Implement Metric methods and tests

2025-08-17 00:26:09 +00:00 · 2021-05-23 20:32:33 +02:00
parent ba73a7f5c0
commit a19f2cc12a
6 changed files with 265 additions and 2 deletions
--- a/cfs/tests/Metrics_test.py
+++ b/cfs/tests/Metrics_test.py
@@ -0,0 +1,90 @@
+import unittest
+from sklearn.datasets import load_iris
+from mdlp import MDLP
+import numpy as np
+from ..Selection import Metrics
+
+
+class Metrics_test(unittest.TestCase):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        mdlp = MDLP(random_state=1)
+        X, self.y = load_iris(return_X_y=True)
+        self.X = mdlp.fit_transform(X, self.y).astype("int64")
+        self.m, self.n = self.X.shape
+
+    # @classmethod
+    # def setup(cls):
+
+    def test_entropy(self):
+        metric = Metrics()
+        datasets = [
+            ([0, 0, 0, 0, 1, 1, 1, 1], 2, 1.0),
+            ([0, 1, 0, 2, 1, 2], 3, 1.0),
+            ([0, 0, 0, 0, 0, 0, 0, 2, 2, 2], 2, 0.8812908992306927),
+            ([1, 1, 1, 5, 2, 2, 3, 3, 3], 4, 0.9455305560363263),
+            ([1, 1, 1, 2, 2, 3, 3, 3, 5], 4, 0.9455305560363263),
+            ([1, 1, 5], 2, 0.9182958340544896),
+            (self.y, 3, 0.999999999),
+        ]
+        for dataset, base, entropy in datasets:
+            computed = metric.entropy(dataset, base)
+            self.assertAlmostEqual(entropy, computed)
+
+    def test_conditional_entropy(self):
+        metric = Metrics()
+        results_expected = [
+            0.490953458537736,
+            0.7110077966379169,
+            0.15663362014829718,
+            0.13032469395094992,
+        ]
+        for expected, col in zip(results_expected, range(self.n)):
+            computed = metric.conditional_entropy(self.X[:, col], self.y, 3)
+            self.assertAlmostEqual(expected, computed)
+        self.assertAlmostEqual(
+            0.6309297535714573,
+            metric.conditional_entropy(
+                [1, 3, 2, 3, 2, 1], [1, 2, 0, 1, 1, 2], 3
+            ),
+        )
+        # https://planetcalc.com/8414/?joint=0.4%200%0A0.2%200.4&showDetails=1
+        self.assertAlmostEqual(
+            0.5509775004326938,
+            metric.conditional_entropy([1, 1, 2, 2, 2], [0, 0, 0, 2, 2], 2),
+        )
+
+    def test_information_gain(self):
+        metric = Metrics()
+        results_expected = [
+            0.5090465414622638,
+            0.28899220336208287,
+            0.8433663798517026,
+            0.8696753060490499,
+        ]
+        for expected, col in zip(results_expected, range(self.n)):
+            computed = metric.information_gain(self.X[:, col], self.y, 3)
+            self.assertAlmostEqual(expected, computed)
+        # https://planetcalc.com/8419/
+        # ?_d=FrDfFN2COAhqh9Pb5ycqy5CeKgIOxlfSjKgyyIR.Q5L0np-g-hw6yv8M1Q8_
+        results_expected = [
+            0.806819679,
+            0.458041805,
+            1.336704086,
+            1.378402748,
+        ]
+        for expected, col in zip(results_expected, range(self.n)):
+            computed = metric.information_gain(self.X[:, col], self.y, 2)
+            self.assertAlmostEqual(expected, computed)
+
+    def test_symmetrical_uncertainty(self):
+        metric = Metrics()
+        results_expected = [
+            0.33296547388990266,
+            0.19068147573570668,
+            0.810724587460511,
+            0.870521418179061,
+        ]
+        for expected, col in zip(results_expected, range(self.n)):
+            computed = metric.symmetrical_uncertainty(self.X[:, col], self.y)
+            self.assertAlmostEqual(expected, computed)