在 sklearn cross_val_score 上评估多个分数

Question

我正在尝试使用 sklearn 评估多个机器学习算法的几个指标（准确度、召回率、精度等）。

根据我从文档here 和源代码（我使用的是 sklearn 0.17）中了解到的内容，cross_val_score 函数每次执行只接收一个记分器。所以为了计算多个分数，我必须：

1. 多次执行

2. 实现我的（耗时且容易出错的）记分器

   我已经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

这没关系，但我自己的数据很慢。如何衡量所有分数？

Answer 1

from sklearn import model_selection

def error_metrics(model, train_data, train_targ, kfold):
    scoring = ["accuracy","roc_auc","neg_log_loss","r2",
             "neg_mean_squared_error","neg_mean_absolute_error"] 

    error_metrics = pd.DataFrame()
    error_metrics["model"] = model
    for scor in scoring:
        score = []
        for mod in model:
           
            result = model_selection.cross_val_score(estimator= mod, X=train_data, y=train_targ,cv=kfold,scoring=scor )
            score.append(result.mean())
            
        error_metrics[scor] =pd.Series(score)
        
    return error_metrics

Answer 2

我遇到了同样的问题，我在cross_val_score 中创建了一个可以支持多个指标的模块。
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查看和下载此模块。 希望对您有所帮助。

Answer 3

自从写这篇文章 scikit-learn 已经更新并且我的答案已经过时了，请看下面更简洁的解决方案

---

您可以编写自己的评分函数来捕获所有三个信息，但是用于交叉验证的评分函数只能返回 scikit-learn 中的单个数字（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'

给出：

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 

---

从 scikit-learn 0.19.0 开始，解决方案变得更加更容易

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'])  

这给出了：

['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.        ]