使用sklearn对网格搜索使用显式（预定义）验证集

问题描述

我有一个数据集，该数据集以前被分为3组：训练，验证和测试。这些集合必须按照给定的方式使用，以便比较不同算法的性能。

I have a dataset, which has previously been split into 3 sets: train, validation and test. These sets have to be used as given in order to compare the performance across different algorithms.

我现在想使用验证集优化我的SVM的参数。但是，我找不到如何将验证集明确输入到 sklearn.grid_search.GridSearchCV（）中。以下是我先前用于在训练集上进行K折交叉验证的一些代码。但是，对于此问题，我需要使用给定的验证集。我该怎么做？

I would now like to optimize the parameters of my SVM using the validation set. However, I cannot find how to input the validation set explicitly into sklearn.grid_search.GridSearchCV(). Below is some code I've previously used for doing K-fold cross-validation on the training set. However, for this problem I need to use the validation set as given. How can I do that?

from sklearn import svm, cross_validation
from sklearn.grid_search import GridSearchCV

# (some code left out to simplify things)

skf = cross_validation.StratifiedKFold(y_train, n_folds=5, shuffle = True)
clf = GridSearchCV(svm.SVC(tol=0.005, cache_size=6000,
                             class_weight=penalty_weights),
                     param_grid=tuned_parameters,
                     n_jobs=2,
                     pre_dispatch="n_jobs",
                     cv=skf,
                     scoring=scorer)
clf.fit(X_train, y_train)

推荐答案

使用

ps = PredefinedSplit(test_fold=your_test_fold)

然后在<$ c $中设置 cv = ps c> GridSearchCV

then set cv=ps in GridSearchCV

test_fold [i]给出样本i的测试集折叠。值为-1表示相应的样本不是任何测试集折叠的一部分，而是将始终放入训练折叠中。

test_fold[i] gives the test set fold of sample i. A value of -1 indicates that the corresponding sample is not part of any test set folds, but will instead always be put into the training fold.

也请参见

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