oct*_*ian 7 python jupyter xgboost
我使用xgboost以下方式:
from xgboost import XGBClassifier
clf = XGBClassifier()
clf = clf.fit(df_train, df_train_labels, verbose=True)
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这很好用.但是,如果我添加一个early_stopping_rounds参数,如下所示:
clf = clf.fit(df_train, df_train_labels, early_stopping_rounds=10, verbose=True)
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我收到此错误:
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-16-786925228ae5> in <module>()
9
10
---> 11 clf = clf.fit(df_train, df_train_labels, early_stopping_rounds=10, verbose=True)
12 print("after fit")
13 prediction = np.exp(clf.predict(df_test))
~/anaconda3/envs/python3/lib/python3.6/site-packages/xgboost/sklearn.py in fit(self, X, y, sample_weight, eval_set, eval_metric, early_stopping_rounds, verbose)
443 early_stopping_rounds=early_stopping_rounds,
444 evals_result=evals_result, obj=obj, feval=feval,
--> 445 verbose_eval=verbose)
446
447 self.objective = xgb_options["objective"]
~/anaconda3/envs/python3/lib/python3.6/site-packages/xgboost/training.py in train(params, dtrain, num_boost_round, evals, obj, feval, maximize, early_stopping_rounds, evals_result, verbose_eval, learning_rates, xgb_model, callbacks)
203 evals=evals,
204 obj=obj, feval=feval,
--> 205 xgb_model=xgb_model, callbacks=callbacks)
206
207
~/anaconda3/envs/python3/lib/python3.6/site-packages/xgboost/training.py in _train_internal(params, dtrain, num_boost_round, evals, obj, feval, xgb_model, callbacks)
99 end_iteration=num_boost_round,
100 rank=rank,
--> 101 evaluation_result_list=evaluation_result_list))
102 except EarlyStopException:
103 break
~/anaconda3/envs/python3/lib/python3.6/site-packages/xgboost/callback.py in callback(env)
190 def callback(env):
191 """internal function"""
--> 192 score = env.evaluation_result_list[-1][1]
193 if len(state) == 0:
194 init(env)
IndexError: list index out of range
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我看了这个,我看到fit方法可以传递大量的参数,所以我不相信我添加的事实early_stopping_rounds会导致问题.
知道这个错误的原因是什么?
此错误的原因是您没有指定 eval_set,xgboost 使用它来确定何时停止以进行提前停止。
在此处查看适合方法的文档。
eval_set (list, optional) – 一个 (X, y) 元组对列表,用作提前停止的验证集
例如,如果您已将数据拆分为训练集和测试集,则可以使用以下内容:
eval_set = [(X_test, y_test)]
clf = clf.fit(df_train,
df_train_labels,
eval_set=eval_set,
early_stopping_rounds=10,
verbose=True)
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