mirror of
https://github.com/webclinic017/drift.git
synced 2026-08-03 22:27:47 +00:00
Mark Aron Szulyovszky
f85ee6bb9c
* fix(MetaLabeling): previously misinterpreted meta-labeling, now also multiplying base model's prediction with the meta model's prediction * fix(Evaluate): print results * fix(Evaluate): make sure we have numerical stability in returns * fix(Inference): only output and print stats in training mode * fix(Evaluate): don't add miniscule amount to result
27 lines
938 B
Python
27 lines
938 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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output_stats: bool
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) -> EnsembleOutcome:
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weights = pd.concat(input_weights, axis=1).mean(axis=1)
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if output_stats:
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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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discretize = True,
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)
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print(stats)
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else:
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stats = None
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return EnsembleOutcome(weights, stats)
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