mirror of
https://github.com/IBM/ai-privacy-toolkit.git
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324 lines
13 KiB
Python
324 lines
13 KiB
Python
from abc import ABCMeta, abstractmethod
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from typing import Any, Optional, Callable, Tuple, Union
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from enum import Enum, auto
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import numpy as np
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from apt.utils.datasets import Dataset, Data, OUTPUT_DATA_ARRAY_TYPE
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from art.estimators.classification import BlackBoxClassifier
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from art.utils import check_and_transform_label_format
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def is_one_hot(y: OUTPUT_DATA_ARRAY_TYPE) -> bool:
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return len(y.shape) == 2 and y.shape[1] > 1
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def get_nb_classes(y: OUTPUT_DATA_ARRAY_TYPE) -> int:
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"""
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Get the number of classes from an array of labels
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:param y: the labels
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:type y: numpy array
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:return: the number of classes as integer
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"""
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if y is None:
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return 0
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if type(y) != np.ndarray:
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raise ValueError("Input should be numpy array")
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if is_one_hot(y):
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return y.shape[1]
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else:
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return int(np.max(y) + 1)
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class ModelOutputType(Enum):
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CLASSIFIER_PROBABILITIES = auto() # vector of probabilities
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CLASSIFIER_LOGITS = auto() # vector of logits
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CLASSIFIER_SCALAR = auto() # label only
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REGRESSOR_SCALAR = auto() # value
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class ModelType(Enum):
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SKLEARN_DECISION_TREE = auto()
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SKLEARN_GRADIENT_BOOSTING = auto()
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class ScoringMethod(Enum):
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ACCURACY = auto() # number of correct predictions divided by the number of samples
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MEAN_SQUARED_ERROR = auto() # mean squared error between the predictions and true labels
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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: `Dataset`
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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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class BlackboxClassifier(Model):
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"""
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Wrapper for black-box ML classification models.
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:param model: The training and/or test data along with the model's predictions for the data or a callable predict
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method.
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:type model: `Data` object or Callable
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:param output_type: The type of output the model yields (vector/label only)
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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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Always assumed to be True (black box) for this wrapper.
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:type black_box_access: boolean, optional
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:param unlimited_queries: Boolean indicating whether a user can perform unlimited queries to the model API.
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:type unlimited_queries: boolean, optional
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:param model_type: The type of model this BlackboxClassifier represents. Needed in order to build and/or fit
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similar dummy/shadow models.
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:type model_type: Either a (unfitted) model object of the underlying framework, or a ModelType representing the
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type of the model, 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, model_type: Optional[Union[Any, ModelType]] = None,
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**kwargs):
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super().__init__(model, output_type, black_box_access=True, unlimited_queries=unlimited_queries, **kwargs)
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self._nb_classes = None
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self._input_shape = None
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self._model_type = model_type
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@property
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def nb_classes(self) -> int:
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"""
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Return the number of prediction classes of the model.
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:return: Number of prediction classes of the model.
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"""
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return self._nb_classes
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@property
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def input_shape(self) -> Tuple[int, ...]:
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"""
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Return the shape of input to the model.
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:return: Shape of input to the model.
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"""
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return self._input_shape
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@property
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def model_type(self) -> Optional[Union[Any, ModelType]]:
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"""
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Return the type of the model.
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:return: Either a (unfitted) model object of the underlying framework, or a ModelType representing the type of
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the model, or None (of none provided at init).
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"""
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return self._model_type
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def fit(self, train_data: Dataset, **kwargs) -> None:
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"""
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A blackbox model cannot be fit.
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"""
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raise NotImplementedError
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def predict(self, x: Dataset, **kwargs) -> OUTPUT_DATA_ARRAY_TYPE:
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"""
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Get predictions from the model for input `x`. `x` must be a subset of the data provided in the `model` data in
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`__init__()`.
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:param x: Input samples.
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:type x: `Dataset`
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:return: Predictions from the model as numpy array.
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"""
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return self._art_model.predict(x.get_samples())
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def score(self, test_data: Dataset, scoring_method: Optional[ScoringMethod] = ScoringMethod.ACCURACY, **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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:param scoring_method: The method for scoring predictions. Default is ACCURACY.
