fairevaluation.py

# 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
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# 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.
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# huggingface packages
import evaluate
import datasets

# faireval functions
from .FairEval import *

# packages to manage input formats
import importlib
from typing import List, Optional, Union
from seqeval.metrics.v1 import check_consistent_length
from seqeval.scheme import Entities, Token, auto_detect

_CITATION = """\
@inproceedings{ortmann2022,
    title = {Fine-Grained Error Analysis and Fair Evaluation of Labeled Spans},
    author = {Katrin Ortmann},
    url = {https://aclanthology.org/2022.lrec-1.150},
    year = {2022},
    date = {2022-06-21},
    booktitle = {Proceedings of the Language Resources and Evaluation Conference (LREC)},
    pages = {1400-1407},
    publisher = {European Language Resources Association},
    address = {Marseille, France},
    pubstate = {published},
    type = {inproceedings}
}
"""

_DESCRIPTION = """\
New evaluation method that more accurately reflects true annotation quality by ensuring that every error is counted 
only once - avoiding the penalty to close-to-target annotations happening in traditional evaluation. 
In addition to the traditional categories of true positives (TP), false positives (FP), and false negatives 
(FN), the new method takes into account the more fine-grained error types suggested by Manning: labeling errors (LE), 
boundary errors (BE), and labeling-boundary errors (LBE). Additionally, the system also distinguishes different types 
of boundary errors: BES (the system's annotation is smaller than the target span), BEL (the system's annotation is 
larger than the target span) and BEO (the system span overlaps with the target span)
"""

_KWARGS_DESCRIPTION = """
Counts the number of redefined traditional errors (FP, FN), newly defined errors (BE, LE, LBE) and fine-grained 
boundary errors (BES, BEL, BEO). Then computes the fair Precision, Recall and F1-Score. 
For the computation of the metrics from the error count please refer to: https://aclanthology.org/2022.lrec-1.150.pdf
Args:
    predictions: list of predictions to score. Each predicted sentence
        should be a list of IOB-formatted labels corresponding to each sentence token.
        Predicted sentences must have the same number of tokens as the references'.
    references: list of reference for each prediction. Each reference sentence
        should be a list of IOB-formatted labels corresponding to each sentence token.
Returns:
    A dictionary with:
        TP: count of True Positives
        FP: count of False Positives
        FN: count of False Negatives
        LE: count of Labeling Errors
        BE: count of Boundary Errors
        BEO: segment of the BE where the prediction overlaps with the reference
        BES: segment of the BE where the prediction is smaller than the reference
        BEL: segment of the BE where the prediction is larger than the reference
        LBE : count of Label-and-Boundary Errors
        Prec: fair precision
        Rec: fair recall
        F1: fair F1-score
Examples:
    >>> faireval = evaluate.load("illorca/fairevaluation")
    >>> pred = ['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O', 'B-PER', 'I-PER', 'O']
    >>> ref =  ['O', 'O', 'O',      'B-MISC', 'I-MISC', 'I-MISC', 'O', 'B-PER', 'I-PER', 'O']
    >>> results = faireval.compute(predictions=pred, references=ref)
    >>> print(results)
    {'TP': 1,
     'FP': 0,
     'FN': 0,
     'LE': 0,
     'BE': 1,
     'BEO': 0,
     'BES': 0,
     'BEL': 1,
     'LBE': 0,
     'Prec': 0.6666666666666666,
     'Rec': 0.6666666666666666,
     'F1': 0.6666666666666666}
"""


@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class FairEvaluation(evaluate.Metric):
    """Counts the number of redefined traditional errors (FP, FN), newly defined errors (BE, LE, LBE) and fine-grained
    boundary errors (BES, BEL, BEO). Then computes the fair Precision, Recall and F1-Score. """

    def _info(self):
        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", id="label"), id="sequence"),
                "references": datasets.Sequence(datasets.Value("string", id="label"), id="sequence"),
            }),
            # Homepage of the module for documentation
            homepage="https://huggingface.co/spaces/illorca/fairevaluation",
            # Additional links to the codebase or references
            codebase_urls=["https://github.com/rubcompling/FairEval#acknowledgement"],
            reference_urls=["https://aclanthology.org/2022.lrec-1.150.pdf"]
        )

    def _compute(
            self,
            predictions,
            references,
            suffix: bool = False,
            scheme: Optional[str] = None,
            mode: Optional[str] = 'fair',
            error_format: Optional[str] = 'count',
            sample_weight: Optional[List[int]] = None,
            zero_division: Union[str, int] = "warn",
    ):
        """Returns the error counts and fair scores"""
        # (1) SEQEVAL INPUT MANAGEMENT
        if scheme is not None:
            try:
                scheme_module = importlib.import_module("seqeval.scheme")
                scheme = getattr(scheme_module, scheme)
            except AttributeError:
                raise ValueError(f"Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}")

        y_true = references
        y_pred = predictions

        check_consistent_length(y_true, y_pred)

        if scheme is None or not issubclass(scheme, Token):
            scheme = auto_detect(y_true, suffix)

        true_spans = Entities(y_true, scheme, suffix).entities
        pred_spans = Entities(y_pred, scheme, suffix).entities

