mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-31 20:57:51 +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
40 lines
983 B
Python
40 lines
983 B
Python
from __future__ import annotations
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from typing import Literal, Optional, Union
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from abc import ABC, abstractmethod
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import numpy as np
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# import numpy as np
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class Model(ABC):
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data_transformation: Literal["transformed", "original"]
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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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