#!/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 theoryStats import TheoryStatistics
from theoryModels import TheoryModels
from dictionaries import Dictionaries
#from fullNaiveBayes import NBClassifier
from sparseNaiveBayes 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/20121212/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('--learnTheories',default=False,action='store_true',help="Uses a two-lvl prediction mode. First the theories, then the premises. Default=False.")
# Theory Parameters
parser.add_argument('--theoryDefValPos',default=-7.5,help="Default value for positive unknown features. Default=-7.5.",type=float)
parser.add_argument('--theoryDefValNeg',default=-10.0,help="Default value for negative unknown features. Default=-15.0.",type=float)
parser.add_argument('--theoryPosWeight',default=2.0,help="Weight value for positive features. Default=2.0.",type=float)
parser.add_argument('--nb',default=False,action='store_true',help="Use Naive Bayes for learning. This is the default learning method.")
# NB Parameters
parser.add_argument('--NBDefaultPriorWeight',default=20.0,help="Initializes classifiers with value * p |- p. Default=20.0.",type=float)
parser.add_argument('--NBDefVal',default=-15.0,help="Default value for unknown features. Default=-15.0.",type=float)
parser.add_argument('--NBPosWeight',default=10.0,help="Weight value for positive features. Default=10.0.",type=float)
# TODO: Rename to sineFeatures
parser.add_argument('--sineFeatures',default=False,action='store_true',help="Uses a SInE like prior for premise lvl predictions. Default=False.")
parser.add_argument('--sineWeight',default=0.5,help="How much the SInE prior is weighted. Default=0.5.",type=float)
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',help="Use predefined predictions. Used only for comparison with the actual learning. Argument is the filename of the predictions.")
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.")
parser.add_argument('--modelFile', default='../tmp/model.pickle', help='Model file name. Default=../tmp/model.pickle')
parser.add_argument('--dictsFile', default='../tmp/dict.pickle', help='Dict file name. Default=../tmp/dict.pickle')
parser.add_argument('--theoryFile', default='../tmp/theory.pickle', help='Model file name. Default=../tmp/theory.pickle')
def mash(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')
logger = logging.getLogger('main.py')
"""
# remove old handler for tester
# TODO: Comment out for Jasmins version. This crashes python 2.6.1
logger.root.handlers[0].stream.close()
logger.root.removeHandler(logger.root.handlers[0])
file_handler = logging.FileHandler(args.log)
file_handler.setLevel(logging.DEBUG)
formatter = logging.Formatter('%(asctime)s %(name)-12s %(levelname)-8s %(message)s')
file_handler.setFormatter(formatter)
logger.root.addHandler(file_handler)
#"""
if args.quiet:
logger.setLevel(logging.WARNING)
#console.setLevel(logging.WARNING)
else:
console = logging.StreamHandler(sys.stdout)
console.setLevel(logging.INFO)
formatter = logging.Formatter('# %(message)s')
console.setFormatter(formatter)
logging.getLogger('').addHandler(console)
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(args.NBDefaultPriorWeight,args.NBPosWeight,args.NBDefVal)
elif args.snow:
logger.info('Using naive bayes (SNoW) for learning.')
model = SNoW()
elif args.predef:
logger.info('Using predefined predictions.')
model = Predefined(args.predef)
else:
logger.info('No algorithm specified. Using sparse Naive Bayes.')
model = sparseNBClassifier(args.NBDefaultPriorWeight,args.NBPosWeight,args.NBDefVal)
# Initializing model
if args.init:
logger.info('Initializing Model.')
startTime = time()
# Load all data
dicts = Dictionaries()
dicts.init_all(args)
# Create Model
trainData = dicts.featureDict.keys()
model.initializeModel(trainData,dicts)
if args.learnTheories:
depFile = os.path.join(args.inputDir,args.depFile)
theoryModels = TheoryModels(args.theoryDefValPos,args.theoryDefValNeg,args.theoryPosWeight)
theoryModels.init(depFile,dicts)
theoryModels.save(args.theoryFile)
model.save(args.modelFile)
dicts.save(args.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()
theoryModels = TheoryModels(args.theoryDefValPos,args.theoryDefValNeg,args.theoryPosWeight)
# Load Files
if os.path.isfile(args.dictsFile):
#logger.info('Loading Dictionaries')
#startTime = time()
dicts.load(args.dictsFile)
#logger.info('Done %s',time()-startTime)
if os.path.isfile(args.modelFile):
#logger.info('Loading Model')
#startTime = time()
model.load(args.modelFile)
#logger.info('Done %s',time()-startTime)
if os.path.isfile(args.theoryFile) and args.learnTheories:
#logger.info('Loading Theory Models')
#startTime = time()
theoryModels.load(args.theoryFile)
#logger.info('Done %s',time()-startTime)
logger.info('All loading completed')
# IO Streams
OS = open(args.predictions,'w')
IS = open(args.inputFile,'r')
# Statistics
if args.statistics:
stats = Statistics(args.cutOff)
if args.learnTheories:
theoryStats = TheoryStatistics()
