feat: mark optional hyperparameters as keyword only (#296)

Closes #278.

### Summary of Changes

* Mark optional hyperparameters as keyword-only
* Add guideline to consider marking optional parameters as keyword-only

docs/development/guidelines.md

@@ -72,6 +72,26 @@ Write full words rather than abbreviations. The increased verbosity is offset by
     figure.scs(CS.AUT)
     ```
 
+### Consider marking optional parameters as keyword-only
+
+_Keyword-only parameters_ are parameters that can only be passed by name. It prevents users from accidentally passing a value to the wrong parameter. This can happen easily if several parameters have the same type. Moreover, marking a parameter as keyword-only allows us to change the order of parameters without breaking client code. Because of this, strongly consider marking optional parameters as keyword-only. In particular, optional hyperparameters of models should be keyword-only.
+
+!!! success "**DO** (library code):"
+
+    ```py
+    class RandomForest:
+        def __init__(self, *, number_of_trees: int = 100) -> None:
+            ...
+    ```
+
+!!! failure "**DON'T** (library code):"
+
+    ```py
+    class RandomForest:
+        def __init__(self, number_of_trees: int = 100) -> None:
+            ...
+    ```
+
 ### Specify types of parameters and results
 
 Use [type hints](https://docs.python.org/3/library/typing.html) to describe the types of parameters and results of functions. This enables static type checking of client code.

src/safeds/ml/classical/classification/_ada_boost.py

@@ -37,6 +37,7 @@ class AdaBoost(Classifier):
 
     def __init__(
         self,
+        *,
         learner: Classifier | None = None,
         maximum_number_of_learners: int = 50,
         learning_rate: float = 1.0,

src/safeds/ml/classical/classification/_gradient_boosting.py

@@ -33,7 +33,7 @@ class GradientBoosting(Classifier):
         If `number_of_trees` is less than or equal to 0 or `learning_rate` is non-positive.
     """
 
-    def __init__(self, number_of_trees: int = 100, learning_rate: float = 0.1) -> None:
+    def __init__(self, *, number_of_trees: int = 100, learning_rate: float = 0.1) -> None:
         # Validation
         if number_of_trees <= 0:
             raise ValueError("The parameter 'number_of_trees' has to be greater than 0.")

src/safeds/ml/classical/classification/_random_forest.py

@@ -28,7 +28,7 @@ class RandomForest(Classifier):
         If `number_of_trees` is less than or equal to 0.
     """
 
-    def __init__(self, number_of_trees: int = 100) -> None:
+    def __init__(self, *, number_of_trees: int = 100) -> None:
         # Validation
         if number_of_trees < 1:
             raise ValueError("The parameter 'number_of_trees' has to be greater than 0.")
@@ -65,7 +65,7 @@ def fit(self, training_set: TaggedTable) -> RandomForest:
         wrapped_classifier = self._get_sklearn_classifier()
         fit(wrapped_classifier, training_set)
 
-        result = RandomForest(self._number_of_trees)
+        result = RandomForest(number_of_trees=self._number_of_trees)
         result._wrapped_classifier = wrapped_classifier
         result._feature_names = training_set.features.column_names
         result._target_name = training_set.target.name

src/safeds/ml/classical/classification/_support_vector_machine.py

@@ -29,7 +29,7 @@ class SupportVectorMachine(Classifier):
         If `c` is less than or equal to 0.
     """
 
-    def __init__(self, c: float = 1.0) -> None:
+    def __init__(self, *, c: float = 1.0) -> None:
         # Internal state
         self._wrapped_classifier: sk_SVC | None = None
         self._feature_names: list[str] | None = None
@@ -63,7 +63,7 @@ def fit(self, training_set: TaggedTable) -> SupportVectorMachine:
         wrapped_classifier = self._get_sklearn_classifier()
         fit(wrapped_classifier, training_set)
 
