--- a/src/HOL/Tools/Sledgehammer/MaSh/src/mashOld.py Fri Aug 23 15:49:27 2013 +0200
+++ /dev/null Thu Jan 01 00:00:00 1970 +0000
@@ -1,329 +0,0 @@
-#!/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())