feat: add InvalidFitDataError

src/safeds/exceptions/__init__.py

@@ -18,6 +18,7 @@
     DatasetMissesFeaturesError,
     FeatureDataMismatchError,
     InputSizeError,
+    InvalidFitDataError,
     InvalidModelStructureError,
     LearningError,
     ModelNotFittedError,
@@ -69,6 +70,7 @@ class OutOfBoundsError(SafeDsError):
     "DatasetMissesDataError",
     "DatasetMissesFeaturesError",
     "FeatureDataMismatchError",
+    "InvalidFitDataError",
     "InputSizeError",
     "InvalidModelStructureError",
     "LearningError",

src/safeds/exceptions/_ml.py

@@ -22,6 +22,13 @@ def __init__(self) -> None:
         super().__init__("Dataset contains no rows")
 
 
+class InvalidFitDataError(Exception):
+    """Raised when a Neural Network is fitted on invalid data."""
+
+    def __init__(self, reason: str) -> None:
+        super().__init__(f"The given Fit Data is invalid:\n{reason}")
+
+
 class LearningError(Exception):
     """
     Raised when an error occurred while training a model.

src/safeds/ml/nn/converters/_input_converter_table.py

@@ -4,6 +4,7 @@
 
 from safeds.data.labeled.containers import TabularDataset
 from safeds.data.tabular.containers import Column, Table
+from safeds.exceptions import InvalidFitDataError
 
 from ._input_converter import InputConversion
 
@@ -43,6 +44,24 @@ def _is_fit_data_valid(self, input_data: TabularDataset) -> bool:
             self._feature_names = input_data.features.column_names
             self._target_name = input_data.target.name
             self._first = False
+
+            columns_with_missing_values = []
+            columns_with_non_numerical_data = []
+
+            for col in input_data.features.add_columns([input_data.target]).to_columns():
+                if col.missing_value_count() > 0:
+                    columns_with_missing_values.append(col.name)
+                if not col.type.is_numeric:
+                    columns_with_non_numerical_data.append(col.name)
+
+            reason = ""
+            if len(columns_with_missing_values) > 0:
+                reason += f"The following Columns contain missing values: {columns_with_missing_values}\n"
+            if len(columns_with_non_numerical_data) > 0:
+                reason += f"The following Columns contain non-numerical data: {columns_with_non_numerical_data}"
+            if reason != "":
+                raise InvalidFitDataError(reason)
+
         return (sorted(input_data.features.column_names)).__eq__(sorted(self._feature_names))
 
     def _is_predict_data_valid(self, input_data: Table) -> bool:

tests/safeds/ml/nn/test_model.py

@@ -1,11 +1,13 @@
 import pickle
+import re
 
 import pytest
 from safeds.data.image.typing import ImageSize
 from safeds.data.labeled.containers import TabularDataset
 from safeds.data.tabular.containers import Table
 from safeds.exceptions import (
     FeatureDataMismatchError,
+    InvalidFitDataError,
     InvalidModelStructureError,
     ModelNotFittedError,
     OutOfBoundsError,
@@ -231,6 +233,54 @@ def test_should_raise_if_train_features_mismatch(self, device: Device) -> None:
         ):
             learned_model.fit(Table.from_dict({"k": [0.1, 0, 0.2], "l": [0, 0.15, 0.5]}).to_tabular_dataset("k"))
 
