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Added LSTM model

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Victor Mylle
2023-11-28 22:27:15 +00:00
parent ffa19592f9
commit eba10c8f83
6 changed files with 117 additions and 38 deletions
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21
src/training_scripts/autoregressive_quantiles.py
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@@ -1,3 +1,4 @@
from src.models.lstm_model import LSTMModel, GRUModel
from src.data import DataProcessor, DataConfig
from src.trainers.quantile_trainer import AutoRegressiveQuantileTrainer, NonAutoRegressiveQuantileRegression
from src.trainers.probabilistic_baseline import ProbabilisticBaselineTrainer
@@ -28,19 +29,21 @@ data_config.LOAD_FORECAST = True
data_config.QUARTER = True
data_config.DAY_OF_WEEK = True
data_config = task.connect(data_config, name="data_features")
# data_config = task.connect(data_config, name="data_features")
data_processor = DataProcessor(data_config, path="")
data_processor.set_batch_size(1024)
data_processor = DataProcessor(data_config, path="", lstm=True)
data_processor.set_batch_size(512)
data_processor.set_full_day_skip(False)
#### Hyperparameters ####
data_processor.set_output_size(1)
inputDim = data_processor.get_input_size()
learningRate = 0.0001
learningRate = 0.001
epochs = 100
print("Input dim: ", inputDim)
# add parameters to clearml
quantiles = task.get_parameter("general/quantiles", cast=True)
if quantiles is None:
@@ -49,8 +52,9 @@ if quantiles is None:
# 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)
model = nn.Sequential(time_embedding, non_linear_regression_model)
# 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)
#### Trainer ####
@@ -62,9 +66,10 @@ trainer = AutoRegressiveQuantileTrainer(
"cuda",
debug=True,
)
trainer.add_metrics_to_track(
[PinballLoss(quantiles), MSELoss(), L1Loss(), CRPSLoss()]
)
trainer.early_stopping(patience=10)
trainer.plot_every(5)
trainer.train(task=task, epochs=epochs, remotely=False)
trainer.plot_every(100)
trainer.train(task=task, epochs=epochs, remotely=True)