from tensorlog import learn
from tensorlog import plearn
from tensorlog import comline
if __name__=="__main__":
logging.basicConfig(level=logging.INFO)
logging.info('level is info')
db = comline.parseDBSpec('tmp-cache/cora.db|inputs/cora.cfacts')
trainData = comline.parseDatasetSpec('tmp-cache/cora-train.dset|inputs/train.examples', db)
testData = comline.parseDatasetSpec('tmp-cache/cora-test.dset|inputs/test.examples', db)
prog = comline.parseProgSpec("cora.ppr",db,proppr=True)
prog.setRuleWeights()
prog.db.markAsParam('kaw',1)
prog.db.markAsParam('ktw',1)
prog.db.markAsParam('kvw',1)
prog.maxDepth = 1
# learner = learn.FixedRateGDLearner(prog,regularizer=learn.L2Regularizer(),epochs=5)
learner = plearn.ParallelFixedRateGDLearner(prog,regularizer=learn.L2Regularizer(),parallel=5,epochs=5)
params = {'prog':prog,
'trainData':trainData, 'testData':testData,
'targetMode':'samebib/io',
'savedModel':'tmp-cache/cora-trained.db',
'savedTestPredictions':'tmp-cache/cora-test.solutions.txt',
'savedTrainExamples':'tmp-cache/cora-train.examples',
'savedTestExamples':'tmp-cache/cora-test.examples',
'learner':learner
}
print 'maxdepth',prog.maxDepth
expt.Expt(params).run()
def runMain(): params,_ = setExptParams() return expt.Expt(params).run()
def runMain(num=250):
logging.basicConfig(level=logging.INFO)
masterconfig.masterConfig().matrixdb.allow_weighted_tuples=False
params = setExptParams(num)
return expt.Expt(params).run()
def runMain():
logging.basicConfig(level=logging.INFO)
params = setExptParams()
return expt.Expt(params).run()