Merge pull request #2395 from Trusted-AI/dev_1.17.1

Update to ART 1.17.1

Author: beat-buesser

.github/actions/deepspeech-v3/Dockerfile

@@ -1,5 +1,5 @@
-pod# Get base from a pytorch image
-FROM pytorch/pytorch:2.1.1-cuda12.1-cudnn8-runtime
+# Get base from a pytorch image
+FROM python:3.10.13-slim-bullseye
 
 # Set to install things in non-interactive mode
 ENV DEBIAN_FRONTEND noninteractive
@@ -26,10 +26,10 @@ RUN apt-get update \
 RUN pip install --ignore-installed PyYAML torch==2.1.1 tensorflow==2.14.1 torchaudio==2.1.1 pytorch-lightning==2.1.2 scikit-learn==1.3.2
 RUN pip install --no-build-isolation fairscale==0.4.13
 
-RUN git clone https://github.com/SeanNaren/deepspeech.pytorch.git
-RUN cd deepspeech.pytorch && sed -i '/^sklearn/d' requirements.txt && pip install -r requirements.txt && pip install -e .
+RUN git clone https://github.com/SeanNaren/deepspeech.pytorch.git && cd deepspeech.pytorch && sed -i '/^sklearn/d' requirements.txt && pip install -r requirements.txt && pip install -e .
 
 RUN pip install numba==0.56.4 pytest-cov==4.1.0 pydub==0.25.1
-RUN pip list
 
 RUN mkdir -p /root/.art/data && cd /root/.art/data && curl -LJO "https://github.com/SeanNaren/deepspeech.pytorch/releases/download/V3.0/librispeech_pretrained_v3.ckpt"
+
+CMD ["/bin/bash"]

.github/workflows/ci-lingvo.yml

@@ -50,7 +50,7 @@ jobs:
           sudo apt-get update
           sudo apt-get -y -q install ffmpeg libavcodec-extra
           python -m pip install --upgrade pip setuptools wheel
-          pip install -q -r <(sed '/^scipy/d;/^matplotlib/d;/^pandas/d;/^statsmodels/d;/^numba/d;/^jax/d;/^h5py/d;/^Pillow/d;/^pytest/d;/^pytest-mock/d;/^torch/d;/^torchaudio/d;/^torchvision/d;/^xgboost/d;/^requests/d;/^tensorflow/d;/^keras/d;/^kornia/d;/^librosa/d;/^tqdm/d;/^timm/d;/^catboost/d' requirements_test.txt)
+          pip install -q -r <(sed '/^scipy/d;/^matplotlib/d;/^pandas/d;/^statsmodels/d;/^numba/d;/^jax/d;/^h5py/d;/^Pillow/d;/^pytest/d;/^pytest-mock/d;/^torch/d;/^torchaudio/d;/^torchvision/d;/^xgboost/d;/^requests/d;/^tensorflow/d;/^keras/d;/^kornia/d;/^librosa/d;/^tqdm/d;/^timm/d;/^catboost/d;/^scikit-learn/d' requirements_test.txt)
           pip install scipy==1.5.4
           pip install matplotlib==3.3.4
           pip install pandas==1.1.5
@@ -75,6 +75,7 @@ jobs:
           pip install librosa==0.9.2
           pip install tqdm==4.64.1
           pip install catboost==1.1.1
+          pip install scikit-learn==0.24.2
           pip list
       - name: Run ${{ matrix.name }} Tests
         run: pytest --cov-report=xml --cov=art --cov-append -q -vv tests/estimators/speech_recognition/test_tensorflow_lingvo.py --framework=${{ matrix.framework }} --durations=0

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

@@ -28,18 +28,14 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - 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 3.10)
-            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: scikit-learn 1.4.0 (Python 3.10)
+            framework: scikitlearn
+            scikit-learn: 1.4.0
+            python: '3.10'
 
     name: ${{ matrix.name }}
     steps:

.github/workflows/ci-tensorflow-v1.yml

@@ -48,7 +48,7 @@ jobs:
           sudo apt-get update
           sudo apt-get -y -q install ffmpeg libavcodec-extra
           python -m pip install --upgrade pip setuptools wheel
-          pip install -q -r <(sed '/^pandas/d;/^scipy/d;/^matplotlib/d;/^xgboost/d;/^tensorflow/d;/^keras/d;/^jax/d;/^torch/d;/^Pillow/d;/^h5py/d' requirements_test.txt)
+          pip install -q -r <(sed '/^pandas/d;/^scipy/d;/^matplotlib/d;/^xgboost/d;/^tensorflow/d;/^keras/d;/^jax/d;/^torch/d;/^Pillow/d;/^h5py/d;/^scikit-learn/d' requirements_test.txt)
           pip install pandas==1.3.5
           pip install scipy==1.7.2
           pip install matplotlib==3.5.3
@@ -62,6 +62,7 @@ jobs:
           pip install torchvision==0.14.1+cpu
           pip install Pillow==9.5.0
           pip install h5py==3.8.0
+          pip install scikit-learn==1.0.2
           pip list
       - name: Run Tests
         run: ./run_tests.sh ${{ matrix.framework }}

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,<1.4.0
+scikit-learn==1.4.0
 six==1.16.0
 Pillow==10.2.0
 tqdm==4.66.1

setup.py

@@ -9,7 +9,7 @@
 install_requires = [
     "numpy>=1.18.0",
     "scipy>=1.4.1",
-    "scikit-learn>=0.22.2,<1.2.0",
+    "scikit-learn>=0.22.2",
     "six",
     "setuptools",
     "tqdm",
@@ -24,7 +24,7 @@
     "numpy>=1.18.0",
     "scipy>=1.4.1",
     "six>=1.13.0",
-    "scikit-learn>=0.22.2,<1.2.0",
+    "scikit-learn>=0.22.2",
     "Pillow>=6.0.0",
 ]
 

tests/attacks/evasion/test_lowprofool.py

@@ -128,6 +128,7 @@ def train_nn(self, nn_model, x, y, learning_rate, epochs):
             optimizer.step()
 
