refactor(Naming): use new convention, added Ensemble model parameter back, support multiple Meta-Labeling models (#132)

* 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

sweep_primary_models.yaml

@@ -0,0 +1,61 @@
+program: run_sweep.py
+method: bayes
+project: price-forecasting
+name: Level-1 models
+metric:
+  goal: maximize
+  name: sharpe
+parameters:
+    primary_models_meta_labeling:
+      value: True
+    assets:
+      value: ['daily_crypto']
+    other_assets:
+      value: ['daily_etf']
+    exogenous_data:
+      value: ['daily_glassnode']
+    expanding_window_primary:
+      values: [True, False]
+      distribution: categorical
+    expanding_window_meta_labeling:
+      value: False
+    n_features_to_select:
+      values: [10, 20, 30]
+      distribution: categorical
+    dimensionality_reduction:
+      value: True
+    sliding_window_size_primary: 
+      values: [180, 280, 380, 480, 580]
+      distribution: categorical
+    sliding_window_size_meta_labeling: 
+      value: 1
+    retrain_every: 
+      values: [10, 20, 30]
+      distribution: categorical
+    scaler:
+      value: 'minmax'
+    method: 
+      value: 'classification'
+    no_of_classes:
+      values: ['two', 'three-balanced', 'three-imbalanced']
+      distribution: categorical
+    forecasting_horizon: 
+      value: 1
+    load_non_target_asset: 
+      values: [True, False]
+      distribution: categorical
+    log_returns: 
+      value: True
+    index_column: 
+      value: 'int'
+    primary_models: 
+      values: [["LR_two_class"], ["LDA"], ["KNN"], ["CART"], ["NB"], ["AB"], ["RF"]]
+      distribution: categorical
+    meta_labeling_models: 
+      value: []
+    own_features:
+      values: [['single_mom', 'date_days'], [], ['level_1', 'date_days'], ['date_days', 'level_2']]
+      distribution: categorical
+    other_features:
+      values: [[], ['level_1'], ['level_2']]
+      distribution: categorical