Scikit learn 对于一个VSRESTClassifier中的多个类，分类器的参数是否可以不同

有人知道sklearn是否支持OneVsRestClassifier中不同分类器的不同参数吗？例如，在这个例子中，我希望对于不同的类有不同的C值

from sklearn.multiclass import OneVsRestClassifier
from sklearn.svm import LinearSVC
text_clf = OneVsRestClassifier(LinearSVC(C=1.0, class_weight="balanced"))

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目前没有一个VSREST分类器不使用不同的估计量参数或不同类别的不同估计量

还有一些是在其他方面实现的，比如会根据类自动调整参数的不同值，但它还没有扩展到OneVsRestClassifier

但是如果您需要，我们可以在源代码中进行更改以实现这一点

电流源：

正如您所看到的，同一个估计器self.estimator正在传递给所有要训练的类。因此，我们将制作新版本的OneVsRestClassifier来改变这一点：

from sklearn.multiclass import OneVsRestClassifier
from sklearn.preprocessing import LabelBinarizer
from sklearn.externals.joblib import Parallel, delayed
from sklearn.multiclass import _fit_binary

class CustomOneVsRestClassifier(OneVsRestClassifier):

    # Changed the estimator to estimators which can take a list now
    def __init__(self, estimators, n_jobs=1):
        self.estimators = estimators
        self.n_jobs = n_jobs

    def fit(self, X, y):

        self.label_binarizer_ = LabelBinarizer(sparse_output=True)
        Y = self.label_binarizer_.fit_transform(y)
        Y = Y.tocsc()
        self.classes_ = self.label_binarizer_.classes_
        columns = (col.toarray().ravel() for col in Y.T)

        # This is where we change the training method
        self.estimators_ = Parallel(n_jobs=self.n_jobs)(delayed(_fit_binary)(
            estimator, X, column, classes=[
                "not %s" % self.label_binarizer_.classes_[i],
                self.label_binarizer_.classes_[i]])
            for i, (column, estimator) in enumerate(zip(columns, self.estimators)))
        return self

现在你可以使用它了

# Make sure you add those many estimators as there are classes
# In binary case, only a single estimator should be used
estimators = []

# I am considering 3 classes as of now
estimators.append(LinearSVC(C=1.0, class_weight="balanced"))
estimators.append(LinearSVC(C=0.1, class_weight="balanced"))
estimators.append(LinearSVC(C=10, class_weight="balanced"))
clf = CustomOneVsRestClassifier(estimators)

clf.fit(X, y)

注意：我还没有在it中实现部分_-fit。如果你打算使用它，我们可以处理它

# Make sure you add those many estimators as there are classes
# In binary case, only a single estimator should be used
estimators = []

# I am considering 3 classes as of now
estimators.append(LinearSVC(C=1.0, class_weight="balanced"))
estimators.append(LinearSVC(C=0.1, class_weight="balanced"))
estimators.append(LinearSVC(C=10, class_weight="balanced"))
clf = CustomOneVsRestClassifier(estimators)

clf.fit(X, y)