add print

tensnsorflow/ffm.py

@@ -218,15 +218,23 @@ def get_predict_set(ucity_id,model,ccity_name,manufacturer,channel):
 
     print("before filter:")
     print(df.shape)
+    print(df.loc[df["device_id"]=="358035085192742"].shape)
     df = df[df["ucity_id"].isin(ucity_id)]
     print("after ucity filter:")
     print(df.shape)
+    print(df.loc[df["device_id"] == "358035085192742"].shape)
     df = df[df["ccity_name"].isin(ccity_name)]
     print("after ccity_name filter:")
     print(df.shape)
+    print(df.loc[df["device_id"] == "358035085192742"].shape)
     df = df[df["manufacturer"].isin(manufacturer)]
+    print("after manufacturer filter:")
+    print(df.shape)
+    print(df.loc[df["device_id"] == "358035085192742"].shape)
     df = df[df["channel"].isin(channel)]
+    print("after channel filter:")
     print(df.shape)
+    print(df.loc[df["device_id"] == "358035085192742"].shape)
     df["cid_id"] = df["cid_id"].astype("str")
     df["clevel1_id"] = df["clevel1_id"].astype("str")
     df["top"] = df["top"].astype("str")
@@ -251,15 +259,23 @@ def get_predict_set(ucity_id,model,ccity_name,manufacturer,channel):
     df["data"] = df["seq"].str.cat(df["data"], sep=",")
     df = df.drop([0, "seq"], axis=1)
     print(df.head())
-
+    print("after transform")
+    print(df.shape)
+    print(df.loc[df["device_id"] == "358035085192742"].shape)
     native_pre = df[df["label"] == "0"]
     native_pre = native_pre.drop("label", axis=1)
+    print("native")
+    print(native_pre.shape)
+    print(native_pre.loc[native_pre["device_id"] == "358035085192742"].shape)
     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)
+    print("nearby")
+    print(nearby_pre.shape)
+    print(nearby_pre.loc[nearby_pre["device_id"] == "358035085192742"].shape)
     nearby_pre.to_csv(path + "nearby.csv", sep="\t", index=False)
     # print("nearby_pre shape")
     # print(nearby_pre.shape)