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')
import sys
import os
from configparser import ConfigParser
from utils.ranking_data import RankingData
from utils.write_parsed import write_parsed
from utils.human_ranking import HumanRanking
from processors.process import Process
from features.feature_extractor import FeatureExtractor
from utils.wmt import write_wmt_format
from utils.process_semeval import process_semeval
from nltk.corpus import stopwords
# Read configuration file
config = ConfigParser()
config.readfp(open('test.cfg'))
# Prepare dataset
ranking_data = RankingData(config)
ranking_data.read_dataset()
ranking_data.write_dataset()
write_parsed(config.get('Data', 'input_dir').replace('plain', 'parse'), config.get('Data', 'working_dir'), ['cs-en'])
# Process dataset
process = Process(config)
sentences_target, sentences_reference = process.run_processors()
cobalt_scores = FeatureExtractor.extract_features_static(['cobalt'], sentences_target, sentences_reference)
# print(str(cobalt_scores[-1][0]))
ranking_data.write_scores_wmt_format(cobalt_scores, metric='cobalt', output_path='output/cobalt.scores')