解决方法是将 GridsearchCV 的 verbose 属性设置为 3,以便记录候选参数和分数并将日志的输出捕获到文件中。你可以这样做:
from sklearn import svm, datasets
from sklearn.model_selection import GridSearchCV
import sys
old_stdout = sys.stdout
log_file = open("cv.log","w")
sys.stdout = log_file
iris = datasets.load_iris()
parameters = {'kernel':('linear', 'rbf'), 'C':[1, 10]}
svc = svm.SVC()
clf = GridSearchCV(svc, parameters, verbose=3)
clf.fit(iris.data, iris.target)
sys.stdout = old_stdout
log_file.close()
这会将结果以及每次迭代的参数写入文件。您可以在下面看到cv.log 内容的示例:
Fitting 5 folds for each of 4 candidates, totalling 20 fits
[CV 1/5] END ................C=1, kernel=linear;, score=0.967 total time= 0.0s
[CV 2/5] END ................C=1, kernel=linear;, score=1.000 total time= 0.0s
[CV 3/5] END ................C=1, kernel=linear;, score=0.967 total time= 0.0s
[CV 4/5] END ................C=1, kernel=linear;, score=0.967 total time= 0.0s
[CV 5/5] END ................C=1, kernel=linear;, score=1.000 total time= 0.0s