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40 lines
1.4 KiB
Python
40 lines
1.4 KiB
Python
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from config.types import RawConfig
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from run_pipeline import run_pipeline
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from config.presets import get_default_config
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import optuna
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from utils.helpers import powerset
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config = get_default_config()
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config.save_models = False
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def train(trial):
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search_space = {
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"n_features_to_select": trial.suggest_int("n_features_to_select", 10, 200),
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"forecasting_horizon": trial.suggest_int("forecasting_horizon", 3, 300),
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"own_features": trial.suggest_categorical(
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"own_features", powerset(["z_score", "level_2", "level_1"])
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),
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"other_features": trial.suggest_categorical(
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"other_features", powerset(["z_score", "level_2", "level_1"])
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),
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"labeling": trial.suggest_categorical(
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"labeling", ["two_class", "three_class_balanced", "three_class_imbalanced"]
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),
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"scaler": trial.suggest_categorical(
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"scaler",
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["normalize", "minmax", "standardize", "robust", "box-cox", "quantile"],
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),
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}
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trial_config = RawConfig(**vars(config) | search_space)
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outcome, _ = run_pipeline(
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project_name="test", with_wandb=False, raw_config=trial_config
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)
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return 1 - outcome.get_output_stats()["sharpe"]
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study = optuna.create_study()
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study.optimize(train, n_trials=40)
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print(study.best_params)
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study.trials_dataframe().to_csv("output/trials.csv")
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