GridSearch用于OneVsRestClassifier内的估算器

Question

我想在SVC模型中执行GridSearchCV,但是它使用one-vs-all策略.对于后者,我可以这样做:

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

我的问题是参数.假设我想尝试以下值:

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

为了执行GridSearchCV,我应该做类似的事情:

 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,然后我执行它得到:

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在OneVsRestClassifier中,并且是我发送给GridSearchCV的估算器,因此无法访问SVC的参数.

为了实现我想要的,我看到两个解决方案:

1. 在创建SVC时,不知何故告诉它不要使用一对一策略,而是使用一对一策略.
2. 以某种方式指示GridSearchCV,参数对应于OneVsRestClassifier内的估计器.

我还没有办法做任何提到的替代方案.你知道有没有办法做他们中的任何一个？或者你可以建议另一种方法来获得相同的结果？

谢谢!

Answer 1

将嵌套估算器与网格搜索结合使用时,可以将参数__作为分隔符进行范围调整.在这种情况下,SVC模型存储为模型estimator内部命名的属性OneVsRestClassifier:

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_

产量:

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

Answer 2

对于Python 3，应使用以下代码

from sklearn.datasets import load_iris
from sklearn.multiclass import OneVsRestClassifier
from sklearn.svm import SVC
from sklearn.model_selection 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,
                             scoring='f1_weighted')

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

print(model_tunning.best_score_)
print(model_tunning.best_params_)

Answer 3

param_grid  = {"estimator__alpha": [10**-5, 10**-3, 10**-1, 10**1, 10**2]}

clf = OneVsRestClassifier(SGDClassifier(loss='log',penalty='l1'))

model = GridSearchCV(clf,param_grid, scoring = 'f1_micro', cv=2,n_jobs=-1)

model.fit(x_train_multilabel, y_train)