main.py

import sys
import json
import numpy as np 

from model import Model
from corpus import Corpus

def main():

	QAfile 	   		= sys.argv[1]
	ReviewFile 		= sys.argv[2]
	minReview  		= int(sys.argv[3])
	k		   	= int(sys.argv[4])
	numiter	   		= int(sys.argv[5])
	Lambda			= float(sys.argv[6])
	predictionsOut 		= sys.argv[7]
	rankingOut 		= sys.argv[8]
	
	corpus = Corpus(QAfile, ReviewFile, minReview)
	
	corpus.construct_QAnswersAndQPerItem()
	corpus.construct_SentencesAndSPerItem()
	corpus.Calculate_PairWiseFeature()

	print "Vocabulary Size: " + str(corpus.Map.V)
	print "Number of Questions: " + str(len(corpus.QAnswers))
	print "Number of Reviews: " + str(len(corpus.Sentences))
	print "Number of Items " + str(len(corpus.Map.ItemIDMap))
	print "Avg review length " + str(sum(corpus.Avgdl.values())/len(corpus.Avgdl))

	model	  	= Model(k, numiter, Lambda, corpus)
	
	sess = model.train_model()
			
	print "\nModel is trained and optimal model loaded!\n"
							
	valid_accuracy, test_accuracy, topRanked 	= model.valid_test_perf(sess)
	
	if (predictionsOut):
		model.save_predictions(topRanked, predictionsOut)
		
	if (rankingOut):
		topRanked = model.top_ranked(sess, 10)
		model.save_top_ranked(topRanked, rankingOut)

	print "Predictions are saved\n"

	valid_AUC, test_AUC	= model.AUC(sess)

	print "-----------------------------------------------"
	print "----------------------------------------------\n"
	print "Accuracy: "
	print "\tValidation: "+str(valid_accuracy)
	print "\tTest: "+str(test_accuracy)
	print "\n"
	print "AUC: "
	print "\tValidation: "+str(valid_AUC)
	print "\tTest: "+str(test_AUC)
	print "\n"
	print "-----------------------------------------------"
	print "----------------------------------------------\n"

if __name__ == "__main__":
	main()