scikit-learn FeatureUnion 对特征子集的网格搜索

Question

如何在 scikit learn 中使用 FeatureUnion，以便 Gridsearch 可以选择处理其部分？

下面的代码可以工作并使用 TfidfVectorizer 为单词和字符 TfidfVectorizer 设置一个 FeatureUnion。

在进行 Gridsearch 时，除了测试定义的参数空间之外，我还想仅测试带有 ngram_range 参数的 'vect__wordvect'（没有用于字符的 TfidfVectorizer），并且仅测试带有小写参数 True 的 'vect__lettervect'和 False，另一个 TfidfVectorizer 被禁用。

编辑：基于 maxymoo 建议的完整代码示例。

如何才能做到这一点？

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.pipeline import Pipeline, FeatureUnion
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.grid_search import GridSearchCV
from sklearn.datasets import fetch_20newsgroups

# setup the featureunion
wordvect = TfidfVectorizer(analyzer='word')
lettervect = CountVectorizer(analyzer='char')
featureunionvect = FeatureUnion([("lettervect", lettervect), ("wordvect", wordvect)])

# setup the pipeline
classifier = LogisticRegression(class_weight='balanced')
pipeline = Pipeline([('vect', featureunionvect), ('classifier', classifier)])

# gridsearch parameters 
parameters = {
            'vect__wordvect__ngram_range': [(1, 1), (1, 2)],  # commenting out these two lines
            'vect__lettervect__lowercase': [True, False],     # runs, but there is no parameterization anymore
            'vect__transformer_list': [[('wordvect', wordvect)],
                                        [('lettervect', lettervect)],
                                        [('wordvect', wordvect), ('lettervect', lettervect)]]}
gs_clf = GridSearchCV(pipeline, parameters)

# data
newsgroups_train = fetch_20newsgroups(subset='train', categories=['alt.atheism', 'sci.space'])

# gridsearch CV
gs_clf = GridSearchCV(pipeline, parameters)
gs_clf = gs_clf.fit(newsgroups_train.data, newsgroups_train.target)
for score in gs_clf.grid_scores_:
    print "gridsearch scores: ", score

Answer 1

有FeatureUnion一个名为的参数transformer_list，您可以使用它来进行网格搜索；所以在你的情况下你的网格搜索参数将变成

parameters = {'vect__wordvect__ngram_range': [(1, 1), (1, 2)],
              'vect__lettervect__lowercase': [True, False],
              'vect__transformer_weights': [{"lettervect":1,"wordvect":0}, 
                                            {"lettervect":0,"wordvect":1}, 
                                            {"lettervect":1,"wordvect":1}]}