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)
我看到了以下回溯:
/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 GridSearchCV 对象到多个标签。我应该如何解决这个问题?我是否需要使用 label_binarizer 明确地遍历唯一类,在每个子估计器上运行网格搜索?
最佳答案
我认为 grid_search.py 中存在错误
您是否尝试过将 y
作为 numpy 数组?
import numpy as np
Y = np.asarray(Y)
关于python - 多标签 OneVsRestClassifier 的 GridSearch?,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/14225882/