feat(Data): add option to predict 3 classes (#79)

* feat(Data): add option to predict 3 classes

* feat(Evaluation): added ability to evaluate 3 class predictions

* chore(Config): set sensible config for regression models

* feat(Data): added option to use balanced or imbalanced three-class data

* feat(Evaluate): correctly track "no_of_samples" now that we have three classes

* chore(Sweep): remove probably not useful scaler values from sweep

sweep.yaml

@@ -21,22 +21,23 @@ parameters:
       values: [10, 20, 30]
       distribution: categorical
     scaler:
-      values: ['minmax', 'normalize', 'minmax', 'standardize', 'none']
+      values: ['minmax', 'none']
       distribution: categorical
     include_original_data_in_ensemble:
-      values: [True, False]
-      distribution: categorical
+      values: False
     method: 
       value: 'classification'
+    no_of_classes:
+      values: ['two', 'three-balanced', 'three-imbalanced']
+      distribution: categorical
     forecasting_horizon: 
-      values: [1,2,3,4,5,6,7,8,9,10]
+      values: 1
       distribution: categorical
     load_other_assets: 
       values: [True, False]
       distribution: categorical
     log_returns: 
-      values: [True, False]
-      distribution: categorical
+      value: True
     index_column: 
       value: 'int'
     level_1_models: 
@@ -44,7 +45,6 @@ parameters:
       distribution: categorical
     level_2_models: 
       value: []
-      distribution: constant
     own_features:
       values: [['only_mom', 'date_days'], [], ['level_1', 'date_days'], ['level_1', 'date_days', 'level_2']]
       distribution: categorical