Ejemplo n.º 1
0
    def test_train_test_are_files(self):
        lm = CharacterLanguageModel('witten-bell', order=3)
        lm.fit(self.words)
        output = lm.predict(self.words)
        log_probs = output['log_probs']
        ppls = output['ppls']
        ppl1s = output['ppl1s']

        self.assertEquals(len(self.words), len(log_probs))
        self.assertEquals(len(self.words), len(ppls))
        self.assertEquals(len(self.words), len(ppl1s))
Ejemplo n.º 2
0
    def test_language_model_classifier(self):
        lm_real_words = CharacterLanguageModel('witten-bell', order=3)
        lm_real_words.fit(self.words)

        real_words = self.words
        non_words = lm_real_words.generate(1, len(real_words))

        lm_non_words = CharacterLanguageModel('witten-bell', order=3)
        lm_non_words.fit(non_words)

        clf = LanguageModelClassifier([lm_non_words, lm_real_words])
        real_words_pred = clf.predict(real_words)
        non_words_pred = clf.predict(non_words)

        real_words_bincount = np.bincount(real_words_pred)
        non_words_bincount = np.bincount(non_words_pred)

        self.assertTrue(real_words_bincount[0] < real_words_bincount[1])
        self.assertTrue(non_words_bincount[0] > non_words_bincount[1])
def train_lm(pos_words, neg_words, discount='witten-bell', order=3, debug=False):
    lm_pos = CharacterLanguageModel(discount, order, debug=debug)
    lm_pos.fit(pos_words)
    lm_neg = CharacterLanguageModel(discount, order, debug=debug)
    lm_neg.fit(neg_words)
    return LanguageModelClassifier([lm_pos, lm_neg])