# scikit-learn
from sklearn.feature_extraction import DictVectorizer
from sklearn.linear_model import Perceptron
from sklearn.naive_bayes import MultinomialNB
from sklearn.svm import LinearSVC

def train_sk(ldocs, clname='perc'):
    docs = [bow_to_avmap(doc) for _, doc in ldocs]
    lbls = [l for l, _ in ldocs]
    vec = DictVectorizer()
    encoded_docs = vec.fit_transform(docs)
    if clname == 'perc':  # perceptron
        classifier = Perceptron(n_iter=20)
    elif clname == 'nb':  # Naive Bayes
        classifier = MultinomialNB()
    elif clname == 'svm': # linear support vector machine
        classifier = LinearSVC()
    classifier.fit(encoded_docs, lbls)
    return vec, classifier

def bow_to_avmap(doc):
    return dict((w, True) for w in doc)

def classify_sk(v_c, doc):
    vec, classifier = v_c
    encoded_doc = vec.transform(bow_to_avmap(doc))
    return classifier.predict(encoded_doc)