mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-29 03:37:45 +00:00
Mark Aron Szulyovszky
6b26643ece
* feat(Transformations): removed feature-selection pre-processing step completely * fix(Core): removed unnecessary `original_X` * fix(Transformations): use the X_expanding_window to transform subsequent data * fix(RFE): should check for model correctly * fix(Config): only re-train the model every 40 timestamp * fix(MetaLabeling): pass in the correct X to meta-labeling step * fix(Transformation): PCA should at least keep as many features as sliding_window_size * feat(Transformations): cache transformations across the same asset * fix(Tests): missing preloaded_transformations arg * chore(Config): got rid of unnecessary 'classification_models' and 'regression_models' dictionary keys
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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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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def get_name(self) -> str:
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return self.model.__class__.__name__
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