text: from sklearn.feature_extraction.text import TfidfVectorizer
  from sklearn.feature_extraction.text import HashingVectorizer
  from sklearn.linear_model import SGDClassifier
  from sklearn.svm import SVC
  from sklearn.naive_bayes import MultinomialNB
  from sklearn.pipeline import Pipeline
  from sklearn.cross_validation import cross_val_score
  from jpjplearn.datasets import load_clf_corpus
  from jpjplearn.analyzer import mecab_analyzer
  
  # ../all/0/*.txt
  #       /1/*.txt
  X, y = load_clf_corpus("../all")
  
  nb = Pipeline([('vec', TfidfVectorizer(analyzer=mecab_analyzer)), ('clf', MultinomialNB(alpha=0.05))])
  #nb = Pipeline([('vec', HashingVectorizer(analyzer=mecab_analyzer, non_negative=True)), ('clf', MultinomialNB(alpha=0.05))])
  svc = Pipeline([('vec', TfidfVectorizer(analyzer=mecab_analyzer)), ('clf', SVC(C=1.0, kernel='linear'))])
  #svc = Pipeline([('vec', HashingVectorizer(analyzer=mecab_analyzer, non_negative=True)), ('clf', SVC(C=1.0, kernel='linear'))])
  sgd = Pipeline([('vec', HashingVectorizer(analyzer=mecab_analyzer)), ('clf', SGDClassifier(n_iter=100, penalty='l2'))])
  
  cross_val_score(nb, X, y, scoring='accuracy').mean()
  cross_val_score(nb, X, y, scoring='precision').mean()
  cross_val_score(nb, X, y, scoring='recall').mean()
  cross_val_score(nb, X, y, scoring='f1').mean()
  
  cross_val_score(svc, X, y, scoring='accuracy').mean()
  cross_val_score(svc, X, y, scoring='precision').mean()
  cross_val_score(svc, X, y, scoring='recall').mean()
  cross_val_score(svc, X, y, scoring='f1').mean()
  