Merge branch 'zhao' into 'master'

esmm模型增加日记是否是视频日记特征

See merge request !22

eda/esmm/Model_pipline/feature_engineering.py

@@ -159,6 +159,8 @@ def feature_engineer():
     sql = "select distinct recover_time from knowledge"
     unique_values.extend(get_unique(db, sql))
 
+    unique_values.append("video")
+
     db = pymysql.connect(host='172.16.40.158', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
     sql = "select max(stat_date) from esmm_train_data_dwell"
     validate_date = con_sql(db, sql)[0].values.tolist()[0]
@@ -177,15 +179,15 @@ def feature_engineer():
                 "channel", "top", "time", "stat_date", "hospital_id",
                 "treatment_method", "price_min", "price_max", "treatment_time", "maintain_time", "recover_time",
                 "app_list", "level3_ids", "level2_ids", "tag1", "tag2", "tag3", "tag4", "tag5", "tag6", "tag7",
-                "search_tag2", "search_tag3"]
+                "search_tag2", "search_tag3","is_video"]
     unique_values.extend(features)
     print("unique_values length")
     print(len(unique_values))
     print("特征维度：")
     print(apps_number + level2_number + level3_number + len(unique_values))
 
-    temp = list(range(28 + apps_number + level2_number + level3_number,
-                      28 + apps_number + level2_number + level3_number + len(unique_values)))
+    temp = list(range(29 + apps_number + level2_number + level3_number,
+                      29 + apps_number + level2_number + level3_number + len(unique_values)))
     value_map = dict(zip(unique_values, temp))
 
     sql = "select e.y,e.z,e.stat_date,e.ucity_id,feat.level2_ids,e.ccity_name,u.device_type,u.manufacturer," \
@@ -193,7 +195,7 @@ def feature_engineer():
           "wiki.tag as tag1,question.tag as tag2,search.tag as tag3,budan.tag as tag4," \
           "ot.tag as tag5,sixin.tag as tag6,cart.tag as tag7,doris.search_tag2,doris.search_tag3," \
           "k.treatment_method,k.price_min,k.price_max,k.treatment_time,k.maintain_time,k.recover_time," \
-          "e.device_id,e.cid_id " \
+          "e.device_id,e.cid_id,video.is_video " \
           "from jerry_test.esmm_train_data_dwell 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 " \
@@ -210,13 +212,14 @@ def feature_engineer():
           "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 " \
           "left join jerry_test.search_doris doris on e.device_id = doris.device_id and e.stat_date = doris.get_date " \
+          "left join jerry_prod.diary_video video on e.cid_id = video.cid and e.stat_date = video.stat_date " \
           "where e.stat_date >= '{}'".format(start)
 
     df = spark.sql(sql)
 
     df = df.drop_duplicates(["ucity_id", "level2_ids", "ccity_name", "device_type", "manufacturer",
                              "channel", "top", "time", "stat_date", "app_list", "hospital_id", "level3_ids",
-                             "tag1", "tag2", "tag3", "tag4", "tag5", "tag6", "tag7"])
+                             "tag1", "tag2", "tag3", "tag4", "tag5", "tag6", "tag7","is_video"])
 
     df = df.na.fill(dict(zip(features, features)))
 
@@ -224,7 +227,7 @@ def feature_engineer():
                     "tag1", "tag2", "tag3", "tag4", "tag5", "tag6", "tag7",
                     "ucity_id", "ccity_name", "device_type", "manufacturer", "channel", "top", "time",
                     "hospital_id", "treatment_method", "price_min", "price_max", "treatment_time",
-                    "maintain_time", "recover_time", "search_tag2", "search_tag3","cid_id","device_id")\
+                    "maintain_time", "recover_time", "search_tag2", "search_tag3","is_video","cid_id","device_id")\
         .rdd.repartition(200).map(
         lambda x: (x[0], float(x[1]), float(x[2]), app_list_func(x[3], app_list_map), app_list_func(x[4], leve2_map),
                    app_list_func(x[5], leve3_map), app_list_func(x[6], leve2_map), app_list_func(x[7], leve2_map),
@@ -234,8 +237,8 @@ def feature_engineer():
                     value_map.get(x[16], 5), value_map.get(x[17], 6), value_map.get(x[18], 7), value_map.get(x[19], 8),
                     value_map.get(x[20], 9), value_map.get(x[21], 10),
                     value_map.get(x[22], 11), value_map.get(x[23], 12), value_map.get(x[24], 13),
-                    value_map.get(x[25], 14), value_map.get(x[26], 15)],
-                   app_list_func(x[27], leve2_map), app_list_func(x[28], leve3_map),x[13],x[29],x[30]
+                    value_map.get(x[25], 14), value_map.get(x[26], 15),value_map.get(x[29], 16)],
+                   app_list_func(x[27], leve2_map), app_list_func(x[28], leve3_map),x[13],x[30],x[31]
                    ))
 
