#!/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()