mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-28 11:17:47 +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
31 lines
836 B
Python
31 lines
836 B
Python
from __future__ import annotations
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from statsmodels.tsa.base.tsa_model import TimeSeriesModel
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from models.base import Model
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import numpy as np
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from copy import deepcopy
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class StatsModel(Model):
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# This is work in progress
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data_transformation = 'transformed'
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only_column = None
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model_type = 'ml'
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predict_window_size = 'single_timestamp'
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def __init__(self, model: TimeSeriesModel):
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self.model = model
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def fit(self, X: np.ndarray, y: np.ndarray) -> None:
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self.model.fit(X, y)
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def predict(self, X) -> tuple[float, np.ndarray]:
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pred = self.model.predict(X).item()
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return (pred, np.array([0]))
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def clone(self) -> StatsModel:
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return StatsModel(deepcopy(self.model))
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def initialize_network(self, input_dim:int, output_dim:int):
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pass
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