cachingallocator option max_split_size_mb too small, must be > 20: str
时间: 2023-09-17 10:02:01 浏览: 55
cachingallocator选项max_split_size_mb太小,必须大于20:str。
这个错误表明在使用缓存分配器时,max_split_size_mb设置的值太小了,必须要大于20。在计算机中,缓存分配器是一种用来管理内存分配和释放的工具,它可以提高内存的利用率和性能。max_split_size_mb是缓存分配器的一个参数,它指定了当需要分配内存时,内存块的最大切分大小。
在这个错误中,使用的max_split_size_mb的值小于20,而根据要求,它必须大于20才能正常工作。可能的原因是在设置这个参数时,输入了错误的值或者没有将其设置为大于20的合适值。
为了解决这个问题,我们需要修改max_split_size_mb的值,使其大于20。可以通过修改配置文件或者调整程序代码中对该参数的设置来实现。根据具体的环境和使用场景,我们可以选择一个合适的值,以满足系统的需求。
总结起来,cachingallocator选项的max_split_size_mb参数设置错误,必须设置为大于20的值才能正常工作。我们需要修改配置,将该参数设置为合适的值,以解决这个错误。
相关问题
option 'max_allowed_packet': unsigned value 107374182400 adjusted to 1073741
这个错误提示通常是因为 MySQL 数据库中的 `max_allowed_packet` 参数设置太大,导致无法处理数据包大小超过限制的请求。该参数限制了 MySQL 服务器能够接收和处理的最大数据包大小。
根据提示信息,你提供的参数值为 107374182400,即 100GB,但是 MySQL 通常会将该参数值限制在 1GB 左右,因此 MySQL 会自动将该值调整为 1073741,即 1MB。
如果你需要处理大型数据包,可以尝试在 MySQL 配置文件中将该参数值适当调大,但是要注意不要设置过大,否则可能会导致服务器性能下降或者出现其他问题。
rain_test_split
```python
from sklearn.model_selection import train_test_split
# 输出train_test_split函数的文档字符串
help(train_test_split)
```
输出结果如下:
```
Help on function train_test_split in module sklearn.model_selection._split:
train_test_split(*arrays, **options)
Split arrays or matrices into random train and test subsets
Quick utility that wraps input validation and next(ShuffleSplit().split(X, y)).
Parameters
----------
*arrays : sequence of indexables with same length / shape[0]
Allowed inputs are lists, numpy arrays, scipy-sparse
matrices or pandas dataframes.
test_size : float or int, default=None
If float, should be between 0.0 and 1.0 and represent the proportion
of the dataset to include in the test split. If int, represents the
absolute number of test samples. If None, the value is set to the
complement of the train size. If ``train_size`` is also None, it will
be set to 0.25.
train_size : float or int, default=None
If float, should be between 0.0 and 1.0 and represent the
proportion of the dataset to include in the train split. If
int, represents the absolute number of train samples. If None,
the value is automatically set to the complement of the test size.
random_state : int or RandomState instance, default=None
Controls the shuffling applied to the data before applying the split.
Pass an int for reproducible output across multiple function calls.
See :term:`Glossary <random_state>`.
shuffle : bool, default=True
Whether or not to shuffle the data before splitting. If shuffle=False
then stratify must be None.
stratify : array-like, default=None
If not None, data is split in a stratified fashion, using this as
the class labels.
Returns
-------
splitting : list, length=2 * len(arrays)
List containing train-test split of inputs.
.. versionadded:: 0.16
Added option to specify a list of arrays to be split
Examples
--------
>>> import numpy as np
>>> from sklearn.model_selection import train_test_split
>>> X, y = np.arange(10).reshape((5, 2)), range(5)
>>> X
array([[0, 1],
[2, 3],
[4, 5],
[6, 7],
[8, 9]])
>>> list(y)
[0, 1, 2, 3, 4]
>>> X_train, X_test, y_train, y_test = train_test_split(
... X, y, test_size=0.33, random_state=42)
...
>>> X_train
array([[4, 5],
[0, 1],
[6, 7]])
>>> y_train
[2, 0, 3]
>>> X_test
array([[2, 3],
[8, 9]])
>>> y_test
[1, 4]
```
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