import random
import math
from operator import itemgetter
def Splitdata(data, M, k, seed):
test = dict()
train = dict()
random.seed(seed)
for user, item in data:
rdm = random.randint(0, M)
if rdm == k:
if user not in test:
test[user] = set()
test[user].add(item)
# test.append([user, item])
else:
if user not in train:
train[user] = set()
train[user].add(item)
# train.append([user, item])
return train, test
def Recall(train, test, N, K):
hit = 0
all = 0
W = UserSimilarity(train)
for user in train.keys():
if user in test:
tu = test[user]
rank = Recommend(user, train, W, K)
rk = sorted(rank.items(), key=itemgetter(1), reverse=True)[0:N]
for item, pui in rk:
if item in tu:
hit += 1
all += len(tu)
return hit / (all * 1.0)
def Precision(train, test, N, K):
hit = 0
all = 0
W = UserSimilarity(train)
for user in train.keys:
tu = test[user]
rank = Recommend(user, train, W, K)
rk = sorted(rank.items(), key=itemgetter(1), reverse=True)[0:N]
for item, pui in rk:
if item in tu:
hit += 1
all += N
return hit / (all * 1.0)
def Coverage(train, test, N, K):
recommend_items = set()
all_items = set()
W = UserSimilarity(train)
for user in train.keys:
for item in train[user]:
all_items.add(item)
rank = Recommend(user, train, W, K)
rk = sorted(rank.items(), key=itemgetter(1), reverse=True)[0:N]
for item, pui in rk:
recommend_items.add(item)
return len(recommend_items) / (len(all_items)*1.0)
def popularity(train, test, N, K):
item_popularity = dict()
for user, items in train.items():
for item in items:
if item not in item_popularity:
item_popularity[item] = 0
item_popularity[item] += 1
ret = 0
n = 0
W = UserSimilarity(train)
for user in train.keys():
rank = Recommend(user, train, W, K)
rk = sorted(rank.items(), key=itemgetter(1), reverse=True)[0:N]
for item, pui in rk:
ret += math.log(1 + item_popularity[item])
n += 1
ret /= n * 1.0
return ret
def UserSimilarity(train):
item_users = dict()
# print(train.items())
for u, items in train.items():
for i in items:
if i not in item_users:
item_users[i] = set()
item_users[i].add(u)
C = dict()
N = dict()
for i, users in item_users.items():
for u in users:
if u not in N:
N[u] = 0
N[u] += 1
for v in users:
if u == v:
continue
if u not in C:
C[u] = dict()
if v not in C[u]:
val = 1 / math.log(1 + len(users))
C[u].update({v:val})
else:
val = C[u][v] + 1 / math.log(1 + len(users))
C[u].update({v: val})
W = dict()
for u, related_users in C.items():
if u not in W:
W[u] = dict()
for v, cuv in related_users.items():
if v not in W[u]:
val = cuv / math.sqrt(N[u] * N[v])
W[u].update({v:val})
return W
def Recommend(user, train, W, K):
rank = dict()
interacted_items = train[user]
li = W[user].items()
for v, wuv in sorted(W[user].items(), key=itemgetter(1), reverse=True)[0:K]:
for i in train[v]:
if i not in interacted_items:
if(i in rank):
rank[i] += wuv
else:
rank[i] = wuv
return rank
def ItemSimilarity(train):
C = dict()
N = dict()
for u, items in train.items():
for i in items:
if i not in N:
N[i] = 0
N[i] += 1
for j in items:
if i == j:
continue
if i not in C:
C[i] = dict()
if j not in C[i]:
val = 1 / math.log(1 + len(items)*1.0)
C[i].update({j: val})
else:
val = C[i][j] + 1 / math.log(1 + len(items)*1.0)
C[i].update({j: val})
W = dict()
for i, related_items in C.items():
for j, cij in related_items.items():
if i not in W:
W[i] = dict()
val = cij / math.sqrt(N[i] * N[j])
W[i].update({j: val})
return W
def ItemCFRecommend(train, user_id, W, K):
rank = dict()
ru = train[user_id]
for i in ru:
for j, wj in sorted(W[i].items(), key=itemgetter(1), reverse=True)[0:K]:
if j in ru:
continue
if j not in rank:
rank[j] = wj
else:
rank[j] += wj
return rank
def RandomSelectNegativeSample(self, items):
ret = dict()
for i in items.keys():
ret[i] = 1
n = 0
for i in range(0, len(items) * 3):
item = items_
path = 'F:\\Project\\python\\ml-100k\\u.data'
datalines = open(path)
data = []
for line in datalines.readlines():
arr = line.split('\t')
data.append((arr[0], arr[1]))
trn, tst = Splitdata(data, 10, 1, 10)
print(len(trn))
print(len(tst))
itemW = ItemSimilarity(trn)
rk = ItemCFRecommend(trn, '1', itemW, 5)
print(rk)
#
# recall = Recall(trn, tst, 100, 80)
# print('recall: ', recall)
# print 'recall: '+ recall
# Wtmp = UserSimilarity(trn)
# rk = Recommend('1', trn, Wtmp, 3)
# print(rk)
# t = dict()
# t['A'] = 1
# t['B'] = 2
# print(t)
# r = sorted(t.items(), key=itemgetter(1),reverse=True)
# print(r)
# for a1, a2 in r:
# print(a1,a2)
