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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. | |
| """MSE - Mean Squared Error Metric""" | |
| import datasets | |
| from sklearn.metrics import mean_squared_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 Squared Error(MSE) is the average of the square of 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. | |
| squared : bool, default=True | |
| If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value. | |
| Returns: | |
| mse : mean squared error. | |
| Examples: | |
| >>> mse_metric = evaluate.load("mse") | |
| >>> predictions = [2.5, 0.0, 2, 8] | |
| >>> references = [3, -0.5, 2, 7] | |
| >>> results = mse_metric.compute(predictions=predictions, references=references) | |
| >>> print(results) | |
| {'mse': 0.375} | |
| >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False) | |
| >>> print(rmse_result) | |
| {'mse': 0.6123724356957945} | |
| If you're using multi-dimensional lists, then set the config as follows : | |
| >>> mse_metric = evaluate.load("mse", "multilist") | |
| >>> predictions = [[0.5, 1], [-1, 1], [7, -6]] | |
| >>> references = [[0, 2], [-1, 2], [8, -5]] | |
| >>> results = mse_metric.compute(predictions=predictions, references=references) | |
| >>> print(results) | |
| {'mse': 0.7083333333333334} | |
| >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput='raw_values') | |
| >>> print(results) # doctest: +NORMALIZE_WHITESPACE | |
| {'mse': array([0.41666667, 1. ])} | |
| """ | |
| class Mse(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_squared_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", squared=True): | |
| mse = mean_squared_error( | |
| references, predictions, sample_weight=sample_weight, multioutput=multioutput, squared=squared | |
| ) | |
| return {"mse": mse} | |