def main(args): dm = DataModel(args.gig_file, args.chat_file) dm.read_data() exp = Experimenter(dm) if args.classify is True: scores = exp.classify_gigs() if args.feature_values is True: scores = exp.evaluate_feature_values() return dm
def main(args): dm = DataModel(args.data_file) dm.read_data(to_read_count=10000) exp = Experimenter(dm, \ process_datamodel=True, \ serialise=False) t1 = time.time() exp.perform_multiclass_experiment( pred_mode=INDEPENDENT, use_exclusion=True, need_to_extract_features=True, prediction_file='../results/predictions_multiclass_independent_englishonly_legibleonly_wordunibigram_chartrigram_10000.csv', result_file='../results/results_multiclass_independent_englishonly_legibleonly_wordunibigram_chartrigram_10000.txt', english_only=True, legible_only=True) t2 = time.time() timeused = t2 - t1 logging.getLogger(LOGGER).info('Time used in experiment (hour:min:sec): %d:%d:%d' % \ (timeused/3600, timeused/60, timeused%60)) return exp
def main(args): dm = DataModel(args.data_file) dm.read_data(to_read_count=10000) exp = Experimenter(dm, \ process_datamodel=True, \ serialise=False) t1 = time.time() exp.perform_multiclass_experiment( pred_mode=INDEPENDENT, use_exclusion=True, need_to_extract_features=True, prediction_file= '../results/predictions_multiclass_independent_englishonly_legibleonly_wordunibigram_chartrigram_10000.csv', result_file= '../results/results_multiclass_independent_englishonly_legibleonly_wordunibigram_chartrigram_10000.txt', english_only=True, legible_only=True) t2 = time.time() timeused = t2 - t1 logging.getLogger(LOGGER).info('Time used in experiment (hour:min:sec): %d:%d:%d' % \ (timeused/3600, timeused/60, timeused%60)) return exp
def main(args): dm = DataModel() dm.read_data(to_read_count=10, normalize_data=True) dm.write_data('../data/data_imp.csv')