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>

utils/launch_wandb.py

@@ -0,0 +1,27 @@
+
+
+def launch_wandb(project_name:str, default_config:dict, sweep:bool=False):
+    from wandb_setup import get_wandb
+    wandb = get_wandb()   
+    
+    if type(wandb) == type(None): 
+        return None
+    elif sweep:
+        wandb.init(project=project_name,  config = default_config)             
+        return wandb
+    else:
+        wandb.init(project=project_name, config = default_config, reinit=True)
+        return wandb
+    
+    
+def seperate_configs(wandb, model_config:dict, training_config:dict, data_config:dict) -> tuple[dict,dict,dict]:
+    config:dict = wandb.config
+
+    if type(wandb) is not type(None):
+        for k in training_config: training_config[k] = config[k]
+        for k in model_config: model_config[k] = config[k]
+        # for k in data_config: data_config[k] = config[k]
+
+    return model_config, training_config, data_config
+
+