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| # 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. | |
| # 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. | |
| """MASE - Mean Absolute Scaled Error Metric""" | |
| import datasets | |
| import numpy as np | |
| from sklearn.metrics import mean_absolute_error | |
| import evaluate | |
| _CITATION = """\ | |
| @article{HYNDMAN2006679, | |
| title = {Another look at measures of forecast accuracy}, | |
| journal = {International Journal of Forecasting}, | |
| volume = {22}, | |
| number = {4}, | |
| pages = {679--688}, | |
| year = {2006}, | |
| issn = {0169-2070}, | |
| doi = {https://doi.org/10.1016/j.ijforecast.2006.03.001}, | |
| url = {https://www.sciencedirect.com/science/article/pii/S0169207006000239}, | |
| author = {Rob J. Hyndman and Anne B. Koehler}, | |
| } | |
| """ | |
| _DESCRIPTION = """\ | |
| Mean Absolute Scaled Error (MASE) is the mean absolute error of the forecast values, divided by the mean absolute error of the in-sample one-step naive forecast. | |
| """ | |
| _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. | |
| training: array-like of shape (n_train_samples,) or (n_train_samples, n_outputs) | |
| In sample training data for naive forecast. | |
| periodicity: int, default=1 | |
| Seasonal periodicity of training data. | |
| 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: | |
| mase : mean absolute scaled 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. | |
| MASE output is non-negative floating point. The best value is 0.0. | |
| Examples: | |
| >>> mase_metric = evaluate.load("mase") | |
| >>> predictions = [2.5, 0.0, 2, 8, 1.25] | |
| >>> references = [3, -0.5, 2, 7, 2] | |
| >>> training = [5, 0.5, 4, 6, 3, 5, 2] | |
| >>> results = mase_metric.compute(predictions=predictions, references=references, training=training) | |
| >>> print(results) | |
| {'mase': 0.18333333333333335} | |
| If you're using multi-dimensional lists, then set the config as follows : | |
| >>> mase_metric = evaluate.load("mase", "multilist") | |
| >>> predictions = [[0, 2], [-1, 2], [8, -5]] | |
| >>> references = [[0.5, 1], [-1, 1], [7, -6]] | |
| >>> training = [[0.5, 1], [-1, 1], [7, -6]] | |
| >>> results = mase_metric.compute(predictions=predictions, references=references, training=training) | |
| >>> print(results) | |
| {'mase': 0.18181818181818182} | |
| >>> results = mase_metric.compute(predictions=predictions, references=references, training=training, multioutput='raw_values') | |
| >>> print(results) | |
| {'mase': array([0.10526316, 0.28571429])} | |
| >>> results = mase_metric.compute(predictions=predictions, references=references, training=training, multioutput=[0.3, 0.7]) | |
| >>> print(results) | |
| {'mase': 0.21935483870967742} | |
| """ | |
| class Mase(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://otexts.com/fpp3/accuracy.html#scaled-errors"], | |
| ) | |
| 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, | |
| training, | |
| periodicity=1, | |
| sample_weight=None, | |
| multioutput="uniform_average", | |
| ): | |
| y_pred_naive = training[:-periodicity] | |
| mae_naive = mean_absolute_error(training[periodicity:], y_pred_naive, multioutput=multioutput) | |
| mae_score = mean_absolute_error( | |
| references, | |
| predictions, | |
| sample_weight=sample_weight, | |
| multioutput=multioutput, | |
| ) | |
| epsilon = np.finfo(np.float64).eps | |
| mase_score = mae_score / np.maximum(mae_naive, epsilon) | |
| return {"mase": mase_score} | |