add print

tensnsorflow/ffm.py

@@ -245,9 +245,10 @@ def get_predict_set(ucity_id,model,ccity_name,manufacturer,channel):
         [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","label","device_id","cid_id"], axis=1).fillna(0.0)
-    print(df.head(2))
-    df = model.transform(df,n=160000, processes=22)
-    df = pd.DataFrame(df)
+    print("before transform")
+    print(df.shape)
+    temp_series = model.transform(df,n=160000, processes=22)
+    df = pd.DataFrame(temp_series)
     print("after transform")
     print(df.shape)
     df["label"] = df[0].apply(lambda x: x.split(",")[0])
@@ -286,8 +287,8 @@ def get_predict_set(ucity_id,model,ccity_name,manufacturer,channel):
 if __name__ == "__main__":
     path = "/home/gmuser/ffm/"
     a = time.time()
-    df, validate_date, ucity_id,ccity_name,manufacturer,channel = get_data()
-    model = transform(df, validate_date)
+    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)
     b = time.time()
     print("cost(分钟)")