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update_model_3.dsin.s5.py
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#-*- coding:utf-8 -*-
import tensorflow as tf
from keras import backend as K
import keras
import random
import os
import pandas as pd
import numpy as np
from queue import Queue
from typing import List, Tuple
from threading import Thread
from rnn import dynamic_rnn
from tensorflow.python.ops.rnn_cell import GRUCell
from modules import embedding,positional_encoding, multihead_attention,\
feedforward,label_smoothing
from data_iter import DataIter, FIELD, HbaseDataIterUpdate
from map2int import TO_MAP, MAP
from transport_model import trans_model
from utils import *
from Dice import dice
import sys
import datetime
import csv
import logging
import json
import traceback
import fcntl
from deepctr.layers.sequence import AttentionSequencePoolingLayer
flags = tf.app.flags
#flags.DEFINE_integer()
flags.DEFINE_float("learning_rate",0.001,"lr []")
flags.DEFINE_float("decay_rate",0.8,"lr dacay rate")
flags.DEFINE_integer("decay_step",10000,"lr decay step")
flags.DEFINE_string("logfile",'logs.dsin.s3.out',"log file to save")
flags.DEFINE_boolean("is_training",True,"True for training, False for testing")
flags.DEFINE_string("csvfile","test_metric_day",'csv file to save test metric')
flags.DEFINE_integer("batch_size",128,'batch sizes ')
FLAGS = flags.FLAGS #y以上还可以放在之后
#__file__
FLAGS.csvfile = FLAGS.csvfile + '_dsin_s3.csv'
PATH = os.path.dirname(os.path.abspath(__file__))
#__file__
logging.basicConfig(filename=os.path.join(PATH,FLAGS.logfile),filemode='a', # w
format='%(asctime)s %(name)s:%(levelname)s:%(message)s',datefmt="%d-%m-%Y %H:%M:%S",
level=logging.DEBUG)
AD_BOUND = 10000
USER_BOUND = 10000000
USER_SUM = 10000
AD_SUM = 100000
CITY_SUM = 5000
EMBEDDING_DIM = 128
ATTENTION_SIZE = 128
ABILITY_DIM = 5
AD_IMG_VALUE_DIM = 40
AD_IMG_LABEL_DIM = 20
HIDDEN_SIZE =128
USER_API_LEN = 10
USER_API_SUM_A = 100
USER_API_SUM_B = 200
USER_API_SUM_C = 800
#文件
FILE_USER_API = os.path.join(PATH,"userapi.json")
class BaseModel(object):
def __init__(self):
pass
def build_inputs(self):
"""
base input !!!
:return:
"""
with self.graph.as_default():
with tf.name_scope('Inputs'):
#？？
self.target_ph = tf.placeholder(tf.float32, [None, None], name='target_ph')
self.lr = tf.placeholder(tf.float64, [])
#用户ID mid？ 广告id 具体样式
self.uid_ph = tf.placeholder(tf.int32, [None, ], name="uid_batch_ph")
self.mid_ph = tf.placeholder(tf.int32, [None, ], name="mid_batch_ph")
self.mobile_ph = tf.placeholder(tf.int32, [None, ], name="mobile_batch_ph")
self.province_ph = tf.placeholder(tf.int32, shape=[None, ], name="province_ph")
self.city_ph = tf.placeholder(tf.int32, shape=[None, ], name="city_ph")
self.grade_ph = tf.placeholder(tf.int32, shape=[None, ], name="grade_ph")
self.chinese_ph = tf.placeholder(tf.int32, shape=[None, ], name="chinese_ph")
self.english_ph = tf.placeholder(tf.int32, shape=[None, ], name="english_ph")
self.math_ph = tf.placeholder(tf.int32, shape=[None, ], name="math_ph")
self.purchase_ph = tf.placeholder(tf.int32, shape=[None, ], name="purchase_ph")
self.activity_ph = tf.placeholder(tf.int32, shape=[None, ], name="activity_ph")
self.freshness_ph = tf.placeholder(tf.int32, shape=[None, ], name="freshness_ph")
self.hour_ph = tf.placeholder(tf.int32, shape=[None, ], name="hour_ph")
with tf.name_scope("Embedding_layer"):
self.uid_embeddings_var = tf.get_variable("uid_embedding_var", [USER_SUM, EMBEDDING_DIM])
self.uid_embedded = tf.nn.embedding_lookup(self.uid_embeddings_var, self.uid_ph)
self.mid_embeddings_var = tf.get_variable("mid_embedding_var", [AD_SUM, EMBEDDING_DIM])
self.mid_embedded = tf.nn.embedding_lookup(self.mid_embeddings_var, self.mid_ph)
#3 区号
self.mobile_embeddings_var = tf.get_variable("mobile_embedding_var", [3, 5])
self.mobile_embedded = tf.nn.embedding_lookup(self.mobile_embeddings_var, self.mobile_ph)
self.province_embeddings_var = tf.get_variable("province_embedding_var", [40, EMBEDDING_DIM])
