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pig_lite/decision_tree/dt_base.py

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+from pig_lite.decision_tree.dt_node import DecisionTreeNodeBase
+import scipy.stats as stats
+
+def entropy(y: list):
+    """
+    Compute the entropy of a binary label distribution.
+
+    This function calculates the entropy of a binary classification label list `y` as a wrapper 
+    around `scipy.stats.entropy`. It assumes the labels are binary (0 or 1) and computes the 
+    proportion of positive labels (1s) to calculate the entropy.
+
+    Parameters
+    ----------
+    y : list
+        A list of binary labels (0 or 1).
+
+    Returns
+    -------
+    float
+        The entropy of the label distribution. If the list is empty, returns 0.0.
+
+    Notes
+    -----
+    - Entropy is calculated using the formula:
+        H = -p*log2(p) - (1-p)*log2(1-p)
+      where `p` is the proportion of positive labels (1s).
+    - If `y` is empty, entropy is defined as 0.0.
+
+    Examples
+    --------
+    >>> entropy([0, 0, 1, 1])
+    1.0
+
+    >>> entropy([1, 1, 1, 1])
+    0.0
+
+    >>> entropy([])
+    0.0
+    """
+    if len(y) == 0: return 0.0
+    positive = sum(y) / len(y)
+    return stats.entropy([positive, 1 - positive], base=2)
+
+# these two dummy classes are only used so we can import them and load trees from a pickle file before they are implemented by the students
+class DecisionTree():
+    def __init__(self) -> None:
+        pass
+
+    def get_height(self, node):
+        if node is None:
+            return 0
+        return max(self.get_height(node.left_child), self.get_height(node.right_child)) + 1
+    
+    def print(self):
+        if self.root is not None:
+            height = self.get_height(self.root)
+            self.root.print_tree(height)
+
+class DecisionTreeNode(DecisionTreeNodeBase):
+    def __init__(self) -> None:
+        pass