change path

tensnsorflow/ffm.py

@@ -138,7 +138,7 @@ class multiFFMFormatPandas:
 
 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"
+    sql = "select max(stat_date) from esmm_train_test"
     validate_date = con_sql(db, sql)[0].values.tolist()[0]
     print("validate_date:" + validate_date)
     temp = datetime.datetime.strptime(validate_date, "%Y-%m-%d")
@@ -174,7 +174,7 @@ def get_data():
 
 def transform(a,validate_date):
     model = multiFFMFormatPandas()
-    df = model.fit_transform(a, y="y", n=160000, processes=26)
+    df = model.fit_transform(a, y="y", n=160000, processes=22)
     df = pd.DataFrame(df)
     df["stat_date"] = df[0].apply(lambda x: x.split(",")[0])
     df["device_id"] = df[0].apply(lambda x: x.split(",")[1])
@@ -194,8 +194,8 @@ def transform(a,validate_date):
     test = test.drop("stat_date",axis=1)
     # print("train shape")
     # print(train.shape)
-    # train.to_csv(path + "train.csv", sep="\t", index=False)
-    # test.to_csv(path + "test.csv", sep="\t", index=False)
+    train.to_csv(path + "tr.csv", sep="\t", index=False)
+    test.to_csv(path + "va.csv", sep="\t", index=False)
 
     return model
 
@@ -245,20 +245,20 @@ def get_predict_set(ucity_id, cid,model):
 
     native_pre = df[df["label"] == "0"]
     native_pre = native_pre.drop("label", axis=1)
-    native_pre.to_csv(path+"native_pre.csv",sep="\t",index=False)
+    native_pre.to_csv(path+"native.csv",sep="\t",index=False)
     # print("native_pre shape")
     # print(native_pre.shape)
 
     nearby_pre = df[df["label"] == "1"]
     nearby_pre = nearby_pre.drop("label", axis=1)
-    nearby_pre.to_csv(path + "nearby_pre.csv", sep="\t", index=False)
+    nearby_pre.to_csv(path + "nearby.csv", sep="\t", index=False)
     # print("nearby_pre shape")
     # print(nearby_pre.shape)
 
 
 
 if __name__ == "__main__":
-    path = "/home/gmuser/ffm/"
+    path = "/home/gaoyazhe/esmm/data/"
     a = time.time()
     df, validate_date, ucity_id, cid = get_data()
     model = transform(df, validate_date)