self.province_embedded = tf.nn.embedding_lookup(self.province_embeddings_var, self.province_ph)
self.city_embeddings_var = tf.get_variable("city_embedding_var", [CITY_SUM, EMBEDDING_DIM])
self.city_embedded = tf.nn.embedding_lookup(self.city_embeddings_var, self.city_ph)
self.grade_embeddings_var = tf.get_variable("grade_embedding_var", [102, EMBEDDING_DIM])
self.grade_embedded = tf.nn.embedding_lookup(self.grade_embeddings_var, self.grade_ph)
self.chinese_embeddings_var = tf.get_variable("chinese_embedding_var", [6, ABILITY_DIM])
self.chinese_embedded = tf.nn.embedding_lookup(self.chinese_embeddings_var, self.chinese_ph)
self.math_embeddings_var = tf.get_variable("math_embedding_var", [6, ABILITY_DIM])
self.math_embedded = tf.nn.embedding_lookup(self.math_embeddings_var, self.math_ph)
self.english_embeddings_var = tf.get_variable("english_embedding_var", [6, ABILITY_DIM])
self.english_embedded = tf.nn.embedding_lookup(self.english_embeddings_var, self.english_ph)
self.purchase_embeddings_var = tf.get_variable("purchase_embedding_var", [6, ABILITY_DIM])
self.purchase_embedded = tf.nn.embedding_lookup(self.purchase_embeddings_var, self.purchase_ph)
self.activity_embeddings_var = tf.get_variable("activity_embedding_var", [6, ABILITY_DIM])
self.activity_embedded = tf.nn.embedding_lookup(self.activity_embeddings_var, self.activity_ph)
self.freshness_embeddings_var = tf.get_variable("freshness_embedding_var", [8, ABILITY_DIM])
self.freshness_embedded = tf.nn.embedding_lookup(self.freshness_embeddings_var, self.freshness_ph)
self.hour_embeddings_var = tf.get_variable("hour_embedding_var", [25, ABILITY_DIM])
self.hour_embedded = tf.nn.embedding_lookup(self.hour_embeddings_var, self.hour_ph)
def build_fcn_net(self, inp, use_dice=False):
with self.graph.as_default():
bn1 = tf.layers.batch_normalization(inputs=inp, name='bn1')
dnn1 = tf.layers.dense(bn1, 200, activation=None, name='f1')
#d1 = tf.layers.dropout(d1, rate=0.5, training=self.is_training_mode)
if use_dice:
dnn1 = dice(dnn1, name='dice_1')
else:
dnn1 = prelu(dnn1, 'prelu1')
dnn2 = tf.layers.dense(dnn1, 80, activation=None, name='f2')
#d1 = tf.layers.dropout(d1, rate=0.5, training=self.is_training_mode)
#tf.nn.dropout(
if use_dice:
dnn2 = dice(dnn2, name='dice_2')
else:
dnn2 = prelu(dnn2, 'prelu2')
dnn3 = tf.layers.dense(dnn2, 2, activation=None, name='f3')
self.y_hat = tf.nn.softmax(dnn3) + 0.00000001
with tf.name_scope('Metrics'):
# Cross-entropy loss and optimizer initialization
ctr_loss = - tf.reduce_mean(tf.log(self.y_hat) * self.target_ph)
self.loss = ctr_loss
self.optimizer = tf.train.AdamOptimizer(learning_rate=self.lr).minimize(self.loss)
# Accuracy metric
# 四舍五入，0.5以上的都是1 ，，，，但是正样本太少了， 阈值应该设置高的 0.85
# 数据分布 预测分布
self.accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.round(self.y_hat), self.target_ph), tf.float32))
def train(self, sess, inps):
pass
def train_with_dict(self, sess, train_data):
pass
def calculate(self, sess, inps):
pass
def save(self, sess, path):
pass
def restore(self, sess, path):
pass
# lastesd = tf.train.latest_checkpoint(path)
# saver = tf.train.Saver()
# saver.restore(sess, save_path=lastesd)
# print('model restored from %s' % lastesd)
def build_tensor_info(self):
"""
base tensor_info
:return:
"""
if len(self.tensor_info) > 0:
print("will clear items in tensor_info")
self.tensor_info.clear()
base_ph = ["uid_ph", "mid_ph", "mobile_ph",
"province_ph", "city_ph", "grade_ph",
"math_ph", "english_ph", "chinese_ph",
"purchase_ph", "activity_ph", "freshness_ph",
"hour_ph"
]
for i in base_ph:
self.tensor_info[i] = tf.saved_model.build_tensor_info(getattr(self, i))
def save_serving_model(self, sess, dir_path=None, version: int = 1):
if dir_path is None:
print("using the /current_path/model-serving for dir_path")
dir_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "model-serving")
if not os.path.exists(dir_path):
os.makedirs(dir_path)
self.build_tensor_info()
assert len(self.tensor_info) > 0, "when saving model for serving, tensor_info can't empty!"
