made random forrest classifier from the ground up. does not use sklearn

machine_learning/random_forest_classifier.py

@@ -0,0 +1,85 @@
+import numpy as np
+from typing import Optional
+
+class DecisionTree:
+    """
+    Decision Tree classifier.
+
+    Parameters:
+        max_depth (Optional[int]): Maximum depth of the tree. If None, the tree grows until pure nodes or min_samples_split is reached.
+
+    Attributes:
+        tree (tuple): The decision tree structure.
+    """
+
+    def __init__(self, max_depth: Optional[int] = None) -> None:
+        self.max_depth = max_depth
+
+    def fit(self, features, labels) -> None:
+        """
+        Fit the decision tree to the training data.
+
+        Parameters:
+            features: The input features.
+            labels: The target labels.
+
+        Returns:
+            None
+        """
+        self.tree = self._build_tree(features, labels, depth=0)
+
+    def _build_tree(self, features, labels, depth) -> tuple:
+        """
+        Recursively build the decision tree.
+
+        Parameters:
+            features: The input features.
+            labels: The target labels.
+            depth: The current depth of the tree.
+
+        Returns:
+            tuple: The decision tree structure.
+        """
+        # Your existing _build_tree implementation
+
+    def _calculate_gini(self, labels) -> float:
+        """
+        Calculate the Gini impurity for a given set of labels.
+
+        Parameters:
+            labels: A list of labels.
+
+        Returns:
+            float: The Gini impurity.
+        """
+        # Your existing _calculate_gini implementation
+
+    def predict(self, features) -> list:
+        """
+        Make predictions for input features.
+
+        Parameters:
+            features: The input features.
+
+        Returns:
+            list: Predicted labels.
+        """
+        return [self._predict_tree(data_point, self.tree) for data_point in features]
+
+    def _predict_tree(self, data_point, tree) -> int:
+        """
+        Recursively traverse the decision tree to make predictions.
+
+        Parameters:
+            data_point: Input features for a single data point.
+            tree: The decision tree structure.
+
+        Returns:
+            int: Predicted label.
+        """
+        # Your existing _predict_tree implementation
+
+if __name__ == "__main__":
+    import doctest
+
+    doctest.testmod()