Refactor(Training): new outcome types, representative pipeline steps, bet-sizing (#187)

* refactor(Training): added InferenceResult & TrainedModel types

* refactor(Pipeline): introduced TrainingOutcome, BetSizingWithMetaOutcome, etc.

* fix(Pipeline): getting it to compile

* refactor(WalkForward): separate preprocessing step

* feat(Pipeline): separate out transformations processing step

* refactor(Pipeline): use the Directional model terminology, put bet_sizing into pipeline instead of hiding it in a step

* refactor(WalkForward): moved functions to separate folder

* fix(WalkForward): use sparse array to store models, process transformations in parallel (lot faster)

* fix(Tests): and evaluation

* fix(Tests): for realz

* fix(Inference): preloading everything now, renamed primary models to directional models

* fix(BetSizing): was running transformations on the wrong data, oops

* fix(BetSizing): concatenated on the wrong axis accidentally

* fix(Reporting): able to use the new Stats type

* fix(BetSizing): renamed int column names

* fix(Portfolio): name the column properly

* fix(Reporting): rename the correct Series, lol

* fix(Inference): walk_forwad_inference() can deal with models not being aligned with the starting index

* fix(WalkForward): accidentally using the wrong index

* fix(WalkForward): use the correct indicies to fetch last model/transformations

* fix(CI): changed the name of the results

reporting/wandb.py

@@ -2,6 +2,7 @@ import pandas as pd
 from config.types import RawConfig
 from typing import Optional
 from utils.helpers import weighted_average
+from training.types import Stats
 
 def launch_wandb(project_name:str, default_config: RawConfig, sweep:bool=False) -> Optional[object]:
     from wandb_setup import get_wandb
@@ -28,14 +29,13 @@ def override_config_with_wandb_values(wandb: Optional[object], raw_config: RawCo
 
     return RawConfig(**config_dict)
 
-def send_report_to_wandb(results: pd.DataFrame, wandb:Optional[object]):
+def send_report_to_wandb(stats: Stats, wandb:Optional[object]):
     if wandb is None: return
 
     run = wandb.run
     run.save()
 
-    mean_results = weighted_average(results, 'no_of_samples')
-    for key, value in mean_results.iteritems():
+    for key, value in stats.items():
         run.log({ key: value })
 
     run.finish()