wehub-resource-sync
647 lines
27 KiB
Python
647 lines
27 KiB
Python
import subprocess
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import os
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import re
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from fairseq import options
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import eval_lm
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import preprocess
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from contextlib import redirect_stdout
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import math
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def reprocess(fle):
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# takes in a file of generate.py translation generate_output
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# returns a source dict and hypothesis dict, where keys are the ID num (as a string)
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# and values and the corresponding source and translation. There may be several translations
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# per source, so the values for hypothesis_dict are lists.
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# parses output of generate.py
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with open(fle, 'r') as f:
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txt = f.read()
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"""reprocess generate.py output"""
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p = re.compile(r"[STHP][-]\d+\s*")
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hp = re.compile(r"(\s*[-]?\d+[.]?\d+\s*)|(\s*(-inf)\s*)")
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source_dict = {}
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hypothesis_dict = {}
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score_dict = {}
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target_dict = {}
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pos_score_dict = {}
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lines = txt.split("\n")
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for line in lines:
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line += "\n"
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prefix = re.search(p, line)
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if prefix is not None:
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assert len(prefix.group()) > 2, "prefix id not found"
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_, j = prefix.span()
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id_num = prefix.group()[2:]
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id_num = int(id_num)
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line_type = prefix.group()[0]
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if line_type == "H":
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h_txt = line[j:]
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hypo = re.search(hp, h_txt)
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assert hypo is not None, ("regular expression failed to find the hypothesis scoring")
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_, i = hypo.span()
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score = hypo.group()
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if id_num in hypothesis_dict:
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hypothesis_dict[id_num].append(h_txt[i:])
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score_dict[id_num].append(float(score))
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else:
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hypothesis_dict[id_num] = [h_txt[i:]]
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score_dict[id_num] = [float(score)]
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elif line_type == "S":
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source_dict[id_num] = (line[j:])
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elif line_type == "T":
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target_dict[id_num] = (line[j:])
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elif line_type == "P":
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pos_scores = (line[j:]).split()
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pos_scores = [float(x) for x in pos_scores]
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if id_num in pos_score_dict:
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pos_score_dict[id_num].append(pos_scores)
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else:
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pos_score_dict[id_num] = [pos_scores]
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return source_dict, hypothesis_dict, score_dict, target_dict, pos_score_dict
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def reprocess_nbest(fle):
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"""reprocess interactive.py output"""
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with open(fle, 'r') as f:
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txt = f.read()
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source_dict = {}
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hypothesis_dict = {}
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score_dict = {}
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target_dict = {}
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pos_score_dict = {}
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lines = txt.split("\n")
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hp = re.compile(r'[-]?\d+[.]?\d+')
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j = -1
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for _i, line in enumerate(lines):
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line += "\n"
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line_type = line[0]
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if line_type == "H":
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hypo = re.search(hp, line)
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_, start_index = hypo.span()
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score = hypo.group()
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if j in score_dict:
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score_dict[j].append(float(score))
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hypothesis_dict[j].append(line[start_index:].strip("\t"))
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else:
