mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-28 03:08:01 +00:00
Mark Aron Szulyovszky
3eb3ea94e3
* 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
39 lines
1.3 KiB
Python
39 lines
1.3 KiB
Python
import pickle
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import datetime
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from config.types import Config
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from typing import Optional
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import os
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import warnings
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from training.types import PipelineOutcome
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def save_models(pipeline_outcome: PipelineOutcome, config: Config) -> None:
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dict_for_pickle = dict()
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dict_for_pickle['config'] = config
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dict_for_pickle['pipeline_outcome'] = pipeline_outcome
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date_string = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M")
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if not os.path.exists('output/models'):
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warnings.warn("No folder exists, creating one.")
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os.makedirs('output/models')
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pickle.dump( dict_for_pickle, open( "output/models/{}.p".format(date_string), "wb" ) )
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def load_models(file_name: Optional[str]) -> tuple[PipelineOutcome, Config]:
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if file_name is None:
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warnings.warn("No file name provided, will load latest models and configurations.")
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files_in_directory:list = os.listdir('output/models')
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assert len(files_in_directory) > 0, "No models found in output/models."
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file_name = sorted(files_in_directory)[-1]
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packacked_dict = pickle.load( open( "output/models/{}".format(file_name), "rb" ) )
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config = packacked_dict.pop("config", None)
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pipeline_outcome = packacked_dict.pop("pipeline_outcome", None)
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return pipeline_outcome, config
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