--- a/src/HOL/Tools/Sledgehammer/MaSh/src/mash.py Thu Jan 17 17:55:02 2013 +0100
+++ b/src/HOL/Tools/Sledgehammer/MaSh/src/mash.py Thu Jan 17 17:55:03 2013 +0100
@@ -51,46 +51,62 @@
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=-15.0,help="Default value for negative unknown features. Default=-15.0.",type=float)
-parser.add_argument('--theoryPosWeight',default=10.0,help="Weight value for positive features. Default=10.0.",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)
-parser.add_argument('--NBSinePrior',default=False,action='store_true',help="Uses a SInE like prior for premise lvl predictions. Default=False.")
-parser.add_argument('--NBSineWeight',default=100.0,help="How much the SInE prior is weighted. Default=100.0.",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',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('--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 main(argv = sys.argv[1:]):
+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')
- console = logging.StreamHandler(sys.stdout)
- console.setLevel(logging.INFO)
- formatter = logging.Formatter('# %(message)s')
- console.setFormatter(formatter)
- logging.getLogger('').addHandler(console)
+ filemode='w')
logger = logging.getLogger('main.py')
+
+ #"""
+ # remove old handler for tester
+ #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)
+ #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)
@@ -98,24 +114,16 @@
# Pick algorithm
if args.nb:
logger.info('Using sparse Naive Bayes for learning.')
- model = sparseNBClassifier(args.NBDefaultPriorWeight,args.NBPosWeight,args.NBDefVal,args.NBSinePrior,args.NBSineWeight)
- modelFile = os.path.join(args.outputDir,'NB.pickle')
+ model = sparseNBClassifier(args.NBDefaultPriorWeight,args.NBPosWeight,args.NBDefVal)
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')
+ model = Predefined(args.predef)
else:
logger.info('No algorithm specified. Using sparse Naive Bayes.')
- model = sparseNBClassifier(args.NBDefaultPriorWeight,args.NBPosWeight,args.NBDefVal,args.NBSinePrior,args.NBSineWeight)
- modelFile = os.path.join(args.outputDir,'NB.pickle')
- dictsFile = os.path.join(args.outputDir,'dicts.pickle')
- theoryFile = os.path.join(args.outputDir,'theory.pickle')
+ model = sparseNBClassifier(args.NBDefaultPriorWeight,args.NBPosWeight,args.NBDefVal)
# Initializing model
if args.init:
@@ -124,7 +132,7 @@
# Load all data
dicts = Dictionaries()
- dicts.init_all(args.inputDir,depFileName=args.depFile)
+ dicts.init_all(args)
# Create Model
trainData = dicts.featureDict.keys()
@@ -134,10 +142,10 @@
depFile = os.path.join(args.inputDir,args.depFile)
theoryModels = TheoryModels(args.theoryDefValPos,args.theoryDefValNeg,args.theoryPosWeight)
theoryModels.init(depFile,dicts)
- theoryModels.save(theoryFile)
+ theoryModels.save(args.theoryFile)
- model.save(modelFile)
- dicts.save(dictsFile)
+ model.save(args.modelFile)
+ dicts.save(args.dictsFile)
logger.info('All Done. %s seconds needed.',round(time()-startTime,2))
return 0
@@ -149,12 +157,22 @@
dicts = Dictionaries()
theoryModels = TheoryModels(args.theoryDefValPos,args.theoryDefValNeg,args.theoryPosWeight)
# Load Files
- if os.path.isfile(dictsFile):
- dicts.load(dictsFile)
- if os.path.isfile(modelFile):
- model.load(modelFile)
- if os.path.isfile(theoryFile) and args.learnTheories:
- theoryModels.load(theoryFile)
+ 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')
@@ -173,7 +191,7 @@
# try:
if True:
if line.startswith('!'):
- problemId = dicts.parse_fact(line)
+ problemId = dicts.parse_fact(line)
# Statistics
if args.statistics and computeStats:
computeStats = False
@@ -183,7 +201,7 @@
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)
+ 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], ',')
@@ -211,7 +229,8 @@
computeStats = True
if args.predef:
continue
- name,features,accessibles,hints = dicts.parse_problem(line)
+ name,features,accessibles,hints = dicts.parse_problem(line)
+
# Create predictions
logger.info('Starting computation for problem on line %s',lineCounter)
# Update Models with hints
@@ -223,11 +242,29 @@
pass
else:
model.update('hints',features,hints)
-
+
# Predict premises
if args.learnTheories:
predictedTheories,accessibles = theoryModels.predict(features,accessibles,dicts)
- predictions,predictionValues = model.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
@@ -268,10 +305,10 @@
# Save
if args.saveModel:
- model.save(modelFile)
+ model.save(args.modelFile)
if args.learnTheories:
- theoryModels.save(theoryFile)
- dicts.save(dictsFile)
+ theoryModels.save(args.theoryFile)
+ dicts.save(args.dictsFile)
if not args.saveStats == None:
if args.learnTheories:
theoryStatsFile = os.path.join(args.outputDir,'theoryStats')
@@ -282,25 +319,37 @@
if __name__ == '__main__':
# Example:
+ #List
+ # ISAR Theories
+ #args = ['-l','testNB.log','-o','../tmp/','--statistics','--init','--inputDir','../data/20130110/List/','--learnTheories']
+ #args = ['-i', '../data/20130110/List/mash_commands','-p','../tmp/testNB.pred','-l','../tmp/testNB.log','--nb','-o','../tmp/','--statistics','--saveStats','../tmp/JinjaIsarNB.stats','--cutOff','500','--learnTheories']
+ # ISAR predef mesh
+ #args = ['-l','testIsabelle.log','-o','../tmp/','--statistics','--init','--inputDir','../data/20130110/List/','--predef','../data/20130110/List/mesh_suggestions']
+ #args = ['-i', '../data/20130110/List/mash_commands','-p','../tmp/JinjaMePo.pred','-l','testIsabelle.log','--predef','../data/20130110/List/mesh_suggestions','-o','../tmp/','--statistics','--saveStats','../tmp/JinjaMePo.stats']
+
+
# Auth
# ISAR Theories
- #args = ['-l','testNB.log','-o','../tmp/','--statistics','--init','--inputDir','../data/20121227b/Auth/','--learnTheories']
+ #args = ['-l','testNB.log','-o','../tmp/','--statistics','--init','--inputDir','../data/20121227b/Auth/','--learnTheories','--sineFeatures']
#args = ['-i', '../data/20121227b/Auth/mash_commands','-p','../tmp/testNB.pred','-l','../tmp/testNB.log','--nb','-o','../tmp/','--statistics','--saveStats','../tmp/JinjaIsarNB.stats','--cutOff','500','--learnTheories']
- # ISAR MePo
- #args = ['-l','testIsabelle.log','-o','../tmp/','--statistics','--init','--inputDir','../data/20121227b/Auth/','--predef']
- #args = ['-i', '../data/20121227b/Auth/mash_commands','-p','../tmp/JinjaMePo.pred','-l','testIsabelle.log','--predef','-o','../tmp/','--statistics','--saveStats','../tmp/JinjaMePo.stats']
+ # ISAR predef mesh
+ #args = ['-l','testIsabelle.log','-o','../tmp/','--statistics','--init','--inputDir','../data/20121227b/Auth/','--predef','../data/20121227b/Auth/mesh_suggestions']
+ #args = ['-i', '../data/20121227b/Auth/mash_commands','-p','../tmp/JinjaMePo.pred','-l','testIsabelle.log','--predef','../data/20121227b/Auth/mesh_suggestions','-o','../tmp/','--statistics','--saveStats','../tmp/JinjaMePo.stats']
# Jinja
# ISAR Theories
- #args = ['-l','testNB.log','-o','../tmp/','--statistics','--init','--inputDir','../data/20121227b/Jinja/','--learnTheories']
- #args = ['-i', '../data/20121227b/Jinja/mash_commands','-p','../tmp/testNB.pred','-l','../tmp/testNB.log','--nb','-o','../tmp/','--statistics','--saveStats','../tmp/JinjaIsarNB.stats','--cutOff','500','--learnTheories']
