feat: improve error handling for predict (#145)

Closes #9.

### Summary of Changes

* Use appropriate exception types.
* Improve messages of raised exception.
* Improve documentation.

src/safeds/data/tabular/transformation/_imputer.py

@@ -8,7 +8,7 @@
 
 from safeds.data.tabular.containers import Table
 from safeds.data.tabular.transformation._table_transformer import TableTransformer
-from safeds.exceptions import NotFittedError, UnknownColumnNameError
+from safeds.exceptions import TransformerNotFittedError, UnknownColumnNameError
 
 
 class ImputerStrategy(ABC):
@@ -139,12 +139,12 @@ def transform(self, table: Table) -> Table:
 
         Raises
         ------
-        NotFittedError
+        TransformerNotFittedError
             If the transformer has not been fitted yet.
         """
         # Transformer has not been fitted yet
         if self._wrapped_transformer is None or self._column_names is None:
-            raise NotFittedError
+            raise TransformerNotFittedError
 
         # Input table does not contain all columns used to fit the transformer
         missing_columns = set(self._column_names) - set(table.get_column_names())

src/safeds/data/tabular/transformation/_label_encoder.py

@@ -9,7 +9,7 @@
 from safeds.data.tabular.transformation._table_transformer import (
     InvertibleTableTransformer,
 )
-from safeds.exceptions import NotFittedError, UnknownColumnNameError
+from safeds.exceptions import TransformerNotFittedError, UnknownColumnNameError
 
 
 def warn(*_: Any, **__: Any) -> None:
@@ -77,12 +77,12 @@ def transform(self, table: Table) -> Table:
 
         Raises
         ------
-        NotFittedError
+        TransformerNotFittedError
             If the transformer has not been fitted yet.
         """
         # Transformer has not been fitted yet
         if self._wrapped_transformer is None or self._column_names is None:
-            raise NotFittedError
+            raise TransformerNotFittedError
 
         # Input table does not contain all columns used to fit the transformer
         missing_columns = set(self._column_names) - set(table.get_column_names())
@@ -110,12 +110,12 @@ def inverse_transform(self, transformed_table: Table) -> Table:
 
         Raises
         ------
-        NotFittedError
+        TransformerNotFittedError
             If the transformer has not been fitted yet.
         """
         # Transformer has not been fitted yet
         if self._wrapped_transformer is None or self._column_names is None:
-            raise NotFittedError
+            raise TransformerNotFittedError
 
         data = transformed_table._data.copy()
         data.columns = transformed_table.get_column_names()

src/safeds/data/tabular/transformation/_one_hot_encoder.py

@@ -7,11 +7,11 @@
 from safeds.data.tabular.transformation._table_transformer import (
     InvertibleTableTransformer,
 )
-from safeds.exceptions import NotFittedError, UnknownColumnNameError
+from safeds.exceptions import TransformerNotFittedError, UnknownColumnNameError
 
 
 class OneHotEncoder(InvertibleTableTransformer):
-    """The OneHotEncoder encodes categorical columns to numerical features [0,1] that represent the existence for each value."""
+    """Encodes categorical columns to numerical features [0,1] that represent the existence for each value."""
 
     def __init__(self) -> None:
         self._wrapped_transformer: sk_OneHotEncoder | None = None
@@ -70,12 +70,12 @@ def transform(self, table: Table) -> Table:
 
         Raises
         ------
-        NotFittedError
+        TransformerNotFittedError
             If the transformer has not been fitted yet.
         """
         # Transformer has not been fitted yet
         if self._wrapped_transformer is None or self._column_names is None:
-            raise NotFittedError
+            raise TransformerNotFittedError
 
         # Input table does not contain all columns used to fit the transformer
         missing_columns = set(self._column_names) - set(table.get_column_names())
@@ -109,12 +109,12 @@ def inverse_transform(self, transformed_table: Table) -> Table:
 
         Raises
         ------
-        NotFittedError
+        TransformerNotFittedError
             If the transformer has not been fitted yet.
         """
         # Transformer has not been fitted yet
         if self._wrapped_transformer is None or self._column_names is None:
-            raise NotFittedError
+            raise TransformerNotFittedError
 
         data = transformed_table._data.copy()
         data.columns = transformed_table.get_column_names()

src/safeds/data/tabular/transformation/_table_transformer.py

@@ -45,7 +45,7 @@ def transform(self, table: Table) -> Table:
 
