refactor(Reporting): only report the last model's results, moved wandb-related functions to reporting (#69)

* refactor(Reporting): only report the last model's results, moved wandb-related functions to `reporting`

* fix(Reporting): use .mean() on axis 1 to retain the metrics, fixed get_model_name()

* fix(Config): sweep file syntax

* fix(Config): changed hyperparameter search method to "bayes"

* chore(Sweep): adjusted sweep config based on the results we saw (removed Momentum as well)

* fix(Sweep): only use classification method for now, we're not yet prepared for regression

config.py

@@ -0,0 +1,56 @@
+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
+
+
+def validate_config(model_config:dict, training_config:dict, data_config:dict):
+    # We need to make sure there's only one output from the pipeline
+    # We're not prepared for more than 1 level-2 models at the moment
+    assert len(model_config["level_2_models"]) <= 1
+    # If level-2 model is there, we need more than one level-1 models to train
+    if len(model_config["level_2_models"]) == 1: assert len(model_config["level_1_models"]) > 0
+    # If there's no level-2 model, we need to have only one level-1 model
+    if len(model_config["level_2_models"]) == 0: assert len(model_config["level_1_models"]) == 1
+
+def get_model_name(model_config:dict) -> str:
+    if len(model_config["level_2_models"]) == 1:
+        return model_config["level_2_models"][0][0]
+    elif len(model_config["level_1_models"]) == 1:
+        return model_config["level_1_models"][0][0]
+    else:
+        raise Exception("No model name found")