+ #args = ['-l','testNB.log','-o','../tmp/','--statistics','--init','--inputDir','../data/20130111/Jinja/','--learnTheories']
+ #args = ['-i', '../data/20130111/Jinja/mash_commands','-p','../tmp/testNB.pred','-l','../tmp/testNB.log','--nb','-o','../tmp/','--statistics','--cutOff','500','--learnTheories']
+ # ISAR Theories SinePrior
+ #args = ['-l','testNB.log','-o','../tmp/','--statistics','--init','--inputDir','../data/20130111/Jinja/','--learnTheories','--sineFeatures']
+ #args = ['-i', '../data/20130111/Jinja/mash_commands','-p','../tmp/testNB.pred','-l','../tmp/testNB.log','--nb','-o','../tmp/','--statistics','--saveStats','../tmp/JinjaIsarNB.stats','--cutOff','500','--learnTheories','--sineFeatures']
# ISAR NB
- #args = ['-l','testNB.log','-o','../tmp/','--statistics','--init','--inputDir','../data/20121221/Jinja/']
- #args = ['-i', '../data/20121221/Jinja/mash_commands','-p','../tmp/testNB.pred','-l','../tmp/testNB.log','--nb','-o','../tmp/','--statistics','--saveStats','../tmp/JinjaIsarNB.stats','--cutOff','500']
- # ISAR MePo
- #args = ['-l','testIsabelle.log','-o','../tmp/','--statistics','--init','--inputDir','../data/20121212/Jinja/','--predef']
- #args = ['-i', '../data/20121212/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/20130111/Jinja/']
+ #args = ['-i', '../data/20130111/Jinja/mash_commands','-p','../tmp/testNB.pred','-l','../tmp/testNB.log','--nb','-o','../tmp/','--statistics','--saveStats','../tmp/JinjaIsarNB.stats','--cutOff','500']
+ # ISAR predef mesh
+ #args = ['-l','testIsabelle.log','-o','../tmp/','--statistics','--init','--inputDir','../data/20130111/Jinja/','--predef','../data/20130111/Jinja/mesh_suggestions']
+ #args = ['-i', '../data/20130111/Jinja/mash_commands','-p','../tmp/JinjaMePo.pred','-l','testIsabelle.log','--predef','../data/20130111/Jinja/mesh_suggestions','-o','../tmp/','--statistics','--saveStats','../tmp/JinjaMePo.stats']
# ISAR NB ATP
#args = ['-l','testNB.log','-o','../tmp/','--statistics','--init','--inputDir','../data/20121212/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']
@@ -313,28 +362,5 @@
#args = ['-i', '../data/20121212/Jinja/mash_commands','-p','../tmp/testNB.pred','-l','../tmp/testNB.log','--snow','-o','../tmp/','--statistics','--saveStats','../tmp/JinjaIsarNB.stats','--cutOff','500']
-
- # Probability
- # ISAR Theories
- #args = ['-l','testNB.log','-o','../tmp/','--statistics','--init','--inputDir','../data/20121213/Probability/','--learnTheories']
- #args = ['-i', '../data/20121213/Probability/mash_commands','-p','../tmp/testNB.pred','-l','../tmp/testNB.log','--nb','-o','../tmp/','--statistics','--saveStats','../tmp/JinjaIsarNB.stats','--cutOff','500','--learnTheories']
- # ISAR NB
- #args = ['-l','testNB.log','-o','../tmp/','--statistics','--init','--inputDir','../data/20121213/Probability/']
- #args = ['-i', '../data/20121213/Probability/mash_commands','-p','../tmp/testNB.pred','-l','../tmp/testNB.log','--nb','-o','../tmp/','--statistics','--saveStats','../tmp/ProbIsarNB.stats','--cutOff','500']
- # ISAR MePo
- #args = ['-l','testIsabelle.log','-o','../tmp/','--statistics','--init','--inputDir','../data/20121213/Probability/','--predef']
- #args = ['-i', '../data/20121213/Probability/mash_commands','-p','../tmp/JinjaMePo.pred','-l','testIsabelle.log','--predef','-o','../tmp/','--statistics','--saveStats','../tmp/JinjaMePo.stats']
- # ISAR NB ATP
- #args = ['-l','testNB.log','-o','../tmp/','--statistics','--init','--inputDir','../data/20121212/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())
+ #sys.exit(mash(args))
+ sys.exit(mash())