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ffm-baseline
Commit 7c880884 authored by 王志伟's avatar 王志伟
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Merge branch 'master' of http://git.wanmeizhensuo.com/ML/ffm-baseline

parents aa7db93b a3b3de0c
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Showing with 397 additions and 30 deletions
tensnsorflow/multi.py
View file @ 7c880884
......@@ -9,7 +9,7 @@ import pandas as pd
def app_list_func(x,l):
b = x.split(",")
b = str(x).split(",")
e = []
for i in b:
if i in l.keys():
......@@ -22,7 +22,7 @@ def app_list_func(x,l):
def multi_hot(df,column,n):
v = df.select(column).distinct().rdd.map(lambda x: x[0]).collect()
app_list_value = [i.split(",") for i in v]
app_list_value = [str(i).split(",") for i in v]
app_list_unique = []
for i in app_list_value:
app_list_unique.extend(i)
......@@ -42,33 +42,20 @@ def feature_engineer():
print(start)
sql = "select e.y,e.z,e.stat_date,e.ucity_id,feat.level2_ids,e.ccity_name,u.device_type,u.manufacturer," \
"u.channel,c.top,cut.time,dl.app_list,e.diary_service_id,feat.level3_ids," \
"u.channel,c.top,cut.time,dl.app_list,feat.level3_ids,doctor.hospital_id," \
"k.treatment_method,k.price_min,k.price_max,k.treatment_time,k.maintain_time,k.recover_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_cut cut on e.cid_id = cut.cid " \
"left join device_app_list dl on e.device_id = dl.device_id " \
"left join diary_feat feat on e.cid_id = feat.diary_id " \
"left join train_Knowledge_network_data k on feat.level2 = k.level2_id " \
"from jerry_test.esmm_train_data e left join jerry_test.user_feature u on e.device_id = u.device_id " \
"left join jerry_test.cid_type_top c on e.device_id = c.device_id " \
"left join jerry_test.cid_time_cut cut on e.cid_id = cut.cid " \
"left join jerry_test.device_app_list dl on e.device_id = dl.device_id " \
"left join jerry_test.diary_feat feat on e.cid_id = feat.diary_id " \
"left join jerry_test.train_Knowledge_network_data k on feat.level2 = k.level2_id " \
"left join eagle.src_zhengxing_api_service service on e.diary_service_id = service.id " \
"left join eagle.src_zhengxing_api_doctor doctor on service.doctor_id = doctor.id " \
"where e.stat_date >= '{}'".format(start)
df = spark.sql(sql)
# TODO 把下面的库改成tidb的数据库
url = "jdbc:mysql://172.16.30.143:3306/zhengxing"
jdbcDF = spark.read.format("jdbc").option("driver", "com.mysql.jdbc.Driver").option("url", url) \
.option("dbtable", "api_service").option("user", 'work').option("password", 'BJQaT9VzDcuPBqkd').load()
jdbcDF.createOrReplaceTempView("api_service")
jdbc = spark.read.format("jdbc").option("driver", "com.mysql.jdbc.Driver").option("url", url) \
.option("dbtable", "api_doctor").option("user", 'work').option("password", 'BJQaT9VzDcuPBqkd').load()
jdbc.createOrReplaceTempView("api_doctor")
sql = "select s.id as diary_service_id,d.hospital_id " \
"from api_service s left join api_doctor d on s.doctor_id = d.id"
hospital = spark.sql(sql)
df = df.join(hospital,"diary_service_id","left_outer").fillna("na")
df = df.drop("diary_service_id")
df = df.drop_duplicates(["ucity_id", "level2_ids", "ccity_name", "device_type", "manufacturer",
"channel", "top", "time", "stat_date", "app_list", "hospital_id", "level3_ids"])
......@@ -113,13 +100,13 @@ def feature_engineer():
spark.createDataFrame(test).toDF("app_list","level2_ids","level3_ids","stat_date","ucity_id", "ccity_name", "device_type", "manufacturer",