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score_dict[j] = [float(score)]
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hypothesis_dict[j] = [line[start_index:].strip("\t")]
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elif line_type == "O":
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j += 1
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source_dict[j] = line[2:]
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# we don't have the targets for interactive.py
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target_dict[j] = "filler"
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elif line_type == "P":
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pos_scores = [float(pos_score) for pos_score in line.split()[1:]]
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if j in pos_score_dict:
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pos_score_dict[j].append(pos_scores)
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else:
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pos_score_dict[j] = [pos_scores]
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assert source_dict.keys() == hypothesis_dict.keys()
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assert source_dict.keys() == pos_score_dict.keys()
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assert source_dict.keys() == score_dict.keys()
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return source_dict, hypothesis_dict, score_dict, target_dict, pos_score_dict
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def write_reprocessed(sources, hypos, targets, source_outfile,
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hypo_outfile, target_outfile, right_to_left=False,
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prefix_len=None, bpe_symbol=None,
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target_prefix_frac=None, source_prefix_frac=None):
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"""writes nbest hypothesis for rescoring"""
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assert not (prefix_len is not None and target_prefix_frac is not None), \
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"in writing reprocessed, only one type of prefix may be used"
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assert not (prefix_len is not None and source_prefix_frac is not None), \
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"in writing reprocessed, only one type of prefix may be used"
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assert not (target_prefix_frac is not None and source_prefix_frac is not None), \
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"in writing reprocessed, only one type of prefix may be used"
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with open(source_outfile, 'w') as source_file, \
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open(hypo_outfile, 'w') as hypo_file, \
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open(target_outfile, 'w') as target_file:
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assert len(sources) == len(hypos), "sources and hypos list length mismatch"
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if right_to_left:
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for i in range(len(sources)):
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for j in range(len(hypos[i])):
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if prefix_len is None:
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hypo_file.write(make_right_to_left(hypos[i][j])+"\n")
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else:
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raise NotImplementedError()
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source_file.write(make_right_to_left(sources[i])+"\n")
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target_file.write(make_right_to_left(targets[i])+"\n")
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else:
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for i in sorted(sources.keys()):
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for j in range(len(hypos[i])):
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if prefix_len is not None:
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shortened = get_prefix_no_bpe(hypos[i][j], bpe_symbol, prefix_len)+"\n"
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hypo_file.write(shortened)
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source_file.write(sources[i])
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target_file.write(targets[i])
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elif target_prefix_frac is not None:
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num_words, shortened, num_bpe_tokens = \
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calc_length_from_frac(hypos[i][j], target_prefix_frac, bpe_symbol)
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shortened += "\n"
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hypo_file.write(shortened)
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source_file.write(sources[i])
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target_file.write(targets[i])
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elif source_prefix_frac is not None:
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num_words, shortened, num_bpe_tokensn = \
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calc_length_from_frac(sources[i], source_prefix_frac, bpe_symbol)
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shortened += "\n"
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hypo_file.write(hypos[i][j])
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source_file.write(shortened)
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target_file.write(targets[i])
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else:
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hypo_file.write(hypos[i][j])
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source_file.write(sources[i])
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target_file.write(targets[i])
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def calc_length_from_frac(bpe_sentence, prefix_frac, bpe_symbol):
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# return number of words, (not bpe tokens) that we want
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no_bpe_sen = remove_bpe(bpe_sentence, bpe_symbol)
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len_sen = len(no_bpe_sen.split())
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num_words = math.ceil(len_sen * prefix_frac)
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prefix = get_prefix_no_bpe(bpe_sentence, bpe_symbol, num_words)
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num_bpe_tokens = len(prefix.split())
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return num_words, prefix, num_bpe_tokens
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def get_prefix(sentence, prefix_len):
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"""assuming no bpe, gets the prefix of the sentence with prefix_len words"""
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tokens = sentence.strip("\n").split()
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if prefix_len >= len(tokens):
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return sentence.strip("\n")
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else:
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return " ".join(tokens[:prefix_len])
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def get_prefix_no_bpe(sentence, bpe_symbol, prefix_len):
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if bpe_symbol is None:
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return get_prefix(sentence, prefix_len)
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else:
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return " ".join(get_prefix_from_len(sentence.split(), bpe_symbol, prefix_len))
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def get_prefix_from_len(sentence, bpe_symbol, prefix_len):
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"""get the prefix of sentence with bpe, with prefix len in terms of words, not bpe tokens"""
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bpe_count = sum([bpe_symbol.strip(" ") in t for t in sentence[:prefix_len]])
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if bpe_count == 0:
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return sentence[:prefix_len]
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else:
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return sentence[:prefix_len]+get_prefix_from_len(sentence[prefix_len:], bpe_symbol, bpe_count)
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def get_num_bpe_tokens_from_len(sentence, bpe_symbol, prefix_len):
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"""given a prefix length in terms of words, return the number of bpe tokens"""
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prefix = get_prefix_no_bpe(sentence, bpe_symbol, prefix_len)
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assert len(remove_bpe(prefix, bpe_symbol).split()) <= prefix_len
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return len(prefix.split(" "))
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def make_right_to_left(line):
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tokens = line.split()
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tokens.reverse()
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new_line = " ".join(tokens)
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return new_line
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def remove_bpe(line, bpe_symbol):
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line = line.replace("\n", '')
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line = (line + ' ').replace(bpe_symbol, '').rstrip()
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return line+("\n")
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def remove_bpe_dict(pred_dict, bpe_symbol):
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new_dict = {}
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for i in pred_dict:
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if type(pred_dict[i]) == list:
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new_list = [remove_bpe(elem, bpe_symbol) for elem in pred_dict[i]]
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new_dict[i] = new_list
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else:
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new_dict[i] = remove_bpe(pred_dict[i], bpe_symbol)
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return new_dict
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def parse_bleu_scoring(line):
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p = re.compile(r'(BLEU4 = )\d+[.]\d+')
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res = re.search(p, line)
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assert res is not None, line
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return float(res.group()[8:])
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def get_full_from_prefix(hypo_prefix, hypos):
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"""given a hypo prefix, recover the first hypo from the list of complete hypos beginning with that prefix"""
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for hypo in hypos:
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hypo_prefix = hypo_prefix.strip("\n")
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len_prefix = len(hypo_prefix)
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if hypo[:len_prefix] == hypo_prefix:
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return hypo
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# no match found
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raise Exception()
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def get_score(a, b, c, target_len, bitext_score1, bitext_score2=None, lm_score=None,
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lenpen=None, src_len=None, tgt_len=None, bitext1_backwards=False,
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bitext2_backwards=False, normalize=False):
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if bitext1_backwards:
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bitext1_norm = src_len
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else:
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bitext1_norm = tgt_len
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if bitext_score2 is not None:
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if bitext2_backwards:
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bitext2_norm = src_len
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else:
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bitext2_norm = tgt_len
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else:
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bitext2_norm = 1
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bitext_score2 = 0
