Scikit-learn 的带有线性内核 svm 的 GridSearchCV 耗时太长
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【中文标题】Scikit-learn 的带有线性内核 svm 的 GridSearchCV 耗时太长【英文标题】:Scikit-learn's GridSearchCV with linear kernel svm takes too long 【发布时间】:2012-09-18 22:48:37 【问题描述】:我从 sklearn 网站获取了示例代码,即
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 是一个大约 70 行的 pandas DataFrame。
输出是
[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 .............................................
然后它永远不会结束。我用 Lion 在 Mac Book Pro 上运行它。我做错了什么?
【问题讨论】:
如果您使用n_jobs=1
运行它会完成吗?
【参考方案1】:
在运行网格搜索之前通过规范化数据集来修复它,如下所示:normalize-data-in-pandas。
【讨论】:
确实 SVC 似乎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?如果你可以分享它,请在github.com/scikit-learn/scikit-learn/issues 上报告问题(只是带有触发数据冻结的参数的 SVC 调用)。邮件列表中有一些讨论在 libsvm 中添加max_iter
参数以避免此问题。
@fspirit 你是个天才以上是关于Scikit-learn 的带有线性内核 svm 的 GridSearchCV 耗时太长的主要内容,如果未能解决你的问题,请参考以下文章
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