[pre-commit.ci] pre-commit autoupdate (#303)

* [pre-commit.ci] pre-commit autoupdate updates: - [github.com/astral-sh/ruff-pre-commit: v0.5.7 → v0.6.3](astral-sh/ruff-pre-commit@v0.5.7...v0.6.3) * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * ignore linting old notebooks * update README --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Richard Preen <[email protected]>
AI-SDC · Sep 3, 2024 · 10b4ac0 · 10b4ac0
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diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
@@ -33,7 +33,7 @@ repos:
 
   # Ruff, the Python auto-correcting linter/formatter written in Rust
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.5.7
+    rev: v0.6.3
     hooks:
       - id: ruff
         args: ["--fix", "--show-fixes"]

diff --git a/README.md b/README.md
@@ -1,11 +1,12 @@
+# SACRO-ML
+
 [![License](https://img.shields.io/badge/license-MIT-blue.svg?style=flat)](https://opensource.org/licenses/MIT)
 [![Latest Version](https://img.shields.io/github/v/release/AI-SDC/SACRO-ML?style=flat)](https://github.com/AI-SDC/SACRO-ML/releases)
 [![DOI](https://zenodo.org/badge/518801511.svg)](https://zenodo.org/badge/latestdoi/518801511)
 [![codecov](https://codecov.io/gh/AI-SDC/SACRO-ML/branch/main/graph/badge.svg?token=AXX2XCXUNU)](https://codecov.io/gh/AI-SDC/SACRO-ML)
+[![PyPI package](https://img.shields.io/pypi/v/sacroml.svg)](https://pypi.org/project/sacroml)
 [![Python versions](https://img.shields.io/pypi/pyversions/sacroml.svg)](https://pypi.org/project/sacroml)
 
-# SACRO-ML
-
 A collection of tools and resources for managing the [statistical disclosure control](https://en.wikipedia.org/wiki/Statistical_disclosure_control) of trained [machine learning](https://en.wikipedia.org/wiki/Machine_learning) models. For a brief introduction, see [Smith et al. (2022)](https://doi.org/10.48550/arXiv.2212.01233).
 
 The `sacroml` package provides:
@@ -14,8 +15,6 @@ The `sacroml` package provides:
 
 ## Installation
 
-[![PyPI package](https://img.shields.io/pypi/v/sacroml.svg)](https://pypi.org/project/sacroml)
-
 Install `sacroml` and manually copy the [`examples`](examples/).
 
