使用scikit-learn进行递归特征消除和网格搜索 [英] Recursive feature elimination and grid search using scikit-learn

问题描述

我想使用scikit-learn通过嵌套网格搜索和交叉验证对每个特征子集执行递归特征消除.在 RFECV 文档中，听起来好像使用estimator_params参数支持这种类型的操作:

I would like to perform recursive feature elimination with nested grid search and cross-validation for each feature subset using scikit-learn. From the RFECV documentation it sounds like this type of operation is supported using the estimator_params parameter:

estimator_params : dict

    Parameters for the external estimator. Useful for doing grid searches.

但是，当我尝试将超参数网格传递给RFECV对象

However, when I try to pass a grid of hyperparameters to the RFECV object

from sklearn.datasets import make_friedman1
from sklearn.feature_selection import RFECV
from sklearn.svm import SVR
X, y = make_friedman1(n_samples=50, n_features=10, random_state=0)
estimator = SVR(kernel="linear")
selector = RFECV(estimator, step=1, cv=5, estimator_params={'C': [0.1, 10, 100, 1000]})
selector = selector.fit(X, y)

我收到类似错误

  File "U:/My Documents/Code/ModelFeatures/bin/model_rcc_gene_features.py", line 130, in <module>
    selector = selector.fit(X, y)
  File "C:\Python27\lib\site-packages\sklearn\feature_selection\rfe.py", line 336, in fit
    ranking_ = rfe.fit(X_train, y_train).ranking_
  File "C:\Python27\lib\site-packages\sklearn\feature_selection\rfe.py", line 146, in fit
    estimator.fit(X[:, features], y)
  File "C:\Python27\lib\site-packages\sklearn\svm\base.py", line 178, in fit
    fit(X, y, sample_weight, solver_type, kernel, random_seed=seed)
  File "C:\Python27\lib\site-packages\sklearn\svm\base.py", line 233, in _dense_fit
    max_iter=self.max_iter, random_seed=random_seed)
  File "libsvm.pyx", line 59, in sklearn.svm.libsvm.fit (sklearn\svm\libsvm.c:1628)
TypeError: a float is required

如果有人可以告诉我我在做错什么，将不胜感激，谢谢！

If anyone could show me what I'm doing wrong it would be greatly appreciated, thanks!

在Andreas的响应变得更加清晰之后，下面是RFECV与网格搜索相结合的一个有效示例.

After Andreas' response things became clearer, below is a working example of RFECV combined with grid search.

from sklearn.datasets import make_friedman1
from sklearn.feature_selection import RFECV
from sklearn.grid_search import GridSearchCV
from sklearn.svm import SVR
X, y = make_friedman1(n_samples=50, n_features=10, random_state=0)
param_grid = [{'C': 0.01}, {'C': 0.1}, {'C': 1.0}, {'C': 10.0}, {'C': 100.0}, {'C': 1000.0}, {'C': 10000.0}]
estimator = SVR(kernel="linear")
selector = RFECV(estimator, step=1, cv=4)
clf = GridSearchCV(selector, {'estimator_params': param_grid}, cv=7)
clf.fit(X, y)
clf.best_estimator_.estimator_
clf.best_estimator_.grid_scores_
clf.best_estimator_.ranking_

推荐答案

不幸的是，RFECV仅限于交叉验证组件的数量.您无法使用它搜索SVM的参数.该错误是因为SVC期望浮点数为C，并且您给了它一个列表.

Unfortunately, RFECV is limited to cross-validating the number of components. You can not search over the parameters of the SVM with it. The error is because SVC is expecting a float as C, and you gave it a list.

您可以执行以下两项操作之一:在RFECV上运行GridSearchCV，这将导致将数据拆分为两次折叠(一次在GridSearchCV内，一次在RFECV内)，但是对组件数量的搜索将很有效，或者您可以仅在RFE上执行GridSearchCV，这将导致数据的单个拆分，但导致RFE估计器的参数扫描效率非常低.

You can do one of two things: Run GridSearchCV on RFECV, which will result in splitting the data into folds two times (ones inside GridSearchCV and once inside RFECV), but the search over the number of components will be efficient, OR you could do GridSearchCV just on RFE, which would result in a single splitting of the data, but in very inefficient scanning of the parameters of the RFE estimator.

如果您想减少文档字符串的含糊性，欢迎提出请求:)

If you would like to make the docstring less ambiguous, a pull request would be welcome :)

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