mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-27 18:57:55 +00:00
Mark Aron Szulyovszky
92 lines
3.1 KiB
Python
92 lines
3.1 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 (
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presets as feature_extractor_presets,
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)
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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(
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[feature_extractor_presets[preset_name] for preset_name in preset_names]
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)
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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 = [
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get_model(model_name) for model_name in model_config["directional_models"]
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]
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model_config.pop("directional_models")
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model_config["directional_model"] = SKLearnModel(
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VotingClassifier([(m.name, m) for m in directional_models], voting="soft")
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)
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if len(model_config["meta_models"]) > 0:
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meta_models = [
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get_model(model_name) for model_name in model_config["meta_models"]
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]
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model_config["meta_model"] = SKLearnModel(
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VotingClassifier([(m.name, m) for m in meta_models], voting="soft")
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)
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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(
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[data_collections[preset_name] for preset_name in preset_names]
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)
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target_asset = next(
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iter(
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[
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asset
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for asset in data_dict["assets"]
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if asset[1] == data_dict["target_asset"]
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]
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),
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None,
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)
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if target_asset is None:
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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"]](
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config_dict["forecasting_horizon"]
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)
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return config_dict
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