         Raises
         ------
-        NotFittedError
+        TransformerNotFittedError
             If the transformer has not been fitted yet.
         """
 
@@ -101,6 +101,6 @@ def inverse_transform(self, transformed_table: Table) -> Table:
 
         Raises
         ------
-        NotFittedError
+        TransformerNotFittedError
             If the transformer has not been fitted yet.
         """

src/safeds/exceptions/__init__.py

@@ -9,21 +9,31 @@
     MissingSchemaError,
     NonNumericColumnError,
     SchemaMismatchError,
+    TransformerNotFittedError,
     UnknownColumnNameError,
 )
-from ._learning_exceptions import LearningError, NotFittedError, PredictionError
+from ._ml_exceptions import (
+    DatasetContainsTargetError,
+    DatasetMissesFeaturesError,
+    LearningError,
+    ModelNotFittedError,
+    PredictionError,
+)
 
 __all__ = [
     "ColumnLengthMismatchError",
     "ColumnSizeError",
+    "DatasetContainsTargetError",
+    "DatasetMissesFeaturesError",
     "DuplicateColumnNameError",
     "IndexOutOfBoundsError",
     "LearningError",
     "MissingDataError",
     "MissingSchemaError",
+    "ModelNotFittedError",
     "NonNumericColumnError",
-    "NotFittedError",
     "PredictionError",
     "SchemaMismatchError",
+    "TransformerNotFittedError",
     "UnknownColumnNameError",
 ]

src/safeds/exceptions/_data_exceptions.py

@@ -89,3 +89,10 @@ class MissingDataError(Exception):
 
     def __init__(self, missing_data_info: str):
         super().__init__(f"The function is missing data: \n{missing_data_info}")
+
+
+class TransformerNotFittedError(Exception):
+    """Raised when a transformer is used before fitting it."""
+
+    def __init__(self) -> None:
+        super().__init__("The transformer has not been fitted yet.")

src/safeds/exceptions/_ml_exceptions.py

@@ -0,0 +1,67 @@
+class DatasetContainsTargetError(ValueError):
+    """
+    Raised when a dataset contains the target column already.
+
+    Parameters
+    ----------
+    target_name: str
+        The name of the target column.
+    """
+
+    def __init__(self, target_name: str):
+        super().__init__(f"Dataset already contains the target column '{target_name}'.")
+
+
+class DatasetMissesFeaturesError(ValueError):
+    """
+    Raised when a dataset misses feature columns.
+
+    Parameters
+    ----------
+    missing_feature_names: list[str]
+        The names of the missing feature columns.
+    """
+
+    def __init__(self, missing_feature_names: list[str]):
+        super().__init__(f"Dataset misses the feature columns '{missing_feature_names}'.")
+
+
+class LearningError(Exception):
+    """
+    Raised when an error occurred while training a model.
+
+    Parameters
+    ----------
+    reason: str | None
+        The reason for the error.
+    """
+
+    def __init__(self, reason: str | None):
+        if reason is None:
+            super().__init__("Error occurred while learning")
+        else:
+            super().__init__(f"Error occurred while learning: {reason}")
+
+
+class ModelNotFittedError(Exception):
+    """Raised when a model is used before fitting it."""
+
+    def __init__(self) -> None:
+        super().__init__("The model has not been fitted yet.")
+
+
+class PredictionError(Exception):
+    """
+    Raised when an error occurred while prediction a target vector using a model.
+
+    Parameters
+    ----------
+    reason: str | None
+        The reason for the error.
+    """
+
+    def __init__(self, reason: str | None):
+        if reason is None:
+            super().__init__("Error occurred while predicting")
+        else:
+            super().__init__(f"Error occurred while predicting: {reason}")

src/safeds/ml/_util_sklearn.py

@@ -1,9 +1,13 @@
 from typing import Any
 
-from sklearn.exceptions import NotFittedError
-
 from safeds.data.tabular.containers import Table, TaggedTable
-from safeds.exceptions import LearningError, PredictionError
+from safeds.exceptions import (
+    DatasetContainsTargetError,
+    DatasetMissesFeaturesError,
+    LearningError,
+    ModelNotFittedError,
+    PredictionError,
+)
 
