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Compared more policy results

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Victor Mylle
2024-01-18 17:06:44 +00:00
parent b87ad1bf42
commit 51014ea7bb
3 changed files with 88 additions and 10 deletions
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2
Result-Reports/Policies.md
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@@ -145,4 +145,4 @@ Test data: 01-01-2023 until 08-10–2023
- [ ] Meer verschil bekijken tussen GRU en diffusion - [ ] Meer verschil bekijken tussen GRU en diffusion
- [ ] Andere lagen voor diffusion model (GRU, kijken naar TSDiff) - [ ] Andere lagen voor diffusion model (GRU, kijken naar TSDiff)
- [ ] Policies met andere modellen (Linear, Non Linear) - [x] Policies met andere modellen (Linear, Non Linear)

89
src/policies/plot_combiner.ipynb
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7
src/training_scripts/autoregressive_quantiles.py
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@@ -13,7 +13,7 @@ from src.models.time_embedding_layer import TimeEmbedding
#### ClearML #### #### ClearML ####
clearml_helper = ClearMLHelper(project_name="Thesis/NrvForecast") clearml_helper = ClearMLHelper(project_name="Thesis/NrvForecast")
task = clearml_helper.get_task(task_name="Autoregressive Quantile Regression: Non Linear + Quarter + DoW + Load + Wind + Net") task = clearml_helper.get_task(task_name="Autoregressive Quantile Regression: Linear + Quarter + DoW + Load + Wind + Net")
#### Data Processor #### #### Data Processor ####
@@ -68,9 +68,10 @@ model_parameters = task.connect(model_parameters, name="model_parameters")
time_embedding = TimeEmbedding(data_processor.get_time_feature_size(), model_parameters["time_feature_embedding"]) 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"]) # 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"])
non_linear_model = NonLinearRegression(time_embedding.output_dim(inputDim), len(quantiles), hiddenSize=model_parameters["hidden_size"], numLayers=model_parameters["num_layers"], dropout=model_parameters["dropout"]) # non_linear_model = NonLinearRegression(time_embedding.output_dim(inputDim), len(quantiles), hiddenSize=model_parameters["hidden_size"], numLayers=model_parameters["num_layers"], dropout=model_parameters["dropout"])
linear_model = LinearRegression(time_embedding.output_dim(inputDim), len(quantiles))
model = nn.Sequential(time_embedding, non_linear_model) model = nn.Sequential(time_embedding, linear_model)
optimizer = torch.optim.Adam(model.parameters(), lr=model_parameters["learning_rate"]) optimizer = torch.optim.Adam(model.parameters(), lr=model_parameters["learning_rate"])
#### Trainer #### #### Trainer ####