import torch
torch.manual_seed(1)
from model import Trainer
if __name__ == '__main__':
FEATURES = 4
WIDTH = 4
DEVICE = torch.device('cpu')
dataset_inputs = torch.randn((2 ** 20, 16, FEATURES)) * WIDTH
dataset_outputs = dataset_inputs.cos().mean(-1) * dataset_inputs.square().mean(-1).sqrt()
model = Trainer(FEATURES, (dataset_inputs, dataset_outputs), DEVICE, max_loss=(WIDTH*FEATURES)**2)
model.fit(100)
print("*********************************")
dataset = Dataset(name=hyperparams_dict["dataset"],
horizon=hyperparams_dict["horizon"],
history_length=hyperparams_dict["history_length"],
path=DATADIR)
hyperparams_dict["num_nodes"] = dataset.num_nodes
hyperparams = Parameters(**hyperparams_dict)
print("*********************************")
print("TRAINING MODELS")
print("*********************************")
trainer = Trainer(hyperparams=hyperparams, logdir=LOGDIR)
trainer.fit(dataset=dataset)
print("*********************************")
print("COMPUTING METRICS")
print("*********************************")
early_stop_mae_h_repeats = dict()
early_stop_mape_h_repeats = dict()
early_stop_rmse_h_repeats = dict()
early_stop_mae_h_ave = dict()
early_stop_mape_h_ave = dict()
early_stop_rmse_h_ave = dict()
for i, h in enumerate(trainer.history):
early_stop_idx = np.argmin(h['mae_val'])
early_stop_mae = np.round(h['mae_test'][early_stop_idx], decimals=3)
print(f"Early stop test error model {trainer.folder_names[i]}:", "Avg MAE",
log_dir=log_dir,
num_resblock=args.num_resblock)
trainer.summary()
try:
if weights_path is not None:
print('loading weights')
trainer.load_checkpoint(weights_path)
else:
print('no weights for initalization are available')
except Exception as e:
print(e)
if args.train_generator:
trainer.fit(train_dataset=train_ds,
valid_dataset=valid_ds,
epochs=args.epochs,
valid_lr=valid_lr,
valid_hr=valid_hr)
print('training finished, saving model now')
trainer.save_model('_only_generator')
if args.train_gan:
trainer.train_gan(train_dataset=train_ds,
valid_dataset=valid_ds,
epochs=args.epochs,
valid_lr=valid_lr,
valid_hr=valid_hr)
print('training finished, saving model now')
trainer.save_model()
dataset, target = readTrainCSV(filecsv, fea_sel=0)
# Split
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(
dataset, target, test_size=0.2, random_state=0)
X_train = np.asarray(X_train).astype(np.float)
X_test = np.asarray(X_test).astype(np.float)
y_train = np.asarray(y_train).astype(np.float)
y_test = np.asarray(y_test).astype(np.float)
model_name = 'SVM'
classifer = Trainer(model_name, kernel='rbf', C=1, gamma='scale')
# classifer.load_model('kNN_classifier.txt')
# parameters = {'kernel':('linear', 'rbf'), 'C':[1,2]}
# classifer.gridsearchCV(parameters)
classifer.fit(X_train, y_train)
classifer.predict(X_test)
classifer.report(y_test)
print(classifer.model)
classifer.save_model('SVM_classifier.sav')