, supervised_dataset: SupervisedDataset) -> None:
         LearningError
             If the supervised dataset contains invalid values or if the training failed.
         """
-        self.target_name = safeds._util._util_sklearn.fit(
+        self.target_name = safeds.ml._util_sklearn.fit(
             self._regression, supervised_dataset
         )
 
@@ -57,7 +57,7 @@ def predict(self, dataset: Table, target_name: Optional[str] = None) -> Table:
         PredictionError
             If prediction with the given dataset failed.
         """
-        return safeds._util._util_sklearn.predict(
+        return safeds.ml._util_sklearn.predict(
             self._regression,
             dataset,
             target_name if target_name is not None else self.target_name,

src/safeds/ml/regression/_decision_tree.py

@@ -1,7 +1,7 @@
 from typing import Optional
 
 # noinspection PyProtectedMember
-import safeds._util._util_sklearn
+import safeds.ml._util_sklearn
 from safeds.data import SupervisedDataset
 from safeds.data.tabular import Table
 from sklearn.tree import DecisionTreeRegressor as sk_DecisionTreeRegressor
@@ -32,7 +32,7 @@ def fit(self, supervised_dataset: SupervisedDataset) -> None:
         LearningError
             If the supervised dataset contains invalid values or if the training failed.
         """
-        self.target_name = safeds._util._util_sklearn.fit(
+        self.target_name = safeds.ml._util_sklearn.fit(
             self._regression, supervised_dataset
         )
 
@@ -57,7 +57,7 @@ def predict(self, dataset: Table, target_name: Optional[str] = None) -> Table:
         PredictionError
             If prediction with the given dataset failed.
         """
-        return safeds._util._util_sklearn.predict(
+        return safeds.ml._util_sklearn.predict(
             self._regression,
             dataset,
             target_name if target_name is not None else self.target_name,

src/safeds/ml/regression/_elastic_net_regression.py

@@ -1,7 +1,7 @@
 from typing import Optional
 
 # noinspection PyProtectedMember
-import safeds._util._util_sklearn
+import safeds.ml._util_sklearn
 from safeds.data import SupervisedDataset
 from safeds.data.tabular import Table
 from sklearn.linear_model import ElasticNet as sk_ElasticNet
@@ -32,7 +32,7 @@ def fit(self, supervised_dataset: SupervisedDataset) -> None:
         LearningError
             If the supervised dataset contains invalid values or if the training failed.
         """
-        self.target_name = safeds._util._util_sklearn.fit(
+        self.target_name = safeds.ml._util_sklearn.fit(
             self._regression, supervised_dataset
         )
 
@@ -57,7 +57,7 @@ def predict(self, dataset: Table, target_name: Optional[str] = None) -> Table:
         PredictionError
             If prediction with the given dataset failed
         """
-        return safeds._util._util_sklearn.predict(
+        return safeds.ml._util_sklearn.predict(
             self._regression,
             dataset,
             target_name if target_name is not None else self.target_name,

src/safeds/ml/regression/_gradient_boosting_regression.py

@@ -1,7 +1,7 @@
 from typing import Optional
 
 # noinspection PyProtectedMember
-import safeds._util._util_sklearn
+import safeds.ml._util_sklearn
 from safeds.data import SupervisedDataset
 from safeds.data.tabular import Table
 from sklearn.ensemble import GradientBoostingRegressor
@@ -33,7 +33,7 @@ def fit(self, supervised_dataset: SupervisedDataset) -> None:
         LearningError
             If the supervised dataset contains invalid values or if the training failed.
         """
-        self.target_name = safeds._util._util_sklearn.fit(
+        self.target_name = safeds.ml._util_sklearn.fit(
             self._regression, supervised_dataset
         )
 
