def test_classic_lm(): train_pos = FileCorpusSource(data.LM_TRAIN_POSITIVE).entries() train_neg = FileCorpusSource(data.LM_TRAIN_NEGATIVE).entries() test_pos = FileCorpusSource(data.LM_TEST_POSITIVE).entries() test_neg = FileCorpusSource(data.LM_TEST_NEGATIVE).entries() run_classic( train_pos, train_neg, test_pos, test_neg, verbose=False )
def test_classic_lm_filtered(): train_pos = filter_lines(FileCorpusSource(data.LM_TRAIN_POSITIVE).entries(), positive_emoji) train_neg = filter_lines(FileCorpusSource(data.LM_TRAIN_NEGATIVE).entries(), negative_emoji) test_pos = filter_lines(FileCorpusSource(data.LM_TEST_POSITIVE).entries(), positive_emoji) test_neg = filter_lines(FileCorpusSource(data.LM_TEST_NEGATIVE).entries(), negative_emoji) run_classic( train_pos, train_neg, test_pos, test_neg, verbose=False )
def test_classic_rt(): positive_training_corpus = itertools.chain(*[ FileCorpusSource(data.RT_CORPUS_FILE_POSITIVE).entries(), FileCorpusSource(data.BIASED_FILE_POSITIVE).entries(), ]) negative_training_corpus = itertools.chain(*[ FileCorpusSource(data.RT_CORPUS_FILE_NEGATIVE).entries(), FileCorpusSource(data.BIASED_FILE_NEGATIVE).entries(), ]) positive_test_corpus = FileCorpusSource(data.TEST_FILE_POSITIVE).entries() negative_test_corpus = FileCorpusSource(data.TEST_FILE_NEGATIVE).entries() run_classic( positive_training_corpus, negative_training_corpus, positive_test_corpus, negative_test_corpus, verbose=False )