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Refractor(Main Pipeline): Refractored the two main steps and the data processing. (#156)
* refr: Took out main primary and secondary loops and data processing. * feat: Tidied the code up. * feat: Saving models and results now works in a type safe way. * fix: There was error in the saving function. * chore: Took out some remaining comments. * fix: Fixed the previous data checking process. * feat: Fixed model selection method. I will continue the inference after we merged. Co-authored-by: Daniel Szemerey <szemereydaniel@gmail.com>
This commit is contained in:
Daniel Szemerey
co-authored by
Daniel Szemerey
Daniel Szemerey
parent
c611481eb6
commit
3084f5e271
+35
-130
@@ -1,26 +1,30 @@
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from config.hashing import hash_data_config
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from data_loader.load_data import load_data
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import pandas as pd
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from training.primary_model import train_primary_model
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from typing import Callable, Optional
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from operator import itemgetter
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from data_loader.load_data import load_data
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from data_loader.process_data import process_data, check_data
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from reporting.wandb import launch_wandb, register_config_with_wandb
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from models.model_map import default_feature_selector_regression, default_feature_selector_classification
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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 utils.helpers import has_enough_samples_to_train
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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 feature_selection.feature_selection import select_features
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from feature_selection.dim_reduction import reduce_dimensionality
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from training.meta_labeling import train_meta_labeling_model
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from reporting.reporting import report_results
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from typing import Callable, Optional
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from training.training_steps import primary_step, secondary_step
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from utils.encapsulation import Reporting, Asset, Training_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, get_config: Callable) -> tuple[dict, dict, dict, pd.DataFrame, pd.DataFrame, pd.DataFrame]:
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def run_pipeline(project_name:str, with_wandb: bool, sweep: bool, get_config: Callable) -> tuple[list[Asset], 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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results, all_predictions, all_probabilities, all_models_all_assets = __run_training(model_config, training_config, data_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)
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@@ -36,134 +40,35 @@ def __setup_config(project_name:str, with_wandb: bool, sweep: bool, get_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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results = pd.DataFrame()
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all_predictions = pd.DataFrame()
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all_probabilities = pd.DataFrame()
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all_models_for_all_assets = dict()
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validate_config(model_config, training_config, data_config)
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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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# 1. Load data
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data_params = data_config.copy()
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data_params['target_asset'] = asset
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configs['data_config']['target_asset'] = asset
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X, y, target_returns = load_data(**data_params)
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original_X = X.copy()
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if has_enough_samples_to_train(X, y, training_config) == False:
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print("Not enough samples to train")
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continue
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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, training_config) is False: continue
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X, original_X, X_pca = process_data(X, y, configs)
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# 2a. Dimensionality Reduction (optional)
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if training_config['dimensionality_reduction']:
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X_pca = reduce_dimensionality(X, int(len(X.columns) / 2))
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X = X_pca.copy()
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else:
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X_pca = X.copy()
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# 2b. Feature Selection
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print("Feature Selection started")
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# TODO: this needs to be done per model!
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backup_model = default_feature_selector_regression if data_config['method'] == 'regression' else default_feature_selector_classification
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X = select_features(X = X, y = y, model = model_config['primary_models'][0][1], n_features_to_select = training_config['n_features_to_select'], backup_model = backup_model, scaling = training_config['scaler'])
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# 3. Train Primary models
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current_result, current_predictions, current_probabilities, all_models_for_single_asset = train_primary_model(
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ticker_to_predict = asset[1],
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original_X = original_X,
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X = X,
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y = y,
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target_returns = target_returns,
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models = model_config['primary_models'],
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method = data_config['method'],
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expanding_window = training_config['expanding_window_primary'],
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sliding_window_size = training_config['sliding_window_size_primary'],
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retrain_every = training_config['retrain_every'],
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scaler = training_config['scaler'],
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no_of_classes = data_config['no_of_classes'],
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level = 'primary',
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print_results= True
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)
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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, original_X, X_pca, asset, target_returns, configs, reporting)
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all_models_for_all_assets[asset[1]] = dict(
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name=asset[1],
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primary_models = all_models_for_single_asset
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)
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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, original_X, X_pca, current_predictions, asset, target_returns, configs, reporting)
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# 4. Train a Meta-Labeling model for each Primary model and replace their predictions with the meta-labeling predictions
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if training_config['primary_models_meta_labeling'] == True:
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for model_name in current_result.columns:
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primary_model_predictions = current_predictions[model_name]
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primary_meta_result, primary_meta_preds, primary_meta_probabilities, meta_labeling_models = train_meta_labeling_model(
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target_asset=asset[1],
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X_pca = X_pca,
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input_predictions= primary_model_predictions,
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y = y,
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target_returns = target_returns,
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models = model_config['meta_labeling_models'],
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data_config= data_config,
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model_config= model_config,
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training_config= training_config,
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model_suffix = 'meta'
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)
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current_result[model_name] = primary_meta_result
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current_predictions[model_name] = primary_meta_preds
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all_models_for_all_assets[asset[1]]['primary_models'][model_name]['meta_labeling'] = meta_labeling_models
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results = pd.concat([results, current_result], axis=1)
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# With static models, because of the lag in the indicator, the first prediction is NA, so we fill it with zero.
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all_predictions = pd.concat([all_predictions, current_predictions], axis=1).fillna(0.)
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all_probabilities = pd.concat([all_probabilities, current_probabilities], axis=1).fillna(0.)
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# 5. Ensemble primary model predictions (If Ensemble model is present)
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if model_config['ensemble_model'] is not None:
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ensemble_result, ensemble_predictions, _, ensemble_models_one_asset = train_primary_model(
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ticker_to_predict = asset[1],
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original_X = current_predictions,
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X = current_predictions,
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y = y,
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target_returns = target_returns,
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models = [model_config['ensemble_model']],
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method = data_config['method'],
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expanding_window = False,
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sliding_window_size = 1,
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retrain_every = training_config['retrain_every'],
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scaler = training_config['scaler'],
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no_of_classes = data_config['no_of_classes'],
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level = 'ensemble',
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print_results= True,
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)
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ensemble_result, ensemble_predictions = ensemble_result.iloc[:,0], ensemble_predictions.iloc[:,0]
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all_models_for_all_assets[asset[1]]['secondary_model'] = ensemble_models_one_asset
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if len(model_config['meta_labeling_models']) > 0:
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# 3. Train a Meta-labeling model on the averaged level-1 model predictions
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ensemble_meta_result, ensemble_meta_predictions, ensemble_meta_probabilities, ensemble_meta_labeling_models = train_meta_labeling_model(
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target_asset=asset[1],
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X_pca = X_pca,
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input_predictions= ensemble_predictions,
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y = y,
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target_returns = target_returns,
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models = model_config['meta_labeling_models'],
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data_config= data_config,
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model_config= model_config,
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training_config= training_config,
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model_suffix = 'ensemble'
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)
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all_models_for_all_assets[asset[1]]['secondary_model'][model_config['ensemble_model']] = dict(meta_labeling=ensemble_meta_labeling_models)
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results = pd.concat([results, ensemble_meta_result], axis=1)
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all_predictions = pd.concat([all_predictions, ensemble_meta_predictions], axis=1)
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all_probabilities = pd.concat([all_probabilities, ensemble_meta_probabilities], axis=1).fillna(0.)
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return results, all_predictions, all_probabilities, all_models_for_all_assets
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# 4. Save the models
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reporting.all_assets.append(Asset(ticker=asset[1], primary=training_step_primary, secondary=training_step_secondary))
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return reporting
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