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https://github.com/webclinic017/drift.git
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Mark Aron SzulyovszkyandGitHub
75157c6285
* feat(Labeling): purge overlapping events, sort dataframe at loading time * fix(Linter): ran * refactor(Labeling): moved purge_overlapping_events one abstraction level higher * fix(Data): renamed class * fix(Data): corrected parameter name * fix(Config): parameters * fix(Data): fixed path * fix(Data): uncommented required code * feat(EventFilters): use vol based CUSUM * fix(Config): only retrain every 2000 samples * fix(Config): filter out even more events * fix(Inference): added remove_overlapping_events * refactor(Types): simplified type hierarchy
62 lines
1.9 KiB
Python
62 lines
1.9 KiB
Python
import pandas as pd
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from transformations.base import Transformation
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from .types import TrainingOutcome
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from training.train_model import train_model
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from training.walk_forward import walk_forward_process_transformations
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from typing import Optional
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from config.types import Config
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from models.base import Model
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def train_directional_model(
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X: pd.DataFrame,
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y: pd.Series,
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forward_returns: pd.Series,
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config: Config,
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model: Model,
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transformations: list[Transformation],
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from_index: Optional[pd.Timestamp],
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preloaded_training_step: Optional[TrainingOutcome] = None,
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) -> TrainingOutcome:
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if preloaded_training_step is None:
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print("Preprocess transformations")
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transformations_over_time = walk_forward_process_transformations(
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X=X,
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y=y,
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forward_returns=forward_returns,
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window_size=config.sliding_window_size,
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retrain_every=config.retrain_every,
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from_index=from_index,
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transformations=transformations,
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)
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else:
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transformations_over_time = preloaded_training_step.transformations
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training_outcome = train_model(
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ticker_to_predict=config.target_asset[1],
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X=X,
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y=y,
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forward_returns=forward_returns,
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model=model,
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sliding_window_size=config.sliding_window_size,
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retrain_every=config.retrain_every,
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from_index=from_index,
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no_of_classes=config.no_of_classes,
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level="primary",
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output_stats=config.mode == "training",
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transformations_over_time=transformations_over_time,
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model_over_time=preloaded_training_step.model_over_time
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if preloaded_training_step
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else None,
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)
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if config.mode == "training":
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print(training_outcome.stats)
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return TrainingOutcome(
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**vars(training_outcome), transformations=transformations_over_time
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)
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