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我正在尝试使用 GridsearchCV 在 sklearn 中调整 GB 分类器。这是代码：

from sklearn.grid_search import GridSearchCV
from sklearn.ensemble import GradientBoostingClassifier

param_grid = {'learning_rate': [0.1, 0.01, 0.001],
              'max_depth': [4, 6],
              'min_samples_leaf': [9, 17],
              'max_features': [0.3, 0.1]}

est = GradientBoostingClassifier(n_estimators=3000)
# this may take some minutes
gs_cv = GridSearchCV(est, param_grid, scoring='f1', n_jobs=-1, verbose=1, pre_dispatch=5).fit(X.values, y)

# best hyperparameter setting
print 'Best hyperparameters: %r' % gs_cv.best_params_

数据集 X 是 100 万行 * 245 个特征。我在接近 32 个内核的机器上运行。运行上述代码时出现以下错误，

error                                     Traceback (most recent call last)
<ipython-input-22-cb545fec9989> in <module>()
      9 est = GradientBoostingClassifier(n_estimators=3000)
     10 # this may take some minutes
---> 11 gs_cv = GridSearchCV(est, param_grid, scoring='f1', n_jobs=-1, verbose=1, pre_dispatch=5).fit(X.values, y)
     12 
     13 # best hyperparameter setting

/var/webeng/opensource/aetna-anaconda/lib/python2.7/site-packages/sklearn/grid_search.pyc in fit(self, X, y)
    594 
    595         """
--> 596         return self._fit(X, y, ParameterGrid(self.param_grid))
    597 
    598 

/var/webeng/opensource/aetna-anaconda/lib/python2.7/site-packages/sklearn/grid_search.pyc in _fit(self, X, y, parameter_iterable)
    376                                     train, test, self.verbose, parameters,
    377                                     self.fit_params, return_parameters=True)
--> 378             for parameters in parameter_iterable
    379             for train, test in cv)
    380 

/var/webeng/opensource/aetna-anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in __call__(self, iterable)
    658                 # consumption.
    659                 self._iterating = False
--> 660             self.retrieve()
    661             # Make sure that we get a last message telling us we are done
    662             elapsed_time = time.time() - self._start_time

/var/webeng/opensource/aetna-anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in retrieve(self)
    510                 self._lock.release()
    511             try:
--> 512                 self._output.append(job.get())
    513             except tuple(self.exceptions) as exception:
    514                 try:

/var/webeng/opensource/aetna-anaconda/lib/python2.7/multiprocessing/pool.pyc in get(self, timeout)
    556             return self._value
    557         else:
--> 558             raise self._value
    559 
    560     def _set(self, i, obj):

error: 'i' format requires -2147483648 <= number <= 2147483647

当我使用 1000 行的子集运行相同的代码时，它可以工作。尝试了不同的 pre_dispatch 但仍然遇到问题。是因为数据大小还是其他原因？谢谢。

在 Python 2.7.9 上使用 sklearn 0.15.2

4

1 回答 1

2

我看到了 3 种可能的方法来解决这个问题：

1）尝试将sklearn更新到最新版本

2）尝试更换

from sklearn.grid_search import GridSearchCV

和：

from sklearn.model_selection import GridSearchCV

3) 如果你想在 GridSearchCV 中使用n_jobs > 1那么你必须使用if __name__ == '__main__' 来保护脚本：

例如

if __name__ == '__main__':
    clf = MLPClassifier()
    my_param_grid = {'activation': ('tanh', 'relu')}
    grid= model_selection.GridSearchCV(clf,   
    param_grid=my_param_grid,n_jobs=-1)
    grid.fit(X, y)

考虑完成所有 3 个步骤

于 2017-06-01T14:52:02.917 回答