mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-28 03:08:01 +00:00
Daniel Szemerey
ee35332f58
* feat: Added base functions for Neural Net. * feat: Added function to handle Neural Nets. * fix: Fixed fit loop * feat: Neural Net trains now, need to test it. * feat: Prediction now works on the neural net. * fix: Put back config and run_pipeline.py * fix: Took out import from run_pipeline. * fix(Models): added get_name(), adjusted pytorch model output size * fix(Tests): fixed tests Co-authored-by: Mark Aron Szulyovszky <mark.szulyovszky@gmail.com>
106 lines
2.9 KiB
Python
106 lines
2.9 KiB
Python
from __future__ import annotations
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from typing import Literal, Optional, Union
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from sklearn.base import clone
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from abc import ABC, abstractmethod
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import numpy as np
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import copy
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import pytorch_lightning as pl
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import numpy as np
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from data_loader.pytorch_dataset import get_dataloader
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class Model(ABC):
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data_scaling: Literal["scaled", "unscaled"]
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feature_selection: Literal["on", "off"]
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# data_format: Literal["wide", "narrow"]
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only_column: Optional[str]
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model_type: Literal['ml', 'static']
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predict_window_size: Literal['single_timestamp', 'window_size']
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@abstractmethod
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def fit(self, X: np.ndarray, y: np.ndarray) -> None:
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raise NotImplementedError
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@abstractmethod
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def predict(self, X: np.ndarray) -> tuple[float, np.ndarray]:
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raise NotImplementedError
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@abstractmethod
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def clone(self) -> Model:
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raise NotImplementedError
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@abstractmethod
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def get_name(self) -> str:
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raise NotImplementedError
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@abstractmethod
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def initialize_network(self, input_dim:int, output_dim:int):
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pass
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class SKLearnModel(Model):
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data_scaling = 'scaled'
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only_column = None
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feature_selection = 'on'
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model_type = 'ml'
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predict_window_size = 'single_timestamp'
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def __init__(self, model):
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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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probability = self.model.predict_proba(X).squeeze()
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return (pred, probability)
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def clone(self) -> SKLearnModel:
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return SKLearnModel(clone(self.model))
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def get_name(self) -> str:
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return self.model.__class__.__name__
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def initialize_network(self, input_dim:int, output_dim:int):
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pass
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class LightningNeuralNetModel(Model):
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data_scaling = 'scaled'
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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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