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Complete implementation of both algorithms

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Complete coverage tests
This commit is contained in:
Ricardo Montañana Gómez
2021-05-25 11:59:24 +02:00
parent 70560506f1
commit 17d44080f5
6 changed files with 112 additions and 52 deletions
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38
mfs/Selection.py
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@@ -1,4 +1,4 @@
from math import log
from math import log, sqrt
from sys import float_info
from itertools import combinations
import numpy as np
@@ -145,7 +145,7 @@ class MFS:
k = len(features)
for pair in list(combinations(features, 2)):
rff += self._compute_su_features(*pair)
return rcf / ((k ** 2 - k) * rff)
return rcf / sqrt(k + (k ** 2 - k) * rff)
def cfs(self, X, y):
"""CFS forward best first heuristic search
@@ -161,34 +161,41 @@ class MFS:
self.X_ = X
self.y_ = y
s_list = self._compute_su_labels()
# Descending orders
# Descending order
feature_order = (-s_list).argsort().tolist()
merit = float_info.min
exit_condition = 0
continue_condition = True
candidates = []
# start with the best feature (max symmetrical uncertainty wrt label)
first_candidate = feature_order.pop(0)
candidates.append(first_candidate)
self._scores.append(s_list[first_candidate])
while exit_condition < 5: # as proposed in the original algorithm
id_selected = -1
while continue_condition:
merit = float_info.min
id_selected = None
for idx, feature in enumerate(feature_order):
candidates.append(feature)
merit_new = self._compute_merit(candidates)
if merit_new > merit:
id_selected = idx
merit = merit_new
exit_condition = 0
candidates.pop()
if id_selected == -1:
exit_condition += 1
else:
candidates.append(feature_order[id_selected])
self._scores.append(merit_new)
del feature_order[id_selected]
candidates.append(feature_order[id_selected])
self._scores.append(merit)
del feature_order[id_selected]
if len(feature_order) == 0:
# Force leaving the loop
exit_condition = 5
continue_condition = False
if len(self._scores) >= 5:
item_ant = -1
for item in self._scores[-5:]:
if item_ant == -1:
item_ant = item
if item > item_ant:
break
else:
item_ant = item
else:
continue_condition = False
self._result = candidates
return self
@@ -213,7 +220,6 @@ class MFS:
break
# Remove redundant features
for index_q in feature_dup:
# test if feature(index_q) su with feature(index_p) is
su_pq = self._compute_su_features(index_p, index_q)
if su_pq >= s_list[index_q]:
# remove feature from list

83
mfs/tests/MFS_test.py
View File

@@ -1,6 +1,6 @@
import unittest
from mdlp import MDLP
from sklearn.datasets import load_wine
from sklearn.datasets import load_wine, load_iris
from ..Selection import MFS
@@ -9,33 +9,53 @@ class MFS_test(unittest.TestCase):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
mdlp = MDLP(random_state=1)
X, self.y = load_wine(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):
# pass
X, self.y_w = load_wine(return_X_y=True)
self.X_w = mdlp.fit_transform(X, self.y_w).astype("int64")
X, self.y_i = load_iris(return_X_y=True)
mdlp = MDLP(random_state=1)
self.X_i = mdlp.fit_transform(X, self.y_i).astype("int64")
def test_initialize(self):
mfs = MFS()
mfs.fcbs(self.X, self.y, 0.05)
mfs.fcbs(self.X_w, self.y_w, 0.05)
mfs._initialize()
self.assertIsNone(mfs.get_results())
self.assertListEqual([], mfs.get_scores())
self.assertDictEqual({}, mfs._su_features)
self.assertIsNone(mfs._su_labels)
def test_csf(self):
def test_csf_wine(self):
mfs = MFS()
expected = [6, 4]
self.assertListEqual(expected, mfs.cfs(self.X, self.y).get_results())
expected = [0.5218299405215557, 2.4168234005280964]
expected = [6, 12, 9, 4, 10, 0]
self.assertListEqual(
expected, mfs.cfs(self.X_w, self.y_w).get_results()
)
expected = [
0.5218299405215557,
0.602513857132804,
0.4877384978817362,
0.3743688234383051,
0.28795671854246285,
0.2309165735173175,
]
self.assertListEqual(expected, mfs.get_scores())
def test_fcbs(self):
def test_csf_iris(self):
mfs = MFS()
computed = mfs.fcbs(self.X, self.y, threshold=0.05).get_results()
expected = [3, 2, 0, 1]
computed = mfs.cfs(self.X_i, self.y_i).get_results()
self.assertListEqual(expected, computed)
expected = [
0.870521418179061,
0.8968651482682227,
0.5908278453318913,
0.40371971570693366,
]
self.assertListEqual(expected, mfs.get_scores())
def test_fcbs_wine(self):
mfs = MFS()
computed = mfs.fcbs(self.X_w, self.y_w, threshold=0.05).get_results()
expected = [6, 9, 12, 0, 11, 4]
self.assertListEqual(expected, computed)
expected = [
@@ -47,3 +67,36 @@ class MFS_test(unittest.TestCase):
0.24972405134844652,
]
self.assertListEqual(expected, mfs.get_scores())
def test_fcbs_iris(self):
mfs = MFS()
computed = mfs.fcbs(self.X_i, self.y_i, threshold=0.05).get_results()
expected = [3, 2]
self.assertListEqual(expected, computed)
expected = [0.870521418179061, 0.810724587460511]
self.assertListEqual(expected, mfs.get_scores())
def test_compute_su_labels(self):
mfs = MFS()
mfs.fcbs(self.X_i, self.y_i, threshold=0.05)
expected = [0.0, 0.0, 0.810724587460511, 0.870521418179061]
self.assertListEqual(expected, mfs._compute_su_labels().tolist())
mfs._su_labels = [1, 2, 3, 4]
self.assertListEqual([1, 2, 3, 4], mfs._compute_su_labels())
def test_invalid_threshold(self):
mfs = MFS()
with self.assertRaises(ValueError):
mfs.fcbs(self.X_i, self.y_i, threshold=1e-5)
def test_fcbs_exit_threshold(self):
mfs = MFS()
computed = mfs.fcbs(self.X_w, self.y_w, threshold=0.4).get_results()
expected = [6, 9, 12]
self.assertListEqual(expected, computed)
expected = [
0.5218299405215557,
0.46224298637417455,
0.44518278979085646,
]
self.assertListEqual(expected, mfs.get_scores())