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Wrapper minimizer (#20)

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* apply dataset wrapper on minimizer
* apply changes on minimization notebook
* add black_box_access and unlimited_queries params
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Ola Saadi 2022-04-18 13:14:49 +03:00 • committed by GitHub Enterprise
parent 6b04fd5564
commit ac5d82aab6
6 changed files with 583 additions and 215 deletions
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32
apt/utils/models/sklearn_model.py
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@ -1,3 +1,5 @@
from typing import Optional
import numpy as np
from sklearn.preprocessing import OneHotEncoder
@ -28,13 +30,23 @@ class SklearnClassifier(SklearnModel):
"""
Wrapper class for scikitlearn classification models.
"""
def __init__(self, model: BaseEstimator, output_type: ModelOutputType, **kwargs):
def __init__(self, model: BaseEstimator, output_type: ModelOutputType, black_box_access: Optional[bool] = True,
unlimited_queries: Optional[bool] = True, **kwargs):
"""
Initialize a `SklearnClassifier` wrapper object.
:param model: The original sklearn model object
:param model: The original sklearn model object.
:param output_type: The type of output the model yields (vector/label only for classifiers,
value for regressors)
:param black_box_access: Boolean describing the type of deployment of the model (when in production).
Set to True if the model is only available via query (API) access, i.e.,
only the outputs of the model are exposed, and False if the model internals
are also available. Optional, Default is True.
:param unlimited_queries: If black_box_access is True, this boolean indicates whether a user can perform
unlimited queries to the model API or whether there is a limit to the number of
queries that can be submitted. Optional, Default is True.
"""
super().__init__(model, output_type, **kwargs)
super().__init__(model, output_type, black_box_access, unlimited_queries, **kwargs)
self._art_model = ArtSklearnClassifier(model)
def fit(self, train_data: Dataset, **kwargs) -> None:
@ -63,13 +75,21 @@ class SklearnRegressor(SklearnModel):
"""
Wrapper class for scikitlearn regression models.
"""
def __init__(self, model: BaseEstimator, **kwargs):
def __init__(self, model: BaseEstimator, black_box_access: Optional[bool] = True,
unlimited_queries: Optional[bool] = True, **kwargs):
"""
Initialize a `SklearnRegressor` wrapper object.
:param model: The original sklearn model object
:param model: The original sklearn model object.
:param black_box_access: Boolean describing the type of deployment of the model (when in production).
Set to True if the model is only available via query (API) access, i.e.,
only the outputs of the model are exposed, and False if the model internals
are also available. Optional, Default is True.
:param unlimited_queries: If black_box_access is True, this boolean indicates whether a user can perform
unlimited queries to the model API or whether there is a limit to the number of
queries that can be submitted. Optional, Default is True.
"""
super().__init__(model, ModelOutputType.REGRESSOR_SCALAR, **kwargs)
super().__init__(model, ModelOutputType.REGRESSOR_SCALAR, black_box_access, unlimited_queries, **kwargs)
self._art_model = ScikitlearnRegressor(model)
def fit(self, train_data: Dataset, **kwargs) -> None: