我从sklearn网站上获取了示例代码，如下所示：

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是一个大约有70行的pandas DataFrame。

输出结果是

[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 .............................................

然后它就再也没有完成。我在装有Lion系统的Mac Book Pro上运行的。我做错了什么？

回答：

通过在运行网格搜索之前对数据集进行标准化解决了这个问题，具体方法如这里所示：normalize-data-in-pandas。