Spaces:
Running
Running
| #!/usr/bin/env python3 | |
| """ | |
| Copyright 2019 Brian Thompson | |
| Licensed under the Apache License, Version 2.0 (the "License"); | |
| you may not use this file except in compliance with the License. | |
| You may obtain a copy of the License at | |
| https://www.apache.org/licenses/LICENSE-2.0 | |
| Unless required by applicable law or agreed to in writing, software | |
| distributed under the License is distributed on an "AS IS" BASIS, | |
| WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| See the License for the specific language governing permissions and | |
| limitations under the License. | |
| """ | |
| import argparse | |
| import logging | |
| import pickle | |
| from math import ceil | |
| from random import seed as seed | |
| import numpy as np | |
| logger = logging.getLogger('vecalign') | |
| logger.setLevel(logging.WARNING) | |
| logFormatter = logging.Formatter("%(asctime)s %(levelname)-5.5s %(message)s") | |
| consoleHandler = logging.StreamHandler() | |
| consoleHandler.setFormatter(logFormatter) | |
| logger.addHandler(consoleHandler) | |
| from dp_utils import make_alignment_types, print_alignments, read_alignments, \ | |
| read_in_embeddings, make_doc_embedding, vecalign | |
| from score import score_multiple, log_final_scores | |
| def _main(): | |
| # make runs consistent | |
| seed(42) | |
| np.random.seed(42) | |
| parser = argparse.ArgumentParser('Sentence alignment using sentence embeddings and FastDTW', | |
| formatter_class=argparse.ArgumentDefaultsHelpFormatter) | |
| parser.add_argument('-s', '--src', type=str, nargs='+', required=True, | |
| help='preprocessed source file to align') | |
| parser.add_argument('-t', '--tgt', type=str, nargs='+', required=True, | |
| help='preprocessed target file to align') | |
| parser.add_argument('-g', '--gold_alignment', type=str, nargs='+', required=False, | |
| help='preprocessed target file to align') | |
| parser.add_argument('--src_embed', type=str, nargs=2, required=True, | |
| help='Source embeddings. Requires two arguments: first is a text file, sencond is a binary embeddings file. ') | |
| parser.add_argument('--tgt_embed', type=str, nargs=2, required=True, | |
| help='Target embeddings. Requires two arguments: first is a text file, sencond is a binary embeddings file. ') | |
| parser.add_argument('-a', '--alignment_max_size', type=int, default=4, | |
| help='Searches for alignments up to size N-M, where N+M <= this value. Note that the the embeddings must support the requested number of overlaps') | |
| parser.add_argument('-d', '--del_percentile_frac', type=float, default=0.2, | |
| help='Deletion penalty is set to this percentile (as a fraction) of the cost matrix distribution. Should be between 0 and 1.') | |
| parser.add_argument('-v', '--verbose', help='sets consle to logging.DEBUG instead of logging.WARN', | |
| action='store_true') | |
| parser.add_argument('--max_size_full_dp', type=int, default=300, # org: 300 | |
| help='Maximum size N for which is is acceptable to run full N^2 dynamic programming.') | |
| parser.add_argument('--costs_sample_size', type=int, default=20000, | |
| help='Sample size to estimate costs distribution, used to set deletion penalty in conjunction with deletion_percentile.') | |
| parser.add_argument('--num_samps_for_norm', type=int, default=100, # org 100 | |
| help='Number of samples used for normalizing embeddings') | |
| parser.add_argument('--search_buffer_size', type=int, default=5, | |
| help='Width (one side) of search buffer. Larger values makes search more likely to recover from errors but increases runtime.') | |
| parser.add_argument('--debug_save_stack', type=str, | |
| help='Write stack to pickle file for debug purposes') | |
| args = parser.parse_args() | |
| if len(args.src) != len(args.tgt): | |
| raise Exception('number of source files must match number of target files') | |
| if args.gold_alignment is not None: | |
| if len(args.gold_alignment) != len(args.src): | |
| raise Exception('number of gold alignment files, if provided, must match number of source and target files') | |
| if args.verbose: | |
| import logging | |
| logger.setLevel(logging.INFO) | |
| if args.alignment_max_size < 2: | |
| logger.warning('Alignment_max_size < 2. Increasing to 2 so that 1-1 alignments will be considered') | |
| args.alignment_max_size = 2 | |
| src_sent2line, src_line_embeddings = read_in_embeddings(args.src_embed[0], args.src_embed[1]) | |
| tgt_sent2line, tgt_line_embeddings = read_in_embeddings(args.tgt_embed[0], args.tgt_embed[1]) | |
| width_over2 = ceil(args.alignment_max_size / 2.0) + args.search_buffer_size | |
| test_alignments = [] | |
| stack_list = [] | |
| for src_file, tgt_file in zip(args.src, args.tgt): | |
| logger.info('Aligning src="%s" to tgt="%s"', src_file, tgt_file) | |
| src_lines = open(src_file, 'rt', encoding="utf-8").readlines() | |
| vecs0 = make_doc_embedding(src_sent2line, src_line_embeddings, src_lines, args.alignment_max_size) | |
| tgt_lines = open(tgt_file, 'rt', encoding="utf-8").readlines() | |
| vecs1 = make_doc_embedding(tgt_sent2line, tgt_line_embeddings, tgt_lines, args.alignment_max_size) | |
| final_alignment_types = make_alignment_types(args.alignment_max_size) | |
| logger.debug('Considering alignment types %s', final_alignment_types) | |
| stack = vecalign(vecs0=vecs0, | |
| vecs1=vecs1, | |
| final_alignment_types=final_alignment_types, | |
| del_percentile_frac=args.del_percentile_frac, | |
| width_over2=width_over2, | |
| max_size_full_dp=args.max_size_full_dp, | |
| costs_sample_size=args.costs_sample_size, | |
| num_samps_for_norm=args.num_samps_for_norm) | |
| # write final alignments to stdout | |
| print_alignments(stack[0]['final_alignments'], stack[0]['alignment_scores']) | |
| test_alignments.append(stack[0]['final_alignments']) | |
| stack_list.append(stack) | |
| if args.gold_alignment is not None: | |
| gold_list = [read_alignments(x) for x in args.gold_alignment] | |
| res = score_multiple(gold_list=gold_list, test_list=test_alignments) | |
| log_final_scores(res) | |
| if args.debug_save_stack: | |
| pickle.dump(stack_list, open(args.debug_save_stack, 'wb')) | |
| if __name__ == '__main__': | |
| _main() | |