feat: Add parameter number_of_tree to RandomForest classifier and…

… regressor (#230)

Closes #161.

### Summary of Changes

Added number_of_trees parameter to initiator of random_forest_classifier
and random_forest_regressor.

---------

Co-authored-by: megalinter-bot <[email protected]>
Co-authored-by: Alexander <[email protected]>
Co-authored-by: Lars Reimann <[email protected]>

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

@@ -13,9 +13,23 @@
 
 
 class RandomForest(Classifier):
-    """Random forest classification."""
-
-    def __init__(self) -> None:
+    """Random forest classification.
+
+    Parameters
+    ----------
+    number_of_trees : int
+        The number of trees to be used in the random forest. Has to be greater than 0.
+
+    Raises
+    ------
+    ValueError
+        If the number of trees is less than or equal to 0.
+    """
+
+    def __init__(self, number_of_trees: int = 100) -> None:
+        if number_of_trees < 1:
+            raise ValueError("The number of trees has to be greater than 0.")
+        self.number_of_trees = number_of_trees
         self._wrapped_classifier: sk_RandomForestClassifier | None = None
         self._feature_names: list[str] | None = None
         self._target_name: str | None = None
@@ -41,10 +55,10 @@ def fit(self, training_set: TaggedTable) -> RandomForest:
         LearningError
             If the training data contains invalid values or if the training failed.
         """
-        wrapped_classifier = sk_RandomForestClassifier(n_jobs=-1)
+        wrapped_classifier = sk_RandomForestClassifier(self.number_of_trees, n_jobs=-1)
         fit(wrapped_classifier, training_set)
 
-        result = RandomForest()
+        result = RandomForest(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/regression/_random_forest.py

@@ -13,9 +13,23 @@
 
 
 class RandomForest(Regressor):
-    """Random forest regression."""
-
-    def __init__(self) -> None:
+    """Random forest regression.
+
+    Parameters
+    ----------
+    number_of_trees : int
+        The number of trees to be used in the random forest. Has to be greater than 0.
+
+    Raises
+    ------
+    ValueError
+        If the number of trees is less than or equal to 0.
+    """
+
+    def __init__(self, number_of_trees: int = 100) -> None:
+        if number_of_trees < 1:
+            raise ValueError("The number of trees has to be greater than 0.")
+        self.number_of_trees = number_of_trees
         self._wrapped_regressor: sk_RandomForestRegressor | None = None
         self._feature_names: list[str] | None = None
         self._target_name: str | None = None
@@ -41,10 +55,10 @@ def fit(self, training_set: TaggedTable) -> RandomForest:
         LearningError
             If the training data contains invalid values or if the training failed.
         """
-        wrapped_regressor = sk_RandomForestRegressor(n_jobs=-1)
+        wrapped_regressor = sk_RandomForestRegressor(self.number_of_trees, n_jobs=-1)
         fit(wrapped_regressor, training_set)
 
-        result = RandomForest()
+        result = RandomForest(self.number_of_trees)
         result._wrapped_regressor = wrapped_regressor
         result._feature_names = training_set.features.column_names
         result._target_name = training_set.target.name

tests/safeds/ml/classical/classification/test_random_forest.py

@@ -0,0 +1,17 @@
+import pytest
+from safeds.data.tabular.containers import Table
+from safeds.ml.classical.classification import RandomForest
+
+
+def test_number_of_trees_invalid() -> None:
+    with pytest.raises(ValueError, match="The number of trees has to be greater than 0."):
+        RandomForest(-1)
+
+
+def test_number_of_trees_valid() -> None:
+    training_set = Table.from_dict({"col1": [1, 2, 3, 4], "col2": [1, 2, 3, 4]})
+    tagged_training_set = training_set.tag_columns(target_name="col1", feature_names=["col2"])
+
+    random_forest = RandomForest(10).fit(tagged_training_set)
+    assert random_forest._wrapped_classifier is not None
+    assert random_forest._wrapped_classifier.n_estimators == 10

tests/safeds/ml/classical/regression/test_random_forest.py

@@ -0,0 +1,17 @@
+import pytest
+from safeds.data.tabular.containers import Table
+from safeds.ml.classical.regression import RandomForest
+
+
+def test_number_of_trees_invalid() -> None:
+    with pytest.raises(ValueError, match="The number of trees has to be greater than 0."):
+        RandomForest(-1)
+
+
+def test_number_of_trees_valid() -> None:
+    training_set = Table.from_dict({"col1": [1, 2, 3, 4], "col2": [1, 2, 3, 4]})
+    tagged_training_set = training_set.tag_columns(target_name="col1", feature_names=["col2"])
+
+    random_forest = RandomForest(10).fit(tagged_training_set)
+    assert random_forest._wrapped_regressor is not None
+    assert random_forest._wrapped_regressor.n_estimators == 10