def generate_sentence_examples(ref,
outs,
src=None,
score_type='sentbleu',
report_length=10,
compare_directions='0-1',
title=None,
case_insensitive=False,
to_cache=False,
cache_dicts=None):
"""
Generate examples of sentences that satisfy some criterion, usually score of one system better
Args:
ref: Tokens from the reference
outs: Tokens from the output file(s)
src: Tokens from the source (optional)
score_type: The type of scorer to use
report_length: Number of sentences to print for each system being better or worse
compare_directions: A string specifying which systems to compare
title: A string specifying the caption of the printed table
case_insensitive: A boolean specifying whether to turn on the case insensitive option
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
report_length = int(report_length)
if type(case_insensitive) == str:
case_insensitive = True if case_insensitive == 'True' else False
# compute statistics
scorer = scorers.create_scorer_from_profile(
score_type, case_insensitive=case_insensitive)
cache_key_list = ['scores', 'strs']
scores, strs = cache_utils.extract_cache_dicts(cache_dicts, cache_key_list,
len(outs))
src = [None for _ in ref] if src is None else src
if cache_dicts is None:
scores, strs = [], []
for out in outs:
scores_i, strs_i = [], []
for (r, o, s) in zip(ref, out, src):
score, string = scorer.score_sentence(r, o, s)
scores_i.append(score)
strs_i.append(string)
scores.append(scores_i)
strs.append(strs_i)
if to_cache:
cache_dict = cache_utils.return_cache_dict(cache_key_list,
[scores, strs])
return cache_dict
direcs = arg_utils.parse_compare_directions(compare_directions)
scorediff_lists = []
for (left, right) in direcs:
scorediff_list = []
deduplicate_set = set()
for i, (o1, o2, r) in enumerate(zip(outs[left], outs[right], ref)):
if (tuple(o1), tuple(o2), tuple(r)) in deduplicate_set:
continue
deduplicate_set.add((tuple(o1), tuple(o2), tuple(r)))
s1, str1 = scores[left][i], strs[left][i]
s2, str2 = scores[right][i], strs[right][i]
scorediff_list.append((s2 - s1, s1, s2, str1, str2, i))
scorediff_list.sort()
scorediff_lists.append(scorediff_list)
# generate reports
reporter = reporters.SentenceExampleReport(report_length=report_length,
scorediff_lists=scorediff_lists,
scorer=scorer,
ref=ref,
outs=outs,
src=src,
compare_directions=direcs,
title=title)
reporter.generate_report()
return reporter
def generate_ngram_report(ref,
outs,
min_ngram_length=1,
max_ngram_length=4,
report_length=50,
alpha=1.0,
compare_type='match',
ref_labels=None,
out_labels=None,
compare_directions='0-1',
title=None,
case_insensitive=False,
to_cache=False,
cache_dicts=None):
"""
Generate a report comparing aggregate n-gram statistics in both plain text and graphs
Args:
ref: Tokens from the reference
outs: Tokens from the output file(s)
min_ngram_length: minimum n-gram length
max_ngram_length: maximum n-gram length
report_length: the number of n-grams to report
alpha: when sorting n-grams for salient features, the smoothing coefficient. A higher smoothing coefficient
will result in more frequent phenomena (sometimes this is good).
compare_type: what type of statistic to compare
(match: n-grams that match the reference, over: over-produced ngrams, under: under-produced ngrams)
ref_labels: either a filename of a file full of reference labels, or a list of strings corresponding to `ref`.
If specified, will aggregate statistics over labels instead of n-grams.
out_labels: output labels. must be specified if ref_labels is specified.
compare_directions: A string specifying which systems to compare
title: A string specifying the caption of the printed table
case_insensitive: A boolean specifying whether to turn on the case insensitive option
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
min_ngram_length, max_ngram_length, report_length = int(
min_ngram_length), int(max_ngram_length), int(report_length)
alpha = float(alpha) if type(alpha) == str else alpha
if type(case_insensitive) == str:
case_insensitive = True if case_insensitive == 'True' else False
if out_labels is not None:
out_labels = arg_utils.parse_files(out_labels)
if len(out_labels) != len(outs):
raise ValueError(
f'The number of output files should be equal to the number of output labels.'
