Trouble training xgboost on categorical column

Question

I am trying to run a Python notebook (link). At line below In [446]: where author train XGBoost, I am getting an error

ValueError: DataFrame.dtypes for data must be int, float or bool. Did not expect the data types in fields StateHoliday, Assortment

# XGB with xgboost library
dtrain = xgb.DMatrix(X_train[predictors], y_train)
dtest = xgb.DMatrix(X_test[predictors], y_test)

watchlist = [(dtrain, 'train'), (dtest, 'test')]

xgb_model = xgb.train(params, dtrain, 300, evals = watchlist,
                      early_stopping_rounds = 50, feval = rmspe_xg, verbose_eval = True)

Here is the minimal code for testing

import pickle
import numpy as np
import xgboost as xgb
from sklearn.model_selection import train_test_split

with open('train_store', 'rb') as f:
    train_store = pickle.load(f)

train_store.shape

predictors = ['Store', 'DayOfWeek', 'Open', 'Promo', 'StateHoliday', 'SchoolHoliday', 'Year', 'Month', 'Day', 
              'WeekOfYear', 'StoreType', 'Assortment', 'CompetitionDistance', 'CompetitionOpenSinceMonth', 
              'CompetitionOpenSinceYear', 'Promo2', 'Promo2SinceWeek', 'Promo2SinceYear', 'CompetitionOpen', 
              'PromoOpen']

y = np.log(train_store.Sales) # log transformation of Sales
X = train_store

# split the data into train/test set
X_train, X_test, y_train, y_test = train_test_split(X, y, 
                                                    test_size = 0.3, # 30% for the evaluation set
                                                    random_state = 42)

# base parameters
params = {
    'booster': 'gbtree', 
    'objective': 'reg:linear', # regression task
    'subsample': 0.8,          # 80% of data to grow trees and prevent overfitting
    'colsample_bytree': 0.85,  # 85% of features used
    'eta': 0.1, 
    'max_depth': 10, 
    'seed': 42} # for reproducible results

num_round = 60 # default 300

dtrain = xgb.DMatrix(X_train[predictors], y_train)
dtest  = xgb.DMatrix(X_test[predictors],  y_test)

watchlist = [(dtrain, 'train'), (dtest, 'test')]

xgb_model = xgb.train(params, dtrain, num_round, evals = watchlist,
                      early_stopping_rounds = 50, feval = rmspe_xg, verbose_eval = True)

Link to train_store data file: Link 1

Answer 1

Try this

train_store['StateHoliday'] = pd.to_numeric(train_store['StateHoliday'])
train_store['Assortment'] = pd.to_numeric(train_store['Assortment'])