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fix(Selection): dynamic step size for feature selection (#123)

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* fix(Selection): dynamic step size for feature selection

* refactor(Pipeline): type definition

* chore(Cache): renamed clear_cache script

* feat(Config): dynamic feature selection is now a toggleable feature

* fix(Training): not passing in necessary parameter
This commit is contained in:
Mark Aron Szulyovszky
2022-01-07 18:45:03 +01:00
committed by GitHub
parent 34105f7ca1
commit 57f63f1e93
5 changed files with 12 additions and 7 deletions
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training/meta_labeling.py
+2 -2
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@@ -24,7 +24,7 @@ def run_meta_labeling_training(
print("Feature Selection started")
backup_model = default_feature_selector_regression if data_config['method'] == 'regression' else default_feature_selector_classification
meta_feature_selection_input_X, meta_feature_selection_input_y = drop_until_first_valid_index(X_pca, meta_y)
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))
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'], dynamic_feature_selection = training_config['dynamic_feature_selection'], data_config_hash = random_string(10))
meta_selected_features_X = X_pca[feature_selection_output.columns]
meta_X = pd.concat([meta_selected_features_X, input_predictions, discretized_predictions], axis = 1)
@@ -59,4 +59,4 @@ def run_meta_labeling_training(
)
meta_result.rename("model_" + target_asset + "_meta_lvl" + str(2), inplace=True)
return meta_result, avg_predictions_with_sizing, meta_probabilities, all_models_single_asset
return meta_result, avg_predictions_with_sizing, meta_probabilities, all_models_single_asset