Source code for tensorly.metrics.regression

from .. import backend as T

# Author: Jean Kossaifi <jean.kossaifi+tensors@gmail.com>


[docs]def MSE(y_true, y_pred, axis=None): """Returns the mean squared error between the two predictions Parameters ---------- y_true : array of shape (n_samples, ) Ground truth (correct) target values. y_pred : array of shape (n_samples, ) Estimated target values. Returns ------- float """ return T.mean((y_true - y_pred) ** 2, axis=axis)
[docs]def RMSE(y_true, y_pred, axis=None): """Returns the regularised mean squared error between the two predictions (the square-root is applied to the mean_squared_error) Parameters ---------- y_true : array of shape (n_samples, ) Ground truth (correct) target values. y_pred : array of shape (n_samples, ) Estimated target values. Returns ------- float """ return T.sqrt(MSE(y_true, y_pred, axis=axis))
def reflective_correlation_coefficient(y_true, y_pred, axis=None): """Reflective variant of Pearson's product moment correlation coefficient where the predictions are not centered around their mean values. Parameters ---------- y_true : array of shape (n_samples, ) Ground truth (correct) target values. y_pred : array of shape (n_samples, ) Estimated target values. Returns ------- float: reflective correlation coefficient """ return T.sum(y_true*y_pred, axis=axis)/T.sqrt(T.sum(y_true**2, axis=axis)*T.sum(y_pred**2, axis=axis)) def covariance(y_true, y_pred, axis=None): centered_true = T.mean(y_true, axis=axis) centered_pred = T.mean(y_pred, axis=axis) if axis is not None: # TODO: write a function to do this.. shape = list(T.shape(y_true)) shape[axis] = 1 centered_true = T.reshape(centered_true, shape) shape = list(T.shape(y_pred)) shape[axis] = 1 centered_pred = T.reshape(centered_pred, shape) return T.mean((y_true - centered_true)*(y_pred - centered_pred), axis=axis) def variance(y, axis=None): return covariance(y, y, axis=axis) def standard_deviation(y, axis=None): return T.sqrt(variance(y, axis=axis)) def correlation(y_true, y_pred, axis=None): """Pearson's product moment correlation coefficient""" return covariance(y_true, y_pred, axis=axis)/T.sqrt(variance(y_true, axis)*variance(y_pred, axis))