Select implementaion of diff entropy and mi

This commit is contained in:
2021-06-02 12:05:21 +02:00
parent 365b9b6668
commit 5a5f06b6b9
5 changed files with 62 additions and 420 deletions

View File

@@ -47,20 +47,19 @@ class MFS_test(unittest.TestCase):
def test_csf_wine_cont(self):
mfs = MFS(discrete=False)
expected = [6, 11, 9, 0, 12, 5]
# self.assertListAlmostEqual(
# expected, mfs.cfs(self.X_wc, self.y_w).get_results()
# )
expected = [10, 6, 0, 2, 9, 7]
self.assertListEqual(
expected, mfs.cfs(self.X_wc, self.y_w).get_results()
)
expected = [
0.5218299405215557,
0.602513857132804,
0.4877384978817362,
0.3743688234383051,
0.28795671854246285,
0.2309165735173175,
0.735264150416997,
0.8279580852902876,
0.7828768186880067,
0.7279815238718462,
0.6287944059925545,
0.5416637958201808,
]
# self.assertListAlmostEqual(expected, mfs.get_scores())
print(expected, mfs.get_scores())
self.assertListAlmostEqual(expected, mfs.get_scores())
def test_csf_max_features(self):
mfs = MFS(max_features=3)

View File

@@ -1,7 +1,6 @@
import unittest
import numpy as np
from sklearn.datasets import load_iris, load_wine
from ..entropy_estimators import entropy
from mdlp import MDLP
from ..Selection import Metrics
@@ -71,29 +70,6 @@ class Metrics_test(unittest.TestCase):
)
self.assertAlmostEqual(computed, res_expected)
def test_dif_ent(self):
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 enumerate(expected):
computed = entropy(
self.X_w_c[:, c].reshape(-1, 1), k=n_samples - 2
)
print("-*-", computed)
def test_conditional_entropy(self):
metric = Metrics()
results_expected = [
@@ -142,6 +118,34 @@ class Metrics_test(unittest.TestCase):
computed = metric.information_gain(self.X_i[:, col], self.y_i, 2)
self.assertAlmostEqual(expected, computed)
def test_information_gain_continuous(self):
metric = Metrics()
# Wine
results_expected = [
0.4993916064992192,
0.4049969724847222,
0.2934244372102506,
0.16970372100970632,
]
for expected, col in zip(results_expected, range(self.X_w_c.shape[1])):
computed = metric.information_gain_cont(
self.X_w_c[:, col], self.y_w
)
self.assertAlmostEqual(expected, computed)
# Iris
results_expected = [
0.32752672968734586,
0.0,
0.5281084030413838,
0.0,
]
for expected, col in zip(results_expected, range(self.X_i_c.shape[1])):
computed = metric.information_gain_cont(
self.X_i_c[:, col].reshape(-1, 1), # reshape for coverage
self.y_i,
)
self.assertAlmostEqual(expected, computed)
def test_symmetrical_uncertainty(self):
metric = Metrics()
results_expected = [
@@ -168,5 +172,4 @@ class Metrics_test(unittest.TestCase):
computed = metric.symmetrical_unc_continuous(
self.X_w_c[:, col], self.y_w
)
# print(computed)
self.assertAlmostEqual(expected, computed)