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import argparse
import logging
import pickle
import sys
from pathlib import Path
import lightning.pytorch as pl
import optuna
import torch
import yaml
from lightning.pytorch.callbacks import EarlyStopping, ModelCheckpoint
from lightning.pytorch.loggers import TensorBoardLogger
import utils.helpers as hp
import utils.utility as ut
from utils.callbacks import OptunaPruningCallback, WaVoCallback
from data_tools.data_module import WaVoDataModule
from models.ensemble_models import WaVoLightningEnsemble, WaVoLightningAttentionEnsemble
use_cuda = torch.cuda.is_available()
if use_cuda:
accelerator = "cuda"
torch.set_float32_matmul_precision("high")
max_free = 0
best_device = None
for j in range(torch.cuda.device_count()):
free, _ = torch.cuda.mem_get_info(j)
if free > max_free:
max_free = free
best_device = j
devices = [best_device]
else:
accelerator = "cpu"
devices = "auto"
storage_base = "sqlite:///../../../../data-project/KIWaVo/models/optuna/"
if ut.debugger_is_active():
max_epochs = 2
default_storage_name = "sqlite:///../../../data-project/KIWaVo/models/optuna/icaart_ensemble_debug_01.db"
else:
max_epochs = 2000
default_storage_name = (
"sqlite:///../../../../data-project/KIWaVo/models/optuna/icaart_ensemble_01.db"
)
class Objective:
"""
This class defines the objective function for hyperparameter tuning using Optuna library.
Args:
filename (str|Path): Path to .csv file, first column should be a timeindex
model_dir (str|Path): Path to the directory containing the base models
log_dir (str|Path): Path to the logging directory
select_strat (str): How to select the base models (random or first n)
model_count (int): How many base models to use
monitor (str, optional): metric to monitor. Defaults to 'hp/val_loss'.
"""
def __init__(
self,
filename,
modeldir,
logdir,
select_strat,
model_count,
monitor="hp/val_loss",
**kwargs,
):
# Hold these implementation specific arguments as the fields of the class.
self.filename = filename
self.model_dir = modeldir
self.log_dir = logdir
self.select_strat = select_strat
self.model_count = model_count
self.monitor = monitor
def _get_callbacks(self, trial):
checkpoint_callback = ModelCheckpoint(
save_top_k=1, monitor=self.monitor, save_weights_only=True
)
pruning_callback = OptunaPruningCallback(trial, monitor=self.monitor)
early_stop_callback = EarlyStopping(
monitor=self.monitor, mode="min", patience=3
)
my_callback = WaVoCallback(ensemble=True)
return my_callback, [
checkpoint_callback,
pruning_callback,
early_stop_callback,
my_callback,
]
def _get_model_params(self, trial):
model_params = dict(
hidden_size=trial.suggest_int("hidden_size", 32, 512),
num_layers=trial.suggest_int("n_layers", 2, 4),
dropout=0.25,
learning_rate=trial.suggest_float("lr", 0.00001, 0.01),
norm_func=trial.suggest_categorical("norm_func", ["softmax", "minmax"]),
)
return model_params
def __call__(self, trial):
model_params = self._get_model_params(trial)
# log_dir = '../../../data-project/KIWaVo/models/ensemble_debug/'
# model_dir = Path('../../../data-project/KIWaVo/models/icaarts_hollingstedt/lightning_logs/')
model_list = []
model_path_list = []
yaml_data = None
if self.select_strat == "random":
all_models = [x.name.split("_")[1] for x in self.model_dir.iterdir()]
elif self.select_strat == "first":
model_choice = list(range(self.model_count))
elif self.select_strat == "hardcoded":
model_choice = [37,88,106,110,116,137,171,175,181,186]
else:
raise ValueError("Invalid selection strategy")
# for s in [0,1,2]:
for s in model_choice:
temp_dir = self.model_dir / f"version_{s}/"
model_list.append(
hp.load_model_cuda(temp_dir, use_cuda=use_cuda, devices=devices)
)
model_path_list.append(str(temp_dir.resolve()))
if yaml_data is None:
yaml_data = hp.load_settings_model(temp_dir)
# with open(temp_dir / 'hparams.yaml', 'r') as file:
# yaml_data = yaml.load(file, Loader=yaml.FullLoader)
# yaml_data['scaler'] = pickle.loads(yaml_data['scaler'])
config = {
"scaler": yaml_data[
"scaler"
], # TODO test how this works without giving scaler etc. outside of jupyterlab
#'filename' : yaml_data['filename'],
"filename": str(self.filename),
"level_name_org": yaml_data["level_name_org"],
"out_size": yaml_data["out_size"],
"threshold": yaml_data["threshold"],
"feature_count": yaml_data["feature_count"],
"differencing": yaml_data["differencing"],
"model_architecture": "ensemble",
}
print("ACHTUNG GGF. FALSCHES MODELL")
ensemble_model = WaVoLightningEnsemble(model_list,model_path_list,**model_params)#TODO 'ÄNDERN!
