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https://github.com/IBM/ai-privacy-toolkit.git
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Revert to having generalize_using_transform as an instance param (passed at init) and throwing an exception when used incorrectly.
Signed-off-by: abigailt <abigailt@il.ibm.com>
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abigailt
parent
256dfbbc71
commit
5e84f3fac4
2 changed files with 64 additions and 32 deletions
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@ -68,6 +68,12 @@ class GeneralizeToRepresentative(BaseEstimator, MetaEstimatorMixin, TransformerM
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:param is_regression: Whether the model is a regression model or not (if False, assumes a classification model).
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Default is False.
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:type is_regression: boolean, optional
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:param generalize_using_transform: Indicates how to calculate NCP and accuracy during the generalization
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process. True means that the `transform` method is used to transform original
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data into generalized data that is used for accuracy and NCP calculation.
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False indicates that the `generalizations` structure should be used.
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Default is True.
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:type generalize_using_transform: boolean, optional
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"""
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def __init__(self, estimator: Union[BaseEstimator, Model] = None,
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@ -77,7 +83,8 @@ class GeneralizeToRepresentative(BaseEstimator, MetaEstimatorMixin, TransformerM
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encoder: Optional[Union[OrdinalEncoder, OneHotEncoder]] = None,
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features_to_minimize: Optional[Union[np.ndarray, list]] = None,
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train_only_features_to_minimize: Optional[bool] = True,
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is_regression: Optional[bool] = False):
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is_regression: Optional[bool] = False,
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generalize_using_transform: bool = True):
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self.estimator = estimator
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if estimator is not None and not issubclass(estimator.__class__, Model):
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@ -96,6 +103,7 @@ class GeneralizeToRepresentative(BaseEstimator, MetaEstimatorMixin, TransformerM
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self.train_only_features_to_minimize = train_only_features_to_minimize
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self.is_regression = is_regression
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self.encoder = encoder
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self.generalize_using_transform = generalize_using_transform
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self._ncp_scores = NCPScores()
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self._feature_data = None
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self._categorical_values = {}
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@ -199,11 +207,14 @@ class GeneralizeToRepresentative(BaseEstimator, MetaEstimatorMixin, TransformerM
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:return: Array containing the representative values to which each record in ``X`` is mapped, as numpy array or
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pandas DataFrame (depending on the type of ``X``), shape (n_samples, n_features)
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"""
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if not self.generalize_using_transform:
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raise ValueError('fit_transform method called even though generalize_using_transform parameter was False. '
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'This can lead to inconsistent results.')
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self.fit(X, y, features_names, dataset=dataset)
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return self.transform(X, features_names, dataset=dataset)
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def fit(self, X: Optional[DATA_PANDAS_NUMPY_TYPE] = None, y: Optional[DATA_PANDAS_NUMPY_TYPE] = None,
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features_names: Optional = None, dataset: ArrayDataset = None, generalize_using_transform: bool = True):
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features_names: Optional = None, dataset: ArrayDataset = None):
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"""Learns the generalizations based on training data. Also sets the fit_score and generalizations_score in
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self.ncp.
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@ -217,12 +228,6 @@ class GeneralizeToRepresentative(BaseEstimator, MetaEstimatorMixin, TransformerM
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:param dataset: Data wrapper containing the training input samples and the predictions of the original model
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on the training data. Either ``X``, ``y`` OR ``dataset`` need to be provided, not both.
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:type dataset: `ArrayDataset`, optional
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:param generalize_using_transform: Indicates how to calculate NCP and accuracy during the generalization
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process. True means that the `transform` method is used to transform original
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data into generalized data that is used for accuracy and NCP calculation.
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False indicates that the `generalizations` structure should be used.
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Default is True.
