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

sweep_meta.yaml

@@ -0,0 +1,56 @@
+program: run_sweep.py
+method: grid
+project: price-forecasting
+name: Meta labelling
+metric:
+  goal: maximize
+  name: sharpe
+parameters:
+    meta_labeling_lvl_1:
+      values: [True, False]
+      distribution: categorical
+    assets:
+      value: ['daily_crypto']
+    other_assets:
+      value: ['daily_etf']
+    exogenous_data:
+      value: ['daily_glassnode']
+    expanding_window_level1:
+      value: False
+    expanding_window_level2:
+      value: True
+    sliding_window_size_level1: 
+      value: 380
+    sliding_window_size_level2: 
+      value: 380
+    n_features_to_select:
+      value: 30
+    dimensionality_reduction:
+      value: True
+    retrain_every: 
+      value: 20
+    scaler:
+      value: 'minmax'
+    method: 
+      value: 'classification'
+    no_of_classes:
+      value: 'two'
+    forecasting_horizon: 
+      value: 1
+    load_non_target_asset: 
+      value: True
+    log_returns: 
+      value: True
+    index_column: 
+      value: 'int'
+    level_1_models: 
+      value: ["LDA", "KNN", "SVC", "CART", "NB", "AB", "RF", "StaticMom"]
+    level_2_model: 
+      values: ["LDA", "KNN", "NB", "AB", "RF", "XGB_two_class"]
+      distribution: categorical
+    own_features:
+      value: ['date_days', 'level_2', 'lags_up_to_5']
+    other_features:
+      value: ['level_2', 'lags_up_to_5']
+    exogenous_features:
+      value: ['standard_scaling']