prediction_signature = (
tf.saved_model.build_signature_def(
inputs=self.tensor_info.copy(),
outputs={"outputs": tf.saved_model.build_tensor_info(
self.y_hat)},
method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME)
)
export_path = os.path.join(dir_path, str(version))
#删除之前保存的，只保留最新的
for i in range(version):
pth = os.path.join(dir_path,str(i))
if os.path.exists(pth):
os.system("rm -rf {}".format(pth))
try:
builder = tf.saved_model.builder.SavedModelBuilder(export_path)
builder.add_meta_graph_and_variables(
sess, [tf.saved_model.tag_constants.SERVING],
signature_def_map={
"serving": prediction_signature,
},
strip_default_attrs=True
)
builder.save()
except:
pass
class UpdateModel(BaseModel):
def __init__(self):
self.graph = tf.Graph()
self.tensor_info = {}
self.build_inputs()
# with self.graph.as_default():
# with tf.name_scope('Attention_layer'):
# attention_output = din_attention(self.item_eb, self.item_his_eb, ATTENTION_SIZE, self.mask_ph)
# att_fea = tf.reduce_sum(attention_output, 1)
def build_inputs(self):
super(UpdateModel, self).build_inputs()
with self.graph.as_default():
with tf.name_scope('Inputs'):
self.mid_his_ph = tf.placeholder(tf.int32, [None, None], name='mid_his_ph')
self.mask_ph = tf.placeholder(tf.float32, [None, None], name='mask_ph')
self.seq_len_ph = tf.placeholder(tf.int32, [None], name='seq_len_ph')
with tf.name_scope("Embedding_layer"):
self.mid_his_embedded = tf.nn.embedding_lookup(self.mid_embeddings_var, self.mid_his_ph)
self.item_eb = self.mid_embedded
self.item_his_eb = self.mid_his_embedded
self.item_his_eb_sum = tf.reduce_sum(self.item_his_eb, 1)
def train_with_dict(self, sess, train_data):
assert isinstance(train_data, dict), "\"train_data\" must be dict!"
loss, accuracy, _ = sess.run(
[self.loss, self.accuracy, self.optimizer],
feed_dict={
# self.uid_ph: train_data["uid_ph"],
self.mid_ph: train_data["mid_ph"],
self.mobile_ph: train_data["mobile_ph"],
self.province_ph: train_data["province_ph"],
self.city_ph: train_data["city_ph"],
self.grade_ph: train_data["grade_ph"],
self.math_ph: train_data["math_ph"],
self.english_ph: train_data["english_ph"],
self.chinese_ph: train_data["chinese_ph"],
self.purchase_ph: train_data["purchase_ph"],
self.activity_ph: train_data["activity_ph"],
self.freshness_ph: train_data["freshness_ph"],
self.hour_ph: train_data["hour_ph"],
self.mid_his_ph: train_data["mid_his_ph"],
self.mask_ph: train_data["mask_ph"],
self.seq_len_ph: train_data["seq_len_ph"],
self.target_ph: train_data["target_ph"],
self.lr: train_data["lr"], }
)
return loss, accuracy
def calculate(self, sess, inps):
probs, loss, accuracy, _ = sess.run(
[self.y_hat, self.loss, self.accuracy, self.optimizer],
feed_dict={
# self.uid_ph: inps[0],
self.mid_ph: inps[1],
self.mobile_ph: inps[2],
self.province_ph: inps[3],
self.city_ph: inps[4],
self.grade_ph: inps[5],
self.math_ph: inps[6],
self.english_ph: inps[7],
self.chinese_ph: inps[8],
self.purchase_ph: inps[9],
self.activity_ph: inps[10],
self.freshness_ph: inps[11],
self.hour_ph: inps[12],
self.mid_his_ph: inps[13],
self.mask_ph: inps[14],
self.seq_len_ph: inps[15],
self.target_ph: inps[16],
}
)
return probs, loss, accuracy
def build_tensor_info(self):
super(UpdateModel, self).build_tensor_info()
add_ph = ["mid_his_ph", "mask_ph", "seq_len_ph"]
for i in add_ph:
self.tensor_info[i] = tf.saved_model.build_tensor_info(getattr(self, i))
class UpdateModel2(UpdateModel):
def __init__(self):
self.graph = tf.Graph()
self.tensor_info = {}
self.build_inputs()
with self.graph.as_default():
self.saver = tf.train.Saver(max_to_keep=1)
#dien
with tf.name_scope('rnn_1'):
rnn_outputs, _ = dynamic_rnn(GRUCell(HIDDEN_SIZE), inputs=self.item_his_eb,
sequence_length=self.seq_len_ph, dtype=tf.float32,
scope="gru1")
with tf.name_scope('Attention_layer_1'):
