def get_pairwise_performance_gap(sys1: SysOutputInfo,
sys2: SysOutputInfo) -> SysOutputInfo:
sys = copy.deepcopy(sys1)
orm, or1, or2 = (unwrap(x.results.overall) for x in (sys, sys1, sys2))
for metric_name, performance_unit in orm.items():
orm[metric_name].value = float(or1[metric_name].value) - float(
or2[metric_name].value)
orm[metric_name].confidence_score_low = None
orm[metric_name].confidence_score_high = None
fgr, fgr1, fgr2 = (unwrap(x.results.fine_grained)
for x in (sys, sys1, sys2))
for bucket_attr, buckets in fgr.items():
for bucket_id, bucket in enumerate(buckets):
for perf_id, perf in enumerate(bucket.performances):
perf.value = float(
fgr1[bucket_attr][bucket_id].performances[perf_id].value
) - float(
fgr2[bucket_attr][bucket_id].performances[perf_id].value)
# TODO(gneubig): these could be done via pairwise bootstraps
perf.confidence_score_low = None
perf.confidence_score_high = None
return sys
def get_tasks(task: TaskType, system_outputs: list[str]) -> list[TaskType]:
"""
Get the task for each system output.
:param task: Explicitly specified task. Use if present
:param system_outputs: System output files, load from metadata in these files if
an explicit task is not set
:return: A list of task types for each system
"""
real_tasks: list[TaskType] = []
if task:
real_tasks = [task] * len(system_outputs)
if task not in TaskType.list():
raise ValueError(
f'Task name {task} was not recognized. ExplainaBoard currently '
f'supports:{TaskType.list()}')
return real_tasks
else:
for sys_output in system_outputs:
# give me a task, or give me death (by exception)
task_or_die: TaskType | None = None
msg: str = ''
try:
metadata = FileLoaderMetadata.from_file(sys_output)
task_or_die = TaskType(unwrap(metadata.task_name))
except Exception as e:
msg = str(e)
if task_or_die is None:
raise ValueError(
'Must either specify a task explicitly or have one '
'specified in metadata, but could find neither for '
f'{sys_output}. {msg}')
real_tasks.append(unwrap(task_or_die))
return real_tasks
def _complete_features(self,
sys_info: SysOutputInfo,
sys_output: list[dict],
external_stats=None) -> list[str]:
"""
This function takes in meta-data about system outputs, system outputs, and a few
other optional pieces of information, then calculates feature functions and
modifies `sys_output` to add these feature values
:param sys_info: Information about the system output
:param sys_output: The system output itself
:param external_stats: Training set statistics that are used to calculate
training set specific features
:return: The features that are active (e.g. skipping training set features when
no training set available)
"""
sys_features = unwrap(sys_info.features)
active_features = list(
sys_features.get_bucket_features(
include_training_dependent=external_stats is not None))
# One pass over the test set to find token test frequency
all_tokens = [
unwrap(sys_info.source_tokenizer)(x['output']) for x in sys_output
]
all_log_probs = [self._get_predicted_label(x) for x in sys_output]
test_freq: dict[str, int] = {}
for tokens in all_tokens:
for tok in tokens:
test_freq[tok] = test_freq.get(tok, 0) + 1
sent_feats: list[str] = []
tok_feats: list[str] = []
for x in active_features:
(sent_feats if (x in sys_features) else tok_feats).append(x)
for _id, (dict_sysout, tokens, log_probs) in progress(
enumerate(zip(sys_output, all_tokens, all_log_probs)),
desc="featurizing"):
