mirror of
https://github.com/webclinic017/drift.git
synced 2026-08-03 22:27:47 +00:00
Mark Aron Szulyovszky
9488e92597
* feature(MetaLabeling): added hacky prototype * fix(MetaLabeling): drop index until first valid X & y * fix(MetaLabeling): transform both X & y before feature selection * fix(MetaLabeling): got feature selection to work * fix(MetaLabeling): correct values for meta_y * feat(MetaLabeling): created predictions multiplied by bet sizes * feat(Pipeline): print out averaged result * fix(Evaluation): correctly deal with non-discretized data * fix(Pipeline): use the right column names * refactor(Pipeline): move out meta-labeling * refactor(Pipeline): complete refactoring * feat(CI): post results to PR * fix(Pipeline): use the correct filename * chore(Config): removed now redundant feature_selection flag * feat(Models): added SVC * fix(Pipeline): accidentally switched two return values * feat(Sweep): prepared sweep_meta.yaml, moved report_results() into a separate file * fix(Pipeline): wrong function name * fix(Sweep): yaml + run_sweep * fix(Sweep): typo in name * fix(Reporting): only save averaged results * feat(MetaLabeling): use optional meta-labeling step for every lvl1 models, before averaging * feat(Reporting): print out sharpe improvement in meta-labeling step * fix(Sweep): adjusted config, defaulted to good defaults * fix(Sweep): adjusted sweep
62 lines
3.2 KiB
Python
62 lines
3.2 KiB
Python
from utils.evaluate import discretize_threeway_threshold, evaluate_predictions
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from utils.helpers import random_string, equal_except_nan, drop_until_first_valid_index
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from training.training import run_single_asset_trainig
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from feature_selection.feature_selection import select_features
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import pandas as pd
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from models.model_map import default_feature_selector_regression, default_feature_selector_classification
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def run_meta_labeling_training(
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target_asset: str,
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X_pca: pd.DataFrame,
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input_predictions: pd.Series,
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y: pd.Series,
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target_returns: pd.Series,
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data_config: dict,
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model_config: dict,
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training_config: dict
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) -> tuple[pd.Series, pd.Series, pd.DataFrame]:
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discretize = discretize_threeway_threshold(0.33)
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discretized_predictions = input_predictions.apply(discretize)
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meta_y: pd.Series = pd.concat([discretized_predictions, y], axis=1).apply(equal_except_nan, axis = 1)
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print("Feature Selection started")
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backup_model = default_feature_selector_regression if data_config['method'] == 'regression' else default_feature_selector_classification
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meta_feature_selection_input_X, meta_feature_selection_input_y = drop_until_first_valid_index(X_pca, meta_y)
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feature_selection_output = select_features(X = meta_feature_selection_input_X, y = meta_feature_selection_input_y, model = model_config['level_1_models'][0][1], n_features_to_select = training_config['n_features_to_select'], backup_model = backup_model, scaling = training_config['scaler'], data_config_hash = random_string(10))
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meta_selected_features_X = X_pca[feature_selection_output.columns]
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meta_X = pd.concat([meta_selected_features_X, input_predictions, discretized_predictions], axis = 1)
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_, meta_preds, meta_probabilities = run_single_asset_trainig(
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ticker_to_predict = "prediction_correct",
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original_X = meta_X,
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X = meta_X,
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y = meta_y,
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target_returns = target_returns,
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models = [model_config['level_2_model']],
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method = data_config['method'],
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expanding_window = training_config['expanding_window_level2'],
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sliding_window_size = training_config['sliding_window_size_level2'],
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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 = 'two',
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level = 2
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)
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bet_size = meta_probabilities.iloc[:,1]
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avg_predictions_with_sizing = input_predictions * bet_size
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avg_predictions_with_sizing.rename("model_" + target_asset + "_meta_lvl" + str(2), inplace=True)
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meta_result = evaluate_predictions(
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model_name = "Meta",
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target_returns = target_returns,
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y_pred = avg_predictions_with_sizing,
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y_true = y,
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method = 'classification',
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no_of_classes = data_config['no_of_classes'],
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discretize=False
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)
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meta_result.rename("model_" + target_asset + "_meta_lvl" + str(2), inplace=True)
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return meta_result, avg_predictions_with_sizing, meta_probabilities |