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import tensorflow as tf
import pymysql
import datetime
import pandas as pd
from datetime import date, timedelta
import time
import os
import sys
from sqlalchemy import create_engine
def input_fn(filenames, batch_size=32, num_epochs=1, perform_shuffle=False):
print('Parsing', filenames)
def _parse_fn(record):
features = {
"y": tf.FixedLenFeature([], tf.float32),
"z": tf.FixedLenFeature([], tf.float32),
"ids": tf.FixedLenFeature([15], tf.int64),
"app_list": tf.VarLenFeature(tf.int64),
"level2_list": tf.VarLenFeature(tf.int64),
"level3_list": tf.VarLenFeature(tf.int64),
"tag1_list": tf.VarLenFeature(tf.int64),
"tag2_list": tf.VarLenFeature(tf.int64),
"tag3_list": tf.VarLenFeature(tf.int64),
"tag4_list": tf.VarLenFeature(tf.int64),
"tag5_list": tf.VarLenFeature(tf.int64),
"tag6_list": tf.VarLenFeature(tf.int64),
"tag7_list": tf.VarLenFeature(tf.int64),
"search_tag2_list": tf.VarLenFeature(tf.int64),
"search_tag3_list": tf.VarLenFeature(tf.int64),
"uid": tf.VarLenFeature(tf.string),
"city": tf.VarLenFeature(tf.string),
"cid_id": tf.VarLenFeature(tf.string)
}
parsed = tf.parse_single_example(record, features)
y = parsed.pop('y')
z = parsed.pop('z')
return parsed, {"y": y, "z": z}
# Extract lines from input files using the Dataset API, can pass one filename or filename list
dataset = tf.data.TFRecordDataset(filenames).map(_parse_fn, num_parallel_calls=10).prefetch(500000) # multi-thread pre-process then prefetch
# Randomizes input using a window of 256 elements (read into memory)
if perform_shuffle:
dataset = dataset.shuffle(buffer_size=256)
# epochs from blending together.
dataset = dataset.repeat(num_epochs)
dataset = dataset.batch(batch_size) # Batch size to use
# dataset = dataset.padded_batch(batch_size, padded_shapes=({"feeds_ids": [None], "feeds_vals": [None], "title_ids": [None]}, [None])) #不定长补齐
#return dataset.make_one_shot_iterator()
iterator = dataset.make_one_shot_iterator()
batch_features, batch_labels = iterator.get_next()
#return tf.reshape(batch_ids,shape=[-1,field_size]), tf.reshape(batch_vals,shape=[-1,field_size]), batch_labels
#print("-"*100)
#print(batch_features,batch_labels)
return batch_features, batch_labels
def model_fn(features, labels, mode, params):
"""Bulid Model function f(x) for Estimator."""
#------hyperparameters----
field_size = params["field_size"]
feature_size = params["feature_size"]
embedding_size = params["embedding_size"]
l2_reg = params["l2_reg"]
learning_rate = params["learning_rate"]
#optimizer = params["optimizer"]
layers = list(map(int, params["deep_layers"].split(',')))
dropout = list(map(float, params["dropout"].split(',')))
ctr_task_wgt = params["ctr_task_wgt"]
common_dims = field_size*embedding_size
#------bulid weights------
Feat_Emb = tf.get_variable(name='embeddings', shape=[feature_size, embedding_size], initializer=tf.glorot_normal_initializer())
feat_ids = features['ids']
app_list = features['app_list']
level2_list = features['level2_list']
level3_list = features['level3_list']
tag1_list = features['tag1_list']
tag2_list = features['tag2_list']
tag3_list = features['tag3_list']
tag4_list = features['tag4_list']
tag5_list = features['tag5_list']
tag6_list = features['tag6_list']
tag7_list = features['tag7_list']
search_tag2_list = features['search_tag2_list']
search_tag3_list = features['search_tag3_list']
uid = features['uid']
city = features['city']
cid_id = features['cid_id']
#------build f(x)------
with tf.variable_scope("Shared-Embedding-layer"):
embedding_id = tf.nn.embedding_lookup(Feat_Emb,feat_ids)
app_id = tf.nn.embedding_lookup_sparse(Feat_Emb, sp_ids=app_list, sp_weights=None, combiner="sum")
level2 = tf.nn.embedding_lookup_sparse(Feat_Emb, sp_ids=level2_list, sp_weights=None, combiner="sum")
level3 = tf.nn.embedding_lookup_sparse(Feat_Emb, sp_ids=level3_list, sp_weights=None, combiner="sum")
tag1 = tf.nn.embedding_lookup_sparse(Feat_Emb, sp_ids=tag1_list, sp_weights=None, combiner="sum")
tag2 = tf.nn.embedding_lookup_sparse(Feat_Emb, sp_ids=tag2_list, sp_weights=None, combiner="sum")
tag3 = tf.nn.embedding_lookup_sparse(Feat_Emb, sp_ids=tag3_list, sp_weights=None, combiner="sum")
tag4 = tf.nn.embedding_lookup_sparse(Feat_Emb, sp_ids=tag4_list, sp_weights=None, combiner="sum")
tag5 = tf.nn.embedding_lookup_sparse(Feat_Emb, sp_ids=tag5_list, sp_weights=None, combiner="sum")
tag6 = tf.nn.embedding_lookup_sparse(Feat_Emb, sp_ids=tag6_list, sp_weights=None, combiner="sum")
