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| # 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} | |
| """ | |
| 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 | |
| } |