无法在分类列上训练 xgboost

Question

我正在尝试运行 Python 笔记本（ 链接）。在下面一行 In [446]: where author train XGBoost ，我收到一个错误

ValueError：数据的 DataFrame.dtypes 必须是 int、float 或 bool。没想到StateHoliday、Assortment字段的数据类型

# 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)

这是用于测试的最小代码

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)

链接到 train_store 数据文件： Link 1

原文由 arush1836 发布，翻译遵循 CC BY-SA 4.0 许可协议

Answer 1

试试这个

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

原文由 Atinesh 发布，翻译遵循 CC BY-SA 4.0 许可协议

Answer 2

我在做罗斯曼销售预测项目时遇到了完全相同的问题。似乎新版本的 xgboost 不接受 StateHoliday 、 Assortment 和 StoreType 的数据类型。您可以使用 Mykhailo Lisovyi 建议的方式检查数据类型

print(test_train.dtypes)

_你需要在这里用你的 X_train 替换 testtrain

你可能会得到

DayOfWeek                      int64
Promo                          int64
StateHoliday                   int64
SchoolHoliday                  int64
StoreType                     object
Assortment                    object
CompetitionDistance          float64
CompetitionOpenSinceMonth    float64
CompetitionOpenSinceYear     float64
Promo2                         int64
Promo2SinceWeek              float64
Promo2SinceYear              float64
Year                           int64
Month                          int64
Day                            int64

错误引发了 对象 类型。您可以将它们转换为

from sklearn import preprocessing
lbl = preprocessing.LabelEncoder()
test_train['StoreType'] = lbl.fit_transform(test_train['StoreType'].astype(str))
test_train['Assortment'] = lbl.fit_transform(test_train['Assortment'].astype(str))

完成这些步骤后一切都会好起来的。

原文由 Zhi Yuan 发布，翻译遵循 CC BY-SA 4.0 许可协议