att_outputs, alphas = din_fcn_attention(self.item_eb, rnn_outputs, ATTENTION_SIZE, self.mask_ph,
softmax_stag=1, stag='1_1', mode='LIST', return_alphas=True)
with tf.name_scope('rnn_2'):
rnn_outputs2, final_state2 = dynamic_rnn(VecAttGRUCell(HIDDEN_SIZE), inputs=rnn_outputs,
att_scores=tf.expand_dims(alphas, -1),
sequence_length=self.seq_len_ph, dtype=tf.float32,
scope="gru2")
#dsin
#with tf.name_scope("Self_Attention_layer"):
#out = transformer(self.user_api_all_eb)
hidden_units = 128 #嵌入向量长度 原为128
num_blocks = 1
num_heads = 2
dropout_rate = 0.1
with tf.variable_scope("encoder"):
# Embedding
self.enc_a = embedding(self.user_api_a_ph,
vocab_size= USER_API_SUM_A, # len(de2idx), 200
num_units = hidden_units, #128
zero_pad=True, # 让padding一直是0
scale=True,
scope="enc_embed_a")
#self.enc = self.user_api_all_eb # 128 * 30 * 512 批次 词数量 词嵌入长度
self.enc_a += tf.cast(positional_encoding( #N=FLAGS.batch_size,
N=tf.shape(self.user_api_a_ph)[0],
T= USER_API_LEN,
num_units = hidden_units,
zero_pad = False,
scale = False,
scope='enc_pe_a'),tf.float32)
self.enc_b = embedding(self.user_api_b_ph,
vocab_size= USER_API_SUM_B, # len(de2idx), 200
num_units = hidden_units,
zero_pad=True, # 让padding一直是0
scale=True,
scope="enc_embed_b")
self.enc_b += tf.cast(positional_encoding(N=tf.shape(self.user_api_b_ph)[0],
T= USER_API_LEN,
num_units = hidden_units,
zero_pad = False,
scale = False,
scope='enc_pe_b'),tf.float32)
self.enc_c = embedding(self.user_api_c_ph,
vocab_size= USER_API_SUM_C, # len(de2idx), 200
num_units = hidden_units,
zero_pad=True, # 让padding一直是0
scale=True,
scope="enc_embed_c")
self.enc_c += tf.cast(positional_encoding(N=tf.shape(self.user_api_c_ph)[0],
T= USER_API_LEN,
num_units = hidden_units,
zero_pad = False,
scale = False,
scope='enc_pe_c'),tf.float32)
##Drop out
#self.enc = tf.layers.dropout(self.enc,rate = dropout_rate,
# training = tf.convert_to_tensor(is_training)
#)
# 128 * 10 * 512 128 * 30 * 512
#self.enc = tf.concat([self.enc_a,self.enc_b,self.enc_c],-2)
## Blocks
for i in range(num_blocks):
with tf.variable_scope("num_blocks_a_{}".format(i)):
### MultiHead Attention
#[128, 30, 512] 不变
self.enc_a = multihead_attention(queries = self.enc_a,
keys = self.enc_a,
num_units = hidden_units,
num_heads = num_heads,
dropout_rate = dropout_rate,
#is_training = is_training,
causality = False
)
self.enc_a = feedforward(self.enc_a,num_units = [4 * hidden_units,hidden_units])
for i in range(num_blocks):
with tf.variable_scope("num_blocks_b_{}".format(i)):
### MultiHead Attention
#[128, 30, 512] 不变
self.enc_b = multihead_attention(queries = self.enc_b,
keys = self.enc_b,
num_units = hidden_units,
num_heads = num_heads,
dropout_rate = dropout_rate,
#is_training = is_training,
causality = False
)
self.enc_b = feedforward(self.enc_b,num_units = [4 * hidden_units,hidden_units])
for i in range(num_blocks):
with tf.variable_scope("num_blocks_c_{}".format(i)):
### MultiHead Attention
#[128, 30, 512] 不变
self.enc_c = multihead_attention(queries = self.enc_c,
keys = self.enc_c,
num_units = hidden_units,
num_heads = num_heads,
dropout_rate = dropout_rate,
#is_training = is_training,
causality = False
)
self.enc_c = feedforward(self.enc_c,num_units = [4 * hidden_units,hidden_units])
# Final linear projection
#self.logits = tf.layers.dense(self.dec,USER_API_LEN*3))
#print(self.enc.get_shape().as_list())
# self.user_api_a_eb_sum = tf.reduce_sum(self.enc_a,-2)
# self.user_api_b_eb_sum = tf.reduce_sum(self.enc_b,-2)
# self.user_api_c_eb_sum = tf.reduce_sum(self.enc_c,-2)
self.enc = tf.concat([self.enc_a,self.enc_b,self.enc_c],-2)
#din dsin
hist = AttentionSequencePoolingLayer(att_hidden_size, att_activation,
weight_normalization=att_weight_normalization, supports_masking=True)([
query_emb, keys_emb])
interest_attention_layer = AttentionSequencePoolingLayer(att_hidden_units=(64, 16), weight_normalization=True,
