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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學習,以便Gridsearch可以選擇處理其部分?
下面的代碼工作並設置一個FeatureUnion,其中包含用於單詞的TfidfVectorizer和用於字符的TfidfVectorizer。
在進行Gridsearch時,除了測試已定義的參數空間外,我還想測試'vect__wordvect'及其ngram_range參數(沒有用於字符的TfidfVectorizer),也只測試帶有小寫參數True的'vect__lettervect'和False,另一個TfidfVectorizer被禁用。
編輯:基於maxymoo建議的完整代碼示例。
如何才能做到這一點?
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
有一個名為transformer_list
的參數,您可以使用它來進行網格搜索; 所以在你的情況下你的網格搜索參數會變成
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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