mirror of
https://github.com/webclinic017/drift.git
synced 2026-08-02 05:37:47 +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
39 lines
1.2 KiB
Python
39 lines
1.2 KiB
Python
from __future__ import annotations
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from models.base import Model
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import numpy as np
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from models.pytorch.pytorch_dataset import get_dataloader
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import copy
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import pytorch_lightning as pl
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class LightningNeuralNetModel(Model):
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method = 'regression'
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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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''' Standard lightning methods '''
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def __init__(self, model, max_epochs=5):
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self.model = model
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self.trainer = pl.Trainer(max_epochs=max_epochs)
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def fit(self, X: np.ndarray, y: np.ndarray) -> None:
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train_dataloader = self.__prepare_data(X.astype(float), y.astype(float))
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self.trainer.fit(self.model, train_dataloader)
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def predict(self, X: np.ndarray) -> tuple[float, np.ndarray]:
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return self.model(X)
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def clone(self):
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model_copy = copy.deepcopy(self.model)
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return LightningNeuralNetModel(model_copy)
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''' Non-standard lightning methods '''
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def __prepare_data(self, X:np.ndarray, y:np.ndarray):
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dataloader = get_dataloader(X, y)
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return dataloader
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def initialize_network(self, input_dim:int, output_dim:int):
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self.model.initialize_network(input_dim, output_dim)
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