"channel", "top", "time", "hospital_id","treatment_method", "price_min",
"price_max", "treatment_time","maintain_time", "recover_time","y","z")\
.write.format("tfrecords").option("recordType", "Example").save(path=path+"va/", mode="overwrite")
.repartition(1).write.format("tfrecords").option("recordType", "Example").save(path=path+"va/", mode="overwrite")
print("va write done")
spark.createDataFrame(train).toDF("app_list","level2_ids","level3_ids","stat_date","ucity_id", "ccity_name", "device_type", "manufacturer",
"channel", "top", "time", "hospital_id","treatment_method", "price_min",
"price_max", "treatment_time","maintain_time", "recover_time","y","z")\
.write.format("tfrecords").option("recordType", "Example").save(path=path+"tr/", mode="overwrite")
"price_max", "treatment_time","maintain_time", "recover_time","y","z") \
.repartition(1).write.format("tfrecords").option("recordType", "Example").save(path=path+"tr/", mode="overwrite")
print("done")
rdd.unpersist()
......@@ -178,7 +165,7 @@ def get_predict(date,value_map,app_list_map,level2_map,level3_map):
.toDF("app_list", "level2_ids", "level3_ids","y","z","ucity_id",
"ccity_name", "device_type","manufacturer", "channel", "time", "hospital_id",
"treatment_method", "price_min", "price_max", "treatment_time", "maintain_time",
"recover_time", "top","stat_date").write.format("tfrecords").option("recordType", "Example")\
"recover_time", "top","stat_date").repartition(1).write.format("tfrecords").option("recordType", "Example") \
.save(path=path+"native/", mode="overwrite")
nearby_pre = spark.createDataFrame(rdd.filter(lambda x: x[6] == 1).map(lambda x: (x[3], x[4], x[5]))) \
......@@ -194,7 +181,7 @@ def get_predict(date,value_map,app_list_map,level2_map,level3_map):
.toDF("app_list", "level2_ids", "level3_ids","y","z", "ucity_id",
"ccity_name", "device_type", "manufacturer", "channel", "time", "hospital_id",
"treatment_method", "price_min", "price_max", "treatment_time", "maintain_time",
"recover_time","top","stat_date").write.format("tfrecords").option("recordType", "Example")\
"recover_time","top","stat_date").repartition(1).write.format("tfrecords").option("recordType", "Example") \
.save(path=path+"nearby/", mode="overwrite")
rdd.unpersist()
......@@ -235,7 +222,7 @@ if __name__ == '__main__':
spark = SparkSession.builder.config(conf=sparkConf).enableHiveSupport().getOrCreate()
ti = pti.TiContext(spark)
ti.tidbMapDatabase("jerry_test")
# ti.tidbMapDatabase("eagle")
ti.tidbMapDatabase("eagle")
spark.sparkContext.setLogLevel("WARN")
path = "hdfs:///strategy/esmm/"
local_path = "/home/gmuser/test/"
......
tensnsorflow/train_multi.py 0 → 100644
View file @ 7c880884
#coding=utf-8
#from __future__ import absolute_import
#from __future__ import division
#from __future__ import print_function
#import argparse
import shutil
import os
import json
import glob
from datetime import date, timedelta
import random
import tensorflow as tf
#################### CMD Arguments ####################
FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_integer("dist_mode", 0, "distribuion mode {0-loacal, 1-single_dist, 2-multi_dist}")
tf.app.flags.DEFINE_string("ps_hosts", '', "Comma-separated list of hostname:port pairs")
tf.app.flags.DEFINE_string("worker_hosts", '', "Comma-separated list of hostname:port pairs")
tf.app.flags.DEFINE_string("job_name", '', "One of 'ps', 'worker'")
tf.app.flags.DEFINE_integer("task_index", 0, "Index of task within the job")
