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31dc847be1
* feat: Added optional import of models. * fix: Models weren't wrapped into abstract class, fixed it. * chore: Deleted leftover comments. * fix: Same merge commit as on remote. * fix: System wasn't putting in RF because there was no differentiation between RF as regressor and RF as classificator. * fix(Models): use the XGBoostModel wrapper Co-authored-by: Daniel Szemerey <szemereydaniel@gmail.com> Co-authored-by: Mark Aron Szulyovszky <mark.szulyovszky@gmail.com>
78 lines
3.7 KiB
Python
78 lines
3.7 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 get_model_map
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from models.base import Model
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from reporting.types import Reporting
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from typing import Union
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def train_meta_labeling_model(
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target_asset: str,
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X: 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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preloaded_models: Union[list[Reporting.Single_Model], None] = None
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) -> tuple[pd.Series, pd.Series, pd.DataFrame, list[Reporting.Single_Model]]:
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_, _, _, default_feature_selector_regression, default_feature_selector_classification = get_model_map(model_config)
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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, 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[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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preloaded_models = preloaded_models
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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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