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update notebooks

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olasaadi 2022-03-23 21:52:06 +02:00
parent 06158c8508
commit 137167fb0c
3 changed files with 175 additions and 506 deletions
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notebooks/membership_inference_anonymization_adult.ipynb
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@ -29,7 +29,7 @@
},
{
"cell_type": "code",
"execution_count": 97,
"execution_count": 6,
"metadata": {},
"outputs": [
{
@ -44,6 +44,18 @@
" [ 26. 11. 0. 0. 48.]\n",
" [ 27. 9. 0. 0. 40.]]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/9b/qbtw28w53355cvpjs4qn83yc0000gn/T/ipykernel_85828/3975777015.py:22: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
"Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
" y_train = y_train.astype(np.int)\n",
"/var/folders/9b/qbtw28w53355cvpjs4qn83yc0000gn/T/ipykernel_85828/3975777015.py:26: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
"Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
" y_test = y_test.astype(np.int)\n"
]
}
],
"source": [
@ -90,14 +102,14 @@
},
{
"cell_type": "code",
"execution_count": 116,
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Base model accuracy: 0.8075056814691972\n"
"Base model accuracy: 0.8074442601805786\n"
]
}
],
@ -126,9 +138,18 @@
},
{
"cell_type": "code",
"execution_count": 124,
"execution_count": 8,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/olasaadi/PycharmProjects/ai-privacy-toolkit-internal/venv/lib/python3.8/site-packages/art/attacks/inference/membership_inference/black_box.py:262: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" self.attack_model.fit(np.c_[x_1, x_2], y_ready) # type: ignore\n"
]
}
],
"source": [
"from art.attacks.inference.membership_inference import MembershipInferenceBlackBox\n",
"\n",
@ -154,14 +175,14 @@
},
{
"cell_type": "code",
"execution_count": 125,
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.5440363591696352\n"
"0.545264709495148\n"
]
}
],
@ -197,7 +218,7 @@
},
{
"cell_type": "code",
"execution_count": 128,
"execution_count": 10,
"metadata": {},
"outputs": [
{
@ -215,6 +236,7 @@
}
],
"source": [
"from apt.utils.datasets import ArrayDataset\n",
"import os\n",
"import sys\n",
"sys.path.insert(0, os.path.abspath('..'))\n",
@ -223,22 +245,20 @@
"# QI = (age, education-num, capital-gain, hours-per-week)\n",
"QI = [0, 1, 2, 4]\n",
"anonymizer = Anonymize(100, QI)\n",
"anon = anonymizer.anonymize(x_train, x_train_predictions)\n",
"anon = anonymizer.anonymize(ArrayDataset(x_train, x_train_predictions))\n",
"print(anon)"
]
},
{
"cell_type": "code",
"execution_count": 104,
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"6739"
]
"text/plain": "6739"
},
"execution_count": 104,
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
@ -250,16 +270,14 @@
},
{
"cell_type": "code",
"execution_count": 129,
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"658"
]
"text/plain": "658"
},
"execution_count": 129,
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
@ -278,14 +296,14 @@
},
{
"cell_type": "code",
"execution_count": 130,
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Anonymized model accuracy: 0.8304158221239482\n"
"Anonymized model accuracy: 0.83078434985566\n"
]
}
],
@ -308,14 +326,22 @@
},
{
"cell_type": "code",
"execution_count": 131,
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/olasaadi/PycharmProjects/ai-privacy-toolkit-internal/venv/lib/python3.8/site-packages/art/attacks/inference/membership_inference/black_box.py:262: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" self.attack_model.fit(np.c_[x_1, x_2], y_ready) # type: ignore\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.5034393809114359\n"
"0.5047291487532244\n"
]
}
],
@ -345,15 +371,15 @@
},
{
"cell_type": "code",
"execution_count": 132,
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(0.5298924372550654, 0.7806166318634075)\n",
"(0.5030507735890172, 0.5671293452892765)\n"
"(0.5312420517168291, 0.7696843139663432)\n",
"(0.5048372911169745, 0.4935511607910576)\n"
]
}
],
@ -419,4 +445,4 @@
},
"nbformat": 4,
"nbformat_minor": 2
}
}