ai-privacy-toolkit/apt/anonymization/anonymizer.py

import numpy as np
import pandas as pd
from scipy.spatial import distance
from collections import Counter

from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
from sklearn.preprocessing import OneHotEncoder
from apt.utils.datasets import ArrayDataset, DATA_PANDAS_NUMPY_TYPE

from typing import Union, Optional


class Anonymize:
    """
    Class for performing tailored, model-guided anonymization of training datasets for ML models.

    Based on the implementation described in: https://arxiv.org/abs/2007.13086
    """

    def __init__(self, k: int, quasi_identifiers: Union[np.ndarray, list], categorical_features: Optional[list] = None,
                 is_regression=False):
        """
        :param k: The privacy parameter that determines the number of records that will be indistinguishable from each
                  other (when looking at the quasi identifiers). Should be at least 2.
        :param quasi_identifiers: The features that need to be minimized. It can be a list of feature names (strings) if
                  dataset.feature_names is set, otherwise a list of indexes (integers).
        :param categorical_features: The list of categorical features. It can be a list of feature names (strings) if
                  dataset.feature_names is set, otherwise a list of indexes (integers).
        :param is_regression: Boolean param indicates that is is a regression problem.
        """
        if k < 2:
            raise ValueError("k should be a positive integer with a value of 2 or higher")
        if quasi_identifiers is None or len(quasi_identifiers) < 1:
            raise ValueError("The list of quasi-identifiers cannot be empty")
        self.k = k
        self.quasi_identifiers = quasi_identifiers
        self.categorical_features = categorical_features
        self.is_regression = is_regression
        self.features_names = None

    def anonymize(self, dataset: ArrayDataset) -> DATA_PANDAS_NUMPY_TYPE:
        """
        Method for performing model-guided anonymization.

        :param dataset: Data wrapper containing the training data for the model and the predictions of the
                        original model on the training data.
        :return: An array containing the anonymized training dataset.
        """
        if dataset.features_names is not None:
            self.features_names = dataset.features_names
            # if features is None, use numbers instead of names
        elif dataset.get_samples().shape[1] != 0:
            self.features_names = [i for i in range(dataset.get_samples().shape[1])]
        else:
            raise ValueError('No data provided')
        if not set(self.quasi_identifiers).issubset(set(self.features_names)):
            raise ValueError('Quasi identifiers should bs a subset of the supplied features or indexes in range of '
                             'the data columns')
        if self.categorical_features and not set(self.categorical_features).issubset(set(self.features_names)):
            raise ValueError('Categorical features should bs a subset of the supplied features or indexes in range of '
                             'the data columns')
        self.quasi_identifiers = [i for i, v in enumerate(self.features_names) if v in self.quasi_identifiers]
        if self.categorical_features:
            self.categorical_features = [i for i, v in enumerate(self.features_names) if v in self.categorical_features]

        transformed = self._anonymize(dataset.get_samples().copy(), dataset.get_labels())
        if dataset.is_pandas:
            return pd.DataFrame(transformed, columns=self.features_names)
        else:
            return transformed

    def _anonymize(self, x, y):
        if x.shape[0] != y.shape[0]:
            raise ValueError("x and y should have same number of rows")
        x_anonymizer_train = x[:, self.quasi_identifiers]
        if x.dtype.kind not in 'iufc':
            if not self.categorical_features:
                raise ValueError('when supplying an array with non-numeric data, categorical_features must be defined')
            x_prepared = self._modify_categorical_features(x_anonymizer_train)
        else:
            x_prepared = x_anonymizer_train
        if self.is_regression:
            self.anonymizer = DecisionTreeRegressor(random_state=10, min_samples_split=2, min_samples_leaf=self.k)
        else:
            self.anonymizer = DecisionTreeClassifier(random_state=10, min_samples_split=2, min_samples_leaf=self.k)

        self.anonymizer.fit(x_prepared, y)
        cells_by_id = self._calculate_cells(x, x_prepared)
        return self._anonymize_data(x, x_prepared, cells_by_id)

    def _calculate_cells(self, x, x_anonymizer_train):
        # x is original data, x_anonymizer_train is only QIs + 1-hot encoded
        cells_by_id = {}
        leaves = []
        for node, feature in enumerate(self.anonymizer.tree_.feature):
            if feature == -2:  # leaf node
                leaves.append(node)
                hist = [int(i) for i in self.anonymizer.tree_.value[node][0]]
                # TODO we may change the method for choosing representative for cell
                # label_hist = self.anonymizer.tree_.value[node][0]
                # label = int(self.anonymizer.classes_[np.argmax(label_hist)])
                cell = {'label': 1, 'hist': hist, 'id': int(node)}
                cells_by_id[cell['id']] = cell
        self.nodes = leaves
        self._find_representatives(x, x_anonymizer_train, cells_by_id.values())
        return cells_by_id

    def _find_representatives(self, x, x_anonymizer_train, cells):
        # x is original data, x_anonymizer_train is only QIs + 1-hot encoded
        node_ids = self._find_sample_nodes(x_anonymizer_train)
        for cell in cells:
            cell['representative'] = {}
            # get all rows in cell
            indexes = [index for index, node_id in enumerate(node_ids) if node_id == cell['id']]
            # TODO: should we filter only those with majority label? (using hist)
            rows = x[indexes]
            for feature in self.quasi_identifiers:
                values = rows[:, feature]
                if self.categorical_features and feature in self.categorical_features:
                    # find most common value
                    cell['representative'][feature] = Counter(values).most_common(1)[0][0]
                else:
                    # find the mean value (per feature)
                    median = np.median(values)
                    min_value = max(values)
                    min_dist = float("inf")
                    for value in values:
                        dist = distance.euclidean(value, median)
                        if dist < min_dist:
                            min_dist = dist
                            min_value = value
                    cell['representative'][feature] = min_value

    def _find_sample_nodes(self, samples):
        paths = self.anonymizer.decision_path(samples).toarray()
        node_set = set(self.nodes)
        return [(list(set([i for i, v in enumerate(p) if v == 1]) & node_set))[0] for p in paths]

    def _find_sample_cells(self, samples, cells_by_id):
        node_ids = self._find_sample_nodes(samples)
        return [cells_by_id[node_id] for node_id in node_ids]

    def _anonymize_data(self, x, x_anonymizer_train, cells_by_id):
        cells = self._find_sample_cells(x_anonymizer_train, cells_by_id)
        index = 0
        for row in x:
            cell = cells[index]
            index += 1
            for feature in cell['representative']:
                row[feature] = cell['representative'][feature]
        return x

    def _modify_categorical_features(self, x):
        encoder = OneHotEncoder()
        one_hot_encoded = encoder.fit_transform(x)
        return one_hot_encoded