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Wrote more about non-autoregressive linear quantile regression

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Victor Mylle
2024-04-19 23:28:08 +02:00
parent e0fbf54347
commit 3a40959a32
25 changed files with 279 additions and 222 deletions
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48
src/training_scripts/autoregressive_quantiles.py
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@@ -2,9 +2,7 @@ from src.utils.clearml import ClearMLHelper
#### ClearML ####
clearml_helper = ClearMLHelper(project_name="Thesis/NrvForecast")
task = clearml_helper.get_task(
task_name="AQR: Linear Baseline + Load + PV + Wind + Net Position + Quarter (dim 5)"
)
task = clearml_helper.get_task(task_name="AQR: Non-Linear (2 - 256 - 0.2)")
task.execute_remotely(queue_name="default", exit_process=True)
from src.policies.PolicyEvaluator import PolicyEvaluator
@@ -29,19 +27,20 @@ from src.models.time_embedding_layer import TimeEmbedding, TrigonometricTimeEmbe
data_config = DataConfig()
data_config.NRV_HISTORY = True
data_config.LOAD_HISTORY = True
data_config.LOAD_FORECAST = True
data_config.WIND_FORECAST = True
data_config.WIND_HISTORY = True
data_config.LOAD_HISTORY = False
data_config.LOAD_FORECAST = False
data_config.PV_FORECAST = True
data_config.PV_HISTORY = True
data_config.WIND_FORECAST = False
data_config.WIND_HISTORY = False
data_config.QUARTER = True
data_config.PV_FORECAST = False
data_config.PV_HISTORY = False
data_config.QUARTER = False
data_config.DAY_OF_WEEK = False
data_config.NOMINAL_NET_POSITION = True
data_config.NOMINAL_NET_POSITION = False
data_config = task.connect(data_config, name="data_features")
@@ -91,26 +90,25 @@ time_embedding = TimeEmbedding(
# 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))
# linear_model = LinearRegression(inputDim, len(quantiles))
# 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)
# model = linear_model
model.output_size = 1
optimizer = torch.optim.Adam(model.parameters(), lr=model_parameters["learning_rate"])
### Policy Evaluator ###
battery = Battery(2, 1)
baseline_policy = BaselinePolicy(battery, data_path="")
policy_evaluator = PolicyEvaluator(baseline_policy, task)
# battery = Battery(2, 1)
# baseline_policy = BaselinePolicy(battery, data_path="")
# policy_evaluator = PolicyEvaluator(baseline_policy, task)
#### Trainer ####
trainer = AutoRegressiveQuantileTrainer(