mirror of
https://github.com/webclinic017/drift.git
synced 2026-08-03 06:07:50 +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
63 lines
2.5 KiB
Python
63 lines
2.5 KiB
Python
import pandas as pd
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from .types import DirectionalTrainingOutcome
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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_models(
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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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models: list[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_outcomes = [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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print_results= True,
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transformations_over_time = transformations_over_time,
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model_over_time = preloaded_training_step.training[index].model_over_time if preloaded_training_step else None
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) for index, model in enumerate(models)]
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return DirectionalTrainingOutcome(training_outcomes, transformations_over_time)
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