@@ -59,7 +59,7 @@ def predict(self, dataset: Table, target_name: Optional[str] = None) -> Table:
         PredictionError
             If prediction with the given dataset failed.
         """
-        return safeds._util._util_sklearn.predict(
+        return safeds.ml._util_sklearn.predict(
             self._regression,
             dataset,
             target_name if target_name is not None else self.target_name,

src/safeds/ml/regression/_k_nearest_neighbors.py

@@ -1,7 +1,7 @@
 from typing import Optional
 
 # noinspection PyProtectedMember
-import safeds._util._util_sklearn
+import safeds.ml._util_sklearn
 from safeds.data import SupervisedDataset
 from safeds.data.tabular import Table
 from sklearn.neighbors import KNeighborsRegressor
@@ -36,7 +36,7 @@ def fit(self, supervised_dataset: SupervisedDataset) -> None:
         LearningError
             If the supervised dataset contains invalid values or if the training failed.
         """
-        self.target_name = safeds._util._util_sklearn.fit(
+        self.target_name = safeds.ml._util_sklearn.fit(
             self._regression, supervised_dataset
         )
 
@@ -61,7 +61,7 @@ def predict(self, dataset: Table, target_name: Optional[str] = None) -> Table:
         PredictionError
             If prediction with the given dataset failed.
         """
-        return safeds._util._util_sklearn.predict(
+        return safeds.ml._util_sklearn.predict(
             self._regression,
             dataset,
             target_name if target_name is not None else self.target_name,

src/safeds/ml/regression/_lasso_regression.py

@@ -1,7 +1,7 @@
 from typing import Optional
 
 # noinspection PyProtectedMember
-import safeds._util._util_sklearn
+import safeds.ml._util_sklearn
 from safeds.data import SupervisedDataset
 from safeds.data.tabular import Table
 from sklearn.linear_model import Lasso as sk_Lasso
@@ -32,7 +32,7 @@ def fit(self, supervised_dataset: SupervisedDataset) -> None:
         LearningError
             If the supervised dataset contains invalid values or if the training failed.
         """
-        self.target_name = safeds._util._util_sklearn.fit(
+        self.target_name = safeds.ml._util_sklearn.fit(
             self._regression, supervised_dataset
         )
 
@@ -57,7 +57,7 @@ def predict(self, dataset: Table, target_name: Optional[str] = None) -> Table:
         PredictionError
             If prediction with the given dataset failed.
         """
-        return safeds._util._util_sklearn.predict(
+        return safeds.ml._util_sklearn.predict(
             self._regression,
             dataset,
             target_name if target_name is not None else self.target_name,

src/safeds/ml/regression/_linear_regression.py

@@ -1,7 +1,7 @@
 from typing import Optional
 
 # noinspection PyProtectedMember
-import safeds._util._util_sklearn
+import safeds.ml._util_sklearn
 from safeds.data import SupervisedDataset
 from safeds.data.tabular import Table
 from sklearn.linear_model import LinearRegression as sk_LinearRegression
@@ -32,7 +32,7 @@ def fit(self, supervised_dataset: SupervisedDataset) -> None:
         LearningError
             If the supervised dataset contains invalid values or if the training failed.
         """
-        self.target_name = safeds._util._util_sklearn.fit(
+        self.target_name = safeds.ml._util_sklearn.fit(
             self._regression, supervised_dataset
         )
 
@@ -57,7 +57,7 @@ def predict(self, dataset: Table, target_name: Optional[str] = None) -> Table:
         PredictionError
             If prediction with the given dataset failed.
         """
-        return safeds._util._util_sklearn.predict(
+        return safeds.ml._util_sklearn.predict(
             self._regression,
             dataset,
             target_name if target_name is not None else self.target_name,

src/safeds/ml/regression/_random_forest.py

@@ -1,7 +1,7 @@
 from typing import Optional
 
 # noinspection PyProtectedMember
-import safeds._util._util_sklearn
+import safeds.ml._util_sklearn
 from safeds.data import SupervisedDataset
 from safeds.data.tabular import Table
 from sklearn.ensemble import RandomForestRegressor
@@ -31,7 +31,7 @@ def fit(self, supervised_dataset: SupervisedDataset) -> None:
         LearningError
             If the supervised dataset contains invalid values or if the training failed.
         """
-        self.target_name = safeds._util._util_sklearn.fit(
+        self.target_name = safeds.ml._util_sklearn.fit(
             self._regression, supervised_dataset
         )
 
@@ -56,7 +56,7 @@ def predict(self, dataset: Table, target_name: Optional[str] = None) -> Table:
         PredictionError
             If prediction with the given dataset failed.
         """
-        return safeds._util._util_sklearn.predict(
+        return safeds.ml._util_sklearn.predict(
             self._regression,
             dataset,
             target_name if target_name is not None else self.target_name,