mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-28 11:17:47 +00:00
Mark Aron Szulyovszky
9d47ee942d
* 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
63 lines
2.8 KiB
Python
63 lines
2.8 KiB
Python
from .types 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
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from data_loader.collections import data_collections
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from labeling.eventfilters_map import eventfilters_map
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from labeling.labellers_map import labellers_map
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from models.sklearn import SKLearnModel
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from sklearn.ensemble import VotingClassifier
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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_dict = __preprocess_event_filter_config(config_dict)
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config_dict = __preprocess_event_labeller_config(config_dict)
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config_dict['no_of_classes'] = 'two'
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config_dict['mode'] = 'training'
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config = Config(**config_dict)
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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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directional_models = [get_model(model_name) for model_name in model_config['directional_models']]
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model_config.pop('directional_models')
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model_config['directional_model'] = SKLearnModel(VotingClassifier([(m.name, m)for m in directional_models], voting ='soft'))
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if len(model_config['meta_models']) > 0:
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meta_models = [get_model(model_name) for model_name in model_config['meta_models']]
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model_config['meta_model'] = SKLearnModel(VotingClassifier([(m.name, m)for m in meta_models], voting ='soft'))
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model_config.pop('meta_models')
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return model_config
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def __preprocess_data_collections_config(data_dict: dict) -> dict:
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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 __preprocess_event_filter_config(data_dict: dict) -> dict:
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data_dict['event_filter'] = eventfilters_map[data_dict['event_filter']]
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return data_dict
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def __preprocess_event_labeller_config(config_dict: dict) -> dict:
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config_dict['labeling'] = labellers_map[config_dict['labeling']](config_dict['forecasting_horizon'])
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return config_dict
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