Added new training scripts

src/training_scripts/autoregressive_quantiles.py

@@ -0,0 +1,72 @@
+from src.data import DataProcessor, DataConfig
+from src.trainers.quantile_trainer import AutoRegressiveQuantileTrainer, NonAutoRegressiveQuantileRegression
+from src.trainers.probabilistic_baseline import ProbabilisticBaselineTrainer
+from src.trainers.autoregressive_trainer import AutoRegressiveTrainer
+from src.trainers.trainer import Trainer
+from src.utils.clearml import ClearMLHelper
+from src.models import *
+from src.losses import *
+import torch
+import numpy as np
+from torch.nn import MSELoss, L1Loss
+from datetime import datetime
+import torch.nn as nn
+from src.models.time_embedding_layer import TimeEmbedding
+
+
+#### ClearML ####
+clearml_helper = ClearMLHelper(project_name="Thesis/NrvForecast")
+task = clearml_helper.get_task(task_name="None")
+
+
+#### Data Processor ####
+data_config = DataConfig()
+data_config.NRV_HISTORY = True
+data_config.LOAD_HISTORY = False
+data_config.LOAD_FORECAST = False
+
+data_config.WIND_FORECAST = False
+data_config.WIND_HISTORY = False
+
+data_config.QUARTER = True
+data_config.DAY_OF_WEEK = False
+
+data_processor = DataProcessor(data_config, path="")
+data_processor.set_batch_size(1024)
+data_processor.set_full_day_skip(False)
+
+
+#### Hyperparameters ####
+data_processor.set_output_size(1)
+inputDim = data_processor.get_input_size()
+learningRate = 0.0001
+epochs = 100
+
+# add parameters to clearml
+quantiles = task.get_parameter("general/quantiles", cast=True)
+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)
+
+
+# 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)
+optimizer = torch.optim.Adam(model.parameters(), lr=learningRate)
+
+#### Trainer ####
+trainer = AutoRegressiveQuantileTrainer(
+    model,
+    optimizer,
+    data_processor,
+    quantiles,
+    "cuda",
+    debug=True,
+)
+trainer.add_metrics_to_track(
+    [PinballLoss(quantiles), MSELoss(), L1Loss(), CRPSLoss(quantiles)]
+)
+trainer.early_stopping(patience=10)
+trainer.plot_every(5)
+trainer.train(task=task, epochs=epochs, remotely=True)