mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-27 18:57:55 +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
66 lines
2.5 KiB
Python
66 lines
2.5 KiB
Python
import pandas as pd
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from .types import DirectionalTrainingOutcome, 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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from models.model_map import default_feature_selector_classification
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from transformations.scaler import get_scaler
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from transformations.rfe import RFETransformation
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from transformations.pca import PCATransformation
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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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from_index: Optional[pd.Timestamp],
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preloaded_training_step: Optional[DirectionalTrainingOutcome] = None,
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) -> DirectionalTrainingOutcome:
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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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expanding_window = config.expanding_window_base,
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window_size = config.sliding_window_size_base,
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retrain_every = config.retrain_every,
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from_index = from_index,
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transformations= [
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get_scaler(config.scaler),
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PCATransformation(ratio_components_to_keep=0.5, sliding_window_size=config.sliding_window_size_base),
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RFETransformation(n_feature_to_select=40, model=default_feature_selector_classification)
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],
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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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expanding_window = config.expanding_window_base,
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sliding_window_size = config.sliding_window_size_base,
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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.training.model_over_time if preloaded_training_step 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 DirectionalTrainingOutcome(training_outcome, transformations_over_time)
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