mirror of
https://github.com/webclinic017/drift.git
synced 2026-08-08 00:27:50 +00:00
Mark Aron Szulyovszky
74 lines
2.5 KiB
Python
74 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(
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ratio_components_to_keep=0.5,
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sliding_window_size=config.sliding_window_size_base,
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),
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RFETransformation(
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n_feature_to_select=40,
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model=default_feature_selector_classification,
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),
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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
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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 DirectionalTrainingOutcome(training_outcome, transformations_over_time)
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