-
Notifications
You must be signed in to change notification settings - Fork 2
/
Copy pathexstracs_configparser.py
210 lines (179 loc) · 9.97 KB
/
exstracs_configparser.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
"""
Name: ExSTraCS_ConfigParser.py
Authors: Ryan Urbanowicz - Written at Dartmouth College, Hanover, NH, USA
Contact: [email protected]
Created: April 25, 2014
Modified: August 25,2014
Description: Manages the configuration file by loading, parsing, and passing it's values to ExSTraCS_Constants. Also includes a method for generating
datasets for cross validation.
---------------------------------------------------------------------------------------------------------------------------------------------------------
ExSTraCS V2.0: Extended Supervised Tracking and Classifying System - An advanced LCS designed specifically for complex, noisy classification/data mining tasks,
such as biomedical/bioinformatics/epidemiological problem domains. This algorithm should be well suited to any supervised learning problem involving
classification, prediction, data mining, and knowledge discovery. This algorithm would NOT be suited to function approximation, behavioral modeling,
or other multi-step problems. This LCS algorithm is most closely based on the "UCS" algorithm, an LCS introduced by Ester Bernado-Mansilla and
Josep Garrell-Guiu (2003) which in turn is based heavily on "XCS", an LCS introduced by Stewart Wilson (1995).
Copyright (C) 2014 Ryan Urbanowicz
This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the
Free Software Foundation; either version 3 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABLILITY
or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation,
Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
---------------------------------------------------------------------------------------------------------------------------------------------------------
"""
#Import Required Modules-------------------------------
from exstracs_constants import *
import os
import copy
import random
#------------------------------------------------------
class ConfigParser:
def __init__(self, filename):
random.seed(1) #Same Random Seed always used here, such that the same CV datasets will be generated if run again.
self.commentChar = '#'
self.paramChar = '='
self.parameters = self.parseConfig(filename) #Parse the configuration file and get all parameters.
cons.setConstants(self.parameters) #Begin building constants Class using parameters from configuration file
if cons.internalCrossValidation == 0 or cons.internalCrossValidation == 1:
pass
else: #Do internal CV
self.CVPart()
def parseConfig(self, filename):
""" Parse the Configuration File"""
parameters = {}
f = open(filename,'rU')
for line in f:
# First, remove comments:
if self.commentChar in line:
# split on comment char, keep only the part before
line, comment = line.split(self.commentChar, 1)
# Second, find lines with an parameter=value:
if self.paramChar in line:
# split on parameter char:
parameter, value = line.split(self.paramChar, 1)
# strip spaces:
parameter = parameter.strip()
value = value.strip()
# store in dictionary:
parameters[parameter] = value
f.close()
return parameters
def CVPart(self):
""" Given a data set, CVPart randomly partitions it into X random balanced
partitions for cross validation which are individually saved in the specified file.
filePath - specifies the path and name of the new datasets. """
numPartitions = cons.internalCrossValidation
folderName = copy.deepcopy(cons.trainFile)
fileName = folderName.split('\\')
fileName = fileName[len(fileName)-1]
filePath = folderName+'\\'+fileName
#Make folder for CV Datasets
if not os.path.exists(folderName):
os.mkdir(folderName)
# Open the specified data file.
f = open(cons.trainFile+'.txt', 'rU')
datasetList = []
headerList = f.readline().rstrip('\n').split('\t') #strip off first row
for line in f:
lineList = line.strip('\n').split('\t')
datasetList.append(lineList)
f.close()
dataLength = len(datasetList)
#Characterize Phenotype----------------------------------------------------------------------------
discretePhenotype = True
if cons.labelPhenotype in headerList:
phenotypeRef = headerList.index(cons.labelPhenotype)
else:
print("Error: ConfigParser - Phenotype Label not found.")
inst = 0
classDict = {}
while len(list(classDict.keys())) <= cons.discreteAttributeLimit and inst < dataLength: #Checks which discriminate between discrete and continuous attribute
target = datasetList[inst][phenotypeRef]
if target in list(classDict.keys()): #Check if we've seen this attribute state yet.
classDict[target] += 1
else: #New state observed
classDict[target] = 1
inst += 1
if len(list(classDict.keys())) > cons.discreteAttributeLimit:
discretePhenotype = False
else:
pass
#---------------------------------------------------------------------------------------------------
CVList = [] #stores all partitions
for x in range(numPartitions):
CVList.append([])
if discretePhenotype:
masterList = []
classKeys = list(classDict.keys())
for i in range(len(classKeys)):
masterList.append([])
for i in datasetList:
notfound = True
j = 0
while notfound:
if i[phenotypeRef] == classKeys[j]:
masterList[j].append(i)
notfound = False
j += 1
#Randomize class instances before partitioning------------------
from random import shuffle
for i in range(len(classKeys)):
shuffle(masterList[i])
#---------------------------------------------------------------
for currentClass in masterList:
currPart = 0
counter = 0
for x in currentClass:
CVList[currPart].append(x)
counter += 1
currPart = counter%numPartitions
self.makePartitions(CVList,numPartitions,filePath,headerList)
else: #Continuous Endpoint
from random import shuffle
shuffle(datasetList)
currPart = 0
counter = 0
for x in datasetList:
CVList[currPart].append(x)
counter += 1
currPart = counter%numPartitions
self.makePartitions(CVList,numPartitions,filePath,headerList)
def makePartitions(self,CVList,numPartitions,filePath,headerList):
for part in range(numPartitions): #Builds CV data files.
if not os.path.exists(filePath+'_CV_'+str(part)+'_Train.txt') or not os.path.exists(filePath+'_CV_'+str(part)+'_Test.txt'):
print("Making new CV files: "+filePath+'_CV_'+str(part))
trainFile = open(filePath+'_CV_'+str(part)+'_Train.txt','w')
testFile = open(filePath+'_CV_'+str(part)+'_Test.txt','w')
for i in range(len(headerList)):
if i < len(headerList)-1:
testFile.write(headerList[i] + "\t")
trainFile.write(headerList[i] + "\t")
else:
testFile.write(headerList[i] + "\n")
trainFile.write(headerList[i] + "\n")
testList=CVList[part]
trainList=[]
tempList = []
for x in range(numPartitions):
tempList.append(x)
tempList.pop(part)
for v in tempList: #for each training partition
trainList.extend(CVList[v])
for i in testList: #Write to Test Datafile
tempString = ''
for point in range(len(i)):
if point < len(i)-1:
tempString = tempString + str(i[point])+"\t"
else:
tempString = tempString +str(i[point])+"\n"
testFile.write(tempString)
for i in trainList: #Write to Train Datafile
tempString = ''
for point in range(len(i)):
if point < len(i)-1:
tempString = tempString + str(i[point])+"\t"
else:
tempString = tempString +str(i[point])+"\n"
trainFile.write(tempString)
trainFile.close()
testFile.close()