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