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main.py
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from argparse import ArgumentParser
import pytorch_lightning as pl
from torch.utils.data import DataLoader
import os
import logging
import src.utils as utils
import src.plots as plots
from src.training_and_evaluation.loss import MSELoss, TotalCostLoss
from src.plants.plants import MicroGridSystem
from src.models.controller import Controller, OptimisationLayer
from src.dataloader.csv_dataset import CSVDataset
from src.training_and_evaluation.learner import OnlineLearner
from syne_tune import Reporter
import logging
logging = logging.getLogger('pytorch_lightning')
def main(args: ArgumentParser) -> None:
# Set seed
pl.seed_everything(args.seed)
# Create experiment structure
if args.experiment_name is None:
experiment_name = utils.get_current_time()
else:
experiment_name = args.experiment_name
experiment_path = os.path.join(args.save_dir, experiment_name)
experiment_path = utils.create_experiment_folder(experiment_path, "./src")
# Set the logger
utils.config_logger(experiment_path)
logging.info("Beginning experiment: %s", experiment_name)
logging.info("Arguments: %s", args)
# Save the arguments
utils.save_pickle(args, utils.args_file(experiment_path))
# Load data
dataset = CSVDataset(site_id=args.site_id,
lookback_window=args.lookback_window,
prediction_horizon=args.prediction_horizon,
train_batchsize=args.train_batchsize,
valid_batchsize=args.valid_batchsize,
input_features=args.input_features,
output_features=args.output_features)
if args.load_dir is not None:
logging.info(
"Loading dataset scaling constants from %s", args.load_dir)
utils.load_dataset(dataset, utils.dataset_file(args.load_dir))
dataloader = DataLoader(dataset, batch_size=1, shuffle=False, num_workers=1)
# Create the plant model
microgrid = MicroGridSystem(site_id=args.site_id,
sample_time=args.sample_time,
self_discharge=args.self_discharge,
initial_state=args.initial_state,
grid_power_min_max=args.grid_power_min_max,)
# Create the NN and MPC optimiser
optimisation_layer = OptimisationLayer(prediction_horizon=args.prediction_horizon,
A_matrix=microgrid.get_model_matrices()[0],
B_matrix=microgrid.get_model_matrices()[1],
charge_min_max=microgrid.charge_min_max,
storage_power_min_max=microgrid.storage_power_min_max,
grid_power_min_max=microgrid.grid_power_min_max)
# Controller definition
controller = Controller(optimisation_layer=optimisation_layer,
dataset=dataset,
hidden_dim=args.hidden_dim,
num_layers=args.num_layers)
if args.load_dir is not None:
logging.info("Loading model from %s", args.load_dir)
controller = utils.load_model(
controller, utils.model_file(args.load_dir))
# Create loss
regression_loss = MSELoss()
total_cost_loss = TotalCostLoss()
# Create trainer
tb_logger = pl.loggers.TensorBoardLogger(
save_dir=experiment_path, name="logs") if args.st_checkpoint_dir is None else None
trainer = pl.Trainer(gpus=args.gpu, max_epochs=1, enable_checkpointing=False,
accelerator="auto", logger=tb_logger,
enable_progress_bar=True if args.st_checkpoint_dir is None else False)
# Create Learner
learner = OnlineLearner(plant=microgrid,
controller=controller,
regression_loss=regression_loss,
total_cost_loss=total_cost_loss,
dataset=dataset,
learning_rate=args.learning_rate,
weight_decay_start_weight=args.weight_decay_start_weight,
weight_decay_end_weight=args.weight_decay_start_weight,
weight_decay_start_time_step=args.weight_decay_start_time_step,
weight_decay_end_time_step=args.weight_decay_end_time_step,
swa_gamma=args.swa_gamma,
swa_start_time_step=args.swa_start_time_step,
swa_end_time_step=args.swa_end_time_step,
swa_replace_frequency=args.swa_replace_frequency,
mse_start_weight=args.mse_start_weight,
mse_exp_decay=args.mse_exp_decay,
mse_end_weight=args.mse_end_weight,
mse_start_time_step=args.mse_start_time_step,
mse_end_time_step=args.mse_end_time_step,
