refactor(Project): removed regression method (we can still use regression models, but we'll need map them to classification later) (#182)

* refactor(Project): removed regression method (we can still use regression models, but we'll need map them to classes later)

* fix(Training): removed mistakenly left in `method` parameter

config/sweep_primary_models.yaml

@@ -0,0 +1,55 @@
+program: run_sweep.py
+method: grid
+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:
+      value: 50
+    dimensionality_reduction:
+      value: True
+    sliding_window_size_primary: 
+      value: 380
+    sliding_window_size_meta_labeling: 
+      value: 380
+    retrain_every: 
+      values: [10, 20, 30]
+      distribution: categorical
+    scaler:
+      value: 'minmax'
+    no_of_classes:
+      value: 'two'
+    forecasting_horizon: 
+      value: 1
+    load_non_target_asset: 
+      value: True
+    log_returns: 
+      value: True
+    index_column: 
+      value: 'int'
+    primary_models: 
+      values: [['LogisticRegression_two_class'], ['SVC'], ['LDA'], ['KNN'], ['CART'], ['MNB'], ['NB'], ['AB'], ['RFC'], ['XGB_two_class'], ['LGBM']]
+      distribution: categorical
+    meta_labeling_models: 
+      value: ["LGBM", "LogisticRegression_two_class"]
+    own_features:
+      value: ['date_days', 'level_2', 'lags_up_to_5']
+    other_features:
+      value: ['level_2', 'lags_up_to_5']
+    exogenous_features:
+      value: ['z_score']