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
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
import pandas as pd
import pymysql
import datetime
import tensorflow as tf
def con_sql(db,sql):
cursor = db.cursor()
try:
cursor.execute(sql)
result = cursor.fetchall()
df = pd.DataFrame(list(result))
except Exception:
print("发生异常", Exception)
df = pd.DataFrame()
finally:
db.close()
return df
def get_data():
db = pymysql.connect(host='10.66.157.22', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
sql = "select max(stat_date) from esmm_train_data"
validate_date = con_sql(db, sql)[0].values.tolist()[0]
print("validate_date:" + validate_date)
temp = datetime.datetime.strptime(validate_date, "%Y-%m-%d")
start = (temp - datetime.timedelta(days=30)).strftime("%Y-%m-%d")
print(start)
db = pymysql.connect(host='10.66.157.22', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
sql = "select e.y,e.z,e.stat_date,e.ucity_id,e.clevel1_id,e.ccity_name," \
"u.device_type,u.manufacturer,u.channel,c.top,cid_time.time " \
"from esmm_train_data e left join user_feature u on e.device_id = u.device_id " \
"left join cid_type_top c on e.device_id = c.device_id left join cid_time on e.cid_id = cid_time.cid_id " \
"where e.stat_date >= '{}'".format(start)
df = con_sql(db, sql)
print(df.shape)
df = df.rename(columns={0: "y", 1: "z", 2: "stat_date", 3: "ucity_id", 4: "clevel1_id", 5: "ccity_name",
6: "device_type", 7: "manufacturer", 8: "channel", 9: "top", 10: "time"})
print("esmm data ok")
print(df.head(2))
df = df.fillna("na")
print(df.count())
ucity_id = {v:i for i,v in df["ucity_id"].unique()}
clevel1_id = {v:i for i,v in df["clevel1_id"].unique()}
ccity_name = {v:i for i,v in df["ccity_name"].unique()}
device_type = {v:i for i,v in df["device_type"].unique()}
manufacturer = {v:i for i,v in df["manufacturer"].unique()}
channel = {v:i for i,v in df["channel"].unique()}
top = {v:i for i,v in df["top"].unique()}
time = {v:i for i,v in df["time"].unique()}
df["ucity_id"] = df["ucity_id"].map(ucity_id)
df["clevel1_id"] = df["clevel1_id"].map(clevel1_id)
df["ccity_name"] = df["ccity_name"].map(ccity_name)
df["device_type"] = df["device_type"].map(device_type)
df["manufacturer"] = df["manufacturer"].map(manufacturer)
df["channel"] = df["channel"].map(channel)
df["top"] = df["top"].map(top)
df["time"] = df["time"].map(time)
train = df.loc[df["stat_date"] == validate_date]
test = df.loc[df["stat_date"] != validate_date]
features = ["ucity_id","clevel1_id","ccity_name","device_type","manufacturer","channel","top","time"]
train_values = train[features].values
train_labels = train[["y","z"]].values
test_values = test[features].values
test_labels = test[["y","z"]].values
ucity_id_max = len(ucity_id)
clevel1_id_max = len(clevel1_id)
ccity_name_max = len(ccity_name)
device_type_max = len(device_type)
manufacturer_max = len(manufacturer)
channel_max = len(channel)
top_max = len(top)
time_max = len(time)
return train_values,train_labels,test_values,test_labels,ucity_id_max,clevel1_id_max,ccity_name_max,\
device_type_max,manufacturer_max,channel_max,top_max,time_max
def get_inputs():
ucity_id = tf.placeholder(tf.int32, [None, 1], name="ucity_id")
clevel1_id = tf.placeholder(tf.int32, [None, 1], name="clevel1_id")
ccity_name = tf.placeholder(tf.int32, [None, 1], name="ccity_name")