-        result = SupportVectorMachine(self._c)
+        result = SupportVectorMachine(c=self._c)
         result._wrapped_classifier = wrapped_classifier
         result._feature_names = training_set.features.column_names
         result._target_name = training_set.target.name

src/safeds/ml/classical/regression/_ada_boost.py

@@ -37,6 +37,7 @@ class AdaBoost(Regressor):
 
     def __init__(
         self,
+        *,
         learner: Regressor | None = None,
         maximum_number_of_learners: int = 50,
         learning_rate: float = 1.0,

src/safeds/ml/classical/regression/_elastic_net_regression.py

@@ -33,7 +33,7 @@ class ElasticNetRegression(Regressor):
         If `alpha` is negative or `lasso_ratio` is not between 0 and 1.
     """
 
-    def __init__(self, alpha: float = 1.0, lasso_ratio: float = 0.5) -> None:
+    def __init__(self, *, alpha: float = 1.0, lasso_ratio: float = 0.5) -> None:
         # Validation
         if alpha < 0:
             raise ValueError("The parameter 'alpha' must be non-negative")

src/safeds/ml/classical/regression/_gradient_boosting.py

@@ -33,7 +33,7 @@ class GradientBoosting(Regressor):
         If `number_of_trees` is less than or equal to 0 or `learning_rate` is non-positive.
     """
 
-    def __init__(self, number_of_trees: int = 100, learning_rate: float = 0.1) -> None:
+    def __init__(self, *, number_of_trees: int = 100, learning_rate: float = 0.1) -> None:
         # Validation
         if number_of_trees <= 0:
             raise ValueError("The parameter 'number_of_trees' has to be greater than 0.")

src/safeds/ml/classical/regression/_lasso_regression.py

@@ -29,7 +29,7 @@ class LassoRegression(Regressor):
         If `alpha` is negative.
     """
 
-    def __init__(self, alpha: float = 1.0) -> None:
+    def __init__(self, *, alpha: float = 1.0) -> None:
         # Validation
         if alpha < 0:
             raise ValueError("The parameter 'alpha' must be non-negative")

src/safeds/ml/classical/regression/_random_forest.py

@@ -28,7 +28,7 @@ class RandomForest(Regressor):
         If `number_of_trees` is less than or equal to 0.
     """
 
-    def __init__(self, number_of_trees: int = 100) -> None:
+    def __init__(self, *, number_of_trees: int = 100) -> None:
         # Validation
         if number_of_trees < 1:
             raise ValueError("The parameter 'number_of_trees' has to be greater than 0.")
@@ -65,7 +65,7 @@ def fit(self, training_set: TaggedTable) -> RandomForest:
         wrapped_regressor = self._get_sklearn_regressor()
         fit(wrapped_regressor, training_set)
 
-        result = RandomForest(self._number_of_trees)
+        result = RandomForest(number_of_trees=self._number_of_trees)
         result._wrapped_regressor = wrapped_regressor
         result._feature_names = training_set.features.column_names
         result._target_name = training_set.target.name

src/safeds/ml/classical/regression/_ridge_regression.py

@@ -30,7 +30,7 @@ class RidgeRegression(Regressor):
         If `alpha` is negative.
     """
 
-    def __init__(self, alpha: float = 1.0) -> None:
+    def __init__(self, *, alpha: float = 1.0) -> None:
         # Validation
         if alpha < 0:
             raise ValueError("The parameter 'alpha' must be non-negative")

src/safeds/ml/classical/regression/_support_vector_machine.py

@@ -29,7 +29,7 @@ class SupportVectorMachine(Regressor):
         If `c` is less than or equal to 0.
     """
 
-    def __init__(self, c: float = 1.0) -> None:
+    def __init__(self, *, c: float = 1.0) -> None:
         # Internal state
         self._wrapped_regressor: sk_SVR | None = None
         self._feature_names: list[str] | None = None
@@ -63,7 +63,7 @@ def fit(self, training_set: TaggedTable) -> SupportVectorMachine:
         wrapped_regressor = self._get_sklearn_regressor()
         fit(wrapped_regressor, training_set)
 
-        result = SupportVectorMachine(self._c)
+        result = SupportVectorMachine(c=self._c)
         result._wrapped_regressor = wrapped_regressor
         result._feature_names = training_set.features.column_names
         result._target_name = training_set.target.name