mirror of
https://github.com/webclinic017/drift.git
synced 2026-08-09 17:11:05 +00:00
Mark Aron Szulyovszky
f85ee6bb9c
* fix(MetaLabeling): previously misinterpreted meta-labeling, now also multiplying base model's prediction with the meta model's prediction * fix(Evaluate): print results * fix(Evaluate): make sure we have numerical stability in returns * fix(Inference): only output and print stats in training mode * fix(Evaluate): don't add miniscule amount to result
68 lines
2.7 KiB
Python
68 lines
2.7 KiB
Python
import pandas as pd
|
|
|
|
from .types import DirectionalTrainingOutcome, TrainingOutcome
|
|
from training.train_model import train_model
|
|
from training.walk_forward import walk_forward_process_transformations
|
|
|
|
from typing import Optional
|
|
from config.types import Config
|
|
from models.base import Model
|
|
from models.model_map import default_feature_selector_classification
|
|
|
|
from transformations.scaler import get_scaler
|
|
from transformations.rfe import RFETransformation
|
|
from transformations.pca import PCATransformation
|
|
|
|
def train_directional_models(
|
|
X: pd.DataFrame,
|
|
y: pd.Series,
|
|
forward_returns: pd.Series,
|
|
config: Config,
|
|
models: list[Model],
|
|
from_index: Optional[pd.Timestamp],
|
|
preloaded_training_step: Optional[DirectionalTrainingOutcome] = None,
|
|
) -> DirectionalTrainingOutcome:
|
|
|
|
if preloaded_training_step is None:
|
|
print("Preprocess transformations")
|
|
transformations_over_time = walk_forward_process_transformations(
|
|
X = X,
|
|
y = y,
|
|
forward_returns = forward_returns,
|
|
expanding_window = config.expanding_window_base,
|
|
window_size = config.sliding_window_size_base,
|
|
retrain_every = config.retrain_every,
|
|
from_index = from_index,
|
|
transformations= [
|
|
get_scaler(config.scaler),
|
|
PCATransformation(ratio_components_to_keep=0.5, sliding_window_size=config.sliding_window_size_base),
|
|
RFETransformation(n_feature_to_select=40, model=default_feature_selector_classification)
|
|
],
|
|
)
|
|
else:
|
|
transformations_over_time = preloaded_training_step.transformations
|
|
|
|
def print_stats(outcome: TrainingOutcome) -> TrainingOutcome:
|
|
if config.mode == 'training':
|
|
print(outcome.stats)
|
|
return outcome
|
|
|
|
training_outcomes = [print_stats(train_model(
|
|
ticker_to_predict = config.target_asset[1],
|
|
X = X,
|
|
y = y,
|
|
forward_returns = forward_returns,
|
|
model = model,
|
|
expanding_window = config.expanding_window_base,
|
|
sliding_window_size = config.sliding_window_size_base,
|
|
retrain_every = config.retrain_every,
|
|
from_index = from_index,
|
|
no_of_classes = config.no_of_classes,
|
|
level = 'primary',
|
|
output_stats= config.mode == 'training',
|
|
transformations_over_time = transformations_over_time,
|
|
model_over_time = preloaded_training_step.training[index].model_over_time if preloaded_training_step else None
|
|
)) for index, model in enumerate(models)]
|
|
return DirectionalTrainingOutcome(training_outcomes, transformations_over_time)
|
|
|