tag7 = tf.nn.embedding_lookup_sparse(Feat_Emb, sp_ids=tag7_list, sp_weights=None, combiner="sum")
search_tag2 = tf.nn.embedding_lookup_sparse(Feat_Emb, sp_ids=search_tag2_list, sp_weights=None, combiner="sum")
search_tag3 = tf.nn.embedding_lookup_sparse(Feat_Emb, sp_ids=search_tag3_list, sp_weights=None, combiner="sum")
# x_concat = tf.reshape(embedding_id,shape=[-1, common_dims]) # None * (F * K)
x_concat = tf.concat([tf.reshape(embedding_id, shape=[-1, common_dims]), app_id, level2, level3, tag1,
tag2, tag3, tag4, tag5, tag6, tag7,search_tag2,search_tag3], axis=1)
uid = tf.sparse.to_dense(uid,default_value="")
city = tf.sparse.to_dense(city,default_value="")
cid_id = tf.sparse.to_dense(cid_id,default_value="")
with tf.name_scope("CVR_Task"):
if mode == tf.estimator.ModeKeys.TRAIN:
train_phase = True
else:
train_phase = False
x_cvr = x_concat
for i in range(len(layers)):
x_cvr = tf.contrib.layers.fully_connected(inputs=x_cvr, num_outputs=layers[i], \
weights_regularizer=tf.contrib.layers.l2_regularizer(l2_reg), scope='cvr_mlp%d' % i)
y_cvr = tf.contrib.layers.fully_connected(inputs=x_cvr, num_outputs=1, activation_fn=tf.identity, \
weights_regularizer=tf.contrib.layers.l2_regularizer(l2_reg), scope='cvr_out')
y_cvr = tf.reshape(y_cvr,shape=[-1])
with tf.name_scope("CTR_Task"):
if mode == tf.estimator.ModeKeys.TRAIN:
train_phase = True
else:
train_phase = False
x_ctr = x_concat
for i in range(len(layers)):
x_ctr = tf.contrib.layers.fully_connected(inputs=x_ctr, num_outputs=layers[i], \
weights_regularizer=tf.contrib.layers.l2_regularizer(l2_reg), scope='ctr_mlp%d' % i)
y_ctr = tf.contrib.layers.fully_connected(inputs=x_ctr, num_outputs=1, activation_fn=tf.identity, \
weights_regularizer=tf.contrib.layers.l2_regularizer(l2_reg), scope='ctr_out')
y_ctr = tf.reshape(y_ctr,shape=[-1])
with tf.variable_scope("MTL-Layer"):
pctr = tf.sigmoid(y_ctr)
pcvr = tf.sigmoid(y_cvr)
pctcvr = pctr*pcvr
predictions = {"pctcvr": pctcvr, "uid": uid, "city": city, "cid_id": cid_id}
export_outputs = {tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: tf.estimator.export.PredictOutput(predictions)}
# Provide an estimator spec for `ModeKeys.PREDICT`
if mode == tf.estimator.ModeKeys.PREDICT:
return tf.estimator.EstimatorSpec(
mode=mode,
predictions=predictions,
export_outputs=export_outputs)
def main(te_file):
dt_dir = (date.today() + timedelta(-1)).strftime('%Y%m%d')
model_dir = "hdfs://172.16.32.4:8020/strategy/esmm/model_ckpt/DeepCvrMTL/" + dt_dir
te_files = ["hdfs://172.16.32.4:8020/strategy/esmm/nearby/part-r-00000"]
model_params = {
"field_size": 15,
"feature_size": 600000,
"embedding_size": 16,
"learning_rate": 0.0001,
"l2_reg": 0.005,
"deep_layers": '512,256,128,64,32',
"dropout": '0.3,0.3,0.3,0.3,0.3',
"ctr_task_wgt":0.5
}
config = tf.estimator.RunConfig().replace(session_config = tf.ConfigProto(device_count={'GPU':0, 'CPU':36}),
log_step_count_steps=100, save_summary_steps=100)
Estimator = tf.estimator.Estimator(model_fn=model_fn, model_dir=model_dir, params=model_params, config=config)
preds = Estimator.predict(input_fn=lambda: input_fn(te_file, num_epochs=1, batch_size=10000), predict_keys=["pctcvr","uid","city","cid_id"])
result = []
for prob in preds:
result.append([str(prob["uid"][0]), str(prob["city"][0]), str(prob["cid_id"][0]), str(prob['pctcvr'])])
def trans(x):
return str(x)[2:-1] if str(x)[0] == 'b' else x
def set_join(lst):
l = lst.unique().tolist()
r = [str(i) for i in l]
r =r[:500]
return ','.join(r)
if __name__ == "__main__":
if sys.argv[1] == "native":
b = time.time()
print("infer native task")
path = "hdfs://172.16.32.4:8020/strategy/esmm/"
# df = spark.read.format("tfrecords").load(path+"test_native/part-r-00000")
# df.show()
te_files = ["hdfs://172.16.32.4:8020/strategy/esmm/test_native/part-r-00000"]
print("dist predict native")
print("耗时(秒)：")
print((time.time()-b))
if sys.argv[1] == "nearby":
print("infer nearby task")
b = time.time()
path = "hdfs://172.16.32.4:8020/strategy/esmm/"
# df = spark.read.format("tfrecords").load(path+"test_nearby/part-r-00000")
# df.show()
te_files = ["hdfs://172.16.32.4:8020/strategy/esmm/test_nearby/part-r-00000"]
result = main(te_files)
df = pd.DataFrame(result, columns=["uid", "city", "cid_id", "pctcvr"])
df.head(10)
host = '172.16.40.158'
port = 4000
user = 'root'
password = '3SYz54LS9#^9sBvC'
db = 'jerry_test'
charset = 'utf8'
print("耗时(min)：")
print((time.time()-b)/60)