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DeepWalk.py
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import numpy as np
import time
import math
import os
from collections import defaultdict
from gensim.models import Word2Vec
class AliasTable:
def __init__(self, prob, obj):
self.n = len(prob)
if len(obj) == self.n:
self.obj = obj
else:
self.obj = range(self.n)
sum_prob = sum(prob)
if not sum_prob:
prob = [1] * self.n
sum_prob = self.n
self.alias = list(range(self.n))
self.prob = [i * self.n / sum_prob for i in prob]
small, large = [], []
for i in range(self.n):
if self.prob[i] > 1:
large.append(i)
else:
small.append(i)
while small and large:
sm, la = small.pop(), large.pop()
self.alias[sm] = la
self.prob[la] -= 1-self.prob[sm]
if self.prob[la] > 1:
large.append(la)
else:
small.append(la)
def sample_one(self):
pos = np.random.randint(self.n)
ap = pos if np.random.rand() < self.prob[pos] else self.alias[pos]
return self.obj[ap]
def sample_n(self, n_times):
return [self.sample_one() for _ in range(n_times)]
class Model:
def __init__(self, data_fold, save_fold):
init_time = time.time()
self.first_data, self.second_data = os.listdir(data_fold)
self.first_data = os.path.join(data_fold, self.first_data)
self.second_data = os.path.join(data_fold, self.second_data)
self.save_fold = save_fold
self.embedding_size = 64
self.num_batches = 1000
self.path_len = 10
self.exact_stage = 100
self.web_gap = 0.1
self.hits = 10
self.first_info = []
self.second_info = []
self.first_name = []
self.second_name = []
self.get_info()
self.first_count = len(self.first_info)
self.second_count = len(self.second_info)
self.user_count = self.first_count+self.second_count
self.total_count = 0
self.web_index = {}
self.web_count = 0
self.all_edge = []
self.make_graph()
self.neighbor_sample = []
self.make_sample()
self.model = Word2Vec([[str(i)]for i in range(self.total_count)],
vector_size=self.embedding_size, window=3, min_count=1, sg=2) # sg:1为skip-gram，2为CBOW
self.init_time = time.time() - init_time
print('initial time: %s, first count: %s, second count: %s, web count: %s'
% (time.time() - init_time, self.first_count, self.second_count, self.web_count))
def get_info(self):
for i in os.listdir(self.first_data):
with open(os.path.join(self.first_data, i), 'r') as f:
cur_info = []
for j in f.readlines():
piece = j.split(',')
if len(piece) == 4:
name, x, y, t = piece
cur_info.append((name, float(x), float(y), int(t)))
if cur_info:
self.first_info.append(cur_info)
self.first_name.append(cur_info[0][0])
for i in os.listdir(self.second_data):
with open(os.path.join(self.second_data, i), 'r') as f:
cur_info = []
for j in f.readlines():
piece = j.split(',')
if len(piece) == 4:
name, x, y, t = piece
cur_info.append((name, float(x), float(y), int(t)))
if cur_info:
self.second_info.append(cur_info)
self.second_name.append(cur_info[0][0])
def make_graph(self):
def to_cell(xx, yy):
return '%s-%s' % (int(xx / self.web_gap), int(yy / self.web_gap))
first_web_tf = []
second_web_tf = []
for i in self.first_info:
cur_info = defaultdict(float)
for name, x, y, t in i:
cur_cell = to_cell(x, y)
if cur_cell not in self.web_index:
self.web_index[cur_cell] = self.web_count
self.web_count += 1
cur_info[self.web_index[cur_cell]] += 1
cur_sum = sum(cur_info.values())
for j in cur_info:
cur_info[j] /= cur_sum
first_web_tf.append(cur_info)
for i in self.second_info:
cur_info = defaultdict(float)
for name, x, y, t in i:
cur_cell = to_cell(x, y)
if cur_cell not in self.web_index:
self.web_index[cur_cell] = self.web_count
self.web_count += 1
cur_info[self.web_index[cur_cell]] += 1
cur_sum = sum(cur_info.values())
for j in cur_info:
cur_info[j] /= cur_sum
second_web_tf.append(cur_info)
web_idf = [0]*self.web_count
self.total_count = self.user_count + self.web_count
for i in range(self.first_count):
for j in first_web_tf[i]:
web_idf[j] += 1
for i in range(self.second_count):
for j in second_web_tf[i]:
web_idf[j] += 1
sum_idf = sum(web_idf)
web_idf = [math.log(sum_idf / web_idf[i])for i in range(self.web_count)]
for i in range(self.first_count):
for j in first_web_tf[i]:
self.all_edge.append((i, j+self.user_count, first_web_tf[i][j]*web_idf[j]))
# self.all_edge.append((j+self.user_count, i, first_web_tf[i][j]*web_idf[j]))
for i in range(self.second_count):
for j in second_web_tf[i]:
self.all_edge.append((i+self.first_count, j+self.user_count, second_web_tf[i][j]*web_idf[j]))
# self.all_edge.append((j+self.user_count, i+self.first_count, second_web_tf[i][j]*web_idf[j]))
def make_sample(self):
neighbor = [[[], []]for _ in range(self.total_count)]
for node_a, node_b, weight in self.all_edge:
neighbor[node_a][0].append(node_b)
neighbor[node_a][1].append(weight)
neighbor[node_b][0].append(node_a)
neighbor[node_b][1].append(weight)
for i in range(self.total_count):
self.neighbor_sample.append(AliasTable(neighbor[i][1], neighbor[i][0]))
def fetch_path(self):
paths = []
for i in range(self.total_count):
cur = i
path = []
for j in range(self.path_len):
path.append(str(cur))
cur = self.neighbor_sample[cur].sample_one()
paths.append(path)
return paths
def run_train(self):
self.save_init()
total_sample_time, total_train_time = 0, 0
print('start training')
for i in range(1, self.num_batches+1):
sample_time = time.time()
paths = self.fetch_path()
total_sample_time += time.time()-sample_time
train_time = time.time()
self.model.train(paths, total_words=self.total_count, epochs=1)
total_train_time += time.time()-train_time
if not i % self.exact_stage:
print('batch: %s, sampling_time:%.2f, train_time:%.2f, running time: %.2f'
% (i, total_sample_time, total_train_time,total_sample_time+total_train_time+self.init_time))
self.cal_exact_rate()
print('finish training')
self.save_res()
def save_init(self):
pass
def save_res(self):
pass
def cal_exact_rate(self):
sim_time = time.time()
first_exact_rate = 0
for i in range(self.first_count):
order = sorted(range(self.second_count),
key=lambda x: -self.model.wv.similarity(str(i), str(x+self.first_count)))
for j in range(self.hits):
if self.first_name[i] == self.second_name[order[j]]:
first_exact_rate += 1
second_exact_rate = 0
for i in range(self.second_count):
order = sorted(range(self.first_count),
key=lambda x: -self.model.wv.similarity(str(i+self.first_count), str(x)))
for j in range(self.hits):
if self.second_name[i] == self.first_name[order[j]]:
second_exact_rate += 1
print('exact time: %.2f' % (time.time() - sim_time))
first_exact_rate /= self.first_count
second_exact_rate /= self.second_count
print('first / second exact rate: %.4f / %.4f' % (first_exact_rate, second_exact_rate))
def main(data_fold, save_fold):
total_time = time.time()
ob = Model(data_fold, save_fold)
ob.run_train()
print('total time: %.2f' % (time.time()-total_time))
if __name__ == '__main__':
main(data_fold='Data2', save_fold='Result')
'''
from matplotlib import pyplot as plt
x = '''
'''
y = '''
'''
x = list(map(int, x.split()))
y = list(map(int, y.split()))
plt.xlabel('')
plt.ylabel('')
plt.plot(x, y)
plt.show()
'''