# Get values of bucketing features
text = dict_sysout["output"]
# sentence_length
dict_sysout["text_length"] = len(tokens)
dict_sysout["text_chars"] = len(text)
# sentence-level training set dependent features
if external_stats is not None:
dict_sysout["num_oov"] = self._get_num_oov(
tokens, external_stats)
dict_sysout["fre_rank"] = self._get_fre_rank(
tokens, external_stats)
# span features for true and predicted spans
dict_sysout["tok_info"] = self._complete_tok_features(
tokens, log_probs, test_freq, statistics=external_stats)
return active_features
def _statistics_func(self, samples: Iterator, sys_info: SysOutputInfo):
return {
'source_vocab':
accumulate_vocab_from_samples(samples, lambda x: x['source'],
unwrap(sys_info.source_tokenizer)),
'target_vocab':
accumulate_vocab_from_samples(samples, lambda x: x['reference'],
unwrap(sys_info.target_tokenizer)),
}
def _get_answer_length(self, sys_info: SysOutputInfo,
existing_features: dict):
if isinstance(existing_features["answers"]["text"], list):
return len(
unwrap(sys_info.source_tokenizer)(
existing_features["answers"]["text"][0]))
else:
return len(
unwrap(sys_info.source_tokenizer)(
existing_features["answers"]["text"]))
def bucket_attribute_specified_bucket_interval(
sample_features: list[tuple[BucketCase, T]],
bucket_number: int,
bucket_setting: list[tuple],
) -> list[BucketCaseCollection]:
intervals = unwrap(bucket_setting)
bucket2examp: dict[tuple,
list[BucketCase]] = {k: list()
for k in intervals}
if isinstance(list(intervals)[0][0],
str): # discrete value, such as entity tags
for k, v in sample_features:
if v in bucket2examp:
bucket2examp[(v, )].append(k)
else:
for examp, value in sample_features:
res_key = find_key(bucket2examp, value)
if res_key is None:
continue
bucket2examp[res_key].append(examp)
bucket_collections = [
BucketCaseCollection((k, ), v) for k, v in bucket2examp.items()
]
return bucket_collections
def _statistics_func(self, samples, sys_info: SysOutputInfo):
vocab: dict[str, float] = {}
length_fre: dict[int, float] = {}
total_samps = 0
tokenizer = unwrap(sys_info.source_tokenizer)
for sample in progress(samples):
text = sample["text"]
tokens = tokenizer(text)
length = len(tokens)
length_fre[length] = length_fre.get(length, 0.0) + 1.0
# update vocabulary
for w in tokens:
vocab[w] = vocab.get(w, 0.0) + 1.0
total_samps += 1
# the rank of each word based on its frequency
sorted_dict = {
key: rank
for rank, key in enumerate(
sorted(set(vocab.values()), reverse=True), 1)
}
vocab_rank = {k: sorted_dict[v] for k, v in vocab.items()}
for k, v in length_fre.items():
length_fre[k] = v * 1.0 / total_samps
return {
"vocab": vocab,
"vocab_rank": vocab_rank,
"length_fre": length_fre
}
def _gen_metric_stats(self, sys_info: SysOutputInfo,
sys_output: list[dict]) -> list[MetricStats]:
"""Generate sufficient statistics for scoring different metrics.
:param sys_info: Information about the system outputs
:param sys_output: The system output itself
:return: Statistics sufficient for scoring
"""
metrics = unwrap(self._get_metrics(sys_info))
true_data = [self._get_true_label(x) for x in sys_output]
pred_data = [self._get_predicted_label(x) for x in sys_output]
rank_data = [self._get_rank_data(x)
for x in sys_output] # rank of true entity in predictions
if any(item is None for item in rank_data):
raise ValueError(
'Some data points do not have rank information; check system outputs.'
