added tree entropy calculation

requirements.txt

@@ -1,4 +1,5 @@
 numpy
 scikit-learn
 pandas
-polars
+polars
+SciPy

src/tree/decision_tree_classifier.py

@@ -0,0 +1,21 @@
+import numpy as np
+
+class DecisionTreeClassifier:
+    def __init__(self):
+        pass
+
+    def get_entropy(self, y: np.array):
+        y_size = len(y)
+
+        if y_size <= 1:
+            return 0
+
+        count_per_index = np.bincount(y)
+        total_count = count_per_index[np.nonzero(count_per_index)]
+        probabilities = total_count / y_size
+
+        if len(probabilities) <= 1:
+            return 0
+
+        return - np.sum(probabilities * np.log2(probabilities))
+

tests/tree/test_decision_tree_classifier.py

@@ -0,0 +1,19 @@
+from unittest import TestCase
+import numpy as np
+from scipy.stats import entropy
+from src.tree.decision_tree_classifier import DecisionTreeClassifier
+
+class TestDecisionTreeClassifier(TestCase):
+    def test_should_calculate_entropy(self):
+        y_labels = np.asarray([
+            1, 2, 1, 3, 4, 5, 1, 1, 4, 4, 5, 0
+        ])
+        total_count = np.bincount(y_labels)
+
+        expected = entropy(total_count, base=2)
+
+        classifier = DecisionTreeClassifier()
+        actual = classifier.get_entropy(y_labels)
+
+        assert round(expected, 4) == round(actual, 4)
+