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| import evaluate | |
| import datasets | |
| import numpy as np | |
| # 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 metric is used for evaluating the quality of relation extraction output. By calculating the Micro and Macro F1 score of every relation extraction outputs to ensure the quality. | |
| """ | |
| _KWARGS_DESCRIPTION = """ | |
| Calculates how good are predictions given some references, using Precision, Recall, F1 Score. | |
| Args: | |
| predictions (list of list of dictionary): A list of predicted relations from the model. | |
| references (list of list of dictionary): A list of ground-truth or reference relations to compare the predictions against. | |
| Returns: | |
| **output** (`dictionary` of `dictionary`s) A dictionary mapping each entity type to its respective scoring metrics such as Precision, Recall, F1 Score. | |
| - **entity type** (`dictionary`): score of selected relation type | |
| - **tp** : true positive count | |
| - **fp** : false positive count | |
| - **fn** : false negative count | |
| - **p** : precision | |
| - **r** : recall | |
| - **f1** : micro f1 score | |
| - **Macro_f1** : macro f1 score | |
| - **Macro_p** : macro precision | |
| - **Macro_r** : macro recall | |
| Examples: | |
| metric_path = "Ikala-allen/relation_extraction" | |
| module = evaluate.load(metric_path) | |
| references = [ | |
| [ | |
| {"head": "phipigments", "head_type": "brand", "type": "sell", "tail": "國際認證之色乳", "tail_type": "product"}, | |
| {"head": "tinadaviespigments", "head_type": "brand", "type": "sell", "tail": "國際認證之色乳", "tail_type": "product"}, | |
| {'head': 'A醛賦活緊緻精華', 'tail': 'Serum', 'head_type': 'product', 'tail_type': 'category', 'type': 'belongs_to'}, | |
| ] | |
| ] | |
| predictions = [ | |
| [ | |
| {"head": "phipigments", "head_type": "product", "type": "sell", "tail": "國際認證之色乳", "tail_type": "product"}, | |
| {"head": "tinadaviespigments", "head_type": "brand", "type": "sell", "tail": "國際認證之色乳", "tail_type": "product"}, | |
| ] | |
| ] | |
| evaluation_scores = module.compute(predictions=predictions, references=references, mode="strict", detailed_scores=False, relation_types=[]) | |
| print(evaluation_scores) | |
| {'tp': 1, 'fp': 1, 'fn': 2, 'p': 50.0, 'r': 33.333333333333336, 'f1': 40.0, 'Macro_f1': 25.0, 'Macro_p': 25.0, 'Macro_r': 25.0} | |
| """ | |
| def convert_format(data:list): | |
| """ | |
| Args: | |
| data (list) : list of dictionaries with different entity elements | |
| e.g | |
| [ | |
| {'head': ['phipigments', 'tinadaviespigments'...], | |
| 'head_type': ['product', 'brand'...], | |
| 'type': ['sell', 'sell'...], | |
| 'tail': ['國際認證之色乳', '國際認證之色乳'...], | |
| 'tail_type': ['product', 'product'...]}, | |
| {'head': ['SABONTAIWAN', 'SNTAIWAN'...], | |
| 'head_type': ['brand', 'brand'...], | |
| 'type': ['sell', 'sell'...], | |
| 'tail': ['大馬士革玫瑰有機光燦系列', '大馬士革玫瑰有機光燦系列'...], | |
| 'tail_type': ['product', 'product'...]} | |
| ... | |
| ] | |
| """ | |
| predictions = [] | |
| for item in data: | |
| prediction_group = [] | |
| for i in range(len(item['head'])): | |
| prediction = { | |
| 'head': item['head'][i], | |
| 'head_type': item['head_type'][i], | |
| 'type': item['type'][i], | |
| 'tail': item['tail'][i], | |
| 'tail_type': item['tail_type'][i], | |
| } | |
| prediction_group.append(prediction) | |
| predictions.append(prediction_group) | |
| return predictions | |
| class relation_extraction(evaluate.Metric): | |
| """evaluating the quality of relation extraction output""" | |
| 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({ | |
| "head": datasets.Value("string"), | |
| "head_type": datasets.Value("string"), | |
| "type": datasets.Value("string"), | |
| "tail": datasets.Value("string"), | |
| "tail_type": datasets.Value("string"), | |
| }), | |
| 'references': datasets.Sequence({ | |
| "head": datasets.Value("string"), | |
| "head_type": datasets.Value("string"), | |
| "type": datasets.Value("string"), | |
| "tail": datasets.Value("string"), | |
| "tail_type": 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): | |
| pass | |
| def _compute(self, predictions, references, mode, detailed_scores=False, relation_types=[]): | |
| """ | |
