mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-28 11:17:47 +00:00
Daniel Szemerey
516c8bcc87
* fix, feat: Fixed inference processing data. Add transformation attribute. * feat: Added transformations step, refractored the loop to make more sense (divided the train and inference loop). * feat: Truncated models over time and transformations over time. Fixed some typing aswell. * fix: Fixed a number of out of array problems. * feat: Inference now works! * fix(Steps): runtime error not checking for None * fix(Steps): preloaded transformers are not optional anymore, sped up training by temporary increasing the retrain_every * fix(CI): disable ray memory monitoring * refactor(Inference): removed truncate_models and replaced it with filling X with NaN until inference should start * feat(Inference): added index_from parameter * fix(Tests): walk_forward test * refactor(Pipeline): only predict one asset * refactor(Inference): removed select_models step, inference code moved to run_inference.py so it matches convention (similar to run_pipeline.py) * fix(Evaluation): adjust transaction costs * fix(Config): adjusted retrain_every Co-authored-by: Daniel Szemerey <szemereydaniel@gmail.com> Co-authored-by: Mark Aron Szulyovszky <mark.szulyovszky@gmail.com>
60 lines
3.1 KiB
Python
60 lines
3.1 KiB
Python
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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(model_config:dict, training_config:dict, data_config:dict) -> tuple[dict, dict, dict]:
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model_config = __preprocess_model_config(model_config, data_config['method'])
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data_config = __preprocess_feature_extractors_config(data_config)
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data_config = __preprocess_data_collections_config(data_config)
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validate_config(model_config, training_config, data_config)
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return model_config, training_config, data_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, method:str) -> 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(model_config:dict, training_config:dict, data_config:dict):
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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(model_config["meta_labeling_models"]) > 1: assert len(model_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(model_config["meta_labeling_models"]) == 0: assert len(model_config["primary_models"]) == 1
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def get_model_name(model_config:dict) -> str:
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if len(model_config["meta_labeling_models"]) > 0:
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return model_config["meta_labeling_models"][0][0]
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elif len(model_config["primary_models"]) == 1:
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return model_config["primary_models"][0][0]
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else:
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raise Exception("No model name found")
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