tf.app.flags.DEFINE_integer("num_threads", 16, "Number of threads")
tf.app.flags.DEFINE_integer("feature_size", 0, "Number of features")
tf.app.flags.DEFINE_integer("field_size", 0, "Number of common fields")
tf.app.flags.DEFINE_integer("embedding_size", 32, "Embedding size")
tf.app.flags.DEFINE_integer("num_epochs", 10, "Number of epochs")
tf.app.flags.DEFINE_integer("batch_size", 64, "Number of batch size")
tf.app.flags.DEFINE_integer("log_steps", 1000, "save summary every steps")
tf.app.flags.DEFINE_float("learning_rate", 0.0005, "learning rate")
tf.app.flags.DEFINE_float("l2_reg", 0.0001, "L2 regularization")
tf.app.flags.DEFINE_string("loss_type", 'log_loss', "loss type {square_loss, log_loss}")
tf.app.flags.DEFINE_float("ctr_task_wgt", 0.5, "loss weight of ctr task")
tf.app.flags.DEFINE_string("optimizer", 'Adam', "optimizer type {Adam, Adagrad, GD, Momentum}")
tf.app.flags.DEFINE_string("deep_layers", '256,128,64', "deep layers")
tf.app.flags.DEFINE_string("dropout", '0.5,0.5,0.5', "dropout rate")
tf.app.flags.DEFINE_boolean("batch_norm", False, "perform batch normaization (True or False)")
tf.app.flags.DEFINE_float("batch_norm_decay", 0.9, "decay for the moving average(recommend trying decay=0.9)")
tf.app.flags.DEFINE_string("data_dir", '', "data dir")
tf.app.flags.DEFINE_string("dt_dir", '', "data dt partition")
tf.app.flags.DEFINE_string("model_dir", '', "model check point dir")
tf.app.flags.DEFINE_string("servable_model_dir", '', "export servable model for TensorFlow Serving")
tf.app.flags.DEFINE_string("task_type", 'train', "task type {train, infer, eval, export}")
tf.app.flags.DEFINE_boolean("clear_existing_model", False, "clear existing model or not")
#40362692,0,0,216:9342395:1.0 301:9351665:1.0 205:7702673:1.0 206:8317829:1.0 207:8967741:1.0 508:9356012:2.30259 210:9059239:1.0 210:9042796:1.0 210:9076972:1.0 210:9103884:1.0 210:9063064:1.0 127_14:3529789:2.3979 127_14:3806412:2.70805
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([FLAGS.field_size], 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)
}
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']
if FLAGS.task_type != "infer":
y = labels['y']
z = labels['z']
#------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")
# 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], axis=1)
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)
if FLAGS.batch_norm:
x_cvr = batch_norm_layer(x_cvr, train_phase=train_phase, scope_bn='cvr_bn_%d' %i) #放在RELU之后 https://github.com/ducha-aiki/caffenet-benchmark/blob/master/batchnorm.md#bn----before-or-after-relu
if mode == tf.estimator.ModeKeys.TRAIN:
x_cvr = tf.nn.dropout(x_cvr, keep_prob=dropout[i]) #Apply Dropout after all BN layers and set dropout=0.8(drop_ratio=0.2)
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)
if FLAGS.batch_norm:
x_ctr = batch_norm_layer(x_ctr, train_phase=train_phase, scope_bn='ctr_bn_%d' %i) #放在RELU之后 https://github.com/ducha-aiki/caffenet-benchmark/blob/master/batchnorm.md#bn----before-or-after-relu
if mode == tf.estimator.ModeKeys.TRAIN:
x_ctr = tf.nn.dropout(x_ctr, keep_prob=dropout[i]) #Apply Dropout after all BN layers and set dropout=0.8(drop_ratio=0.2)
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={"pcvr": pcvr, "pctr": pctr, "pctcvr": pctcvr}
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)
if FLAGS.task_type != "infer":
#------bulid loss------
ctr_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=y_ctr, labels=y))
