scikit-learn joblib 错误:多处理池 self.value 超出“i"格式代码的范围，仅适用于大型 numpy 数组

My code runs fine with smaller test samples, like 10000 rows of data in X_train, y_train. When I call it for millions of rows, I get the resulting error. Is the bug in a package, or can I do something differently? I am using Python 2.7.7 from Anaconda 2.0.1, and I put the pool.py from Anaconda's multiprocessing package and parallel.py from scikit-learn's external package on my Dropbox for you.

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:userslaszlosappdatalocal	emp4joblib_memmaping_pool_6172_787659766172-284752304-75223296-0.pkl
Failed to save <type 'numpy.ndarray'> to .npy file:
Traceback (most recent call last):
  File "C:Anacondalibsite-packagessklearnexternalsjoblib
umpy_pickle.py", line 240, in save
    obj, filename = self._write_array(obj, filename)
  File "C:Anacondalibsite-packagessklearnexternalsjoblib
umpy_pickle.py", line 203, in _write_array
    self.np.save(filename, array)
  File "C:Anacondalibsite-packages
umpylib
pyio.py", line 453, in save
    format.write_array(fid, arr)
  File "C:Anacondalibsite-packages
umpylibformat.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:userslaszlosappdatalocal	emp4joblib_memmaping_pool_6172_787659766172-284752304-75223296-0.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:laszlogridsearch_largearray.py", line 33, in <module>
    main()
  File "S:laszlogridsearch_largearray.py", line 28, in main
    gs.fit(X_train, y_train)
  File "C:Anacondalibsite-packagessklearngrid_search.py", line 597, in fit
    return self._fit(X, y, ParameterGrid(self.param_grid))
  File "C:Anacondalibsite-packagessklearngrid_search.py", line 379, in _fit
    for parameters in parameter_iterable
  File "C:Anacondalibsite-packagessklearnexternalsjoblibparallel.py", line 651, in __call__
    self.retrieve()
  File "C:Anacondalibsite-packagessklearnexternalsjoblibparallel.py", line 503, in retrieve
    self._output.append(job.get())
  File "C:Anacondalibmultiprocessingpool.py", line 558, in get
    raise self._value
struct.error: integer out of range for 'i' format code

ogrisel 的答案确实适用于 scikit-learn-0.15.0b1 的手动内存映射.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过多.(我的 CSV 文件大小约为 12.5 GB 的数据，使用 8 个线程，运行大约 60 GB.)

from sklearn.externals import joblib

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

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