feat(Sweep): separated level-1 and level-2 sweep configs, skip assets with too few samples to train on, simplified model mapping (#84)

* feat: Added ensemble models to sweep and configured naming convention.

* fix: Default value was misconfigured.

* feat(Sweep): separated level-1 and level-2 sweep configs, skip assets with too few samples to train on, simplified model mapping

* fix(Sweep): syntax error

* chore(Sweep): set sweep names accordingly

* fix(Sweep): set sliding window

* fix(Sweep): adjusted sweep config

* fix(Sweep): removed invalid feature extractor preset

Co-authored-by: Mark Aron Szulyovszky <mark.szulyovszky@gmail.com>

sweep_level_2.yaml

@@ -0,0 +1,49 @@
+program: run_sweep.py
+method: bayes
+project: price-forecasting
+name: Level-2 models
+metric:
+  goal: maximize
+  name: sharpe
+parameters:
+    path : 
+      value: 'data/'
+    expanding_window:
+      values: [True, False]
+      distribution: categorical
+    sliding_window_size: 
+      values: [180, 280, 380]
+      distribution: categorical
+    retrain_every: 
+      values: [10, 20, 30]
+      distribution: categorical
+    scaler:
+      value: 'minmax'
+    include_original_data_in_ensemble:
+      values: [True, False]
+      distribution: categorical
+    method: 
+      value: 'classification'
+    no_of_classes:
+      values: ['two', 'three-balanced', 'three-imbalanced']
+      distribution: categorical
+    forecasting_horizon: 
+      value: 1
+    load_other_assets: 
+      values: [True, False]
+      distribution: categorical
+    log_returns: 
+      value: True
+    index_column: 
+      value: 'int'
+    level_1_models: 
+      value: ["LR", "LDA", "KNN", "CART", "NB", "AB", "RF", "StaticMom"]
+    level_2_models: 
+      values: [["LR"], ["LDA"], ["KNN"], ["CART"], ["NB"], ["AB"], ["RF"], ["Ensemble_Average"]]
+      distribution: categorical
+    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