mirror of
https://github.com/webclinic017/drift.git
synced 2026-08-03 14:17:47 +00:00
Mark Aron Szulyovszky
b1c04afb13
* refactor(Naming): use `primary_models` & `meta_labeling_models` * refactor(Naming): using primary * meta_labeling across config and in pipeline * feat(Pipeline): added back Ensemble models * fix(Pipeline): compiler error * fix(Config): typo * chore(Pipeline): removed unused averaging step * revert the changes in discretizing * chore(Pipeline): remove sharpe improvement logging * fix(Pipeline): ensemble predictions should be a pd.Series instead of a DataFrame * fix(Pipeline): discard unnecessary ensemble_probabilities * fix(Pipeline): fixes regarding various meta-labeling ensemble bugs * fix(Reporting): use the new naming convention * fix(Reporting): use the right variable * feat(Sweep): new sweep for ensemble models * fix(Sweep): config reference * fix(Config): simplified dev config * fix(Models): use the faster LR model * fix(Models): use LGBM in the meta-labeling model for speed * fix(Selection): always use the first model for feature selection, commented out caching from select_features() as it's close to redundant in terms of speed
72 lines
3.5 KiB
Python
72 lines
3.5 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.primary_model import train_primary_model
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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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from models.base import Model
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def train_meta_labeling_model(
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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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models: list[tuple[str, Model]],
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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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model_suffix: str
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) -> tuple[pd.Series, pd.Series, pd.DataFrame, dict]:
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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 = 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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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, all_models_single_asset = train_primary_model(
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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 = models,
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method = 'classification',
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expanding_window = training_config['expanding_window_meta_labeling'],
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sliding_window_size = training_config['sliding_window_size_meta_labeling'],
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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 = 'meta_labeling',
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print_results = False
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)
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if len(models) > 1:
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meta_preds = meta_preds.mean(axis = 1)
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bet_size = meta_probabilities[meta_probabilities.columns[1::2]].mean(axis = 1)
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else:
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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 + "_" + model_suffix, 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 = 'two',
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print_results = True,
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discretize=False
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)
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meta_result.rename("model_" + target_asset + "_" + model_suffix, inplace=True)
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return meta_result, avg_predictions_with_sizing, meta_probabilities, all_models_single_asset
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