src/transformers/data/metrics/__init__.py

# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
#
# 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.
from sklearn.metrics import mean_squared_error,r2_score

try:
    from scipy.stats import pearsonr, spearmanr
    import numpy as np
    from sklearn.metrics import matthews_corrcoef, precision_score, recall_score, f1_score, roc_auc_score, average_precision_score

    _has_sklearn = True
except (AttributeError, ImportError):
    _has_sklearn = False


def is_sklearn_available():
    return _has_sklearn


if _has_sklearn:

    def simple_accuracy(preds, labels):
        return (preds == labels).mean()

    def acc_and_f1(preds, labels):
        acc = simple_accuracy(preds, labels)
        f1 = f1_score(y_true=labels, y_pred=preds)
        return {
            "acc": acc,
            "f1": f1,
            "acc_and_f1": (acc + f1) / 2,
        }
    
    def acc_f1_mcc(preds, labels):
        acc = simple_accuracy(preds, labels)
        f1 = f1_score(y_true=labels, y_pred=preds)
        mcc = matthews_corrcoef(labels, preds)
        return {
            "acc": acc,
            "f1": f1,
            "mcc": mcc
        }

    def acc_f1_mcc_auc_aupr_pre_rec(preds, labels, probs):
        acc = simple_accuracy(preds, labels)
        precision = precision_score(y_true=labels, y_pred=preds)
        recall = recall_score(y_true=labels, y_pred=preds)
        f1 = f1_score(y_true=labels, y_pred=preds)
        mcc = matthews_corrcoef(labels, preds)
        auc = roc_auc_score(labels, probs)
        aupr = average_precision_score(labels, probs)
        return {
            "acc": acc,
            "f1": f1,
            "mcc": mcc,
            "auc": auc,
            "aupr": aupr,
            "precision": precision,
            "recall": recall,
        }
    def acc_f1_mcc_auc_aupr_pre_rec_5cls(preds, labels, probs):
    # 计算准确率
        acc = simple_accuracy(labels, preds)
        
        # 计算精确率、召回率、F1-score
        precision = precision_score(y_true=labels, y_pred=preds, average='macro')
        recall = recall_score(y_true=labels, y_pred=preds, average='macro')
        f1 = f1_score(y_true=labels, y_pred=preds, average='macro')
        
        # 计算MCC
        mcc = matthews_corrcoef(labels, preds)
        
        # 计算AUC和AUPR
        # 对于多分类，需要使用one-vs-rest策略
        auc = roc_auc_score(labels, probs, multi_class='ovr')
        aupr = average_precision_score(labels, probs, average='macro')
        
        return {
            "acc": acc,
            "f1": f1,
            "mcc": mcc,
            "auc": auc,
            "aupr": aupr,
            "precision": precision,
            "recall": recall,
        }


    def acc_f1_mcc_auc_pre_rec(preds, labels, probs):
        acc = simple_accuracy(preds, labels)
        precision = precision_score(y_true=labels, y_pred=preds, average="macro")
        recall = recall_score(y_true=labels, y_pred=preds, average="macro")
        f1 = f1_score(y_true=labels, y_pred=preds, average="macro")
        mcc = matthews_corrcoef(labels, preds)
        auc = roc_auc_score(labels, probs, average="macro", multi_class="ovo")
        return {
            "acc": acc,
            "f1": f1,
            "mcc": mcc,
            "auc": auc,
            "precision": precision,
            "recall": recall,
        }

    def pearson_and_spearman(preds, labels):
        pearson_corr = pearsonr(preds, labels)[0]
        spearman_corr = spearmanr(preds, labels)[0]
        rmse_score = np.sqrt(mean_squared_error(labels, preds))
        return {
            "pearson": pearson_corr,
            "spearmanr": spearman_corr,
            "corr": (pearson_corr + spearman_corr) / 2,
            "rmse":rmse_score,
            "r2": r2_score(labels, preds),
        }

    def rmse(preds, labels):
        rmse_score = np.sqrt(mean_squared_error(labels, preds))
        return {"rmse":rmse_score}
       
    
    def glue_compute_metrics(task_name, preds, labels, probs=None):
        assert len(preds) == len(labels)
        if task_name == "cola":
            return {"mcc": matthews_corrcoef(labels, preds)}
        elif task_name == "sst-2":
            return {"acc": simple_accuracy(preds, labels)}
        elif task_name in ["dna690", "dnapair"]:
            return acc_f1_mcc_auc_aupr_pre_rec(preds, labels, probs)
        elif task_name == "dnaprom":
            return acc_f1_mcc_auc_pre_rec(preds, labels, probs)
            # return {"acc": simple_accuracy(preds, labels)}
        elif task_name == "dnasplice":
            return acc_f1_mcc_auc_pre_rec(preds, labels, probs)
        elif task_name == "mrpc":
            return acc_and_f1(preds, labels)
        elif task_name == "sts-b":
            return pearson_and_spearman(preds, labels)
        elif task_name == "qqp":
            return acc_and_f1(preds, labels)
        elif task_name == "mnli":
            return {"acc": simple_accuracy(preds, labels)}
        elif task_name == "mnli-mm":
            return {"acc": simple_accuracy(preds, labels)}
        elif task_name == "qnli":
            return {"acc": simple_accuracy(preds, labels)}
        elif task_name == "rte":
            return {"acc": simple_accuracy(preds, labels)}
        elif task_name == "wnli":
            return {"acc": simple_accuracy(preds, labels)}
        elif task_name == "hans":
            return {"acc": simple_accuracy(preds, labels)}
        elif task_name == "genepred":
            return pearson_and_spearman(preds, labels)
        elif task_name == "cre5clas":
            return acc_f1_mcc_auc_aupr_pre_rec_5cls(preds, labels, probs)
        elif task_name == "cre4clas":
            return acc_f1_mcc_auc_aupr_pre_rec_5cls(preds, labels, probs)
        else:
            raise KeyError(task_name)

    def xnli_compute_metrics(task_name, preds, labels):
        assert len(preds) == len(labels)
        if task_name == "xnli":
            return {"acc": simple_accuracy(preds, labels)}
        else:
            raise KeyError(task_name)