)
metric_stats = []
for metric in metrics:
if (isinstance(metric, MeanReciprocalRank)
or isinstance(metric, MeanRank)
or isinstance(metric, Hits)):
metric_stats.append(metric.calc_stats_from_rank(rank_data))
else:
metric_stats.append(
metric.calc_stats_from_data(true_data, pred_data))
return metric_stats
def _get_fre_rank(self, sys_info: SysOutputInfo, existing_features: dict,
statistics: Any):
return explainaboard.utils.feature_funcs.feat_freq_rank(
existing_features,
statistics,
lambda x: x['context'],
unwrap(sys_info.source_tokenizer),
)
def _get_ref_fre_rank(self, sys_info: SysOutputInfo,
existing_features: dict, statistics: Any):
return explainaboard.utils.feature_funcs.feat_freq_rank(
existing_features,
statistics['target_vocab'],
lambda x: x['reference'],
unwrap(sys_info.target_tokenizer),
)
def _get_src_num_oov(self, sys_info: SysOutputInfo,
existing_features: dict, statistics: Any):
return explainaboard.utils.feature_funcs.feat_num_oov(
existing_features,
statistics['source_vocab'],
lambda x: x['source'],
unwrap(sys_info.source_tokenizer),
)
def _get_absolute_blank_position(self, sys_info: SysOutputInfo,
existing_features: dict):
source_tokens = unwrap(sys_info.source_tokenizer)(
existing_features["context"]).strs
if existing_features["question_mark"] not in source_tokens:
return 0
else:
return source_tokens.index(existing_features["question_mark"])
def _get_config(self, config: Optional[MetricConfig] = None) -> MetricConfig:
"""
Get the configuration or overwritten configuration
:param config: Optional configuration to override the default configuration
:return: Either the default or overridden configuration
"""
ret_config: MetricConfig = unwrap(config) if config is not None else self.config
return ret_config
def _complete_features(self,
sys_info: SysOutputInfo,
sys_output: list[dict],
external_stats=None) -> list[str]:
"""
This function takes in meta-data about system outputs, system outputs, and a few
other optional pieces of information, then calculates feature functions and
modifies `sys_output` to add these feature values
:param sys_info: Information about the system output
:param sys_output: The system output itself
:param external_stats: Training set statistics that are used to calculate
training set specific features
:return: The features that are active (e.g. skipping training set features when
no training set available)
"""
sys_features = unwrap(sys_info.features)
active_features = list(
sys_features.get_bucket_features(
include_training_dependent=external_stats is not None))
sent_feats: list[str] = []
tok_feats: list[str] = []
for x in active_features:
(sent_feats if (x in sys_features) else tok_feats).append(x)
for _id, dict_sysout in progress(enumerate(sys_output),
desc="featurizing"):
# Get values of bucketing features
tokens = dict_sysout["tokens"]
# sentence_length
dict_sysout["sentence_length"] = len(tokens)
# entity density
dict_sysout["span_density"] = len(
self._span_ops.get_spans_simple(
tags=dict_sysout["true_tags"])) / len(tokens)
# sentence-level training set dependent features
if external_stats is not None:
dict_sysout["num_oov"] = self._get_num_oov(
tokens, external_stats)
dict_sysout["fre_rank"] = self._get_fre_rank(
tokens, external_stats)
# span features for true and predicted spans
dict_sysout["span_info"] = self._complete_span_features(
tokens,
dict_sysout["true_tags"],
dict_sysout["pred_tags"],
statistics=external_stats,
)
# This is not used elsewhere, so just keep it as-is
return active_features
def _get_length_fre(self, sys_info: SysOutputInfo, existing_features: dict,
statistics: Any):
length_fre = 0
length = len(
unwrap(sys_info.source_tokenizer)(existing_features["text"]))
if length in statistics['length_fre'].keys():
length_fre = statistics['length_fre'][length]
return length_fre
def get_bucket_performance_lm(
self,
sys_info: SysOutputInfo,
sys_output: list[dict],
samples_over_bucket: list[BucketCaseCollection],
) -> list[BucketPerformance]:
"""
This function defines how to get bucket-level performance w.r.t a given feature
(e.g., sentence length)
:param sys_info: Information about the system output
:param sys_output: The system output itself
:param samples_over_bucket: a dictionary mapping bucket interval names to
true sample IDs
:return: bucket_performances: a list of bucket intervals to
bucket performance
"""
bucket_performances = []
for bucket_collection in samples_over_bucket:
bucket_metrics = [
x.to_metric() for x in unwrap(sys_info.metric_configs)
]
log_probs = []
for bucket_case in bucket_collection.samples:
bcp = cast(BucketCaseSpan, bucket_case)
log_probs.append(sys_output[bcp.sample_id]['tok_info'][
