20ng.py

from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV
from sklearn.datasets import fetch_20newsgroups

categories = [
    'alt.atheism',
    'talk.religion.misc',
]
data = fetch_20newsgroups(subset='train', categories=categories)
pipeline = Pipeline([('vect', CountVectorizer()),
                     ('tfidf', TfidfTransformer()),
                     ('clf', LogisticRegression())])
grid = {'vect__ngram_range': [(1, 2)],
        'tfidf__norm': ['l1', 'l2'],
        'clf__C': [100, 10, 1.0, 1e-1, 1e-2, 1e-3, 1e-4, 1e-5]}

if __name__=='__main__':
    grid_search = GridSearchCV(pipeline, grid, cv=5, n_jobs=-1)
    grid_search.fit(data.data, data.target)
    print("Best score: %0.3f" % grid_search.best_score_)
    print("-------------------------------------------")
    print("Best parameters set:", grid_search.best_estimator_.get_params())