mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-28 03:08:01 +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
49 lines
2.2 KiB
Python
49 lines
2.2 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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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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expanding_window: bool,
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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 = [pd.Series(index=y.index, dtype='object').rename(t.get_name()) for t in transformations]
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first_nonzero_return = max(get_first_valid_return_index(forward_returns), get_first_valid_return_index(X.iloc[:,0]), get_first_valid_return_index(y))
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train_from = first_nonzero_return + window_size + 1 if from_index is None else X.index.to_list().index(from_index)
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train_till = len(y)
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iterations_before_retrain = 0
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for index in tqdm(range(train_from, train_till)):
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train_window_start = X.index[first_nonzero_return] if expanding_window else X.index[index - window_size - 1]
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if iterations_before_retrain <= 0 or pd.isna(transformations_over_time[0][index-1]):
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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_index, transformation in enumerate(current_transformations):
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X_expanding_window = transformation.fit_transform(X_expanding_window, y_expanding_window)
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iterations_before_retrain = retrain_every
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for transformation_index, transformation in enumerate(current_transformations):
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transformations_over_time[transformation_index][X.index[index]] = transformation
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iterations_before_retrain -= 1
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return transformations_over_time
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