Function main

tasks/AutoEAP/code/experiment.py:159–191

(config, indir, out_dir, label)

    return str("{0:0.2f}".format(stats.pearsonr(Y, pred[i].squeeze())[0]))
 
def main(config, indir, out_dir, label):
    data = pd.read_table(indir)
    params = LoadConfig(config)

    X_train, Y_train = prepare_input(data[data['set'] == "Train"], params)
    X_valid, Y_valid = prepare_input(data[data['set'] == "Val"], params)
    X_test, Y_test = prepare_input(data[data['set'] == "Test"], params)

    DeepSTARR(params)[0].summary() 
    DeepSTARR(params)[1] 
    main_model, main_params = DeepSTARR(params)
    main_model, my_history = train(main_model, X_train, Y_train, X_valid, Y_valid, main_params)

    endTime=time.time()
    seconds=endTime-startTime
    print("Total training time:",round(seconds/60,2),"minutes")

    dev_results = summary_statistics(X_test, Y_test[0], "test", "Dev", main_model, main_params, out_dir)
    hk_results = summary_statistics(X_test, Y_test[1], "test", "Hk", main_model, main_params, out_dir)

    result = {
        "AutoEAP": {
            "means": {
                "PCC(Dev)": dev_results,
                "PCC(Hk)": hk_results
            }
        }
    }
    
    with open(f"{out_dir}/final_info.json", "w") as file:
        json.dump(result, file, indent=4)

    main_model.save(out_dir + '/' + label + '.h5')

if __name__ == "__main__":
    try: