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67 lines
1.8 KiB
Python
67 lines
1.8 KiB
Python
from abc import ABCMeta, abstractmethod
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from typing import Any
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from enum import Enum, auto
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from apt.utils.datasets import Dataset, OUTPUT_DATA_ARRAY_TYPE
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class ModelOutputType(Enum):
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CLASSIFIER_VECTOR = auto() # probabilities or logits
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CLASSIFIER_SCALAR = auto() # label only
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REGRESSOR_SCALAR = auto() # value
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class Model(metaclass=ABCMeta):
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"""
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Abstract base class for ML model wrappers.
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"""
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def __init__(self, model: Any, output_type: ModelOutputType, **kwargs):
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"""
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Initialize a `Model` wrapper object.
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:param model: The original model object (of the underlying ML framework)
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:param output_type: The type of output the model yields (vector/label only for classifiers,
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value for regressors)
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"""
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self._model = model
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self._output_type = output_type
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@abstractmethod
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def fit(self, train_data: Dataset, **kwargs) -> None:
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"""
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Fit the model using the training data.
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:param train_data: Training data.
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:type train_data: `Dataset`
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"""
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raise NotImplementedError
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@abstractmethod
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def predict(self, x: Dataset, **kwargs) -> OUTPUT_DATA_ARRAY_TYPE:
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"""
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Perform predictions using the model for input `x`.
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:param x: Input samples.
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:type x: `np.ndarray` or `pandas.DataFrame`
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:return: Predictions from the model.
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"""
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raise NotImplementedError
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@property
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def model(self) -> Any:
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"""
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Return the model.
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:return: The model.
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"""
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return self._model
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@property
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def output_type(self) -> ModelOutputType:
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"""
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Return the model's output type.
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:return: The model's output type.
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"""
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return self._output_type
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