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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 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/aciton_generation") | |
| >>> results = metric.compute(references=[0, 1], predictions=[0, 1]) | |
| >>> print(results) | |
| {'accuracy': 1.0} | |
| """ | |
| # TODO: Define external resources urls if needed | |
| BAD_WORDS_URL = "http://url/to/external/resource/bad_words.txt" | |
| VALID_LABELS = [ | |
| "/開箱", | |
| "/教學", | |
| "/表達", | |
| "/分享/外部資訊", | |
| "/分享/個人資訊", | |
| "/推薦/產品", | |
| "/推薦/服務", | |
| "/推薦/其他", | |
| "" | |
| ] | |
| class BaseEvaluater: | |
| eps = 1e-8 | |
| valid_labels = None | |
| def __call__(self, preds, labels): | |
| return self._compute(preds, labels) | |
| def _compute(self, preds, labels): | |
| # calculate precision, recall, f1 | |
| tp, fp, fn = 0, 0, 0 | |
| for pred, label in zip(preds, labels): | |
| tp += len(set(pred) & set(label)) | |
| fp += len(set(pred) - set(label)) | |
| fn += len(set(label) - set(pred)) | |
| precision = tp / (tp + fp + self.eps) | |
| recall = tp / (tp + fn + self.eps) | |
| f1 = 2 * precision * recall / (precision + recall + self.eps) | |
| return { | |
| "precision": round(precision, 4), | |
| "recall": round(recall, 4), | |
| "f1": round(f1, 4) | |
| } | |
| def _init_valid_labels(self): | |
| if self.valid_labels is None: | |
| self.valid_labels = VALID_LABELS | |
| class ClassEvaluater(BaseEvaluater): | |
| def __init__(self, valid_labels=None): | |
| self.valid_labels = valid_labels | |
| self._init_valid_labels() | |
| def __call__(self, preds, labels): | |
| preds = map(self.extract_class, preds) | |
| labels = map(self.extract_class, labels) | |
| # helper function to extract valid tags | |
| preds = list(map(self.extract_valid, preds)) | |
| labels = list(map(self.extract_valid, labels)) | |
| return self._compute(preds, labels) | |
| def extract_valid(self, tags): | |
| tags = list(filter(lambda tag: tag in self.valid_labels, tags)) | |
| return tags | |
| def extract_class(self, tags): | |
| tags = map(lambda tag: tag.replace("/ ", "/"), tags) | |
| tags = list(map(self.batch_extract_class, tags)) | |
| # deduplicate | |
| tags = list(dict.fromkeys(tags)) | |
| return tags | |
| def batch_extract_class(self, tag): | |
| # filter out invalid tags | |
| tag = tag.split('/') | |
| if len(tag)==3: | |
| _class = '/'.join(tag[:2]) | |
| elif len(tag)==4: | |
| _class = '/'.join(tag[:3]) | |
| elif len(tag)==1: | |
| _class = '' | |
| else: | |
| _class = None | |
| if _class in self.valid_labels: | |
| return _class | |
| else: | |
| return "" | |
| class PhraseEvaluater(BaseEvaluater): | |
| def __init__(self, valid_labels=None): | |
| self.valid_labels = valid_labels | |
| self._init_valid_labels() | |
| def __call__(self, preds, labels): | |
| preds = map(self.extract_phrase, preds) | |
| labels = map(self.extract_phrase, labels) | |
| return self._compute(preds, labels) | |
| def extract_phrase(self, tags): | |
| tags = map(lambda tag: tag.replace("/ ", "/"), tags) | |
| tags = list(map(self.batch_extract_phrase, tags)) | |
| # deduplicate | |
| tags = list(dict.fromkeys(tags)) | |
| return tags | |
| def batch_extract_phrase(self, phrase): | |
| # filter out invalid tags | |
| tag = phrase.split('/') | |
| if len(tag)==3: | |
| _class = '/'.join(tag[:2]) | |
| elif len(tag)==4: | |
| _class = '/'.join(tag[:3]) | |
| elif len(tag)==1: | |
| _class = '' | |
| else: | |
| _class = None | |
| if _class in self.valid_labels: | |
| return phrase.replace(_class, '') | |
| else: | |
| return "" | |
| class action_generation(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 _compute(self, predictions, references, | |
| valid_labels=None, detailed_scores=False, | |
| weights={"class": 0.8, "phrase": 0.2} | |
| ): | |
| """Returns the scores""" | |
| class_eval = ClassEvaluater(valid_labels)(predictions, references) | |
| phrase_eval = PhraseEvaluater(valid_labels)(predictions, references) | |
| weight_sum = { | |
| key: round((class_eval[key] * weights["class"]) + (phrase_eval[key] * weights["phrase"]), 4) | |
| for key in class_eval | |
| } | |
| if detailed_scores: | |
| results = { | |
| "class": class_eval, | |
| "phrase": phrase_eval, | |
| "weighted_sum": weight_sum | |
| } | |
| else: | |
| results = weight_sum | |
| return results | |