Revision d38084d993f6b218862f3aa0693aacc2e3b4b1b3 authored by TUNA Caglayan on 29 April 2021, 08:44:25 UTC, committed by TUNA Caglayan on 12 May 2021, 12:26:22 UTC
1 parent e54a8ff
regression.py
from .. import backend as T
# Author: Jean Kossaifi <jean.kossaifi+tensors@gmail.com>
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)
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))
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