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[英]GridSearch over RegressorChain using Scikit-Learn?
[英]scikit-learn FeatureUnion gridsearch over subsets of features
如何在scikit中使用FeatureUnion学习,以便Gridsearch可以选择处理其部分?
下面的代码工作并设置一个FeatureUnion,其中包含用于单词的TfidfVectorizer和用于字符的TfidfVectorizer。
在进行Gridsearch时,除了测试已定义的参数空间外,我还想测试'vect__wordvect'及其ngram_range参数(没有用于字符的TfidfVectorizer),也只测试带有小写参数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
FeatureUnion
有一个名为transformer_list
的参数,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会变成
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}]}
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