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CI fixes #567

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de17eb6
security fixes for model loading
hoffmansc May 26, 2026
6427d3b
fix type error and python versions
hoffmansc Jun 8, 2026
b3354d1
fix deprecations
hoffmansc Jun 8, 2026
8f3c109
explicitly download torch cpu
hoffmansc Jun 8, 2026
8878ee8
flatten labels
hoffmansc Jun 9, 2026
c3f9ba6
update versions
hoffmansc Jun 9, 2026
de8c2bd
remove path modifications
hoffmansc Jun 15, 2026
1dc572a
Merge branch 'main' into security_fixes
hoffmansc Jun 15, 2026
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2 changes: 1 addition & 1 deletion .github/workflows/ci.yml
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Original file line number Diff line number Diff line change
Expand Up @@ -45,7 +45,7 @@ jobs:
run: |
sudo apt install libglpk-dev libxml2-dev
python -m pip install --upgrade pip setuptools wheel
pip install '.[all]' --extra-index-url https://download.pytorch.org/whl/cpu
pip install -e '.[all]' --extra-index-url https://download.pytorch.org/whl/cpu
pip install flake8
pip list
python -m rpy2.situation
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36 changes: 17 additions & 19 deletions examples/demo_adversarial_debiasing.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -10,7 +10,7 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": null,
"metadata": {},
"outputs": [
{
Expand All @@ -25,8 +25,6 @@
"source": [
"%matplotlib inline\n",
"# Load all necessary packages\n",
"import sys\n",
"sys.path.append(\"../\")\n",
"from aif360.datasets import BinaryLabelDataset\n",
"from aif360.datasets import AdultDataset, GermanDataset, CompasDataset\n",
"from aif360.metrics import BinaryLabelDatasetMetric\n",
Expand Down Expand Up @@ -72,7 +70,7 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": null,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -180,7 +178,7 @@
"display(Markdown(\"#### Protected attribute names\"))\n",
"print(dataset_orig_train.protected_attribute_names)\n",
"display(Markdown(\"#### Privileged and unprivileged protected attribute values\"))\n",
"print(dataset_orig_train.privileged_protected_attributes, \n",
"print(dataset_orig_train.privileged_protected_attributes,\n",
" dataset_orig_train.unprivileged_protected_attributes)\n",
"display(Markdown(\"#### Dataset feature names\"))\n",
"print(dataset_orig_train.feature_names)"
Expand All @@ -195,7 +193,7 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": null,
"metadata": {},
"outputs": [
{
Expand All @@ -221,20 +219,20 @@
],
"source": [
"# Metric for the original dataset\n",
"metric_orig_train = BinaryLabelDatasetMetric(dataset_orig_train, \n",
"metric_orig_train = BinaryLabelDatasetMetric(dataset_orig_train,\n",
" unprivileged_groups=unprivileged_groups,\n",
" privileged_groups=privileged_groups)\n",
"display(Markdown(\"#### Original training dataset\"))\n",
"print(\"Train set: Difference in mean outcomes between unprivileged and privileged groups = %f\" % metric_orig_train.mean_difference())\n",
"metric_orig_test = BinaryLabelDatasetMetric(dataset_orig_test, \n",
"metric_orig_test = BinaryLabelDatasetMetric(dataset_orig_test,\n",
" unprivileged_groups=unprivileged_groups,\n",
" privileged_groups=privileged_groups)\n",
"print(\"Test set: Difference in mean outcomes between unprivileged and privileged groups = %f\" % metric_orig_test.mean_difference())"
]
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": null,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -262,12 +260,12 @@
"min_max_scaler = MaxAbsScaler()\n",
"dataset_orig_train.features = min_max_scaler.fit_transform(dataset_orig_train.features)\n",
"dataset_orig_test.features = min_max_scaler.transform(dataset_orig_test.features)\n",
"metric_scaled_train = BinaryLabelDatasetMetric(dataset_orig_train, \n",
"metric_scaled_train = BinaryLabelDatasetMetric(dataset_orig_train,\n",
