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Run_svi tweaks #10

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50 changes: 43 additions & 7 deletions examples/run_svi.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,25 +10,40 @@
parser = argparse.ArgumentParser(description="Train DeepSequence with SVI.")
parser.add_argument("--dataset", type=str, default="BLAT_ECOLX",
help="Dataset name for fitting model.")
parser.add_argument("--neff-override", type=float, default=None,
help="Override the model Neff.")
parser.add_argument("--theta-override", type=float, default=None,
help="Override the model theta.")
parser.add_argument("--n-latent-override", type=int, default=None,
help="Override the model n_latent.")
parser.add_argument("--weights_dir", type=str, default="", help="Location of precomputed weights, if possible")
parser.add_argument("--alignments_dir", type=str, help="Location of alignments")
parser.add_argument("--seed", type=int, help="Random seed override (for model ensembling).")
args = parser.parse_args()

args.dataset = args.dataset.split(".a2m")[0]

data_params = {
"dataset" : args.dataset,
"dataset" : args.dataset,
"weights_dir" : args.weights_dir,
}

if args.seed is not None:
print("Using seed: {}".format(args.seed))

model_params = {
"bs" : 100,
"encode_dim_zero" : 1500,
"encode_dim_one" : 1500,
"decode_dim_zero" : 100,
"decode_dim_one" : 500,
"decode_dim_one" : 2000, # 500 in the repo
"n_latent" : 30,
"logit_p" : 0.001,
"sparsity" : "logit",
"final_decode_nonlin": "sigmoid",
"final_pwm_scale" : True,
"n_pat" : 4,
"r_seed" : 12345,
"r_seed" : args.seed if args.seed is not None else 12345,
"conv_pat" : True,
"d_c_size" : 40
}
Expand All @@ -37,13 +52,24 @@
"num_updates" : 300000,
"save_progress" : True,
"verbose" : True,
"save_parameters" : False,
"save_parameters" : 50000,
}

if __name__ == "__main__":
if args.n_latent_override:
model_params['n_latent'] = args.n_latent_override

if __name__ == "__main__":
start_time = time.time()
data_helper = helper.DataHelper(dataset=data_params["dataset"],
calc_weights=True)
working_dir='.',
calc_weights=False, # Use precomputed weights
theta=args.theta_override,
weights_dir=data_params["weights_dir"],
alignments_dir=args.alignments_dir,
)
print("Data loaded.")
if args.neff_override:
data_helper.Neff = args.neff_override

vae_model = model.VariationalAutoencoder(data_helper,
batch_size = model_params["bs"],
Expand All @@ -63,16 +89,26 @@
n_patterns = model_params["n_pat"],
random_seed = model_params["r_seed"],
)
print("Model loaded")

job_string = helper.gen_job_string(data_params, model_params)
if args.neff_override:
job_string += "_neff-" + str(args.neff_override)
if args.seed is not None:
job_string += "_seed-" + str(args.seed)

print ("job string: ", job_string)

print (job_string)
print("Starting training")

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)
print("Training complete")

vae_model.save_parameters(file_prefix=job_string)

print("Done in " + str(time.time() - start_time) + " seconds")