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90 lines
3.2 KiB
Python
Executable File
90 lines
3.2 KiB
Python
Executable File
import unittest
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from sklearn.datasets import load_iris
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from mdlp import MDLP
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from ..Selection import Metrics
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class Metrics_test(unittest.TestCase):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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mdlp = MDLP(random_state=1)
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X, self.y = load_iris(return_X_y=True)
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self.X = mdlp.fit_transform(X, self.y).astype("int64")
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self.m, self.n = self.X.shape
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# @classmethod
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# def setup(cls):
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def test_entropy(self):
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metric = Metrics()
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datasets = [
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([0, 0, 0, 0, 1, 1, 1, 1], 2, 1.0),
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([0, 1, 0, 2, 1, 2], 3, 1.0),
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([0, 0, 0, 0, 0, 0, 0, 2, 2, 2], 2, 0.8812908992306927),
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([1, 1, 1, 5, 2, 2, 3, 3, 3], 4, 0.9455305560363263),
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([1, 1, 1, 2, 2, 3, 3, 3, 5], 4, 0.9455305560363263),
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([1, 1, 5], 2, 0.9182958340544896),
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(self.y, 3, 0.999999999),
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]
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for dataset, base, entropy in datasets:
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computed = metric.entropy(dataset, base)
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self.assertAlmostEqual(entropy, computed)
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def test_conditional_entropy(self):
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metric = Metrics()
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results_expected = [
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0.490953458537736,
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0.7110077966379169,
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0.15663362014829718,
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0.13032469395094992,
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]
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for expected, col in zip(results_expected, range(self.n)):
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computed = metric.conditional_entropy(self.X[:, col], self.y, 3)
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self.assertAlmostEqual(expected, computed)
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self.assertAlmostEqual(
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0.6309297535714573,
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metric.conditional_entropy(
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[1, 3, 2, 3, 2, 1], [1, 2, 0, 1, 1, 2], 3
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),
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)
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# https://planetcalc.com/8414/?joint=0.4%200%0A0.2%200.4&showDetails=1
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self.assertAlmostEqual(
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0.5509775004326938,
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metric.conditional_entropy([1, 1, 2, 2, 2], [0, 0, 0, 2, 2], 2),
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)
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def test_information_gain(self):
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metric = Metrics()
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results_expected = [
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0.5090465414622638,
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0.28899220336208287,
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0.8433663798517026,
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0.8696753060490499,
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]
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for expected, col in zip(results_expected, range(self.n)):
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computed = metric.information_gain(self.X[:, col], self.y, 3)
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self.assertAlmostEqual(expected, computed)
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# https://planetcalc.com/8419/
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# ?_d=FrDfFN2COAhqh9Pb5ycqy5CeKgIOxlfSjKgyyIR.Q5L0np-g-hw6yv8M1Q8_
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results_expected = [
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0.806819679,
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0.458041805,
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1.336704086,
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1.378402748,
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]
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for expected, col in zip(results_expected, range(self.n)):
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computed = metric.information_gain(self.X[:, col], self.y, 2)
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self.assertAlmostEqual(expected, computed)
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def test_symmetrical_uncertainty(self):
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metric = Metrics()
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results_expected = [
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0.33296547388990266,
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0.19068147573570668,
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0.810724587460511,
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0.870521418179061,
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]
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for expected, col in zip(results_expected, range(self.n)):
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computed = metric.symmetrical_uncertainty(self.X[:, col], self.y)
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self.assertAlmostEqual(expected, computed)
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