feat(Project): use SKLearn models directly, removed custom ensembling, use 5 minute data, batch inference, numba cusum filter (#192)

* feat(Project): use 5 minute data, running training in parallel, sped up cusum filter by 10x with numba

* fix(WalkForward): inference mini-batch parallelization

* fix(WalkForward): don't use the parallel version of any of the functions

* feat(CI): download the data required

* fix(Project): 5min_crypto folder added

* fix(Evaluate): make sure we have numerical stability in returns

* feat(Models): use SKLearn models directly to enable composability

* feat(Inference): batched inference now working, added forecasting_horizon

* fix(Inference): works again

* fix(Inference)

* chore(Models): remove unused Ensemble model

* fix(Labeller): don't just forward shift returns, also take the sum of the data happened until then

* Update test.yml

training/train_model.py

@@ -1,29 +1,10 @@
 import pandas as pd
 from typing import Literal, Optional
-from training.walk_forward import walk_forward_train, walk_forward_inference
+from training.walk_forward import walk_forward_train, walk_forward_inference, walk_forward_inference_batched
 from utils.evaluate import evaluate_predictions
 from models.base import Model
 from .types import ModelOverTime, TransformationsOverTime, TrainingOutcome
 
-def train_models(
-                ticker_to_predict: str,
-                X: pd.DataFrame,
-                y: pd.Series,
-                forward_returns: pd.Series,
-                models: list[Model],
-                expanding_window: bool,
-                sliding_window_size: int,
-                retrain_every: int,
-                from_index: Optional[pd.Timestamp],
-                no_of_classes: Literal['two', 'three-balanced', 'three-imbalanced'],
-                level: str,
-                output_stats: bool,
-                transformations_over_time: TransformationsOverTime,
-                models_over_time: Optional[list[ModelOverTime]]
-    ) -> list[TrainingOutcome]:
-    return [train_model(ticker_to_predict, X, y, forward_returns, model, expanding_window, sliding_window_size, retrain_every, from_index, no_of_classes, level, output_stats, transformations_over_time, models_over_time[index] if models_over_time else None) for index, model in enumerate(models)]
-
-
 def train_model(
                 ticker_to_predict: str,
                 X: pd.DataFrame,
@@ -61,7 +42,8 @@ def train_model(
     else:
         model_id = model_over_time.name
 
-    predictions, probabilities = walk_forward_inference(
+    inference_function = walk_forward_inference if from_index is not None else walk_forward_inference_batched
+    predictions, probabilities = inference_function(
         model_name = model_id,
         model_over_time= model_over_time,
         transformations_over_time = transformations_over_time,