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https://github.com/webclinic017/drift.git
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Mark Aron Szulyovszky
e80fffdb65
* refactor(Config): use a Config object instead of dictionary of dictionaries! * fix(Config): use default_ensemble_config * fix(Portfolio): fixed portfolio construction
56 lines
2.6 KiB
Python
56 lines
2.6 KiB
Python
from config.config import Config, RawConfig
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from utils.helpers import flatten
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from feature_extractors.feature_extractor_presets import presets as feature_extractor_presets
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from models.model_map import get_model_map
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from data_loader.collections import data_collections
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def preprocess_config(raw_config: RawConfig) -> Config:
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config_dict = vars(raw_config)
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config_dict = __preprocess_model_config(config_dict)
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config_dict = __preprocess_feature_extractors_config(config_dict)
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config_dict = __preprocess_data_collections_config(config_dict)
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config = Config(**config_dict)
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validate_config(config)
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return config
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def __preprocess_feature_extractors_config(data_dict: dict) -> dict:
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data_dict = data_dict.copy()
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keys = ['own_features', 'other_features', 'exogenous_features']
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for key in keys:
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preset_names = data_dict[key]
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data_dict[key] = flatten([feature_extractor_presets[preset_name] for preset_name in preset_names])
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return data_dict
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def __preprocess_model_config(model_config:dict) -> dict:
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model_map = get_model_map(model_config)
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model_config['primary_models'] = [(model_name, model_map['primary_models'][model_name]) for model_name in model_config['primary_models']]
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if len(model_config['meta_labeling_models']) > 0:
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model_config['meta_labeling_models'] = [(model_name, model_map['primary_models'][model_name]) for model_name in model_config['meta_labeling_models']]
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if model_config['ensemble_model'] is not None:
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model_config['ensemble_model'] = (model_config['ensemble_model'], model_map['ensemble_models'][model_config['ensemble_model']])
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return model_config
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def __preprocess_data_collections_config(data_dict: dict) -> dict:
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data_dict = data_dict.copy()
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keys = ['assets', 'other_assets', 'exogenous_data']
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for key in keys:
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preset_names = data_dict[key]
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data_dict[key] = flatten([data_collections[preset_name] for preset_name in preset_names])
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target_asset = next(iter([asset for asset in data_dict['assets'] if asset[1] == data_dict['target_asset']]), None)
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if target_asset is None: raise Exception('Target asset wasnt found in assets')
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data_dict['target_asset'] = target_asset
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return data_dict
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def validate_config(config: Config):
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# We need to make sure there's only one output from the pipeline
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# If level-2 model is there, we need more than one level-1 models to train
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if len(config.meta_labeling_models) > 1: assert len(config.primary_models) > 0
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# If there's no level-2 model, we need to have only one level-1 model
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if len(config.meta_labeling_models) == 0: assert len(config.primary_models) == 1
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