gavindiaz/drift
1
0
Fork 0
mirror of https://github.com/webclinic017/drift.git synced 2026-08-15 20:08:08 +00:00
Code Issues Packages Projects Releases Wiki Activity

refactor(Config): use a Config object instead of dictionary of dictionaries! (#184)

Browse Source 
* refactor(Config): use a Config object instead of dictionary of dictionaries!

* fix(Config): use default_ensemble_config

* fix(Portfolio): fixed portfolio construction
This commit is contained in:
Mark Aron Szulyovszky
2022-01-23 18:37:43 +01:00
committed by GitHub
parent 5c4a5b0cf1
commit e80fffdb65
19 changed files with 223 additions and 196 deletions
Download Patch File Download Diff File Expand all files Collapse all files
run_pipeline.py
+34 -22
View File
@@ -4,12 +4,12 @@ from typing import Callable, Optional
from data_loader.load_data import load_data
from data_loader.process_data import check_data
from reporting.wandb import launch_wandb, register_config_with_wandb
from reporting.wandb import launch_wandb, override_config_with_wandb_values
from reporting.reporting import report_results
from reporting.saving import save_models
from config.config import get_default_ensemble_config
from config.config import Config, get_default_ensemble_config, get_lightweight_ensemble_config
from config.preprocess import validate_config, preprocess_config
from training.training_steps import primary_step, secondary_step
@@ -20,46 +20,58 @@ import ray
ray.init()
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]:
wandb, model_config, training_config, data_config = __setup_config(project_name, with_wandb, sweep, get_config)
reporting = __run_training(model_config, training_config, data_config)
def run_pipeline(project_name:str, with_wandb: bool, sweep: bool, get_config: Callable) -> tuple[Reporting.Asset, Config, pd.DataFrame, pd.DataFrame, pd.DataFrame]:
wandb, config = __setup_config(project_name, with_wandb, sweep, get_config)
reporting = __run_training(config)
results, all_predictions, all_probabilities, all_models = reporting.get_results()
report_results(results, all_predictions, model_config, wandb, sweep, project_name)
save_models(all_models, data_config, training_config, model_config)
report_results(results, all_predictions, config, wandb, sweep, project_name)
save_models(all_models, config)
return all_models, data_config, training_config, model_config, results, all_predictions, all_probabilities
return all_models, config, results, all_predictions, all_probabilities
def __setup_config(project_name:str, with_wandb: bool, sweep: bool, get_config: Callable) -> tuple[Optional[object], dict, dict, dict]:
model_config, training_config, data_config = get_config()
def __setup_config(project_name:str, with_wandb: bool, sweep: bool, get_config: Callable) -> tuple[Optional[object], Config]:
raw_config = get_config()
wandb = None
if with_wandb:
wandb = launch_wandb(project_name=project_name, default_config=dict(**model_config, **training_config, **data_config), sweep=sweep)
model_config, training_config, data_config = register_config_with_wandb(wandb, model_config, training_config, data_config)
model_config, training_config, data_config = preprocess_config(model_config, training_config, data_config)
wandb = launch_wandb(project_name=project_name, default_config=raw_config, sweep=sweep)
raw_config = override_config_with_wandb_values(wandb, raw_config)
config = preprocess_config(raw_config)
return wandb, model_config, training_config, data_config
return wandb, config
def __run_training(model_config:dict, training_config:dict, data_config:dict):
def __run_training(config: Config):
validate_config(model_config, training_config, data_config)
configs = dict(model_config=model_config, training_config=training_config, data_config=data_config)
validate_config(config)
reporting = Reporting()
# 1. Load data, check for validity
X, y, target_returns = load_data(**configs['data_config'])
assert check_data(X, y, configs['training_config']) == True, "Data is not valid."
X, y, target_returns = load_data(
assets = config.assets,
other_assets = config.other_assets,
exogenous_data = config.exogenous_data,
target_asset = config.target_asset,
load_non_target_asset = config.load_non_target_asset,
log_returns = config.log_returns,
forecasting_horizon = config.forecasting_horizon,
own_features = config.own_features,
other_features = config.other_features,
exogenous_features = config.exogenous_features,
index_column = config.index_column,
no_of_classes = config.no_of_classes,
)
assert check_data(X, y, config) == True, "Data is not valid."
# 2. Train a Primary model with optional metalabeling for each asset
training_step_primary, current_predictions = primary_step(X, y, target_returns, configs, reporting, from_index = None)
training_step_primary, current_predictions = primary_step(X, y, target_returns, config, reporting, from_index = None)
# 3. Train an Ensemble model with optional metalabeling for each asset
training_step_secondary = secondary_step(X, y, current_predictions, target_returns, configs, reporting, from_index = None)
training_step_secondary = secondary_step(X, y, current_predictions, target_returns, config, reporting, from_index = None)
# 4. Save the models
reporting.asset = Reporting.Asset(ticker= data_config['target_asset'][1], primary=training_step_primary, secondary=training_step_secondary)
reporting.asset = Reporting.Asset(ticker= config.target_asset[1], primary=training_step_primary, secondary=training_step_secondary)
return reporting