)
if type(ref_labels) == str:
label_files_str = f' ref_labels={ref_labels},'
for i, out_label in enumerate(out_labels):
label_files_str += f' out{i}_labels={out_label},'
label_files = (label_files_str)
else:
label_files = None
# compute statistics
cache_key_list = ['totals', 'matches', 'overs', 'unders']
totals, matches, overs, unders = cache_utils.extract_cache_dicts(
cache_dicts, cache_key_list, len(outs))
if cache_dicts is None:
if not type(ref_labels) == str and case_insensitive:
ref = corpus_utils.lower(ref)
outs = [corpus_utils.lower(out) for out in outs]
ref_labels = corpus_utils.load_tokens(ref_labels) if type(
ref_labels) == str else ref_labels
out_labels = [
corpus_utils.load_tokens(out_labels[i])
if not out_labels is None else None for i in range(len(outs))
]
totals, matches, overs, unders = zip(*[
ngram_utils.compare_ngrams(ref,
out,
ref_labels=ref_labels,
out_labels=out_label,
min_length=min_ngram_length,
max_length=max_ngram_length)
for out, out_label in zip(outs, out_labels)
])
if to_cache:
cache_dict = cache_utils.return_cache_dict(
cache_key_list, [totals, matches, overs, unders])
return cache_dict
direcs = arg_utils.parse_compare_directions(compare_directions)
scores = []
for (left, right) in direcs:
if compare_type == 'match':
scores.append(
stat_utils.extract_salient_features(matches[left],
matches[right],
alpha=alpha))
elif compare_type == 'over':
scores.append(
stat_utils.extract_salient_features(overs[left],
overs[right],
alpha=alpha))
elif compare_type == 'under':
scores.append(
stat_utils.extract_salient_features(unders[left],
unders[right],
alpha=alpha))
else:
raise ValueError(f'Illegal compare_type "{compare_type}"')
scorelist = [
sorted(score.items(), key=operator.itemgetter(1), reverse=True)
for score in scores
]
# generate reports
reporter = reporters.NgramReport(scorelist=scorelist,
report_length=report_length,
min_ngram_length=min_ngram_length,
max_ngram_length=max_ngram_length,
matches=matches,
compare_type=compare_type,
alpha=alpha,
compare_directions=direcs,
label_files=label_files,
title=title)
reporter.generate_report(
output_fig_file=
f'ngram-min{min_ngram_length}-max{max_ngram_length}-{compare_type}',
output_fig_format='pdf',
output_directory='outputs')
return reporter
def generate_sentence_bucketed_report(ref,
outs,
src=None,
bucket_type='score',
bucket_cutoffs=None,
statistic_type='count',
score_measure='sentbleu',
label_set=None,
ref_labels=None,
out_labels=None,
title=None,
case_insensitive=False,
output_bucket_details=False,
to_cache=False,
cache_dicts=None):
"""
Generate a report of sentences by bucket in both plain text and graphs
Args:
ref: Tokens from the reference
outs: Tokens from the output file(s)
bucket_type: The type of bucketing method to use
score_measure: If using 'score' as either bucket_type or statistic_type, which scorer to use
ref_labels: either a filename of a file full of reference labels, or a list of strings corresponding to `ref`. Would overwrite out_labels if specified.
out_labels: output labels.
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 ref_labels is not None:
ref_labels = corpus_utils.load_tokens(ref_labels) if type(
ref_labels) == str else ref_labels
if len(ref_labels) != len(ref):
raise ValueError(
f'The number of labels should be equal to the number of sentences.'
)
elif out_labels is not None:
out_labels = arg_utils.parse_files(out_labels)
if len(out_labels) != len(outs):
raise ValueError(
f'The number of output files should be equal to the number of output labels.'
)
out_labels = [
corpus_utils.load_tokens(out_label)
if type(out_label) == str else out_label
for out_label in out_labels
]
for out, out_label in zip(outs, out_labels):
if len(out_label) != len(out):
raise ValueError(
f'The number of labels should be equal to the number of sentences.'
