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exstracs_test.py
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exstracs_test.py
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"""
Name: ExSTraCS_Test.py
Authors: Ryan Urbanowicz - Written at Dartmouth College, Hanover, NH, USA
Contact: [email protected]
Created: April 25, 2014
Modified: August 25,2014
Description: This module is for developing and testing the ExSTraCS algorithm locally. This module will run ExSTraCS directly within an editor
(e.g. Eclipse with PyDev). Initialization of the algorithm and key mechanisms takes place here.
---------------------------------------------------------------------------------------------------------------------------------------------------------
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_timer import Timer
from exstracs_configparser import ConfigParser
from exstracs_offlineenv import Offline_Environment
from exstracs_onlineenv import Online_Environment
from exstracs_algorithm import ExSTraCS
from exstracs_constants import *
from exstracs_at import AttributeTracking
from exstracs_ek import ExpertKnowledge
#------------------------------------------------------
helpstr = """
Failed attempt to run ExSTraCS.
"""
#Obtain path to configuration file
configurationFile = "ExSTraCS_Configuration_File_Complete.txt"#"ExSTraCS_Configuration_File_Minimum.txt"#"ExSTraCS_Configuration_File_Complete.txt"
#Initialize the Parameters object - this will parse the configuration file and store all constants and parameters.
ConfigParser(configurationFile)
if cons.offlineData:
print('ExSTraCS Offline Environment Mode Initiated.')
if cons.internalCrossValidation == 0 or cons.internalCrossValidation == 1: #No internal Cross Validation
#Engage Timer - tracks run time of algorithm and it's components.
timer = Timer() #TIME
cons.referenceTimer(timer)
cons.timer.startTimeInit()
#Initialize the Environment object - this manages the data presented to ExSTraCS
env = Offline_Environment()
cons.referenceEnv(env) #Send reference to environment object to constants - to access from anywhere in ExSTraCS
cons.parseIterations()
#Instantiate ExSTraCS Algorithm
algorithm = ExSTraCS()
if cons.onlyTest:
cons.timer.stopTimeInit()
algorithm.runTestonly()
else:
if cons.onlyRC:
cons.timer.stopTimeInit()
algorithm.runRConly()
else:
if cons.onlyEKScores:
cons.timer.stopTimeInit()
EK = ExpertKnowledge(cons)
print("Algorithm Run Complete")
else: #Run the ExSTraCS algorithm.
if cons.useExpertKnowledge: #Transform EK scores into probabilities weights for covering. Done once. EK must be externally provided.
cons.timer.startTimeEK()
EK = ExpertKnowledge(cons)
cons.referenceExpertKnowledge(EK)
cons.timer.stopTimeEK()
if cons.doAttributeTracking:
cons.timer.startTimeAT()
AT = AttributeTracking(True)
cons.timer.stopTimeAT()
else:
AT = AttributeTracking(False)
cons.referenceAttributeTracking(AT)
cons.timer.stopTimeInit()
algorithm.runExSTraCS()
else:
print("Running ExSTraCS with Internal Cross Validation")
for part in range(cons.internalCrossValidation):
cons.updateFileNames(part)
#Engage Timer - tracks run time of algorithm and it's components.
timer = Timer() #TIME
cons.referenceTimer(timer)
cons.timer.startTimeInit()
#Initialize the Environment object - this manages the data presented to ExSTraCS
env = Offline_Environment()
cons.referenceEnv(env) #Send reference to environment object to constants - to access from anywhere in ExSTraCS
cons.parseIterations()
#Instantiate ExSTraCS Algorithm
algorithm = ExSTraCS()
if cons.onlyTest:
cons.timer.stopTimeInit()
algorithm.runTestonly()
else:
if cons.onlyRC:
cons.timer.stopTimeInit()
algorithm.runRConly()
else:
if cons.onlyEKScores:
cons.timer.stopTimeInit()
cons.runFilter()
print("Algorithm Run Complete")
else: #Run the ExSTraCS algorithm.
if cons.useExpertKnowledge: #Transform EK scores into probabilities weights for covering. Done once. EK must be externally provided.
cons.timer.startTimeEK()
EK = ExpertKnowledge(cons)
cons.referenceExpertKnowledge(EK)
cons.timer.stopTimeEK()
if cons.doAttributeTracking:
cons.timer.startTimeAT()
AT = AttributeTracking(True)
cons.timer.stopTimeAT()
else:
AT = AttributeTracking(False)
cons.referenceAttributeTracking(AT)
cons.timer.stopTimeInit()
algorithm.runExSTraCS()
else: #Online Dataset (Does not allow Expert Knowledge, Attribute Tracking, Attribute Feedback, or cross-validation)
#Engage Timer - tracks run time of algorithm and it's components.
print("ExSTraCS Online Environment Mode Initiated.")
timer = Timer() #TIME
cons.referenceTimer(timer)
cons.timer.startTimeInit()
cons.overrideParameters()
#Initialize the Environment object - this manages the data presented to ExSTraCS
env = Online_Environment()
cons.referenceEnv(env) #Send reference to environment object to constants - to access from anywhere in ExSTraCS
cons.parseIterations()
#Instantiate ExSTraCS Algorithm
algorithm = ExSTraCS()
cons.timer.stopTimeInit()
if cons.onlyRC:
algorithm.runRConly()
else:
algorithm.runExSTraCS()