vecalign.py

#!/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,
                        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,
                        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()