metrics.py

# coding=utf-8
# Copyright 2020 
#
# 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.

# Modifications made to original implementation available here:
# https://github.com/huggingface/transformers/blob/master/examples/run_glue.py 
# Credits: 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.

import csv
import sys
import logging

logger = logging.getLogger(__name__)

try:
    from scipy.stats import pearsonr, spearmanr
    from sklearn.metrics import matthews_corrcoef, f1_score
    _has_sklearn = True
except (AttributeError, ImportError) as e:
    logger.warning("To use data.metrics please install scikit-learn. See https://scikit-learn.org/stable/index.html")
    _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, average=None)
        return {
            "acc": acc,
            "f1": f1,
            "acc_and_f1": (acc + f1) / 2,
        }


    def pearson_and_spearman(preds, labels):
        pearson_corr = pearsonr(preds, labels)[0]
        spearman_corr = spearmanr(preds, labels)[0]
        return {
            "pearson": pearson_corr,
            "spearmanr": spearman_corr,
            "corr": (pearson_corr + spearman_corr) / 2,
        }


    def compute_metrics(task_name, preds, labels):
        assert len(preds) == len(labels)
        if task_name == "seq-classification":
            return {"acc": simple_accuracy(preds, labels)}
            #return {"Eval results: ": acc_and_f1(preds, labels)}
        else:
            raise KeyError(task_name)