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main_experiment.py
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main_experiment.py
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import json
import os
import argparse
import numpy as np
from hpo.optimizers.qt_metadataset import QuickTuneMetaDataset
from hpo.optimizers.quick_tune.cost_metalearner import CostMetaLearner
from hpo.optimizers.quick_tune.factory import create_qt_optimizer, SPLITS
from hpo.optimizers.bo import BO
from hpo.optimizers.random_search import RandomSearchOptimizer
if __name__ == "__main__":
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument("--max_budget", type=int, default=50)
parser.add_argument("--seed", type=int, default=100)
parser.add_argument("--budget_limit", type=int, default=10000)
parser.add_argument("--output_dir", type=str, default="output/")
parser.add_argument("--aggregate_data", type=int, default=0)
parser.add_argument("--metadataset_version", type=str, default="micro")
parser.add_argument("--hidden_dim", type=int, default=64)
parser.add_argument("--output_dim", type=int, default=32)
parser.add_argument("--learning_rate", type=float, default=0.001)
parser.add_argument("--meta_learning_rate", type=float, default=0.01)
parser.add_argument("--train_iter", type=int, default=10000)
parser.add_argument("--device", type=str, default="cuda")
parser.add_argument("--with_scheduler", type=int, default=0)
parser.add_argument("--include_metafeatures", type=int, default=1)
parser.add_argument("--acqf_fc", type=str, default="ei")
parser.add_argument("--explore_factor", type=float, default=0.1)
parser.add_argument("--experiment_id", type=str, default="")
parser.add_argument("--meta_test_id", type=str, default="mt0")
parser.add_argument("--load_meta_trained", type=int, default=0)
parser.add_argument("--load_cost_predictor", type=int, default=0)
parser.add_argument("--output_dim_metafeatures", type=int, default=2)
parser.add_argument("--freeze_feature_extractor", type=int, default=0)
parser.add_argument("--run_random", type=int, default=0)
parser.add_argument("--meta_train", type=int, default=1)
parser.add_argument("--load_only_dataset_descriptors", type=int, default=1)
parser.add_argument("--cost_aware", type=int, default=0)
parser.add_argument("--use_encoders_for_model", type=int, default=0)
parser.add_argument("--observe_cost", type=int, default=0)
parser.add_argument("--target_model", type=str, default=None)
parser.add_argument("--test_generalization_to_model", type=int, default=0)
parser.add_argument("--use_only_target_model", type=int, default=0)
parser.add_argument("--split_id", type=int, default=None)
parser.add_argument("--dataset_id_in_split", type=str, default=None)
parser.add_argument("--conditioned_time_limit", type=int, default=0)
parser.add_argument("--subsample_models_in_hub", type=int, default=None)
parser.add_argument("--measure_for_target_model", type=str, default=None)
parser.add_argument("--file_with_init_indices", type=str, default=None)
args = parser.parse_args()
print(args)
load_meta_trained = args.load_meta_trained
load_cost_predictor = args.load_cost_predictor
results = {}
rootdir = os.path.dirname(os.path.abspath(__file__))
output_dir = os.path.join(rootdir, args.output_dir, args.experiment_id)
if args.file_with_init_indices is not None:
args.file_with_init_indices = os.path.join(
rootdir, "hpo", "meta_data", args.file_with_init_indices
)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
metadataset = QuickTuneMetaDataset(
aggregate_data=args.aggregate_data,
path="data",
version=args.metadataset_version,
load_only_dataset_descriptors=args.load_only_dataset_descriptors,
)
if not args.meta_train:
args.output_dim_metafeatures = 0
# not used for now
augmentation_ids = [None]
