我有一个很大的输入向量。目前，程序在运行 calibrated_clf.fit(x_train, y_train) 时已经卡了几小时。我不知道程序是死机了还是怎么回事。我怎样才能在 calibrated_clf.fit(x_train, y_train) 函数调用中打印出某种进度信息呢？

clf = ensemble.RandomForestClassifier(criterion = 'entropy', n_estimators = 350, max_features = 200,n_jobs=-1)calibrated_clf = CalibratedClassifierCV(clf, method='isotonic')print "Here 1"calibrated_clf.fit(x_train, y_train)print "Here 2"

x_train 是一个大小为 (51733, 250) 的向量。我在打印输出中卡在 “Here 1” 已经几个小时了。

回答：

你可以简单地将 verbose 设置为大于 0 的值。

来自

from sklearn.externals import joblibhelp(joblib.parallel)

verbose: int, 可选 详细程度级别：如果非零，将打印进度消息。当大于 50 时，输出将发送到 stdout。消息的频率随详细程度级别的增加而增加。如果大于 10，将报告所有迭代。

RandomForestClassifier 使用了 joblib 库中的 parallel 函数。

import numpyfrom sklearn.datasets import make_blobsfrom sklearn.ensemble import RandomForestClassifiern = 1000X, y = make_blobs(n_samples=n)X_train, y_train = X[0:n // 2], y[0:n // 2]X_valid, y_valid = X[n // 2:], y[n // 2:]clf = RandomForestClassifier(n_estimators=350, verbose=100)clf.fit(X_train, y_train)

输出

building tree 1 of 350[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0sbuilding tree 2 of 350[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0sbuilding tree 3 of 350[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0sbuilding tree 4 of 350[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0sbuilding tree 5 of 350[...]building tree 100 of 350building tree 101 of 350building tree 102 of 350building tree 103 of 350building tree 104 of 350[...]building tree 350 of 350[Parallel(n_jobs=1)]: Done 350 out of 350 | elapsed:    1.6s finished