Merge pull request #2372 from Trusted-AI/dependabot/pip/scikit-learn-…

…gte-0.22.2-and-lt-1.4.0

Update scikit-learn requirement from <1.2.0,>=0.22.2 to >=0.22.2,<1.4.0

.github/workflows/ci-scikit-learn.yml

@@ -28,22 +28,18 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - name: scikit-learn 0.24.2 (Python 3.9)
-            framework: scikitlearn
-            scikit-learn: 0.24.2
-            python: 3.9
           - name: scikit-learn 1.1.3 (Python 3.9)
             framework: scikitlearn
             scikit-learn: 1.1.3
             python: 3.9
-          - name: scikit-learn 1.2.2 (Python 3.9)
-            framework: scikitlearn
-            scikit-learn: 1.2.2
-            python: 3.9
           - name: scikit-learn 1.2.2 (Python 3.10)
             framework: scikitlearn
             scikit-learn: 1.2.2
             python: '3.10'
+          - name: scikit-learn 1.3.2 (Python 3.10)
+            framework: scikitlearn
+            scikit-learn: 1.3.2
+            python: '3.10'
 
     name: ${{ matrix.name }}
     steps:

art/attacks/evasion/adversarial_patch/utils.py

@@ -62,11 +62,11 @@ def insert_transformed_patch(x: np.ndarray, patch: np.ndarray, image_coords: np.
     height, _ = cv2.findHomography(patch_coords, image_coords)
 
     # warp patch to destination coordinates
-    x_out = cv2.warpPerspective(patch, height, (x.shape[1], x.shape[0]), cv2.INTER_CUBIC)
+    x_out = cv2.warpPerspective(patch, height, (x.shape[1], x.shape[0]), cv2.INTER_CUBIC)  # type: ignore
 
     # mask to aid with insertion
     mask = np.ones(patch.shape)
-    mask_out = cv2.warpPerspective(mask, height, (x.shape[1], x.shape[0]), cv2.INTER_CUBIC)
+    mask_out = cv2.warpPerspective(mask, height, (x.shape[1], x.shape[0]), cv2.INTER_CUBIC)  # type: ignore
 
     # save image before adding shadows
     x_neg_patch = np.copy(x)

art/attacks/evasion/graphite/graphite_blackbox.py

@@ -346,7 +346,7 @@ def _perturb(
         mask_copy = mask_array.copy()
         x_noise = cv2.resize(x_copy, self.noise_size)
         x_tar_noise = cv2.resize(x_tar_copy, self.noise_size)
-        mask_noise = cv2.resize(mask_copy, self.noise_size)
+        mask_noise = cv2.resize(mask_copy, self.noise_size).astype(float)
         mask_noise = np.where(mask_noise > 0.5, 1.0, 0.0)
 
         if len(x_noise.shape) < 3:

art/attacks/evasion/graphite/utils.py

@@ -155,7 +155,7 @@ def apply_transformation(
     table = np.empty((256), np.uint8)
     for i in range(256):
         table[i] = np.clip(pow(i / 255.0, gamma) * 255.0, 0, 255)
-    att_uint = cv2.LUT(att_uint, table)
+    att_uint = cv2.LUT(att_uint, table)  # type: ignore
     att = (att_uint / 255.0).astype(np.float32)
     att = np.clip(att, 0.0, 1.0)
 
@@ -227,12 +227,12 @@ def add_noise(
     """
     import cv2
 
-    theta_full = cv2.resize(theta, (x.shape[1], x.shape[0]))
+    theta_full = cv2.resize(theta, (x.shape[1], x.shape[0])).astype(float)
     if len(theta_full.shape) < 3:
         theta_full = theta_full[:, :, np.newaxis]
     comb = x + lbd * theta_full
 
