diff --git a/examples/notebooks/example-notebook-SVC.ipynb b/examples/notebooks/example-notebook-SVC.ipynb
index 1b81384b..a7b93488 100644
--- a/examples/notebooks/example-notebook-SVC.ipynb
+++ b/examples/notebooks/example-notebook-SVC.ipynb
@@ -1,319 +1,314 @@
 {
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "6105c66c",
-            "metadata": {},
-            "source": [
-                "# Safe Support Vector Machine Notebook \n",
-                "## A Quick Start Guide to implementing Safer Support Vector Machines\n",
-                "### Commands commented out for path manipulation are for developers only\n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 1,
-            "id": "82797236",
-            "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",
-                "\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",
-                "# sys.path.append(ROOT_DIR)\n",
-                "# home = expanduser(\"~\")\n",
-                "# sys.path.append(os.path.abspath(home + \"/AI-SDC\"))\n",
-                "# sys.path.insert(0, os.path.abspath(\"..\"))\n",
-                "\n",
-                "logging.basicConfig()\n",
-                "logger = logging.getLogger(\"wrapper_svm\")\n",
-                "logger.setLevel(logging.INFO)\n",
-                "# ROOT_PROJECT_FOLDER = os.path.dirname(os.path.dirname(__file__))\n",
-                "# sys.path.append(ROOT_PROJECT_FOLDER)\n",
-                "from aisdc.safemodel.classifiers import SafeSVC"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "190329f4",
-            "metadata": {},
-            "source": [
-                "## Use the sklearn Wisconsin breast cancer dataset"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 2,
-            "id": "85af89a0",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "cancer = datasets.load_breast_cancer()\n",
-                "x = np.asarray(cancer.data, dtype=np.float64)\n",
-                "y = np.asarray(cancer.target, dtype=np.float64)"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "1410e2f4",
-            "metadata": {},
-            "source": [
-                "## Kernel for approximator: equivalent to rbf."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 3,
-            "id": "69481c5c",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "def rbf(x, y, gamma=1):\n",
-                "    return np.exp(-gamma * np.sum((x - y) ** 2))\n",
-                "\n",
-                "\n",
-                "def rbf_svm(x, y, gamma=1):\n",
-                "    r = np.zeros((x.shape[0], y.shape[0]))\n",
-                "    for i in range(x.shape[0]):\n",
-                "        for j in range(y.shape[0]):\n",
-                "            r[i, j] = rbf(x[i, :], y[j, :], gamma)\n",
-                "    return r"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "f7bdc592",
-            "metadata": {},
-            "source": [
-                "## Set parameters"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 4,
-            "id": "7cd6c7f2",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "gamma = 0.1  # Kernel width\n",
-                "C = 1  # Penalty term\n",
-                "dhat = 5  # Dimension of approximator\n",
-                "eps = 500  # DP level (not very private)"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cab1eed3",
-            "metadata": {},
-            "source": [
-                "# Define Differentially Private version with DP level (approximate)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 5,
-            "id": "1a7734dc",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "clf3 = SafeSVC(eps=eps, dhat=dhat, C=C, gamma=gamma)\n",
-                "clf3.fit(x, y)\n",
-                "c3 = clf3.predict(x)\n",
-                "p3 = clf3.predict_proba(x)"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "6c08d536",
-            "metadata": {},
-            "source": [
-                "## Define the model and fit it.\n",
-                "## Save and Request Release\n",
-                "### We are warned that dhat is too low."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 6,
-            "id": "c3b7e21f",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "clf3 = SafeSVC(eps=eps, dhat=dhat, C=C, gamma=gamma)\n",
-                "clf3.fit(x, y)\n",
-                "clf3.save(name=\"testSaveSVC.pkl\")\n",
-                "clf3.request_release(path=\"testSaveSVC\", ext=\"pkl\")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 7,
-            "id": "9aa22802",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "{\n",
-                        "    \"data_name\": \"\",\n",
-                        "    \"n_samples\": 0,\n",
-                        "    \"features\": {},\n",
