h0rton.train_utils.logging_utils

Module Contents

Functions

get_logdet(tril_elements, Y_dim)	Returns the log determinant of the covariance matrix
get_1d_mapping_fig(name, mu, Y)	Plots the marginal 1D mapping of the mean predictions
get_mae(pred_mu, true_mu, Y_cols)	Get the total RMSE of predicted mu of the primary Gaussian wrt the transformed labels mu in a batch of validation data
interpret_pred(pred, Y_dim)	Slice the network prediction into means and cov matrix elements

h0rton.train_utils.logging_utils.get_logdet(tril_elements, Y_dim)

Returns the log determinant of the covariance matrix

h0rton.train_utils.logging_utils.get_1d_mapping_fig(name, mu, Y)

Plots the marginal 1D mapping of the mean predictions

name : str

name of the parameter

mu : np.array of shape [batch_size,]

network prediction of the Gaussian mean

Y : np.array of shape [batch_size,]

truth label

which_normal_i : int

which Gaussian (0 for first, 1 for second)

matplotlib.FigureCanvas object

plot of network predictions against truth

h0rton.train_utils.logging_utils.get_mae(pred_mu, true_mu, Y_cols)

Get the total RMSE of predicted mu of the primary Gaussian wrt the transformed labels mu in a batch of validation data

pred_mu : np.array of shape [batch_size, Y_dim]

predicted means of the primary Gaussian

true_mu : np.array of shape [batch_size, Y_dim]

true (label) Gaussian means

Y_cols : np.array of shape [Y_dim,]

the column names

dict

total mean of the RMSE for that batch

h0rton.train_utils.logging_utils.interpret_pred(pred, Y_dim)

Slice the network prediction into means and cov matrix elements

pred : np.array of shape [batch_size, out_dim] Y_dim : int

number of parameters to predict

Currently hardcoded for DoubleGaussianNLL. (Update: no longer used; slicing function replaced by the BNNPosterior class.)

dict

pred sliced into parameters of the Gaussians to predict