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()
