latentmi.lmi

Classes

EarlyStopper

Early stopping that returns best weights

Functions

train(model, X_train, Y_train, X_test, Y_test[, ...])	training loop for LMI models
learn_representation(Xs, Ys, train_indices, test_indices)	train paired AE model and embed data
estimate(Xs, Ys[, regularizer, alpha, lam, N_dims, ...])	return pMIs, with NaNs for points not included in KSG estimate

Module Contents

class latentmi.lmi.EarlyStopper(patience=1)

Early stopping that returns best weights trying to replicate the Keras callback

early_stop(validation_loss, model)

latentmi.lmi.train(model, X_train, Y_train, X_test, Y_test, batch_size=512, lr=0.0001, epochs=300, patience=30, quiet=True)

training loop for LMI models

Parameters:

• model – LMI model

• X_train – train samples, shape (N_samples, N_features)

• Y_train – train samples, shape (N_samples, N_features)

• X_test – test samples, shape (N_samples, N_features)

• Y_test – test samples, shape (N_samples, N_features)

• batch_size – samples per batch, defaults to 512

• lr – learning rate for Adam optimizer, defaults to 1e-4

• epochs – max number of epochs, defaults to 300

• patience – epochs without val. loss decline before early stopping, defaults to 300

• quiet – suppress training progress display, defaults to True

latentmi.lmi.learn_representation(Xs, Ys, train_indices, test_indices, regularizer='models.AECross', alpha=1, lam=1, N_dims=8, batch_size=512, lr=0.0001, epochs=300, validation_split=0.3, patience=30, quiet=True, device='cpu')

train paired AE model and embed data

Parameters:

• Xs

• Ys

• train_indices

• test_indices

• regularizer

• alpha

• lam

• N_dims

• batch_size

• lr

• epochs

• validation_split

• patience

• quiet

latentmi.lmi.estimate(Xs, Ys, regularizer='models.AECross', alpha=1, lam=1, N_dims=8, validation_split=0.5, estimate_on_val=True, batch_size=512, lr=0.0001, epochs=300, patience=30, quiet=True, device=None)

return pMIs, with NaNs for points not included in KSG estimate

Parameters:

• Xs

• Ys

• regularizer

• alpha

• lam

• N_dims

• batch_size

• lr

• epochs

• validation_split

• patience

• quiet