device_type = tf.placeholder(tf.int32, [None, 1], name="device_type")
manufacturer = tf.placeholder(tf.int32, [None, 1], name="manufacturer")
channel = tf.placeholder(tf.int32, [None, 1], name="channel")
top = tf.placeholder(tf.int32, [None, 1], name="top")
time = tf.placeholder(tf.int32, [None, 1], name="time")
targets = tf.placeholder(tf.float32, [None, 2], name="targets")
LearningRate = tf.placeholder(tf.float32, name="LearningRate")
return ucity_id,clevel1_id,ccity_name,device_type,manufacturer,channel,top,time,targets,LearningRate
def define_embedding_layers(combiner,embed_dim,ucity_id, ucity_id_max, clevel1_id_max,clevel1_id,
ccity_name_max,ccity_name,device_type_max,device_type,manufacturer_max,
manufacturer,channel,channel_max,top,top_max,time,time_max):
ucity_id_embed_matrix = tf.Variable(tf.random_normal([ucity_id_max, embed_dim], 0, 0.001))
ucity_id_embed_layer = tf.nn.embedding_lookup(ucity_id_embed_matrix, ucity_id)
if combiner == "sum":
ucity_id_embed_layer = tf.reduce_sum(ucity_id_embed_layer, axis=1, keep_dims=True)
clevel1_id_embed_matrix = tf.Variable(tf.random_uniform([clevel1_id_max, embed_dim], 0, 0.001))
clevel1_id_embed_layer = tf.nn.embedding_lookup(clevel1_id_embed_matrix, clevel1_id)
if combiner == "sum":
clevel1_id_embed_layer = tf.reduce_sum(clevel1_id_embed_layer, axis=1, keep_dims=True)
ccity_name_embed_matrix = tf.Variable(tf.random_uniform([ccity_name_max, embed_dim], 0, 0.001))
ccity_name_embed_layer = tf.nn.embedding_lookup(ccity_name_embed_matrix,ccity_name)
if combiner == "sum":
ccity_name_embed_layer = tf.reduce_sum(ccity_name_embed_layer, axis=1, keep_dims=True)
device_type_embed_matrix = tf.Variable(tf.random_uniform([device_type_max, embed_dim], 0, 0.001))
device_type_embed_layer = tf.nn.embedding_lookup(device_type_embed_matrix, device_type)
if combiner == "sum":
device_type_embed_layer = tf.reduce_sum(device_type_embed_layer, axis=1, keep_dims=True)
manufacturer_embed_matrix = tf.Variable(tf.random_uniform([manufacturer_max, embed_dim], 0, 0.001))
manufacturer_embed_layer = tf.nn.embedding_lookup(manufacturer_embed_matrix, manufacturer)
if combiner == "sum":
manufacturer_embed_layer = tf.reduce_sum(manufacturer_embed_layer, axis=1, keep_dims=True)
channel_embed_matrix = tf.Variable(tf.random_uniform([channel_max, embed_dim], 0, 0.001))
channel_embed_layer = tf.nn.embedding_lookup(channel_embed_matrix, channel)
if combiner == "sum":
channel_embed_layer = tf.reduce_sum(channel_embed_layer, axis=1, keep_dims=True)
top_embed_matrix = tf.Variable(tf.random_uniform([top_max, embed_dim], 0, 0.001))
top_embed_layer = tf.nn.embedding_lookup(top_embed_matrix, top)
if combiner == "sum":
top_embed_layer = tf.reduce_sum(top_embed_layer, axis=1, keep_dims=True)
time_embed_matrix = tf.Variable(tf.random_uniform([time_max, embed_dim], 0, 0.001))
time_embed_layer = tf.nn.embedding_lookup(time_embed_matrix, time)
if combiner == "sum":
time_embed_layer = tf.reduce_sum(time_embed_layer, axis=1, keep_dims=True)
esmm_embedding_layer = tf.concat([ucity_id_embed_layer, clevel1_id_embed_layer,ccity_name_embed_layer,
device_type_embed_layer,manufacturer_embed_layer,channel_embed_layer,
top_embed_layer,time_embed_layer], axis=1)
esmm_embedding_layer = tf.reshape(esmm_embedding_layer, [-1, embed_dim * 8])