#ensemble_model = WaVoLightningAttentionEnsemble(
# model_list, model_path_list, **model_params
#)
config["in_size"] = ensemble_model.max_in_size
data_module = WaVoDataModule(**config)
logging.info("Params: %s", trial.params)
my_callback, callbacks = self._get_callbacks(trial)
logger = TensorBoardLogger(self.log_dir, default_hp_metric=False)
trainer = pl.Trainer(
default_root_dir=self.log_dir,
gradient_clip_val=0.5,
logger=logger,
accelerator=accelerator,
devices=devices,
callbacks=callbacks,
max_epochs=max_epochs,
log_every_n_steps=10,
)
trainer.fit(ensemble_model, data_module)
# save metrics to optuna
model_path = str(Path(trainer.log_dir).resolve())
logging.info("model_path: %s", model_path)
trial.set_user_attr("model_path", model_path)
for metric in ["hp/val_nse", "hp/val_mae", "hp/val_mae_flood"]:
for i in [23, 47]:
trial.set_user_attr(
f"{metric}_{i}", my_callback.metrics[metric][i].item()
)
return my_callback.metrics[self.monitor].item()
def parse_args() -> argparse.Namespace:
"""Parse all the arguments and provides some help in the command line"""
parser: argparse.ArgumentParser = argparse.ArgumentParser(
description="Execute experiments for exp_icaart."
)
parser.add_argument(
"filename", metavar="datafile", type=Path, help="The path to your input data."
)
parser.add_argument(
"modeldir", metavar="modeldir", type=Path, help="The path to your base models."
)
parser.add_argument(
"logdir", type=Path, help="set a directory for logs and model checkpoints."
)
parser.add_argument(
"trials",
metavar="trials",
type=int,
default=100,
help="How many trials to run.",
)
parser.add_argument("select_strat", choices=["random", "first","hardcoded"], help="How to select the base models.")
parser.add_argument(
"model_count",
metavar="mc",
type=int,
default=5,
help="How many base models to use.",
)
parser.add_argument(
"--expname",
metavar="experiment_name",
type=str,
default="nameless",
help="The name of the experiment.",
)
parser.add_argument(
"--storagename",
metavar="storage_name",
type=str,
default=None,
help="The database for the experiment.",
)
return parser.parse_args()
def main():
parsed_args = parse_args()
if not parsed_args.logdir.exists():
parsed_args.logdir.mkdir(parents=True)
if False:
pruner = optuna.pruners.HyperbandPruner(
min_resource=1, max_resource="auto", reduction_factor=3, bootstrap_count=0
)
else:
pruner = optuna.pruners.NopPruner()
study_name = f"{parsed_args.filename.stem} {parsed_args.expname}" # Unique identifier of the study.
storage_name = (
default_storage_name
if parsed_args.storagename is None
else f"{storage_base}{parsed_args.storagename}.db"
)
# Logging, add stream handler of stdout to show the messages
logging.basicConfig(level=logging.INFO)
logFormatter = logging.Formatter(
"%(asctime)s;%(levelname)s;%(message)s", datefmt="%Y-%m-%d %H:%M:%S"
)
fileHandler = logging.FileHandler(
parsed_args.logdir / "ensemble_hyper.log",
)
consoleHandler = logging.StreamHandler(sys.stdout)
fileHandler.setFormatter(logFormatter)
consoleHandler.setFormatter(logFormatter)
logging.getLogger().addHandler(fileHandler)
# logging.getLogger().addHandler(consoleHandler)
optuna.logging.get_logger("optuna").addHandler(fileHandler)
optuna.logging.get_logger("optuna").addHandler(consoleHandler)
logging.info(
"Start of this execution======================================================================"
)
logging.info(
"Executing %s with device %s and parameters %s ",
sys.argv[0],
devices,
sys.argv[1:],
)
study = optuna.create_study(
study_name=study_name,
storage=storage_name,
direction="minimize",
pruner=pruner,
load_if_exists=True,
)
study.set_metric_names(["hp/val_loss"])
objective = Objective(**vars(parsed_args), gc_after_trial=True)
study.optimize(
objective,
n_trials=parsed_args.trials,
timeout=None,
callbacks=[lambda study, trial: torch.cuda.empty_cache()],
)
if __name__ == "__main__":
# TODO command line arguments
main()