Feature: Added sweep functionality (#65)

* feat: Parametricized model selection works now.

* feat: Fixed errors. Sweep generates and you can run it, but it gives an error for model.only_columns attribute.

* feat: Factored the wandb management, default config managment and the model_dictionary out of the run_pipeline to a seperate file.

* fix: Took out prints and fixed the mismatch of ensemble models when classifing.

* fix(Models): added StaticMomentum model to the dictionary, hopefully fixed sklearn-ex RandomForestRegressor problem

* fix(Dependencies): pin scikit-learn-ex's version, moved map_model_name_to_function to `models`

* feat(Sweep): added `run_sweep.py` shortcut

* feat(Pipeline): skip training a meta model if array is empty

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

default_config.py

@@ -0,0 +1,38 @@
+from utils.load_data import get_crypto_assets
+import feature_extractors.feature_extractor_presets as feature_extractor_presets
+from models.model_map import model_names_classification, model_names_regression
+
+def get_default_config() -> tuple[dict, dict, dict]:
+  
+    training_config = dict(
+        sliding_window_size = 150,
+        retrain_every = 20,
+        scaler = 'minmax', # 'normalize' 'minmax' 'standardize' 'none'
+        include_original_data_in_ensemble = True,
+    )
+
+    data_config = dict(
+        path='data/',
+        all_assets = get_crypto_assets('data/'),
+        load_other_assets= False,
+        log_returns= True,
+        forecasting_horizon = 1,
+        own_features= feature_extractor_presets.date + feature_extractor_presets.level1,
+        other_features= [],
+        index_column= 'int',
+        method= 'classification',
+    )
+
+    # regression_models = ["Lasso", "Ridge", "BayesianRidge", "KNN", "AB", "LR", "MLP", "RF", "SVR"]
+    regression_models = model_names_regression
+    regression_ensemble_models = ['Ensemble_Average']
+    # classification_models = ["LR", "LDA", "KNN", "CART", "NB", "AB", "RF", "StaticMom"]
+    classification_models = model_names_classification        
+    classification_ensemble_models = ['Ensemble_Average']
+
+    model_config = dict(
+        level_1_models = regression_models if data_config['method'] == 'regression' else classification_models,
+        level_2_models = regression_ensemble_models if data_config['method'] == 'regression' else classification_ensemble_models
+    )
+    
+    return model_config, training_config, data_config