-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathdeepFmMTL2.py
132 lines (99 loc) · 5.91 KB
/
deepFmMTL2.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
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2019/8/22 11:27
# @Author : Daishijun
# @Site :
# @File : deepFmMTL2.py
# @Software : PyCharm
import tensorflow as tf
import pandas as pd
from sklearn.preprocessing import LabelEncoder, MinMaxScaler
from sklearn.metrics import accuracy_score, log_loss, roc_auc_score
from sklearn.model_selection import train_test_split
# from deepctr.models import xDeepFM, DeepFM, DCN
from deepFMwithMMoE2 import DeepFM
from deepctr.inputs import SparseFeat, DenseFeat, get_fixlen_feature_names
# from keras.optimizers import Adam
from tensorflow.python.keras.optimizers import Adam
# from tensorfl import Adam
from tensorflow.python.keras.callbacks import EarlyStopping
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "3"
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
session = tf.Session(config=config)
CHECKPOINT_ROOT_DIR ='./checkpoint/Experiment'
print(os.path.isdir(CHECKPOINT_ROOT_DIR))
data = pd.read_csv('./data/final_track2_train.txt', sep='\t', names=[
'uid', 'user_city', 'item_id', 'author_id', 'item_city', 'channel', 'finish', 'like', 'music_id', 'device', 'time', 'duration_time'])
sparse_features = ['uid', 'user_city', 'item_id', 'author_id', 'item_city', 'channel', 'music_id', 'device']
dense_features = ['time', 'duration_time']
data[sparse_features] = data[sparse_features].fillna('-1', )
data[dense_features] = data[dense_features].fillna(0,)
# target = ['finish']
target = ['finish','like']
for feat in sparse_features:
lbe = LabelEncoder()
data[feat] = lbe.fit_transform(data[feat])
mms = MinMaxScaler(feature_range=(0, 1))
data[dense_features] = mms.fit_transform(data[dense_features])
sparse_feature_columns = [SparseFeat(feat, data[feat].nunique()) #(特征名, 特征不同取值个数)生成SparseFeat对象，name == 特征名，dimension==该特征不同取值个数， dtype ==int32
for feat in sparse_features]
dense_feature_columns = [DenseFeat(feat, 1) #（特征名， dimension==1） 数据dtype == float32
for feat in dense_features]
dnn_feature_columns = sparse_feature_columns + dense_feature_columns
linear_feature_columns = sparse_feature_columns + dense_feature_columns
## 这里有多余的步骤，该方法中间为每个特征设置了Input层，但是没有返回，只返回了特征名称list，其实可以直接从上面的两个list合并得到。
feature_names = get_fixlen_feature_names(linear_feature_columns + dnn_feature_columns)
train, test = train_test_split(data, test_size=0.1)
train_model_input = [train[name] for name in feature_names]
test_model_input = [test[name] for name in feature_names]
train_labels = [train[target[0]].values, train[target[1]].values]
# test_labels = [test[target[0]].values, test[target[1]].values]
from tensorflow.python.keras import backend as K
def auc(y_true, y_pred):
auc = tf.metrics.auc(y_true, y_pred)[1]
K.get_session().run(tf.local_variables_initializer())
return auc
#model = xDeepFM(linear_feature_columns, dnn_feature_columns, task='binary')
model = DeepFM(linear_feature_columns, dnn_feature_columns, task='binary',use_fm=True,
dnn_hidden_units=(128,128), dnn_dropout=0)
# model = DCN(dnn_feature_columns, embedding_size=8)
model.compile(Adam(lr=0.0001), "binary_crossentropy", metrics=['binary_crossentropy', auc], loss_weights=[0.6,0.4])
#es = EarlyStopping(monitor='val_binary_crossentropy')
checkpoint_path = os.path.join(CHECKPOINT_ROOT_DIR, "DeepFM_finish_like_HS", "DeepFM_finish_like_HS-{epoch:04d}.ckpt")
checkpoint_dir = os.path.dirname(checkpoint_path)
if not os.path.isdir(checkpoint_dir):
os.mkdir(checkpoint_dir)
# Create checkpoint callback
callbacks = [
# tf.keras.callbacks.ModelCheckpoint(checkpoint_path, verbose=1, monitor='val_finish_output_auc',
# save_weights_only=True, save_best_only=True, mode='max'),
tf.keras.callbacks.EarlyStopping(monitor='val_finish_output_auc', patience=2, verbose=1)
]
# latest = tf.train.latest_checkpoint(checkpoint_dir)
# if latest is not None:
# model.load_weights(latest)
# print("load model from %s"%(latest))
history = model.fit(train_model_input, {"finish_output":train["finish"].values, "like_output":train["like"].values},
batch_size=4096, epochs=10, verbose=1, validation_split=0.2,
callbacks = callbacks
)
# latest = tf.train.latest_checkpoint(checkpoint_dir)
# if latest is not None:
# model.load_weights(latest)
# print("load model from %s"%(latest))
# history = model.fit(train_model_input, y={"finish_output":train["finish"].values, "like_output":train["like"].values}, validation_split=0.3, callbacks=[EarlyStopping(monitor='val_finish_output_auc', patience=2, verbose=0, mode='auto')],
# batch_size=4096, epochs=10, verbose=1)
pred_ans_finish, pred_ans_like = model.predict(test_model_input, batch_size=2**14)
pred_finish = (pred_ans_finish*2).astype(int)
pred_like = (pred_ans_like*2).astype(int)
# print("test accuracy", round(accuracy_score(test[target].values, pred_finish), 4))
# print("test LogLoss", round(log_loss(test[target].values, pred_ans_finish), 4))
# print("test AUC", round(roc_auc_score(test[target].values, pred_ans_like), 4))
print('test accuracy ==> finish:{} \t like:{}'.format(round(accuracy_score(test['finish'].values, pred_finish), 4),\
round(accuracy_score(test['like'].values, pred_like), 4)))
print('test LogLoss ==> finish:{} \t like:{}'.format(round(log_loss(test['finish'].values, pred_ans_finish), 4),\
round(log_loss(test['like'].values, pred_ans_like), 4)))
print('test AUC ==> finish:{} \t like:{}'.format(round(roc_auc_score(test['finish'].values, pred_ans_finish), 4),\
round(roc_auc_score(test['like'].values, pred_ans_like), 4)))