mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-27 18:57:55 +00:00
Mark Aron Szulyovszky
83 lines
3.2 KiB
Python
83 lines
3.2 KiB
Python
import pandas as pd
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from typing import Callable, Optional
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from config.types import Config, RawConfig
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from config.preprocess import preprocess_config, validate_config
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from config.presets import get_default_ensemble_config, get_lightweight_ensemble_config
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from data_loader.load import load_data
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from data_loader.process import check_data
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from labeling.process import label_data
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from reporting.wandb import launch_wandb, override_config_with_wandb_values
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from reporting.reporting import report_results
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from reporting.saving import save_models
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from reporting.types import Reporting
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from training.training_steps import primary_step, secondary_step
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import ray
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ray.init()
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def run_pipeline(project_name:str, with_wandb: bool, sweep: bool, raw_config: RawConfig) -> tuple[Reporting.Asset, Config, pd.DataFrame, pd.DataFrame, pd.DataFrame]:
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wandb, config = __setup_config(project_name, with_wandb, sweep, raw_config)
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reporting = __run_training(config)
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results, all_predictions, all_probabilities, all_models = reporting.get_results()
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report_results(results, all_predictions, config, wandb, sweep, project_name)
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save_models(all_models, config)
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return all_models, config, results, all_predictions, all_probabilities
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def __setup_config(project_name:str, with_wandb: bool, sweep: bool, raw_config: RawConfig) -> tuple[Optional[object], Config]:
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wandb = None
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if with_wandb:
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wandb = launch_wandb(project_name=project_name, default_config=raw_config, sweep=sweep)
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raw_config = override_config_with_wandb_values(wandb, raw_config)
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config = preprocess_config(raw_config)
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return wandb, config
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def __run_training(config: Config):
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validate_config(config)
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reporting = Reporting()
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# 1. Load data, check for validity
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X, returns, forward_returns = load_data(
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assets = config.assets,
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other_assets = config.other_assets,
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exogenous_data = config.exogenous_data,
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target_asset = config.target_asset,
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load_non_target_asset = config.load_non_target_asset,
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own_features = config.own_features,
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other_features = config.other_features,
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exogenous_features = config.exogenous_features,
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)
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assert check_data(X, config) == True, "Data is not valid."
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# 2. Filter for significant events when we want to trade, and label data
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events, X, y, forward_returns = label_data(config.event_filter, config.labeling, X, returns, forward_returns)
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# 3. Train a Primary model with optional metalabeling for each asset
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training_step_primary, current_predictions = primary_step(X, y, forward_returns, config, reporting, from_index = None)
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# 4. Train an Ensemble model with optional metalabeling for each asset
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training_step_secondary = secondary_step(X, y, current_predictions, forward_returns, config, reporting, from_index = None)
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# 5. Save the models
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reporting.asset = Reporting.Asset(name = config.target_asset[1], primary = training_step_primary, secondary = training_step_secondary)
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return reporting
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if __name__ == '__main__':
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run_pipeline(project_name='price-prediction', with_wandb = False, sweep = False, raw_config=get_default_ensemble_config()) |