def generate_src_word_accuracy_report(ref,
outs,
src,
ref_align_file=None,
acc_type='rec',
bucket_type='freq',
bucket_cutoffs=None,
freq_count_file=None,
freq_corpus_file=None,
label_set=None,
src_labels=None,
title=None,
case_insensitive=False):
"""
Generate a report for source word analysis in both plain text and graphs.
Args:
ref: Tokens from the reference
outs: Tokens from the output file(s)
src: Tokens from the source
ref_align_file: Alignment file for the reference
acc_type: The type of accuracy to show (prec/rec/fmeas). Can also have multiple separated by '+'.
bucket_type: A string specifying the way to bucket words together to calculate F-measure (freq/tag)
bucket_cutoffs: The boundaries between buckets, specified as a colon-separated string.
freq_corpus_file: When using "freq" as a bucketer, which corpus to use to calculate frequency.
By default this uses the frequency in the reference test set, but it's often more informative
se the frequency in the training set, in which case you specify the path of the target side
he training corpus.
freq_count_file: An alternative to freq_corpus that uses a count file in "word\tfreq" format.
src_labels: either a filename of a file full of source labels, or a list of strings corresponding to `ref`.
title: A string specifying the caption of the printed table
case_insensitive: A boolean specifying whether to turn on the case insensitive option
"""
case_insensitive = True if case_insensitive == 'True' else False
if acc_type != 'rec':
raise ValueError(
"Source word analysis can only use recall as an accuracy type")
if not src or not ref_align_file:
raise ValueError(
"Must specify the source and the alignment file when performing source analysis."
)
ref_align = corpus_utils.load_alignments(ref_align_file)
bucketer = bucketers.create_word_bucketer_from_profile(
bucket_type,
bucket_cutoffs=bucket_cutoffs,
freq_count_file=freq_count_file,
freq_corpus_file=freq_corpus_file,
freq_data=src,
label_set=label_set,
case_insensitive=case_insensitive)
statistics, examples = bucketer.calc_statistics_and_examples(
ref, outs, src=src, src_labels=src_labels, ref_aligns=ref_align)
reporter = reporters.WordReport(bucketer=bucketer,
statistics=statistics,
examples=examples,
src_sents=src,
ref_sents=ref,
ref_aligns=ref_align,
out_sents=outs,
src_labels=src_labels,
acc_type=acc_type,
header="Source Word Accuracy Analysis",
title=title)
reporter.generate_report(output_fig_file=f'src-word-acc',
output_fig_format='pdf',
output_directory='outputs')
return reporter
def generate_src_word_accuracy_report(ref,
outs,
src,
ref_align_file=None,
acc_type='rec',
bucket_type='freq',
bucket_cutoffs=None,
freq_count_file=None,
freq_corpus_file=None,
label_set=None,
src_labels=None,
title=None,
case_insensitive=False,
output_bucket_details=False,
to_cache=False,
cache_dicts=None):
"""
Generate a report for source word analysis in both plain text and graphs.
Args:
ref: Tokens from the reference
outs: Tokens from the output file(s)
src: Tokens from the source
ref_align_file: Alignment file for the reference
acc_type: The type of accuracy to show (prec/rec/fmeas). Can also have multiple separated by '+'.
bucket_type: A string specifying the way to bucket words together to calculate F-measure (freq/tag)
bucket_cutoffs: The boundaries between buckets, specified as a colon-separated string.
freq_corpus_file: When using "freq" as a bucketer, which corpus to use to calculate frequency.
By default this uses the frequency in the reference test set, but it's often more informative
se the frequency in the training set, in which case you specify the path of the target side
he training corpus.
freq_count_file: An alternative to freq_corpus that uses a count file in "word\tfreq" format.
src_labels: either a filename of a file full of source labels, or a list of strings corresponding to `ref`.
title: A string specifying the caption of the printed table
case_insensitive: A boolean specifying whether to turn on the case insensitive option
output_bucket_details: A boolean specifying whether to output the number of words in each bucket
to_cache: Return a list of computed statistics if True
cache_dicts: A list of dictionaries that store cached statistics for each output
"""
# check and set parameters
if type(case_insensitive) == str:
case_insensitive = True if case_insensitive == 'True' else False
if type(output_bucket_details) == str:
output_bucket_details = True if output_bucket_details == 'True' else False
if acc_type != 'rec':
raise ValueError(
"Source word analysis can only use recall as an accuracy type")
if not src or not ref_align_file:
raise ValueError(
"Must specify the source and the alignment file when performing source analysis."
)
if type(src_labels) == str:
src_labels = corpus_utils.load_tokens(src_labels)
ref_align = corpus_utils.load_alignments(ref_align_file)
# compute statistics
bucketer = bucketers.create_word_bucketer_from_profile(
bucket_type,
bucket_cutoffs=bucket_cutoffs,
freq_count_file=freq_count_file,
freq_corpus_file=freq_corpus_file,
freq_data=src,
label_set=label_set,
case_insensitive=case_insensitive)
cache_key_list = [
'statistics', 'my_ref_total_list', 'my_out_totals_list',
'my_out_matches_list'
]
statistics, my_ref_total_list, my_out_totals_list, my_out_matches_list = cache_utils.extract_cache_dicts(
cache_dicts, cache_key_list, len(outs))
if cache_dicts is not None:
my_ref_total_list = my_ref_total_list[0]
my_out_totals_list = list(np.concatenate(my_out_totals_list, 1))
my_out_matches_list = list(np.concatenate(my_out_matches_list, 1))
else:
statistics, my_ref_total_list, my_out_totals_list, my_out_matches_list = bucketer.calc_statistics(
ref, outs, src=src, src_labels=src_labels, ref_aligns=ref_align)
examples = bucketer.calc_examples(len(ref), len(outs), statistics,
my_ref_total_list, my_out_matches_list)
bucket_cnts, bucket_intervals = bucketer.calc_bucket_details(
my_ref_total_list, my_out_totals_list,
my_out_matches_list) if output_bucket_details else (None, None)
if to_cache:
cache_dict = cache_utils.return_cache_dict(cache_key_list, [
statistics, [my_ref_total_list], [my_out_totals_list],
[my_out_matches_list]
])
return cache_dict
# generate reports
reporter = reporters.WordReport(bucketer=bucketer,
statistics=statistics,
examples=examples,
bucket_cnts=bucket_cnts,
bucket_intervals=bucket_intervals,
src_sents=src,
ref_sents=ref,
ref_aligns=ref_align,
out_sents=outs,
src_labels=src_labels,
acc_type=acc_type,
header="Source Word Accuracy Analysis",
title=title)
reporter.generate_report(output_fig_file=f'src-word-acc',
output_fig_format='pdf',
output_directory='outputs')
return reporter