mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-28 19:27:47 +00:00
Mark Aron Szulyovszky
3eb3ea94e3
* refactor(Training): added InferenceResult & TrainedModel types * refactor(Pipeline): introduced TrainingOutcome, BetSizingWithMetaOutcome, etc. * fix(Pipeline): getting it to compile * refactor(WalkForward): separate preprocessing step * feat(Pipeline): separate out transformations processing step * refactor(Pipeline): use the Directional model terminology, put bet_sizing into pipeline instead of hiding it in a step * refactor(WalkForward): moved functions to separate folder * fix(WalkForward): use sparse array to store models, process transformations in parallel (lot faster) * fix(Tests): and evaluation * fix(Tests): for realz * fix(Inference): preloading everything now, renamed primary models to directional models * fix(BetSizing): was running transformations on the wrong data, oops * fix(BetSizing): concatenated on the wrong axis accidentally * fix(Reporting): able to use the new Stats type * fix(BetSizing): renamed int column names * fix(Portfolio): name the column properly * fix(Reporting): rename the correct Series, lol * fix(Inference): walk_forwad_inference() can deal with models not being aligned with the starting index * fix(WalkForward): accidentally using the wrong index * fix(WalkForward): use the correct indicies to fetch last model/transformations * fix(CI): changed the name of the results
23 lines
829 B
Python
23 lines
829 B
Python
from .types import WeightsSeries, EnsembleOutcome
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import pandas as pd
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from utils.evaluate import evaluate_predictions
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from data_loader.types import ForwardReturnSeries, ySeries
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from typing import Literal
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def ensemble_weights(
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input_weights: list[WeightsSeries],
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forward_returns: ForwardReturnSeries,
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y: ySeries,
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no_of_classes: Literal['two', 'three-balanced', 'three-imbalanced'],
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) -> EnsembleOutcome:
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weights = pd.concat(input_weights, axis=1).mean(axis=1)
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stats = evaluate_predictions(
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forward_returns = forward_returns,
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y_pred = weights,
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y_true = y,
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no_of_classes = no_of_classes,
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print_results = False,
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discretize = True,
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)
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return EnsembleOutcome(weights, stats)
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