Select Git revision
nlu_metric.py
Code owners
Assign users and groups as approvers for specific file changes. Learn more.
nlu_metric.py 5.26 KiB
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""NLU Metric"""
import datasets
from convlab2.base_models.t5.nlu.serialization import deserialize_dialogue_acts
# TODO: Add BibTeX citation
_CITATION = """\
"""
_DESCRIPTION = """\
Metric to evaluate text-to-text models on the natural language understanding task.
"""
_KWARGS_DESCRIPTION = """
Calculates sequence exact match, dialog acts accuracy and f1
Args:
predictions: list of predictions to score. Each predictions
should be a string.
references: list of reference for each prediction. Each
reference should be a string.
Returns:
seq_em: sequence exact match
accuracy: dialog acts accuracy
overall_f1: dialog acts overall f1
binary_f1: binary dialog acts f1
categorical_f1: categorical dialog acts f1
non-categorical_f1: non-categorical dialog acts f1
Examples:
>>> nlu_metric = datasets.load_metric("nlu_metric.py")
>>> predictions = ["[binary][thank][general][]", "[non-categorical][inform][taxi][leave at][17:15]"]
>>> references = ["[binary][thank][general][]", "[non-categorical][inform][train][leave at][17:15]"]
>>> results = nlu_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'seq_em': 0.5, 'accuracy': 0.5,
'overall_f1': 0.5, 'overall_precision': 0.5, 'overall_recall': 0.5,
'binary_f1': 1.0, 'binary_precision': 1.0, 'binary_recall': 1.0,
'categorical_f1': 0.0, 'categorical_precision': 0.0, 'categorical_recall': 0.0,
'non-categorical_f1': 0.0, 'non-categorical_precision': 0.0, 'non-categorical_recall': 0.0}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class NLUMetrics(datasets.Metric):
"""Metric to evaluate text-to-text models on the natural language understanding task."""
def _info(self):
return datasets.MetricInfo(
description=_DESCRIPTION,
citation=_CITATION,
inputs_description=_KWARGS_DESCRIPTION,
# This defines the format of each prediction and reference
features=datasets.Features({
'predictions': datasets.Value('string'),
'references': datasets.Value('string'),
})
)
def _compute(self, predictions, references):
"""Returns the scores: sequence exact match, dialog acts accuracy and f1"""
seq_em = []
acc = []
f1_metrics = {x: {'TP':0, 'FP':0, 'FN':0} for x in ['overall', 'binary', 'categorical', 'non-categorical']}
for prediction, reference in zip(predictions, references):
seq_em.append(prediction.strip()==reference.strip())
pred_da = deserialize_dialogue_acts(prediction)
gold_da = deserialize_dialogue_acts(reference)
flag = True
for da_type in ['binary', 'categorical', 'non-categorical']:
if da_type == 'binary':
predicts = sorted(list({(x['intent'], x['domain'], x['slot']) for x in pred_da[da_type]}))
labels = sorted(list({(x['intent'], x['domain'], x['slot']) for x in gold_da[da_type]}))
else:
predicts = sorted(list({(x['intent'], x['domain'], x['slot'], ''.join(x['value'].split()).lower()) for x in pred_da[da_type]}))
labels = sorted(list({(x['intent'], x['domain'], x['slot'], ''.join(x['value'].split()).lower()) for x in gold_da[da_type]}))
for ele in predicts:
if ele in labels:
f1_metrics['overall']['TP'] += 1
f1_metrics[da_type]['TP'] += 1
else:
f1_metrics['overall']['FP'] += 1
f1_metrics[da_type]['FP'] += 1
for ele in labels:
if ele not in predicts:
f1_metrics['overall']['FN'] += 1
f1_metrics[da_type]['FN'] += 1
flag &= (predicts==labels)
acc.append(flag)
for metric in list(f1_metrics.keys()):
TP = f1_metrics[metric].pop('TP')
FP = f1_metrics[metric].pop('FP')
FN = f1_metrics[metric].pop('FN')
precision = 1.0 * TP / (TP + FP) if TP + FP else 0.
recall = 1.0 * TP / (TP + FN) if TP + FN else 0.
f1 = 2.0 * precision * recall / (precision + recall) if precision + recall else 0.
f1_metrics.pop(metric)
f1_metrics[f'{metric}_f1'] = f1
f1_metrics[f'{metric}_precision'] = precision
f1_metrics[f'{metric}_recall'] = recall
return {
"seq_em": sum(seq_em)/len(seq_em),
"accuracy": sum(acc)/len(acc),
**f1_metrics
}