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ffm-baseline
Commit 88db6d8d authored by 张彦钊's avatar 张彦钊
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eda/esmm/Feature_pipline/data2ffm.py
View file @ 88db6d8d
...@@ -254,7 +254,7 @@ def get_predict_set(model,columns): ...@@ -254,7 +254,7 @@ def get_predict_set(model,columns):
df = df.fillna("na") df = df.fillna("na")
print("before transform") print("before transform")
print(df.shape) print(df.shape)
print(df.count()) # print(df.count())
temp_series = model.transform(df,n=160000, processes=22) temp_series = model.transform(df,n=160000, processes=22)
df = pd.DataFrame(temp_series) df = pd.DataFrame(temp_series)
print("after transform") print("after transform")
......
eda/esmm/Feature_pipline/get_tfrecord.py
View file @ 88db6d8d
...@@ -26,7 +26,7 @@ tf.app.flags.DEFINE_integer("threads", 16, "threads num") ...@@ -26,7 +26,7 @@ tf.app.flags.DEFINE_integer("threads", 16, "threads num")
#Ad_Fileds = set(['205','206','207','210','216']) #Ad_Fileds = set(['205','206','207','210','216'])
#Context_Fileds = set(['508','509','702','853','301']) #Context_Fileds = set(['508','509','702','853','301'])
#Common_Fileds = {'1':'1','2':'2','3':'3','4':'4','5':'5','6':'6','7':'7','8':'8','9':'9','10':'10','11':'11','12':'12','13':'13','14':'14','15':'15','16':'16','17':'17','18':'18','19':'19','20':'20','21':'21','22':'22','23':'23'} #Common_Fileds = {'1':'1','2':'2','3':'3','4':'4','5':'5','6':'6','7':'7','8':'8','9':'9','10':'10','11':'11','12':'12','13':'13','14':'14','15':'15','16':'16','17':'17','18':'18','19':'19','20':'20','21':'21','22':'22','23':'23'}
Common_Fileds = {'1':'1','2':'2','3':'3','4':'4','5':'5','6':'6','7':'7','8':'8'} Common_Fileds = {'1':'1','2':'2','3':'3','4':'4','5':'5','6':'6','7':'7','8':'8','9':'9'}
UMH_Fileds = {'109_14':('u_cat','12'),'110_14':('u_shop','13'),'127_14':('u_brand','14'),'150_14':('u_int','15')} #user multi-hot feature UMH_Fileds = {'109_14':('u_cat','12'),'110_14':('u_shop','13'),'127_14':('u_brand','14'),'150_14':('u_int','15')} #user multi-hot feature
Ad_Fileds = {'206':('a_cat','16'),'207':('a_shop','17'),'210':('a_int','18'),'216':('a_brand','19')} #ad feature for DIN Ad_Fileds = {'206':('a_cat','16'),'207':('a_shop','17'),'210':('a_int','18'),'216':('a_brand','19')} #ad feature for DIN
......
eda/esmm/Model_pipline/submit.sh
View file @ 88db6d8d
...@@ -55,7 +55,7 @@ currentTimeStamp=$((timeStamp*1000+`date "+%N"`/1000000)) ...@@ -55,7 +55,7 @@ currentTimeStamp=$((timeStamp*1000+`date "+%N"`/1000000))
echo $current echo $current
echo "train..." echo "train..."
