# Copyright 2022 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.
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#
#     http://www.apache.org/licenses/LICENSE-2.0
#
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# distributed under the License is distributed on an "AS IS" BASIS,
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"""MAPE - Mean Absolute Percentage Error Metric"""

import datasets
from sklearn.metrics import mean_absolute_percentage_error

import evaluate


_CITATION = """\
@article{scikit-learn,
 title={Scikit-learn: Machine Learning in {P}ython},
 author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
 journal={Journal of Machine Learning Research},
 volume={12},
 pages={2825--2830},
 year={2011}
}
"""

_DESCRIPTION = """\
Mean Absolute Percentage Error (MAPE) is the mean percentage error difference between the predicted and actual
values.
"""


_KWARGS_DESCRIPTION = """
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

                 "raw_values" : Returns a full set of errors in case of multioutput input.

                 "uniform_average" : Errors of all outputs are averaged with uniform weight.

Returns:
    mape : mean absolute percentage error.
        If multioutput is "raw_values", then mean absolute percentage error is returned for each output separately. If multioutput is "uniform_average" or an ndarray of weights, then the weighted average of all output errors is returned.
        MAPE output is non-negative floating point. The best value is 0.0.
Examples:

    >>> mape_metric = evaluate.load("mape")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mape_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mape': 0.3273809523809524}

    If you're using multi-dimensional lists, then set the config as follows :

    >>> mape_metric = evaluate.load("mape", "multilist")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0.1, 2], [-1, 2], [8, -5]]
    >>> results = mape_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mape': 0.8874999875823658}
    >>> results = mape_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
    >>> print(results)
    {'mape': array([1.37499998, 0.4       ])}
"""


@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class Mape(evaluate.Metric):
    def _info(self):
        return evaluate.MetricInfo(
            description=_DESCRIPTION,
            citation=_CITATION,
            inputs_description=_KWARGS_DESCRIPTION,
            features=datasets.Features(self._get_feature_types()),
            reference_urls=[
                "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_percentage_error.html"
            ],
        )

    def _get_feature_types(self):
        if self.config_name == "multilist":
            return {
                "predictions": datasets.Sequence(datasets.Value("float")),
                "references": datasets.Sequence(datasets.Value("float")),
            }
        else:
            return {
                "predictions": datasets.Value("float"),
                "references": datasets.Value("float"),
            }

    def _compute(self, predictions, references, sample_weight=None, multioutput="uniform_average"):

        mape_score = mean_absolute_percentage_error(
            references,
            predictions,
            sample_weight=sample_weight,
            multioutput=multioutput,
        )

        return {"mape": mape_score}