Files
drift/training/types.py
T
Mark Aron Szulyovszky 3eb3ea94e3 Refactor(Training): new outcome types, representative pipeline steps, bet-sizing (#187)
* 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
2022-01-29 06:41:40 +01:00

51 lines
1.4 KiB
Python

import pandas as pd
from dataclasses import dataclass
from typing import Optional, Dict
PredictionsSeries = pd.Series
WeightsSeries = pd.Series
ProbabilitiesDataFrame = pd.DataFrame
Stats = Dict[str, float]
ModelOverTime = pd.Series
TransformationsOverTime = list[pd.Series]
@dataclass
class TrainingOutcome:
model_id: str
predictions: PredictionsSeries
probabilities: ProbabilitiesDataFrame
stats: Stats
model_over_time: ModelOverTime
@dataclass
class EnsembleOutcome:
weights: WeightsSeries
stats: Stats
@dataclass
class BetSizingWithMetaOutcome:
model_id: str
meta_training: list[TrainingOutcome]
meta_transformations: TransformationsOverTime
weights: WeightsSeries
stats: Stats
@dataclass
class DirectionalTrainingOutcome:
training: list[TrainingOutcome]
transformations: TransformationsOverTime
@dataclass
class PipelineOutcome:
directional_training: DirectionalTrainingOutcome
bet_sizing: list[BetSizingWithMetaOutcome]
ensemble: EnsembleOutcome
secondary_bet_sizing: Optional[BetSizingWithMetaOutcome]
def get_output_weights(self) -> WeightsSeries:
return self.secondary_bet_sizing.weights if self.secondary_bet_sizing else self.ensemble.weights
def get_output_stats(self) -> Stats:
return self.secondary_bet_sizing.stats if self.secondary_bet_sizing else self.ensemble.stats