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Fixed small summary with model architectures until now

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Victor Mylle
2023-11-30 21:53:35 +00:00
parent eba10c8f83
commit 120b6aa5bd
23 changed files with 402 additions and 185 deletions
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45
src/training_scripts/autoregressive_quantiles.py
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@@ -17,19 +17,18 @@ from src.models.time_embedding_layer import TimeEmbedding
#### ClearML ####
clearml_helper = ClearMLHelper(project_name="Thesis/NrvForecast")
task = clearml_helper.get_task(task_name="Autoregressive Quantile Regression")
task = clearml_helper.get_task(task_name="Autoregressive Quantile Regression: GRU + Quarter + Load Forecast")
#### Data Processor ####
data_config = DataConfig()
data_config.NRV_HISTORY = True
data_config.LOAD_HISTORY = True
data_config.LOAD_FORECAST = True
data_config.QUARTER = True
data_config.DAY_OF_WEEK = True
data_config.DAY_OF_WEEK = False
# data_config = task.connect(data_config, name="data_features")
data_config = task.connect(data_config, name="data_features")
data_processor = DataProcessor(data_config, path="", lstm=True)
data_processor.set_batch_size(512)
@@ -39,37 +38,49 @@ data_processor.set_full_day_skip(False)
#### Hyperparameters ####
data_processor.set_output_size(1)
inputDim = data_processor.get_input_size()
learningRate = 0.001
epochs = 100
print("Input dim: ", inputDim)
epochs = 400
# add parameters to clearml
quantiles = task.get_parameter("general/quantiles", cast=True)
# make sure it is a list
if quantiles is None:
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]
task.set_parameter("general/quantiles", quantiles)
else:
# 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]""
if isinstance(quantiles, str):
quantiles = eval(quantiles)
model_parameters = {
"learning_rate": 0.0001,
"hidden_size": 512,
"num_layers": 2,
"dropout": 0.2,
"time_feature_embedding": 4,
}
model_parameters = task.connect(model_parameters, name="model_parameters")
time_embedding = TimeEmbedding(data_processor.get_time_feature_size(), model_parameters["time_feature_embedding"])
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"])
# model = LinearRegression(inputDim, len(quantiles))
time_embedding = TimeEmbedding(data_processor.get_time_feature_size(), 4)
# non_linear_regression_model = NonLinearRegression(time_embedding.output_dim(inputDim), len(quantiles), hiddenSize=1024, numLayers=5)
lstm_model = GRUModel(time_embedding.output_dim(inputDim), len(quantiles), hidden_size=512, num_layers=2)
model = nn.Sequential(time_embedding, lstm_model)
optimizer = torch.optim.Adam(model.parameters(), lr=learningRate)
optimizer = torch.optim.Adam(model.parameters(), lr=model_parameters["learning_rate"])
#### Trainer ####
trainer = AutoRegressiveQuantileTrainer(
model,
inputDim,
optimizer,
data_processor,
quantiles,
"cuda",
debug=True,
debug=False,
)
trainer.add_metrics_to_track(
[PinballLoss(quantiles), MSELoss(), L1Loss(), CRPSLoss()]
)
trainer.early_stopping(patience=10)
trainer.plot_every(100)
trainer.train(task=task, epochs=epochs, remotely=True)
trainer.early_stopping(patience=30)
trainer.plot_every(5)
trainer.train(task=task, epochs=epochs, remotely=True)