SKlearn中具有嵌套交叉验证的分类报告(平均值/单个值) [英] Classification report with Nested Cross Validation in SKlearn (Average/Individual values)

问题描述

是否可以通过一些变通办法从cross_val_score获取分类报告?我正在使用嵌套的交叉验证，在这里可以获得模型的各种评分，但是，我希望看到外部循环的分类报告.有什么建议吗?

Is it possible to get classification report from cross_val_score through some workaround? I'm using nested cross-validation and I can get various scores here for a model, however, I would like to see the classification report of the outer loop. Any recommendations?

# Choose cross-validation techniques for the inner and outer loops,
# independently of the dataset.
# E.g "LabelKFold", "LeaveOneOut", "LeaveOneLabelOut", etc.
inner_cv = KFold(n_splits=4, shuffle=True, random_state=i)
outer_cv = KFold(n_splits=4, shuffle=True, random_state=i)

# Non_nested parameter search and scoring
clf = GridSearchCV(estimator=svr, param_grid=p_grid, cv=inner_cv)

# Nested CV with parameter optimization
nested_score = cross_val_score(clf, X=X_iris, y=y_iris, cv=outer_cv)

我想在这里看到分数值旁边的分类报告. http://scikit-learn.org/stable/modules/genic /sklearn.metrics.classification_report.html

I would like to see a classification report here along side the score values. http://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html

推荐答案

我们可以定义自己的评分函数，如下所示:

We can define our own scoring function as below:

from sklearn.metrics import classification_report, accuracy_score, make_scorer

def classification_report_with_accuracy_score(y_true, y_pred):

    print classification_report(y_true, y_pred) # print classification report
    return accuracy_score(y_true, y_pred) # return accuracy score

现在，只需使用make_scorer使用新的评分功能调用cross_val_score:

Now, just call cross_val_score with our new scoring function, using make_scorer:

# Nested CV with parameter optimization
nested_score = cross_val_score(clf, X=X_iris, y=y_iris, cv=outer_cv, \
               scoring=make_scorer(classification_report_with_accuracy_score))
print nested_score 

它将分类报告作为文本打印，同时将nested_score作为数字返回.

It will print the classification report as text at the same time return the nested_score as a number.

http://scikit-learn.org/stable/auto_examples/model_selection/使用此新评分功能运行时，plot_nested_cross_validation_iris.html 示例，输出的最后几行如下:

http://scikit-learn.org/stable/auto_examples/model_selection/plot_nested_cross_validation_iris.html example when run with this new scoring function, the last few lines of the output will be as follows:

#   precision    recall  f1-score   support    
#0       1.00      1.00      1.00        14
#1       1.00      1.00      1.00        14
#2       1.00      1.00      1.00         9

#avg / total       1.00      1.00      1.00        37

#[ 0.94736842  1.          0.97297297  1. ]

#Average difference of 0.007742 with std. dev. of 0.007688.

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