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ffm-baseline
Commit f84b3822 authored by 张彦钊's avatar 张彦钊
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esm pyspark 代码重构

parent dccbc937
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Showing with 87 additions and 268 deletions
tensnsorflow/feature_engineering.py → eda/esmm/Model_pipline/feature_engineering.py
View file @ f84b3822
......@@ -206,7 +206,7 @@ def feature_engineer():
# TODO 上线后把下面train fliter 删除，因为最近一天的数据也要作为训练集
train = rdd.filter(lambda x: x[0] != validate_date).map(
train = rdd.map(
lambda x: (x[1], x[2], x[3], x[4], x[5], x[6], x[7], x[8], x[9],
x[10], x[11], x[12], x[13]))
f = time.time()
......
eda/esmm/Model_pipline/submit.sh
View file @ f84b3822
#! /bin/bash
git checkout master
PYTHON_PATH=/opt/anaconda3/envs/esmm/bin/python
MODEL_PATH=/srv/apps/ffm-baseline/eda/esmm/Model_pipline
DATA_PATH=/home/gmuser/esmm
echo "rm leave tfrecord"
rm ${DATA_PATH}/tr/*
rm ${DATA_PATH}/va/*
rm ${DATA_PATH}/native/*
rm ${DATA_PATH}/nearby/*
rm -r ${DATA_PATH}/model_ckpt/DeepCvrMTL/20*
echo "data"
${PYTHON_PATH} ${MODEL_PATH}/feature.py > ${DATA_PATH}/feature.log
echo "csv to tfrecord"
${PYTHON_PATH} ${MODEL_PATH}/to_tfrecord.py --input_dir=${DATA_PATH}/tr/ --output_dir=${DATA_PATH}/tr/
${PYTHON_PATH} ${MODEL_PATH}/to_tfrecord.py --input_dir=${DATA_PATH}/va/ --output_dir=${DATA_PATH}/va/
${PYTHON_PATH} ${MODEL_PATH}/to_tfrecord.py --input_dir=${DATA_PATH}/native/ --output_dir=${DATA_PATH}/native/
${PYTHON_PATH} ${MODEL_PATH}/to_tfrecord.py --input_dir=${DATA_PATH}/nearby/ --output_dir=${DATA_PATH}/nearby/
cat ${DATA_PATH}/tr/*.tfrecord > ${DATA_PATH}/tr/tr.tfrecord
cat ${DATA_PATH}/va/*.tfrecord > ${DATA_PATH}/va/va.tfrecord
cat ${DATA_PATH}/native/*.tfrecord > ${DATA_PATH}/native/native.tfrecord
cat ${DATA_PATH}/nearby/*.tfrecord > ${DATA_PATH}/nearby/nearby.tfrecord
rm ${DATA_PATH}/tr/tr_*
rm ${DATA_PATH}/va/va_*
rm ${DATA_PATH}/native/native_*
rm ${DATA_PATH}/nearby/nearby_*
PYTHON_PATH=/srv/envs/esmm/bin/python
MODEL_PATH=/srv/apps/ffm-baseline_git/eda/esmm/Model_pipline
LOCAL_PATH=/home/gmuser/esmm
HDFS_PATH=hdfs://172.16.32.4:8020/strategy/esmm
echo "rm model file"
rm -r ${LOCAL_PATH}/model_ckpt/DeepCvrMTL/20*
echo "train..."
${PYTHON_PATH} ${MODEL_PATH}/train.py --ctr_task_wgt=0.5 --learning_rate=0.0001 --deep_layers=512,256,128,64,32 --dropout=0.3,0.3,0.3,0.3,0.3 --optimizer=Adam --num_epochs=1 --embedding_size=16 --batch_size=2000 --field_size=15 --feature_size=300000 --l2_reg=0.005 --log_steps=100 --num_threads=36 --model_dir=${DATA_PATH}/model_ckpt/DeepCvrMTL/ --data_dir=${DATA_PATH} --task_type=train
CLASSPATH="$(hadoop classpath --glob)" ${PYTHON_PATH} ${MODEL_PATH}/train.py --ctr_task_wgt=0.5 --learning_rate=0.0001 --deep_layers=512,256,128,64,32 --dropout=0.3,0.3,0.3,0.3,0.3 --optimizer=Adam --num_epochs=1 --embedding_size=16 --batch_size=10000 --field_size=15 --feature_size=600000 --l2_reg=0.005 --log_steps=100 --num_threads=36 --model_dir=${LOCAL_PATH}/model_ckpt/DeepCvrMTL/ --local_dir=${LOCAL_PATH} --task_type=train
echo "infer native..."