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if normalize:
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score = a*bitext_score1/bitext1_norm + b*bitext_score2/bitext2_norm+c*lm_score/src_len
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else:
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score = a*bitext_score1 + b*bitext_score2+c*lm_score
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if lenpen is not None:
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score /= (target_len) ** float(lenpen)
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return score
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class BitextOutput(object):
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def __init__(self, output_file, backwards, right_to_left, bpe_symbol,
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prefix_len=None, target_prefix_frac=None, source_prefix_frac=None):
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"""process output from rescoring"""
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source, hypo, score, target, pos_score = reprocess(output_file)
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if backwards:
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self.hypo_fracs = source_prefix_frac
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else:
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self.hypo_fracs = target_prefix_frac
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# remove length penalty so we can use raw scores
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score, num_bpe_tokens = get_score_from_pos(pos_score, prefix_len, hypo, bpe_symbol, self.hypo_fracs, backwards)
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source_lengths = {}
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target_lengths = {}
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assert hypo.keys() == source.keys(), "key mismatch"
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if backwards:
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tmp = hypo
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hypo = source
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source = tmp
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for i in source:
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# since we are reranking, there should only be one hypo per source sentence
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if backwards:
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len_src = len(source[i][0].split())
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# record length without <eos>
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if len_src == num_bpe_tokens[i][0] - 1:
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source_lengths[i] = num_bpe_tokens[i][0] - 1
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else:
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source_lengths[i] = num_bpe_tokens[i][0]
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target_lengths[i] = len(hypo[i].split())
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source[i] = remove_bpe(source[i][0], bpe_symbol)
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target[i] = remove_bpe(target[i], bpe_symbol)
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hypo[i] = remove_bpe(hypo[i], bpe_symbol)
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score[i] = float(score[i][0])
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pos_score[i] = pos_score[i][0]
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else:
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len_tgt = len(hypo[i][0].split())
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# record length without <eos>
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if len_tgt == num_bpe_tokens[i][0] - 1:
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target_lengths[i] = num_bpe_tokens[i][0] - 1
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else:
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target_lengths[i] = num_bpe_tokens[i][0]
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source_lengths[i] = len(source[i].split())
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if right_to_left:
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source[i] = remove_bpe(make_right_to_left(source[i]), bpe_symbol)
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target[i] = remove_bpe(make_right_to_left(target[i]), bpe_symbol)
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hypo[i] = remove_bpe(make_right_to_left(hypo[i][0]), bpe_symbol)
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score[i] = float(score[i][0])
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pos_score[i] = pos_score[i][0]
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else:
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assert len(hypo[i]) == 1, "expected only one hypothesis per source sentence"
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source[i] = remove_bpe(source[i], bpe_symbol)
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target[i] = remove_bpe(target[i], bpe_symbol)
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hypo[i] = remove_bpe(hypo[i][0], bpe_symbol)
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score[i] = float(score[i][0])
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pos_score[i] = pos_score[i][0]
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self.rescore_source = source
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self.rescore_hypo = hypo
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self.rescore_score = score
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self.rescore_target = target
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self.rescore_pos_score = pos_score
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self.backwards = backwards
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self.right_to_left = right_to_left
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self.target_lengths = target_lengths
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self.source_lengths = source_lengths
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class BitextOutputFromGen(object):
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def __init__(self, predictions_bpe_file, bpe_symbol=None, nbest=False, prefix_len=None, target_prefix_frac=None):
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if nbest:
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pred_source, pred_hypo, pred_score, pred_target, pred_pos_score = reprocess_nbest(predictions_bpe_file)
