OneVsRestClassifier 中的 GridSearch 估计器 [英] GridSearch for an estimator inside a OneVsRestClassifier

问题描述

我想在 SVC 模型中执行 GridSearchCV，但它使用一对多策略.对于后一部分，我可以这样做:

I want to perform GridSearchCV in a SVC model, but that uses the one-vs-all strategy. For the latter part, I can just do this:

model_to_set = OneVsRestClassifier(SVC(kernel="poly"))

我的问题是参数.假设我想尝试以下值:

My problem is with the parameters. Let's say I want to try the following values:

parameters = {"C":[1,2,4,8], "kernel":["poly","rbf"],"degree":[1,2,3,4]}

为了执行 GridSearchCV，我应该这样做:

In order to perform GridSearchCV, I should do something like:

 cv_generator = StratifiedKFold(y, k=10)
 model_tunning = GridSearchCV(model_to_set, param_grid=parameters, score_func=f1_score, n_jobs=1, cv=cv_generator)

但是，然后我执行它我得到:

However, then I execute it I get:

Traceback (most recent call last):
  File "/.../main.py", line 66, in <module>
    argclass_sys.set_model_parameters(model_name="SVC", verbose=3, file_path=PATH_ROOT_MODELS)
  File "/.../base.py", line 187, in set_model_parameters
    model_tunning.fit(self.feature_encoder.transform(self.train_feats), self.label_encoder.transform(self.train_labels))
  File "/usr/local/lib/python2.7/dist-packages/sklearn/grid_search.py", line 354, in fit
    return self._fit(X, y)
  File "/usr/local/lib/python2.7/dist-packages/sklearn/grid_search.py", line 392, in _fit
    for clf_params in grid for train, test in cv)
  File "/usr/local/lib/python2.7/dist-packages/sklearn/externals/joblib/parallel.py", line 473, in __call__
    self.dispatch(function, args, kwargs)
  File "/usr/local/lib/python2.7/dist-packages/sklearn/externals/joblib/parallel.py", line 296, in dispatch
    job = ImmediateApply(func, args, kwargs)
  File "/usr/local/lib/python2.7/dist-packages/sklearn/externals/joblib/parallel.py", line 124, in __init__
    self.results = func(*args, **kwargs)
  File "/usr/local/lib/python2.7/dist-packages/sklearn/grid_search.py", line 85, in fit_grid_point
    clf.set_params(**clf_params)
  File "/usr/local/lib/python2.7/dist-packages/sklearn/base.py", line 241, in set_params
    % (key, self.__class__.__name__))
ValueError: Invalid parameter kernel for estimator OneVsRestClassifier

基本上，由于 SVC 位于 OneVsRestClassifier 内并且这是我发送到 GridSearchCV 的估算器，因此无法访问 SVC 的参数.

Basically, since the SVC is inside a OneVsRestClassifier and that's the estimator I send to the GridSearchCV, the SVC's parameters can't be accessed.

为了完成我想要的，我看到了两个解决方案:

In order to accomplish what I want, I see two solutions:

1. 在创建 SVC 时，以某种方式告诉它不要使用一对一的策略，而是使用一对一的策略.
2. 以某种方式指示参数对应于 OneVsRestClassifier 中的估计器的 GridSearchCV.

我还没有找到一种方法来做任何提到的替代品.你知道有没有办法做到其中的任何一个?或者，也许您可​​以建议另一种方法来获得相同的结果?

I'm yet to find a way to do any of the mentioned alternatives. Do you know if there's a way to do any of them? Or maybe you could suggest another way to get to the same result?

谢谢！

推荐答案

当您在网格搜索中使用嵌套估计器时，您可以使用 __ 作为分隔符来限定参数的范围.在这种情况下，SVC 模型作为一个名为 estimator 的属性存储在 OneVsRestClassifier 模型中:

When you use nested estimators with grid search you can scope the parameters with __ as a separator. In this case the SVC model is stored as an attribute named estimator inside the OneVsRestClassifier model:

from sklearn.datasets import load_iris
from sklearn.multiclass import OneVsRestClassifier
from sklearn.svm import SVC
from sklearn.grid_search import GridSearchCV
from sklearn.metrics import f1_score

iris = load_iris()

model_to_set = OneVsRestClassifier(SVC(kernel="poly"))

parameters = {
    "estimator__C": [1,2,4,8],
    "estimator__kernel": ["poly","rbf"],
    "estimator__degree":[1, 2, 3, 4],
}

model_tunning = GridSearchCV(model_to_set, param_grid=parameters,
                             score_func=f1_score)

model_tunning.fit(iris.data, iris.target)

print model_tunning.best_score_
print model_tunning.best_params_

结果:

0.973290762737
{'estimator__kernel': 'poly', 'estimator__C': 1, 'estimator__degree': 2}

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