" unprivileged_groups=unprivileged_groups,\n",
" privileged_groups=privileged_groups)\n",
"display(Markdown(\"#### Scaled dataset - Verify that the scaling does not affect the group label statistics\"))\n",
"print(\"Train set: Difference in mean outcomes between unprivileged and privileged groups = %f\" % metric_scaled_train.mean_difference())\n",
"metric_scaled_test = BinaryLabelDatasetMetric(dataset_orig_test, \n",
"metric_scaled_test = BinaryLabelDatasetMetric(dataset_orig_test,\n",
" unprivileged_groups=unprivileged_groups,\n",
" privileged_groups=privileged_groups)\n",
"print(\"Test set: Difference in mean outcomes between unprivileged and privileged groups = %f\" % metric_scaled_test.mean_difference())\n"
Expand Down Expand Up @@ -437,7 +435,7 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": null,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -488,20 +486,20 @@
"source": [
"# Metrics for the dataset from plain model (without debiasing)\n",
"display(Markdown(\"#### Plain model - without debiasing - dataset metrics\"))\n",
"metric_dataset_nodebiasing_train = BinaryLabelDatasetMetric(dataset_nodebiasing_train, \n",
"metric_dataset_nodebiasing_train = BinaryLabelDatasetMetric(dataset_nodebiasing_train,\n",
" unprivileged_groups=unprivileged_groups,\n",
" privileged_groups=privileged_groups)\n",
"\n",
"print(\"Train set: Difference in mean outcomes between unprivileged and privileged groups = %f\" % metric_dataset_nodebiasing_train.mean_difference())\n",
"\n",
"metric_dataset_nodebiasing_test = BinaryLabelDatasetMetric(dataset_nodebiasing_test, \n",
"metric_dataset_nodebiasing_test = BinaryLabelDatasetMetric(dataset_nodebiasing_test,\n",
" unprivileged_groups=unprivileged_groups,\n",
" privileged_groups=privileged_groups)\n",
"\n",
"print(\"Test set: Difference in mean outcomes between unprivileged and privileged groups = %f\" % metric_dataset_nodebiasing_test.mean_difference())\n",
"\n",
"display(Markdown(\"#### Plain model - without debiasing - classification metrics\"))\n",
"classified_metric_nodebiasing_test = ClassificationMetric(dataset_orig_test, \n",
"classified_metric_nodebiasing_test = ClassificationMetric(dataset_orig_test,\n",
" dataset_nodebiasing_test,\n",
" unprivileged_groups=unprivileged_groups,\n",
" privileged_groups=privileged_groups)\n",
Expand Down Expand Up @@ -695,7 +693,7 @@
},
{
"cell_type": "code",
"execution_count": 14,
"execution_count": null,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -795,13 +793,13 @@
"\n",
"# Metrics for the dataset from model with debiasing\n",
"display(Markdown(\"#### Model - with debiasing - dataset metrics\"))\n",
"metric_dataset_debiasing_train = BinaryLabelDatasetMetric(dataset_debiasing_train, \n",
"metric_dataset_debiasing_train = BinaryLabelDatasetMetric(dataset_debiasing_train,\n",
" unprivileged_groups=unprivileged_groups,\n",
" privileged_groups=privileged_groups)\n",
"\n",
"print(\"Train set: Difference in mean outcomes between unprivileged and privileged groups = %f\" % metric_dataset_debiasing_train.mean_difference())\n",
"\n",
"metric_dataset_debiasing_test = BinaryLabelDatasetMetric(dataset_debiasing_test, \n",
"metric_dataset_debiasing_test = BinaryLabelDatasetMetric(dataset_debiasing_test,\n",
" unprivileged_groups=unprivileged_groups,\n",
" privileged_groups=privileged_groups)\n",
"\n",
Expand All @@ -823,7 +821,7 @@
"\n",
"\n",
"display(Markdown(\"#### Model - with debiasing - classification metrics\"))\n",
"classified_metric_debiasing_test = ClassificationMetric(dataset_orig_test, \n",
"classified_metric_debiasing_test = ClassificationMetric(dataset_orig_test,\n",
" dataset_debiasing_test,\n",
" unprivileged_groups=unprivileged_groups,\n",
" privileged_groups=privileged_groups)\n",
Expand Down
56 changes: 27 additions & 29 deletions examples/demo_calibrated_eqodds_postprocessing.ipynb
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