        # (2) TRANSFORM FROM SEQEVAL TO FAIREVAL SPAN FORMAT
        true_spans = seq_to_fair(true_spans)
        pred_spans = seq_to_fair(pred_spans)

        # (3) COUNT ERRORS AND CALCULATE SCORES
        total_errors = compare_spans([], [])  # initialize empty error count dictionary

        for i in range(len(true_spans)):
            sentence_errors = compare_spans(true_spans[i], pred_spans[i])
            total_errors = add_dict(total_errors, sentence_errors)

        results = calculate_results(total_errors)
        del results['conf']

        # (4) SELECT OUTPUT MODE AND REFORMAT AS SEQEVAL HUGGINGFACE OUTPUT
        output = {}
        total_trad_errors = results['overall']['traditional']['FP'] + results['overall']['traditional']['FN']
        total_fair_errors = results['overall']['fair']['FP'] + results['overall']['fair']['FN'] + \
                            results['overall']['fair']['LE'] + results['overall']['fair']['BE'] + \
                            results['overall']['fair']['LBE']

        assert mode in ['traditional', 'fair'], 'mode must be \'traditional\' or \'fair\''
        assert error_format in ['count', 'proportion'], 'error_format must be \'count\' or \'proportion\''

        if mode == 'traditional':
            for k, v in results['per_label'][mode].items():
                if error_format == 'count':
                    output[k] = {'precision': v['Prec'], 'recall': v['Rec'], 'f1': v['F1'], 'TP': v['TP'],
                                 'FP': v['FP'], 'FN': v['FN']}
                elif error_format == 'proportion':
                    output[k] = {'precision': v['Prec'], 'recall': v['Rec'], 'f1': v['F1'], 'TP': v['TP'],
                                 'FP': v['FP'] / total_trad_errors, 'FN': v['FN'] / total_trad_errors}
        elif mode == 'fair':
            for k, v in results['per_label'][mode].items():
                if error_format == 'count':
                    output[k] = {'precision': v['Prec'], 'recall': v['Rec'], 'f1': v['F1'], 'TP': v['TP'],
                                 'FP': v['FP'], 'FN': v['FN'], 'LE': v['LE'], 'BE': v['BE'], 'LBE': v['LBE']}
                elif error_format == 'proportion':
                    output[k] = {'precision': v['Prec'], 'recall': v['Rec'], 'f1': v['F1'], 'TP': v['TP'],
                                 'FP': v['FP'] / total_fair_errors, 'FN': v['FN'] / total_fair_errors,
                                 'LE': v['LE'] / total_fair_errors, 'BE': v['BE'] / total_fair_errors,
                                 'LBE': v['LBE'] / total_fair_errors}

        output['overall_precision'] = results['overall'][mode]['Prec']
        output['overall_recall'] = results['overall'][mode]['Rec']
        output['overall_f1'] = results['overall'][mode]['F1']

        if mode == 'traditional':
            output['TP'] = results['overall'][mode]['TP']
            output['FP'] = results['overall'][mode]['FP']
            output['FN'] = results['overall'][mode]['FN']
            if error_format == 'proportion':
                output['FP'] = output['FP'] / total_trad_errors
                output['FN'] = output['FN'] / total_trad_errors
        elif mode == 'fair':
            output['TP'] = results['overall'][mode]['TP']
            output['FP'] = results['overall'][mode]['FP']
            output['FN'] = results['overall'][mode]['FN']
            output['LE'] = results['overall'][mode]['LE']
            output['BE'] = results['overall'][mode]['BE']
            output['LBE'] = results['overall'][mode]['LBE']
            if error_format == 'proportion':
                output['FP'] = output['FP'] / total_fair_errors
                output['FN'] = output['FN'] / total_fair_errors
                output['LE'] = output['LE'] / total_fair_errors
                output['BE'] = output['BE'] / total_fair_errors
                output['LBE'] = output['LBE'] / total_fair_errors

        return output


def seq_to_fair(seq_sentences):
    out = []
    for seq_sentence in seq_sentences:
        sentence = []
        for entity in seq_sentence:
            span = str(entity).replace('(', '').replace(')', '').replace(' ', '').split(',')
            span = span[1:]
            span[-1] = int(span[-1]) - 1
            span[1] = int(span[1])
            span.append({i for i in range(span[1], span[2] + 1)})
            sentence.append(span)
        out.append(sentence)
    return out