feature(MetaLabeling): replaced previous non-functional Ensembling method with Meta-labeling method available for both lvl1 and lvl2 models (#110)

* 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

training/averaged.py

@@ -0,0 +1,25 @@
+
+import pandas as pd
+from utils.evaluate import evaluate_predictions
+
+def average_and_evaluate_predictions(predictions: pd.DataFrame, y: pd.Series, target_returns: pd.Series, data_config: dict) -> tuple[pd.Series, pd.DataFrame]:
+    averaged_predictions = predictions.mean(axis = 1)
+    non_discretized_result = evaluate_predictions(
+        model_name = 'Averaged - Non-discrete',
+        target_returns = target_returns,
+        y_pred = averaged_predictions,
+        y_true = y,
+        method = 'classification',
+        no_of_classes = data_config['no_of_classes'],
+        discretize=False
+    )
+    discretized_result = evaluate_predictions(
+        model_name = 'Averaged - Discrete',
+        target_returns = target_returns,
+        y_pred = averaged_predictions,
+        y_true = y,
+        method = 'classification',
+        no_of_classes = data_config['no_of_classes'],
+        discretize=True
+    )
+    return averaged_predictions, pd.concat([non_discretized_result, discretized_result], axis = 1)