-    mask_full = cv2.resize(mask, (x.shape[1], x.shape[0]))
+    mask_full = cv2.resize(mask, (x.shape[1], x.shape[0])).astype(float)
     if len(mask_full.shape) < 3:
         mask_full = mask_full[:, :, np.newaxis]
     mask_full = np.where(mask_full > 0.5, 1.0, 0.0)
@@ -334,7 +334,7 @@ def transform_wb(
     if blur != 0:
         kernel = np.zeros((blur * 2 - 1, blur * 2 - 1))
         kernel[blur - 1, blur - 1] = 1
-        kernel = cv2.GaussianBlur(kernel, (blur, blur), 0)
+        kernel = cv2.GaussianBlur(kernel, (blur, blur), 0).astype(float)
         kernel = kernel[blur // 2 : blur // 2 + blur, blur // 2 : blur // 2 + blur]
         kernel = kernel[np.newaxis, :, :]
         kernel = np.repeat(kernel[np.newaxis, :, :, :], x_adv.size()[1], axis=0)

requirements_test.txt

@@ -3,7 +3,7 @@
 numpy>=1.18.5,<1.27
 scipy==1.10.1
 matplotlib==3.7.1
-scikit-learn>=0.22.2,<1.2.0
+scikit-learn>=0.22.2,<1.4.0
 six==1.16.0
 Pillow==10.1.0
 tqdm==4.66.1

tests/attacks/inference/attribute_inference/test_black_box.py

@@ -86,8 +86,8 @@ def transform_feature(x):
         # check accuracy
         train_acc = np.sum(inferred_train == x_train_feature.reshape(1, -1)) / len(inferred_train)
         test_acc = np.sum(inferred_test == x_test_feature.reshape(1, -1)) / len(inferred_test)
-        assert pytest.approx(0.8285, abs=0.3) == train_acc
-        assert pytest.approx(0.8888, abs=0.3) == test_acc
+        assert pytest.approx(0.8285, abs=0.35) == train_acc
+        assert pytest.approx(0.8888, abs=0.35) == test_acc
         print(model_type, train_acc, test_acc)
 
     except ARTTestException as e:
@@ -184,8 +184,8 @@ def transform_feature(x):
         # check accuracy
         train_acc = np.sum(inferred_train == x_train_feature.reshape(1, -1)) / len(inferred_train)
         test_acc = np.sum(inferred_test == x_test_feature.reshape(1, -1)) / len(inferred_test)
-        assert pytest.approx(0.8285, abs=0.3) == train_acc
-        assert pytest.approx(0.8888, abs=0.3) == test_acc
+        assert pytest.approx(0.8285, abs=0.35) == train_acc
+        assert pytest.approx(0.8888, abs=0.35) == test_acc
         print(model_type, train_acc, test_acc)
 
     except ARTTestException as e:
@@ -236,8 +236,8 @@ def transform_feature(x):
         # check accuracy
         train_acc = np.sum(inferred_train == x_train_feature.reshape(1, -1)) / len(inferred_train)
         test_acc = np.sum(inferred_test == x_test_feature.reshape(1, -1)) / len(inferred_test)
-        assert pytest.approx(0.8285, abs=0.3) == train_acc
-        assert pytest.approx(0.8888, abs=0.3) == test_acc
+        assert pytest.approx(0.8285, abs=0.35) == train_acc
+        assert pytest.approx(0.8888, abs=0.35) == test_acc
         print(model_type, train_acc, test_acc)
 
     except ARTTestException as e:
@@ -286,8 +286,8 @@ def transform_feature(x):
         # check accuracy
         train_acc = np.sum(inferred_train == x_train_feature.reshape(1, -1)) / len(inferred_train)
         test_acc = np.sum(inferred_test == x_test_feature.reshape(1, -1)) / len(inferred_test)
-        assert pytest.approx(0.8285, abs=0.3) == train_acc
-        assert pytest.approx(0.8888, abs=0.3) == test_acc
+        assert pytest.approx(0.8285, abs=0.35) == train_acc
+        assert pytest.approx(0.8888, abs=0.35) == test_acc
         print(model_type, train_acc, test_acc)
 
     except ARTTestException as e:

tests/utils.py

@@ -28,6 +28,7 @@
 import unittest
 import warnings
 
+import sklearn
 import numpy as np
 
 from art.estimators.classification.tensorflow import TensorFlowV2Classifier
@@ -1746,6 +1747,13 @@ def get_tabular_classifier_scikit_list(clipped=False, model_list_names=None):
         ScikitlearnSVC,
     )
 