| This method computes and returns various scoring metrics for the prediction model based on the mode specified, including Precision, Recall, F1-Score and others. It evaluates the model's predictions against the provided reference data. | |
| Parameters: | |
| predictions: A list of predicted relations from the model. | |
| references: A list of ground-truth or reference relations to compare the predictions against. | |
| mode: Evaluation mode - 'strict' or 'boundaries'. 'strict' mode takes into account both entities type and their relationships | |
| while 'boundaries' mode only considers the entity spans of the relationships. | |
| detailed_scores: Boolean value, if True it returns scores for each relation type specifically, | |
| if False it returns the overall scores. | |
| relation_types: A list of relation types to consider while evaluating. If not provided, relation types will be constructed | |
| from the ground truth or reference data. | |
| Returns: | |
| A dictionary mapping each entity type to its respective scoring metrics such as Precision, Recall, F1 Score. | |
| """ | |
| predictions = convert_format(predictions) | |
| references = convert_format(references) | |
| assert mode in ["strict", "boundaries"] | |
| # construct relation_types from ground truth if not given | |
| if len(relation_types) == 0: | |
| for triplets in references: | |
| for triplet in triplets: | |
| relation = triplet["type"] | |
| if relation not in relation_types: | |
| relation_types.append(relation) | |
| scores = {rel: {"tp": 0, "fp": 0, "fn": 0} for rel in relation_types + ["ALL"]} | |
| # Count GT relations and Predicted relations | |
| n_sents = len(references) | |
| n_rels = sum([len([rel for rel in sent]) for sent in references]) | |
| n_found = sum([len([rel for rel in sent]) for sent in predictions]) | |
| # Count TP, FP and FN per type | |
| for pred_sent, gt_sent in zip(predictions, references): | |
| for rel_type in relation_types: | |
| # strict mode takes argument types into account | |
| if mode == "strict": | |
| pred_rels = {(rel["head"], rel["head_type"], rel["tail"], rel["tail_type"]) for rel in pred_sent if | |
| rel["type"] == rel_type} | |
| gt_rels = {(rel["head"], rel["head_type"], rel["tail"], rel["tail_type"]) for rel in gt_sent if | |
| rel["type"] == rel_type} | |
| # boundaries mode only takes argument spans into account | |
| elif mode == "boundaries": | |
| pred_rels = {(rel["head"], rel["tail"]) for rel in pred_sent if rel["type"] == rel_type} | |
| gt_rels = {(rel["head"], rel["tail"]) for rel in gt_sent if rel["type"] == rel_type} | |
| scores[rel_type]["tp"] += len(pred_rels & gt_rels) | |
| scores[rel_type]["fp"] += len(pred_rels - gt_rels) | |
| scores[rel_type]["fn"] += len(gt_rels - pred_rels) | |
| # Compute per entity Precision / Recall / F1 | |
| for rel_type in scores.keys(): | |
| if scores[rel_type]["tp"]: | |
| scores[rel_type]["p"] = 100 * scores[rel_type]["tp"] / (scores[rel_type]["fp"] + scores[rel_type]["tp"]) | |
| scores[rel_type]["r"] = 100 * scores[rel_type]["tp"] / (scores[rel_type]["fn"] + scores[rel_type]["tp"]) | |
| else: | |
| scores[rel_type]["p"], scores[rel_type]["r"] = 0, 0 | |
| if not scores[rel_type]["p"] + scores[rel_type]["r"] == 0: | |
| scores[rel_type]["f1"] = 2 * scores[rel_type]["p"] * scores[rel_type]["r"] / ( | |
| scores[rel_type]["p"] + scores[rel_type]["r"]) | |
| else: | |
| scores[rel_type]["f1"] = 0 | |
| # Compute micro F1 Scores | |
| tp = sum([scores[rel_type]["tp"] for rel_type in relation_types]) | |
| fp = sum([scores[rel_type]["fp"] for rel_type in relation_types]) | |
| fn = sum([scores[rel_type]["fn"] for rel_type in relation_types]) | |
| if tp: | |
| precision = 100 * tp / (tp + fp) | |
| recall = 100 * tp / (tp + fn) | |
| f1 = 2 * precision * recall / (precision + recall) | |
| else: | |
| precision, recall, f1 = 0, 0, 0 | |
| scores["ALL"]["p"] = precision | |
| scores["ALL"]["r"] = recall | |
| scores["ALL"]["f1"] = f1 | |
| scores["ALL"]["tp"] = tp | |
| scores["ALL"]["fp"] = fp | |
| scores["ALL"]["fn"] = fn | |
| # Compute Macro F1 Scores | |
| scores["ALL"]["Macro_f1"] = np.mean([scores[ent_type]["f1"] for ent_type in relation_types]) | |
| scores["ALL"]["Macro_p"] = np.mean([scores[ent_type]["p"] for ent_type in relation_types]) | |
| scores["ALL"]["Macro_r"] = np.mean([scores[ent_type]["r"] for ent_type in relation_types]) | |
| if detailed_scores: | |
| return scores | |
| return scores["ALL"] |