mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-28 03:08:01 +00:00
Mark Aron Szulyovszky
80 lines
2.5 KiB
Python
80 lines
2.5 KiB
Python
import pandas as pd
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from training.types import TransformationsOverTime
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from utils.helpers import get_first_valid_return_index
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from tqdm import tqdm
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from transformations.base import Transformation
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from typing import Optional
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from data_loader.types import ForwardReturnSeries, XDataFrame, ySeries
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import ray
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from utils.parallel import parallel_compute_with_bar
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def walk_forward_process_transformations(
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X: XDataFrame,
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y: ySeries,
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forward_returns: ForwardReturnSeries,
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window_size: int,
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retrain_every: int,
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from_index: Optional[pd.Timestamp],
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transformations: list[Transformation],
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) -> TransformationsOverTime:
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transformations_over_time = [
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pd.Series(index=y.index).rename(t.get_name()) for t in transformations
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]
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first_nonzero_return = max(
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get_first_valid_return_index(forward_returns),
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get_first_valid_return_index(X.iloc[:, 0]),
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get_first_valid_return_index(y),
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)
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train_from = (
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first_nonzero_return + window_size + 1
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if from_index is None
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else X.index.to_list().index(from_index)
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)
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train_till = len(y)
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processed_transformations = parallel_compute_with_bar(
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[
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preprocess_transformations_window.remote(
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X,
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y,
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transformations,
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first_nonzero_return,
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index,
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)
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for index in range(train_from, train_till, retrain_every)
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]
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)
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for transformation, index_time in processed_transformations:
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for transformation_index, transformation in enumerate(transformation):
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transformations_over_time[transformation_index][
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X.index[index_time]
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] = transformation
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return transformations_over_time
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@ray.remote
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def preprocess_transformations_window(
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X: XDataFrame,
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y: ySeries,
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transformations: list[Transformation],
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first_nonzero_return: int,
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index: int,
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) -> tuple[list[Transformation], int]:
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train_window_start = X.index[first_nonzero_return]
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train_window_end = X.index[index - 1]
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X_expanding_window = X[train_window_start:train_window_end]
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y_expanding_window = y[train_window_start:train_window_end]
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current_transformations = [t.clone() for t in transformations]
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for transformation in current_transformations:
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X_expanding_window = transformation.fit_transform(
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X_expanding_window, y_expanding_window
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)
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return (current_transformations, index)
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