Concrete-Autoencoders

To install, use pip install concrete-autoencoder

To see how to use concrete autoencoders, you can take a look at this colab notebook: https://colab.research.google.com/drive/11NMLrmToq4bo6WQ_4WX5G4uIjBHyrzXd

An implementation of the ideas in https://arxiv.org/abs/1901.09346.

Description:

Concrete Autoencoders is a kind of an autoencoder, which does feature selection when you train it.

Documentation:

class ConcreteAutoencoderFeatureSelector:

Constructor takes a number of parameters to initalize the class. They are:

K: the number of features one wants to select

output_function: the decoder function

num_epochs: number of epochs to start training concrete autoencoders

batch_size: the batch size during training

learning_rate: learning rate of the adam optimizer used during training

start_temp: the starting temperature of the concrete select layer

min_temp: the ending temperature of the concrete select layer

tryout_limit: number of times to double the number of epochs and try again in case it doesn't converge

fit(X, Y = None): trains the concrete autoencoder

X: the data for which you want to do feature selection

Y: labels, in case labels are given, it will do supervised feature selection, if not, then unsupervised feature selection

transform(X): filters X's features after fit has been called

X: the data to be filtered

fit_transform(X): calls fit and transform in a sequence

X: the data to do feature selection on and filter

get_support(indices = False): if indices is True, returns indices of the features selected, if not, returns a mask

indices: boolean flag to determine whether to return a list of indices or a boolean mask

get_params(): returns the underlying keras model for the concrete autoencoder