from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import SGDClassifier

class Classifier(object):
        def __init__(self, datagrabber):
                self.grabber = datagrabber
                self.reload()
                
        def reload(self):
                Xs, Ys = self.grabber()

                self.vect = CountVectorizer(analyzer='word',ngram_range=(1,3))
                self.train_vec = self.vect.fit_transform(Xs)
                
                self.clf = SGDClassifier(loss='hinge', penalty='l2',alpha=1e-3, n_iter=500, random_state=42)
                self.text_clf = self.clf.fit(self.train_vec, Ys)

        def scan(self, name):
                v = self.vect.transform([name])
                return self.text_clf.decision_function(v)[0]

        def add(self, name, state):
                # implement add using partial_fit
                # this would mean switching to hashing vectorizer, which means we can't reverse the model
                # so for now we're just going to reload completely
                self.reload()