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

reporting/wandb.py

@@ -0,0 +1,43 @@
+import pandas as pd
+from typing import Optional
+
+def launch_wandb(project_name:str, default_config:dict, sweep:bool=False):
+    from wandb_setup import get_wandb
+    wandb = get_wandb()   
+    
+    if wandb is 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: Optional[object], 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
+
+
+def send_report_to_wandb(results: pd.DataFrame, wandb:Optional[object], project_name: str, model_name: str):
+    if wandb is None: return
+
+    run = wandb.init(project=project_name, config={"model_type": model_name}, reinit=True)
+    wandb.run.name = model_name+ "-" + wandb.run.id
+    wandb.run.save()
+
+    mean_results = results.mean(axis = 1)
+    for key, value in mean_results.iteritems():
+        run.log({"model_type": model_name, key: value })
+
+    run.finish()
+
+
+