XGBoost Python error: "Size of labels must equal to number of rows"

Question

I am using xgboost in Python.

import pandas as pd
import numpy as np
import xgboost as xgb
from sklearn.model_selection import train_test_split

df=pd.read_csv('442.csv')
y=df.columnone
X=df.columnfive

X_train,X_test,Y_train,Y_test=train_test_split(X,y,test_size=0.2)
dtrain = xgb.DMatrix(X_train, label=Y_train)
dtest = xgb.DMatrix(X_test, label=Y_test)

The shape of the label seem to be uniform with the training set?

X_train.shape
>(405020,)
Y_train.shape
>(405020,)

param = {
   'eta': 0.3,
   'max_depth': 3, 
   'objective': 'multi:softprob', 
   'num_class': 2}
steps = 20  # The number of training iterations

But running this gives me this result:

model = xgb.train(param, dtrain, steps)
>XGBoostError: Check failed: labels_.Size() == num_row_ (405020 vs. 1) : Size of labels must equal to number of rows.

When I run

dtrain.num_row()
>1
dtrain.num_col()
>405020

This might have to do with the error? But I still have no idea how that could have happened. My initial X and y variables both have the correct number of rows and one column each.

Answer 1

Xgboost expects a 2-d array of inputs, and a vector of outputs. You are giving it two vectors, so it is confused. using df[["columnone"]] for the input should work.