Scikit学习GridSearch给出“ ValueError:不支持多类格式”错误
machine-learning
python
scikit-learn
6
0

我正在尝试使用GridSearch对LinearSVC()进行参数估算,如下所示-

clf_SVM = LinearSVC()
params = {
          'C': [0.5, 1.0, 1.5],
          'tol': [1e-3, 1e-4, 1e-5],
          'multi_class': ['ovr', 'crammer_singer'],
          }
gs = GridSearchCV(clf_SVM, params, cv=5, scoring='roc_auc')
gs.fit(corpus1, y)

corpus1的形状为(1726,7001),y的形状为(1726,)

这是一个多类分类,并且y的值从0到3(包括两个端点),即有四个类。

但这给了我以下错误-

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-220-0c627bda0543> in <module>()
      5           }
      6 gs = GridSearchCV(clf_SVM, params, cv=5, scoring='roc_auc')
----> 7 gs.fit(corpus1, y)

/usr/local/lib/python2.7/dist-packages/sklearn/grid_search.pyc in fit(self, X, y)
    594 
    595         """
--> 596         return self._fit(X, y, ParameterGrid(self.param_grid))
    597 
    598 

/usr/local/lib/python2.7/dist-packages/sklearn/grid_search.pyc in _fit(self, X, y, parameter_iterable)
    376                                     train, test, self.verbose, parameters,
    377                                     self.fit_params, return_parameters=True)
--> 378             for parameters in parameter_iterable
    379             for train, test in cv)
    380 

/usr/local/lib/python2.7/dist-packages/sklearn/externals/joblib/parallel.pyc in __call__(self, iterable)
    651             self._iterating = True
    652             for function, args, kwargs in iterable:
--> 653                 self.dispatch(function, args, kwargs)
    654 
    655             if pre_dispatch == "all" or n_jobs == 1:

/usr/local/lib/python2.7/dist-packages/sklearn/externals/joblib/parallel.pyc in dispatch(self, func, args, kwargs)
    398         """
    399         if self._pool is None:
--> 400             job = ImmediateApply(func, args, kwargs)
    401             index = len(self._jobs)
    402             if not _verbosity_filter(index, self.verbose):

/usr/local/lib/python2.7/dist-packages/sklearn/externals/joblib/parallel.pyc in __init__(self, func, args, kwargs)
    136         # Don't delay the application, to avoid keeping the input
    137         # arguments in memory
--> 138         self.results = func(*args, **kwargs)
    139 
    140     def get(self):

/usr/local/lib/python2.7/dist-packages/sklearn/cross_validation.pyc in _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters)
   1238     else:
   1239         estimator.fit(X_train, y_train, **fit_params)
-> 1240     test_score = _score(estimator, X_test, y_test, scorer)
   1241     if return_train_score:
   1242         train_score = _score(estimator, X_train, y_train, scorer)

/usr/local/lib/python2.7/dist-packages/sklearn/cross_validation.pyc in _score(estimator, X_test, y_test, scorer)
   1294         score = scorer(estimator, X_test)
   1295     else:
-> 1296         score = scorer(estimator, X_test, y_test)
   1297     if not isinstance(score, numbers.Number):
   1298         raise ValueError("scoring must return a number, got %s (%s) instead."

/usr/local/lib/python2.7/dist-packages/sklearn/metrics/scorer.pyc in __call__(self, clf, X, y)
    136         y_type = type_of_target(y)
    137         if y_type not in ("binary", "multilabel-indicator"):
--> 138             raise ValueError("{0} format is not supported".format(y_type))
    139 
    140         try:

ValueError: multiclass format is not supported
参考资料:
Stack Overflow
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共 3 个回答
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删除scoring='roc_auc' ,它将起作用,因为roc_auc曲线不支持分类数据。

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因为已经指出的那样,你必须先双稳态y

y = label_binarize(y, classes=[0, 1, 2, 3])

然后使用诸如OneVsRestClassifierOneVsOneClassifier的多类学习算法。例如:

clf_SVM = OneVsRestClassifier(LinearSVC())
params = {
      'estimator__C': [0.5, 1.0, 1.5],
      'estimator__tol': [1e-3, 1e-4, 1e-5],
      }
gs = GridSearchCV(clf_SVM, params, cv=5, scoring='roc_auc')
gs.fit(corpus1, y)
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从:

http://scikit-learn.org/stable/modules/generation/sklearn.metrics.roc_auc_score.html#sklearn.metrics.roc_auc_score

“注意:此实现仅限于标签指示符格式的二进制分类任务或多标签分类任务。”

尝试:

from sklearn import preprocessing
y = preprocessing.label_binarize(y, classes=[0, 1, 2, 3])

在训练之前。这将对您的y执行“一次性”编码。

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