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

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* 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
This commit is contained in:
Mark Aron Szulyovszky
2022-01-29 06:41:40 +01:00
committed by GitHub
parent 42a1bc59cb
commit 3eb3ea94e3
42 changed files with 772 additions and 736 deletions
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config/preprocess.py
+6 -8
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@@ -28,11 +28,9 @@ def __preprocess_feature_extractors_config(data_dict: dict) -> dict:
return data_dict
def __preprocess_model_config(model_config:dict) -> dict:
model_config['primary_models'] = [(model_name, get_model(model_name)) for model_name in model_config['primary_models']]
if len(model_config['meta_labeling_models']) > 0:
model_config['meta_labeling_models'] = [(model_name, get_model(model_name)) for model_name in model_config['meta_labeling_models']]
if model_config['ensemble_model'] is not None:
model_config['ensemble_model'] = (model_config['ensemble_model'], get_model(model_config['ensemble_model']))
model_config['directional_models'] = [get_model(model_name) for model_name in model_config['directional_models']]
if len(model_config['meta_models']) > 0:
model_config['meta_models'] = [get_model(model_name) for model_name in model_config['meta_models']]
return model_config
@@ -57,9 +55,9 @@ def __preprocess_event_labeller_config(data_dict: dict) -> dict:
def validate_config(config: Config):
# We need to make sure there's only one output from the pipeline
# If level-2 model is there, we need more than one level-1 models to train
if len(config.meta_labeling_models) > 1: assert len(config.primary_models) > 0
# If meta model is there, we need more than one directional models to train
if len(config.meta_models) > 1: assert len(config.directional_models) > 0
# If there's no level-2 model, we need to have only one level-1 model
if len(config.meta_labeling_models) == 0: assert len(config.primary_models) == 1
if len(config.meta_models) == 0: assert len(config.directional_models) == 1