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

tests/test_evaluation.py

@@ -53,9 +53,7 @@ class EvenOddStubModel(Model):
 
     def clone(self):
         return self
-    
-    def get_name(self) -> str:
-        return 'test'
+
 
     def initialize_network(self, input_dim: int, output_dim: int):
         pass
@@ -65,28 +63,28 @@ def test_evaluation():
     X, y = __generate_even_odd_test_data(no_of_rows)
     
     window_length = 10
+    retrain_every = 10
 
     model = EvenOddStubModel(window_length = window_length)
     
-    model_over_time, transformations_over_time = walk_forward_train(
-        model_name='test',
+    model_over_time = walk_forward_train(
         model=model,
         X=X,
         y=y,
         forward_returns=y,
         expanding_window=False,
         window_size=window_length,
-        retrain_every=10,
+        retrain_every=retrain_every,
         from_index=None,
-        transformations=[],
-        preloaded_transformations=None)
+        transformations_over_time=[])
     predictions, _ = walk_forward_inference(
         model_name='test',
         model_over_time=model_over_time,
-        transformations_over_time=transformations_over_time,
+        transformations_over_time=[],
         X=X,
         expanding_window=False,
         window_size=window_length,
+        retrain_every = retrain_every,
         from_index=None,
     )
     
@@ -98,7 +96,6 @@ def test_evaluation():
     processed_predictions_to_match_returns = predictions * 0.1
 
     result = evaluate_predictions(
-        model_name='test',
         forward_returns=fake_forward_returns,
         y_pred=processed_predictions_to_match_returns,
         y_true=y,

tests/test_walk_forward.py

@@ -11,16 +11,16 @@ def __generate_incremental_test_data(no_of_rows) -> tuple[pd.DataFrame, pd.Serie
     
     no_columns = 6
     X = [[row] * no_columns for row in range(no_of_rows)]
-    assert X[0][0] == 0
     assert X[1][0] == 1
     assert X[2][0] == 2
     assert X[3][0] == 3
+    assert X[4][0] == 4
     X = pd.DataFrame(X)
 
     y = [row+1 for row in range(no_of_rows)]
-    assert y[0] == 1
     assert y[1] == 2
     assert y[2] == 3
+    assert y[3] == 4
     y = pd.Series(y)
 
     return X, y
@@ -52,9 +52,6 @@ class IncrementingStubModel(Model):
 
     def clone(self):
         return self
-
-    def get_name(self) -> str:
-        return 'test'
     
     def initialize_network(self, input_dim: int, output_dim: int):
         pass
@@ -63,29 +60,29 @@ def test_walk_forward_train_test():
     X, y = __generate_incremental_test_data(no_of_rows)
 
     window_length = 10
+    retrain_every = 10
 
     model = IncrementingStubModel(window_length = window_length)
 
-    model_over_time, transformations_over_time = walk_forward_train(
-        model_name='test',
+    model_over_time = walk_forward_train(
         model=model,
         X=X,
         y=y,
         forward_returns=y,
         expanding_window=False,
         window_size=window_length,
-        retrain_every=10,
+        retrain_every=retrain_every,
         from_index=None,
-        transformations=[],
-        preloaded_transformations=None
+        transformations_over_time=[],
     )
     predictions, _ = walk_forward_inference(
         model_name='test',
         model_over_time=model_over_time,
-        transformations_over_time=transformations_over_time,
+        transformations_over_time=[],
         X=X,
         expanding_window=False,
         window_size=window_length,
+        retrain_every=retrain_every,
         from_index=None,
     )