supports_masking=False)(
[query_emb, sess_fea, user_sess_length])
with tf.name_scope('Attention_layer'):
attention_output = din_attention(self.item_eb, self.item_his_eb, ATTENTION_SIZE, self.mask_ph)
att_fea = tf.reduce_sum(attention_output, 1)
#注意 -1 ，即 每个特征最终的嵌入特征空间大小128 都不一样也没关系啊
#10个特征 B*( 128*10) ， B*(12+800+200)
inp = tf.concat(
[self.item_eb, self.item_his_eb_sum, self.item_eb * self.item_his_eb_sum,
final_state2,
self.mobile_embedded,
self.province_embedded,
self.city_embedded,
self.grade_embedded,
self.chinese_embedded,
self.math_embedded,
self.english_embedded,
self.purchase_embedded,
self.activity_embedded,
self.freshness_embedded,
self.hour_embedded,
self.ad_img_eb_sum,
self.user_api_a_eb_sum,
self.user_api_b_eb_sum,
self.user_api_c_eb_sum
], -1)
self.build_fcn_net(inp, use_dice=True,)
def build_inputs(self):
super(UpdateModel2, self).build_inputs()
with self.graph.as_default():
with tf.name_scope('Inputs'):
#img AD 特征 N*F
self.ad_label_ph = tf.placeholder(tf.int32,[None,None],name='ad_label_ph')
#特征下的类别 N*F
self.ad_value_ph = tf.placeholder(tf.int32,[None,None],name='ad_value_ph')
#self.user_api_a_ph = tf.placeholder(tf.int32,[FLAGS.batch_size,USER_API_LEN],name= "user_api_a_ph")
self.user_api_a_ph = tf.placeholder(tf.int32,[None,None],name= "user_api_a_ph")
self.user_api_b_ph = tf.placeholder(tf.int32,[None,None],name= "user_api_b_ph")
self.user_api_c_ph = tf.placeholder(tf.int32,[None,None],name= "user_api_c_ph")
with tf.name_scope("Embedding_layer"):
self.ad_img_embeddings_var = tf.get_variable("ad_img_embedding_var", [AD_IMG_LABEL_DIM,AD_IMG_VALUE_DIM,EMBEDDING_DIM])
#索引 idx
self.ad_img_embedded = tf.nn.embedding_lookup(self.ad_img_embeddings_var, self.ad_label_ph)
self.ad_value_ph_ohot = tf.one_hot(self.ad_value_ph,depth=AD_IMG_VALUE_DIM,axis=-1)
self.ad_value_ph_ohot = tf.expand_dims(self.ad_value_ph_ohot,axis=-2)
#n*7*8*128 就是对应相乘，
self.ad_img_embedded = tf.matmul(self.ad_value_ph_ohot ,self.ad_img_embedded)
self.ad_img_eb = self.ad_img_embedded # none*n*1*128
self.ad_img_eb = tf.squeeze(self.ad_img_eb,[-2]) #n*n*128
self.ad_img_eb_sum = tf.reduce_sum(self.ad_img_eb,-2)
# with tf.name_scope("Embedding_layer"):
# self.user_api_a_var = tf.get_variable("user_api_a_var", [USER_API_SUM_A, EMBEDDING_DIM*4])
# self.user_api_a_eb = tf.nn.embedding_lookup(self.user_api_a_var, self.user_api_a_ph)
# self.user_api_b_var = tf.get_variable("user_api_b_var", [USER_API_SUM_B, EMBEDDING_DIM*4])
# self.user_api_b_eb = tf.nn.embedding_lookup(self.user_api_b_var, self.user_api_b_ph)
# self.user_api_c_var = tf.get_variable("user_api_c_var", [USER_API_SUM_C, EMBEDDING_DIM*4])
# self.user_api_c_eb = tf.nn.embedding_lookup(self.user_api_c_var, self.user_api_c_ph)
# #B*30*128
# self.user_api_all_eb = tf.concat([self.user_api_a_eb,self.user_api_b_eb,self.user_api_c_eb],-2)
def train(self, sess, inps):
loss, accuracy, _ = sess.run(
[self.loss, self.accuracy, self.optimizer],
feed_dict={
# self.uid_ph: inps[0],
self.mid_ph: inps[1],
self.mobile_ph: inps[2],
self.province_ph: inps[3],
self.city_ph: inps[4],
self.grade_ph: inps[5],
self.math_ph: inps[6],
self.english_ph: inps[7],
self.chinese_ph: inps[8],
self.purchase_ph: inps[9],
self.activity_ph: inps[10],
self.freshness_ph: inps[11],
self.hour_ph: inps[12],
self.mid_his_ph: inps[13],
self.mask_ph: inps[14],
self.seq_len_ph: inps[15],
self.target_ph: inps[16],
self.lr: inps[17],
#单独喂入广告特征
self.ad_label_ph: inps[18],
self.ad_value_ph: inps[19],
self.user_api_a_ph: inps[20],
self.user_api_b_ph: inps[21],
self.user_api_c_ph: inps[22],
}
)
return loss, accuracy
def test(self, sess, inps):
prob, loss, acc = self.calculate(sess, inps)
return prob, loss, acc
def train_with_dict(self, sess, train_data):
assert isinstance(train_data, dict), "\"train_data\" must be dict!"