)
# compute statistics
bucketer = bucketers.create_sentence_bucketer_from_profile(
bucket_type,
bucket_cutoffs=bucket_cutoffs,
score_type=score_measure,
label_set=label_set,
case_insensitive=case_insensitive)
src = [None for _ in ref] if src is None else src
if statistic_type == 'count':
scorer = None
if bucket_type != 'score' and bucket_type != 'lengthdiff':
ref = ref_label = None
aggregator = lambda out, refs, src: len(out)
elif statistic_type == 'score':
scorer = scorers.create_scorer_from_profile(
score_measure, case_insensitive=case_insensitive)
aggregator = lambda out, ref, src: scorer.score_corpus(ref, out, src)[0
]
else:
raise ValueError(f'Illegal statistic_type {statistic_type}')
cache_key_list = ['stats']
stats = cache_utils.extract_cache_dicts(cache_dicts, cache_key_list,
len(outs))
if cache_dicts is None:
bcs = [
bucketer.create_bucketed_corpus(
out,
ref=ref,
src=src,
ref_labels=ref_labels if ref_labels else None,
out_labels=out_labels[i] if out_labels else None)
for i, out in enumerate(outs)
]
stats = [[aggregator(out, ref, src) for (out, ref, src) in bc]
for bc in bcs]
if output_bucket_details and statistic_type == 'score':
bucket_cnt_calculator = lambda out, ref, src: len(out)
bucket_interval_calculator = lambda out, ref: sign_utils.eval_with_paired_bootstrap(
ref, [out], src, scorer, None)[1][0]
if cache_dicts is not None: # we don't cache bcs
bcs = [
bucketer.create_bucketed_corpus(
out,
ref=ref,
src=src,
ref_labels=ref_labels if ref_labels else None,
out_labels=out_labels[i] if out_labels else None)
for i, out in enumerate(outs)
]
bucket_cnts = [
bucket_cnt_calculator(out, ref, src) for (out, ref, src) in bcs[0]
]
bucket_intervals = [[
bucket_interval_calculator(out, ref, src) for (out, ref, src) in bc
] for bc in bcs]
else:
bucket_cnts = bucket_intervals = None
if to_cache:
cache_dict = cache_utils.return_cache_dict(cache_key_list, [stats])
return cache_dict
# generate reports
reporter = reporters.SentenceReport(bucketer=bucketer,
sys_stats=stats,
statistic_type=statistic_type,
scorer=scorer,
bucket_cnts=bucket_cnts,
bucket_intervals=bucket_intervals,
title=title)
reporter.generate_report(
output_fig_file=f'sentence-{statistic_type}-{score_measure}',
output_fig_format='pdf',
output_directory='outputs')
return reporter
def generate_score_report(ref,
outs,
src=None,
score_type='bleu',
bootstrap=0,
prob_thresh=0.05,
meteor_directory=None,
options=None,
title=None,
case_insensitive=False,
to_cache=False,
cache_dicts=None):
"""
Generate a report comparing overall scores of system(s) in both plain text and graphs.
Args:
ref: Tokens from the reference
outs: Tokens from the output file(s)
src: Tokens for the source
score_type: A string specifying the scoring type (bleu/length)
bootstrap: Number of samples for significance test (0 to disable)
prob_thresh: P-value threshold for significance test
meteor_directory: Path to the directory of the METEOR code
options: Options when using external program
compare_directions: A string specifying which systems to compare
title: A string specifying the caption of the printed table
case_insensitive: A boolean specifying whether to turn on the case insensitive option
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
bootstrap = int(bootstrap)
prob_thresh = float(prob_thresh)
if type(case_insensitive) == str:
case_insensitive = True if case_insensitive == 'True' else False
# compute statistics
scorer = scorers.create_scorer_from_profile(
score_type,
case_insensitive=case_insensitive,
meteor_directory=meteor_directory,
options=options)
cache_key_list = ['scores', 'strs', 'sign_stats']
scores, strs, sign_stats = cache_utils.extract_cache_dicts(
cache_dicts, cache_key_list, len(outs))
if cache_dicts is None:
scores, strs = zip(
*[scorer.score_corpus(ref, out, src=src) for out in outs])
if to_cache:
cache_dict = cache_utils.return_cache_dict(
cache_key_list,
[scores, strs, [scorer.cache_stats(ref, outs[0], src=src)]])
return cache_dict
if bootstrap != 0:
direcs = []
for i in range(len(scores)):
for j in range(i + 1, len(scores)):
direcs.append((i, j))
wins, sys_stats = sign_utils.eval_with_paired_bootstrap(
ref,
outs,
src,
scorer,
direcs,
num_samples=bootstrap,
cache_stats=sign_stats)
wins = list(zip(direcs, wins))
else:
wins = sys_stats = None
# generate reports
reporter = reporters.ScoreReport(scorer=scorer,
scores=scores,
strs=strs,
wins=wins,
sys_stats=sys_stats,
prob_thresh=prob_thresh,
title=title)
reporter.generate_report(output_fig_file=f'score-{score_type}-{bootstrap}',
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
def generate_word_accuracy_report(ref,
outs,
src=None,
acc_type='fmeas',
bucket_type='freq',
bucket_cutoffs=None,
freq_count_file=None,
freq_corpus_file=None,
label_set=None,
ref_labels=None,
out_labels=None,
title=None,
case_insensitive=False,
to_cache=False,
cache_dicts=None):
"""
Generate a report comparing the word accuracy in both plain text and graphs.
Args:
ref: Tokens from the reference
outs: Tokens from the output file(s)
src: Tokens from the source
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
to use the frequency in the training set, in which case you specify the path of the
training corpus.
freq_count_file: An alternative to freq_corpus that uses a count file in "word\tfreq" format.
ref_labels: either a filename of a file full of reference labels, or a list of strings corresponding to `ref`.
out_labels: output labels. must be specified if ref_labels is specified.
title: A string specifying the caption of the printed table
case_insensitive: A boolean specifying whether to turn on the case insensitive option
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(ref_labels) == str:
ref_labels = corpus_utils.load_tokens(ref_labels)
if out_labels is not None:
out_label_files = arg_utils.parse_files(out_labels)
out_labels = [corpus_utils.load_tokens(x) for x in out_label_files]
if len(out_labels) != len(outs):
raise ValueError(
f'The number of output files should be equal to the number of output labels.'
)
for i, (o, ol) in enumerate(zip(outs, out_labels)):
if len(o) != len(ol):
raise ValueError(
f'The labels in {out_label_files[i]} do not match the length of the output file {outs[i]}.'
)
# 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=ref,
label_set=label_set,
case_insensitive=case_insensitive)
cache_key_list = ['statistics', 'my_ref_total_list', 'my_out_matches_list']
statistics, my_ref_total_list, my_out_matches_list = cache_utils.extract_cache_dicts(
cache_dicts, cache_key_list, len(outs))
if cache_dicts is None:
statistics, my_ref_total_list, my_out_matches_list = bucketer.calc_statistics(
ref, outs, ref_labels=ref_labels, out_labels=out_labels)
else:
my_ref_total_list = my_ref_total_list[0]
my_out_matches_list = list(np.concatenate(my_out_matches_list, 1))
examples = bucketer.calc_examples(len(ref), len(outs), statistics,
my_ref_total_list, my_out_matches_list)
if to_cache:
cache_dict = cache_utils.return_cache_dict(
cache_key_list,
[statistics, [my_ref_total_list], [my_out_matches_list]])
return cache_dict
# generate reports
reporter = reporters.WordReport(bucketer=bucketer,
statistics=statistics,
examples=examples,
src_sents=src,
ref_sents=ref,
ref_labels=ref_labels,
out_sents=outs,
out_labels=out_labels,
acc_type=acc_type,
header="Word Accuracy Analysis",
title=title)
reporter.generate_report(output_fig_file=f'word-acc',
output_fig_format='pdf',
output_directory='outputs')
return reporter
