add test

tensnsorflow/applist.py

@@ -100,3 +100,6 @@ def sort_app():
 
 if __name__ == "__main__":
     sort_app()
+
+
+

tensnsorflow/ffm.py

@@ -147,9 +147,13 @@ def get_data():
     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,cid_time.time,e.device_id " \
+          "u.device_type,u.manufacturer,u.channel,c.top,cid_time.time,e.device_id," \
+          "a.competitor,a.dianshang,a.kuajing_dianshang,a.zhibo,a.youxizhibo,a.short_video,a.meitu,a.tiyu," \
+          "a.read,a.finance,a.fashion_clothes,a.muying,a.fresh,a.bijia,a.travel,a.airplane," \
+          "a.love,a.stock,a.car,a.child,a.homework,a.work,a.job " \
           "from esmm_train_data e left join user_feature_clean 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 on e.cid_id = cid_time.cid_id " \
+          "left join app_list_sort a " \
           "where e.stat_date >= '{}'".format(start)
     df = con_sql(db, sql)
     print(df.shape)
@@ -158,24 +162,25 @@ def get_data():
     print("esmm data ok")
     print(df.head(2))
 
+    for i in range(12,36):
+        df[i] = df[i].astype("str")
     df["clevel1_id"] = df["clevel1_id"].astype("str")
     df["y"] = df["y"].astype("str")
     df["z"] = df["z"].astype("str")
     df["top"] = df["top"].astype("str")
     df["y"] = df["stat_date"].str.cat([df["device_id"].values.tolist(),df["y"].values.tolist(),df["z"].values.tolist()], sep=",")
-    df = df.drop(["z","stat_date","device_id"], axis=1).fillna(0.0)
+    df = df.drop(["z","stat_date","device_id"], axis=1).fillna(0)
     print(df.head(2))
     features = 0
     for i in ["ucity_id","clevel1_id","ccity_name","device_type","manufacturer","channel"]:
         features = features + len(df[i].unique())
     print("fields:{}".format(df.shape[1]-1))
-    print("features:{}".format(features))
+    print("features:{}".format(features+48))
     ccity_name = list(set(df["ccity_name"].values.tolist()))
     ucity_id = list(set(df["ucity_id"].values.tolist()))
     manufacturer = list(set(df["manufacturer"].values.tolist()))
     channel = list(set(df["channel"].values.tolist()))
 
-
     return df,validate_date,ucity_id,ccity_name,manufacturer,channel
 
 
@@ -291,7 +296,7 @@ if __name__ == "__main__":
     a = time.time()
     temp, validate_date, ucity_id,ccity_name,manufacturer,channel = get_data()
     model = transform(temp, validate_date)
-    get_predict_set(ucity_id,model,ccity_name,manufacturer,channel)
+    # get_predict_set(ucity_id,model,ccity_name,manufacturer,channel)
     b = time.time()
     print("cost(分钟)")
     print((b-a)/60)