#!/usr/bin/python
# Title: HOL/Tools/Sledgehammer/MaSh/src/mash.py
# Author: Daniel Kuehlwein, ICIS, Radboud University Nijmegen
# Copyright 2012
#
# Entry point for MaSh (Machine Learning for Sledgehammer).
'''
MaSh - Machine Learning for Sledgehammer
MaSh allows to use different machine learning algorithms to predict relevant fact for Sledgehammer.
Created on July 12, 2012
@author: Daniel Kuehlwein
'''
import logging,datetime,string,os,sys
from argparse import ArgumentParser,RawDescriptionHelpFormatter
from time import time
from stats import Statistics
from dictionaries import Dictionaries
#from fullNaiveBayes import NBClassifier
from sparseNaiveBayes import sparseNBClassifier
#from naiveBayes import sparseNBClassifier
from snow import SNoW
from predefined import Predefined
# Set up command-line parser
parser = ArgumentParser(description='MaSh - Machine Learning for Sledgehammer. \n\n\
MaSh allows to use different machine learning algorithms to predict relevant facts for Sledgehammer.\n\n\
--------------- Example Usage ---------------\n\
First initialize:\n./mash.py -l test.log -o ../tmp/ --init --inputDir ../data/Jinja/ \n\
Then create predictions:\n./mash.py -i ../data/Jinja/mash_commands -p ../data/Jinja/mash_suggestions -l test.log -o ../tmp/ --statistics\n\
\n\n\
Author: Daniel Kuehlwein, July 2012',formatter_class=RawDescriptionHelpFormatter)
parser.add_argument('-i','--inputFile',help='File containing all problems to be solved.')
parser.add_argument('-o','--outputDir', default='../tmp/',help='Directory where all created files are stored. Default=../tmp/.')
parser.add_argument('-p','--predictions',default='../tmp/%s.predictions' % datetime.datetime.now(),
help='File where the predictions stored. Default=../tmp/dateTime.predictions.')
parser.add_argument('--numberOfPredictions',default=200,help="Number of premises to write in the output. Default=200.",type=int)
parser.add_argument('--init',default=False,action='store_true',help="Initialize Mash. Requires --inputDir to be defined. Default=False.")
parser.add_argument('--inputDir',default='../data/Jinja/',\
help='Directory containing all the input data. MaSh expects the following files: mash_features,mash_dependencies,mash_accessibility')
parser.add_argument('--depFile', default='mash_dependencies',
help='Name of the file with the premise dependencies. The file must be in inputDir. Default = mash_dependencies')
parser.add_argument('--saveModel',default=False,action='store_true',help="Stores the learned Model at the end of a prediction run. Default=False.")
parser.add_argument('--nb',default=False,action='store_true',help="Use Naive Bayes for learning. This is the default learning method.")
parser.add_argument('--snow',default=False,action='store_true',help="Use SNoW's naive bayes instead of Naive Bayes for learning.")
parser.add_argument('--predef',default=False,action='store_true',\
help="Use predefined predictions. Used only for comparison with the actual learning. Expects mash_mepo_suggestions in inputDir.")
parser.add_argument('--statistics',default=False,action='store_true',help="Create and show statistics for the top CUTOFF predictions.\
WARNING: This will make the program a lot slower! Default=False.")
parser.add_argument('--saveStats',default=None,help="If defined, stores the statistics in the filename provided.")
parser.add_argument('--cutOff',default=500,help="Option for statistics. Only consider the first cutOff predictions. Default=500.",type=int)
parser.add_argument('-l','--log', default='../tmp/%s.log' % datetime.datetime.now(), help='Log file name. Default=../tmp/dateTime.log')
parser.add_argument('-q','--quiet',default=False,action='store_true',help="If enabled, only print warnings. Default=False.")
def main(argv = sys.argv[1:]):
# Initializing command-line arguments
args = parser.parse_args(argv)
# Set up logging
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s %(name)-12s %(levelname)-8s %(message)s',
datefmt='%d-%m %H:%M:%S',
filename=args.log,
filemode='w')
console = logging.StreamHandler(sys.stdout)
console.setLevel(logging.INFO)
formatter = logging.Formatter('# %(message)s')
console.setFormatter(formatter)
logging.getLogger('').addHandler(console)
logger = logging.getLogger('main.py')
if args.quiet:
logger.setLevel(logging.WARNING)
console.setLevel(logging.WARNING)
if not os.path.exists(args.outputDir):
os.makedirs(args.outputDir)
logger.info('Using the following settings: %s',args)
# Pick algorithm
if args.nb:
logger.info('Using sparse Naive Bayes for learning.')
model = sparseNBClassifier()
modelFile = os.path.join(args.outputDir,'NB.pickle')
elif args.snow:
logger.info('Using naive bayes (SNoW) for learning.')
model = SNoW()
modelFile = os.path.join(args.outputDir,'SNoW.pickle')
elif args.predef:
logger.info('Using predefined predictions.')
#predictionFile = os.path.join(args.inputDir,'mash_meng_paulson_suggestions')
predictionFile = os.path.join(args.inputDir,'mash_mepo_suggestions')
model = Predefined(predictionFile)
modelFile = os.path.join(args.outputDir,'mepo.pickle')
else:
logger.info('No algorithm specified. Using sparse Naive Bayes.')
model = sparseNBClassifier()
modelFile = os.path.join(args.outputDir,'NB.pickle')
dictsFile = os.path.join(args.outputDir,'dicts.pickle')
# Initializing model
if args.init:
logger.info('Initializing Model.')
startTime = time()
# Load all data
dicts = Dictionaries()
dicts.init_all(args.inputDir,depFileName=args.depFile)
# Create Model
trainData = dicts.featureDict.keys()
if args.predef:
model.initializeModel(trainData,dicts)
else:
model.initializeModel(trainData,dicts)
model.save(modelFile)
dicts.save(dictsFile)
logger.info('All Done. %s seconds needed.',round(time()-startTime,2))
return 0
# Create predictions and/or update model
else:
lineCounter = 1
statementCounter = 1
computeStats = False
dicts = Dictionaries()
# Load Files
if os.path.isfile(dictsFile):
dicts.load(dictsFile)
if os.path.isfile(modelFile):
model.load(modelFile)
# IO Streams
OS = open(args.predictions,'w')
IS = open(args.inputFile,'r')
# Statistics
if args.statistics:
stats = Statistics(args.cutOff)
predictions = None
#Reading Input File
for line in IS:
# try:
if True:
if line.startswith('!'):
problemId = dicts.parse_fact(line)
# Statistics
if args.statistics and computeStats:
computeStats = False
acc = dicts.accessibleDict[problemId]
if args.predef:
predictions = model.predict(problemId)
else:
if args.snow:
predictions,_predictionsValues = model.predict(dicts.featureDict[problemId],dicts.expand_accessibles(acc),dicts)
else:
predictions,_predictionsValues = model.predict(dicts.featureDict[problemId],dicts.expand_accessibles(acc))
stats.update(predictions,dicts.dependenciesDict[problemId],statementCounter)
if not stats.badPreds == []:
bp = string.join([str(dicts.idNameDict[x]) for x in stats.badPreds], ',')
logger.debug('Bad predictions: %s',bp)
statementCounter += 1
# Update Dependencies, p proves p
dicts.dependenciesDict[problemId] = [problemId]+dicts.dependenciesDict[problemId]
if args.snow:
model.update(problemId,dicts.featureDict[problemId],dicts.dependenciesDict[problemId],dicts)
else:
model.update(problemId,dicts.featureDict[problemId],dicts.dependenciesDict[problemId])
elif line.startswith('p'):
# Overwrite old proof.
problemId,newDependencies = dicts.parse_overwrite(line)
newDependencies = [problemId]+newDependencies
model.overwrite(problemId,newDependencies,dicts)
dicts.dependenciesDict[problemId] = newDependencies
elif line.startswith('?'):
startTime = time()
computeStats = True
if args.predef:
continue
name,features,accessibles = dicts.parse_problem(line)
# Create predictions
logger.info('Starting computation for problem on line %s',lineCounter)
if args.snow:
predictions,predictionValues = model.predict(features,accessibles,dicts)
else:
predictions,predictionValues = model.predict(features,accessibles)
assert len(predictions) == len(predictionValues)
logger.info('Done. %s seconds needed.',round(time()-startTime,2))
# Output
predictionNames = [str(dicts.idNameDict[p]) for p in predictions[:args.numberOfPredictions]]
predictionValues = [str(x) for x in predictionValues[:args.numberOfPredictions]]
predictionsStringList = ['%s=%s' % (predictionNames[i],predictionValues[i]) for i in range(len(predictionNames))]
predictionsString = string.join(predictionsStringList,' ')
outString = '%s: %s' % (name,predictionsString)
OS.write('%s\n' % outString)
else:
logger.warning('Unspecified input format: \n%s',line)
sys.exit(-1)
lineCounter += 1
"""
except:
logger.warning('An error occurred on line %s .',line)
