92 lines
2.7 KiB
Python
92 lines
2.7 KiB
Python
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 AutoRegressiveQuantileTrainer
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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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#### ClearML ####
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clearml_helper = ClearMLHelper(project_name="Thesis/NrvForecast")
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task = clearml_helper.get_task(task_name="Autoregressive Quantile Regression: GRU + Quarter + DoW + Load + Wind + Net")
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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.QUARTER = True
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data_config.DAY_OF_WEEK = 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=True)
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data_processor.set_batch_size(512)
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data_processor.set_full_day_skip(False)
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#### Hyperparameters ####
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data_processor.set_output_size(1)
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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": 2,
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"dropout": 0.2,
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"time_feature_embedding": 4,
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}
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model_parameters = task.connect(model_parameters, name="model_parameters")
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time_embedding = TimeEmbedding(data_processor.get_time_feature_size(), model_parameters["time_feature_embedding"])
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lstm_model = GRUModel(time_embedding.output_dim(inputDim), len(quantiles), hidden_size=model_parameters["hidden_size"], num_layers=model_parameters["num_layers"], dropout=model_parameters["dropout"])
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model = nn.Sequential(time_embedding, lstm_model)
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optimizer = torch.optim.Adam(model.parameters(), lr=model_parameters["learning_rate"])
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#### Trainer ####
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trainer = AutoRegressiveQuantileTrainer(
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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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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=30)
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trainer.plot_every(5)
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trainer.train(task=task, epochs=epochs, remotely=True)
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