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143 lines
5.3 KiB
Python
Executable File
143 lines
5.3 KiB
Python
Executable File
import unittest
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import numpy as np
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from sklearn.datasets import load_iris, load_wine
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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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self.X_i_c, self.y_i = load_iris(return_X_y=True)
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self.X_i = mdlp.fit_transform(self.X_i_c, self.y_i).astype("int64")
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self.X_w_c, self.y_w = load_wine(return_X_y=True)
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self.X_w = mdlp.fit_transform(self.X_w_c, self.y_w).astype("int64")
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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_i, 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_differential_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], 6, 1.0026709900837547096),
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([0, 1, 0, 2, 1, 2], 5, 1.3552453009332424),
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([0, 0, 0, 0, 0, 0, 0, 2, 2, 2], 7, 1.7652626150881443),
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([1, 1, 1, 5, 2, 2, 3, 3, 3], 8, 1.9094631320594582),
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([1, 1, 1, 2, 2, 3, 3, 3, 5], 8, 1.9094631320594582),
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([1, 1, 5], 2, 2.5794415416798357),
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(self.X_i_c, 37, 3.06627326925228),
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(self.X_w_c, 37, 63.13827518897429),
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]
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for dataset, base, entropy in datasets:
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computed = metric.differential_entropy(
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np.array(dataset, dtype="float64"), base
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)
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self.assertAlmostEqual(entropy, computed, msg=str(dataset))
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expected = [
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1.6378708764142766,
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2.0291571802275037,
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0.8273865123744271,
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3.203935772642847,
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4.859193341386733,
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1.3707315434976266,
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1.8794952925706312,
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-0.2983180654207054,
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1.4521478934625076,
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2.834404839362728,
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0.4894081282811191,
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1.361210381692561,
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7.6373991502818175,
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]
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n_samples, n_features = self.X_w_c.shape
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for c, res_expected in zip(range(n_features), expected):
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computed = metric.differential_entropy(
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self.X_w_c[:, c], n_samples - 1
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)
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self.assertAlmostEqual(computed, res_expected)
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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_i[:, 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_i[:, 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_i[:, 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_i[:, col], self.y)
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self.assertAlmostEqual(expected, computed)
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def test_symmetrical_uncertainty_continuous(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_unc_continuous(
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self.X_i[:, col], self.y
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)
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print(computed)
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# self.assertAlmostEqual(expected, computed)
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