把ctr参数改回去

eda/esmm/Model_pipline/feature_engineering.py

@@ -301,8 +301,7 @@ def get_predict(date,value_map,app_list_map,leve2_map,leve3_map):
           "left join jerry_test.sixin_tag sixin on e.device_id = sixin.device_id " \
           "left join jerry_test.cart_tag cart on e.device_id = cart.device_id " \
           "left join jerry_test.knowledge k on feat.level2 = k.level2_id " \
-          "left join jerry_test.search_doris doris on e.device_id = doris.device_id and e.stat_date = doris.get_date " \
-          "where e.device_id = '355374101417079' and e.ucity_id = 'beijing'"
+          "left join jerry_test.search_doris doris on e.device_id = doris.device_id and e.stat_date = doris.get_date"
 
     features = ["ucity_id", "ccity_name", "device_type", "manufacturer",
                 "channel", "top", "time", "hospital_id",

eda/esmm/Model_pipline/submit.sh

@@ -19,11 +19,11 @@ echo "rm model file"
 b=`date +%Y%m%d`
 
 echo "train..."
-${PYTHON_PATH} ${MODEL_PATH}/train.py --ctr_task_wgt=0.7 --learning_rate=0.0001 --deep_layers=512,256,128,64,32 --dropout=0.3,0.3,0.3,0.3,0.3 --optimizer=Adam --num_epochs=1 --embedding_size=16 --batch_size=10000 --field_size=15 --feature_size=600000 --l2_reg=0.005 --log_steps=100 --num_threads=36 --model_dir=${HDFS_PATH}/model_ckpt/DeepCvrMTL/ --local_dir=${LOCAL_PATH} --hdfs_dir=${HDFS_PATH}/native --task_type=train
+${PYTHON_PATH} ${MODEL_PATH}/train.py --ctr_task_wgt=0.5 --learning_rate=0.0001 --deep_layers=512,256,128,64,32 --dropout=0.3,0.3,0.3,0.3,0.3 --optimizer=Adam --num_epochs=1 --embedding_size=16 --batch_size=10000 --field_size=15 --feature_size=600000 --l2_reg=0.005 --log_steps=100 --num_threads=36 --model_dir=${HDFS_PATH}/model_ckpt/DeepCvrMTL/ --local_dir=${LOCAL_PATH} --hdfs_dir=${HDFS_PATH}/native --task_type=train
 
 echo "infer native..."
-${PYTHON_PATH} ${MODEL_PATH}/train.py --ctr_task_wgt=0.7 --learning_rate=0.0001 --deep_layers=512,256,128,64,32 --dropout=0.3,0.3,0.3,0.3,0.3 --optimizer=Adam --num_epochs=1 --embedding_size=16 --batch_size=10000 --field_size=15 --feature_size=600000 --l2_reg=0.005 --log_steps=100 --num_threads=36 --model_dir=${HDFS_PATH}/model_ckpt/DeepCvrMTL/ --local_dir=${LOCAL_PATH}/native --hdfs_dir=${HDFS_PATH}/native --task_type=infer
+${PYTHON_PATH} ${MODEL_PATH}/train.py --ctr_task_wgt=0.5 --learning_rate=0.0001 --deep_layers=512,256,128,64,32 --dropout=0.3,0.3,0.3,0.3,0.3 --optimizer=Adam --num_epochs=1 --embedding_size=16 --batch_size=10000 --field_size=15 --feature_size=600000 --l2_reg=0.005 --log_steps=100 --num_threads=36 --model_dir=${HDFS_PATH}/model_ckpt/DeepCvrMTL/ --local_dir=${LOCAL_PATH}/native --hdfs_dir=${HDFS_PATH}/native --task_type=infer
 
 echo "infer nearby..."
-${PYTHON_PATH} ${MODEL_PATH}/train.py --ctr_task_wgt=0.7 --learning_rate=0.0001 --deep_layers=512,256,128,64,32 --dropout=0.3,0.3,0.3,0.3,0.3 --optimizer=Adam --num_epochs=1 --embedding_size=16 --batch_size=10000 --field_size=15 --feature_size=600000 --l2_reg=0.005 --log_steps=100 --num_threads=36 --model_dir=${HDFS_PATH}/model_ckpt/DeepCvrMTL/ --local_dir=${LOCAL_PATH}/nearby --hdfs_dir=${HDFS_PATH}/nearby --task_type=infer
+${PYTHON_PATH} ${MODEL_PATH}/train.py --ctr_task_wgt=0.5 --learning_rate=0.0001 --deep_layers=512,256,128,64,32 --dropout=0.3,0.3,0.3,0.3,0.3 --optimizer=Adam --num_epochs=1 --embedding_size=16 --batch_size=10000 --field_size=15 --feature_size=600000 --l2_reg=0.005 --log_steps=100 --num_threads=36 --model_dir=${HDFS_PATH}/model_ckpt/DeepCvrMTL/ --local_dir=${LOCAL_PATH}/nearby --hdfs_dir=${HDFS_PATH}/nearby --task_type=infer