增加特征

eda/esmm/Model_pipline/feature.py

@@ -32,23 +32,32 @@ def get_data():
     validate_date = con_sql(db, sql)[0].values.tolist()[0]
     print("validate_date:" + validate_date)
     temp = datetime.datetime.strptime(validate_date, "%Y-%m-%d")
-    start = (temp - datetime.timedelta(days=300)).strftime("%Y-%m-%d")
+    start = (temp - datetime.timedelta(days=3)).strftime("%Y-%m-%d")
     print(start)
     db = pymysql.connect(host='172.16.40.158', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
     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,e.device_id,cut.time,dl.app_list,e.diary_service_id,feat.level3_ids,feat.level2 " \
+          "u.channel,c.top,e.device_id,cut.time,dl.app_list,e.diary_service_id,feat.level3_ids,feat.level2," \
+          "wiki.tag,question.tag,search.tag,budan.tag,order.tag,sixin.tag,cart.tag " \
           "from {} 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 wiki_tag wiki on e.device_id = wiki.device_id " \
+          "left join question_tag question on e.device_id = question.device_id " \
+          "left join search_tag search on e.device_id = search.device_id " \
+          "left join budan_tag budan on e.device_id = budan.device_id " \
+          "left join order_tag order on e.device_id = order.device_id " \
+          "left join sixin_tag sixin on e.device_id = sixin.device_id " \
+          "left join cart_tag sixin on e.device_id = cart.device_id " \
           "where e.stat_date >= '{}'".format(train_data_set, start)
 
     df = con_sql(db, sql)
     # print(df.shape)
     df = df.rename(columns={0: "y", 1: "z", 2: "stat_date", 3: "ucity_id", 4: "clevel2_id", 5: "ccity_name",
                             6: "device_type", 7: "manufacturer", 8: "channel", 9: "top", 10: "device_id",
-                            11: "time", 12: "app_list", 13: "service_id", 14: "level3_ids", 15: "level2"})
+                            11: "time", 12: "app_list", 13: "service_id", 14: "level3_ids", 15: "level2",
+                            16:"tag1",17:"tag2",18:"tag3",19:"tag4",20:"tag5",21:"tag6",22:"tag7"})
 
     db = pymysql.connect(host='172.16.40.158', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
     sql = "select level2_id,treatment_method,price_min,price_max,treatment_time,maintain_time,recover_time " \
@@ -89,6 +98,11 @@ def get_data():
     level2_number, level2_map = multi_hot(df, "clevel2_id", 2 + app_list_number)
     level3_number, level3_map = multi_hot(df, "level3_ids", 2 + app_list_number + level2_number)
 
+    for i in ["tag1","tag2","tag3","tag4","tag5","tag6","tag7"]:
+        df[i] = df[i].fillna("lost_na")
+        df[i] = df[i].apply(app_list_func, args=(level2_map,))
+
+
     unique_values = []
     features = ["ucity_id", "ccity_name", "device_type", "manufacturer",
                 "channel", "top", "time", "stat_date", "hospital_id",
@@ -105,8 +119,9 @@ def get_data():
     value_map = dict(zip(unique_values, temp))
 
     df = df.drop("device_id", axis=1)
-    # 最近一天的数据集放进训练集，这样用户的正、负反馈能及时获取
-    train = df
+    # TODO 上线后把最近一天的数据集放进训练集，这样用户的正、负反馈能及时获取
+    # train = df
+    train = df[df["stat_date"] != validate_date + "stat_date"]
     test = df[df["stat_date"] == validate_date + "stat_date"]
     for i in ["ucity_id", "ccity_name", "device_type", "manufacturer",
               "channel", "top", "time", "stat_date", "hospital_id",
@@ -151,17 +166,25 @@ def get_predict(date,value_map,app_list_map,level2_map,level3_map):
     db = pymysql.connect(host='172.16.40.158', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
     sql = "select e.y,e.z,e.label,e.ucity_id,feat.level2_ids,e.ccity_name," \
           "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,feat.level2 " \
+          "dl.app_list,e.hospital_id,feat.level3_ids,feat.level2," \
+          "wiki.tag,question.tag,search.tag,budan.tag,order.tag,sixin.tag,cart.tag " \
           "from esmm_pre_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 diary_feat feat on e.cid_id = feat.diary_id " \
+          "left join wiki_tag wiki on e.device_id = wiki.device_id " \
+          "left join question_tag question on e.device_id = question.device_id " \
+          "left join search_tag search on e.device_id = search.device_id " \
+          "left join budan_tag budan on e.device_id = budan.device_id " \
+          "left join order_tag order on e.device_id = order.device_id " \
+          "left join sixin_tag sixin on e.device_id = sixin.device_id " \
+          "left join cart_tag sixin on e.device_id = cart.device_id"
     df = con_sql(db, sql)
     df = df.rename(columns={0: "y", 1: "z", 2: "label", 3: "ucity_id", 4: "clevel2_id", 5: "ccity_name",
                             6: "device_type", 7: "manufacturer", 8: "channel", 9: "top", 10: "device_id",
                             11: "cid_id", 12: "time", 13: "app_list", 14: "hospital_id", 15: "level3_ids",
-                            16: "level2"})
+                            16: "level2",17:"tag1",18:"tag2",19:"tag3",20:"tag4",21:"tag5",22:"tag6",23:"tag7"})
 
     db = pymysql.connect(host='172.16.40.158', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
     sql = "select level2_id,treatment_method,price_min,price_max,treatment_time,maintain_time,recover_time " \
@@ -185,6 +208,10 @@ def get_predict(date,value_map,app_list_map,level2_map,level3_map):
     df["level3_ids"] = df["level3_ids"].fillna("lost_na")
     df["level3_ids"] = df["level3_ids"].apply(app_list_func, args=(level3_map,))
 
+    for i in ["tag1", "tag2", "tag3", "tag4", "tag5", "tag6", "tag7"]:
+        df[i] = df[i].fillna("lost_na")
+        df[i] = df[i].apply(app_list_func, args=(level2_map,))
+
     # print("predict shape")
     # print(df.shape)
     df["uid"] = df["device_id"]

eda/esmm/Model_pipline/to_tfrecord.py

@@ -36,13 +36,28 @@ def gen_tfrecords(in_file):
         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(","))
+        tag1_list = np.array(str(df["tag1"][i]).split(","))
+        tag2_list = np.array(str(df["tag2"][i]).split(","))
+        tag3_list = np.array(str(df["tag3"][i]).split(","))
+        tag4_list = np.array(str(df["tag4"][i]).split(","))
+        tag5_list = np.array(str(df["tag5"][i]).split(","))
+        tag6_list = np.array(str(df["tag6"][i]).split(","))
+        tag7_list = np.array(str(df["tag7"][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)))
+            "level3_list": tf.train.Feature(int64_list=tf.train.Int64List(value=level3_list.astype(np.int))),
+            "tag1_list": tf.train.Feature(int64_list=tf.train.Int64List(value=tag1_list.astype(np.int))),
+            "tag2_list": tf.train.Feature(int64_list=tf.train.Int64List(value=tag2_list.astype(np.int))),
+            "tag3_list": tf.train.Feature(int64_list=tf.train.Int64List(value=tag3_list.astype(np.int))),
+            "tag4_list": tf.train.Feature(int64_list=tf.train.Int64List(value=tag4_list.astype(np.int))),
+            "tag5_list": tf.train.Feature(int64_list=tf.train.Int64List(value=tag5_list.astype(np.int))),
+            "tag6_list": tf.train.Feature(int64_list=tf.train.Int64List(value=tag6_list.astype(np.int))),
+            "tag7_list": tf.train.Feature(int64_list=tf.train.Int64List(value=tag7_list.astype(np.int)))
         })
 
         example = tf.train.Example(features = features)

eda/esmm/Model_pipline/train.py

@@ -54,7 +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 +110,13 @@ def model_fn(features, labels, mode, params):
     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']
@@ -114,10 +128,17 @@ def model_fn(features, labels, mode, params):
         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], 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: