apunkt/ai-privacy-toolkit
1
0
Fork 0
mirror of https://github.com/IBM/ai-privacy-toolkit.git synced 2026-05-07 02:52:39 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

Support regression models (#19)

Browse source 
* support DecisionTreeRegressor

* support regression models

* Update membership_inference_dp_diabetes_reg.ipynb
This commit is contained in:
olasaadi 2022-01-26 14:30:58 +02:00 • committed by GitHub
parent a9a93c8a3a
commit cb9278ddb5
No known key found for this signature in database
GPG key ID: 4AEE18F83AFDEB23
3 changed files with 273 additions and 12 deletions
Download patch file Download diff file Expand all files Collapse all files

25
apt/anonymization/anonymizer.py
View file

@ -3,7 +3,7 @@ import pandas as pd
from scipy.spatial import distance
from collections import Counter
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
from sklearn.preprocessing import OneHotEncoder
from typing import Union, Optional
@ -15,7 +15,9 @@ class Anonymize:
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):
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.
@ -23,6 +25,7 @@ class Anonymize:
in case of numpy data.
:param categorical_features: The list of categorical features (should only be supplied when passing data as a
pandas dataframe.
: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")
@ -32,6 +35,7 @@ class Anonymize:
self.k = k
self.quasi_identifiers = quasi_identifiers
self.categorical_features = categorical_features
self.is_regression = is_regression
def anonymize(self, x: Union[np.ndarray, pd.DataFrame], y: Union[np.ndarray, pd.DataFrame]) \
-> Union[np.ndarray, pd.DataFrame]:
@ -58,7 +62,10 @@ class Anonymize:
x_prepared = self._modify_categorical_features(x_anonymizer_train)
else:
x_prepared = x_anonymizer_train
self.anonymizer = DecisionTreeClassifier(random_state=10, min_samples_split=2, min_samples_leaf=self.k)
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_numpy(x, x_prepared, cells_by_id)
@ -69,7 +76,10 @@ class Anonymize:
x_anonymizer_train = x.loc[:, self.quasi_identifiers]
# need to one-hot encode before training the decision tree
x_prepared = self._modify_categorical_features(x_anonymizer_train)
self.anonymizer = DecisionTreeClassifier(random_state=10, min_samples_split=2, min_samples_leaf=self.k)
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_pandas(x, x_prepared, cells_by_id)
@ -82,9 +92,10 @@ class Anonymize:
if feature == -2: # leaf node
leaves.append(node)
hist = [int(i) for i in self.anonymizer.tree_.value[node][0]]
label_hist = self.anonymizer.tree_.value[node][0]
label = int(self.anonymizer.classes_[np.argmax(label_hist)])
cell = {'label': label, 'hist': hist, 'id': int(node)}
# 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())