predictions = None
predictedTheories = 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
# Assume '!' comes after '?'
if args.predef:
predictions = model.predict(problemId)
if args.learnTheories:
tmp = [dicts.idNameDict[x] for x in dicts.dependenciesDict[problemId]]
usedTheories = set([x.split('.')[0] for x in tmp])
theoryStats.update((dicts.idNameDict[problemId]).split('.')[0],predictedTheories,usedTheories,len(theoryModels.accessibleTheories))
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.learnTheories:
theoryModels.update(problemId,dicts.featureDict[problemId],dicts.dependenciesDict[problemId],dicts)
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)
if args.learnTheories:
theoryModels.overwrite(problemId,newDependencies,dicts)
dicts.dependenciesDict[problemId] = newDependencies
elif line.startswith('?'):
startTime = time()
computeStats = True
if args.predef:
continue
name,features,accessibles,hints = dicts.parse_problem(line)
# Create predictions
logger.info('Starting computation for problem on line %s',lineCounter)
# Update Models with hints
if not hints == []:
if args.learnTheories:
accessibleTheories = set([(dicts.idNameDict[x]).split('.')[0] for x in accessibles])
theoryModels.update_with_acc('hints',features,hints,dicts,accessibleTheories)
if args.snow:
pass
else:
model.update('hints',features,hints)
# Predict premises
if args.learnTheories:
predictedTheories,accessibles = theoryModels.predict(features,accessibles,dicts)
# Add additional features on premise lvl if sine is enabled
if args.sineFeatures:
origFeatures = [f for f,_w in features]
secondaryFeatures = []
for f in origFeatures:
if dicts.featureCountDict[f] == 1:
continue
triggeredFormulas = dicts.featureTriggeredFormulasDict[f]
for formula in triggeredFormulas:
tFeatures = dicts.triggerFeaturesDict[formula]
#tFeatures = [ff for ff,_fw in dicts.featureDict[formula]]
newFeatures = set(tFeatures).difference(secondaryFeatures+origFeatures)
for fNew in newFeatures:
secondaryFeatures.append((fNew,args.sineWeight))
predictionsFeatures = features+secondaryFeatures
else:
predictionsFeatures = features
predictions,predictionValues = model.predict(predictionsFeatures,accessibles,dicts)
assert len(predictions) == len(predictionValues)
# Delete hints
if not hints == []:
if args.learnTheories:
theoryModels.delete('hints',features,hints,dicts)
if args.snow:
pass
else:
model.delete('hints',features,hints)
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:
if args.learnTheories:
theoryStats.printAvg()
stats.printAvg()
# Save
if args.saveModel:
model.save(args.modelFile)
if args.learnTheories:
theoryModels.save(args.theoryFile)
dicts.save(args.dictsFile)
if not args.saveStats == None:
if args.learnTheories:
theoryStatsFile = os.path.join(args.outputDir,'theoryStats')
theoryStats.save(theoryStatsFile)
statsFile = os.path.join(args.outputDir,args.saveStats)
stats.save(statsFile)
return 0
if __name__ == '__main__':
# Cezary Auth
args = ['--statistics', '--init', '--inputDir', '../data/20130118/Jinja', '--log', '../tmp/auth.log', '--theoryFile', '../tmp/t0', '--modelFile', '../tmp/m0', '--dictsFile', '../tmp/d0','--NBDefaultPriorWeight', '20.0', '--NBDefVal', '-15.0', '--NBPosWeight', '10.0']
mash(args)
args = ['-i', '../data/20130118/Jinja/mash_commands', '-p', '../tmp/auth.pred0', '--statistics', '--cutOff', '500', '--log', '../tmp/auth.log','--modelFile', '../tmp/m0', '--dictsFile', '../tmp/d0']
mash(args)
#sys.exit(mash(args))
sys.exit(mash())