Found input variables with inconsistent numbers of samples

#1 
import xgboost as xgb
from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split

x_train, x_valid, y_train, y_valid = train_test_split(
        pca2_results_train, 
        y_train, test_size=0.2)

d_train = xgb.DMatrix(x_train, label=y_train)
d_valid = xgb.DMatrix(x_valid, label=y_valid)
d_test = xgb.DMatrix(x_test)
d_test = xgb.DMatrix(pca2_results_test)

params = {}
params['objective'] = 'reg:linear'
params['eta'] = 0.02
params['max_depth'] = 4

def xgb_r2_score(preds, dtrain):
    labels = dtrain.get_label()
    return 'r2', r2_score(labels, preds)

watchlist = [(d_train, 'train'), (d_valid, 'valid')]

clf = xgb.train(params, d_train, 
                1000, watchlist, early_stopping_rounds=50, 
                feval=xgb_r2_score, maximize=True, verbose_eval=10)

I have been using this but an error is poping up as below:-

Found input variables with inconsistent numbers of samples: [4209, 3367]

Please help

'XGBClassifier' object has no attribute 'DMatrix'
#2

The error doesn’t appear to have anything to do with XGBoost. See https://datascience.stackexchange.com/questions/20199/train-test-split-error-found-input-variables-with-inconsistent-numbers-of-sam

#3

I faced a similar problem while fitting a regression model . The problem in my case was, Number of rows in X was not equal to number of rows in y. You likely get problems because you remove rows containing nulls in X_train and y_train independent of each other. y_train probably has few, or no nulls and X_train probably has some. So when you remove a row in X_train and the same row is not removed in y_train it will cause your data to be unsynced and have different lenghts. Instead you should remove nulls before you separate X and y.