| ''' |
| Trains a DeepSequence VAE model. |
| Based on open source code from DeepSequence repo. |
| ''' |
|
|
| import argparse |
| import numpy as np |
| import os |
| import sys |
| import time |
|
|
| WORKING_DIR="/share/vault/Users/gz2294/combining-evolutionary-and-assay-labelled-data/DeepSequence/" |
| module_path = os.path.abspath(WORKING_DIR) |
| |
| |
| sys.path.insert(0, "./DeepSequence/DeepSequence/") |
| from model import VariationalAutoencoder |
| import helper |
| import train |
|
|
| model_params = { |
| "bs" : 100, |
| "encode_dim_zero" : 1500, |
| "encode_dim_one" : 1500, |
| "decode_dim_zero" : 100, |
| "decode_dim_one" : 500, |
| "n_latent" : 30, |
| "logit_p" : 0.001, |
| "sparsity" : "logit", |
| "final_decode_nonlin": "sigmoid", |
| "final_pwm_scale" : True, |
| "n_pat" : 4, |
| "conv_pat" : True, |
| "d_c_size" : 40 |
| } |
|
|
| train_params = { |
| "num_updates" : 300000, |
| "save_progress" : True, |
| "verbose" : True, |
| "save_parameters" : False, |
| } |
|
|
|
|
| def main(): |
| parser = argparse.ArgumentParser() |
| parser.add_argument('alignment_file', type=str) |
| parser.add_argument('job_string', type=str) |
| parser.add_argument('seed', type=int) |
| args = parser.parse_args() |
| data_helper = helper.DataHelper( |
| working_dir=WORKING_DIR, |
| alignment_file=args.alignment_file, |
| calc_weights=True |
| ) |
|
|
| vae_model = VariationalAutoencoder(data_helper, |
| batch_size = model_params["bs"], |
| encoder_architecture = [model_params["encode_dim_zero"], |
| model_params["encode_dim_one"]], |
| decoder_architecture = [model_params["decode_dim_zero"], |
| model_params["decode_dim_one"]], |
| n_latent = model_params["n_latent"], |
| logit_p = model_params["logit_p"], |
| sparsity = model_params["sparsity"], |
| encode_nonlinearity_type = "relu", |
| decode_nonlinearity_type = "relu", |
| final_decode_nonlinearity = model_params["final_decode_nonlin"], |
| final_pwm_scale = model_params["final_pwm_scale"], |
| conv_decoder_size = model_params["d_c_size"], |
| convolve_patterns = model_params["conv_pat"], |
| n_patterns = model_params["n_pat"], |
| random_seed = args.seed, |
| working_dir = WORKING_DIR, |
| ) |
|
|
| data_params = {'alignment_file': args.alignment_file} |
| job_string = args.job_string |
|
|
| train.train(data_helper, vae_model, |
| num_updates = train_params["num_updates"], |
| save_progress = train_params["save_progress"], |
| save_parameters = train_params["save_parameters"], |
| verbose = train_params["verbose"], |
| job_string = job_string) |
|
|
| vae_model.save_parameters(file_prefix=job_string) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
