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我是 scikits-learn 的新手，我想使用cross_validation.cross_val_scorewithmetrics.precision_recall_fscore_support这样我就可以获得所有相关的交叉验证指标，而不必为了准确性、一次为了精度、一次为了召回和一次为了准确而运行我的交叉验证f1。但是当我尝试这个时，我得到一个 ValueError： 

from sklearn.datasets import fetch_20newsgroups

from sklearn.svm import LinearSVC
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn import metrics
from sklearn import cross_validation
import numpy as np

data_train = fetch_20newsgroups(subset='train', #categories=categories,
                                shuffle=True, random_state=42)
clf = LinearSVC(loss='l1', penalty='l2')
vectorizer = TfidfVectorizer(
  sublinear_tf=False, 
  max_df=0.5,
  min_df=2, 
  ngram_range = (1,1),
  use_idf=False,
  stop_words='english')

X_train = vectorizer.fit_transform(data_train.data)

# Cross-validate:
scores = cross_validation.cross_val_score(
  clf, X_train, data_train.target, cv=5, 
  scoring=metrics.precision_recall_fscore_support)

这是错误：

  File "<stdin>", line 3, in <module>
  File "sklearn/cross_validation.py", line 1148, in cross_val_score
    for train, test in cv)
  File "sklearn/externals/joblib/parallel.py", line 514, in __call__
    self.dispatch(function, args, kwargs)
  File "sklearn/externals/joblib/parallel.py", line 311, in dispatch
    job = ImmediateApply(func, args, kwargs)
  File "sklearn/externals/joblib/parallel.py", line 135, in __init__
    self.results = func(*args, **kwargs)
  File "sklearn/cross_validation.py", line 1075, in _cross_val_score
    score = scorer(estimator, X_test, y_test)
  File "sklearn/metrics/metrics.py", line 1261, in precision_recall_fscore_support
    print beta
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

注意，您需要 .14-git 版本才能在cross_validation.cross_val_score. 

import sklearn
sklearn.__version__

'0.14-git'
4

1 回答 1

4

您应该将 sci-kit learn 更新到最新版本 0.16。

有关评分参数，请参阅此页面

并非所有的 sklearn.metrics 都有效，而且名称也不同。接受以下参数：

ValueError: 'wrong_choice' is not a valid scoring value. Valid options are        
['accuracy', 'adjusted_rand_score', 'average_precision', 'f1', 'f1_macro', 
'f1_micro', 'f1_samples', 'f1_weighted', 'log_loss', 'mean_absolute_error', 
'mean_squared_error', 'median_absolute_error', 'precision',   
'precision_macro', 'precision_micro', 'precision_samples', 
'precision_weighted', 'r2', 'recall', 'recall_macro', 'recall_micro', 
'recall_samples', 'recall_weighted', 'roc_auc']
于 2015-07-24T21:57:10.897 回答