DietNetworks: Thin parameters for Fat Genomics

This repo contains the code to reproduce the experiments of the paper DietNetworks: Thin parameters for fat genomics.

Before you start

• Download and install Theano and Lasagne.
• Download the 1000 Genomes dataset as described in appendix B of the paper.

Generate the pre-computed embeddings if necessary

cd experiments/common python utils_helpers.py

By default, this generates the histogram per class embeddings. You will need to change the path argument.

Run Experiments

Diet Networks with per class histograms (fold 0)

cd experiments/variant2

Experiments without reconstruction loss:

THEANO_FLAGS='device=gpu' python learn_model.py --which_fold=0 -eni=0.02 -dni=0.02 -ne=3000 --n_hidden_t_enc=[100,100] --n_hidden_t_dec=[100,100] --n_hidden_s=[100] --n_hidden_u=[100] --gamma=0 --learning_rate=0.00003 -lra=.999 --patience=500 --optimizer=adam -bn=1 --embedding_source=histo3x26 -exp_name=dietnet_histo_ -rp=0

Experiments with reconstruction loss:

THEANO_FLAGS='device=gpu' python learn_model.py --which_fold=0 -eni=0.02 -dni=0.02 -ne=3000 --n_hidden_t_enc=[100,100] --n_hidden_t_dec=[100,100] --n_hidden_s=[100] --n_hidden_u=[100] --gamma=20 --learning_rate=0.00003 -lra=.999 --patience=500 --optimizer=adam -bn=1 --embedding_source=histo3x26 -exp_name=dietnet_histo_ -rp=0

Parameters in experiments/variant2/learn_model.py

• dataset: Str. Dataset name. (default: '1000_genomes').
• n_hidden_u: List. Considering the Emb. of Fig. 1 (b)/(c) an MLP -> number of hidden units of each layer of the MLP. The parameters of the embedding are shared among auxiliary networks in Fig. 1 (b) and (c). Set to [100] for DietNetworks experiments, ignored when a particuler embedding is required.
• n_hidden_t_enc/dec: List. Number of hidden units of the MLP depicted in Fig. 1 (b)/(c). Both MLP have the same structure (number of layers, number of hidden units per layer) but do not share parameters. Set to [100, 100] for DietNetworks experiments.
• n_hidden_s: List. Number of hidden units of the second MLP in Fig. 1 (a). Set to [100] for DietNetworks experiments.
• embedding_source: Str. The kind of embedding to be used in the auxiliary networks (histo3x26 for per class histogram, bin for snp2vec, raw for no pre-computed embedding)
• num_epochs: Int. Maximum number of epochs. Set to 3000 for DietNetworks experiments.
• learning_rate: Float. Learning rate. (default: 0.0001)
• learning_rate_annealing: Float. Learning rate annealing. (default: 0.99)
• alpha: Float. Set to 0 for all experiments.
• beta: Float. Set to 0 for all experiments.
• gamma: Float. Set to 0 if no reconstruction loss.
• lmd: Float. Weight decay. Set to 0 for all experiments.
• disc_nonlinearity: Str. Nonlinearity to be used at the top layer of Fig. 1 (a). (default: softmax)
• encoder_net_init/decoder_net_init: Float. Initialization values. (default 0.01)
• keep_labels: Float. Set to 1.0 for all experiments.
• prec_recall_cutoff. Int. Set to 0 for all experiments.
• which_fold: Int. Which fold to use.
• early_stop_criterion: Str. Set to accuracy for all experiments.
• save_tmp: Str. Where to temporarily save the training curves and models (used during training, can be the same as save_perm).
• save_perm: Str. Where to save the final results (used at the end of the training, can be the same a save_tmp).
• dataset_path: Str. Path to dataset.
• resume: Bool. In case we need to resume the training.
• exp_name: Str. If we want a particular experiment name to be concatenated at the beginning of the generated name. (default: '')
• random_proj: Int. Whether we want to use random projections as embedding. (default: 0)