Build the dt on all features anon (#23)

* add param to build the DT on all features and not just on QI
* one-hot encoding only for categorical features

apt/anonymization/anonymizer.py

@@ -3,6 +3,9 @@ import pandas as pd
 from scipy.spatial import distance
 from collections import Counter
 
+from sklearn.compose import ColumnTransformer
+from sklearn.impute import SimpleImputer
+from sklearn.pipeline import Pipeline
 from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
 from sklearn.preprocessing import OneHotEncoder
 
@@ -29,10 +32,13 @@ class Anonymize:
     is_regression : Bool, optional
         Whether the model is a regression model or not (if False, assumes
         a classification model). Default is False.
+    train_only_QI : Bool, optional
+        The required method to train data set for anonymization. Default is
+        to train the tree on all features.
     """
 
     def __init__(self, k: int, quasi_identifiers: Union[np.ndarray, list], categorical_features: Optional[list] = None,
-                 is_regression=False):
+                 is_regression=False, train_only_QI=False):
         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:
@@ -42,6 +48,7 @@ class Anonymize:
         self.quasi_identifiers = quasi_identifiers
         self.categorical_features = categorical_features
         self.is_regression = is_regression
+        self.train_only_QI = train_only_QI
 
     def anonymize(self, x: Union[np.ndarray, pd.DataFrame], y: Union[np.ndarray, pd.DataFrame]) \
             -> Union[np.ndarray, pd.DataFrame]:
@@ -54,8 +61,10 @@ class Anonymize:
         :return: An array containing the anonymized training dataset.
         """
         if type(x) == np.ndarray:
+            self.features = [i for i in range(x.shape[1])]
             return self._anonymize_ndarray(x.copy(), y)
         else:  # pandas
+            self.features = x.columns
             if not self.categorical_features:
                 raise ValueError('When supplying a pandas dataframe, categorical_features must be defined')
             return self._anonymize_pandas(x.copy(), y)
@@ -63,7 +72,10 @@ class Anonymize:
     def _anonymize_ndarray(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]
+        x_anonymizer_train = x
+        if self.train_only_QI:
+            # build DT just on QI features
+            x_anonymizer_train = x[:, self.quasi_identifiers]
         if x.dtype.kind not in 'iufc':
             x_prepared = self._modify_categorical_features(x_anonymizer_train)
         else:
@@ -79,7 +91,10 @@ class Anonymize:
     def _anonymize_pandas(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.loc[:, self.quasi_identifiers]
+        x_anonymizer_train = x
+        if self.train_only_QI:
+            # build DT just on QI features
+            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)
         if self.is_regression:
@@ -169,6 +184,21 @@ class Anonymize:
         return x
 
     def _modify_categorical_features(self, x):
-        encoder = OneHotEncoder()
+        # prepare data for DT
-        one_hot_encoded = encoder.fit_transform(x)
+        used_features = self.features
-        return one_hot_encoded
+        if self.train_only_QI:
+            used_features = self.quasi_identifiers
+        numeric_features = [f for f in x.columns if f in used_features and f not in self.categorical_features]
+        categorical_features = [f for f in self.categorical_features if f in used_features]
+        numeric_transformer = Pipeline(
+            steps=[('imputer', SimpleImputer(strategy='constant', fill_value=0))]
+        )
+        categorical_transformer = OneHotEncoder(handle_unknown="ignore", sparse=False)
+        preprocessor = ColumnTransformer(
+            transformers=[
+                ("num", numeric_transformer, numeric_features),
+                ("cat", categorical_transformer, categorical_features),
+            ]
+        )
+        encoded = preprocessor.fit_transform(x)
+        return encoded

tests/test_anonymizer.py

@@ -1,5 +1,8 @@
 import pytest
 import numpy as np
+from sklearn.compose import ColumnTransformer
+from sklearn.impute import SimpleImputer
+from sklearn.pipeline import Pipeline
 from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
 from sklearn.preprocessing import OneHotEncoder
 
