Spaces:
Sleeping
Sleeping
| # 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()} |