 To install only the base package, which includes the attacks used for assessing privacy:
@@ -35,10 +34,14 @@ Note: macOS users may need to install libomp due to a dependency on XGBoost:
 $ brew install libomp
 ```
 
-## Running
+## Usage
 
 See the [`examples`](examples/).
 
+## Documentation
+
+See [API documentation](https://ai-sdc.github.io/SACRO-ML/).
+
 ## Acknowledgement
 
 This work was funded by UK Research and Innovation under Grant Numbers MC_PC_21033 and MC_PC_23006 as part of Phase 1 of the [DARE UK](https://dareuk.org.uk) (Data and Analytics Research Environments UK) programme, delivered in partnership with Health Data Research UK (HDR UK) and Administrative Data Research UK (ADR UK). The specific projects were Semi-Automatic checking of Research Outputs (SACRO; MC_PC_23006) and Guidelines and Resources for AI Model Access from TrusTEd Research environments (GRAIMATTER; MC_PC_21033).This project has also been supported by MRC and EPSRC [grant number MR/S010351/1]: PICTURES.

diff --git a/examples/notebooks/example-notebook-SVC.ipynb b/examples/notebooks/example-notebook-SVC.ipynb
@@ -17,16 +17,11 @@
             "metadata": {},
             "outputs": [],
             "source": [
-                "import os\n",
-                "import sys\n",
-                "import pylab as plt\n",
-                "import numpy as np\n",
                 "import logging\n",
-                "from sklearn.svm import SVC\n",
-                "from sklearn.linear_model import LogisticRegression\n",
+                "import os\n",
                 "\n",
+                "import numpy as np\n",
                 "from sklearn import datasets\n",
-                "from os.path import expanduser\n",
                 "\n",
                 "# next few commented out lines are for developers only\n",
                 "# ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(\"\")))\n",
@@ -191,7 +186,7 @@
             ],
             "source": [
                 "target_json = os.path.normpath(\"testSaveSVC/target.json\")\n",
-                "with open(target_json, \"r\") as f:\n",
+                "with open(target_json) as f:\n",
                 "    print(f.read())"
             ]
         },
@@ -282,7 +277,7 @@
             ],
             "source": [
                 "target_json = os.path.normpath(\"testSaveSVC/target.json\")\n",
-                "with open(target_json, \"r\") as f:\n",
+                "with open(target_json) as f:\n",
                 "    print(f.read())"
             ]
         },

diff --git a/examples/notebooks/example-notebook-decisiontree.ipynb b/examples/notebooks/example-notebook-decisiontree.ipynb
@@ -17,7 +17,6 @@
             "metadata": {},
             "outputs": [],
             "source": [
-                "import sys\n",
                 "import os\n",
                 "\n",
                 "# from os.path import expanduser\n",
@@ -91,8 +90,8 @@
             "metadata": {},
             "outputs": [],
             "source": [
-                "from sklearn.tree import plot_tree\n",
-                "import matplotlib.pyplot as plt"
+                "import matplotlib.pyplot as plt\n",
+                "from sklearn.tree import plot_tree"
             ]
         },
         {
@@ -229,8 +228,8 @@
             "metadata": {},
             "outputs": [],
             "source": [
-                "from aisdc.safemodel.safemodel import SafeModel\n",
-                "from aisdc.safemodel.classifiers import SafeDecisionTreeClassifier"
+                "from aisdc.safemodel.classifiers import SafeDecisionTreeClassifier\n",
+                "from aisdc.safemodel.safemodel import SafeModel"
             ]
         },
         {
@@ -894,7 +893,7 @@
             ],
             "source": [
                 "target_json = os.path.normpath(\"hacked_unsafe/target.json\")\n",
-                "with open(target_json, \"r\") as f:\n",
+                "with open(target_json) as f:\n",
                 "    print(f.read())"
             ]
         },

diff --git a/examples/notebooks/example-notebook-keras.ipynb b/examples/notebooks/example-notebook-keras.ipynb
@@ -16,7 +16,6 @@
             "metadata": {},
             "outputs": [],
             "source": [
-                "import sys\n",
                 "import os\n",
                 "\n",
                 "\n",
@@ -38,30 +37,26 @@
             "metadata": {},
             "outputs": [],
             "source": [
-                "import numpy as np\n",
                 "import matplotlib.pyplot as plt\n",
+                "import numpy as np\n",
                 "\n",
                 "%matplotlib inline\n",
                 "\n",
                 "# Scikit-learn utils\n",
-                "from sklearn.model_selection import train_test_split\n",
-                "from sklearn.datasets import make_classification, make_moons\n",
-                "from sklearn.metrics import ConfusionMatrixDisplay\n",
-                "from sklearn.metrics import confusion_matrix, classification_report, roc_curve, auc\n",
-                "\n",
                 "# Tensorflow imports\n",
                 "import tensorflow as tf\n",
-                "from tensorflow.keras.models import Model\n",
-                "from tensorflow.keras.layers import Input, Dense, Dropout\n",
-                "import tensorflow_privacy as tf_privacy\n",
-                "from tensorflow_privacy.privacy.analysis import compute_dp_sgd_privacy\n",
-                "\n",
-                "# Classifiers for attack models\n",
-                "from sklearn.linear_model import LogisticRegression\n",
-                "from sklearn.neural_network import MLPClassifier\n",
                 "\n",
                 "# Safe Keras\n",
                 "from aisdc.safemodel.classifiers import SafeKerasModel\n",
+                "from sklearn.datasets import make_classification\n",
+                "\n",
+                "# Classifiers for attack models\n",
+                "from sklearn.metrics import (\n",
+                "    classification_report,\n",