if args.split_id is not None:
split_ids = [args.split_id]
else:
split_ids = np.arange(0, 5)
for split_id in split_ids:
load_meta_trained = args.load_meta_trained
load_cost_predictor = args.load_cost_predictor
datasets = metadataset.get_datasets()
train_splits, test_splits, val_splits = SPLITS[split_id]
datasets_for_split = CostMetaLearner.get_splits(
None,
datasets,
train_splits=train_splits,
test_splits=test_splits,
val_splits=val_splits,
)
if args.dataset_id_in_split is not None:
datasets = [datasets_for_split["test"][int(args.dataset_id_in_split)]]
else:
datasets = datasets_for_split["test"]
for dataset_name in datasets:
for augmentation_id in augmentation_ids:
try:
metadataset.set_dataset_name(
dataset_name, augmentation_id=augmentation_id
)
hyperparameter_candidates = (
metadataset.get_hyperparameters_candidates().values.tolist()
)
log_indicator = [
False for _ in range(len(hyperparameter_candidates[0]))
] # Dont apply log
new_dataset_name = dataset_name.replace("/", "_")
if augmentation_id is not None:
new_dataset_name = (
new_dataset_name + "_aug" + str(augmentation_id)
)
if args.measure_for_target_model is not None:
with open(
os.path.join(
rootdir, "meta_data", "best_models_per_dataset.json"
),
"r",
) as f:
best_models_per_datasets = json.load(f)
target_model = best_models_per_datasets[dataset_name][
args.measure_for_target_model
]
else:
target_model = args.target_model
if args.conditioned_time_limit:
with open(
os.path.join(rootdir, "meta_data", "time_counts.json"), "r"
) as f:
time_limits = json.load(f)
budget_limit = time_limits[args.metadataset_version][new_dataset_name] + 100
else:
budget_limit = args.budget_limit
temp_output_dir = os.path.join(
output_dir, args.meta_test_id, new_dataset_name
)
if not os.path.exists(temp_output_dir):
os.makedirs(temp_output_dir)
if args.run_random:
random_search = RandomSearchOptimizer(
metadataset, seed=args.seed
)
optimizer_budget, optimizer_cost, optimizer_performance = BO(
random_search, metadataset, args.budget_limit
)
results = [
optimizer_budget,
optimizer_cost,
optimizer_performance,
]
else:
optimizer = create_qt_optimizer(
metadataset,
experiment_id=args.experiment_id,
output_dim_metafeatures=args.output_dim_metafeatures,
freeze_feature_extractor=args.freeze_feature_extractor,
explore_factor=args.explore_factor,
load_meta_trained=load_meta_trained,
meta_output_dir=output_dir,
output_dir=temp_output_dir,
dataset_name=dataset_name,
new_dataset_name=new_dataset_name,
log_indicator=log_indicator,
budget_limit=budget_limit,
include_metafeatures=args.include_metafeatures,
meta_train=args.meta_train,
acqf_fc=args.acqf_fc,
learning_rate=args.learning_rate,
meta_learning_rate=args.meta_learning_rate,
train_iter=args.train_iter,
hidden_dim=args.hidden_dim,
output_dim=args.output_dim,
with_scheduler=args.with_scheduler,
cost_aware=args.cost_aware,
use_encoders_for_model=args.use_encoders_for_model,
load_cost_predictor=load_cost_predictor,
split_id=split_id,
augmentation_id=augmentation_id,
observe_cost=args.observe_cost,
target_model=target_model,
seed=args.seed,
test_generalization_to_model=args.test_generalization_to_model,
use_only_target_model=args.use_only_target_model,
subsample_models_in_hub=args.subsample_models_in_hub,
file_with_init_indices=args.file_with_init_indices,
)
optimizer_budget, optimizer_cost, optimizer_performance = BO(
optimizer,
metadataset,
budget_limit,
observe_cost=args.observe_cost,
)
results = [
optimizer_budget,
optimizer_cost,
optimizer_performance,
]
load_meta_trained = True
load_cost_predictor = True
with open(os.path.join(temp_output_dir, f"results.json"), "w") as f:
json.dump(results, f)
except Exception as e:
print(e)
print("Error in dataset: ", dataset_name, augmentation_id)