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Scikit-learn的带线性核SVM的GridSearchCV花费时间太长。

pythonpython-2.7machine-learningscikit-learn
3

我从sklearn网站上获取了示例代码，它是这样的：

tuned_parameters = [{'kernel': ['rbf'], 'gamma': [1e-3, 1e-4], 'C': [1, 10, 100, 1000]},
        {'kernel': ['linear'], 'C': [1, 10, 100, 1000]}]

scores = [('f1', f1_score)]

for score_name, score_func in scores:
    print "# Tuning hyper-parameters for %s" % score_name
    print

    clf = GridSearchCV( SVC(), tuned_parameters, score_func=score_func, n_jobs=-1, verbose=2 )
    clf.fit(X_train, Y_train)

    print "Best parameters set found on development set:"
    print
    print clf.best_estimator_
    print
    print "Grid scores on development set:"

    print
    for params, mean_score, scores in clf.grid_scores_:
        print "%0.3f (+/-%0.03f) for %r" % (
            mean_score, scores.std() / 2, params)
    print

    print "Detailed classification report:"
    print
    print "The model is trained on the full development set."
    print "The scores are computed on the full evaluation set."
    print
    y_true, y_pred = Y_test, clf.predict(X_test)
    print cross_validation.classification_report(y_true, y_pred)
    print

X_train是一个包含大约70行的pandas DataFrame。

输出结果为

[GridSearchCV] kernel=rbf, C=1, gamma=0.001 ....................................
[GridSearchCV] kernel=rbf, C=1, gamma=0.001 ....................................
[GridSearchCV] kernel=rbf, C=1, gamma=0.001 ....................................
[GridSearchCV] kernel=rbf, C=1, gamma=0.0001 ...................................
[Parallel(n_jobs=-1)]: Done   1 jobs       | elapsed:    0.0s
[GridSearchCV] ........................... kernel=rbf, C=1, gamma=0.001 -   0.0s
[GridSearchCV] ........................... kernel=rbf, C=1, gamma=0.001 -   0.0s
[GridSearchCV] ........................... kernel=rbf, C=1, gamma=0.001 -   0.0s
[GridSearchCV] .......................... kernel=rbf, C=1, gamma=0.0001 -   0.0s
[GridSearchCV] kernel=rbf, C=1, gamma=0.0001 ...................................
[GridSearchCV] kernel=rbf, C=1, gamma=0.0001 ...................................
[GridSearchCV] kernel=rbf, C=10, gamma=0.001 ...................................
[GridSearchCV] kernel=rbf, C=10, gamma=0.001 ...................................
[GridSearchCV] .......................... kernel=rbf, C=1, gamma=0.0001 -   0.0s
[GridSearchCV] .......................... kernel=rbf, C=1, gamma=0.0001 -   0.0s
[GridSearchCV] kernel=rbf, C=10, gamma=0.001 ...................................
[GridSearchCV] .......................... kernel=rbf, C=10, gamma=0.001 -   0.0s
[GridSearchCV] .......................... kernel=rbf, C=10, gamma=0.001 -   0.0s
[GridSearchCV] kernel=rbf, C=10, gamma=0.0001 ..................................
[GridSearchCV] .......................... kernel=rbf, C=10, gamma=0.001 -   0.0s
[GridSearchCV] kernel=rbf, C=10, gamma=0.0001 ..................................
[GridSearchCV] kernel=rbf, C=10, gamma=0.0001 ..................................
[GridSearchCV] ......................... kernel=rbf, C=10, gamma=0.0001 -   0.0s
[GridSearchCV] kernel=rbf, C=100, gamma=0.001 ..................................
[GridSearchCV] ......................... kernel=rbf, C=10, gamma=0.0001 -   0.0s
[GridSearchCV] ......................... kernel=rbf, C=10, gamma=0.0001 -   0.0s
[GridSearchCV] kernel=rbf, C=100, gamma=0.001 ..................................
[GridSearchCV] ......................... kernel=rbf, C=100, gamma=0.001 -   0.0s
[GridSearchCV] kernel=rbf, C=100, gamma=0.001 ..................................
[GridSearchCV] kernel=rbf, C=100, gamma=0.0001 .................................
[GridSearchCV] ......................... kernel=rbf, C=100, gamma=0.001 -   0.0s
[GridSearchCV] kernel=rbf, C=100, gamma=0.0001 .................................
[GridSearchCV] ......................... kernel=rbf, C=100, gamma=0.001 -   0.0s
[GridSearchCV] kernel=rbf, C=100, gamma=0.0001 .................................
[GridSearchCV] kernel=rbf, C=1000, gamma=0.001 .................................
[GridSearchCV] ........................ kernel=rbf, C=100, gamma=0.0001 -   0.0s
[GridSearchCV] ........................ kernel=rbf, C=100, gamma=0.0001 -   0.0s
[GridSearchCV] kernel=rbf, C=1000, gamma=0.001 .................................
[GridSearchCV] ........................ kernel=rbf, C=100, gamma=0.0001 -   0.0s
[GridSearchCV] ........................ kernel=rbf, C=1000, gamma=0.001 -   0.0s
[GridSearchCV] kernel=rbf, C=1000, gamma=0.001 .................................
[GridSearchCV] kernel=rbf, C=1000, gamma=0.0001 ................................
[GridSearchCV] kernel=rbf, C=1000, gamma=0.0001 ................................
[GridSearchCV] ........................ kernel=rbf, C=1000, gamma=0.001 -   0.0s
[GridSearchCV] kernel=rbf, C=1000, gamma=0.0001 ................................
[GridSearchCV] ........................ kernel=rbf, C=1000, gamma=0.001 -   0.0s
[GridSearchCV] ....................... kernel=rbf, C=1000, gamma=0.0001 -   0.0s
[GridSearchCV] kernel=linear, C=1 ..............................................
[GridSearchCV] ....................... kernel=rbf, C=1000, gamma=0.0001 -   0.0s
[GridSearchCV] kernel=linear, C=1 ..............................................
[GridSearchCV] kernel=linear, C=1 ..............................................
[GridSearchCV] ....................... kernel=rbf, C=1000, gamma=0.0001 -   0.0s
[GridSearchCV] kernel=linear, C=10 .............................................

然后它就一直不停止了。我在配备有Lion系统的Mac Book Pro上运行它。我做错了什么？

如果你使用 n_jobs=1 运行，它能否完成？ - ogrisel
1个回答
6

在运行网格搜索之前，可以像这里所示normalize-data-in-pandas一样对数据集进行归一化处理来解决该问题。

确实，SVC 对非规范化数据非常敏感。您的数据是私有的还是可以公开的？如果您可以分享它，请在 https://github.com/scikit-learn/scikit-learn/issues 上报问题（只需使用触发数据冻结的 SVC 调用及其参数）。邮件列表上有一些讨论，要添加一个 max_iter 参数到 libsvm 中以避免这个问题。 - ogrisel
@fspirit，您真是个天才。 - ajaanbaahu
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