n_classes = 2
n_features = int(args.n_feat)
n_epochs = 100
pre.load_data(filename=f'training_data_{n_features}.csv', name='training_data')
X_df = pre.get(name='training_data').drop(columns=['target'])
y_df = pre.get(name='training_data')['target']
model = LogReg('log_reg', n_features, n_classes)
train_classifier = TrainClassifier(model, X_df, y_df)
trained_model, optimizer, criterion, loss_hist, loss_validate_hist = train_classifier.run_train(n_epochs = n_epochs)
pre.save_results(loss_hist, loss_validate_hist, f'log_reg_{100}')
m_exporter = ModelExporter('IMDB')
m_exporter.save_nn_model(trained_model, optimizer, n_features, n_classes, n_epochs)
##teeeeeest part
pre.load_data(filename=f'test_data_{n_features}.csv', name='test_data')
X_test_df = pre.get(name='test_data').drop(columns=['target'])
y_test_df = pre.get(name='test_data')['target']
dtype = torch.float
device = torch.device("cpu")
X_test = torch.tensor(X_test_df.values, device=device, dtype=dtype)
y_test = torch.tensor(y_test_df.values, device=device, dtype=torch.long)
y_pred = model(X_test).argmax(1)
args=parser.parse_args()
n_classes = 10
n_epochs = 200
pre = Preprocessing('digits')
pre.load_data(filename='train.csv', name='train')
X_df = pre.get(name='train').drop(columns=['0'])
y_df = pre.get(name='train')['0']
dtype = torch.float
device = torch.device("cpu")
model_name = 'logreg_digits'
model = LogReg(model_name, 256, n_classes)
learning_rate = 0.0001
batch_size = 32
train_classifier = TrainClassifier(model, X_df, y_df)
trained_model , optimizer, criterion, loss_hist, loss_val_hist, best_param = train_classifier.run_train(n_epochs = n_epochs, lr=learning_rate, batch_size=batch_size)
pre.save_results(loss_hist, loss_val_hist, f'{model_name}')
trained_model.load_state_dict(state_dict=best_param)
trained_model.eval()
if args.s_model:
m_exporter = ModelExporter('digits')
m_exporter.save_nn_model(trained_model, optimizer, 0, n_classes, n_epochs, trained_model.get_args())
X_train = model.reshape_data(
torch.tensor(X_train_df.values, device=device, dtype=dtype))
y_train = torch.tensor(y_train_df.values, device=device, dtype=torch.long)
X_pred = trained_model(X_test)
print(f'test accuracy last trained model {c(X_pred, X_test)}')
trained_model.load_state_dict(state_dict=best_model_param)
trained_model.eval()
X_pred = trained_model(X_test)
print(f'test accuracy best model {c(X_pred, X_test)}')
if args.s_model:
m_exporter = ModelExporter('digits')
m_exporter.save_nn_model(trained_model, opt, 0, n_features_encoded,
n_epochs, trained_model.get_args())
X_train_encoded = trained_model.encoder(X_train)
X_test_encoded = trained_model.encoder(X_test)
X_test_decoded = trained_model.decoder(X_test_encoded)
X_train_encoded_df = pd.DataFrame(X_train_encoded.detach().numpy())
X_test_encoded_df = pd.DataFrame(X_test_encoded.detach().numpy())
cols = list(range(1, n_features_encoded + 1))
X_train_encoded_df.columns = cols
X_test_encoded_df.columns = cols