bcp.token_span[0]]['tok_log_prob'])
bucket_samples = self._subsample_bucket_cases(
bucket_collection.samples)
bucket_performance = BucketPerformance(
bucket_interval=bucket_collection.interval,
n_samples=len(bucket_collection),
bucket_samples=bucket_samples,
)
for metric in bucket_metrics:
metric_val = metric.evaluate(None,
log_probs,
conf_value=sys_info.conf_value)
conf_low, conf_high = (metric_val.conf_interval
if metric_val.conf_interval else None)
performance = Performance(
metric_name=metric.config.name,
value=metric_val.value,
confidence_score_low=conf_low,
confidence_score_high=conf_high,
)
bucket_performance.performances.append(performance)
bucket_performances.append(bucket_performance)
bucket_performances.sort(key=lambda x: x.bucket_interval)
return bucket_performances
def process(self, metadata: dict, sys_output: list[dict]) -> SysOutputInfo:
# TODO(Pengfei): Rethink if this is a good way to manipulate `system_output`
overall_statistics = self.get_overall_statistics(metadata, sys_output)
sys_info = unwrap(overall_statistics.sys_info)
metric_stats = overall_statistics.metric_stats
active_features = unwrap(overall_statistics.active_features)
overall_results = sys_info.results.overall
performance_over_bucket = self.bucketing_samples(
sys_info, sys_output, active_features, metric_stats=metric_stats
)
self.sort_bucket_info(
performance_over_bucket,
sort_by=metadata.get('sort_by', 'key'),
sort_by_metric=metadata.get('sort_by_metric', 'first'),
sort_ascending=metadata.get('sort_ascending', False),
)
sys_info.results = Result(
overall=overall_results, fine_grained=performance_over_bucket
)
return sys_info
def _statistics_func(self, samples: Iterator, sys_info: SysOutputInfo):
if sys_info.source_language is None or sys_info.target_language is None:
raise ValueError(
'source or target languages must be specified to load '
f'translation data, but source={sys_info.source_language} '
f', target={sys_info.target_language}'
)
src = FileLoaderField(('translation', sys_info.source_language), '', str)
trg = FileLoaderField(('translation', sys_info.target_language), '', str)
return {
'source_vocab': accumulate_vocab_from_samples(
samples,
lambda x: FileLoader.find_field(x, src),
unwrap(sys_info.source_tokenizer),
),
'target_vocab': accumulate_vocab_from_samples(
samples,
lambda x: FileLoader.find_field(x, trg),
unwrap(sys_info.target_tokenizer),
),
}
def _fetch_metric_stats(self, metric_stats: dict[str, Any]):
"""
A utility function used to lazily fetch the actual scoring dict when it's
necessary.
"""
if 'request_id' in metric_stats:
client: AsyncClient = unwrap(self._eaas_client)
eaas_stats: dict[str, Any] = client.wait_and_get_result(
metric_stats['request_id'])
metric_stats.clear()
for k, v in eaas_stats.items():
metric_stats[k] = v
def get_overall_statistics(
self, metadata: dict, sys_output: list[dict]
) -> OverallStatistics:
"""
Get the overall statistics information, including performance, of the system
output
:param metadata: The metadata of the system
:param sys_output: The system output itself
"""
if metadata is None:
metadata = {}
if "task_name" not in metadata.keys():
metadata["task_name"] = self.task_type().value
sys_info = SysOutputInfo.from_dict(metadata)
if sys_info.metric_configs is None:
sys_info.metric_configs = self.default_metrics(
source_language=sys_info.source_language,
target_language=sys_info.target_language,
)
if sys_info.target_tokenizer is None:
sys_info.target_tokenizer = get_default_tokenizer(
task_type=self.task_type(), lang=sys_info.target_language
)
if sys_info.source_tokenizer is None:
sys_info.source_tokenizer = (
sys_info.target_tokenizer
if sys_info.source_language == sys_info.target_language
else get_default_tokenizer(
task_type=self.task_type(), lang=sys_info.source_language
)
)
# declare customized features: _features will be updated
custom_features: dict = metadata.get('custom_features', {})
sys_info.features = self._customize_features(custom_features)
# get scoring statistics
metric_stats = unwrap(self._gen_metric_stats(sys_info, sys_output))
external_stats = self._gen_external_stats(sys_info, self._statistics_func)
active_features = self._complete_features(
sys_info, sys_output, external_stats=external_stats
)
overall_results = self.get_overall_performance(
sys_info, sys_output, metric_stats=metric_stats
)
sys_info.results = Result(
overall=overall_results, calibration=None, fine_grained=None
)
return OverallStatistics(sys_info, metric_stats, active_features)
def _gen_metric_stats(
self, sys_info: SysOutputInfo, sys_output: list[dict]
) -> list[MetricStats]:
"""Generate sufficient statistics for scoring different metrics.
