mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-27 18:57:55 +00:00
Mark Aron Szulyovszky
b5ddee8dce
* feat(HPO): added `run_hpo` script * fix(Linter): ran * feat(HPO): removed any reference to sweep (superseeded by optuna) * fix(HPO): optimize for sharpe * fix(Config): removed glassnode data, save trials from hpo * feat(Labelling): added three-balanced method works again * fix(BetSizing): set the correct class labels * fix(HPO): powerset should return what's expected, added two new normalization methods * fix(Linter): ran * fix(DataLoader): sort the dataframe when fetching data * fix(Config): only take z-score of other assets
103 lines
3.0 KiB
Python
103 lines
3.0 KiB
Python
import pandas as pd
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from training.types import (
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ModelOverTime,
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TransformationsOverTime,
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PredictionsSeries,
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ProbabilitiesDataFrame,
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)
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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 typing import Optional
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from data_loader.types import XDataFrame
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from tqdm import tqdm
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def walk_forward_inference_batched(
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model_name: str,
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model_over_time: ModelOverTime,
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transformations_over_time: TransformationsOverTime,
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X: XDataFrame,
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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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class_labels: list[int],
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from_index: Optional[pd.Timestamp],
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) -> tuple[PredictionsSeries, ProbabilitiesDataFrame]:
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predictions = pd.Series(index=X.index, dtype="object").rename(model_name)
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probabilities = pd.DataFrame(
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index=X.index, columns=[str(label) for label in class_labels]
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)
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inference_from = (
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max(
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get_first_valid_return_index(model_over_time),
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get_first_valid_return_index(X.iloc[:, 0]),
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)
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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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inference_till = X.shape[0]
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first_model = model_over_time[inference_from]
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if first_model.only_column is not None:
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X = X[[column for column in X.columns if first_model.only_column in column]]
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if first_model.data_transformation == "original":
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transformations_over_time = []
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batch_indices = (
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range(inference_from, inference_till, retrain_every)
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if inference_till - inference_from > retrain_every
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else [inference_from]
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)
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batched_results = [
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__inference_from_window(
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index,
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index + retrain_every,
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X,
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model_over_time,
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transformations_over_time,
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expanding_window,
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window_size,
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)
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for index in tqdm(batch_indices)
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]
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for batch in batched_results:
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for index, prediction, probs in batch:
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predictions[X.index[index]] = prediction
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probabilities.loc[X.index[index]] = probs
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return predictions, probabilities
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def __inference_from_window(
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index_start: int,
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index_end: int,
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X: XDataFrame,
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model_over_time: ModelOverTime,
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transformations_over_time: TransformationsOverTime,
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expanding_window: bool,
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window_size: int,
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) -> list[tuple[int, float, pd.Series]]:
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current_model = model_over_time[X.index[index_start]]
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current_transformations = [
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transformation_over_time[X.index[index_start]]
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for transformation_over_time in transformations_over_time
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]
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input_data = X.iloc[index_start:index_end]
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for transformation in current_transformations:
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input_data = transformation.transform(input_data)
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input_data = input_data.to_numpy()
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predictions = current_model.predict(input_data)
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probs = current_model.predict_proba(input_data)
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results = [
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(index_start + index, predictions[index], probs[index])
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for index in range(len(predictions))
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]
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return results
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