145 lines
4.2 KiB
Python
145 lines
4.2 KiB
Python
from src.utils.clearml import ClearMLHelper
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#### ClearML ####
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clearml_helper = ClearMLHelper(project_name="Thesis/NAQR: Non-Linear")
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task = clearml_helper.get_task(
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task_name="NAQR: Non-Linear (8 - 512) + NRV + LOAD + PV + WIND + NP"
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)
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task.execute_remotely(queue_name="default", exit_process=True)
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from src.policies.PolicyEvaluator import PolicyEvaluator
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from src.policies.simple_baseline import BaselinePolicy, Battery
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from src.models.lstm_model import GRUModel
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from src.data import DataProcessor, DataConfig
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from src.trainers.quantile_trainer import (
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AutoRegressiveQuantileTrainer,
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NonAutoRegressiveQuantileRegression,
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)
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from src.trainers.trainer import Trainer
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from src.utils.clearml import ClearMLHelper
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from src.models import *
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from src.losses import *
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import torch
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from torch.nn import MSELoss, L1Loss
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import torch.nn as nn
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from src.models.time_embedding_layer import TimeEmbedding
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#### Data Processor ####
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data_config = DataConfig()
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data_config.NRV_HISTORY = True
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data_config.LOAD_HISTORY = True
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data_config.LOAD_FORECAST = True
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data_config.WIND_FORECAST = True
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data_config.WIND_HISTORY = True
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data_config.PV_FORECAST = True
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data_config.PV_HISTORY = True
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data_config.NOMINAL_NET_POSITION = True
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data_config = task.connect(data_config, name="data_features")
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data_processor = DataProcessor(data_config, path="", lstm=False)
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data_processor.set_batch_size(64)
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data_processor.set_full_day_skip(True)
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#### Hyperparameters ####
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data_processor.set_output_size(96)
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inputDim = data_processor.get_input_size()
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epochs = 300
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# add parameters to clearml
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quantiles = task.get_parameter("general/quantiles", cast=True)
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# make sure it is a list
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if quantiles is None:
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quantiles = [0.01, 0.05, 0.1, 0.15, 0.3, 0.4, 0.5, 0.6, 0.7, 0.85, 0.9, 0.95, 0.99]
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task.set_parameter("general/quantiles", quantiles)
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else:
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# if string, convert to list "[0.01, 0.05, 0.1, 0.15, 0.3, 0.4, 0.5, 0.6, 0.7, 0.85, 0.9, 0.95, 0.99]"
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if isinstance(quantiles, str):
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quantiles = eval(quantiles)
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model_parameters = {
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"learning_rate": 0.0001,
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"hidden_size": 512,
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"num_layers": 8,
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"dropout": 0.2,
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}
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model_parameters = task.connect(model_parameters, name="model_parameters")
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# linear_model = LinearRegression(inputDim, len(quantiles) * 96)
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non_linear_model = NonLinearRegression(
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inputDim,
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len(quantiles) * 96,
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hiddenSize=model_parameters["hidden_size"],
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numLayers=model_parameters["num_layers"],
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dropout=model_parameters["dropout"],
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)
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model = non_linear_model
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model.output_size = 96
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optimizer = torch.optim.Adam(model.parameters(), lr=model_parameters["learning_rate"])
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### Policy Evaluator ###
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battery = Battery(2, 1)
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baseline_policy = BaselinePolicy(battery, data_path="")
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policy_evaluator = PolicyEvaluator(baseline_policy, task)
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#### Trainer ####
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trainer = NonAutoRegressiveQuantileRegression(
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model,
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inputDim,
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optimizer,
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data_processor,
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quantiles,
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"cuda",
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policy_evaluator=None,
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debug=False,
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)
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trainer.add_metrics_to_track(
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[PinballLoss(quantiles), MSELoss(), L1Loss(), CRPSLoss(quantiles)]
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)
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trainer.early_stopping(patience=5)
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trainer.plot_every(20)
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trainer.train(task=task, epochs=epochs, remotely=True)
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### Policy Evaluation ###
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# idx_samples = trainer.test_set_samples
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# _, test_loader = trainer.data_processor.get_dataloaders(
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# predict_sequence_length=trainer.model.output_size, full_day_skip=False
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# )
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# policy_evaluator.evaluate_test_set(idx_samples, test_loader)
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# policy_evaluator.plot_profits_table()
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# policy_evaluator.plot_thresholds_per_day()
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# optimal_penalty, profit, charge_cycles = (
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# policy_evaluator.optimize_penalty_for_target_charge_cycles(
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# idx_samples=idx_samples,
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# test_loader=test_loader,
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# initial_penalty=1000,
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# target_charge_cycles=283,
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# learning_rate=15,
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# max_iterations=150,
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# tolerance=1,
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# )
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# )
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# print(
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# f"Optimal Penalty: {optimal_penalty}, Profit: {profit}, Charge Cycles: {charge_cycles}"
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# )
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# task.get_logger().report_single_value(name="Optimal Penalty", value=optimal_penalty)
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# task.get_logger().report_single_value(name="Optimal Profit", value=profit)
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# task.get_logger().report_single_value(name="Optimal Charge Cycles", value=charge_cycles)
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task.close()
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