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:type generalize_using_transform: boolean, optional
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:return: self
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"""
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@ -329,7 +334,7 @@ class GeneralizeToRepresentative(BaseEstimator, MetaEstimatorMixin, TransformerM
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# self._cells currently holds the generalization created from the tree leaves
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self._calculate_generalizations(x_test)
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if generalize_using_transform:
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if self.generalize_using_transform:
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generalized = self._generalize_from_tree(x_test, x_prepared_test, nodes, self.cells, self._cells_by_id)
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else:
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generalized = self._generalize_from_generalizations(x_test, self.generalizations)
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@ -359,7 +364,7 @@ class GeneralizeToRepresentative(BaseEstimator, MetaEstimatorMixin, TransformerM
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self._attach_cells_representatives(x_prepared, used_x_train, y_train, nodes)
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self._calculate_generalizations(x_test)
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if generalize_using_transform:
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if self.generalize_using_transform:
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generalized = self._generalize_from_tree(x_test, x_prepared_test, nodes, self.cells,
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self._cells_by_id)
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else:
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@ -384,12 +389,12 @@ class GeneralizeToRepresentative(BaseEstimator, MetaEstimatorMixin, TransformerM
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removed_feature = self._remove_feature_from_generalization(x_test, x_prepared_test,
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nodes, y_test,
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self._feature_data, accuracy,
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generalize_using_transform)
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self.generalize_using_transform)
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if removed_feature is None:
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break
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self._calculate_generalizations(x_test)
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if generalize_using_transform:
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if self.generalize_using_transform:
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generalized = self._generalize_from_tree(x_test, x_prepared_test, nodes, self.cells,
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self._cells_by_id)
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else:
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@ -401,8 +406,8 @@ class GeneralizeToRepresentative(BaseEstimator, MetaEstimatorMixin, TransformerM
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# calculate iLoss
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x_test_dataset = ArrayDataset(x_test, features_names=self._features)
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self._ncp_scores.fit_score = self.calculate_ncp(x_test_dataset, generalize_using_transform)
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self._ncp_scores.generalizations_score = self.calculate_ncp(x_test_dataset, False)
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self._ncp_scores.fit_score = self.calculate_ncp(x_test_dataset)
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self._ncp_scores.generalizations_score = self.calculate_ncp(x_test_dataset)
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# Return the transformer
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return self
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@ -422,12 +427,15 @@ class GeneralizeToRepresentative(BaseEstimator, MetaEstimatorMixin, TransformerM
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:return: Array containing the representative values to which each record in ``X`` is mapped, as numpy array or
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pandas DataFrame (depending on the type of ``X``), shape (n_samples, n_features)
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"""
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if not self.generalize_using_transform:
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raise ValueError('transform method called even though generalize_using_transform parameter was False. This '
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'can lead to inconsistent results.')
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transformed = self._inner_transform(X, features_names, dataset)
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transformed_dataset = ArrayDataset(transformed, features_names=self._features)
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self._ncp_scores.transform_score = self.calculate_ncp(transformed_dataset, True)
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self._ncp_scores.transform_score = self.calculate_ncp(transformed_dataset)
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return transformed
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def calculate_ncp(self, samples: ArrayDataset, generalize_using_transform: bool = True):
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def calculate_ncp(self, samples: ArrayDataset):
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"""
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Compute the NCP score of the generalization. Calculation is based on the value of the
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generalize_using_transform param. If samples are provided, updates stored ncp value to the one computed on the
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@ -438,11 +446,6 @@ class GeneralizeToRepresentative(BaseEstimator, MetaEstimatorMixin, TransformerM
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:param samples: The input samples to compute the NCP score on.
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:type samples: ArrayDataset, optional. feature_names should be set.
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:param generalize_using_transform: Indicates how to calculate NCP and accuracy during the generalization process.
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True means that the `transform` method is used to transform original data into
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generalized data that is used for accuracy and NCP calculation. False indicates
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that the `generalizations` structure should be used. Default is True.
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:type generalize_using_transform: boolean, optional
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:return: NCP score as float.
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"""
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if not samples.features_names:
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@ -454,7 +457,7 @@ class GeneralizeToRepresentative(BaseEstimator, MetaEstimatorMixin, TransformerM
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self._feature_data = self._get_feature_data(samples_pd)
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total_samples = samples_pd.shape[0]
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if generalize_using_transform:
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if self.generalize_using_transform:
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generalizations = self._calculate_cell_generalizations()
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# count how many records are mapped to each cell
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counted = np.zeros(samples_pd.shape[0]) # to mark records we already counted
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@ -256,8 +256,8 @@ def test_minimizer_params_not_transform(cells):
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model = SklearnClassifier(base_est, ModelOutputType.CLASSIFIER_PROBABILITIES)
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model.fit(ArrayDataset(x, y))
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gen = GeneralizeToRepresentative(model, cells=cells)
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ncp = gen.calculate_ncp(samples, generalize_using_transform=False)
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gen = GeneralizeToRepresentative(model, cells=cells, generalize_using_transform=False)
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ncp = gen.calculate_ncp(samples)
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assert (ncp > 0.0)
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@ -304,10 +304,10 @@ def test_minimizer_ncp(data_two_features):
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target_accuracy = 0.4
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train_dataset = ArrayDataset(x, predictions, features_names=features)
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gen1 = GeneralizeToRepresentative(model, target_accuracy=target_accuracy)
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gen1.fit(dataset=train_dataset, generalize_using_transform=False)
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gen1 = GeneralizeToRepresentative(model, target_accuracy=target_accuracy, generalize_using_transform=False)
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gen1.fit(dataset=train_dataset)
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ncp1 = gen1.ncp.fit_score
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ncp2 = gen1.calculate_ncp(ad1, generalize_using_transform=False)
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ncp2 = gen1.calculate_ncp(ad1)
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gen2 = GeneralizeToRepresentative(model, target_accuracy=target_accuracy)
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gen2.fit(dataset=train_dataset)
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@ -348,10 +348,10 @@ def test_minimizer_ncp_categorical(data_four_features):
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train_dataset = ArrayDataset(x, predictions, features_names=features)
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gen1 = GeneralizeToRepresentative(model, target_accuracy=target_accuracy,
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categorical_features=categorical_features)
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gen1.fit(dataset=train_dataset, generalize_using_transform=False)
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categorical_features=categorical_features, generalize_using_transform=False)
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gen1.fit(dataset=train_dataset)
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ncp1 = gen1.ncp.fit_score
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ncp2 = gen1.calculate_ncp(ad1, generalize_using_transform=False)
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ncp2 = gen1.calculate_ncp(ad1)
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gen2 = GeneralizeToRepresentative(model, target_accuracy=target_accuracy, categorical_features=categorical_features)
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gen2.fit(dataset=train_dataset)
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@ -381,10 +381,10 @@ def test_minimizer_fit_not_transform(data_two_features):
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if predictions.shape[1] > 1:
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predictions = np.argmax(predictions, axis=1)
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target_accuracy = 0.5
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gen = GeneralizeToRepresentative(model, target_accuracy=target_accuracy)
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gen = GeneralizeToRepresentative(model, target_accuracy=target_accuracy, generalize_using_transform=False)
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train_dataset = ArrayDataset(x, predictions, features_names=features)
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gen.fit(dataset=train_dataset, generalize_using_transform=False)
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gen.fit(dataset=train_dataset)
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gener = gen.generalizations
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expected_generalizations = {'ranges': {'age': [], 'height': [157.0]}, 'categories': {}, 'untouched': []}
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@ -954,3 +954,32 @@ def test_untouched():
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gener = gen.generalizations
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expected_generalizations = {'ranges': {'age': [38, 39]}, 'categories': {}, 'untouched': ['gender']}
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compare_generalizations(gener, expected_generalizations)
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def test_errors():
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features = ['age', 'height']
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X = np.array([[23, 165],
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[45, 158],
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[56, 123],
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[67, 154],
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[45, 149],
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[42, 166],
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[73, 172],
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[94, 168],
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[69, 175],
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[24, 181],
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[18, 190]])
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y = np.array([1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0])
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base_est = DecisionTreeClassifier(random_state=0, min_samples_split=2,
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min_samples_leaf=1)
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model = SklearnClassifier(base_est, ModelOutputType.CLASSIFIER_PROBABILITIES)
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model.fit(ArrayDataset(X, y))
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ad = ArrayDataset(X)
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predictions = model.predict(ad)
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if predictions.shape[1] > 1:
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predictions = np.argmax(predictions, axis=1)
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gen = GeneralizeToRepresentative(model, generalize_using_transform=False)
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train_dataset = ArrayDataset(X, predictions, features_names=features)
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gen.fit(dataset=train_dataset)
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with pytest.raises(ValueError):
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gen.transform(X)
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