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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" | |
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()} |