def main():
QAfile = sys.argv[1]
ReviewFile = sys.argv[2]
minReview = int(sys.argv[3])
V = int(sys.argv[4])
k = int(sys.argv[5])
numiter = int(sys.argv[6])
Lambda = float(sys.argv[7])
predictionsOut = sys.argv[8]
rankingOut = sys.argv[9]
create_corpus = sys.argv[10] # takes zero and 1 as args
corpus_pickle_file = "./Data/corpus_{}.pkl".format(
QAfile.split("/")[-1].split(".")[0])
if x:
corpus = Corpus(QAfile, ReviewFile, minReview, V)
corpus.construct_QAnswersAndQPerItem()
corpus.construct_SentencesAndSPerItem()
corpus.Calculate_PairWiseFeature()
with open(corpus_pickle_file, 'wb') as f:
pickle.dump(corpus, f)
else:
with open(corpus_pickle_file, 'rb') as f:
corpus = pickle.load(f)
print("corpus is available")
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))))
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"