${PYTHON_PATH} ${MODEL_PATH}/train.py --ctr_task_wgt=0.5 --learning_rate=0.0001 --deep_layers=512,256,128,64,32 --dropout=0.3,0.3,0.3,0.3,0.3 --optimizer=Adam --num_epochs=1 --embedding_size=16 --batch_size=2000 --field_size=15 --feature_size=300000 --l2_reg=0.005 --log_steps=100 --num_threads=36 --model_dir=${DATA_PATH}/model_ckpt/DeepCvrMTL/ --data_dir=${DATA_PATH}/native --task_type=infer
CLASSPATH="$(hadoop classpath --glob)" ${PYTHON_PATH} ${MODEL_PATH}/train.py --ctr_task_wgt=0.5 --learning_rate=0.0001 --deep_layers=512,256,128,64,32 --dropout=0.3,0.3,0.3,0.3,0.3 --optimizer=Adam --num_epochs=1 --embedding_size=16 --batch_size=10000 --field_size=15 --feature_size=600000 --l2_reg=0.005 --log_steps=100 --num_threads=36 --model_dir=${LOCAL_PATH}/model_ckpt/DeepCvrMTL/ --local_dir=${LOCAL_PATH}/native --hdfs_dir=${HDFS_PATH}/native --task_type=infer
echo "infer nearby..."
${PYTHON_PATH} ${MODEL_PATH}/train.py --ctr_task_wgt=0.5 --learning_rate=0.0001 --deep_layers=512,256,128,64,32 --dropout=0.3,0.3,0.3,0.3,0.3 --optimizer=Adam --num_epochs=1 --embedding_size=16 --batch_size=2000 --field_size=15 --feature_size=300000 --l2_reg=0.005 --log_steps=100 --num_threads=36 --model_dir=${DATA_PATH}/model_ckpt/DeepCvrMTL/ --data_dir=${DATA_PATH}/nearby --task_type=infer
CLASSPATH="$(hadoop classpath --glob)" ${PYTHON_PATH} ${MODEL_PATH}/train.py --ctr_task_wgt=0.5 --learning_rate=0.0001 --deep_layers=512,256,128,64,32 --dropout=0.3,0.3,0.3,0.3,0.3 --optimizer=Adam --num_epochs=1 --embedding_size=16 --batch_size=10000 --field_size=15 --feature_size=600000 --l2_reg=0.005 --log_steps=100 --num_threads=36 --model_dir=${LOCAL_PATH}/model_ckpt/DeepCvrMTL/ --local_dir=${LOCAL_PATH}/nearby --hdfs_dir=${HDFS_PATH}/nearby --task_type=infer
echo "sort and 2sql"
${PYTHON_PATH} ${MODEL_PATH}/to_database.py
\ No newline at end of file
eda/esmm/Model_pipline/to_database.py
View file @ f84b3822
......@@ -81,21 +81,33 @@ def main():
tmp = str(to_delete[start:end]).strip('[]')
df_merge_str.append(tmp)
try:
for i in df_merge_str:
delete_str = 'delete from esmm_device_diary_queue where concat(device_id,city_id) in ({0})'.format(i)
con = pymysql.connect(host='172.16.40.158', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
cur = con.cursor()
cur.execute(delete_str)
con.commit()
print("delete done")
con.close()
engine = create_engine(str(r"mysql+pymysql://%s:" + '%s' + "@%s:%s/%s") % (user, password, host, port, db))
df_all.to_sql('esmm_device_diary_queue',con=engine,if_exists='append',index=False,chunksize=8000)
print("insert done")
except Exception as e:
print(e)
for i in df_merge_str:
delete_str = 'delete from esmm_device_diary_queue where concat(device_id,city_id) in ({0})'.format(i)
con = pymysql.connect(host='172.16.40.158', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
cur = con.cursor()
cur.execute(delete_str)
con.commit()
print("delete done")
con.close()
engine = create_engine(str(r"mysql+pymysql://%s:" + '%s' + "@%s:%s/%s") % (user, password, host, port, db))
df_all.to_sql('esmm_device_diary_queue', con=engine, if_exists='append', index=False, chunksize=8000)
print("insert done")
# try:
# for i in df_merge_str:
# delete_str = 'delete from esmm_device_diary_queue where concat(device_id,city_id) in ({0})'.format(i)
# con = pymysql.connect(host='172.16.40.158', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
# cur = con.cursor()
# cur.execute(delete_str)
# con.commit()
# print("delete done")
# con.close()
# engine = create_engine(str(r"mysql+pymysql://%s:" + '%s' + "@%s:%s/%s") % (user, password, host, port, db))
# df_all.to_sql('esmm_device_diary_queue',con=engine,if_exists='append',index=False,chunksize=8000)
# print("insert done")
#
# except Exception as e:
# print(e)
if __name__ == '__main__':
......
