mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-27 18:57:55 +00:00
Daniel Szemerey
516c8bcc87
* fix, feat: Fixed inference processing data. Add transformation attribute. * feat: Added transformations step, refractored the loop to make more sense (divided the train and inference loop). * feat: Truncated models over time and transformations over time. Fixed some typing aswell. * fix: Fixed a number of out of array problems. * feat: Inference now works! * fix(Steps): runtime error not checking for None * fix(Steps): preloaded transformers are not optional anymore, sped up training by temporary increasing the retrain_every * fix(CI): disable ray memory monitoring * refactor(Inference): removed truncate_models and replaced it with filling X with NaN until inference should start * feat(Inference): added index_from parameter * fix(Tests): walk_forward test * refactor(Pipeline): only predict one asset * refactor(Inference): removed select_models step, inference code moved to run_inference.py so it matches convention (similar to run_pipeline.py) * fix(Evaluation): adjust transaction costs * fix(Config): adjusted retrain_every Co-authored-by: Daniel Szemerey <szemereydaniel@gmail.com> Co-authored-by: Mark Aron Szulyovszky <mark.szulyovszky@gmail.com>
70 lines
3.1 KiB
Python
70 lines
3.1 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 reporting.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[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 = 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, data_config, training_config, model_config)
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return all_models, 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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# 1. Load data, check for validity
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X, y, target_returns = load_data(**configs['data_config'])
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assert check_data(X, y, configs['training_config']) == True, "Data is not valid."
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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, target_returns, configs, reporting, from_index = None)
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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, target_returns, configs, reporting, from_index = None)
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# 4. Save the models
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reporting.asset = Reporting.Asset(ticker= data_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, get_config=get_default_ensemble_config) |