${PYTHON_PATH} ${MODEL_PATH}/Model_pipline/DeepCvrMTL.py --ctr_task_wgt=0.3 --learning_rate=0.0001 --deep_layers=256,128 --dropout=0.8,0.5 --optimizer=Adam --num_epochs=1 --embedding_size=16 --batch_size=1024 --field_size=8 --feature_size=2000 --l2_reg=0.005 --log_steps=100 --num_threads=36 --model_dir=${DATA_PATH}/model_ckpt/DeepCvrMTL/ --data_dir=${DATA_PATH} --task_type=train ${PYTHON_PATH} ${MODEL_PATH}/Model_pipline/DeepCvrMTL.py --ctr_task_wgt=0.3 --learning_rate=0.0001 --deep_layers=256,128 --dropout=0.8,0.5 --optimizer=Adam --num_epochs=1 --embedding_size=16 --batch_size=1024 --field_size=9 --feature_size=2000 --l2_reg=0.005 --log_steps=100 --num_threads=36 --model_dir=${DATA_PATH}/model_ckpt/DeepCvrMTL/ --data_dir=${DATA_PATH} --task_type=train
echo "train time" echo "train time"
current=$(date "+%Y-%m-%d %H:%M:%S") current=$(date "+%Y-%m-%d %H:%M:%S")
...@@ -64,11 +64,11 @@ currentTimeStamp=$((timeStamp*1000+`date "+%N"`/1000000)) ...@@ -64,11 +64,11 @@ currentTimeStamp=$((timeStamp*1000+`date "+%N"`/1000000))
echo $current echo $current
echo "infer native..." echo "infer native..."
${PYTHON_PATH} ${MODEL_PATH}/Model_pipline/DeepCvrMTL.py --ctr_task_wgt=0.3 --learning_rate=0.0001 --deep_layers=256,128 --dropout=0.8,0.5 --optimizer=Adam --num_epochs=1 --embedding_size=16 --batch_size=1024 --field_size=8 --feature_size=2000 --l2_reg=0.005 --log_steps=100 --num_threads=36 --model_dir=${DATA_PATH}/model_ckpt/DeepCvrMTL/ --data_dir=${DATA_PATH}/native --task_type=infer > ${DATA_PATH}/infer.log ${PYTHON_PATH} ${MODEL_PATH}/Model_pipline/DeepCvrMTL.py --ctr_task_wgt=0.3 --learning_rate=0.0001 --deep_layers=256,128 --dropout=0.8,0.5 --optimizer=Adam --num_epochs=1 --embedding_size=16 --batch_size=1024 --field_size=9 --feature_size=2000 --l2_reg=0.005 --log_steps=100 --num_threads=36 --model_dir=${DATA_PATH}/model_ckpt/DeepCvrMTL/ --data_dir=${DATA_PATH}/native --task_type=infer > ${DATA_PATH}/infer.log
echo "infer nearby..." echo "infer nearby..."
${PYTHON_PATH} ${MODEL_PATH}/Model_pipline/DeepCvrMTL.py --ctr_task_wgt=0.3 --learning_rate=0.0001 --deep_layers=256,128 --dropout=0.8,0.5 --optimizer=Adam --num_epochs=1 --embedding_size=16 --batch_size=1024 --field_size=8 --feature_size=2000 --l2_reg=0.005 --log_steps=100 --num_threads=36 --model_dir=${DATA_PATH}/model_ckpt/DeepCvrMTL/ --data_dir=${DATA_PATH}/nearby --task_type=infer > ${DATA_PATH}/infer.log ${PYTHON_PATH} ${MODEL_PATH}/Model_pipline/DeepCvrMTL.py --ctr_task_wgt=0.3 --learning_rate=0.0001 --deep_layers=256,128 --dropout=0.8,0.5 --optimizer=Adam --num_epochs=1 --embedding_size=16 --batch_size=1024 --field_size=9 --feature_size=2000 --l2_reg=0.005 --log_steps=100 --num_threads=36 --model_dir=${DATA_PATH}/model_ckpt/DeepCvrMTL/ --data_dir=${DATA_PATH}/nearby --task_type=infer > ${DATA_PATH}/infer.log
echo "sort and 2sql" echo "sort and 2sql"
${PYTHON_PATH} ${MODEL_PATH}/Model_pipline/sort_and_2sql.py ${PYTHON_PATH} ${MODEL_PATH}/Model_pipline/sort_and_2sql.py
......
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