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else:
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pred_source, pred_hypo, pred_score, pred_target, pred_pos_score = reprocess(predictions_bpe_file)
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assert len(pred_source) == len(pred_hypo)
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assert len(pred_source) == len(pred_score)
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assert len(pred_source) == len(pred_target)
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assert len(pred_source) == len(pred_pos_score)
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# remove length penalty so we can use raw scores
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pred_score, num_bpe_tokens = get_score_from_pos(pred_pos_score, prefix_len, pred_hypo,
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bpe_symbol, target_prefix_frac, False)
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self.source = pred_source
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self.target = pred_target
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self.score = pred_score
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self.pos_score = pred_pos_score
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self.hypo = pred_hypo
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self.target_lengths = {}
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self.source_lengths = {}
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self.no_bpe_source = remove_bpe_dict(pred_source.copy(), bpe_symbol)
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self.no_bpe_hypo = remove_bpe_dict(pred_hypo.copy(), bpe_symbol)
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self.no_bpe_target = remove_bpe_dict(pred_target.copy(), bpe_symbol)
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# indexes to match those from the rescoring models
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self.rescore_source = {}
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self.rescore_target = {}
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self.rescore_pos_score = {}
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self.rescore_hypo = {}
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self.rescore_score = {}
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self.num_hypos = {}
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self.backwards = False
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self.right_to_left = False
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index = 0
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for i in sorted(pred_source.keys()):
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for j in range(len(pred_hypo[i])):
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self.target_lengths[index] = len(self.hypo[i][j].split())
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self.source_lengths[index] = len(self.source[i].split())
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self.rescore_source[index] = self.no_bpe_source[i]
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self.rescore_target[index] = self.no_bpe_target[i]
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self.rescore_hypo[index] = self.no_bpe_hypo[i][j]
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self.rescore_score[index] = float(pred_score[i][j])
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self.rescore_pos_score[index] = pred_pos_score[i][j]
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self.num_hypos[index] = len(pred_hypo[i])
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index += 1
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def get_score_from_pos(pos_score_dict, prefix_len, hypo_dict, bpe_symbol, hypo_frac, backwards):
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score_dict = {}
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num_bpe_tokens_dict = {}
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assert prefix_len is None or hypo_frac is None
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for key in pos_score_dict:
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score_dict[key] = []
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num_bpe_tokens_dict[key] = []
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for i in range(len(pos_score_dict[key])):
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if prefix_len is not None and not backwards:
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num_bpe_tokens = get_num_bpe_tokens_from_len(hypo_dict[key][i], bpe_symbol, prefix_len)
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score_dict[key].append(sum(pos_score_dict[key][i][:num_bpe_tokens]))
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num_bpe_tokens_dict[key].append(num_bpe_tokens)
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elif hypo_frac is not None:
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num_words, shortened, hypo_prefix_len = calc_length_from_frac(hypo_dict[key][i], hypo_frac, bpe_symbol)
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score_dict[key].append(sum(pos_score_dict[key][i][:hypo_prefix_len]))
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num_bpe_tokens_dict[key].append(hypo_prefix_len)
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else:
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score_dict[key].append(sum(pos_score_dict[key][i]))
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num_bpe_tokens_dict[key].append(len(pos_score_dict[key][i]))
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return score_dict, num_bpe_tokens_dict
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class LMOutput(object):
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def __init__(self, lm_score_file, lm_dict=None, prefix_len=None, bpe_symbol=None, target_prefix_frac=None):
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lm_sentences, lm_sen_scores, lm_sen_pos_scores, lm_no_bpe_sentences, lm_bpe_tokens = \
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parse_lm(lm_score_file, prefix_len=prefix_len,
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bpe_symbol=bpe_symbol, target_prefix_frac=target_prefix_frac)
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self.sentences = lm_sentences
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self.score = lm_sen_scores
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self.pos_score = lm_sen_pos_scores
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self.lm_dict = lm_dict