 
+@pytest.mark.only_with_platform("scikitlearn")
 def test_general_iris_lr(iris_dataset):
     """
     Check whether the produced adversaries are correct,
@@ -136,7 +137,7 @@ def test_general_iris_lr(iris_dataset):
     (x_train, y_train, x_valid, y_valid), _, clip_values = iris_dataset
 
     # Setup classifier.
-    lr_clf = LogisticRegression(penalty="none")
+    lr_clf = LogisticRegression(penalty=None)
     lr_clf.fit(x_train, y_train)
     clf_slr = ScikitlearnLogisticRegression(model=lr_clf, clip_values=clip_values)
 
@@ -170,6 +171,7 @@ def test_general_iris_lr(iris_dataset):
         _ = lpf_slr.generate(x=np.ones((sample.shape[0], 11)), y=target)
 
 
+@pytest.mark.only_with_platform("scikitlearn")
 def test_general_wines_lr(wine_dataset):
     """
     Check whether the produced adversaries are correct,
@@ -178,7 +180,7 @@ def test_general_wines_lr(wine_dataset):
     (x_train, y_train, x_valid, y_valid), _, clip_values = wine_dataset
 
     # Setup classifier
-    lr_clf = LogisticRegression(penalty="none")
+    lr_clf = LogisticRegression(penalty=None)
     lr_clf.fit(x_train, y_train)
     clf_slr = ScikitlearnLogisticRegression(model=lr_clf, clip_values=clip_values)
 
@@ -204,6 +206,7 @@ def test_general_wines_lr(wine_dataset):
     assert success_rate > expected
 
 
+@pytest.mark.only_with_platform("scikitlearn")
 def test_general_cancer_lr(breast_cancer_dataset):
     """
     Check whether the produced adversaries are correct,
@@ -212,7 +215,7 @@ def test_general_cancer_lr(breast_cancer_dataset):
     (x_train, y_train, x_valid, y_valid), _, clip_values = breast_cancer_dataset
 
     # Setup classifier
-    lr_clf = LogisticRegression(penalty="none")
+    lr_clf = LogisticRegression(penalty=None)
     lr_clf.fit(x_train, y_train)
     clf_slr = ScikitlearnLogisticRegression(model=lr_clf, clip_values=clip_values)
 
@@ -281,6 +284,7 @@ def test_general_iris_nn(iris_dataset):
     assert success_rate > expected
 
 
+@pytest.mark.only_with_platform("scikitlearn")
 def test_general_cancer_svc(breast_cancer_dataset):
     """
     Check whether the produced adversaries are correct,
@@ -316,6 +320,7 @@ def test_general_cancer_svc(breast_cancer_dataset):
     assert success_rate > expected
 
 
+@pytest.mark.only_with_platform("scikitlearn")
 def test_fit_importances(iris_dataset):
     """
     Check whether feature importance is calculated properly.
@@ -329,7 +334,7 @@ def pearson_correlations(x, y):
         return result
 
     # Setup classifier
-    lr_clf = LogisticRegression(penalty="none")
+    lr_clf = LogisticRegression(penalty=None)
     lr_clf.fit(x_train, y_train)
     clf_slr = ScikitlearnLogisticRegression(model=lr_clf, clip_values=clip_values)
 
@@ -385,14 +390,15 @@ def pearson_correlations(x, y):
     assert is_vec_fit_valid
 
 
+@pytest.mark.only_with_platform("scikitlearn")
 def test_clipping(iris_dataset):
     """
     Check weather adversaries are clipped properly.
     """
     (x_train, y_train, x_valid, y_valid), _, clip_values = iris_dataset
 
     # Setup classifier
-    lr_clf = LogisticRegression(penalty="none")
+    lr_clf = LogisticRegression(penalty=None)
     lr_clf.fit(x_train, y_train)
 
     # Dataset min-max clipping values

tests/utils.py

@@ -1748,9 +1748,10 @@ def get_tabular_classifier_scikit_list(clipped=False, model_list_names=None):
     )
 
     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"
+    if sklearn_version[0] == 1 and sklearn_version[1] == 3:
+        suffix = "-eq-1.3.0"
+    elif sklearn_version[0] == 1 and sklearn_version[1] == 4:
+        suffix = "-eq-1.4.0"
     else:
         suffix = ""
 

utils/resources/create_model_weights.py

@@ -154,7 +154,7 @@ def create_scikit_model_weights():
                 os.path.join(
                     os.path.dirname(os.path.dirname(__file__)),
                     "resources/models/scikit/",
-                    "scikit-" + model_name + "-iris-clipped-ge-1.3.0.pickle",
+                    "scikit-" + model_name + "-iris-clipped-eq-1.4.0.pickle",
                 ),
                 "wb",
             ),
@@ -168,7 +168,7 @@ def create_scikit_model_weights():
                 os.path.join(
                     os.path.dirname(os.path.dirname(__file__)),
                     "resources/models/scikit/",
-                    "scikit-" + model_name + "-iris-unclipped-ge-1.3.0.pickle",
+                    "scikit-" + model_name + "-iris-unclipped-eq-1.4.0.pickle",
                 ),
                 "wb",
             ),