def evaluate(true, pred, msg): true, pred = np.array(true), np.array(pred) MAE= mean_absolute_error(np.array(true), np.array(pred)) MSE_sqrt = math.sqrt(mean_squared_error(np.array(true), np.array(pred))) MSE = mean_squared_error(np.array(true), np.array(pred)) R2 = r2_score(np.array(true), np.array(pred)) Pearson_r = pearsonr(np.array(true), np.array(pred)) Spearman_r = spearmanr(np.array(true), np.array(pred)) log_state(msg) log_performance(MSE, MAE, Pearson_r, R2, Spearman_r, MSE_sqrt) return None
logger.info(r"loading corpus from : " + corpus_name) lexicon_name = get_file_path('lexicon') logger.info(r"loading lexicon form : " + lexicon_name) expand_name = get_file_path('neural_cand') logger.info(r"loading expand_word from : " + expand_name) mark_name = get_file_path('mark') logger.info(r"loading mark from : " + mark_name) corpus = load_corpus(corpus_name) lexicon = load_lexicon(lexicon_name) mark = load_mark(mark_name) # log_state('use extend lexicon') lexicon = combine_lexicon(lexicon_name, expand_name) log_state('mean') evaluate_mean(corpus, lexicon, mark) log_state('tf_mean') evaluate_tf_mean(corpus, lexicon, mark) log_state('tfidf_mean') evaluate_tfidf_mean(corpus, lexicon, mark) log_state('geo') evaluate_geo(corpus, lexicon, mark) log_state('tfidf_geo') evaluate_tfidf_geo(corpus, lexicon, mark) log_state('tf_geo') evaluate_tf_geo(corpus, lexicon, mark)