Updates for scikit-learn 1.4

Signed-off-by: Beat Buesser <[email protected]>

Author: beat-buesser

.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 }}

tests/attacks/evasion/test_lowprofool.py

@@ -136,7 +136,7 @@ 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)
 
@@ -178,7 +178,7 @@ 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)
 
@@ -212,7 +212,7 @@ 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)
 
@@ -329,7 +329,7 @@ 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)
 
@@ -392,7 +392,7 @@ def test_clipping(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)
 
     # Dataset min-max clipping values