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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)

42
src/training_scripts/hyperparameter_optimizer.py
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@@ -5,7 +5,6 @@ from clearml.automation.optuna import OptimizerOptuna
from clearml.automation import (
DiscreteParameterRange, HyperParameterOptimizer, RandomSearch,
UniformIntegerParameterRange)
from src.data.preprocessing import DataConfig
# trying to load Bayesian optimizer package
try:
@@ -21,17 +20,28 @@ except ImportError as ex:
'we will be using RandomSearch strategy instead')
aSearchStrategy = RandomSearch
# input task id to optimize
input_task_id = input("Please enter the task id to optimize: ")
# input task id to optimize using argparse
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--task_id", help="Task ID to optimize", type=str)
args = parser.parse_args()
input_task_id = args.task_id
# check if task id is valid
if not Task.get_task(task_id=input_task_id):
raise ValueError("Invalid task id")
task = Task.init(project_name='Hyper-Parameter Optimization',
task_name='Automatic Hyper-Parameter Optimization',
Task.add_requirements("requirements.txt")
Task.ignore_requirements("torch")
Task.ignore_requirements("torchvision")
Task.ignore_requirements("tensorboard")
task = Task.init(project_name='Thesis/NrvForecast',
task_name='Autoregressive Quantile Regression Hyper-Parameter Optimization',
task_type=Task.TaskTypes.optimizer,
reuse_last_task_id=False)
task.set_base_docker(f"docker.io/clearml/pytorch-cuda-gcc:2.0.0-cuda11.7-cudnn8-runtime --env GIT_SSL_NO_VERIFY=true --env CLEARML_AGENT_GIT_USER=VictorMylle --env CLEARML_AGENT_GIT_PASS=Voetballer1" )
task.set_packages("requirements.txt")
execution_queue = "default"
@@ -40,36 +50,42 @@ execution_queue = "default"
#### Quantiles ####
quantile_lists = [
[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9], # Deciles
[0.25, 0.5, 0.75], # Quartiles
[0.05, 0.15, 0.25, 0.35, 0.45, 0.55, 0.65, 0.75, 0.85, 0.95], # 10% Increments, Excluding Extremes
[0.01, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.99], # Combining Deciles with Extremes
[0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1], # Including 0 and 1
[0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99], # Mixed Small and Large Increments
[0.2, 0.4, 0.6, 0.8], # 20% Increments
[0.125, 0.375, 0.625, 0.875], # Eighths
[0.05, 0.10, 0.20, 0.30, 0.40, 0.50, 0.60, 0.70, 0.80, 0.90], # 10% Increments
[0.01, 0.02, 0.03, 0.04, 0.05, 0.1, 0.15, 0.2, 0.3, 0.5] # Mixed Fine and Coarser Increments
[0.05, 0.10, 0.20, 0.30, 0.40, 0.50, 0.60, 0.70, 0.80, 0.90, 0.95], # 10% Increments
]
quantiles_range = DiscreteParameterRange("general/quantiles", values=quantile_lists)
#### Data Config ####
quarter_range = DiscreteParameterRange("data_features/quarter", values=[True, False])
day_of_week_range = DiscreteParameterRange("data_features/day_of_week", values=[True, False])
load_forecast_range = DiscreteParameterRange("data_features/load_forecast", values=[True, False])
load_history_range = DiscreteParameterRange("data_features/load_history", values=[True, False])
### OPTIMIZER OBJECT ###
optimizer = HyperParameterOptimizer(
base_task_id=input_task_id,
objective_metric_title="PinballLoss",
objective_metric_series="test",
objective_metric_title="Summary",
objective_metric_series="test_CRPSLoss",
objective_metric_sign="min",
execution_queue=execution_queue,
max_number_of_concurrent_tasks=1,
optimizer_class=aSearchStrategy,
max_iteration_per_job=50,
# save_top_k_tasks_only=3,
pool_period_min=0.2,
total_max_jobs=15,
hyper_parameters=[
quantiles_range,
quarter_range,
day_of_week_range,
load_forecast_range,
load_history_range
]
)