loss module

DIRESA loss classes/functions

Author:

Geert De Paepe

Email:

geert.de.paepe@vub.be

License:

MIT License

loss.mae_dist_loss(_, distances)

Absolute Error between original and latent distances

Parameters:

  • _ – not used (loss functions need 2 params: the true and predicted values)

  • distances – batch of original and latent distances between twins

Returns:

batch of absolute errors

loss.male_dist_loss(_, distances)

Absolute Error between logarithm of original and latent distances

Parameters:

  • _ – not used (loss functions need 2 params: the true and predicted values)

  • distances – batch of original and latent distances between twins

Returns:

batch of absolute logarithmic errors

loss.mape_dist_loss(_, distances)

Absolute Percentage Error between original and latent distances

Parameters:

  • _ – not used (loss functions need 2 params: the true and predicted values)

  • distances – batch of original and latent distances between twins

Returns:

batch of absolute percentage errors

loss.mse_dist_loss(_, distances)

Squared Error between original and latent distances

Parameters:

  • _ – not used (loss functions need 2 params: the true and predicted values)

  • distances – batch of original and latent distances between twins

Returns:

batch of squared errors

loss.msle_dist_loss(_, distances)

Squared Error between logarithm of original and latent distances

Parameters:

  • _ – not used (loss functions need 2 params: the true and predicted values)

  • distances – batch of original and latent distances between twins

Returns:

batch of squared logarithmic errors

loss.corr_dist_loss(_, distances)

Correlation loss between original and latent distances

Parameters:

  • _ – not used (loss functions need 2 params: the true and predicted values)

  • distances – batch of original and latent distances between twins

Returns:

1 - correlation coefficient

loss.corr_log_dist_loss(_, distances)

Correlation loss between logarithm of original and latent distances

Parameters:

  • _ – not used (loss functions need 2 params: the true and predicted values)

  • distances – batch of original and latent distances between twins

Returns:

1 - correlation coefficient (of logarithmic distances)

class loss.MaleDistLoss(*args: Any, **kwargs: Any)

Mean Absolute Error between logarithm of original and latent distances

__init__(*args: Any, **kwargs: Any) → None
call()
class loss.MsleDistLoss(*args: Any, **kwargs: Any)

Mean Square Error between logarithm of original and latent distances

__init__(*args: Any, **kwargs: Any) → None
call()
class loss.CorrLogDistLoss(*args: Any, **kwargs: Any)

Correlation loss between logarithm of original and latent distances

__init__(*args: Any, **kwargs: Any) → None
call()
class loss.LatentCovLoss(*args: Any, **kwargs: Any)

Latent covariance loss class Latent covariance weight is annealed by AnnealingCallback

__init__(*args: Any, **kwargs: Any) → None
call()