:param sys_info: Information about the system outputs
:param sys_output: The system output itself
:return: Statistics sufficient for scoring
"""
metrics = unwrap(self._get_metrics(sys_info))
true_data = [self._get_true_label(x) for x in sys_output]
pred_data = [self._get_predicted_label(x) for x in sys_output]
metric_stats = []
for metric in metrics:
metric_stats.append(metric.calc_stats_from_data(true_data, pred_data))
return metric_stats
def bucketing_samples(
self,
sys_info: SysOutputInfo,
sys_output: list[dict],
active_features: list[str],
metric_stats: list[MetricStats],
) -> dict[str, list[BucketPerformance]]:
features = unwrap(sys_info.features)
sent_feats: list[str] = []
tok_feats: list[str] = []
for x in active_features:
(sent_feats if (x in features) else tok_feats).append(x)
# First, get the buckets for sentences using the standard protocol
performances_over_bucket = super().bucketing_samples(
sys_info, sys_output, sent_feats, metric_stats)
# Bucketing
feature_lists = self._get_feature_lists(sys_output, tok_feats)
for i, feature_name in enumerate(
progress(tok_feats, desc="token-level bucketing")):
my_feature = features["tok_info"].feature.feature[feature_name]
bucket_info = my_feature.bucket_info
# Get buckets for true spans
bucket_func: Callable[..., list[BucketCaseCollection]] = getattr(
bucketing, bucket_info.method)
samples_over_bucket = bucket_func(
sample_features=feature_lists[i],
bucket_number=bucket_info.number,
bucket_setting=bucket_info.setting,
)
# evaluating bucket: get bucket performance
performances_over_bucket[
feature_name] = self.get_bucket_performance_lm(
sys_info,
sys_output,
samples_over_bucket,
)
return performances_over_bucket
def bucketing_samples(
self,
sys_info: SysOutputInfo,
sys_output: list[dict],
active_features: list[str],
metric_stats: list[MetricStats],
) -> dict[str, list[BucketPerformance]]:
"""
Separate samples into buckets and calculate performance over them
:param sys_info: Information about the system output
:param sys_output: The system output itself, already annotated with features
:param active_features: The features to perform bucketing over
:param metric_stats: The stats from which to calculate performance
:return:
performances_over_bucket:
a dictionary of feature name -> list of performances by bucket
"""
sys_features = unwrap(sys_info.features)
# Bucketing
performances_over_bucket: dict[str, list[BucketPerformance]] = {}
for feature_name in progress(active_features, desc="sample-level bucketing"):
# Preparation for bucketing
bucket_func: Callable[..., list[BucketCaseCollection]] = getattr(
explainaboard.utils.bucketing,
sys_features[feature_name].bucket_info.method,
)
samples_over_bucket = bucket_func(
sample_features=[
(BucketCase(x), sys_output[x][feature_name])
for x in range(len(sys_output))
],
bucket_number=sys_features[feature_name].bucket_info.number,
bucket_setting=sys_features[feature_name].bucket_info.setting,
)
# evaluating bucket: get bucket performance
performances_over_bucket[feature_name] = self.get_bucket_performance(
sys_info,
sys_output,
samples_over_bucket,
metric_stats=metric_stats,
)
return performances_over_bucket
def bucketing_samples(
self,
sys_info: SysOutputInfo,
sys_output: list[dict],
active_features: list[str],
metric_stats: list[MetricStats],
) -> dict[str, list[BucketPerformance]]:
features = unwrap(sys_info.features)
sent_feats: list[str] = []
tok_feats: list[str] = []
for x in active_features:
(sent_feats if (x in features) else tok_feats).append(x)
# First, get the buckets for sentences using the standard protocol
performances_over_bucket = super().bucketing_samples(
sys_info, sys_output, sent_feats, metric_stats)
