Merge branch 'master' of git.wanmeizhensuo.com:ML/ffm-baseline

add esmm model

tensnsorflow/ffm.py

@@ -136,14 +136,13 @@ class multiFFMFormatPandas:
             return False
 
 
-
 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 esmm_train_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=2)).strftime("%Y-%m-%d")
+    start = (temp - datetime.timedelta(days=14)).strftime("%Y-%m-%d")
     db = pymysql.connect(host='10.66.157.22', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
     sql = "select device_id,y,z,stat_date,ucity_id,cid_id,clevel1_id,ccity_name from esmm_train_data " \
           "where stat_date >= '{}'".format(start)
@@ -160,14 +159,7 @@ def get_data():
     df["y"] = df["stat_date"].str.cat([df["device_id"].values.tolist(),df["ucity_id"].values.tolist(), df["cid_id"].values.tolist(),
                                        df["y"].values.tolist(),df["z"].values.tolist()], sep=",")
     df = df.drop("z", axis=1)
-    print(df.head(2))
-    print("shape")
-    print(df.shape)
     df = pd.merge(df,get_statistics(),how='left',on = "device_id").fillna(0)
-    print("merge")
-    # print(df.head())
-    print("shape")
-    print(df.shape)
     df = df.drop("device_id", axis=1)
     print(df.head())
     return df,validate_date,ucity_id,cid
@@ -191,17 +183,20 @@ def transform(a,validate_date):
 
     train = df[df["stat_date"] != validate_date]
     train = train.drop("stat_date",axis=1)
-    # print("train shape")
-    # print(train.shape)
     test = df[df["stat_date"] == validate_date]
     test = test.drop("stat_date",axis=1)
-    # print("test shape")
-    # print(test.shape)
-    # train.to_csv(path+"train.csv",index=None)
-    # test.to_csv(path + "test.csv", index=None)
+    print("train shape")
+    print(train.shape)
+
+    yconnect = create_engine('mysql+pymysql://root:3SYz54LS9#^9sBvC@10.66.157.22:4000/jerry_test?charset=utf8')
+    pd.io.sql.to_sql(train, "train_zhao", yconnect, schema='jerry_test', if_exists='replace', index=False)
+    print("train insert done")
+    pd.io.sql.to_sql(test, "test_zhao", yconnect, schema='jerry_test', if_exists='replace', index=False)
+    print("test insert done")
+
     return model
 
-    # yconnect = create_engine('mysql+pymysql://root:3SYz54LS9#^9sBvC@10.66.157.22:4000/jerry_test?charset=utf8')
+
     # n = 100000
     # for i in range(0,df.shape[0],n):
     #     print(i)
@@ -233,7 +228,6 @@ def get_predict_set(ucity_id, cid,model):
     df = con_sql(db, sql)
     df = df.rename(columns={0: "device_id", 1: "y", 2: "z", 3: "stat_date", 4: "ucity_id", 5: "cid_id",
                             6: "clevel1_id", 7: "ccity_name",8:"label"})
-    print("df ok")
     df = df[df["cid_id"].isin(cid)]
     df = df[df["ucity_id"].isin(ucity_id)]
     print(df.shape)
@@ -269,13 +263,17 @@ def get_predict_set(ucity_id, cid,model):
     native_pre = native_pre.drop("label", axis=1)
     print("native_pre shape")
     print(native_pre.shape)
-    native_pre.to_csv(path + "native_pre.csv", index=None)
 
     nearby_pre = df[df["label"] == "1"]
     nearby_pre = nearby_pre.drop("label", axis=1)
     print("nearby_pre shape")
     print(nearby_pre.shape)
-    nearby_pre.to_csv(path + "nearby_pre.csv", index=None)
+
+    yconnect = create_engine('mysql+pymysql://root:3SYz54LS9#^9sBvC@10.66.157.22:4000/jerry_test?charset=utf8')
+    pd.io.sql.to_sql(native_pre, "native_zhao", yconnect, schema='jerry_test', if_exists='replace', index=False)
+    print("train insert done")
+    pd.io.sql.to_sql(nearby_pre, "nearby_zhao", yconnect, schema='jerry_test', if_exists='replace', index=False)
+    print("test insert done")
 
 
     # df = pd.DataFrame(df)
@@ -294,8 +292,12 @@ def get_predict_set(ucity_id, cid,model):
 
 if __name__ == "__main__":
     path = "/home/gmuser/ffm/"
+    a = time.time()
     df, validate_date, ucity_id, cid = get_data()
     model = transform(df, validate_date)
     get_predict_set(ucity_id, cid,model)
+    b = time.time()
+    print("cost(分钟)")
+    print((b-a)/60)