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mufs/mfs/tests/Metrics_test.py
Ricardo Montañana 794374fe8c Add max_features to selection
Add first approach to continuous variables
2021-06-01 11:30:52 +02:00

143 lines
5.3 KiB
Python
Executable File

import unittest
import numpy as np
from sklearn.datasets import load_iris, load_wine
from mdlp import MDLP
from ..Selection import Metrics
class Metrics_test(unittest.TestCase):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
mdlp = MDLP(random_state=1)
self.X_i_c, self.y_i = load_iris(return_X_y=True)
self.X_i = mdlp.fit_transform(self.X_i_c, self.y_i).astype("int64")
self.X_w_c, self.y_w = load_wine(return_X_y=True)
self.X_w = mdlp.fit_transform(self.X_w_c, self.y_w).astype("int64")
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_i, 3, 0.999999999),
]
for dataset, base, entropy in datasets:
computed = metric.entropy(dataset, base)
self.assertAlmostEqual(entropy, computed)
def test_differential_entropy(self):
metric = Metrics()
datasets = [
([0, 0, 0, 0, 1, 1, 1, 1], 6, 1.0026709900837547096),
([0, 1, 0, 2, 1, 2], 5, 1.3552453009332424),
([0, 0, 0, 0, 0, 0, 0, 2, 2, 2], 7, 1.7652626150881443),
([1, 1, 1, 5, 2, 2, 3, 3, 3], 8, 1.9094631320594582),
([1, 1, 1, 2, 2, 3, 3, 3, 5], 8, 1.9094631320594582),
([1, 1, 5], 2, 2.5794415416798357),
(self.X_i_c, 37, 3.06627326925228),
(self.X_w_c, 37, 63.13827518897429),
]
for dataset, base, entropy in datasets:
computed = metric.differential_entropy(
np.array(dataset, dtype="float64"), base
)
self.assertAlmostEqual(entropy, computed, msg=str(dataset))
expected = [
1.6378708764142766,
2.0291571802275037,
0.8273865123744271,
3.203935772642847,
4.859193341386733,
1.3707315434976266,
1.8794952925706312,
-0.2983180654207054,
1.4521478934625076,
2.834404839362728,
0.4894081282811191,
1.361210381692561,
7.6373991502818175,
]
n_samples, n_features = self.X_w_c.shape
for c, res_expected in zip(range(n_features), expected):
computed = metric.differential_entropy(
self.X_w_c[:, c], n_samples - 1
)
self.assertAlmostEqual(computed, res_expected)
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_i[:, 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_i[:, 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_i[:, 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_i[:, col], self.y)
self.assertAlmostEqual(expected, computed)
def test_symmetrical_uncertainty_continuous(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_unc_continuous(
self.X_i[:, col], self.y
)
print(computed)
# self.assertAlmostEqual(expected, computed)