6

我正在对多标签数据进行网格搜索，如下所示：

#imports
from sklearn.svm import SVC as classifier
from sklearn.pipeline import Pipeline
from sklearn.decomposition import RandomizedPCA
from sklearn.cross_validation import StratifiedKFold
from sklearn.grid_search import GridSearchCV

#classifier pipeline
clf_pipeline = clf_pipeline = OneVsRestClassifier(
                Pipeline([('reduce_dim', RandomizedPCA()),
                          ('clf', classifier())
                          ]
                         ))

C_range = 10.0 ** np.arange(-2, 9)
gamma_range = 10.0 ** np.arange(-5, 4)
n_components_range = (10, 100, 200)
degree_range = (1, 2, 3, 4)

param_grid = dict(estimator__clf__gamma=gamma_range,
                  estimator__clf__c=c_range,
                  estimator__clf__degree=degree_range,
                  estimator__reduce_dim__n_components=n_components_range)

grid = GridSearchCV(clf_pipeline, param_grid,
                                cv=StratifiedKFold(y=Y, n_folds=3), n_jobs=1,
                                verbose=2)
grid.fit(X, Y)

我看到以下回溯：

/Users/andrewwinterman/Documents/sparks-honey/classifier/lib/python2.7/site-packages/sklearn/grid_search.pyc in fit_grid_point(X, y, base_clf, clf_params, train, test, loss_func, score_func, verbose, **fit_params)
    107 
    108     if y is not None:
--> 109         y_test = y[safe_mask(y, test)]
    110         y_train = y[safe_mask(y, train)]
    111         clf.fit(X_train, y_train, **fit_params)

TypeError: only integer arrays with one element can be converted to an index

看起来像多个标签的 GridSearchCV 对象。我应该如何解决这个问题？我是否需要使用 label_binarizer 显式迭代唯一类，在每个子估计器上运行网格搜索？

4

1 回答 1

6

我认为 grid_search.py​​ 中有一个错误

您是否尝试过提供ynumpy 数组？

import numpy as np
Y = np.asarray(Y)
于 2013-02-05T13:08:58.113 回答