#cvr_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=y_ctcvr, labels=z))
cvr_loss = tf.reduce_mean(tf.losses.log_loss(predictions=pctcvr, labels=z))
loss = ctr_task_wgt * ctr_loss + (1 -ctr_task_wgt) * cvr_loss + l2_reg * tf.nn.l2_loss(Feat_Emb)
tf.summary.scalar('ctr_loss', ctr_loss)
tf.summary.scalar('cvr_loss', cvr_loss)
# Provide an estimator spec for `ModeKeys.EVAL`
eval_metric_ops = {
"CTR_AUC": tf.metrics.auc(y, pctr),
#"CTR_F1": tf.contrib.metrics.f1_score(y,pctr),
#"CTR_Precision": tf.metrics.precision(y,pctr),
#"CTR_Recall": tf.metrics.recall(y,pctr),
"CVR_AUC": tf.metrics.auc(z, pcvr),
"CTCVR_AUC": tf.metrics.auc(z, pctcvr)
}
if mode == tf.estimator.ModeKeys.EVAL:
return tf.estimator.EstimatorSpec(
mode=mode,
predictions=predictions,
loss=loss,
eval_metric_ops=eval_metric_ops)
#------bulid optimizer------
if FLAGS.optimizer == 'Adam':
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate, beta1=0.9, beta2=0.999, epsilon=1e-8)
elif FLAGS.optimizer == 'Adagrad':
optimizer = tf.train.AdagradOptimizer(learning_rate=learning_rate, initial_accumulator_value=1e-8)
elif FLAGS.optimizer == 'Momentum':
optimizer = tf.train.MomentumOptimizer(learning_rate=learning_rate, momentum=0.95)
elif FLAGS.optimizer == 'ftrl':
optimizer = tf.train.FtrlOptimizer(learning_rate)
train_op = optimizer.minimize(loss, global_step=tf.train.get_global_step())
# Provide an estimator spec for `ModeKeys.TRAIN` modes
if mode == tf.estimator.ModeKeys.TRAIN:
return tf.estimator.EstimatorSpec(
mode=mode,
predictions=predictions,
loss=loss,
train_op=train_op)
def batch_norm_layer(x, train_phase, scope_bn):
bn_train = tf.contrib.layers.batch_norm(x, decay=FLAGS.batch_norm_decay, center=True, scale=True, updates_collections=None, is_training=True, reuse=None, scope=scope_bn)
bn_infer = tf.contrib.layers.batch_norm(x, decay=FLAGS.batch_norm_decay, center=True, scale=True, updates_collections=None, is_training=False, reuse=True, scope=scope_bn)
z = tf.cond(tf.cast(train_phase, tf.bool), lambda: bn_train, lambda: bn_infer)
return z
def set_dist_env():
if FLAGS.dist_mode == 1: # 本地分布式测试模式1 chief, 1 ps, 1 evaluator
ps_hosts = FLAGS.ps_hosts.split(',')
chief_hosts = FLAGS.chief_hosts.split(',')
task_index = FLAGS.task_index
job_name = FLAGS.job_name
print('ps_host', ps_hosts)
print('chief_hosts', chief_hosts)
print('job_name', job_name)
print('task_index', str(task_index))
# 无worker参数
tf_config = {
'cluster': {'chief': chief_hosts, 'ps': ps_hosts},
'task': {'type': job_name, 'index': task_index }
}
print(json.dumps(tf_config))
os.environ['TF_CONFIG'] = json.dumps(tf_config)
elif FLAGS.dist_mode == 2: # 集群分布式模式
ps_hosts = FLAGS.ps_hosts.split(',')
worker_hosts = FLAGS.worker_hosts.split(',')
chief_hosts = worker_hosts[0:1] # get first worker as chief
worker_hosts = worker_hosts[2:] # the rest as worker
task_index = FLAGS.task_index
job_name = FLAGS.job_name
print('ps_host', ps_hosts)
print('worker_host', worker_hosts)
print('chief_hosts', chief_hosts)
print('job_name', job_name)
print('task_index', str(task_index))
# use #worker=0 as chief
if job_name == "worker" and task_index == 0:
job_name = "chief"
# use #worker=1 as evaluator
if job_name == "worker" and task_index == 1:
job_name = 'evaluator'
task_index = 0
# the others as worker
if job_name == "worker" and task_index > 1:
task_index -= 2
tf_config = {
'cluster': {'chief': chief_hosts, 'worker': worker_hosts, 'ps': ps_hosts},
'task': {'type': job_name, 'index': task_index }
}
print(json.dumps(tf_config))
