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import gzip
import hickle
import _pickle as cPickle
import itertools
import time
def get_num_neighbor(G,etype):
print(G.edges(etype=etype))
for i in G.edges(etype=etype):
print(i)
# exit()
def neighbormap(df,dic,user_dic,new_item_dic,col_user='user_id',col_item='item_id'):
t=time.time()
print('Start time')
for i in range(len(df)):
user=df.at[i,col_user]
item=df.at[i,col_item]
if item in new_item_dic:
dic[user_dic[user]].append(new_item_dic[item])
print('End time',time.time()-t)
return dic
def split_char(str):
english = 'abcdefghijklmnopqrstuvwxyz0123456789'
output = []
buffer = ''
try:
for s in str:
if s in english or s in english.upper(): # English or numeric
buffer += s
elif s in ' （）*()【】/-.': # If it is a special symbol such as a space, skip it
continue
else: # Chinese
if buffer:
output.append(buffer)
buffer = ''
output.append(s)
if buffer:
output.append(buffer)
except:
print(str)
return output
def filter_sample(threshold,dic):
del_index = []
out = []
for key,value in dic.items():
if len(set(value)) < threshold:
del_index.append(key)
else:
neirghbor = value
out.append(neirghbor[:threshold])
return out,del_index
def combination(df,users,col_user='user_id',col_item='item_id'):
df = df[df[col_user].isin(users)] # Filtering, the user must be a user who meets the conditions
df.reset_index(drop=True, inplace=True)
df_item=df[col_item].value_counts()
items = df_item[df_item >= 10].to_dict().keys() # Filtered, the number of users clicked on the item should be greater than a certain value
df = df[df[col_item].isin(items)]
df.reset_index(drop=True, inplace=True)
print(df.shape,len(list(df.groupby([col_item]))))
out = []
for iter in df.groupby([col_item]):
l = iter[1][col_user].tolist()
l = [x for x in l if x in set(users)]
pairs = list(itertools.combinations(l, 2))[:10 if 10>len(l) else len(l)]
out.extend(pairs)
out = list(zip(*set(out)))
print('Number of sides after de-duplication:', len(out[0]))
return out
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