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fd6be8e778
* Bump version to 0.1.0 (breaking changes to some APIs) * Update documentation * Update requirements * gitignore
109 lines
3.9 KiB
Python
109 lines
3.9 KiB
Python
from abc import ABCMeta, abstractmethod
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from typing import Any, Optional
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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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:param model: The original model object (of the underlying ML framework)
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:type model: framework-specific model object
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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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:type output_type: `ModelOutputType`
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:param black_box_access: Boolean describing the type of deployment of the model (when in production).
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Set to True if the model is only available via query (API) access, i.e.,
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only the outputs of the model are exposed, and False if the model internals
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are also available. Default is True.
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:type black_box_access: boolean, optional
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:param unlimited_queries: If black_box_access is True, this boolean indicates whether a user can perform
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unlimited queries to the model API or whether there is a limit to the number of
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queries that can be submitted. Default is True.
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:type unlimited_queries: boolean, optional
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"""
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def __init__(self, model: Any, output_type: ModelOutputType, black_box_access: Optional[bool] = True,
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unlimited_queries: Optional[bool] = True, **kwargs):
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self._model = model
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self._output_type = output_type
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self._black_box_access = black_box_access
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self._unlimited_queries = unlimited_queries
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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 as numpy array.
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"""
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raise NotImplementedError
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@abstractmethod
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def score(self, test_data: Dataset, **kwargs):
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"""
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Score the model using test data.
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:param test_data: Test data.
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:type train_data: `Dataset`
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:return: the score as float (for classifiers, between 0 and 1)
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"""
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return NotImplementedError
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@property
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def model(self) -> Any:
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"""
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Return the underlying 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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@property
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def black_box_access(self) -> bool:
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"""
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Return whether the model is only available via query (API) access, i.e.,
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only the outputs of the model are exposed, or if the model internals are also available.
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:return: True if the model is only available via query (API) access, otherwise False.
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"""
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return self._black_box_access
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@property
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def unlimited_queries(self) -> bool:
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"""
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If black_box_access is True, return whether a user can perform unlimited queries to the model API
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or whether there is a limit to the number of queries that can be submitted.
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:return: True if a user can perform unlimited queries to the model API, otherwise False.
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"""
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return self._unlimited_queries
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