 
 # noinspection PyProtectedMember
@@ -55,13 +59,23 @@ def predict(model: Any, dataset: Table, feature_names: list[str] | None, target_
 
     Raises
     ------
+    ModelNotFittedError
+        If the model has not been fitted yet.
+    DatasetContainsTargetError
+        If the dataset contains the target column already.
+    DatasetMissesFeaturesError
+        If the dataset misses feature columns.
     PredictionError
         If predicting with the given dataset failed.
     """
+    # Validation
     if model is None or target_name is None or feature_names is None:
-        raise PredictionError("The model has not been trained yet.")
+        raise ModelNotFittedError
     if dataset.has_column(target_name):
-        raise ValueError(f"Dataset already contains the target column '{target_name}'.")
+        raise DatasetContainsTargetError(target_name)
+    missing_feature_names = [feature_name for feature_name in feature_names if not dataset.has_column(feature_name)]
+    if missing_feature_names:
+        raise DatasetMissesFeaturesError(missing_feature_names)
 
     dataset_df = dataset.keep_only_columns(feature_names)._data
     dataset_df.columns = feature_names
@@ -73,7 +87,5 @@ def predict(model: Any, dataset: Table, feature_names: list[str] | None, target_
         predicted_target_vector = model.predict(dataset_df.values)
         result_set[target_name] = predicted_target_vector
         return Table(result_set).tag_columns(target_name=target_name, feature_names=feature_names)
-    except NotFittedError as exception:
-        raise PredictionError("The model was not trained") from exception
     except ValueError as exception:
         raise PredictionError(str(exception)) from exception

src/safeds/ml/classification/_ada_boost.py

@@ -67,8 +67,14 @@ def predict(self, dataset: Table) -> TaggedTable:
 
         Raises
         ------
+        ModelNotFittedError
+            If the model has not been fitted yet.
+        DatasetContainsTargetError
+            If the dataset contains the target column already.
+        DatasetMissesFeaturesError
+            If the dataset misses feature columns.
         PredictionError
-            If prediction with the given dataset failed.
+            If predicting with the given dataset failed.
         """
         return predict(self._wrapped_classifier, dataset, self._feature_names, self._target_name)
 

src/safeds/ml/classification/_classifier.py

@@ -52,8 +52,14 @@ def predict(self, dataset: Table) -> TaggedTable:
 
         Raises
         ------
+        ModelNotFittedError
+            If the model has not been fitted yet.
+        DatasetContainsTargetError
+            If the dataset contains the target column already.
+        DatasetMissesFeaturesError
+            If the dataset misses feature columns.
         PredictionError
-            If prediction with the given dataset failed.
+            If predicting with the given dataset failed.
         """
 
     @abstractmethod

src/safeds/ml/classification/_decision_tree.py

@@ -67,8 +67,14 @@ def predict(self, dataset: Table) -> TaggedTable:
 
         Raises
         ------
+        ModelNotFittedError
+            If the model has not been fitted yet.
+        DatasetContainsTargetError
+            If the dataset contains the target column already.
+        DatasetMissesFeaturesError
+            If the dataset misses feature columns.
         PredictionError
-            If prediction with the given dataset failed.
+            If predicting with the given dataset failed.
         """
         return predict(self._wrapped_classifier, dataset, self._feature_names, self._target_name)
 

src/safeds/ml/classification/_gradient_boosting_classification.py

@@ -51,7 +51,6 @@ def fit(self, training_set: TaggedTable) -> GradientBoosting:
 
         return result
 
-    # noinspection PyProtectedMember
     def predict(self, dataset: Table) -> TaggedTable:
         """
         Predict a target vector using a dataset containing feature vectors. The model has to be trained first.
@@ -68,8 +67,14 @@ def predict(self, dataset: Table) -> TaggedTable:
 
         Raises
         ------
+        ModelNotFittedError
+            If the model has not been fitted yet.
+        DatasetContainsTargetError
+            If the dataset contains the target column already.
+        DatasetMissesFeaturesError
+            If the dataset misses feature columns.
         PredictionError
-            If prediction with the given dataset failed.
+            If predicting with the given dataset failed.
         """
         return predict(self._wrapped_classifier, dataset, self._feature_names, self._target_name)