eda/esmm/Model_pipline/train.py
View file @ f84b3822
This diff is collapsed.
tensnsorflow/es/feature.py deleted 100644 → 0
View file @ dccbc937
This diff is collapsed.
tensnsorflow/es/to_database.py deleted 100644 → 0
View file @ dccbc937
#coding=utf-8
from sqlalchemy import create_engine
import pandas as pd
import pymysql
import time
def con_sql(sql):
"""
:type sql : str
:rtype : tuple
"""
db = pymysql.connect(host='10.66.157.22', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
cursor = db.cursor()
cursor.execute(sql)
result = cursor.fetchall()
db.close()
return result
def nearby_set_join(lst):
# return ','.join([str(i) for i in list(lst)])
return ','.join([str(i) for i in lst.unique().tolist()])
def native_set_join(lst):
l = lst.unique().tolist()
d = int(len(l)/2)
if d == 0:
d = 1
r = [str(i) for i in l]
r =r[:d]
return ','.join(r)
def main():
# native queue
df2 = pd.read_csv('/data/esmm/native.csv')
df2['cid_id'] = df2['cid_id'].astype(str)
df1 = pd.read_csv("/data/esmm/native/pred.txt",sep='\t',header=None,names=["ctr","cvr","ctcvr"])
df2["ctr"],df2["cvr"],df2["ctcvr"] = df1["ctr"],df1["cvr"],df1["ctcvr"]
df3 = df2.groupby(by=["uid","city"]).apply(lambda x: x.sort_values(by="ctcvr",ascending=False)).reset_index(drop=True).groupby(by=["uid","city"]).agg({'cid_id':native_set_join}).reset_index(drop=False)
df3.columns = ["device_id","city_id","native_queue"]
print("native_device_count",df3.shape)
# nearby queue
df2 = pd.read_csv('/data/esmm/nearby.csv')
df2['cid_id'] = df2['cid_id'].astype(str)
df1 = pd.read_csv("/data/esmm/nearby/pred.txt",sep='\t',header=None,names=["ctr","cvr","ctcvr"])
df2["ctr"], df2["cvr"], df2["ctcvr"] = df1["ctr"], df1["cvr"], df1["ctcvr"]
df4 = df2.groupby(by=["uid","city"]).apply(lambda x: x.sort_values(by="ctcvr",ascending=False)).reset_index(drop=True).groupby(by=["uid","city"]).agg({'cid_id':nearby_set_join}).reset_index(drop=False)
df4.columns = ["device_id","city_id","nearby_queue"]
print("nearby_device_count",df4.shape)
#union
df_all = pd.merge(df3,df4,on=['device_id','city_id'],how='outer').fillna("")
df_all['device_id'] = df_all['device_id'].astype(str)
df_all['city_id'] = df_all['city_id'].astype(str)
ctime = int(time.time())
df_all["time"] = ctime
print("union_device_count",df_all.shape)
host='10.66.157.22'
port=4000
user='root'
password='3SYz54LS9#^9sBvC'
db='jerry_test'
charset='utf8'
engine = create_engine(str(r"mysql+mysqldb://%s:" + '%s' + "@%s:%s/%s") % (user, password, host, port, db))
df_merge = df_all['device_id'] + df_all['city_id']
df_merge_str = (str(list(df_merge.values))).strip('[]')
try:
# df_merge = df_all[['device_id','city_id']].apply(lambda x: ''.join(x),axis=1)
delete_str = 'delete from esmm_device_diary_queue where concat(device_id,city_id) in ({0})'.format(df_merge_str)
con = pymysql.connect(host='10.66.157.22', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
cur = con.cursor()
cur.execute(delete_str)
con.commit()
df_all.to_sql('esmm_device_diary_queue',con=engine,if_exists='append',index=False,chunksize=8000)
except Exception as e:
print(e)
print("done")
if __name__ == '__main__':
main()
\ No newline at end of file
tensnsorflow/es/to_tfrecord.py deleted 100644 → 0
View file @ dccbc937
#coding=utf-8
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import pandas as pd
import os
import glob
import tensorflow as tf
import numpy as np