 
@@ -283,7 +286,7 @@ def get_predict(date,value_map,app_list_map,leve2_map,leve3_map):
           "u.device_type,u.manufacturer,u.channel,c.top,e.device_id,e.cid_id,cut.time," \
           "dl.app_list,e.hospital_id,feat.level3_ids," \
           "wiki.tag as tag1,question.tag as tag2,search.tag as tag3,budan.tag as tag4," \
-          "ot.tag as tag5,sixin.tag as tag6,cart.tag as tag7,doris.search_tag2,doris.search_tag3," \
+          "ot.tag as tag5,sixin.tag as tag6,cart.tag as tag7,doris.search_tag2,doris.search_tag3,video.is_video," \
           "k.treatment_method,k.price_min,k.price_max,k.treatment_time,k.maintain_time,k.recover_time " \
           "from jerry_test.esmm_pre_data e " \
           "left join jerry_test.user_feature u on e.device_id = u.device_id " \
@@ -299,13 +302,14 @@ 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"
+          "left join jerry_test.search_doris doris on e.device_id = doris.device_id and e.stat_date = doris.get_date " \
+          "left join jerry_prod.diary_video video on e.cid_id = video.cid and e.stat_date = video.stat_date"
 
     features = ["ucity_id", "ccity_name", "device_type", "manufacturer",
                 "channel", "top", "time", "hospital_id",
                 "treatment_method", "price_min", "price_max", "treatment_time", "maintain_time", "recover_time",
                 "app_list", "level3_ids", "level2_ids", "tag1", "tag2", "tag3", "tag4", "tag5", "tag6", "tag7",
-                "search_tag2", "search_tag3"]
+                "search_tag2", "search_tag3","is_video"]
 
     df = spark.sql(sql)
     df = df.drop_duplicates(["ucity_id", "device_id", "cid_id"])
@@ -316,7 +320,7 @@ def get_predict(date,value_map,app_list_map,leve2_map,leve3_map):
                     "tag1", "tag2", "tag3", "tag4", "tag5", "tag6", "tag7",
                     "ucity_id", "ccity_name", "device_type", "manufacturer", "channel", "top", "time",
                     "hospital_id", "treatment_method", "price_min", "price_max", "treatment_time",
-                    "maintain_time", "recover_time", "search_tag2", "search_tag3") \
+                    "maintain_time", "recover_time", "search_tag2", "search_tag3","is_video") \
         .rdd.repartition(200).map(lambda x: (x[0], float(x[1]), float(x[2]), x[3], x[4], x[5],
                                              app_list_func(x[6], app_list_map), app_list_func(x[7], leve2_map),
                                              app_list_func(x[8], leve3_map), app_list_func(x[9], leve2_map),
@@ -330,12 +334,13 @@ def get_predict(date,value_map,app_list_map,leve2_map,leve3_map):
                                               value_map.get(x[23], 9), value_map.get(x[24], 10),
                                               value_map.get(x[25], 11), value_map.get(x[26], 12),
                                               value_map.get(x[27], 13), value_map.get(x[28], 14),
-                                              value_map.get(x[29], 15)], app_list_func(x[30], leve2_map),
-                                             app_list_func(x[31], leve3_map)))
+                                              value_map.get(x[29], 15),value_map.get(x[32], 16)],
+                                             app_list_func(x[30], leve2_map),app_list_func(x[31], leve3_map)))
 
 
 
     rdd.persist(storageLevel= StorageLevel.MEMORY_ONLY_SER)
+    print("预测集样本大小：")
     print(rdd.count())
 
     spark.createDataFrame(rdd.filter(lambda x: x[0] == 0)
@@ -369,6 +374,7 @@ if __name__ == '__main__':
     spark = SparkSession.builder.config(conf=sparkConf).enableHiveSupport().getOrCreate()
     ti = pti.TiContext(spark)
     ti.tidbMapDatabase("jerry_test")
+    ti.tidbMapDatabase("jerry_prod")
     ti.tidbMapDatabase("eagle")
     spark.sparkContext.setLogLevel("WARN")
     path = "hdfs:///strategy/esmm/"

eda/esmm/Model_pipline/submit.sh

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