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:type scoring_method: `ScoringMethod`, optional
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:return: the score as float (for classifiers, between 0 and 1)
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"""
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predicted = self._art_model.predict(test_data.get_samples())
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y = check_and_transform_label_format(test_data.get_labels(), nb_classes=self._nb_classes)
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if scoring_method == ScoringMethod.ACCURACY:
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return np.count_nonzero(np.argmax(y, axis=1) == np.argmax(predicted, axis=1)) / predicted.shape[0]
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else:
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raise NotImplementedError
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class BlackboxClassifierPredictions(BlackboxClassifier):
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"""
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Wrapper for black-box ML classification models using data and predictions.
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:param model: The training and/or test data along with the model's predictions for the data. Assumes that the data
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is represented as numpy arrays. Labels are expected to either be class probabilities (multi-column) or
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a 1D-array of categorical labels (consecutive integers starting at 0).
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:type model: `Data` object
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:param output_type: The type of output the model yields (vector/label only)
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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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Always assumed to be True for this wrapper.
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:type black_box_access: boolean, optional
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:param unlimited_queries: Boolean indicating whether a user can perform unlimited queries to the model API.
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Always assumed to be False for this wrapper.
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:type unlimited_queries: boolean, optional
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"""
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def __init__(self, model: Data, output_type: ModelOutputType, black_box_access: Optional[bool] = True,
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unlimited_queries: Optional[bool] = True, **kwargs):
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super().__init__(model, output_type, black_box_access=True, unlimited_queries=False, **kwargs)
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x_train_pred = model.get_train_samples()
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y_train_pred = model.get_train_labels()
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x_test_pred = model.get_test_samples()
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y_test_pred = model.get_test_labels()
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if y_train_pred is not None and len(y_train_pred.shape) == 1:
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self._nb_classes = get_nb_classes(y_train_pred)
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y_train_pred = check_and_transform_label_format(y_train_pred, nb_classes=self._nb_classes)
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if y_test_pred is not None and len(y_test_pred.shape) == 1:
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if self._nb_classes is None:
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self._nb_classes = get_nb_classes(y_test_pred)
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y_test_pred = check_and_transform_label_format(y_test_pred, nb_classes=self._nb_classes)
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if x_train_pred is not None and y_train_pred is not None and x_test_pred is not None and y_test_pred is not None:
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if type(y_train_pred) != np.ndarray or type(y_test_pred) != np.ndarray \
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or type(y_train_pred) != np.ndarray or type(y_test_pred) != np.ndarray:
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raise NotImplementedError("X/Y Data should be numpy array")
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x_pred = np.vstack((x_train_pred, x_test_pred))
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y_pred = np.vstack((y_train_pred, y_test_pred))
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elif x_test_pred is not None and y_test_pred is not None:
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x_pred = x_test_pred
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y_pred = y_test_pred
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elif x_train_pred is not None and y_train_pred is not None:
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x_pred = x_train_pred
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y_pred = y_train_pred
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else:
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raise NotImplementedError("Invalid data - None")
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self._nb_classes = get_nb_classes(y_pred)
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self._input_shape = x_pred.shape[1:]
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predict_fn = (x_pred, y_pred)
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self._art_model = BlackBoxClassifier(predict_fn, self._input_shape, self._nb_classes, fuzzy_float_compare=True,
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preprocessing=None)
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class BlackboxClassifierPredictFunction(BlackboxClassifier):
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"""
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Wrapper for black-box ML classification models using a predict function.
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:param model: Function that takes in an `np.ndarray` of input data and returns predictions either as class
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probabilities (multi-column) or a 1D-array of categorical labels (consecutive integers starting at 0).
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:type model: Callable
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:param output_type: The type of output the model yields (vector/label only)
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:type output_type: `ModelOutputType`
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:param input_shape: Shape of input to the model.
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:type input_shape: Tuple[int, ...]
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:param nb_classes: Number of prediction classes of the model.
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:type nb_classes: int
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:param black_box_access: Boolean describing the type of deployment of the model (when in production).
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Always assumed to be True for this wrapper.
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:type black_box_access: boolean, optional
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:param unlimited_queries: Boolean indicating whether a user can perform unlimited queries to the model API.
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:type unlimited_queries: boolean, optional
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"""
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def __init__(self, model: Callable, output_type: ModelOutputType, input_shape: Tuple[int, ...], nb_classes: int,
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black_box_access: Optional[bool] = True, unlimited_queries: Optional[bool] = True, **kwargs):
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super().__init__(model, output_type, black_box_access=True, unlimited_queries=unlimited_queries, **kwargs)
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self._nb_classes = nb_classes
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self._input_shape = input_shape
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self._art_model = BlackBoxClassifier(model, self._input_shape, self._nb_classes, preprocessing=None)
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