import os import sys sys.path.append("..") # add top folder to path import numpy as np import pandas as pd import matplotlib.pyplot as plt import torch import impepdom import pickle hla_allele = 'HLA-A01:01' fold_idx = [0, 1, 2] hla_a01_01 = impepdom.PeptideDataset(hla_allele, padding='after2', toy=True) peploader = hla_a01_01.get_peptide_dataloader(fold_idx) mlp = impepdom.MultilayerPerceptron() folder = impepdom.train_nn(mlp, peploader, hla_allele, fold_idx, save_results=True) predictions = mlp(torch.tensor(hla_a01_01.get_fold([3])[0]).float()) print(folder) pred_path = os.path.join(folder, 'pred-3') outfile = open(pred_path, 'wb') pickle.dump(predictions, outfile) outfile.close()
'HLA-B07:02', # >>> Khoi 2 <<< 'HLA-A24:02', 'HLA-B27:05', # >>> Michael 'HLA-A68:01' # <<< Michael ] hla_alleles_test = ['HLA-A01:01'] impepdom.time_tracker.reset_timer() # start counting time for i, hla_allele in enumerate(hla_alleles_test): # change allele list here print(impepdom.time_tracker.now() + 'working with allele {0} out of {1}'.format( i + 1, len(hla_alleles_test))) # change allele list here dataset = impepdom.PeptideDataset(hla_allele=hla_allele, padding='flurry', toy=False) best_config = impepdom.hyperparam_search( model_type='MultilayerPerceptron', dataset=dataset, max_epochs=15, batch_sizes=[32], learning_rates=[1e-3], dropout_input_list=[0.75, 0.65], dropout_hidden_list=[0.50, 0.45], conv_flags=[False], num_conv_layers_list=[2], conv_filt_sz_list=[5], conv_stride_list=[1], )
import os import sys sys.path.append("..") # add top folder to path import numpy as np import pandas as pd import matplotlib.pyplot as plt import pickle from sklearn.metrics import roc_auc_score import torch import impepdom model = impepdom.models.MultilayerPerceptron(num_hidden_layers=2, hidden_layer_size=100) dataset = impepdom.PeptideDataset(hla_allele='HLA-A01:01', padding='flurry', toy=True) folder, baseline_metrics, _ = impepdom.run_experiment(model, dataset, train_fold_idx=[1, 2, 3], val_fold_idx=[0], learning_rate=2e-3, num_epochs=5, batch_size=32) trained_model, train_history = impepdom.load_trained_model(model, folder) impepdom.plot_train_history(train_history, baseline_metrics)