def perform_SVR(self): print( 'SVRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRR' ) model_trainer = ModelTrainer() svr = SVR(gamma='poly', C=1e3, epsilon=0.2) Y_test, Y_pred, y_true_glucose, y_pred_glucose = model_trainer.train_model( svr, self.X_train, self.X_test, self.Y_train, self.Y_test)
def perform_ridge_regression(self): print( '*********************************************RIDGE REGRESSION**************************************************' ) model_trainer = ModelTrainer() ridge = Ridge(alpha=1.0) Y_test, Y_pred, y_true_glucose, y_pred_glucose = model_trainer.train_model( ridge, self.X_train, self.X_test, self.Y_train, self.Y_test) evl = MetricsCalculator() evl.evaluate('root mean square error for ridge regression', y_true_glucose, y_pred_glucose) viz = Visualizer() viz.visualize('ridge regression', y_true_glucose, y_pred_glucose)
def perform_linear_regression(self): print( '------------------------------------------LINEAR REGRESSION------------------------------------------' ) model_trainer = ModelTrainer() linear_reg = LinearRegression() Y_test, Y_pred, y_true_glucose, y_pred_glucose = model_trainer.train_model( linear_reg, self.X_train, self.X_test, self.Y_train, self.Y_test) evl = MetricsCalculator() evl.evaluate('root mean square error for linear regression', y_true_glucose, y_pred_glucose) viz = Visualizer() viz.visualize('linear regression', y_true_glucose, y_pred_glucose)
def perform_lasso_regression(self): print( '................................... LASSO REGRESSION ............................................' ) model_trainer = ModelTrainer() lasso = Lasso() Y_test, Y_pred, y_true_glucose, y_pred_glucose = model_trainer.train_model( lasso, self.X_train, self.X_test, self.Y_train, self.Y_test) evl = MetricsCalculator() evl.evaluate('root mean square error for lasso regression', y_true_glucose, y_pred_glucose) viz = Visualizer() viz.visualize('lasso regression', y_true_glucose, y_pred_glucose)
def perform_PLS(self): print( ',,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, PARTIAL LEAST SQUARE ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,' ) model_trainer = ModelTrainer() pls = PLSRegression(n_components=20, scale=True, max_iter=5000, tol=1e-06, copy=True) Y_test, Y_pred, y_true_glucose, y_pred_glucose = model_trainer.train_model( pls, self.X_train, self.X_test, self.Y_train, self.Y_test) evl = MetricsCalculator() evl.evaluate('root mean square error for partial least square', y_true_glucose, y_pred_glucose) viz = Visualizer() viz.visualize('pls', y_true_glucose, y_pred_glucose)
def perform_NN(self): print( '/////////////////////////////////////////////////// NEURAL NETWORK ///////////////////////////////////' ) model_trainer = ModelTrainer() nn = MLPRegressor(hidden_layer_sizes=(200, ), activation='relu', solver='adam', alpha=0.1, batch_size='auto', learning_rate='constant', learning_rate_init=0.001, power_t=0.5, max_iter=3000, shuffle=True, random_state=None, tol=0.0001, verbose=False, warm_start=False, momentum=0.9, nesterovs_momentum=True, early_stopping=False, validation_fraction=0.1, beta_1=0.9, beta_2=0.999, epsilon=1e-08, n_iter_no_change=10) Y_test, Y_pred, y_true_glucose, y_pred_glucose = model_trainer.train_model( nn, self.X_train, self.X_test, self.Y_train, self.Y_test) evl = MetricsCalculator() evl.evaluate('root mean square error for Neural network', y_true_glucose, y_pred_glucose) viz = Visualizer() viz.visualize('neural network', y_true_glucose, y_pred_glucose)
parser = argparse.ArgumentParser() parser.add_argument("--config_path", default='../config.ini', required=False) args = parser.parse_args() cfg = OCTConfig(args.config_path) oct_logger = OCTLogger(cfg, RUN_TIMESTAMP) oct_logger.print_cfg() generator_resolver = GeneratorResolver(cfg) training_data_iterator, test_data_iterator, val_data_iterator = generator_resolver.resolve_data_iterators( ) model_resolver = ModelResolver(cfg) model = model_resolver.resolve_model() augmented_image_data_generator = generator_resolver.provide_augmented_image_data_generator( ) augmentation_processor = AugmentationProcessor( cfg, augmented_image_data_generator) augmentation_processor.perform_data_augmentation() model_trainer = ModelTrainer(cfg, model, training_data_iterator, val_data_iterator, RUN_TIMESTAMP) model_trainer.train_model() model_evaluator = ModelEvaluator(cfg, model, test_data_iterator) model_evaluator.evaluate_model()