mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-28 03:08:01 +00:00
Mark Aron Szulyovszky
b1c04afb13
* refactor(Naming): use `primary_models` & `meta_labeling_models` * refactor(Naming): using primary * meta_labeling across config and in pipeline * feat(Pipeline): added back Ensemble models * fix(Pipeline): compiler error * fix(Config): typo * chore(Pipeline): removed unused averaging step * revert the changes in discretizing * chore(Pipeline): remove sharpe improvement logging * fix(Pipeline): ensemble predictions should be a pd.Series instead of a DataFrame * fix(Pipeline): discard unnecessary ensemble_probabilities * fix(Pipeline): fixes regarding various meta-labeling ensemble bugs * fix(Reporting): use the new naming convention * fix(Reporting): use the right variable * feat(Sweep): new sweep for ensemble models * fix(Sweep): config reference * fix(Config): simplified dev config * fix(Models): use the faster LR model * fix(Models): use LGBM in the meta-labeling model for speed * fix(Selection): always use the first model for feature selection, commented out caching from select_features() as it's close to redundant in terms of speed
56 lines
2.8 KiB
Python
56 lines
2.8 KiB
Python
|
|
from utils.helpers import flatten
|
|
from feature_extractors.feature_extractor_presets import presets as feature_extractor_presets
|
|
from models.model_map import model_map
|
|
from data_loader.collections import data_collections
|
|
|
|
def preprocess_config(model_config:dict, training_config:dict, data_config:dict) -> tuple[dict, dict, dict]:
|
|
model_config = __preprocess_model_config(model_config, data_config['method'])
|
|
data_config = __preprocess_feature_extractors_config(data_config)
|
|
data_config = __preprocess_data_collections_config(data_config)
|
|
|
|
validate_config(model_config, training_config, data_config)
|
|
return model_config, training_config, data_config
|
|
|
|
def __preprocess_feature_extractors_config(data_dict: dict) -> dict:
|
|
data_dict = data_dict.copy()
|
|
keys = ['own_features', 'other_features', 'exogenous_features']
|
|
for key in keys:
|
|
preset_names = data_dict[key]
|
|
data_dict[key] = flatten([feature_extractor_presets[preset_name] for preset_name in preset_names])
|
|
return data_dict
|
|
|
|
def __preprocess_model_config(model_config:dict, method:str) -> dict:
|
|
model_config['primary_models'] = [(model_name, model_map[method + '_models'][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, model_map[method + '_models'][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'], model_map['ensemble_models'][model_config['ensemble_model']])
|
|
|
|
return model_config
|
|
|
|
def __preprocess_data_collections_config(data_dict: dict) -> dict:
|
|
data_dict = data_dict.copy()
|
|
keys = ['assets', 'other_assets', 'exogenous_data']
|
|
for key in keys:
|
|
preset_names = data_dict[key]
|
|
data_dict[key] = flatten([data_collections[preset_name] for preset_name in preset_names])
|
|
return data_dict
|
|
|
|
|
|
def validate_config(model_config:dict, training_config:dict, data_config:dict):
|
|
# 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(model_config["meta_labeling_models"]) > 1: assert len(model_config["primary_models"]) > 0
|
|
# If there's no level-2 model, we need to have only one level-1 model
|
|
if len(model_config["meta_labeling_models"]) == 0: assert len(model_config["primary_models"]) == 1
|
|
|
|
def get_model_name(model_config:dict) -> str:
|
|
if len(model_config["meta_labeling_models"]) > 0:
|
|
return model_config["meta_labeling_models"][0][0]
|
|
elif len(model_config["primary_models"]) == 1:
|
|
return model_config["primary_models"][0][0]
|
|
else:
|
|
raise Exception("No model name found")
|
|
|