"KeyError: 0" with xgboost, scikit-learn and pandas
"KeyError: 0" with xgboost, scikit-learn and pandas
我创建这个演示是为了演示从库内部抛出的错误。此代码将数据集拆分为 train/eval/test 并使用 train/eval 进行超参数搜索、提前停止，同时保留测试集以供以后评估。我缩小了与 GridSearchCV 交叉验证相关的错误范围，但我无法找出确切的根本原因并进行修复。
from sklearn import svm, datasets
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
import numpy as np
import pandas as pd
import xgboost as xgb
iris = datasets.load_iris()
df = pd.DataFrame(data=np.c_[iris['data'], iris['target']], columns=iris['feature_names'] + ['target'])
X, y = df[['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']], df['target']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
X_train_split, X_eval_split, y_train_split, y_eval_split = train_test_split(X_train, y_train, test_size=0.25, random_state=42)
parameters = {
'max_depth': (2, 3, 4),
}
fit_params = {
'early_stopping_rounds': 2,
'eval_set': (X_eval_split, y_eval_split),
}
model = xgb.XGBClassifier()
gs = GridSearchCV(model, parameters, cv=3)
gs.fit(X_train_split, y_train_split, **fit_params)
但是我收到了这条晦涩难懂的消息：
Traceback (most recent call last):
File "/Users/foo/bar/.env/lib/python3.6/site-packages/pandas/core/indexes/base.py", line 3078, in get_loc
return self._engine.get_loc(key)
File "pandas/_libs/index.pyx", line 140, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/index.pyx", line 162, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/hashtable_class_helper.pxi", line 1492, in pandas._libs.hashtable.PyObjectHashTable.get_item
File "pandas/_libs/hashtable_class_helper.pxi", line 1500, in pandas._libs.hashtable.PyObjectHashTable.get_item
KeyError: 0
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "t.py", line 36, in <module>
gs.fit(X_train_split, y_train_split, **fit_params)
File "/Users/foo/bar/.env/lib/python3.6/site-packages/sklearn/model_selection/_search.py", line 640, in fit
cv.split(X, y, groups)))
File "/Users/foo/bar/.env/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py", line 779, in __call__
while self.dispatch_one_batch(iterator):
File "/Users/foo/bar/.env/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py", line 625, in dispatch_one_batch
self._dispatch(tasks)
File "/Users/foo/bar/.env/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py", line 588, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "/Users/foo/bar/.env/lib/python3.6/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 111, in apply_async
result = ImmediateResult(func)
File "/Users/foo/bar/.env/lib/python3.6/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 332, in __init__
self.results = batch()
File "/Users/foo/bar/.env/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py", line 131, in __call__
return [func(*args, **kwargs) for func, args, kwargs in self.items]
File "/Users/foo/bar/.env/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py", line 131, in <listcomp>
return [func(*args, **kwargs) for func, args, kwargs in self.items]
File "/Users/foo/bar/.env/lib/python3.6/site-packages/sklearn/model_selection/_validation.py", line 458, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "/Users/foo/bar/.env/lib/python3.6/site-packages/xgboost/sklearn.py", line 526, in fit
for i in range(len(eval_set))
File "/Users/foo/bar/.env/lib/python3.6/site-packages/xgboost/sklearn.py", line 526, in <genexpr>
for i in range(len(eval_set))
File "/Users/foo/bar/.env/lib/python3.6/site-packages/pandas/core/frame.py", line 2688, in __getitem__
return self._getitem_column(key)
File "/Users/foo/bar/.env/lib/python3.6/site-packages/pandas/core/frame.py", line 2695, in _getitem_column
return self._get_item_cache(key)
File "/Users/foo/bar/.env/lib/python3.6/site-packages/pandas/core/generic.py", line 2489, in _get_item_cache
values = self._data.get(item)
File "/Users/foo/bar/.env/lib/python3.6/site-packages/pandas/core/internals.py", line 4115, in get
loc = self.items.get_loc(item)
File "/Users/foo/bar/.env/lib/python3.6/site-packages/pandas/core/indexes/base.py", line 3080, in get_loc
return self._engine.get_loc(self._maybe_cast_indexer(key))
File "pandas/_libs/index.pyx", line 140, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/index.pyx", line 162, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/hashtable_class_helper.pxi", line 1492, in pandas._libs.hashtable.PyObjectHashTable.get_item
File "pandas/_libs/hashtable_class_helper.pxi", line 1500, in pandas._libs.hashtable.PyObjectHashTable.get_item
KeyError: 0
有人可以就我收到此错误的原因提供一些建议吗？
当我通读错误跟踪时，我发现 fit 方法有问题。
KeyError : 0 表示解释器正在数据框中寻找位于第 0 个索引位置的 element/item。我尝试了 运行 你的 X_train_split、y_train_split 和 X_eval_split。索引不同，可能会破坏执行。
但是，如果我们不为训练和评估打乱数据集，交叉验证的目的可能会被打败。
尝试在 fit 方法中重置事物的索引，包括评估（如其在参数中使用的那样）。如果问题仍然存在，请通读提前停止参数的概念、训练测试拆分和 Gridsearch cv =3 区域以检查是否存在任何不一致。
希望它能让您对错误有所了解。
eval_set (list, optional) – A list of (X, y) tuple pairs to use as a validation set for early-stopping
eval_set 应该是一个元组列表。但是你只有 eval_set 作为一个元组：
fit_params = {
'early_stopping_rounds': 2,
'eval_set': (X_eval_split, y_eval_split),
}
改成这样：
fit_params = {
'early_stopping_rounds': 2,
'eval_set': [(X_eval_split, y_eval_split)],
}
您的代码将 运行。
我创建这个演示是为了演示从库内部抛出的错误。此代码将数据集拆分为 train/eval/test 并使用 train/eval 进行超参数搜索、提前停止，同时保留测试集以供以后评估。我缩小了与 GridSearchCV 交叉验证相关的错误范围，但我无法找出确切的根本原因并进行修复。
from sklearn import svm, datasets
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
import numpy as np
import pandas as pd
import xgboost as xgb
iris = datasets.load_iris()
df = pd.DataFrame(data=np.c_[iris['data'], iris['target']], columns=iris['feature_names'] + ['target'])
X, y = df[['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']], df['target']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
X_train_split, X_eval_split, y_train_split, y_eval_split = train_test_split(X_train, y_train, test_size=0.25, random_state=42)
parameters = {
'max_depth': (2, 3, 4),
}
fit_params = {
'early_stopping_rounds': 2,
'eval_set': (X_eval_split, y_eval_split),
}
model = xgb.XGBClassifier()
gs = GridSearchCV(model, parameters, cv=3)
gs.fit(X_train_split, y_train_split, **fit_params)
但是我收到了这条晦涩难懂的消息：
Traceback (most recent call last):
File "/Users/foo/bar/.env/lib/python3.6/site-packages/pandas/core/indexes/base.py", line 3078, in get_loc
return self._engine.get_loc(key)
File "pandas/_libs/index.pyx", line 140, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/index.pyx", line 162, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/hashtable_class_helper.pxi", line 1492, in pandas._libs.hashtable.PyObjectHashTable.get_item
File "pandas/_libs/hashtable_class_helper.pxi", line 1500, in pandas._libs.hashtable.PyObjectHashTable.get_item
KeyError: 0
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "t.py", line 36, in <module>
gs.fit(X_train_split, y_train_split, **fit_params)
File "/Users/foo/bar/.env/lib/python3.6/site-packages/sklearn/model_selection/_search.py", line 640, in fit
cv.split(X, y, groups)))
File "/Users/foo/bar/.env/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py", line 779, in __call__
while self.dispatch_one_batch(iterator):
File "/Users/foo/bar/.env/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py", line 625, in dispatch_one_batch
self._dispatch(tasks)
File "/Users/foo/bar/.env/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py", line 588, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "/Users/foo/bar/.env/lib/python3.6/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 111, in apply_async
result = ImmediateResult(func)
File "/Users/foo/bar/.env/lib/python3.6/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 332, in __init__
self.results = batch()
File "/Users/foo/bar/.env/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py", line 131, in __call__
return [func(*args, **kwargs) for func, args, kwargs in self.items]
File "/Users/foo/bar/.env/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py", line 131, in <listcomp>
return [func(*args, **kwargs) for func, args, kwargs in self.items]
File "/Users/foo/bar/.env/lib/python3.6/site-packages/sklearn/model_selection/_validation.py", line 458, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "/Users/foo/bar/.env/lib/python3.6/site-packages/xgboost/sklearn.py", line 526, in fit
for i in range(len(eval_set))
File "/Users/foo/bar/.env/lib/python3.6/site-packages/xgboost/sklearn.py", line 526, in <genexpr>
for i in range(len(eval_set))
File "/Users/foo/bar/.env/lib/python3.6/site-packages/pandas/core/frame.py", line 2688, in __getitem__
return self._getitem_column(key)
File "/Users/foo/bar/.env/lib/python3.6/site-packages/pandas/core/frame.py", line 2695, in _getitem_column
return self._get_item_cache(key)
File "/Users/foo/bar/.env/lib/python3.6/site-packages/pandas/core/generic.py", line 2489, in _get_item_cache
values = self._data.get(item)
File "/Users/foo/bar/.env/lib/python3.6/site-packages/pandas/core/internals.py", line 4115, in get
loc = self.items.get_loc(item)
File "/Users/foo/bar/.env/lib/python3.6/site-packages/pandas/core/indexes/base.py", line 3080, in get_loc
return self._engine.get_loc(self._maybe_cast_indexer(key))
File "pandas/_libs/index.pyx", line 140, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/index.pyx", line 162, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/hashtable_class_helper.pxi", line 1492, in pandas._libs.hashtable.PyObjectHashTable.get_item
File "pandas/_libs/hashtable_class_helper.pxi", line 1500, in pandas._libs.hashtable.PyObjectHashTable.get_item
KeyError: 0
有人可以就我收到此错误的原因提供一些建议吗？
当我通读错误跟踪时，我发现 fit 方法有问题。
KeyError : 0 表示解释器正在数据框中寻找位于第 0 个索引位置的 element/item。我尝试了 运行 你的 X_train_split、y_train_split 和 X_eval_split。索引不同，可能会破坏执行。
但是，如果我们不为训练和评估打乱数据集，交叉验证的目的可能会被打败。
尝试在 fit 方法中重置事物的索引，包括评估（如其在参数中使用的那样）。如果问题仍然存在，请通读提前停止参数的概念、训练测试拆分和 Gridsearch cv =3 区域以检查是否存在任何不一致。
希望它能让您对错误有所了解。
eval_set (list, optional) – A list of (X, y) tuple pairs to use as a validation set for early-stopping
eval_set 应该是一个元组列表。但是你只有 eval_set 作为一个元组：
fit_params = {
'early_stopping_rounds': 2,
'eval_set': (X_eval_split, y_eval_split),
}
改成这样：
fit_params = {
'early_stopping_rounds': 2,
'eval_set': [(X_eval_split, y_eval_split)],
}
您的代码将 运行。