mirror of
https://github.com/IBM/ai-privacy-toolkit.git
synced 2026-04-24 20:36:21 +02:00
Limit scikit-learn version because of API changes (#81)
* Limit scikit-learn versions between 0.22.2 and 1.1.3, remove deprecated load_boston(). * Set pytest configuration option to show test progress in detail. * Change np.int to int according to DeprecationWarning Signed-off-by: Maya Anderson <mayaa@il.ibm.com>
This commit is contained in:
andersonm-ibm
GitHub
parent
be7d248c33
commit
e9a225501f
5 changed files with 84 additions and 79 deletions
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"execution_count": 6,
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"execution_count": 3,
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" [ 26. 11. 0. 0. 48.]\n",
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" [ 27. 9. 0. 0. 40.]]\n"
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"/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",
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"Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
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" y_train = y_train.astype(np.int)\n",
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"/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",
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"Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
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" y_test = y_test.astype(np.int)\n"
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]
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}
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],
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"source": [
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@ -80,11 +71,11 @@
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"\n",
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"y_train[y_train == '<=50K'] = 0\n",
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"y_train[y_train == '>50K'] = 1\n",
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"y_train = y_train.astype(np.int)\n",
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"y_train = y_train.astype(int)\n",
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"\n",
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"y_test[y_test == '<=50K'] = 0\n",
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"y_test[y_test == '>50K'] = 1\n",
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"y_test = y_test.astype(np.int)\n",
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"y_test = y_test.astype(int)\n",
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"\n",
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"# get balanced dataset\n",
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"x_train = x_train[:x_test.shape[0]]\n",
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]
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"attachments": {},
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Base model accuracy: 0.8074442601805786\n"
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"Base model accuracy: 0.8076285240464345\n"
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]
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},
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"name": "stderr",
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"/home/mayaa/Development/GitHub/aiprivacy/ai-privacy-toolkit/venv1/lib/python3.8/site-packages/sklearn/utils/deprecation.py:103: FutureWarning: The attribute `n_features_` is deprecated in 1.0 and will be removed in 1.2. Use `n_features_in_` instead.\n",
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" warnings.warn(msg, category=FutureWarning)\n"
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": 5,
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"metadata": {},
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"text": [
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"/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",
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" self.attack_model.fit(np.c_[x_1, x_2], y_ready) # type: ignore\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"from art.attacks.inference.membership_inference import MembershipInferenceBlackBox\n",
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"\n",
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": 6,
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"metadata": {},
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0.545264709495148\n"
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"0.5460017196904557\n"
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]
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}
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],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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"output_type": "stream",
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"text": [
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"[[38. 13. 0. 0. 40.]\n",
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" [57. 13. 0. 0. 30.]\n",
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" [37. 9. 0. 0. 40.]\n",
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" [46. 13. 0. 0. 35.]\n",
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" [28. 9. 0. 0. 40.]\n",
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" ...\n",
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" [26. 13. 0. 0. 40.]\n",
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" [29. 10. 0. 0. 50.]\n",
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" [25. 9. 0. 0. 40.]]\n"
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" [27. 10. 0. 0. 50.]\n",
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" [28. 9. 0. 0. 40.]]\n"
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]
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}
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],
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"source": [
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"from apt.utils.datasets import ArrayDataset\n",
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"import os\n",
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"import sys\n",
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"sys.path.insert(0, os.path.abspath('..'))\n",
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"from apt.utils.datasets import ArrayDataset\n",
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"from apt.anonymization import Anonymize\n",
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"\n",
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"# QI = (age, education-num, capital-gain, hours-per-week)\n",
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": "6739"
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"text/plain": [
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"6739"
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},
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"execution_count": 11,
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": "658"
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"text/plain": [
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"401"
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]
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},
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"execution_count": 12,
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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"cell_type": "code",
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"execution_count": 13,
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Anonymized model accuracy: 0.83078434985566\n"
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"Anonymized model accuracy: 0.826914808672686\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/home/mayaa/Development/GitHub/aiprivacy/ai-privacy-toolkit/venv1/lib/python3.8/site-packages/sklearn/utils/deprecation.py:103: FutureWarning: The attribute `n_features_` is deprecated in 1.0 and will be removed in 1.2. Use `n_features_in_` instead.\n",
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" warnings.warn(msg, category=FutureWarning)\n"
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]
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}
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],
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"execution_count": 11,
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"/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",
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" self.attack_model.fit(np.c_[x_1, x_2], y_ready) # type: ignore\n"
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]
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},
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"text": [
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"0.5047291487532244\n"
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"0.49692912418621793\n"
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]
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}
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],
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]