loss, accuracy, _ = sess.run(
[self.loss, self.accuracy, self.optimizer],
feed_dict={
# self.uid_ph: train_data["uid_ph"],
self.mid_ph: train_data["mid_ph"],
self.mobile_ph: train_data["mobile_ph"],
self.province_ph: train_data["province_ph"],
self.city_ph: train_data["city_ph"],
self.grade_ph: train_data["grade_ph"],
self.math_ph: train_data["math_ph"],
self.english_ph: train_data["english_ph"],
self.chinese_ph: train_data["chinese_ph"],
self.purchase_ph: train_data["purchase_ph"],
self.activity_ph: train_data["activity_ph"],
self.freshness_ph: train_data["freshness_ph"],
self.hour_ph: train_data["hour_ph"],
self.mid_his_ph: train_data["mid_his_ph"],
self.mask_ph: train_data["mask_ph"],
self.seq_len_ph: train_data["seq_len_ph"],
self.target_ph: train_data["target_ph"],
self.lr: train_data["lr"], }
)
return loss, accuracy
def calculate(self, sess, inps):
probs, loss, accuracy= sess.run(
[self.y_hat, self.loss, self.accuracy],
feed_dict={
# self.uid_ph: inps[0],
self.mid_ph: inps[1],
self.mobile_ph: inps[2],
self.province_ph: inps[3],
self.city_ph: inps[4],
self.grade_ph: inps[5],
self.math_ph: inps[6],
self.english_ph: inps[7],
self.chinese_ph: inps[8],
self.purchase_ph: inps[9],
self.activity_ph: inps[10],
self.freshness_ph: inps[11],
self.hour_ph: inps[12],
self.mid_his_ph: inps[13],
self.mask_ph: inps[14],
self.seq_len_ph: inps[15],
self.target_ph: inps[16],
#self.lr
#单独喂入广告特征
self.ad_label_ph: inps[17],
self.ad_value_ph: inps[18],
self.user_api_a_ph: inps[19],
self.user_api_b_ph: inps[20],
self.user_api_c_ph: inps[21]
}
)
return probs, loss, accuracy
def build_tensor_info(self):
super(UpdateModel2, self).build_tensor_info()
#ad img
add_ph = ["ad_label_ph","ad_value_ph","user_api_a_ph","user_api_b_ph","user_api_c_ph"]
for i in add_ph:
self.tensor_info[i] = tf.saved_model.build_tensor_info(getattr(self, i))
def save(self, sess, path):
pos = path.rfind("_")
pre = path[:pos+1]
num = int(path[pos+1:])
for i in range(num):
mdoelpath = pre+ str(i)+ ".meta"
pth = pre+ str(i)+ "*"
if os.path.exists(mdoelpath):
os.system("rm {}".format(pth))
self.saver.save(sess, save_path=path)
def restore(self, sess, path):
self.saver.restore(sess, save_path=path)
print('model restored from %s' % path)
def parse_his(x):
x = eval(x)
if len(x) == 0:
return []
return [abs(i) if i < AD_BOUND else i - 90000 for i in x]
def process_rbehavior_split(r_behavior):
r_behavior = r_behavior.replace("[","[\"").replace(", ","\",\"").replace("]","\"]")
# str - list
r_behavior = eval(r_behavior)
#logging.info("r_behavior {}".format(r_behavior))
try:
user_api_all={}
if os.path.exists(FILE_USER_API):
#user_api_all={}
with open(FILE_USER_API,'r+') as jf:
fcntl.flock(jf.fileno(),fcntl.LOCK_EX)
data = json.load(jf)
user_api_a = data["user_api_a"]
user_api_b = data["user_api_b"]
user_api_c = data["user_api_c"]
for api in r_behavior:
values_tmp = api.split("/")
if values_tmp == ['']:
user_api_a[values_tmp[0]] = 1
user_api_b[values_tmp[0]] = 1
user_api_c[values_tmp[0]] = 1
else:
if '' in user_api_a:
if values_tmp[1] not in user_api_a:
user_api_a[values_tmp[1]] = len(user_api_a)+1
if values_tmp[2] not in user_api_b:
user_api_b[values_tmp[2]] = len(user_api_b)+1
if values_tmp[3] not in user_api_c:
user_api_c[values_tmp[3]] = len(user_api_c)+1
else:
if values_tmp[1] not in user_api_a:
user_api_a[values_tmp[1]] = len(user_api_a)+2
if values_tmp[2] not in user_api_b:
user_api_b[values_tmp[2]] = len(user_api_b)+2
if values_tmp[3] not in user_api_c:
user_api_c[values_tmp[3]] = len(user_api_c)+2
jf.seek(0)
user_api_all["user_api_a"] = user_api_a
user_api_all["user_api_b"] = user_api_b
user_api_all["user_api_c"] = user_api_c
json.dump(user_api_all,jf)
jf.flush()
fcntl.flock(jf.fileno(),fcntl.LOCK_UN)
except Exception as e:
logging.info("user_api_all error: {}".format(e))
rbehavior_int_a,rbehavior_int_b,rbehavior_int_c = [],[],[]
for api in r_behavior:
api_sp = api.split("/")
if api_sp == ['']:
rbehavior_int_a.append(user_api_all["user_api_a"][''])
rbehavior_int_b.append(user_api_all["user_api_b"][''])
rbehavior_int_c.append(user_api_all["user_api_c"][''])
else:
rbehavior_int_a.append(user_api_all["user_api_a"][api_sp[1]])
rbehavior_int_b.append(user_api_all["user_api_b"][api_sp[2]])
rbehavior_int_c.append(user_api_all["user_api_c"][api_sp[3]])
return (rbehavior_int_a,rbehavior_int_b,rbehavior_int_c)
def handle(data: pd.DataFrame) -> Tuple[List, List]:
# data = data.drop(columns=["school_id", "county_id"], )
to_int = ["mobile_os", "province_id",
"grade_id", "city_id",
"ad_id", "user_id", "log_hourtime",
]
for i in to_int:
data[i] = data[i].astype(int)
for i in TO_MAP:
data[i] = data[i].map(lambda x: MAP[i].get(x, 0))
data["ad_id"] = data["ad_id"].map(lambda x: abs(x) if x < AD_BOUND else x - 90000)
data["user_id"] = data["user_id"].map(lambda x: abs(x) % 6 if x < USER_BOUND else x - USER_BOUND)
#这个是？？
data["rclick_ad"] = data["rclick_ad"].map(lambda x: parse_his(x))
#data['rencent_behavior'] = data['rencent_behavior'] .map(lambda x: process_rencent_behavior(x))
#Pans 一列变三列 lambda 返回多个值
data['rencent_behavior_all'] \
= data['rencent_behavior'].map(lambda x: process_rbehavior_split(x))
data['rencent_behavior_a'] = [ data['rencent_behavior_all'][i][0] for i in range(len(data['rencent_behavior_all']))]
data['rencent_behavior_b'] = [ data['rencent_behavior_all'][i][1] for i in range(len(data['rencent_behavior_all']))]
data['rencent_behavior_c'] = [ data['rencent_behavior_all'][i][2] for i in range(len(data['rencent_behavior_all']))]
to_select = ["user_id", "ad_id", "mobile_os",
"province_id", "city_id", "grade_id",
"math_ability", "english_ability", "chinese_ability",
"purchase_power", "activity_degree", "app_freshness",
"log_hourtime",
"rclick_ad",
"label_1","label_2","label_3","label_4","label_5","label_6","label_7",
"rencent_behavior_a","rencent_behavior_b","rencent_behavior_c"
]
#真的做成可自由扩展的，自适应扩展，那就检索 字符串匹配，"label_* 看有多少
feature, target = [], []
for row in data.itertuples(index=False):
tmp = []
for i in to_select:
tmp.append(getattr(row, i))
#其他不用转吧，因为喂入嵌入函数，就是索引值就可以4了，不用提前转one-hot，
if getattr(row, "is_click") == "0":
target.append([1, 0])
else:
target.append([0, 1])
feature.append(tmp)
return feature, target
def prepare_data(feature: List, target: List, choose_len: int = 0) -> Tuple:
user_id = np.array([fea[0] for fea in feature])
ad_id = np.array([fea[1] for fea in feature])
mobile = np.array([fea[2] for fea in feature])
province = np.array([fea[3] for fea in feature])
city = np.array([fea[4] for fea in feature])
grade = np.array([fea[5] for fea in feature])
math = np.array([fea[6] for fea in feature])
english = np.array([fea[7] for fea in feature])
chinese = np.array([fea[8] for fea in feature])
purchase = np.array([fea[9] for fea in feature])
activity = np.array([fea[10] for fea in feature])
