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# 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.
"""TODO: Add a description here."""
import string
import evaluate
import datasets
# TODO: Add BibTeX citation
_CITATION = """\
@InProceedings{huggingface:module,
title = {A great new module},
authors={huggingface, Inc.},
year={2020}
}
"""
# TODO: Add description of the module here
_DESCRIPTION = """\
This new module is designed to solve this great ML task and is crafted with a lot of care.
"""
# TODO: Add description of the arguments of the module here
_KWARGS_DESCRIPTION = """
Calculates how good are predictions given some references, using certain scores
Args:
predictions: list of predictions to score. Each predictions
should be a string with tokens separated by spaces.
references: list of reference for each prediction. Each
reference should be a string with tokens separated by spaces.
Returns:
accuracy: description of the first score,
another_score: description of the second score,
Examples:
Examples should be written in doctest format, and should illustrate how
to use the function.
>>> metric = evaluate.load("DarrenChensformer/eval_keyphrase")
>>> results = metric.compute(references=[["Hello","World"]], predictions=[["hello","world"]])
>>> print(results)
{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'num_pred': 2.0, 'num_gold': 2.0}
"""
# TODO: Define external resources urls if needed
BAD_WORDS_URL = "http://url/to/external/resource/bad_words.txt"
@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class eval_keyphrase(evaluate.Metric):
"""TODO: Short description of my evaluation module."""
def _info(self):
# TODO: Specifies the evaluate.EvaluationModuleInfo object
return evaluate.MetricInfo(
# This is the description that will appear on the modules page.
module_type="metric",
description=_DESCRIPTION,
citation=_CITATION,
inputs_description=_KWARGS_DESCRIPTION,
# This defines the format of each prediction and reference
features=datasets.Features({
'predictions': datasets.Sequence(datasets.Value('string')),
'references': datasets.Sequence(datasets.Value('string')),
}),
# Homepage of the module for documentation
homepage="http://module.homepage",
# Additional links to the codebase or references
codebase_urls=["http://github.com/path/to/codebase/of/new_module"],
reference_urls=["http://path.to.reference.url/new_module"]
)
def _download_and_prepare(self, dl_manager):
"""Optional: download external resources useful to compute the scores"""
# TODO: Download external resources if needed
pass
def _normalize_keyphrase(self, kp):
def white_space_fix(text):
return ' '.join(text.split())
def remove_punc(text):
exclude = set(string.punctuation)
return ''.join(ch for ch in text if ch not in exclude)
def lower(text):
return text.lower()
return white_space_fix(remove_punc(lower(kp)))
def _compute(self, predictions, references, ignore_empty_label=True):
"""Returns the scores"""
macro_metrics = {'precision': [], 'recall': [], 'f1': [], 'num_pred': [], 'num_gold': []}
for targets, preds in zip(references, predictions):
if ignore_empty_label:
targets = [self._normalize_keyphrase(tmp_key).strip() for tmp_key in targets if len(self._normalize_keyphrase(tmp_key).strip()) != 0]
preds = [self._normalize_keyphrase(tmp_key).strip() for tmp_key in preds if len(self._normalize_keyphrase(tmp_key).strip()) != 0]
else:
targets = [self._normalize_keyphrase(tmp_key).strip() for tmp_key in targets]
preds = [self._normalize_keyphrase(tmp_key).strip() for tmp_key in preds]
total_tgt_set = set(targets)
total_preds = set(preds)
if len(total_tgt_set) == 0: continue
# get the total_correctly_matched indicators
total_correctly_matched = len(total_preds & total_tgt_set)
# macro metric calculating
precision = total_correctly_matched / len(total_preds) if len(total_preds) else 0.0
recall = total_correctly_matched / len(total_tgt_set)
f1 = 2 * precision * recall / (precision + recall) if total_correctly_matched > 0 else 0.0
macro_metrics['precision'].append(precision)
macro_metrics['recall'].append(recall)
macro_metrics['f1'].append(f1)
macro_metrics['num_pred'].append(len(total_preds))
macro_metrics['num_gold'].append(len(total_tgt_set))
return { k: round(sum(v)/len(v), 4) for k, v in macro_metrics.items()} |