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self.no_bpe_sentences = lm_no_bpe_sentences
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self.bpe_tokens = lm_bpe_tokens
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|
|
|
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def parse_lm(input_file, prefix_len=None, bpe_symbol=None, target_prefix_frac=None):
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|
"""parse output of eval_lm"""
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with open(input_file, 'r') as f:
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|
text = f.readlines()
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text = text[7:]
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cleaned_text = text[:-2]
|
|
|
|
sentences = {}
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|
sen_scores = {}
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|
sen_pos_scores = {}
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|
no_bpe_sentences = {}
|
|
num_bpe_tokens_dict = {}
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|
for _i, line in enumerate(cleaned_text):
|
|
tokens = line.split()
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|
if tokens[0].isdigit():
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|
line_id = int(tokens[0])
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|
scores = [float(x[1:-1]) for x in tokens[2::2]]
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|
sentences[line_id] = " ".join(tokens[1::2][:-1])+"\n"
|
|
if bpe_symbol is not None:
|
|
# exclude <eos> symbol to match output from generate.py
|
|
bpe_sen = " ".join(tokens[1::2][:-1])+"\n"
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|
no_bpe_sen = remove_bpe(bpe_sen, bpe_symbol)
|
|
no_bpe_sentences[line_id] = no_bpe_sen
|
|
|
|
if prefix_len is not None:
|
|
num_bpe_tokens = get_num_bpe_tokens_from_len(bpe_sen, bpe_symbol, prefix_len)
|
|
sen_scores[line_id] = sum(scores[:num_bpe_tokens])
|
|
num_bpe_tokens_dict[line_id] = num_bpe_tokens
|
|
elif target_prefix_frac is not None:
|
|
num_words, shortened, target_prefix_len = calc_length_from_frac(bpe_sen, target_prefix_frac,
|
|
bpe_symbol)
|
|
sen_scores[line_id] = sum(scores[:target_prefix_len])
|
|
num_bpe_tokens_dict[line_id] = target_prefix_len
|
|
else:
|
|
sen_scores[line_id] = sum(scores)
|
|
num_bpe_tokens_dict[line_id] = len(scores)
|
|
|
|
sen_pos_scores[line_id] = scores
|
|
|
|
return sentences, sen_scores, sen_pos_scores, no_bpe_sentences, num_bpe_tokens_dict
|
|
|
|
|
|
def get_directories(data_dir_name, num_rescore, gen_subset,
|
|
fw_name, shard_id, num_shards,
|
|
sampling=False, prefix_len=None,
|
|
target_prefix_frac=None, source_prefix_frac=None):
|
|
nbest_file_id = "nbest_" + str(num_rescore) + \
|
|
"_subset_" + gen_subset + \
|
|
"_fw_name_" + fw_name + \
|
|
"_shard_" + str(shard_id) + \
|
|
"_of_" + str(num_shards)
|
|
|
|
if sampling:
|
|
nbest_file_id += "_sampling"
|
|
|
|
# the directory containing all information for this nbest list
|
|
pre_gen = os.path.join(os.path.dirname(__file__))+"/rerank_data/"+data_dir_name+"/"+nbest_file_id
|
|
# the directory to store the preprocessed nbest list, for left to right rescoring
|
|
left_to_right_preprocessed_dir = pre_gen+"/left_to_right_preprocessed"
|
|
if source_prefix_frac is not None:
|
|
left_to_right_preprocessed_dir = left_to_right_preprocessed_dir + "/prefix_frac" + str(source_prefix_frac)
|
|
# the directory to store the preprocessed nbest list, for right to left rescoring
|
|
right_to_left_preprocessed_dir = pre_gen+"/right_to_left_preprocessed"
|
|
# the directory to store the preprocessed nbest list, for backwards rescoring
|
|
backwards_preprocessed_dir = pre_gen+"/backwards"
|
|
if target_prefix_frac is not None:
|
|
backwards_preprocessed_dir = backwards_preprocessed_dir+"/prefix_frac"+str(target_prefix_frac)
|
|
elif prefix_len is not None:
|
|
backwards_preprocessed_dir = backwards_preprocessed_dir+"/prefix_"+str(prefix_len)
|
|
|
|
# the directory to store the preprocessed nbest list, for rescoring with P(T)
|
|
lm_preprocessed_dir = pre_gen+"/lm_preprocessed"
|
|
|
|
return pre_gen, left_to_right_preprocessed_dir, right_to_left_preprocessed_dir, \
|
|
backwards_preprocessed_dir, lm_preprocessed_dir
|
|
|
|
|
|
def lm_scoring(preprocess_directory, bpe_status, gen_output, pre_gen,
|
|
cur_lm_dict, cur_lm_name, cur_language_model, cur_lm_bpe_code,
|
|
batch_size, lm_score_file, target_lang, source_lang, prefix_len=None):
|
|
if prefix_len is not None:
|
|
assert bpe_status == "different", "bpe status must be different to use prefix len"
|
|
if bpe_status == "no bpe":
|
|
# run lm on output without bpe
|
|
write_reprocessed(gen_output.no_bpe_source, gen_output.no_bpe_hypo,
|
|
gen_output.no_bpe_target, pre_gen+"/rescore_data_no_bpe.de",
|
|
pre_gen+"/rescore_data_no_bpe.en", pre_gen+"/reference_file_no_bpe")
|
|
|
|
preprocess_lm_param = ["--only-source",
|
|
"--trainpref", pre_gen+"/rescore_data_no_bpe."+target_lang,
|
|
"--srcdict", cur_lm_dict,
|
|
"--destdir", preprocess_directory]
|
|
preprocess_parser = options.get_preprocessing_parser()
|
|
input_args = preprocess_parser.parse_args(preprocess_lm_param)
|
|
preprocess.main(input_args)
|
|
|
|
eval_lm_param = [preprocess_directory,
|
|
"--path", cur_language_model,
|
|
"--output-word-probs",
|
|
"--batch-size", str(batch_size),
|
|
"--max-tokens", "1024",
|
|
"--sample-break-mode", "eos",
|
|
"--gen-subset", "train"]
|
|
|
|
eval_lm_parser = options.get_eval_lm_parser()
|
|
input_args = options.parse_args_and_arch(eval_lm_parser, eval_lm_param)
|
|
|
|
with open(lm_score_file, 'w') as f:
|
|
with redirect_stdout(f):
|
|
eval_lm.main(input_args)
|
|
|
|
elif bpe_status == "shared":
|
|
preprocess_lm_param = ["--only-source",
|
|
"--trainpref", pre_gen+"/rescore_data."+target_lang,
|
|
"--srcdict", cur_lm_dict,
|
|
"--destdir", preprocess_directory]
|
|
preprocess_parser = options.get_preprocessing_parser()
|
|
input_args = preprocess_parser.parse_args(preprocess_lm_param)
|
|
preprocess.main(input_args)
|
|
|
|
eval_lm_param = [preprocess_directory,
|
|
"--path", cur_language_model,
|
|
"--output-word-probs",
|
|
"--batch-size", str(batch_size),
|
|
"--sample-break-mode", "eos",
|
|
"--gen-subset", "train"]
|
|
|
|
eval_lm_parser = options.get_eval_lm_parser()
|
|
input_args = options.parse_args_and_arch(eval_lm_parser, eval_lm_param)
|
|
|
|
with open(lm_score_file, 'w') as f:
|
|
with redirect_stdout(f):
|
|
eval_lm.main(input_args)
|
|
|
|
elif bpe_status == "different":
|
|
rescore_file = pre_gen+"/rescore_data_no_bpe"
|
|
rescore_bpe = pre_gen+"/rescore_data_new_bpe"
|
|
|
|
rescore_file += "."