os.environ['TF_CONFIG'] = json.dumps(tf_config)
def main(_):
#------check Arguments------
if FLAGS.dt_dir == "":
FLAGS.dt_dir = (date.today() + timedelta(-1)).strftime('%Y%m%d')
FLAGS.model_dir = FLAGS.model_dir + FLAGS.dt_dir
#FLAGS.data_dir = FLAGS.data_dir + FLAGS.dt_dir
print('task_type ', FLAGS.task_type)
print('model_dir ', FLAGS.model_dir)
print('data_dir ', FLAGS.data_dir)
print('dt_dir ', FLAGS.dt_dir)
print('num_epochs ', FLAGS.num_epochs)
print('feature_size ', FLAGS.feature_size)
print('field_size ', FLAGS.field_size)
print('embedding_size ', FLAGS.embedding_size)
print('batch_size ', FLAGS.batch_size)
print('deep_layers ', FLAGS.deep_layers)
print('dropout ', FLAGS.dropout)
print('loss_type ', FLAGS.loss_type)
print('optimizer ', FLAGS.optimizer)
print('learning_rate ', FLAGS.learning_rate)
print('l2_reg ', FLAGS.l2_reg)
print('ctr_task_wgt ', FLAGS.ctr_task_wgt)
#------init Envs------
path = "hdfs:///strategy/esmm/"
tr_files = [path+"tr/part-r-00000"]
va_files = [path+"va/part-r-00000"]
# tr_files = glob.glob("%s/tr/*tfrecord" % FLAGS.data_dir)
# random.shuffle(tr_files)
# print("tr_files:", tr_files)
# va_files = glob.glob("%s/va/*tfrecord" % FLAGS.data_dir)
# print("va_files:", va_files)
# te_files = glob.glob("%s/*tfrecord" % FLAGS.data_dir)
# print("te_files:", te_files)
if FLAGS.clear_existing_model:
try:
shutil.rmtree(FLAGS.model_dir)
except Exception as e:
print(e, "at clear_existing_model")
else:
print("existing model cleaned at %s" % FLAGS.model_dir)
set_dist_env()
#------bulid Tasks------
model_params = {
"field_size": FLAGS.field_size,
"feature_size": FLAGS.feature_size,
"embedding_size": FLAGS.embedding_size,
"learning_rate": FLAGS.learning_rate,
"l2_reg": FLAGS.l2_reg,
"deep_layers": FLAGS.deep_layers,
"dropout": FLAGS.dropout,
"ctr_task_wgt":FLAGS.ctr_task_wgt
}
config = tf.estimator.RunConfig().replace(session_config = tf.ConfigProto(device_count={'GPU':0, 'CPU':FLAGS.num_threads}),
log_step_count_steps=FLAGS.log_steps, save_summary_steps=FLAGS.log_steps)
Estimator = tf.estimator.Estimator(model_fn=model_fn, model_dir=FLAGS.model_dir, params=model_params, config=config)
if FLAGS.task_type == 'train':
train_spec = tf.estimator.TrainSpec(input_fn=lambda: input_fn(tr_files, num_epochs=FLAGS.num_epochs, batch_size=FLAGS.batch_size))
eval_spec = tf.estimator.EvalSpec(input_fn=lambda: input_fn(va_files, num_epochs=1, batch_size=FLAGS.batch_size), steps=None, start_delay_secs=1000, throttle_secs=1200)
result = tf.estimator.train_and_evaluate(Estimator, train_spec, eval_spec)
for key,value in sorted(result[0].items()):
print('%s: %s' % (key,value))
elif FLAGS.task_type == 'eval':
result = Estimator.evaluate(input_fn=lambda: input_fn(va_files, num_epochs=1, batch_size=FLAGS.batch_size))
for key,value in sorted(result.items()):
print('%s: %s' % (key,value))
elif FLAGS.task_type == 'infer':
preds = Estimator.predict(input_fn=lambda: input_fn(te_files, num_epochs=1, batch_size=FLAGS.batch_size), predict_keys=["pctcvr","pctr","pcvr"])
with open(FLAGS.data_dir+"/pred.txt", "w") as fo:
print("-"*100)
with open(FLAGS.data_dir + "/pred.txt", "w") as fo:
for prob in preds:
fo.write("%f\t%f\t%f\n" % (prob['pctr'], prob['pcvr'], prob['pctcvr']))
elif FLAGS.task_type == 'export':
print("Not Implemented, Do It Yourself!")
if __name__ == "__main__":
tf.logging.set_verbosity(tf.logging.INFO)
tf.app.run()
\ No newline at end of file
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