mean_reciprocal_rank.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
#
# 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.
"""TODO: Add a description here."""

import evaluate
import datasets


# TODO: Add BibTeX citation
_CITATION = """\
@InProceedings{posicube:module,
title = {Mean reciprocal mean},
authors={Pocicube, Inc.},
year={2022}
}
"""

# TODO: Add description of the module here
_DESCRIPTION = """\
This module is designed to evaluate a system ranks the list of item. 
mean reciprocal rank is a statistic measure for evaluating any process that produces a list of possible responses to a sample of queries, ordered by probability of correctness
"""


# TODO: Add description of the arguments of the module here
_KWARGS_DESCRIPTION = """
Calculates how good are ranks, using certain scores
Args:
    predictions: list of predicted ranks of gold item, the first rank starts with 0
Returns:
    mean reciprocal rank: mean of inverse of rank of gold item
Examples:
    
    >>> mrr = evaluate.load("poscicube/mean_reciprocal_rank")
    >>> results = mrr.compute(predictions=[0, 4])
    >>> print(results)
    {'mrr': 0.6}
"""


@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class MeanReciprocalRank(evaluate.Metric):
    """a statistic measure for evaluating any process that produces a list of possible responses to a sample of queries, ordered by probability of correctness."""

    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.Value('int64'),
            }),
            # Homepage of the module for documentation
            homepage="https://huggingface.co./spaces/posicube/mean_reciprocal_rank",
            # Additional links to the codebase or references
            codebase_urls=["https://huggingface.co./spaces/posicube/mean_reciprocal_rank"],
            reference_urls=["https://en.wikipedia.org/wiki/Mean_reciprocal_rank"]
        )

    def _download_and_prepare(self, dl_manager):
        """Optional: download external resources useful to compute the scores"""
        pass

    def _compute(self, predictions):
        """Returns the scores"""
        # TODO: Compute the different scores of the module
        q = len(predictions)
        sum_rr = 0.0
        for p in predictions:
            sum_rr += 1/(p+1)
        mrr = sum_rr / q
        return {
            "mrr": mrr
        }