-
Notifications
You must be signed in to change notification settings - Fork 1
/
algo2dl3.py
167 lines (106 loc) · 4.39 KB
/
algo2dl3.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Wed Apr 19 21:14:47 2017
@author: xlychee
"""
import tensorflow as tf
import tflearn
import numpy as np
from tflearn.data_utils import shuffle, to_categorical
from tflearn.layers.core import input_data, dropout, flatten
from tflearn.layers.conv import conv_2d, max_pool_2d, avg_pool_2d
from tflearn.layers.estimator import regression
from sklearn import model_selection
import pandas as pd
import time
import random
import numpy as np
import datetime
import tensorflow as tf
#import matplotlib.pyplot as plt
import math
import pickle
from sklearn.model_selection import train_test_split
from datetime import timedelta
def curtime():
return time.asctime(time.localtime(time.time()))
print curtime()+" Program begin "
df = pd.read_csv('../train_new_41')
df,dftest = train_test_split(df, test_size = 0.2)
sess = tf.InteractiveSession()
places = np.array(list(set(df['last_place'].unique()) |set(df['now_place'].unique())))
num_places = len(places)
map_places = {}
counter = 0
for p in places:
if p not in map_places:
map_places[p] = counter
counter +=1
users = df['user_id'].unique()
num_users = len(users)
map_users = {}
counter = 0
for u in users:
if u not in map_users:
map_users[u]=counter
counter+=1
times = df['now_time'].unique()
num_times = len(times)
map_times ={}
counter = 0
for t in times:
if t not in map_times:
map_times[t] = counter
counter+=1
tf.reset_default_graph()
net_p = tflearn.input_data(shape = [None],dtype='int32')
net_p = tflearn.one_hot_encoding(net_p, n_classes=num_places)
net_p = tflearn.fully_connected(net_p,200,activation='linear',weights_init = 'normal', regularizer='L2')
net_u = tflearn.input_data(shape = [None],dtype='int32')
net_u = tflearn.one_hot_encoding(net_u, n_classes=num_users)
net_u = tflearn.fully_connected(net_u,200,activation='linear',weights_init = 'normal',regularizer='L2')
net_t = tflearn.input_data(shape = [None],dtype='int32')
net_t = tflearn.one_hot_encoding(net_t, n_classes=num_times)
net_t = tflearn.fully_connected(net_t,100,activation='linear',weights_init = 'normal',regularizer='L2')
net = tflearn.fully_connected(net_p,500,activation='Relu',weights_init = 'normal',regularizer='L2')\
+tflearn.fully_connected(net_u,500,activation='Relu',weights_init = 'normal',regularizer='L2')\
+tflearn.fully_connected(net_t,500,activation='Relu',weights_init = 'normal',regularizer='L2')
net = tflearn.batch_normalization(net)
net = tflearn.fully_connected(net,500,activation='Relu',weights_init = 'normal',regularizer='L2')
net = tflearn.batch_normalization(net)
net = tflearn.fully_connected(net,500,activation='Relu',weights_init = 'normal',regularizer='L2')
net = tflearn.batch_normalization(net)
net = tflearn.fully_connected(net,500,activation='Relu',weights_init = 'normal',regularizer='L2')
net = tflearn.batch_normalization(net)
net = tflearn.fully_connected(net,500,activation='Relu',weights_init = 'normal',regularizer='L2')
net = tflearn.batch_normalization(net)
net = tflearn.fully_connected(net,500,activation='Relu',weights_init = 'normal',regularizer='L2')
net = tflearn.batch_normalization(net)
net = tflearn.fully_connected(net, num_places,weights_init = 'normal',activation='softmax')
#net_y = tflearn.input_data(shape = [None],dtype='int32', name='1')
#net_yy = tflearn.one_hot_encoding(net_y, n_classes=num_places,name='2')
net = tflearn.regression(net, to_one_hot=True, n_classes = num_places, optimizer='adam',loss='categorical_crossentropy')
model = tflearn.DNN(net)
X_p = np.array(map(lambda x:map_places[x], df['last_place']))
X_u = np.array(map(lambda x:map_users[x], df['user_id']))
X_t = np.array(map(lambda x:map_times[x], df['now_time']))
y = np.array(map(lambda x:map_places[x], df['now_place']))
X_p_train = X_p[:-10000]
X_u_train = X_u[:-10000]
X_t_train = X_t[:-10000]
y_train = y[:-10000]
X_p_test = X_p[-10000:]
X_u_test = X_u[-10000:]
X_t_test = X_t[-10000:]
y_test = y[-10000:]
model.fit([X_p_train,X_u_train,X_t_train],y_train,validation_set=([X_p_test,X_u_test,X_t_test],y_test),
n_epoch=20,batch_size=500,show_metric=True)
predictions = model.predict([X_p_test,X_u_test,X_t_test])
correct = 0
total_test = y_test.shape[0]
for i in xrange(y_test.shape[0]):
pred = np.array(predictions[i])
if y_test[i] in pred.argsort()[-5:]:
correct+=1
print 'precision:', float(correct)/total_test