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

utils/get_prices.py

@@ -2,9 +2,6 @@
 import requests
 import pandas as pd
 
-AV_API_KEY = 'UY5VGSWBE88SHGI6'
-CC_API_KEY = 'bfb8b5f54b21354608020a6654b370617b2fcabd2c8c2ce04ab881682a1d9dc9'
-
 # %%
 def get_crypto_price_crypto_compare(symbol: str, exchange: str, days: int) -> pd.DataFrame:
     api_url = f'https://min-api.cryptocompare.com/data/v2/histoday?fsym={symbol}&tsym={exchange}&limit={days}&api_key={CC_API_KEY}'

utils/launch_wandb.py

@@ -1,27 +0,0 @@
-
-
-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
-
-