mirror of
https://github.com/webclinic017/drift.git
synced 2026-08-01 13:17:47 +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
51 lines
1.4 KiB
Python
51 lines
1.4 KiB
Python
import pandas as pd
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from dataclasses import dataclass
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from typing import Optional, Dict
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PredictionsSeries = pd.Series
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WeightsSeries = pd.Series
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ProbabilitiesDataFrame = pd.DataFrame
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Stats = Dict[str, float]
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ModelOverTime = pd.Series
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TransformationsOverTime = list[pd.Series]
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@dataclass
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class TrainingOutcome:
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model_id: str
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predictions: PredictionsSeries
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probabilities: ProbabilitiesDataFrame
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stats: Optional[Stats]
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model_over_time: ModelOverTime
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@dataclass
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class EnsembleOutcome:
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weights: WeightsSeries
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stats: Optional[Stats]
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@dataclass
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class BetSizingWithMetaOutcome:
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model_id: str
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meta_training: list[TrainingOutcome]
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meta_transformations: TransformationsOverTime
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weights: WeightsSeries
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stats: Optional[Stats]
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@dataclass
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class DirectionalTrainingOutcome:
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training: list[TrainingOutcome]
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transformations: TransformationsOverTime
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@dataclass
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class PipelineOutcome:
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directional_training: DirectionalTrainingOutcome
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bet_sizing: list[BetSizingWithMetaOutcome]
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ensemble: EnsembleOutcome
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secondary_bet_sizing: Optional[BetSizingWithMetaOutcome]
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def get_output_weights(self) -> WeightsSeries:
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return self.secondary_bet_sizing.weights if self.secondary_bet_sizing else self.ensemble.weights
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def get_output_stats(self) -> Optional[Stats]:
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return self.secondary_bet_sizing.stats if self.secondary_bet_sizing else self.ensemble.stats |