task_start_weight=args.task_start_weight,
task_end_weight=args.task_end_weight,
task_start_time_step=args.task_start_time_step,
task_end_time_step=args.task_end_time_step,
task_window=args.task_window)
# Fit the model
trainer.fit(learner, train_dataloaders=dataloader)
# Plot results
plots.plot_trajectory(experiment_path=experiment_path,
state=learner.state, constraints_state=microgrid.charge_min_max,
controller_outputs=learner.controller_outputs,
constraints_input=[microgrid.storage_power_min_max, microgrid.storage_power_min_max, []],
t_initial=0, t_final=learner.state.shape[0])
# Create empty results dictionary
results = {}
if args.plot:
data_splits = ['train', 'val', 'test']
else:
data_splits = ['test']
for data_split in data_splits:
results[data_split] = {}
plots.plot_predictions(experiment_path=experiment_path,
dataset=dataset,
predictions=learner.predictions[data_split],
targets=learner.targets[data_split],
output_features=args.output_features,
t_initial=0, t_final=learner.predictions[data_split].shape[0],
data_split=data_split)
plots.plot_decision_variables(experiment_path=experiment_path,
decision_variables=learner.decisions[data_split],
constraints={
'Pl': [dataset.get_profile()[:, 1]],
'Pr': [dataset.get_profile()[:, 0]],
'Ps_in': microgrid.storage_power_min_max,
'Ps_out': microgrid.storage_power_min_max,
'Pg': microgrid.grid_power_min_max,
'eps': [1.0, -1.0]},
data_split=data_split)
plots.plot_and_log_total_cost(experiment_path=experiment_path,
price=dataset.get_profile()[:, 2],
Pg=learner.decisions[data_split]["Pg"],
results=results[data_split],
data_split=data_split,
prediction_horizon=args.prediction_horizon)
# Store results
results["Total cost [eur]"] = plots.total_cost(price=dataset.get_profile()[:, 2],
Pg=learner.controller_outputs[:, 2])
utils.store_metrics(
results, metrics=learner.metrics["train"], prefix="train")
utils.store_metrics(results, metrics=learner.metrics["val"], prefix="val")
utils.store_metrics(
results, metrics=learner.metrics["test"], prefix="test")
logging.info("Results: %s", results)
# Save model
utils.save_model(controller, utils.model_file(experiment_path))
# Save results
utils.save_pickle(results, utils.results_file(experiment_path))
# Save learner
utils.save_learner(learner, utils.learner_file(experiment_path))
utils.save_learner_as_csv(learner, experiment_path)
# Save dataset
utils.save_dataset(dataset, utils.dataset_file(experiment_path))
if __name__ == '__main__':
parser = ArgumentParser()
parser.add_argument_group("Data")
parser.add_argument('--data_dir', type=str, default='./data',
help='The directory where the data is stored.')
parser.add_argument('--num_workers', type=int, default=4,
help='The number of workers to be used for loading the data.')
parser.add_argument('--st_checkpoint_dir', type=str, default=None,
help='Directory where syne-tune checkpoints are stored.')
parser.add_argument_group("Microgrid Data")
parser = CSVDataset.add_specific_args(parser)
parser.add_argument_group("Network")
parser = Controller.add_specific_args(parser)
parser.add_argument_group("Plant")
parser = MicroGridSystem.add_specific_args(parser)
parser.add_argument_group("Experiment")
parser.add_argument('--seed', type=int, default=42,
help='The seed to be used for training.')
parser.add_argument('--gpu', type=int, default=0,
help='The gpu to be used for training.')
parser.add_argument('--save_dir', type=str, default='experiments',
help='The directory where the experiment results are stored.')
parser.add_argument('--load_dir', type=str, default=None,
help='The directory where the model is loaded from. Default: None')
parser.add_argument('--plot', type=int, choices=[0, 1], default=1,
help='Enable the plot of the train and validation results.')
parser.add_argument('--experiment_name', type=str, default=None,
help='Name of the experiment directory.')
parser = OnlineLearner.add_specific_args(parser)
args = parser.parse_args()
main(args)