from multiprocessing import Pool as ThreadPool
flags = tf.app.flags
FLAGS = flags.FLAGS
LOG = tf.logging
tf.app.flags.DEFINE_string("input_dir", "./", "input dir")
tf.app.flags.DEFINE_string("output_dir", "./", "output dir")
tf.app.flags.DEFINE_integer("threads", 16, "threads num")
def gen_tfrecords(in_file):
basename = os.path.basename(in_file) + ".tfrecord"
out_file = os.path.join(FLAGS.output_dir, basename)
tfrecord_out = tf.python_io.TFRecordWriter(out_file)
df = pd.read_csv(in_file)
for i in range(df.shape[0]):
feats = ["ucity_id", "ccity_name", "device_type", "manufacturer",
"channel", "top", "time", "stat_date","hospital_id",
"method", "min", "max", "treatment_time", "maintain_time", "recover_time"]
id = np.array([])
for j in feats:
id = np.append(id,df[j][i])
app_list = np.array(str(df["app_list"][i]).split(","))
level2_list = np.array(str(df["clevel2_id"][i]).split(","))
level3_list = np.array(str(df["level3_ids"][i]).split(","))
features = tf.train.Features(feature={
"y": tf.train.Feature(float_list=tf.train.FloatList(value=[df["y"][i]])),
"z": tf.train.Feature(float_list=tf.train.FloatList(value=[df["z"][i]])),
"ids": tf.train.Feature(int64_list=tf.train.Int64List(value=id.astype(np.int))),
"app_list":tf.train.Feature(int64_list=tf.train.Int64List(value=app_list.astype(np.int))),
"level2_list": tf.train.Feature(int64_list=tf.train.Int64List(value=level2_list.astype(np.int))),
"level3_list": tf.train.Feature(int64_list=tf.train.Int64List(value=level3_list.astype(np.int)))
})
example = tf.train.Example(features = features)
serialized = example.SerializeToString()
tfrecord_out.write(serialized)
tfrecord_out.close()
def main(_):
if not os.path.exists(FLAGS.output_dir):
os.mkdir(FLAGS.output_dir)
file_list = glob.glob(os.path.join(FLAGS.input_dir, "*.csv"))
print("total files: %d" % len(file_list))
pool = ThreadPool(FLAGS.threads) # Sets the pool size
pool.map(gen_tfrecords, file_list)
pool.close()
pool.join()
if __name__ == "__main__":
tf.logging.set_verbosity(tf.logging.INFO)
tf.app.run()
\ No newline at end of file
tensnsorflow/es/submit.sh → tensnsorflow/old_version/submit.sh
View file @ f84b3822
#! /bin/bash
git checkout master
PYTHON_PATH=/home/gaoyazhe/miniconda3/bin/python
MODEL_PATH=/srv/apps/ffm-baseline/tensnsorflow/es
DATA_PATH=/data/esmm
PYTHON_PATH=/opt/anaconda3/envs/esmm/bin/python
MODEL_PATH=/srv/apps/ffm-baseline/eda/esmm/Model_pipline
DATA_PATH=/home/gmuser/esmm
echo "rm leave tfrecord"
rm ${DATA_PATH}/tr/*
......@@ -32,15 +32,13 @@ rm ${DATA_PATH}/nearby/nearby_*
echo "train..."
${PYTHON_PATH} ${MODEL_PATH}/train.py --ctr_task_wgt=0.5 --learning_rate=0.0001 --deep_layers=512,256,128,64,32 --dropout=0.3,0.3,0.3,0.3,0.3 --optimizer=Adam --num_epochs=1 --embedding_size=16 --batch_size=1024 --field_size=15 --feature_size=300000 --l2_reg=0.005 --log_steps=100 --num_threads=36 --model_dir=${DATA_PATH}/model_ckpt/DeepCvrMTL/ --data_dir=${DATA_PATH} --task_type=train
${PYTHON_PATH} ${MODEL_PATH}/train.py --ctr_task_wgt=0.5 --learning_rate=0.0001 --deep_layers=512,256,128,64,32 --dropout=0.3,0.3,0.3,0.3,0.3 --optimizer=Adam --num_epochs=1 --embedding_size=16 --batch_size=2000 --field_size=15 --feature_size=300000 --l2_reg=0.005 --log_steps=100 --num_threads=36 --model_dir=${DATA_PATH}/model_ckpt/DeepCvrMTL/ --data_dir=${DATA_PATH} --task_type=train
echo "infer native..."
