test

tensnsorflow/es/feature.py

@@ -18,6 +18,19 @@ def con_sql(db,sql):
     return df
 
 
+def multi_hot(df,column,n):
+    df[column] = df[column].fillna("lost_na")
+    app_list_value = [i.split(",") for i in df[column].unique()]
+    app_list_unique = []
+    for i in app_list_value:
+        app_list_unique.extend(i)
+    app_list_unique = list(set(app_list_unique))
+    number = len(app_list_unique)
+    app_list_map = dict(zip(app_list_unique, list(range(n, number + n))))
+    df[column] = df[column].apply(app_list_func, args=(app_list_map,))
+    return number,app_list_map
+
+
 def get_data():
     db = pymysql.connect(host='10.66.157.22', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
     sql = "select max(stat_date) from {}".format(train_data_set)
@@ -27,17 +40,18 @@ def get_data():
     start = (temp - datetime.timedelta(days=20)).strftime("%Y-%m-%d")
     print(start)
     db = pymysql.connect(host='10.66.157.22', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
-    sql = "select e.y,e.z,e.stat_date,e.ucity_id,e.clevel1_id,e.ccity_name," \
-          "u.device_type,u.manufacturer,u.channel,c.top,cl.l1,cl.l2,e.device_id,cut.time,dl.app_list " \
+    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 " \
           "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_level2 cl on e.cid_id = cl.cid " \
           "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 " \
           "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: "clevel1_id", 5: "ccity_name",
+    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: "l1",11: "l2",
                             12: "device_id", 13: "time",14:"app_list"})
     print("esmm data ok")
@@ -46,30 +60,23 @@ def get_data():
     print(df.shape)
     print("after")
     df = df.drop_duplicates()
-    df = df.drop_duplicates(["ucity_id", "clevel1_id", "ccity_name", "device_type", "manufacturer",
+    df = df.drop_duplicates(["ucity_id", "clevel2_id", "ccity_name", "device_type", "manufacturer",
                              "channel", "top", "l1","l2", "time", "stat_date","app_list"])
 
-    df["app_list"] = df["app_list"].fillna("lost_na")
-    app_list_value = [i.split(",") for i in df["app_list"].unique()]
-    app_list_unique = []
-    for i in app_list_value:
-        app_list_unique.extend(i)
-    app_list_unique = list(set(app_list_unique))
-    app_list_map = dict(zip(app_list_unique, list(range(1, len(app_list_unique) + 1))))
-    df["app_list"] = df["app_list"].apply(app_list_func,args=(app_list_map,))
-    print("after applist map")
-    print(df.head(2))
+    app_list_number,app_list_map = multi_hot(df,"app_list",1)
+    level2_number,level2_map = multi_hot(df,"clevel2_id",1+app_list_number)
+    # df["app_list"] = df["app_list"].fillna("lost_na")
+    # app_list_value = [i.split(",") for i in df["app_list"].unique()]
+    # app_list_unique = []
+    # for i in app_list_value:
+    #     app_list_unique.extend(i)
+    # app_list_unique = list(set(app_list_unique))
+    # app_list_map = dict(zip(app_list_unique, list(range(1, len(app_list_unique) + 1))))
+    # df["app_list"] = df["app_list"].apply(app_list_func,args=(app_list_map,))
 
-    # print(df.shape)
-    # print("exp numbers:")
-    # print(df[df["y"] == 0].shape)
-    # print("click numbers")
-    # print(df[(df["y"] == 1)&(df["z"] == 0)].shape)
-    # print("buy numbers")
-    # print(df[(df["y"] == 1) & (df["z"] == 1)].shape)
 
     unique_values = []
-    features = ["ucity_id", "clevel1_id", "ccity_name", "device_type", "manufacturer",
+    features = ["ucity_id", "ccity_name", "device_type", "manufacturer",
                 "channel", "top", "time", "stat_date"]
     for i in features:
         df[i] = df[i].astype("str")
@@ -88,13 +95,13 @@ def get_data():
     print(len(unique_values))
     print(df.head(2))
 
-    temp = list(range(len(app_list_unique) + 1,len(app_list_unique) + len(unique_values)+1))
+    temp = list(range(1+app_list_number+level2_number, 1 + app_list_number+level2_number + len(unique_values)))
     value_map = dict(zip(unique_values,temp))
 
     df = df.drop("device_id", axis=1)
     train = df[df["stat_date"] != validate_date+"stat_date"]
     test = df[df["stat_date"] == validate_date+"stat_date"]
-    for i in ["ucity_id", "clevel1_id", "ccity_name", "device_type", "manufacturer",
+    for i in ["ucity_id", "ccity_name", "device_type", "manufacturer",
                 "channel", "top", "l1", "time", "stat_date","l2"]:
         train[i] = train[i].map(value_map)
         test[i] = test[i].map(value_map)

tensnsorflow/es/train.py

@@ -110,10 +110,7 @@ def model_fn(features, labels, mode, params):
     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")
-        print("a")
-        print(embedding_id.shape)
-        print("b")
-        print(app_id.shape)
+
         # 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], axis=1)