import evaluate import datasets import numpy as np # Add BibTeX citation _CITATION = """\ @Paper{ author = {Bruno Taillé, Vincent Guigue, Geoffrey Scoutheeten, Patrick Gallinari}, title = {Let's Stop Incorrect Comparisons in End-to-end Relation Extraction!}, year = {2020}, link = https://arxiv.org/abs/2009.10684 } """ # 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. """ # Add description of the arguments of the module here _KWARGS_DESCRIPTION = """ Calculates how good are predictions given some references, using Micro and Macro F1 scores Args: predictions: list of list of dictionary, including relation and its type dictionary should be key value pair like entity name link to its type references: list of list of dictionary, including relation and its type dictionary should be entity name pair like entity name link to its type Returns: evaluation result: - **sell** (`dictionary`): score of type sell - **tp** : true positive count - **fp** : false positive count - **fn** : false negative count - **p** : precision - **r** : recall - **f1** : micro f1 score - **ALL** (`dictionary`): score of all of the type (sell and belongs to) - **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": "phip igments", "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"}, ... ] ... ] >>> 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) >>> print(evaluation_scores) {'sell': {'tp': 1, 'fp': 1, 'fn': 1, 'p': 50.0, 'r': 50.0, 'f1': 50.0}, 'ALL': {'tp': 1, 'fp': 1, 'fn': 1, 'p': 50.0, 'r': 50.0, 'f1': 50.0, 'Macro_f1': 50.0, 'Macro_p': 50.0, 'Macro_r': 50.0}} """ 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'...]}, # first element {'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'...]} # second element ... ] """ 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 @evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION) class relation_extraction(evaluate.Metric): """ evaluation metric of relation extraction inputs: predictions : (`list` of `list`s of `dictionary`s) about relation and its type of prediction references : (`list` of `list`s of `dictionary`s) about references for each relation and its type. """ 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): """Optional: download external resources useful to compute the scores""" # TODO: Download external resources if needed pass def _compute(self, predictions, references, mode="strict", relation_types=[]): """Returns the scores""" # TODO: Compute the different scores of the module 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]) return scores