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},
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},
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"cell_type": "code",
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"execution_count": 15,
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"(0.5312420517168291, 0.7696843139663432)\n",
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"(0.5048372911169745, 0.4935511607910576)\n"
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"(0.5316007088009451, 0.7738607050730868)\n",
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"(0.4971184877823882, 0.5297874953936863)\n"
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]
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}
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],
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]
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},
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"attachments": {},
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"source": [
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.3"
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"version": "3.8.10"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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}
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"\n",
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"y_train[y_train == '<=50K'] = 0\n",
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"y_train[y_train == '>50K'] = 1\n",
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"y_train = y_train.astype(np.int)\n",
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"y_train = y_train.astype(int)\n",
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"\n",
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"y_test[y_test == '<=50K'] = 0\n",
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"y_test[y_test == '>50K'] = 1\n",
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"y_test = y_test.astype(np.int)\n",
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"y_test = y_test.astype(int)\n",
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"\n",
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"print(x_train)"
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]
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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}
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numpy==1.22.0
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pandas~=1.1.0
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scipy==1.4.1
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scikit-learn>=0.22.2
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scikit-learn>=0.22.2,<=1.1.3
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torch>=1.8.0
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tqdm>=4.64.1
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matplotlib>=3.7.0
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exclude =
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venv
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venv1
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[tool:pytest]
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log_cli = True
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log-cli-level = INFO
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from sklearn.compose import ColumnTransformer
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from sklearn.datasets import load_boston, load_diabetes
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from sklearn.datasets import load_diabetes
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from sklearn.impute import SimpleImputer
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from sklearn.model_selection import train_test_split
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from sklearn.pipeline import Pipeline
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@pytest.fixture
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def data():
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return load_boston(return_X_y=True)
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def dataset():
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return load_diabetes()
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def test_minimizer_params(data):
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def test_minimizer_params():
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# Assume two features, age and height, and boolean label
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cells = [{"id": 1, "ranges": {"age": {"start": None, "end": 38}, "height": {"start": None, "end": 170}}, "label": 0,
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'categories': {}, "representative": {"age": 26, "height": 149}},
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gen.transform(dataset=ArrayDataset(X, features_names=features))
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def test_minimizer_fit(data):
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def test_minimizer_fit():
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features = ['age', 'height']
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X = np.array([[23, 165],
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[45, 158],
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assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= 0.05)
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def test_minimizer_fit_pandas(data):
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def test_minimizer_fit_pandas():
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features = ['age', 'height', 'sex', 'ola']
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X = [[23, 165, 'f', 'aa'],
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[45, 158, 'f', 'aa'],
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assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= 0.05)
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def test_minimizer_params_categorical(data):
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def test_minimizer_params_categorical():
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# Assume three features, age, sex and height, and boolean label
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cells = [{'id': 1, 'label': 0, 'ranges': {'age': {'start': None, 'end': None}},
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||||
'categories': {'sex': ['f', 'm']}, 'hist': [2, 0],
|
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|
|
@ -246,7 +246,7 @@ def test_minimizer_params_categorical(data):
|
|||
assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= 0.05)
|
||||
|
||||
|
||||
def test_minimizer_fit_QI(data):
|
||||
def test_minimizer_fit_QI():
|
||||
features = ['age', 'height', 'weight']
|
||||
X = np.array([[23, 165, 70],
|
||||
[45, 158, 67],
|
||||
|
|
@ -301,7 +301,7 @@ def test_minimizer_fit_QI(data):
|
|||
assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= 0.05)
|
||||
|
||||
|
||||
def test_minimizer_fit_pandas_QI(data):
|
||||
def test_minimizer_fit_pandas_QI():
|
||||
features = ['age', 'height', 'weight', 'sex', 'ola']
|
||||
X = [[23, 165, 65, 'f', 'aa'],
|
||||
[45, 158, 76, 'f', 'aa'],
|
||||
|
|
@ -577,8 +577,7 @@ def test_german_credit_pandas():
|
|||
assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= 0.05)
|
||||
|
||||
|
||||
def test_regression():
|
||||
dataset = load_diabetes()
|
||||
def test_regression(dataset):
|
||||
x_train, x_test, y_train, y_test = train_test_split(dataset.data, dataset.target, test_size=0.5, random_state=14)
|
||||
|
||||
base_est = DecisionTreeRegressor(random_state=10, min_samples_split=2)
|
||||
|
|
@ -651,7 +650,7 @@ def test_regression():
|
|||
assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= 0.05)
|
||||
|
||||
|
||||
def test_X_y(data):
|
||||
def test_X_y():
|
||||
features = [0, 1, 2]
|
||||
X = np.array([[23, 165, 70],
|
||||
[45, 158, 67],
|
||||
|
|
@ -705,7 +704,7 @@ def test_X_y(data):
|
|||
assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= 0.05)
|
||||
|
||||
|
||||
def test_X_y_features_names(data):
|
||||
def test_X_y_features_names():
|
||||
features = ['age', 'height', 'weight']
|
||||
X = np.array([[23, 165, 70],
|
||||
[45, 158, 67],
|
||||
|
|
@ -759,7 +758,7 @@ def test_X_y_features_names(data):
|
|||
assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= 0.05)
|
||||
|
||||
|
||||
def test_BaseEstimator_classification(data):
|
||||
def test_BaseEstimator_classification():
|
||||
features = ['age', 'height', 'weight', 'sex', 'ola']
|
||||
X = [[23, 165, 65, 'f', 'aa'],
|
||||
[45, 158, 76, 'f', 'aa'],
|
||||
|
|
@ -833,8 +832,7 @@ def test_BaseEstimator_classification(data):
|
|||
assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= 0.05)
|
||||
|
||||
|
||||
def test_BaseEstimator_regression():
|
||||
dataset = load_diabetes()
|
||||
def test_BaseEstimator_regression(dataset):
|
||||
x_train, x_test, y_train, y_test = train_test_split(dataset.data, dataset.target, test_size=0.5, random_state=14)
|
||||
|
||||
base_est = DecisionTreeRegressor(random_state=10, min_samples_split=2)
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue