评估sklearn cross_val_score上的多个分数

Question

我正在尝试使用sklearn评估多个机器学习算法,以获得几个指标(准确度,召回率,精度等等).

对于我从这里的文档和源代码(我使用sklearn 0.17)中理解的内容,cross_val_score函数只为每次执行接收一个记分器.因此,为了计算多个分数,我必须:

1. 执行多次

2. 实施我的(耗时且容易出错的)得分手

   我用这段代码执行了多次:

   from sklearn.svm import SVC
   from sklearn.naive_bayes import GaussianNB
   from sklearn.tree import DecisionTreeClassifier
   from sklearn.cross_validation import  cross_val_score
   import time
   from sklearn.datasets import  load_iris
   
   iris = load_iris()
   
   models = [GaussianNB(), DecisionTreeClassifier(), SVC()]
   names = ["Naive Bayes", "Decision Tree", "SVM"]
   for model, name in zip(models, names):
       print name
       start = time.time()
       for score in ["accuracy", "precision", "recall"]:
           print score,
           print " : ",
           print cross_val_score(model, iris.data, iris.target,scoring=score, cv=10).mean()
       print time.time() - start
   

我得到这个输出:

Naive Bayes
accuracy  :  0.953333333333
precision  :  0.962698412698
recall  :  0.953333333333
0.0383198261261
Decision Tree
accuracy  :  0.953333333333
precision  :  0.958888888889
recall  :  0.953333333333
0.0494720935822
SVM
accuracy  :  0.98
precision  :  0.983333333333
recall  :  0.98
0.063080072403

哪个没问题,但对我自己的数据来说速度很慢.我如何衡量所有分数？

Answer 1

自写这篇文章以来,scikit-learn已经更新并使我的答案过时,请参阅下面更清晰的解决方案

您可以编写自己的评分函数来捕获所有三条信息,但是交叉验证的评分函数必须只返回一个数字scikit-learn(这可能是出于兼容性原因).下面是一个示例,其中每个交叉验证切片的每个分数都打印到控制台,返回的值只是三个指标的总和.如果要返回所有这些值,则必须对cross_val_score(cross_validation.py的第1351行)和_score(第1601行或同一文件)进行一些更改.

from sklearn.svm import SVC
from sklearn.naive_bayes import GaussianNB
from sklearn.tree import DecisionTreeClassifier
from sklearn.cross_validation import  cross_val_score
import time
from sklearn.datasets import  load_iris
from sklearn.metrics import accuracy_score, precision_score, recall_score

iris = load_iris()

models = [GaussianNB(), DecisionTreeClassifier(), SVC()]
names = ["Naive Bayes", "Decision Tree", "SVM"]

def getScores(estimator, x, y):
    yPred = estimator.predict(x)
    return (accuracy_score(y, yPred), 
            precision_score(y, yPred, pos_label=3, average='macro'), 
            recall_score(y, yPred, pos_label=3, average='macro'))

def my_scorer(estimator, x, y):
    a, p, r = getScores(estimator, x, y)
    print a, p, r
    return a+p+r

for model, name in zip(models, names):
    print name
    start = time.time()
    m = cross_val_score(model, iris.data, iris.target,scoring=my_scorer, cv=10).mean()
    print '\nSum:',m, '\n\n'
    print 'time', time.time() - start, '\n\n'

这使:

Naive Bayes
0.933333333333 0.944444444444 0.933333333333
0.933333333333 0.944444444444 0.933333333333
1.0 1.0 1.0
0.933333333333 0.944444444444 0.933333333333
0.933333333333 0.944444444444 0.933333333333
0.933333333333 0.944444444444 0.933333333333
0.866666666667 0.904761904762 0.866666666667
1.0 1.0 1.0
1.0 1.0 1.0
1.0 1.0 1.0

Sum: 2.86936507937 


time 0.0249638557434 


Decision Tree
1.0 1.0 1.0
0.933333333333 0.944444444444 0.933333333333
1.0 1.0 1.0
0.933333333333 0.944444444444 0.933333333333
0.933333333333 0.944444444444 0.933333333333
0.866666666667 0.866666666667 0.866666666667
0.933333333333 0.944444444444 0.933333333333
0.933333333333 0.944444444444 0.933333333333
1.0 1.0 1.0
1.0 1.0 1.0

Sum: 2.86555555556 


time 0.0237860679626 


SVM
1.0 1.0 1.0
0.933333333333 0.944444444444 0.933333333333
1.0 1.0 1.0
1.0 1.0 1.0
1.0 1.0 1.0
0.933333333333 0.944444444444 0.933333333333
0.933333333333 0.944444444444 0.933333333333
1.0 1.0 1.0
1.0 1.0 1.0
1.0 1.0 1.0

Sum: 2.94333333333 


time 0.043044090271 

截至scikit-learn 0.19.0,解决方案变得更加容易

from sklearn.model_selection import cross_validate
from sklearn.datasets import  load_iris
from sklearn.svm import SVC

iris = load_iris()
clf = SVC()
scoring = {'acc': 'accuracy',
           'prec_macro': 'precision_macro',
           'rec_micro': 'recall_macro'}
scores = cross_validate(clf, iris.data, iris.target, scoring=scoring,
                         cv=5, return_train_score=True)
print(scores.keys())
print(scores['test_acc'])  

这使:

['test_acc', 'score_time', 'train_acc', 'fit_time', 'test_rec_micro', 'train_rec_micro', 'train_prec_macro', 'test_prec_macro']
[ 0.96666667  1.          0.96666667  0.96666667  1.        ]

Answer 2

我遇到了同样的问题,我创建了一个可以支持多个指标的模块cross_val_score.
为了用这个模块完成你想要的,你可以写:

from multiscorer import MultiScorer
import numpy as np
from sklearn.metrics import accuracy_score, precision_score, recall_score          
from sklearn.model_selection import cross_val_score
from numpy import average

scorer = MultiScorer({
    'Accuracy'  : (accuracy_score , {}),
    'Precision' : (precision_score, {'pos_label': 3, 'average':'macro'}),
    'Recall'    : (recall_score   , {'pos_label': 3, 'average':'macro'})
})

for model, name in zip(models, names):
    print name
    start = time.time()

    _ = cross_val_score(model, iris.data, iris.target,scoring=scorer, cv=10) # Added assignment of the result to `_` in order to illustrate that the return value will not be used
    results = scorer.get_results()

    for metric_name in results.keys():
        average_score = np.average(results[metric_name])
        print('%s : %f' % (metric_name, average_score))

    print 'time', time.time() - start, '\n\n'

您可以从GitHub检查并下载此模块.希望能帮助到你.