add hospital and doctor

tensnsorflow/ffm.py

@@ -146,7 +146,7 @@ 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,s.doctor_id " \
+          "u.device_type,u.manufacturer,u.channel,c.top,cid_time.time,s.doctor_id,s.hospital_id " \
           "from esmm_train_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 on e.cid_id = cid_time.cid_id " \
           "left join service_hospital s on e.diary_service_id = s.id " \
@@ -154,11 +154,12 @@ def get_data():
     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",
-                            6:"device_type",7:"manufacturer",8:"channel",9:"top",10:"time",11:"hospital_id"})
+                            6:"device_type",7:"manufacturer",8:"channel",9:"top",10:"time",
+                            11:"doctor_id",12:"hospital_id"})
     print("esmm data ok")
     print(df.head(2))
     features = 0
-    category = ["ucity_id","clevel1_id","ccity_name","device_type","manufacturer","channel","top","hospital_id"]
+    category = ["ucity_id","clevel1_id","ccity_name","device_type","manufacturer","channel","top","hospital_id","doctor_id"]
     for i in category:
         df[i] = df[i].fillna("na")
         features = features + len(df[i].unique())
@@ -196,8 +197,8 @@ def transform(a,validate_date):
     test = test.drop("stat_date",axis=1)
     # print("train shape")
     # print(train.shape)
-    train.to_csv(path + "tr_doctor.csv", sep="\t", index=False)
-    test.to_csv(path + "va_doctor.csv", sep="\t", index=False)
+    train.to_csv(path + "tr_all.csv", sep="\t", index=False)
+    test.to_csv(path + "va_all.csv", sep="\t", index=False)
 
     return model