return esmm_embedding_layer
def define_ctr_layer(esmm_embedding_layer):
ctr_layer_1 = tf.layers.dense(esmm_embedding_layer, 200, activation=tf.nn.relu)
ctr_layer_2 = tf.layers.dense(ctr_layer_1, 80, activation=tf.nn.relu)
ctr_layer_3 = tf.layers.dense(ctr_layer_2, 2) # [nonclick, click]
ctr_prob = tf.nn.softmax(ctr_layer_3) + 0.00000001
return ctr_prob
def define_cvr_layer(esmm_embedding_layer):
cvr_layer_1 = tf.layers.dense(esmm_embedding_layer, 200, activation=tf.nn.relu)
cvr_layer_2 = tf.layers.dense(cvr_layer_1, 80, activation=tf.nn.relu)
cvr_layer_3 = tf.layers.dense(cvr_layer_2, 2) # [nonbuy, buy]
cvr_prob = tf.nn.softmax(cvr_layer_3) + 0.00000001
return cvr_prob
def define_ctr_cvr_layer(esmm_embedding_layer):
layer_1 = tf.layers.dense(esmm_embedding_layer, 128 , activation=tf.nn.relu)
layer_2 = tf.layers.dense(layer_1, 16, activation=tf.nn.relu)
layer_3 = tf.layers.dense(layer_2, 2)
ctr_prob = tf.nn.softmax(layer_3) + 0.00000001
cvr_prob = tf.nn.softmax(layer_3) + 0.00000001
return ctr_prob, cvr_prob
if __name__ == '__main__':
embed_dim = 6
combiner = "sum"
train_values, train_labels, test_values, test_labels, ucity_id_max, clevel1_id_max, ccity_name_max, \
device_type_max, manufacturer_max, channel_max, top_max, time_max = get_data()
tf.reset_default_graph()
train_graph = tf.Graph()
with train_graph.as_default():
ucity_id, clevel1_id, ccity_name, device_type, manufacturer, channel, top, \
time, targets, LearningRate = get_inputs()
esmm_embedding_layer = define_embedding_layers(combiner,embed_dim,ucity_id, ucity_id_max, clevel1_id_max,clevel1_id,
ccity_name_max,ccity_name,device_type_max,device_type,manufacturer_max,
manufacturer,channel,channel_max,top,top_max,time,time_max)
ctr_prob, cvr_prob = define_ctr_cvr_layer(esmm_embedding_layer)
with tf.name_scope("loss"):
ctr_prob_one = tf.slice(ctr_prob, [0, 1], [-1, 1]) # [batch_size , 1]
cvr_prob_one = tf.slice(cvr_prob, [0, 1], [-1, 1]) # [batchsize, 1 ]
ctcvr_prob_one = ctr_prob_one * cvr_prob_one # [ctr*cvr]
ctcvr_prob = tf.concat([1 - ctcvr_prob_one, ctcvr_prob_one], axis=1)
ctr_label = tf.slice(targets, [0, 0], [-1, 1]) # target: [click, buy]
ctr_label = tf.concat([1 - ctr_label, ctr_label], axis=1) # [1-click, click]
cvr_label = tf.slice(targets, [0, 1], [-1, 1])
ctcvr_label = tf.concat([1 - cvr_label, cvr_label], axis=1)
# 单列，判断Click是否=1
ctr_clk = tf.slice(targets, [0, 0], [-1, 1])
ctr_clk_dup = tf.concat([ctr_clk, ctr_clk], axis=1)
# clicked subset CVR loss
cvr_loss = - tf.multiply(tf.log(cvr_prob) * ctcvr_label, ctr_clk_dup)
# batch CTR loss
ctr_loss = - tf.log(ctr_prob) * ctr_label # -y*log(p)-(1-y)*log(1-p)
# batch CTCVR loss
ctcvr_loss = - tf.log(ctcvr_prob) * ctcvr_label
# loss = tf.reduce_mean(ctr_loss + ctcvr_loss + cvr_loss)
# loss = tf.reduce_mean(ctr_loss + ctcvr_loss)
# loss = tf.reduce_mean(ctr_loss + cvr_loss)
loss = tf.reduce_mean(cvr_loss)
ctr_loss = tf.reduce_mean(ctr_loss)
cvr_loss = tf.reduce_mean(cvr_loss)
ctcvr_loss = tf.reduce_mean(ctcvr_loss)
# 优化损失
# train_op = tf.train.AdamOptimizer(lr).minimize(loss) #cost
global_step = tf.Variable(0, name="global_step", trainable=False)
optimizer = tf.train.AdamOptimizer(lr)
gradients = optimizer.compute_gradients(loss) # cost
train_op = optimizer.apply_gradients(gradients, global_step=global_step)