mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-27 18:57:55 +00:00
Mark Aron Szulyovszky
6b26643ece
* feat(Transformations): removed feature-selection pre-processing step completely * fix(Core): removed unnecessary `original_X` * fix(Transformations): use the X_expanding_window to transform subsequent data * fix(RFE): should check for model correctly * fix(Config): only re-train the model every 40 timestamp * fix(MetaLabeling): pass in the correct X to meta-labeling step * fix(Transformation): PCA should at least keep as many features as sliding_window_size * feat(Transformations): cache transformations across the same asset * fix(Tests): missing preloaded_transformations arg * chore(Config): got rid of unnecessary 'classification_models' and 'regression_models' dictionary keys
75 lines
3.4 KiB
Python
75 lines
3.4 KiB
Python
import pandas as pd
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from typing import Callable, Optional
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from data_loader.load_data import load_data
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from data_loader.process_data import check_data
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from reporting.wandb import launch_wandb, register_config_with_wandb
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from reporting.reporting import report_results
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from models.saving import save_models
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from config.config import get_default_ensemble_config
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from config.preprocess import validate_config, preprocess_config
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from training.training_steps import primary_step, secondary_step
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from reporting.types import Reporting
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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, get_config: Callable) -> tuple[list[Reporting.Asset], dict, dict, dict, pd.DataFrame, pd.DataFrame, pd.DataFrame]:
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wandb, model_config, training_config, data_config = __setup_config(project_name, with_wandb, sweep, get_config)
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reporting = __run_training(model_config, training_config, data_config)
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results, all_predictions, all_probabilities, all_models_all_assets = reporting.get_results()
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report_results(results, all_predictions, model_config, wandb, sweep, project_name)
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save_models(all_models_all_assets, data_config, training_config, model_config)
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return all_models_all_assets, data_config, training_config, model_config, results, all_predictions, all_probabilities
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def __setup_config(project_name:str, with_wandb: bool, sweep: bool, get_config: Callable) -> tuple[Optional[object], dict, dict, dict]:
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model_config, training_config, data_config = get_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=dict(**model_config, **training_config, **data_config), sweep=sweep)
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model_config, training_config, data_config = register_config_with_wandb(wandb, model_config, training_config, data_config)
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model_config, training_config, data_config = preprocess_config(model_config, training_config, data_config)
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return wandb, model_config, training_config, data_config
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def __run_training(model_config:dict, training_config:dict, data_config:dict):
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validate_config(model_config, training_config, data_config)
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configs = dict(model_config=model_config, training_config=training_config, data_config=data_config)
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reporting = Reporting()
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for asset in data_config['assets']:
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print('--------\nPredicting: ', asset[1])
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configs['data_config']['target_asset'] = asset
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# 1. Load data, check for validity and process data (feature selection, dimensionality reduction, etc.)
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X, y, target_returns = load_data(**configs['data_config'])
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if check_data(X, y, configs['training_config']) is False: continue
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# 2. Train a Primary model with optional metalabeling for each asset
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training_step_primary, current_predictions = primary_step(X, y, asset, target_returns, configs, reporting)
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# 3. Train an Ensemble model with optional metalabeling for each asset
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training_step_secondary = secondary_step(X, y, current_predictions, asset, target_returns, configs, reporting)
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# 4. Save the models
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reporting.all_assets.append(Reporting.Asset(ticker=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, get_config=get_default_ensemble_config) |