|
|
rescore_bpe += "."
|
|
|
|
write_reprocessed(gen_output.no_bpe_source, gen_output.no_bpe_hypo,
|
|
gen_output.no_bpe_target, rescore_file+source_lang,
|
|
rescore_file+target_lang, pre_gen+"/reference_file_no_bpe",
|
|
bpe_symbol=None)
|
|
|
|
# apply LM bpe to nbest list
|
|
bpe_src_param = ["-c", cur_lm_bpe_code,
|
|
"--input", rescore_file+target_lang,
|
|
"--output", rescore_bpe+target_lang]
|
|
subprocess.call(["python",
|
|
os.path.join(os.path.dirname(__file__),
|
|
"subword-nmt/subword_nmt/apply_bpe.py")] + bpe_src_param,
|
|
shell=False)
|
|
# uncomment to use fastbpe instead of subword-nmt bpe
|
|
# bpe_src_param = [rescore_bpe+target_lang, rescore_file+target_lang, cur_lm_bpe_code]
|
|
# subprocess.call(["/private/home/edunov/fastBPE/fast", "applybpe"] + bpe_src_param, shell=False)
|
|
|
|
preprocess_dir = preprocess_directory
|
|
|
|
preprocess_lm_param = ["--only-source",
|
|
"--trainpref", rescore_bpe+target_lang,
|
|
"--srcdict", cur_lm_dict,
|
|
"--destdir", preprocess_dir]
|
|
preprocess_parser = options.get_preprocessing_parser()
|
|
input_args = preprocess_parser.parse_args(preprocess_lm_param)
|
|
preprocess.main(input_args)
|
|
|
|
eval_lm_param = [preprocess_dir,
|
|
"--path", cur_language_model,
|
|
"--output-word-probs",
|
|
"--batch-size", str(batch_size),
|
|
"--max-tokens", "1024",
|
|
"--sample-break-mode", "eos",
|
|
"--gen-subset", "train"]
|
|
|
|
eval_lm_parser = options.get_eval_lm_parser()
|
|
input_args = options.parse_args_and_arch(eval_lm_parser, eval_lm_param)
|
|
|
|
with open(lm_score_file, 'w') as f:
|
|
with redirect_stdout(f):
|
|
eval_lm.main(input_args)
|
|
|
|
|
|
def rescore_file_name(nbest_dir, prefix_len, scorer_name, lm_file=False,
|
|
target_prefix_frac=None, source_prefix_frac=None, backwards=None):
|
|
if lm_file:
|
|
score_file = nbest_dir+"/lm_score_translations_model_"+scorer_name+".txt"
|
|
else:
|
|
score_file = nbest_dir+"/"+scorer_name+"_score_translations.txt"
|
|
if backwards:
|
|
if prefix_len is not None:
|
|
score_file += "prefix_len"+str(prefix_len)
|
|
elif target_prefix_frac is not None:
|
|
score_file += "target_prefix_frac"+str(target_prefix_frac)
|
|
else:
|
|
if source_prefix_frac is not None:
|
|
score_file += "source_prefix_frac"+str(source_prefix_frac)
|
|
return score_file
|