all_sample_features = self._get_sample_features(sys_output, tok_feats)
# Second, get the buckets for tokens
for feature_id, feature_name in enumerate(
progress(tok_feats, desc="bucketing token features")):
# Choose behavior based on whether this is a feature of samples or spans
my_feature = features["ref_tok_info"].feature.feature[feature_name]
bucket_info = my_feature.bucket_info
# Get buckets for true spans
bucket_func: Callable[..., list[BucketCaseCollection]] = getattr(
bucketing, bucket_info.method)
sample_features = [(case, feats[feature_id])
for case, feats in all_sample_features]
samples_over_bucket = bucket_func(
def _complete_span_features(self,
sentence,
true_tags,
pred_tags,
statistics=None) -> list[Span]:
# Get training set stats if they exist
has_stats = statistics is not None and len(statistics) > 0
econ_dic = statistics["econ_dic"] if has_stats else None
efre_dic = statistics["efre_dic"] if has_stats else None
self._span_ops.set_resources(resources={
"has_stats": has_stats,
"econ_dic": econ_dic,
"efre_dic": efre_dic,
})
# Merge the spans together so that the span tag is "true_tag pred_tag"
# using "_DEFAULT_TAG" if that span doesn't exist in the true or predicted tags
# respectively
# TODO(gneubig): This is probably calculating features twice, could be just once
true_spans = self._span_ops.get_spans(toks=sentence, tags=true_tags)
pred_spans = self._span_ops.get_spans(toks=sentence, tags=pred_tags)
merged_spans = {}
for span in true_spans:
span.span_tag = f'{span.span_tag} {self._DEFAULT_TAG}'
merged_spans[span.span_pos] = span
for span in pred_spans:
merged_span = merged_spans.get(span.span_pos)
if not merged_span:
span.span_tag = f'{self._DEFAULT_TAG} {span.span_tag}'
merged_spans[span.span_pos] = span
else:
true_tag, _ = unwrap(merged_span.span_tag).split(' ')
merged_span.span_tag = f'{true_tag} {span.span_tag}'
return list(merged_spans.values())
def _complete_features(
self, sys_info: SysOutputInfo, sys_output: list[dict], external_stats=None
) -> list[str]:
"""
This function takes in meta-data about system outputs, system outputs, and a few
other optional pieces of information, then calculates feature functions and
modifies `sys_output` to add these feature values
:param sys_info: Information about the system output
:param sys_output: The system output itself
:param external_stats: External statistics that are used to calculate training
set specific features
:return: The features that are active (e.g. skipping training set features when
no training set available)
"""
bucket_feature_funcs: dict[str, tuple[Callable, bool]] = {}
sys_features = unwrap(sys_info.features)
for bucket_feature in sys_features.get_bucket_features():
feature_info = sys_features[bucket_feature]
# Skip training set features if no stats
if external_stats is None and feature_info.require_training_set:
continue
feature_func = self._get_feature_func(
bucket_feature, feature_info.is_custom
)
bucket_feature_funcs[bucket_feature] = (
feature_func,
feature_info.require_training_set,
)
for _id, dict_sysout in progress(enumerate(sys_output), desc="featurizing"):
# Get values of bucketing features
for (
bucket_key,
(
bucket_func,
training_dependent,
),
) in bucket_feature_funcs.items():
feature_info = sys_features[bucket_key]
# handles user-defined features
if feature_info.is_custom:
# TODO(Pengfei): this should be generalized
feature_value = (
"_".join(dict_sysout[bucket_key])
if isinstance(dict_sysout[bucket_key], list)
else dict_sysout[bucket_key]
)
dict_sysout[bucket_key] = feature_value
# handles all other features
else:
dict_sysout[bucket_key] = (
bucket_func(sys_info, dict_sysout, external_stats)
if training_dependent
