Я пытаюсь выбрать лучшие признаки, используя хи -квадрат (scikit -узнать 0,10 ). Из 80 обучающих документов я сначала извлекаю 227 функций, и из этих 227 функций я хочу выбрать 10 лучших.
my_vectorizer = CountVectorizer(analyzer=MyAnalyzer())
X_train = my_vectorizer.fit_transform(train_data)
X_test = my_vectorizer.transform(test_data)
Y_train = np.array(train_labels)
Y_test = np.array(test_labels)
X_train = np.clip(X_train.toarray(), 0, 1)
X_test = np.clip(X_test.toarray(), 0, 1)
ch2 = SelectKBest(chi2, k=10)
print X_train.shape
X_train = ch2.fit_transform(X_train, Y_train)
print X_train.shape
Результаты следующие.
(80, 227)
(80, 14)
Они подобны, если я устанавливаю k
равным 100
.
(80, 227)
(80, 227)
Почему это происходит?
*РЕДАКТИРОВАТЬ :Полный пример вывода, теперь без отсечения, где я запрашиваю 30 и получаю вместо этого 32:
Train instances: 9 Test instances: 1
Feature extraction...
X_train:
[[0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 1 0 1 1 0 1 1 0 0 0 1 0 1 0 0 0 0 1 1 1 0 0 1 0 0 1 0 0 0 0]
[0 0 2 1 0 0 0 0 0 1 0 0 0 1 1 0 0 0 1 0 0 0 0 1 0 1 1 0 0 1 0 1]
[1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0]
[0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0]]
Y_train:
[0 0 0 0 0 0 0 0 1]
32 features extracted from 9 training documents.
Feature selection...
(9, 32)
(9, 32)
Using 32(requested:30) best features from 9 training documents
get support:
[ True True True True True True True True True True True True
True True True True True True True True True True True True
True True True True True True True True]
get support with vocabulary :
[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
25 26 27 28 29 30 31]
Training...
/usr/local/lib/python2.6/dist-packages/scikit_learn-0.10-py2.6-linux-x86_64.egg/sklearn/svm/sparse/base.py:23: FutureWarning: SVM: scale_C will be True by default in scikit-learn 0.11
scale_C)
Classifying...
Другой пример без отсечения, где я запрашиваю 10 и вместо этого получаю 11:
Train instances: 9 Test instances: 1
Feature extraction...
X_train:
[[0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[0 0 1 0 1 1 0 1 1 0 0 0 1 0 1 0 0 0 0 1 1 1 0 0 1 0 0 1 0 0 0 0]
[0 0 2 1 0 0 0 0 0 1 0 0 0 1 1 0 0 0 1 0 0 0 0 1 0 1 1 0 0 1 0 1]
[1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0]
[0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0]]
Y_train:
[0 0 0 0 0 0 0 0 1]
32 features extracted from 9 training documents.
Feature selection...
(9, 32)
(9, 11)
Using 11(requested:10) best features from 9 training documents
get support:
[ True True True False False True False False False False True False
False False True False False False True False True False True True
False False False False True False False False]
get support with vocabulary :
[ 0 1 2 5 10 14 18 20 22 23 28]
Training...
/usr/local/lib/python2.6/dist-packages/scikit_learn-0.10-py2.6-linux-x86_64.egg/sklearn/svm/sparse/base.py:23: FutureWarning: SVM: scale_C will be True by default in scikit-learn 0.11
scale_C)
Classifying...