def get_data(self): process_wmt = PrepareWmt() data_structure1 = process_wmt.get_data_structure(self.config) data_structure2 = process_wmt.get_data_structure2(self.config) process_wmt.print_data_set(self.config, data_structure1) if 'Parse' in loads(self.config.get("Resources", "processors")): process_wmt_parse = PrepareWmt(data_type='parse') data_structure_parse = process_wmt_parse.get_data_structure(self.config) process_wmt_parse.print_data_set(self.config, data_structure_parse) f_judgements = self.config.get('WMT', 'human_ranking') maximum_comparisons = int(self.config.get('WMT', 'maximum_comparisons')) human_rankings = HumanRanking() human_rankings.add_human_data(f_judgements, self.config, max_comparisons=maximum_comparisons) process = Process(self.config) sents_tgt, sents_ref = process.run_processors() extractor = FeatureExtractor(self.config) features_to_extract = FeatureExtractor.read_feature_names(self.config) extractor.extract_features(features_to_extract, sents_tgt, sents_ref) return data_structure2, human_rankings, extractor.vals
def get_data(self): human_scores = read_reference_file(os.path.expanduser(self.config.get('Data', 'human_scores')), '\t') process = Process(self.config) sents_tgt, sents_ref = process.run_processors() extractor = FeatureExtractor(self.config) features_to_extract = FeatureExtractor.read_feature_names(self.config) extractor.extract_features(features_to_extract, sents_tgt, sents_ref) return extractor.vals, human_scores
def prepare_feature_files(self): process_wmt = PrepareWmt() data_structure1 = process_wmt.get_data_structure(self.config) data_structure2 = process_wmt.get_data_structure2(self.config) process_wmt.print_data_set(self.config, data_structure1) if 'Parse' in loads(self.config.get("Resources", "processors")): process_wmt_parse = PrepareWmt(data_type='parse') data_structure_parse = process_wmt_parse.get_data_structure(self.config) process_wmt_parse.print_data_set(self.config, data_structure_parse) process = Process(self.config) sents_tgt, sents_ref = process.run_processors() extractor = FeatureExtractor(self.config) features_to_extract = FeatureExtractor.read_feature_names(self.config) extractor.extract_features(features_to_extract, sents_tgt, sents_ref) feature_values = extractor.vals datasets_language_pairs = set((x[0], x[1]) for x in data_structure2) dataset_for_all = self.config.get('WMT', 'dataset') feature_set_name = os.path.basename(self.config.get('Features', 'feature_set')).replace(".txt", "") f_features_all = open(os.path.expanduser(self.config.get('WMT', 'output_dir')) + '/' + 'x_' + dataset_for_all + '.' + feature_set_name + '.' + 'all' + '.tsv', 'w') f_meta_data_all = open(os.path.expanduser(self.config.get('WMT', 'output_dir')) + '/' + 'meta_' + dataset_for_all + '.' + feature_set_name + '.' + 'all' + '.tsv', 'w') for dataset, lp in sorted(datasets_language_pairs): f_features = open(os.path.expanduser(self.config.get('WMT', 'output_dir')) + '/' + 'x_' + dataset + '.' + feature_set_name + '.' + lp + '.tsv', 'w') for i, sentence_data in enumerate(data_structure2): if dataset in sentence_data and lp in sentence_data: f_features_all.write('\t'.join([str(x) for x in feature_values[i]]) + "\n") f_meta_data_all.write('\t'.join([str(x) for x in sentence_data]) + "\n") f_features.write('\t'.join([str(x) for x in feature_values[i]]) + "\n") f_features.close() f_features_all.close()
def feature_extraction(config_features_path): config = ConfigParser() config.readfp(open(config_features_path)) wd = config.get('WMT', 'working_directory') if not os.path.exists(wd): os.mkdir(wd) data = RankingData(config) data.read_dataset() process = Process(config) sentences_tgt, sentences_ref = process.run_processors() feature_names = FeatureExtractor.read_feature_names(config) feature_values = FeatureExtractor.extract_features_static(feature_names, sentences_tgt, sentences_ref) write_feature_file(wd + '/' + 'x' + '_' + data.datasets[0].name + '.tsv', feature_values) my_dataset = data.plain[0].dataset my_lp = data.plain[0].lp f_path = wd + '/' + 'x' + '_' + my_dataset + '_' + my_lp + '.tsv' f_file = open(f_path, 'w') for i, instance in enumerate(data.plain): if instance.dataset == my_dataset and instance.lp == my_lp: f_file.write('\t'.join([str(x) for x in feature_values[i]]) + "\n") else: f_file.close() my_dataset = instance.dataset my_lp = instance.lp f_path = wd + '/' + 'x' + '_' + my_dataset + '_' + my_lp + '.tsv' f_file = open(f_path, 'w') f_judgements = config.get('WMT', 'human_ranking') human_rankings = HumanRanking() human_rankings.add_human_data(f_judgements, config) human_rankings.get_sentence_ids(data) learn_to_rank(feature_values, human_rankings, wd + '/' + 'x_learn_to_rank.tsv', wd + '/' + 'y_learn_to_rank.tsv')
def test_feature_sets(): cfg = ConfigParser() cfg.readfp(open(os.getcwd() + '/config/system.cfg')) group_name = FE.get_features_group_name(cfg) features_to_test = FE.read_feature_names(cfg) if os.path.exists(cfg.get('Data', 'output') + '/' + group_name + '.' + 'summary'): "Path exists!" return output_file = open(cfg.get('Data', 'output') + '/' + group_name + '.' + 'summary', 'w') name0 = group_name + '_' + 'all' corr0 = corr_feature_set(features_to_test, name0) output_file.write(name0 + '\t' + str(corr0) + '\n') for feat in features_to_test: name1 = group_name + '_' + feat + '_' + 'only' corr1 = corr_feature_set(feat, name1) output_file.write(name1 + '\t' + str(corr1) + '\n') name2 = group_name + '_' + feat + '_' + 'excluded' excluding = [] for ffeat in features_to_test: if ffeat == feat: continue excluding.append(ffeat) corr2 = corr_feature_set(excluding, name2) output_file.write(name2 + '\t' + str(corr2) + '\n') output_file.close()