import gzip
from collections import defaultdict

def readGz(f):
  for l in gzip.open(f):
    yield eval(l)

### Helpfulness baseline: similar to the above. Compute the global average helpfulness rate, and the average helpfulness rate for each user

allHelpful = []
userHelpful = defaultdict(list)

for l in readGz("train.json.gz"):
  user,item = l['reviewerID'],l['itemID']
  allHelpful.append(l['helpful'])
  userHelpful[user].append(l['helpful'])

averageRate = sum([x['nHelpful'] for x in allHelpful]) * 1.0 / sum([x['outOf'] for x in allHelpful])

userRate = {}
for u in userHelpful:
  userRate[u] = sum([x['nHelpful'] for x in userHelpful[u]]) * 1.0 / sum([x['outOf'] for x in userHelpful[u]])

predictions = open("predictions_Helpful.txt", 'w')
for l in open("pairs_Helpful.txt"):
  if l.startswith("userID"):
    #header
    predictions.write(l)
    continue
  u,i,outOf = l.strip().split('-')
  outOf = int(outOf)
  if u in userRate:
    predictions.write(u + '-' + i + '-' + str(outOf) + ',' + str(outOf*userRate[u]) + '\n')
  else:
    predictions.write(u + '-' + i + '-' + str(outOf) + ',' + str(outOf*averageRate) + '\n')

predictions.close()

### Purchasing baseline: just rank which items are popular and which are not, and return '1' if an item is among the top-ranked

itemCount = defaultdict(int)
totalPurchases = 0

for l in readGz("train.json.gz"):
  user,item = l['reviewerID'],l['itemID']
  itemCount[item] += 1
  totalPurchases += 1

mostPopular = [(itemCount[x], x) for x in itemCount]
mostPopular.sort()
mostPopular.reverse()

return1 = set()
count = 0
for ic, i in mostPopular:
  count += ic
  return1.add(i)
  if count > totalPurchases/2: break

predictions = open("predictions_Purchase.txt", 'w')
for l in open("pairs_Purchase.txt"):
  if l.startswith("userID"):
    #header
    predictions.write(l)
    continue
  u,i = l.strip().split('-')
  if i in return1:
    predictions.write(u + '-' + i + ",1\n")
  else:
    predictions.write(u + '-' + i + ",0\n")

predictions.close()