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Quarter embedding using trigonometry + more thesis writing

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Victor Mylle
2024-04-17 21:48:13 +02:00
parent 6b02c9aab8
commit 8fb2a7fc7b
18 changed files with 3467 additions and 55 deletions
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31
src/training_scripts/autoregressive_quantiles.py
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@@ -2,7 +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")
task = clearml_helper.get_task(task_name="AQR: Linear Baseline + Quarter Trigonometric")
task.execute_remotely(queue_name="default", exit_process=True)
from src.policies.PolicyEvaluator import PolicyEvaluator
@@ -20,7 +20,7 @@ from src.losses import *
import torch
from torch.nn import MSELoss, L1Loss
import torch.nn as nn
from src.models.time_embedding_layer import TimeEmbedding
from src.models.time_embedding_layer import TimeEmbedding, TrigonometricTimeEmbedding
#### Data Processor ####
@@ -30,18 +30,21 @@ data_config.NRV_HISTORY = True
data_config.LOAD_HISTORY = True
data_config.LOAD_FORECAST = True
data_config.WIND_FORECAST = False
data_config.WIND_HISTORY = False
data_config.WIND_FORECAST = True
data_config.WIND_HISTORY = True
data_config.QUARTER = False
data_config.PV_FORECAST = True
data_config.PV_HISTORY = True
data_config.QUARTER = True
data_config.DAY_OF_WEEK = False
data_config.NOMINAL_NET_POSITION = False
data_config.NOMINAL_NET_POSITION = True
data_config = task.connect(data_config, name="data_features")
data_processor = DataProcessor(data_config, path="", lstm=True)
data_processor = DataProcessor(data_config, path="", lstm=False)
data_processor.set_batch_size(512)
data_processor.set_full_day_skip(False)
@@ -67,7 +70,7 @@ model_parameters = {
"hidden_size": 256,
"num_layers": 2,
"dropout": 0.2,
"time_feature_embedding": 8,
"time_feature_embedding": 2,
}
model_parameters = task.connect(model_parameters, name="model_parameters")
@@ -76,6 +79,8 @@ model_parameters = task.connect(model_parameters, name="model_parameters")
# data_processor.get_time_feature_size(), model_parameters["time_feature_embedding"]
# )
time_embedding = TrigonometricTimeEmbedding(data_processor.get_time_feature_size())
# lstm_model = GRUModel(
# time_embedding.output_dim(inputDim),
# len(quantiles),
@@ -92,11 +97,11 @@ model_parameters = task.connect(model_parameters, name="model_parameters")
# 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))
# linear_model = LinearRegression(inputDim, len(quantiles))
# model = nn.Sequential(time_embedding, lstm_model)
model = 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"])
@@ -121,7 +126,7 @@ trainer.add_metrics_to_track(
[PinballLoss(quantiles), MSELoss(), L1Loss(), CRPSLoss(quantiles)]
)
trainer.early_stopping(patience=5)
trainer.plot_every(2)
trainer.plot_every(15)
trainer.train(task=task, epochs=epochs, remotely=True)
### Policy Evaluation ###