scikit-learn joblib bug:多处理池self.value超出'i'格式代码的范围,只有大numpy数组

Question

我的代码运行正常较小的测试样品,如10000行的数据X_train,y_train.当我为数百万行调用它时,我得到了结果错误.是包中的错误,还是我可以做不同的事情？我正在使用Anaconda 2.0.1中的Python 2.7.7,我将来自Anaconda的多处理软件包的pool.py和来自scikit-learn的外部软件包的parallel.py放在我的Dropbox上.

测试脚本是:

import numpy as np
import sklearn
from sklearn.linear_model import SGDClassifier
from sklearn import grid_search
import multiprocessing as mp


def main():
    print("Started.")

    print("numpy:", np.__version__)
    print("sklearn:", sklearn.__version__)

    n_samples = 1000000
    n_features = 1000

    X_train = np.random.randn(n_samples, n_features)
    y_train = np.random.randint(0, 2, size=n_samples)

    print("input data size: %.3fMB" % (X_train.nbytes / 1e6))

    model = SGDClassifier(penalty='elasticnet', n_iter=10, shuffle=True)
    param_grid = [{
        'alpha' : 10.0 ** -np.arange(1,7),
        'l1_ratio': [.05, .15, .5, .7, .9, .95, .99, 1],
    }]
    gs = grid_search.GridSearchCV(model, param_grid, n_jobs=8, verbose=100)
    gs.fit(X_train, y_train)
    print(gs.grid_scores_)

if __name__=='__main__':
    mp.freeze_support()
    main()

这导致输出:

Vendor:  Continuum Analytics, Inc.
Package: mkl
Message: trial mode expires in 28 days
Started.
('numpy:', '1.8.1')
('sklearn:', '0.15.0b1')
input data size: 8000.000MB
Fitting 3 folds for each of 48 candidates, totalling 144 fits
Memmaping (shape=(1000000L, 1000L), dtype=float64) to new file c:\users\laszlos\appdata\local\temp\4\joblib_memmaping_pool_6172_78765976\6172-284752304-75223296-0.pkl
Failed to save <type 'numpy.ndarray'> to .npy file:
Traceback (most recent call last):
  File "C:\Anaconda\lib\site-packages\sklearn\externals\joblib\numpy_pickle.py", line 240, in save
    obj, filename = self._write_array(obj, filename)
  File "C:\Anaconda\lib\site-packages\sklearn\externals\joblib\numpy_pickle.py", line 203, in _write_array
    self.np.save(filename, array)
  File "C:\Anaconda\lib\site-packages\numpy\lib\npyio.py", line 453, in save
    format.write_array(fid, arr)
  File "C:\Anaconda\lib\site-packages\numpy\lib\format.py", line 406, in write_array
    array.tofile(fp)
ValueError: 1000000000 requested and 268435456 written

Memmaping (shape=(1000000L, 1000L), dtype=float64) to old file c:\users\laszlos\appdata\local\temp\4\joblib_memmaping_pool_6172_78765976\6172-284752304-75223296-0.pkl
Vendor:  Continuum Analytics, Inc.
Package: mkl
Message: trial mode expires in 28 days
Vendor:  Continuum Analytics, Inc.
Package: mkl
Message: trial mode expires in 28 days
Vendor:  Continuum Analytics, Inc.
Package: mkl
Message: trial mode expires in 28 days
Vendor:  Continuum Analytics, Inc.
Package: mkl
Message: trial mode expires in 28 days
Vendor:  Continuum Analytics, Inc.
Package: mkl
Message: trial mode expires in 28 days
Vendor:  Continuum Analytics, Inc.
Package: mkl
Message: trial mode expires in 28 days
Vendor:  Continuum Analytics, Inc.
Package: mkl
Message: trial mode expires in 28 days
Vendor:  Continuum Analytics, Inc.
Package: mkl
Message: trial mode expires in 28 days
Traceback (most recent call last):
  File "S:\laszlo\gridsearch_largearray.py", line 33, in <module>
    main()
  File "S:\laszlo\gridsearch_largearray.py", line 28, in main
    gs.fit(X_train, y_train)
  File "C:\Anaconda\lib\site-packages\sklearn\grid_search.py", line 597, in fit
    return self._fit(X, y, ParameterGrid(self.param_grid))
  File "C:\Anaconda\lib\site-packages\sklearn\grid_search.py", line 379, in _fit
    for parameters in parameter_iterable
  File "C:\Anaconda\lib\site-packages\sklearn\externals\joblib\parallel.py", line 651, in __call__
    self.retrieve()
  File "C:\Anaconda\lib\site-packages\sklearn\externals\joblib\parallel.py", line 503, in retrieve
    self._output.append(job.get())
  File "C:\Anaconda\lib\multiprocessing\pool.py", line 558, in get
    raise self._value
struct.error: integer out of range for 'i' format code

编辑:ogrisel的答案与scikit-learn-0.15.0b1的手动内存映射一起使用.不要忘记一次只运行一个脚本,否则你仍然会耗尽内存并且线程太多.(对于大小为~12.5 GB的数据,我的运行大约需要60 GB,有8个线程.)

Answer 1

作为一种解决方法,您可以尝试显式地和手动地映射数据,如joblib文档中所述.

编辑#1:这是重要的部分:

from sklearn.externals import joblib

joblib.dump(X_train, some_filename)
X_train = joblib.load(some_filename, mmap_mode='r+')

然后将这个memmap的数据传递给GridSearchCVscikit-learn 0.15+.

编辑#2:此外:如果使用32位版本的Anaconda,每个python进程限制为2GB,这也会限制内存.

我刚刚在Python 3.4中发现了一个错误,numpy.save但即使修复后,对mmap的后续调用也将失败:

OSError: [WinError 8] Not enough storage is available to process this command

所以请使用64位版本的Python(Anaconda作为AFAIK,目前没有其他64位软件包用于numpy/scipy/scikit-learn == 0.15.0b1).

编辑#3:我发现了另一个可能导致Windows内存使用过多的问题:当前joblib.Parallel内存映射输入数据mmap_mode='c'默认情况下:这种写时复制设置似乎会导致窗口耗尽页面文件,有时会触发"[错误1455] ]页面文件太小,无法完成此操作"错误.设置mmap_mode='r'或mmap_mode='r+'不触发该问题.我将运行测试以查看是否可以在下一版本的joblib中更改默认模式.