+                "    confusion_matrix,\n",
+                ")\n",
+                "from sklearn.model_selection import train_test_split\n",
+                "from tensorflow.keras.layers import Dense, Input\n",
                 "\n",
                 "# set tensorflow messages to warning level\n",
                 "tf.get_logger().setLevel(\"WARNING\")"
@@ -614,7 +609,7 @@
             ],
             "source": [
                 "target_json = os.path.normpath(\"safe1/target.json\")\n",
-                "with open(target_json, \"r\") as f:\n",
+                "with open(target_json) as f:\n",
                 "    print(f.read())"
             ]
         },

diff --git a/examples/notebooks/example-notebook-randomforest.ipynb b/examples/notebooks/example-notebook-randomforest.ipynb
@@ -15,7 +15,6 @@
             "metadata": {},
             "outputs": [],
             "source": [
-                "import sys\n",
                 "import os\n",
                 "\n",
                 "##Commented out lines below applies to developers only\n",
@@ -90,8 +89,8 @@
             "metadata": {},
             "outputs": [],
             "source": [
-                "from sklearn.tree import plot_tree\n",
-                "import matplotlib.pyplot as plt"
+                "import matplotlib.pyplot as plt\n",
+                "from sklearn.tree import plot_tree"
             ]
         },
         {
@@ -110,7 +109,6 @@
             "metadata": {},
             "outputs": [],
             "source": [
-                "from aisdc.safemodel.safemodel import SafeModel\n",
                 "from aisdc.safemodel.classifiers import SafeRandomForestClassifier"
             ]
         },
@@ -247,7 +245,7 @@
             ],
             "source": [
                 "target_json = os.path.normpath(\"testSaveRF/target.json\")\n",
-                "with open(target_json, \"r\") as f:\n",
+                "with open(target_json) as f:\n",
                 "    print(f.read())"
             ]
         },
@@ -278,7 +276,6 @@
                 }
             ],
             "source": [
-                "from aisdc.safemodel.safemodel import SafeModel\n",
                 "from aisdc.safemodel.classifiers import SafeRandomForestClassifier\n",
                 "\n",
                 "safeRFModel = SafeRandomForestClassifier(n_estimators=100)  # (criterion=\"entropy\")\n",
@@ -352,7 +349,7 @@
             ],
             "source": [
                 "target_json = os.path.normpath(\"testSaveRF/target.json\")\n",
-                "with open(target_json, \"r\") as f:\n",
+                "with open(target_json) as f:\n",
                 "    print(f.read())"
             ]
         },

diff --git a/examples/risk_examples/python/attribute_inference_cancer.ipynb b/examples/risk_examples/python/attribute_inference_cancer.ipynb
@@ -41,13 +41,14 @@
             "source": [
                 "import random\n",
                 "from itertools import product\n",
+                "\n",
                 "import numpy as np\n",
                 "\n",
                 "np.random.seed(1234)\n",
                 "random.seed(12345)\n",
                 "\n",
-                "from scipy.stats import poisson\n",
                 "import pandas as pd\n",
+                "from scipy.stats import poisson\n",
                 "from sklearn.svm import SVC"
             ]
         },

diff --git a/examples/risk_examples/python/instance_based_mimic.ipynb b/examples/risk_examples/python/instance_based_mimic.ipynb
@@ -29,9 +29,9 @@
                 }
             ],
             "source": [
-                "import os\n",
                 "import logging\n",
-                "import numpy as np\n",
+                "import os\n",
+                "\n",
                 "import pylab as plt\n",
                 "\n",
                 "%matplotlib inline\n",
@@ -123,9 +123,9 @@
                 }
             ],
             "source": [
-                "from sklearn.svm import SVC\n",
-                "from sklearn.model_selection import train_test_split\n",
                 "from sklearn.metrics import roc_curve\n",
+                "from sklearn.model_selection import train_test_split\n",
+                "from sklearn.svm import SVC\n",
                 "\n",
                 "font = {\"size\": 14}\n",
                 "plt.rc(\"font\", **font)\n",

diff --git a/examples/risk_examples/python/membership_inference_cancer.ipynb b/examples/risk_examples/python/membership_inference_cancer.ipynb
@@ -40,16 +40,16 @@
             "outputs": [],
             "source": [
                 "import random\n",
-                "from itertools import product\n",
+                "\n",
                 "import numpy as np\n",
                 "\n",
                 "np.random.seed(1234)\n",
                 "random.seed(12345)\n",
                 "\n",
-                "from scipy.stats import poisson\n",
                 "import pandas as pd\n",
-                "from sklearn.svm import SVC\n",
-                "from sklearn.model_selection import train_test_split"
+                "from scipy.stats import poisson\n",
+                "from sklearn.model_selection import train_test_split\n",
+                "from sklearn.svm import SVC"
             ]
         },
         {

diff --git a/pyproject.toml b/pyproject.toml
@@ -74,6 +74,16 @@ lint.select = [
   "YTT",  # flake8-2020
 ]
 
+exclude = [
+    "**example-notebook-SVC.ipynb",
+    "**example-notebook-decisiontree.ipynb",
+    "**example-notebook-keras.ipynb",
+    "**example-notebook-randomforest.ipynb",
+    "**attribute_inference_cancer.ipynb",
+    "**instance_based_mimic.ipynb",
+    "**membership_inference_cancer.ipynb",
+]
+
 lint.ignore = [
     "ANN101", # missing-type-self
     "EM101",  # raw-string-in-exception

diff --git a/tests/conftest.py b/tests/conftest.py
@@ -93,7 +93,7 @@ def _cleanup():
             os.remove(file)
 
 
-@pytest.fixture()
+@pytest.fixture
 def get_target(request) -> Target:  # pylint: disable=too-many-locals
     """Return a target object with test data and fitted model.