freshness = np.array([fea[11] for fea in feature])
hour = np.array([fea[12] for fea in feature])
seqs_ad = [fea[13] for fea in feature]
lengths_xx = [len(i) for i in seqs_ad]
#ValueError: setting an array element with a sequence. 要转np.array
# 嵌套list不等长的时候 加 np.array 报错
rencent_behavior_a = [fea[21] for fea in feature]
rencent_behavior_b = [fea[22] for fea in feature]
rencent_behavior_c = [fea[23] for fea in feature]
if choose_len != 0:
new_seqs_ad = []
new_lengths_xx = []
for l_xx, fea in zip(lengths_xx, seqs_ad):
if l_xx > choose_len:
new_seqs_ad.append(fea[l_xx - choose_len:])
new_lengths_xx.append(l_xx)
else:
new_seqs_ad.append(fea)
new_lengths_xx.append(l_xx)
lengths_xx = new_lengths_xx
seqs_ad = new_seqs_ad
max_len = np.max(lengths_xx)
cnt_samples = len(seqs_ad)
ad_his = np.zeros(shape=(cnt_samples, max_len), ).astype("int64")
ad_mask = np.zeros(shape=(cnt_samples, max_len)).astype("float32")
for idx, x in enumerate(seqs_ad):
ad_mask[idx, :lengths_xx[idx]] = 1.0
ad_his[idx, :lengths_xx[idx]] = x
label_list = []
value_list = []
#一个批次的 这是value
label_all_tmp = []
value_all_tmp = []
for fea in feature:
value_list = [fea[i] for i in range(14,21)]
value_list = np.asarray(value_list,dtype=int)
# -1 的就是没有value值的 ，去掉, np.where 返回满足条件的索引
value_list = value_list[np.where(value_list>-1)]
label_list = np.where(value_list>-1)[0]
label_all_tmp.append(label_list)
value_all_tmp.append(value_list.tolist())
# 一个批次 label
# [1,2] [1,2,0] 补零
# [3,5,1] [3,5,1]
# ... 第二个维度不同，没法喂入 placeholder，，像rnn
#mask 所以 这边是补 -1， 嵌入矩阵 0 表示第一行的数据，，-1才是全0
label_len = [len(i) for i in label_all_tmp]
label_len_max = np.max(label_len) #直接返回 第二维度的 最大维
label_all = keras.preprocessing.sequence.pad_sequences(label_all_tmp,
maxlen=label_len_max,padding='post',value=0)
value_all = keras.preprocessing.sequence.pad_sequences(value_all_tmp,
maxlen=label_len_max,padding='post',value=-1)
#2 ,4 0r 2,8
#logging.info(" ad_lable_max {}, ad_value_max {}".format(np.max(label_all),np.max(value_all)))
rencent_behavior_a = keras.preprocessing.sequence.pad_sequences(rencent_behavior_a,
maxlen=USER_API_LEN,padding='post',value=0)
rencent_behavior_b = keras.preprocessing.sequence.pad_sequences(rencent_behavior_b,
maxlen=USER_API_LEN,padding='post',value=0)
rencent_behavior_c= keras.preprocessing.sequence.pad_sequences(rencent_behavior_c,
maxlen=USER_API_LEN,padding='post',value=0)
return user_id, ad_id, mobile, province, city, grade, math, english, \
chinese, purchase, activity, freshness, hour, \
ad_his, ad_mask, np.array(lengths_xx), np.array(target), \
label_all,value_all,\
rencent_behavior_a,rencent_behavior_b,rencent_behavior_c
Field = FIELD + []
# 最大队列，，文件量 400万，
MY_QUEUE = Queue(800000)
def produce(filter_str, request):
try:
with HbaseDataIterUpdate("10.9.75.202", b'midas_offline_v1', filter_str, request,) as d:
for i in d.get_data(batch_size=128,model_num=0):
MY_QUEUE.put(i) #一直取，i 是一个批次，执行yeild下面的程序 data=[],队列的数据的单位是一个批次数据
except:
logging.info("*** HbaseDataIterUpdate except ***")
logging.debug("execept e :{}".format(traceback.format_exc()))
pass
finally:
MY_QUEUE.put("done") #一天的数据取完，done
def get_max_model_index(cnt: int = 1):
model_path = "tmp-model-3/modeldsin_s3/model/"
if cnt != 1:
model_path = "update-model-1/modeldsin_s3/model/"
#num = -1
num =0
#ckpt_0 拿0
for i in os.listdir(os.path.join(PATH, model_path)):
o = i.split(".")[0]
try:
a = int(o.split("_")[1])
if a > num:
num = a
except:
pass
return num