${PYTHON_PATH} ${MODEL_PATH}/train.py --ctr_task_wgt=0.5 --learning_rate=0.0001 --deep_layers=512,256,128,64,32 --dropout=0.3,0.3,0.3,0.3,0.3 --optimizer=Adam --num_epochs=1 --embedding_size=16 --batch_size=1024 --field_size=15 --feature_size=300000 --l2_reg=0.005 --log_steps=100 --num_threads=36 --model_dir=${DATA_PATH}/model_ckpt/DeepCvrMTL/ --data_dir=${DATA_PATH}/native --task_type=infer > ${DATA_PATH}/native_infer.log
${PYTHON_PATH} ${MODEL_PATH}/train.py --ctr_task_wgt=0.5 --learning_rate=0.0001 --deep_layers=512,256,128,64,32 --dropout=0.3,0.3,0.3,0.3,0.3 --optimizer=Adam --num_epochs=1 --embedding_size=16 --batch_size=2000 --field_size=15 --feature_size=300000 --l2_reg=0.005 --log_steps=100 --num_threads=36 --model_dir=${DATA_PATH}/model_ckpt/DeepCvrMTL/ --data_dir=${DATA_PATH}/native --task_type=infer
echo "infer nearby..."
${PYTHON_PATH} ${MODEL_PATH}/train.py --ctr_task_wgt=0.5 --learning_rate=0.0001 --deep_layers=512,256,128,64,32 --dropout=0.3,0.3,0.3,0.3,0.3 --optimizer=Adam --num_epochs=1 --embedding_size=16 --batch_size=1024 --field_size=15 --feature_size=300000 --l2_reg=0.005 --log_steps=100 --num_threads=36 --model_dir=${DATA_PATH}/model_ckpt/DeepCvrMTL/ --data_dir=${DATA_PATH}/nearby --task_type=infer > ${DATA_PATH}/nearby_infer.log
${PYTHON_PATH} ${MODEL_PATH}/train.py --ctr_task_wgt=0.5 --learning_rate=0.0001 --deep_layers=512,256,128,64,32 --dropout=0.3,0.3,0.3,0.3,0.3 --optimizer=Adam --num_epochs=1 --embedding_size=16 --batch_size=2000 --field_size=15 --feature_size=300000 --l2_reg=0.005 --log_steps=100 --num_threads=36 --model_dir=${DATA_PATH}/model_ckpt/DeepCvrMTL/ --data_dir=${DATA_PATH}/nearby --task_type=infer
echo "sort and 2sql"
${PYTHON_PATH} ${MODEL_PATH}/to_database.py > ${DATA_PATH}/insert_database.log
tensnsorflow/to_database.py → tensnsorflow/old_version/to_database.py
View file @ f84b3822
......@@ -81,33 +81,21 @@ def main():
tmp = str(to_delete[start:end]).strip('[]')
df_merge_str.append(tmp)
for i in df_merge_str:
delete_str = 'delete from esmm_device_diary_queue where concat(device_id,city_id) in ({0})'.format(i)
con = pymysql.connect(host='172.16.40.158', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
cur = con.cursor()
cur.execute(delete_str)
con.commit()
print("delete done")
con.close()
engine = create_engine(str(r"mysql+pymysql://%s:" + '%s' + "@%s:%s/%s") % (user, password, host, port, db))
df_all.to_sql('esmm_device_diary_queue', con=engine, if_exists='append', index=False, chunksize=8000)
print("insert done")
# try:
# for i in df_merge_str:
# delete_str = 'delete from esmm_device_diary_queue where concat(device_id,city_id) in ({0})'.format(i)
# con = pymysql.connect(host='172.16.40.158', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
# cur = con.cursor()
# cur.execute(delete_str)
# con.commit()
# print("delete done")
# con.close()
# engine = create_engine(str(r"mysql+pymysql://%s:" + '%s' + "@%s:%s/%s") % (user, password, host, port, db))
# df_all.to_sql('esmm_device_diary_queue',con=engine,if_exists='append',index=False,chunksize=8000)
# print("insert done")
#
# except Exception as e:
# print(e)
try:
for i in df_merge_str:
delete_str = 'delete from esmm_device_diary_queue where concat(device_id,city_id) in ({0})'.format(i)
con = pymysql.connect(host='172.16.40.158', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
cur = con.cursor()
cur.execute(delete_str)
con.commit()
print("delete done")
con.close()
engine = create_engine(str(r"mysql+pymysql://%s:" + '%s' + "@%s:%s/%s") % (user, password, host, port, db))
df_all.to_sql('esmm_device_diary_queue',con=engine,if_exists='append',index=False,chunksize=8000)
print("insert done")
except Exception as e:
print(e)
if __name__ == '__main__':
......
