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| # -*- coding: utf-8 -*- | |
| r""" | |
| Regression Metrics | |
| ============== | |
| Metrics to evaluate regression quality of estimator models. | |
| """ | |
| import warnings | |
| import numpy as np | |
| import torch | |
| from scipy.stats import kendalltau, pearsonr, spearmanr | |
| class RegressionReport: | |
| def __init__(self, kendall_type="c"): | |
| super().__init__() | |
| self.metrics = [Pearson(), Kendall(kendall_type), Spearman(), ] | |
| def compute(self, x: np.array, y: np.array) -> float: | |
| """Computes Kendall correlation. | |
| :param x: predicted scores. | |
| :param x: ground truth scores. | |
| :return: Kendall Tau correlation value. | |
| """ | |
| with warnings.catch_warnings(): | |
| warnings.simplefilter("ignore") | |
| return {metric.name: metric.compute(x, y) for metric in self.metrics} | |
| class Kendall: | |
| def __init__(self, variant="c"): | |
| self.name = "kendall" | |
| self.variant = variant | |
| def compute(self, x: np.array, y: np.array) -> float: | |
| """Computes Kendall correlation. | |
| :param x: predicted scores. | |
| :param x: ground truth scores. | |
| :return: Kendall Tau correlation value. | |
| """ | |
| if np.isnan(x).any() or np.isnan(y).any(): | |
| return np.nan | |
| return torch.tensor(kendalltau(x, y,variant=self.variant)[0], dtype=torch.float32) | |
| class Pearson: | |
| def __init__(self): | |
| self.name = "pearson" | |
| def compute(self, x: np.array, y: np.array) -> torch.Tensor: | |
| """Computes Pearson correlation. | |
| :param x: predicted scores. | |
| :param x: ground truth scores. | |
| :return: Pearson correlation value. | |
| """ | |
| # check nan or inf | |
| if np.isnan(x).any() or np.isnan(y).any(): | |
| return np.nan | |
| return torch.tensor(pearsonr(x, y)[0], dtype=torch.float32) | |
| class Spearman: | |
| def __init__(self): | |
| self.name = "spearman" | |
| def compute(self, x: np.array, y: np.array) -> float: | |
| """Computes Spearman correlation. | |
| :param x: predicted scores. | |
| :param x: ground truth scores. | |
| Return: | |
| - Spearman correlation value. | |
| """ | |
| if np.isnan(x).any() or np.isnan(y).any(): | |
| return np.nan | |
| return torch.tensor(spearmanr(x, y)[0], dtype=torch.float32) | |