7) run_applyFeatureExistance.py ''' modelfile = path + '/models/modelDict_%d.model'%numberFE trainAveragesFile = path+'/models/trainAverages_%d.model'%numberFE applyFE(path, modelfile, trainAveragesFile, 20000000000) ''' 8) run_applySentimentMF.py ''' applySMF(path, 50000) ''' 9) run_applyAggregationModel.py ''' applyAM(path,numberAM,200000) ''' 10) run_learnMF.py ''' learnMF(path, 20000) ''' 11) run_applyMF.py ''' applyMF(path, 4255) ''' 12) run_evaluation.py ''' evaluate(path)
from standartAlgorithms.applyMatrixFactorization import applyMF
from params.params import path
if __name__ == '__main__':
logger = logging.getLogger('signature')
logfile = '../../data/log/%d_learnSMF.log'%int(time.time())
logging.basicConfig(filename = logfile, format='%(asctime)s : %(name)-12s: %(levelname)s : %(message)s')
logging.root.setLevel(level=logging.DEBUG)
logger.info("running %s" % ' '.join(sys.argv))
console = logging.StreamHandler()
console.setLevel(logging.DEBUG)
# set a format which is simpler for console use
formatter = logging.Formatter('%(asctime)s : %(name)-12s: %(levelname)-8s %(message)s')
# tell the handler to use this format
console.setFormatter(formatter)
logger.addHandler(console)
'''
10) run_learnMF.py
'''
numberMF = learnMF(path, 200000000)
#numberMF = 46192
'''
11) run_applyMF.py
'''
applyMF(path, numberMF)