tensnsorflow/es/train.py → tensnsorflow/old_version/train.py
View file @ f84b3822
......@@ -6,7 +6,6 @@
#import argparse
import shutil
#import sys
import os
import json
import glob
......@@ -55,7 +54,14 @@ def input_fn(filenames, batch_size=32, num_epochs=1, perform_shuffle=False):
"ids": tf.FixedLenFeature([FLAGS.field_size], tf.int64),
"app_list": tf.VarLenFeature(tf.int64),
"level2_list": tf.VarLenFeature(tf.int64),
"level3_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)
}
parsed = tf.parse_single_example(record, features)
......@@ -103,6 +109,14 @@ def model_fn(features, labels, mode, params):
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']
if FLAGS.task_type != "infer":
y = labels['y']
......@@ -113,10 +127,18 @@ def model_fn(features, labels, mode, params):
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")
# 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], axis=1)
x_concat = tf.concat([tf.reshape(embedding_id,shape=[-1,common_dims]),app_id,level2,level3,tag1,
tag2,tag3,tag4,tag5,tag6,tag7], axis=1)
with tf.name_scope("CVR_Task"):
if mode == tf.estimator.ModeKeys.TRAIN:
......
tensnsorflow/pipe.sh deleted 100644 → 0
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#! /bin/bash
git checkout master
PYTHON_PATH=/srv/envs/esmm/bin/python
MODEL_PATH=/srv/apps/ffm-baseline_git/tensnsorflow
LOCAL_PATH=/home/gmuser/esmm
HDFS_PATH=hdfs://172.16.32.4:8020/strategy/esmm
echo "train..."
CLASSPATH="$(hadoop classpath --glob)" ${PYTHON_PATH} ${MODEL_PATH}/train_multi.py --ctr_task_wgt=0.5 --learning_rate=0.0001 --deep_layers=512,256,128,64,32 --dropout=0.3,0.3,0.3,0.3,0.3 --optimizer=Adam --num_epochs=1 --embedding_size=16 --batch_size=10000 --field_size=15 --feature_size=600000 --l2_reg=0.005 --log_steps=100 --num_threads=36 --model_dir=${LOCAL_PATH}/model_ckpt/DeepCvrMTL/ --local_dir=${LOCAL_PATH} --task_type=train
echo "infer native..."
CLASSPATH="$(hadoop classpath --glob)" ${PYTHON_PATH} ${MODEL_PATH}/train_multi.py --ctr_task_wgt=0.5 --learning_rate=0.0001 --deep_layers=512,256,128,64,32 --dropout=0.3,0.3,0.3,0.3,0.3 --optimizer=Adam --num_epochs=1 --embedding_size=16 --batch_size=10000 --field_size=15 --feature_size=600000 --l2_reg=0.005 --log_steps=100 --num_threads=36 --model_dir=${LOCAL_PATH}/model_ckpt/DeepCvrMTL/ --local_dir=${LOCAL_PATH}/native --hdfs_dir=${HDFS_PATH}/native --task_type=infer
echo "infer nearby..."
CLASSPATH="$(hadoop classpath --glob)" ${PYTHON_PATH} ${MODEL_PATH}/train_multi.py --ctr_task_wgt=0.5 --learning_rate=0.0001 --deep_layers=512,256,128,64,32 --dropout=0.3,0.3,0.3,0.3,0.3 --optimizer=Adam --num_epochs=1 --embedding_size=16 --batch_size=10000 --field_size=15 --feature_size=600000 --l2_reg=0.005 --log_steps=100 --num_threads=36 --model_dir=${LOCAL_PATH}/model_ckpt/DeepCvrMTL/ --local_dir=${LOCAL_PATH}/nearby --hdfs_dir=${HDFS_PATH}/nearby --task_type=infer
echo "sort and 2sql"
${PYTHON_PATH} ${MODEL_PATH}/to_database.py
\ No newline at end of file
tensnsorflow/train_multi.py deleted 100644 → 0
View file @ dccbc937
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