mirror of
https://github.com/webclinic017/drift.git
synced 2026-08-06 15:47:52 +00:00
Mark Aron Szulyovszky
1856fcad22
* feat(Transformations): added Transformations abstraction & handling in walk_forward_train() & inference() * fix(WalkForward): use Dataframes to call Transformation.fit_transform() * feat(WalkForward): restored option for models to recieve unscaled data * fix(Transformations): output DataFrame as expected * fix(Tests): missing new property
42 lines
1.3 KiB
Python
42 lines
1.3 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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data_transformation = 'transformed'
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only_column = None
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feature_selection = 'off'
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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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def get_name(self) -> str:
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return self.model.__class__.__name__
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