@@ -17,7 +20,7 @@ def test_anonymize_ndarray_iris():
 
     k = 10
     QI = [0, 2]
-    anonymizer = Anonymize(k, QI)
+    anonymizer = Anonymize(k, QI, train_only_QI=True)
     anon = anonymizer.anonymize(x_train, pred)
     assert(len(np.unique(anon[:, QI], axis=0)) < len(np.unique(x_train[:, QI], axis=0)))
     _, counts_elements = np.unique(anon[:, QI], return_counts=True)
@@ -27,16 +30,31 @@ def test_anonymize_ndarray_iris():
 
 def test_anonymize_pandas_adult():
     (x_train, y_train), _ = get_adult_dataset()
-    encoded = OneHotEncoder().fit_transform(x_train)
-    model = DecisionTreeClassifier()
-    model.fit(encoded, y_train)
-    pred = model.predict(encoded)
 
     k = 100
+    features = ['age', 'workclass', 'education-num', 'marital-status', 'occupation',
+                'relationship', 'race', 'sex', 'capital-gain', 'capital-loss', 'hours-per-week', 'native-country']
     QI = ['age', 'workclass', 'education-num', 'marital-status', 'occupation', 'relationship', 'race', 'sex',
           'native-country']
     categorical_features = ['workclass', 'marital-status', 'occupation', 'relationship', 'race', 'sex',
                             'native-country']
+    # prepare data for DT
+    numeric_features = [f for f in features if f not in categorical_features]
+    numeric_transformer = Pipeline(
+        steps=[('imputer', SimpleImputer(strategy='constant', fill_value=0))]
+    )
+    categorical_transformer = OneHotEncoder(handle_unknown="ignore", sparse=False)
+    preprocessor = ColumnTransformer(
+        transformers=[
+            ("num", numeric_transformer, numeric_features),
+            ("cat", categorical_transformer, categorical_features),
+        ]
+    )
+    encoded = preprocessor.fit_transform(x_train)
+    model = DecisionTreeClassifier()
+    model.fit(encoded, y_train)
+    pred = model.predict(encoded)
+
     anonymizer = Anonymize(k, QI, categorical_features=categorical_features)
     anon = anonymizer.anonymize(x_train, pred)
 
@@ -48,15 +66,29 @@ def test_anonymize_pandas_adult():
 def test_anonymize_pandas_nursery():
     (x_train, y_train), _ = get_nursery_dataset()
     x_train = x_train.astype(str)
-    encoded = OneHotEncoder().fit_transform(x_train)
+
+    k = 100
+    features = ["parents", "has_nurs", "form", "children", "housing", "finance", "social", "health"]
+    QI = ["finance", "social", "health"]
+    categorical_features = ["parents", "has_nurs", "form", "housing", "finance", "social", "health", 'children']
+    # prepare data for DT
+    numeric_features = [f for f in features if f not in categorical_features]
+    numeric_transformer = Pipeline(
+        steps=[('imputer', SimpleImputer(strategy='constant', fill_value=0))]
+    )
+    categorical_transformer = OneHotEncoder(handle_unknown="ignore", sparse=False)
+    preprocessor = ColumnTransformer(
+        transformers=[
+            ("num", numeric_transformer, numeric_features),
+            ("cat", categorical_transformer, categorical_features),
+        ]
+    )
+    encoded = preprocessor.fit_transform(x_train)
     model = DecisionTreeClassifier()
     model.fit(encoded, y_train)
     pred = model.predict(encoded)
 
-    k = 100
+    anonymizer = Anonymize(k, QI, categorical_features=categorical_features, train_only_QI=True)
-    QI = ["finance", "social", "health"]
-    categorical_features = ["parents", "has_nurs", "form", "housing", "finance", "social", "health", 'children']
-    anonymizer = Anonymize(k, QI, categorical_features=categorical_features)
     anon = anonymizer.anonymize(x_train, pred)
 
     assert(anon.loc[:, QI].drop_duplicates().shape[0] < x_train.loc[:, QI].drop_duplicates().shape[0])
@@ -74,7 +106,7 @@ def test_regression():
     pred = model.predict(x_train)
     k = 10
     QI = [0, 2, 5, 8]
-    anonymizer = Anonymize(k, QI, is_regression=True)
+    anonymizer = Anonymize(k, QI, is_regression=True, train_only_QI=True)
     anon = anonymizer.anonymize(x_train, pred)
     print('Base model accuracy (R2 score): ', model.score(x_test, y_test))
     model.fit(anon, y_train)