修改测试文件

tensnsorflow/multi.py

@@ -29,6 +29,7 @@ def multi_hot(df,column,n):
     app_list_map = dict(zip(app_list_unique, list(range(n, number + n))))
     return number,app_list_map
 
+
 def feature_engineer():
     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"
@@ -123,6 +124,87 @@ def feature_engineer():
     spark.createDataFrame(test).write.csv('/recommend/va', mode='overwrite', header=True)
     spark.createDataFrame(train).write.csv('/recommend/tr', mode='overwrite', header=True)
 
+    return validate_date,value_map,app_list_map,leve2_map,leve3_map
+
+
+# def get_predict(date,value_map,app_list_map,level2_map,level3_map):
+#
+#     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 " \
+#           "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"
+#     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"})
+#
+#     db = pymysql.connect(host='10.66.157.22', 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 " \
+#           "from train_Knowledge_network_data"
+#     knowledge = con_sql(db, sql)
+#     knowledge = knowledge.rename(columns={0: "level2", 1: "method", 2: "min", 3: "max",
+#                                           4: "treatment_time", 5: "maintain_time", 6: "recover_time"})
+#     knowledge["level2"] = knowledge["level2"].astype("str")
+#
+#     df = pd.merge(df, knowledge, on='level2', how='left')
+#     df = df.drop("level2", axis=1)
+#     df = df.drop_duplicates(["ucity_id", "clevel2_id", "ccity_name", "device_type", "manufacturer",
+#                              "channel", "top", "time", "app_list", "hospital_id", "level3_ids"])
+#
+#
+#     df["stat_date"] = date
+#     print(df.head(6))
+#     df["app_list"] = df["app_list"].fillna("lost_na")
+#     df["app_list"] = df["app_list"].apply(app_list_func,args=(app_list_map,))
+#     df["clevel2_id"] = df["clevel2_id"].fillna("lost_na")
+#     df["clevel2_id"] = df["clevel2_id"].apply(app_list_func, args=(level2_map,))
+#     df["level3_ids"] = df["level3_ids"].fillna("lost_na")
+#     df["level3_ids"] = df["level3_ids"].apply(app_list_func, args=(level3_map,))
+#
+#     # print("predict shape")
+#     # print(df.shape)
+#     df["uid"] = df["device_id"]
+#     df["city"] = df["ucity_id"]
+#     features = ["ucity_id", "ccity_name", "device_type", "manufacturer",
+#                 "channel", "top", "time", "stat_date","hospital_id",
+#                 "method", "min", "max", "treatment_time", "maintain_time", "recover_time"]
+#     for i in features:
+#         df[i] = df[i].astype("str")
+#         df[i] = df[i].fillna("lost")
+#         df[i] = df[i] + i
+#
+#     native_pre = df[df["label"] == 0]
+#     native_pre = native_pre.drop("label", axis=1)
+#     nearby_pre = df[df["label"] == 1]
+#     nearby_pre = nearby_pre.drop("label", axis=1)
+#
+#     for i in ["ucity_id", "ccity_name", "device_type", "manufacturer",
+#                 "channel", "top", "time", "stat_date","hospital_id",
+#               "method", "min", "max", "treatment_time", "maintain_time", "recover_time"]:
+#         native_pre[i] = native_pre[i].map(value_map)
+#         # TODO 没有覆盖到的类别会处理成na，暂时用0填充，后续完善一下
+#         native_pre[i] = native_pre[i].fillna(0)
+#
+#         nearby_pre[i] = nearby_pre[i].map(value_map)
+#         # TODO 没有覆盖到的类别会处理成na，暂时用0填充，后续完善一下
+#         nearby_pre[i] = nearby_pre[i].fillna(0)
+#
+#     print("native")
+#     print(native_pre.shape)
+#
+#     native_pre[["uid","city","cid_id"]].to_csv(path+"native.csv",index=False)
+#     write_csv(native_pre, "native",200000)
+#
+#     print("nearby")
+#     print(nearby_pre.shape)
+#
+#     nearby_pre[["uid","city","cid_id"]].to_csv(path+"nearby.csv",index=False)
+#     write_csv(nearby_pre, "nearby", 160000)
 
 def con_sql(db,sql):
     cursor = db.cursor()