+    sklearn_version = list(map(int, sklearn.__version__.split(".")))
+    sklearn_ge_1_3_0 = sklearn_version[0] == 1 and sklearn_version[1] >= 3
+    if sklearn_ge_1_3_0:
+        suffix = "-ge-1.3.0"
+    else:
+        suffix = ""
+
     available_models = {
         "decisionTreeClassifier": ScikitlearnDecisionTreeClassifier,
         # "extraTreeClassifier": ScikitlearnExtraTreeClassifier,
@@ -1775,7 +1783,7 @@ def get_tabular_classifier_scikit_list(clipped=False, model_list_names=None):
                     os.path.join(
                         os.path.dirname(os.path.dirname(__file__)),
                         "utils/resources/models/scikit/",
-                        "scikit-" + model_name + "-iris-clipped.pickle",
+                        "scikit-" + model_name + "-iris-clipped" + suffix + ".pickle",
                     ),
                     "rb",
                 )
@@ -1788,7 +1796,7 @@ def get_tabular_classifier_scikit_list(clipped=False, model_list_names=None):
                     os.path.join(
                         os.path.dirname(os.path.dirname(__file__)),
                         "utils/resources/models/scikit/",
-                        "scikit-" + model_name + "-iris-unclipped.pickle",
+                        "scikit-" + model_name + "-iris-unclipped" + suffix + ".pickle",
                     ),
                     "rb",
                 )

utils/resources/create_model_weights.py

@@ -15,13 +15,13 @@
 # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
 # TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 # SOFTWARE.
-import tensorflow as tf
 import os
 import pickle
+
+import tensorflow as tf
 from tensorflow.keras.models import Sequential
 from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2D
 from sklearn.linear_model import LogisticRegression
-from art.estimators.classification import SklearnClassifier
 from sklearn.svm import SVC, LinearSVC
 from sklearn.tree import DecisionTreeClassifier, ExtraTreeClassifier
 from sklearn.ensemble import AdaBoostClassifier, BaggingClassifier, ExtraTreesClassifier
@@ -139,43 +139,43 @@ def create_scikit_model_weights():
         "linearSVC": LinearSVC(),
     }
 
-    clipped_models = {
-        model_name: SklearnClassifier(model=model, clip_values=(0, 1)) for model_name, model in model_list.items()
-    }
-    unclipped_models = {model_name: SklearnClassifier(model=model) for model_name, model in model_list.items()}
+    clipped_models = {model_name: model for model_name, model in model_list.items()}
+    unclipped_models = {model_name: model for model_name, model in model_list.items()}
 
     (x_train_iris, y_train_iris), (_, _), _, _ = load_dataset("iris")
 
+    y_train_iris = np.argmax(y_train_iris, axis=1)
+
     for model_name, model in clipped_models.items():
-        model.fit(x=x_train_iris, y=y_train_iris)
+        model.fit(X=x_train_iris, y=y_train_iris)
         pickle.dump(
             model,
             open(
                 os.path.join(
                     os.path.dirname(os.path.dirname(__file__)),
-                    "utils/resources/models/scikit/",
-                    model_name + "iris_clipped.sav",
+                    "resources/models/scikit/",
+                    "scikit-" + model_name + "-iris-clipped-ge-1.3.0.pickle",
                 ),
                 "wb",
             ),
         )
 
     for model_name, model in unclipped_models.items():
-        model.fit(x=x_train_iris, y=y_train_iris)
+        model.fit(X=x_train_iris, y=y_train_iris)
         pickle.dump(
             model,
             open(
                 os.path.join(
                     os.path.dirname(os.path.dirname(__file__)),
-                    "utils/resources/models/scikit/",
-                    model_name + "iris_unclipped.sav",
+                    "resources/models/scikit/",
+                    "scikit-" + model_name + "-iris-unclipped-ge-1.3.0.pickle",
                 ),
                 "wb",
             ),
         )
 
 
 if __name__ == "__main__":
-    main_mnist_binary()
+    # main_mnist_binary()
     create_scikit_model_weights()
-    main_diabetes()
+    # main_diabetes()