feat: hyperparameter optimization for fnn models (#897)

Closes #861 

### Summary of Changes

Added fit_by_exhaustive_search method to NNClassifier, NNRegressor
Added FittingWithChoice and FittingWithoutChoice Error
Added Choice options for neuron_count params of all layers
Added contains_choices method to all layers
Added _get_layers_for_all_choices method to all layers
Added ClassifierMetric and RegressorMetric as Enums

fit_by_exhaustive_search currently only works for tables as input,
images and timeseries will be added in a later pr

---------

Co-authored-by: megalinter-bot <129584137+megalinter-bot@users.noreply.github.com>

src/safeds/exceptions/__init__.py

@@ -17,7 +17,10 @@
 from ._ml import (
     DatasetMissesDataError,
     DatasetMissesFeaturesError,
+    EmptyChoiceError,
     FeatureDataMismatchError,
+    FittingWithChoiceError,
+    FittingWithoutChoiceError,
     InputSizeError,
     InvalidFitDataError,
     InvalidModelStructureError,
@@ -72,13 +75,16 @@ class OutOfBoundsError(SafeDsError):
     # ML exceptions
     "DatasetMissesDataError",
     "DatasetMissesFeaturesError",
-    "TargetDataMismatchError",
+    "EmptyChoiceError",
     "FeatureDataMismatchError",
+    "FittingWithChoiceError",
+    "FittingWithoutChoiceError",
     "InvalidFitDataError",
     "InputSizeError",
     "InvalidModelStructureError",
     "LearningError",
     "ModelNotFittedError",
     "PlainTableError",
     "PredictionError",
+    "TargetDataMismatchError",
 ]

src/safeds/exceptions/_ml.py

@@ -42,6 +42,33 @@ def __init__(self) -> None:
         super().__init__("Dataset contains no rows")
 
 
+class EmptyChoiceError(ValueError):
+    """Raised when a choice object is created, but no arguments are provided."""
+
+    def __init__(self) -> None:
+        super().__init__("Please provide at least one Value in a Choice Parameter")
+
+
+class FittingWithChoiceError(Exception):
+    """Raised when a model is fitted with a choice object as a parameter."""
+
+    def __init__(self) -> None:
+        super().__init__(
+            "Error occurred while fitting: Trying to fit with a Choice Parameter. Please use "
+            "fit_by_exhaustive_search() instead.",
+        )
+
+
+class FittingWithoutChoiceError(Exception):
+    """Raised when a model is fitted by exhaustive search without a choice object as a parameter."""
+
+    def __init__(self) -> None:
+        super().__init__(
+            "Error occurred while fitting: Trying to fit by exhaustive search without a Choice "
+            "Parameter. Please use fit() instead.",
+        )
+
+
 class InvalidFitDataError(Exception):
     """Raised when a Neural Network is fitted on invalid data."""
 

src/safeds/ml/hyperparameters/_choice.py

@@ -3,6 +3,8 @@
 from collections.abc import Collection
 from typing import TYPE_CHECKING, TypeVar
 
+from safeds.exceptions import EmptyChoiceError
+
 if TYPE_CHECKING:
     from collections.abc import Iterator
     from typing import Any
@@ -15,14 +17,17 @@ class Choice(Collection[T]):
 
     def __init__(self, *args: T) -> None:
         """
-        Create a new choice.
+        Create a new choice. Duplicate values will be removed.
 
         Parameters
         ----------
         *args:
             The values to choose from.
         """
         self.elements = list(args)
+        if len(args) < 1:
+            raise EmptyChoiceError
+        self.elements = list(dict.fromkeys(args))
 
     def __contains__(self, value: Any) -> bool:
         """
@@ -61,3 +66,10 @@ def __len__(self) -> int:
             The number of values in this choice.
         """
         return len(self.elements)
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, Choice):
+            return NotImplemented
+        if self is other:
+            return True
+        return (self is other) or (self.elements == other.elements)