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/walk_forward/train.py

@@ -5,7 +5,7 @@ from utils.helpers import get_first_valid_return_index
 from tqdm import tqdm
 from typing import Optional
 from data_loader.types import ForwardReturnSeries, XDataFrame, ySeries
-
+from copy import deepcopy
 
 def walk_forward_train(
                     model: Model,
@@ -18,7 +18,7 @@ def walk_forward_train(
                     from_index: Optional[pd.Timestamp],
                     transformations_over_time: TransformationsOverTime,
                 ) -> ModelOverTime:
-    models_over_time = pd.Series(index=y.index).rename(model.name)
+    models_over_time = pd.Series(index=y.index, dtype='object').rename(model.name)
 
     first_nonzero_return = max(get_first_valid_return_index(forward_returns), get_first_valid_return_index(X.iloc[:,0]), get_first_valid_return_index(y))
     train_from = first_nonzero_return + window_size + 1 if from_index is None else X.index.to_list().index(from_index)
@@ -43,13 +43,9 @@ def walk_forward_train(
         X_slice = X_slice.to_numpy()
         y_slice = y[train_window_start:train_window_end].to_numpy()
 
-        current_model = model.clone()
-
-        current_model.initialize_network(input_dim = len(X_slice[0]), output_dim=1) 
+        current_model = deepcopy(model)
         current_model.fit(X_slice, y_slice)
 
         models_over_time[X.index[index]] = current_model
-        for transformation_index, transformation in enumerate(current_transformations):
-            transformations_over_time[transformation_index][X.index[index]] = transformation
 
     return models_over_time