lineCounter += 1
continue
"""
OS.close()
IS.close()
# Statistics
if args.statistics:
stats.printAvg()
# Save
if args.saveModel:
model.save(modelFile)
dicts.save(dictsFile)
if not args.saveStats == None:
statsFile = os.path.join(args.outputDir,args.saveStats)
stats.save(statsFile)
return 0
if __name__ == '__main__':
# Example:
# Jinja
#args = ['-l','testIsabelle.log','-o','../tmp/','--statistics','--init','--inputDir','../data/Jinja/','--predef']
#args = ['-i', '../data/Jinja/mash_commands','-p','../tmp/testIsabelle.pred','-l','testIsabelle.log','--predef','-o','../tmp/','--statistics','--saveStats','../tmp/natATPMP.stats']
#args = ['-l','testNB.log','-o','../tmp/','--statistics','--init','--inputDir','../data/Jinja/']
#args = ['-i', '../data/Jinja/mash_commands','-p','../tmp/testNB.pred','-l','../tmp/testNB.log','--nb','-o','../tmp/','--statistics','--saveStats','../tmp/natATPNB.stats','--cutOff','500']
# List
#args = ['-l','testIsabelle.log','-o','../tmp/','--statistics','--init','--inputDir','../data/List/','--isabelle']
#args = ['-i', '../data/List/mash_commands','-p','../tmp/testIsabelle.pred','-l','testIsabelle.log','--isabelle','-o','../tmp/','--statistics']
# Huffmann
#args = ['-l','testNB.log','-o','../tmp/','--statistics','--init','--inputDir','../data/Huffman/','--depFile','mash_atp_dependencies']
#args = ['-l','testNB.log','-o','../tmp/','--statistics','--init','--inputDir','../data/Huffman/']
#args = ['-i', '../data/Huffman/mash_commands','-p','../tmp/testNB.pred','-l','testNB.log','--nb','-o','../tmp/','--statistics']
# Jinja
# ISAR
#args = ['-l','testNB.log','-o','../tmp/','--statistics','--init','--inputDir','../data/Jinja/']
#args = ['-i', '../data/Jinja/mash_commands','-p','../tmp/testNB.pred','-l','../tmp/testNB.log','--nb','-o','../tmp/','--statistics','--saveStats','../tmp/JinjaIsarNB.stats','--cutOff','500']
#args = ['-l','testIsabelle.log','-o','../tmp/','--statistics','--init','--inputDir','../data/Jinja/','--predef']
#args = ['-i', '../data/Jinja/mash_commands','-p','../tmp/JinjaMePo.pred','-l','testIsabelle.log','--predef','-o','../tmp/','--statistics','--saveStats','../tmp/JinjaMePo.stats']
#args = ['-l','testNB.log','-o','../tmp/','--statistics','--init','--inputDir','../data/Jinja/','--depFile','mash_atp_dependencies','--snow']
#args = ['-i', '../data/Jinja/mash_commands','-p','../tmp/testNB.pred','-l','../tmp/testNB.log','--nb','-o','../tmp/','--statistics','--saveStats','../tmp/JinjaIsarNB.stats','--cutOff','500','--depFile','mash_atp_dependencies']
# ATP
#args = ['-l','testNB.log','-o','../tmp/','--statistics','--init','--inputDir','../data/Jinja/','--depFile','mash_atp_dependencies']
#args = ['-i', '../data/Jinja/mash_commands','-p','../tmp/testNB.pred','-l','../tmp/testNB.log','--nb','-o','../tmp/','--statistics','--saveStats','../tmp/JinjaIsarNB.stats','--cutOff','500','--depFile','mash_atp_dependencies']
#args = ['-l','testIsabelle.log','-o','../tmp/','--statistics','--init','--inputDir','../data/Jinja/','--predef','--depFile','mash_atp_dependencies']
#args = ['-i', '../data/Jinja/mash_commands','-p','../tmp/JinjaMePo.pred','-l','testIsabelle.log','--predef','-o','../tmp/','--statistics','--saveStats','../tmp/JinjaMePo.stats','--depFile','mash_atp_dependencies']
#args = ['-l','testNB.log','-o','../tmp/','--statistics','--init','--inputDir','../data/Jinja/','--depFile','mash_atp_dependencies','--snow']
#args = ['-i', '../data/Jinja/mash_commands','-p','../tmp/testNB.pred','-l','../tmp/testNB.log','--snow','-o','../tmp/','--statistics','--saveStats','../tmp/JinjaIsarNB.stats','--cutOff','500','--depFile','mash_atp_dependencies']
#startTime = time()
#sys.exit(main(args))
#print 'New ' + str(round(time()-startTime,2))
sys.exit(main())