else bucket_func(sys_info, dict_sysout)
)
return list(bucket_feature_funcs.keys())
def _get_metrics(self, sys_info: SysOutputInfo) -> list[Metric]:
return [config.to_metric() for config in unwrap(sys_info.metric_configs)]
def _get_head_entity_length(self, sys_info: SysOutputInfo,
existing_features: dict):
return len(
unwrap(sys_info.source_tokenizer)(
existing_features["true_head_decipher"]))
def draw_bar_chart_from_reports(reports: list[str],
output_dir: str,
sys_names: list[str] | None = None) -> None:
"""
Draw bar charts from report file generated from ExplainaBoard
:param reports: Reports to plot
:param output_dir:
:return:
"""
# TODO(gneubig): This should get the system name from inside the report
if sys_names is None:
sys_names = [os.path.basename(x).replace('.json', '') for x in reports]
elif len(sys_names) != len(reports):
raise ValueError('Length of sys_names must equal that of reports')
report_info: list[SysOutputInfo] = []
for report in reports:
with open(report) as fin:
report_info.append(SysOutputInfo.from_dict(json.load(fin)))
overall_results = [
list(unwrap(x.results.overall).values()) for x in report_info
]
overall_metric_names = list(unwrap(report_info[0].results.overall).keys())
fg_results = [unwrap(x.results.fine_grained) for x in report_info]
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# Overall performance
ys = [[x.value for x in y] for y in overall_results]
y_errs = None
if overall_results[0][0].confidence_score_low is not None:
y_errs = [(
[x.value - unwrap(x.confidence_score_low) for x in y],
[unwrap(x.confidence_score_high) - x.value for x in y],
) for y in overall_results]
make_bar_chart(
ys,
output_dir,
'overall',
output_fig_format='png',
fig_size=(8, 6),
sys_names=sys_names,
errs=y_errs,
title=None,
xticklabels=overall_metric_names,
ylabel='metric value',
)
# Bucket performance: feature name, for example, sentence length
for feature_name in progress(fg_results[0].keys()):
# Make sure that buckets exist
buckets: list[list[BucketPerformance]] = []
for i, fg_result in enumerate(fg_results):
if feature_name not in fg_result:
get_logger().error(
f'error: feature {feature_name} not in {reports[i]}')
else:
buckets.append(fg_result[feature_name])
bnames0, bnames = [x.bucket_interval for x in buckets[0]
], [x.bucket_interval for x in buckets[-1]]
if len(bnames0) != len(bnames):
get_logger().error(
f'error: different number of buckets for {feature_name} in '
f'{reports[0]} and {reports[i]}')
buckets = []
elif bnames0 != bnames:
get_logger().warning(
f'warning: different bucket labels for {feature_name} in '
f'{reports[0]} and {reports[i]}')
if len(buckets) != i + 1:
break
if len(buckets) != len(reports):
continue
bucket0_intervals = [x.bucket_interval for x in buckets[0]]
bucket_metrics = [x.metric_name for x in buckets[0][0].performances]
for metric_id, metric_name in enumerate(bucket_metrics):
performances: list[list[Performance]] = [
[x.performances[metric_id] for x in y] for y in buckets
]
ys = [[x.value for x in y] for y in performances]
y_errs = None
if performances[0][0].confidence_score_low is not None:
y_errs = [(
[x.value - unwrap(x.confidence_score_low) for x in y],
[unwrap(x.confidence_score_high) - x.value for x in y],
) for y in performances]
make_bar_chart(
ys,
output_dir,
f'{feature_name}_{metric_name}',
output_fig_format='png',
fig_size=(8, 6),
sys_names=sys_names,
errs=y_errs,
title=None,
xlabel=feature_name,
xticklabels=bucket0_intervals,
ylabel=metric_name,
)
def _get_attr_compression(self, sys_info: SysOutputInfo, existing_features: dict):
return len(
unwrap(sys_info.source_tokenizer)(existing_features["source"])
) / len(unwrap(sys_info.target_tokenizer)(existing_features["reference"]))