+    @pytest.mark.parametrize(
+        ("table", "reason"),
+        [
+            (
+                Table.from_dict({"a": [1, 2, 3], "b": [1, 2, None], "c": [0, 15, 5]}).to_tabular_dataset("c"),
+                re.escape("The given Fit Data is invalid:\nThe following Columns contain missing values: ['b']\n"),
+            ),
+            (
+                Table.from_dict({"a": ["a", "b", "c"], "b": [1, 2, 3], "c": [0, 15, 5]}).to_tabular_dataset("c"),
+                re.escape("The given Fit Data is invalid:\nThe following Columns contain non-numerical data: ['a']"),
+            ),
+            (
+                Table.from_dict({"a": ["a", "b", "c"], "b": [1, 2, None], "c": [0, 15, 5]}).to_tabular_dataset("c"),
+                re.escape(
+                    "The given Fit Data is invalid:\nThe following Columns contain missing values: ['b']\nThe following Columns contain non-numerical data: ['a']",
+                ),
+            ),
+            (
+                Table.from_dict({"a": [1, 2, 3], "b": [1, 2, 3], "c": [0, None, 5]}).to_tabular_dataset("c"),
+                re.escape(
+                    "The given Fit Data is invalid:\nThe following Columns contain missing values: ['c']\n",
+                ),
+            ),
+            (
+                Table.from_dict({"a": [1, 2, 3], "b": [1, 2, 3], "c": ["a", "b", "a"]}).to_tabular_dataset("c"),
+                re.escape("The given Fit Data is invalid:\nThe following Columns contain non-numerical data: ['c']"),
+            ),
+        ],
+        ids=[
+            "missing value feature",
+            "non-numerical feature",
+            "missing value and non-numerical features",
+            "missing value target",
+            "non-numerical target",
+        ],
+    )
+    def test_should_catch_invalid_fit_data(self, device: Device, table: TabularDataset, reason: str) -> None:
+        configure_test_with_device(device)
+        model = NeuralNetworkClassifier(
+            InputConversionTable(),
+            [ForwardLayer(neuron_count=4), ForwardLayer(1)],
+        )
+        with pytest.raises(
+            InvalidFitDataError,
+            match=reason,
+        ):
+            model.fit(table)
+
     # def test_should_raise_if_table_size_and_input_size_mismatch(self, device: Device) -> None:
     #     configure_test_with_device(device)
     #     model = NeuralNetworkClassifier(
@@ -609,6 +659,54 @@ def test_should_raise_if_train_features_mismatch(self, device: Device) -> None:
                 Table.from_dict({"k": [1, 0, 2], "l": [0, 15, 5]}).to_tabular_dataset("l"),
             )
 
+    @pytest.mark.parametrize(
+        ("table", "reason"),
+        [
+            (
+                Table.from_dict({"a": [1, 2, 3], "b": [1, 2, None], "c": [0, 15, 5]}).to_tabular_dataset("c"),
+                re.escape("The given Fit Data is invalid:\nThe following Columns contain missing values: ['b']\n"),
+            ),
+            (
+                Table.from_dict({"a": ["a", "b", "c"], "b": [1, 2, 3], "c": [0, 15, 5]}).to_tabular_dataset("c"),
+                re.escape("The given Fit Data is invalid:\nThe following Columns contain non-numerical data: ['a']"),
+            ),
+            (
+                Table.from_dict({"a": ["a", "b", "c"], "b": [1, 2, None], "c": [0, 15, 5]}).to_tabular_dataset("c"),
+                re.escape(
+                    "The given Fit Data is invalid:\nThe following Columns contain missing values: ['b']\nThe following Columns contain non-numerical data: ['a']",
+                ),
+            ),
+            (
+                Table.from_dict({"a": [1, 2, 3], "b": [1, 2, 3], "c": [0, None, 5]}).to_tabular_dataset("c"),
+                re.escape(
+                    "The given Fit Data is invalid:\nThe following Columns contain missing values: ['c']\n",
+                ),
+            ),
+            (
+                Table.from_dict({"a": [1, 2, 3], "b": [1, 2, 3], "c": ["a", "b", "a"]}).to_tabular_dataset("c"),
+                re.escape("The given Fit Data is invalid:\nThe following Columns contain non-numerical data: ['c']"),
+            ),
+        ],
+        ids=[
+            "missing value feature",
+            "non-numerical feature",
+            "missing value and non-numerical features",
+            "missing value target",
+            "non-numerical target",
+        ],
+    )
+    def test_should_catch_invalid_fit_data(self, device: Device, table: TabularDataset, reason: str) -> None:
+        configure_test_with_device(device)
+        model = NeuralNetworkRegressor(
+            InputConversionTable(),
+            [ForwardLayer(neuron_count=4), ForwardLayer(1)],
+        )
+        with pytest.raises(
+            InvalidFitDataError,
+            match=reason,
+        ):
+            model.fit(table)
+
     # def test_should_raise_if_table_size_and_input_size_mismatch(self, device: Device) -> None:
     #     configure_test_with_device(device)
     #     model = NeuralNetworkRegressor(