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()