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import random
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import warnings
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warnings.filterwarnings("ignore")
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import numpy as np
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import torchvision
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import wandb
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import torch
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torchvision.disable_beta_transforms_warning()
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import constants as cst
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import hydra
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from config.config import Config
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from run import run_wandb, run, sweep_init
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from preprocessing.lobster import LOBSTERDataBuilder
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from constants import Dataset
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from config.config import MLPLOB, TLOB
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@hydra.main(config_path="config", config_name="config")
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def hydra_app(config: Config):
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set_reproducibility(config.experiment.seed)
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if (cst.DEVICE == "cpu"):
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accelerator = "cpu"
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else:
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accelerator = "gpu"
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if config.experiment.dataset_type == Dataset.FI_2010:
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config.experiment.batch_size = 32
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if config.model.type.value == "MLPLOB" or config.model.type.value == "TLOB":
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config.model.hyperparameters_fixed["hidden_dim"] = 144
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else:
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config.experiment.batch_size = 128
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if config.model.type.value == "MLPLOB" or config.model.type.value == "TLOB":
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config.model.hyperparameters_fixed["hidden_dim"] = 46
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if config.experiment.dataset_type.value == "LOBSTER" and not config.experiment.is_data_preprocessed:
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data_builder = LOBSTERDataBuilder(
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stocks=config.experiment.training_stocks,
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data_dir=cst.DATA_DIR,
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date_trading_days=cst.DATE_TRADING_DAYS,
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split_rates=cst.SPLIT_RATES,
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sampling_type=config.experiment.sampling_type,
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sampling_time=config.experiment.sampling_time,
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sampling_quantity=config.experiment.sampling_quantity,
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)
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data_builder.prepare_save_datasets()
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if config.experiment.is_wandb:
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if config.experiment.is_sweep:
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sweep_config = sweep_init(config)
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sweep_id = wandb.sweep(sweep_config, project=cst.PROJECT_NAME, entity="")
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wandb.agent(sweep_id, run_wandb(config, accelerator), count=sweep_config["run_cap"])
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else:
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start_wandb = run_wandb(config, accelerator)
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start_wandb()
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else:
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run(config, accelerator)
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def set_reproducibility(seed):
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torch.manual_seed(seed)
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np.random.seed(seed)
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random.seed(seed)
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def set_torch():
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torch.set_default_dtype(torch.float32)
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torch.backends.cuda.matmul.allow_tf32 = True
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torch.backends.cudnn.allow_tf32 = True
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torch.autograd.set_detect_anomaly(False)
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torch.set_float32_matmul_precision('high')
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if __name__ == "__main__":
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set_torch()
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hydra_app()
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