-                        "    \"n_features\": 0,\n",
-                        "    \"n_samples_orig\": 0,\n",
-                        "    \"generalisation_error\": \"unknown\",\n",
-                        "    \"safemodel\": [\n",
-                        "        {\n",
-                        "            \"researcher\": \"j4-smith\",\n",
-                        "            \"model_type\": \"SVC\",\n",
-                        "            \"details\": \"WARNING: model parameters may present a disclosure risk:\\n- parameter dhat = 5 identified as less than the recommended min value of 1000.\",\n",
-                        "            \"recommendation\": \"Do not allow release\",\n",
-                        "            \"reason\": \"WARNING: model parameters may present a disclosure risk:\\n- parameter dhat = 5 identified as less than the recommended min value of 1000.\",\n",
-                        "            \"timestamp\": \"2023-10-12 01:49:21\"\n",
-                        "        }\n",
-                        "    ],\n",
-                        "    \"model_path\": \"model.pkl\",\n",
-                        "    \"model_name\": \"SafeSVC\",\n",
-                        "    \"model_params\": {}\n",
-                        "}\n"
-                    ]
-                }
-            ],
-            "source": [
-                "target_json = os.path.normpath(\"testSaveSVC/target.json\")\n",
-                "with open(target_json, \"r\") as f:\n",
-                "    print(f.read())"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cf32d434",
-            "metadata": {},
-            "source": [
-                "## Set Parameters to safe values"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 8,
-            "id": "87689404",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "gamma = 0.1  # Kernel width\n",
-                "C = 1  # Penalty term\n",
-                "dhat = 1000  # Dimension of approximator\n",
-                "eps = 500  # DP level (not very private)"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "75c8400a",
-            "metadata": {},
-            "source": [
-                "## Define the model and fit it.\n",
-                "## Save and Request Release\n",
-                "### Model parameters are within recommended ranges. The saved model can pass through next step of checking procedure"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 9,
-            "id": "90369029",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "clf3 = SafeSVC(eps=eps, dhat=dhat, C=C, gamma=gamma)\n",
-                "clf3.fit(x, y)\n",
-                "clf3.save(name=\"testSaveSVC.pkl\")\n",
-                "clf3.request_release(path=\"testSaveSVC\", ext=\"pkl\")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "f7ae65a4",
-            "metadata": {},
-            "source": [
-                "## Examine the checkfile"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 10,
-            "id": "ca19340f",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "{\n",
-                        "    \"data_name\": \"\",\n",
-                        "    \"n_samples\": 0,\n",
-                        "    \"features\": {},\n",
-                        "    \"n_features\": 0,\n",
-                        "    \"n_samples_orig\": 0,\n",
-                        "    \"generalisation_error\": \"unknown\",\n",
-                        "    \"safemodel\": [\n",
-                        "        {\n",
-                        "            \"researcher\": \"j4-smith\",\n",
-                        "            \"model_type\": \"SVC\",\n",
-                        "            \"details\": \"Model parameters are within recommended ranges.\\n\",\n",
-                        "            \"recommendation\": \"Proceed to next step of checking\",\n",
-                        "            \"timestamp\": \"2023-10-12 01:49:21\"\n",
-                        "        }\n",
-                        "    ],\n",
-                        "    \"model_path\": \"model.pkl\",\n",
-                        "    \"model_name\": \"SafeSVC\",\n",
-                        "    \"model_params\": {}\n",
-                        "}\n"
-                    ]
-                }
-            ],
-            "source": [
-                "target_json = os.path.normpath(\"testSaveSVC/target.json\")\n",
-                "with open(target_json, \"r\") as f:\n",
-                "    print(f.read())"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "bc6c0dfe",
-            "metadata": {},
-            "outputs": [],
-            "source": []
-        }
-    ],
-    "metadata": {
-        "kernelspec": {
-            "display_name": "aisdc-v1.1",
-            "language": "python",
-            "name": "aisdc-v1.1"
-        },
-        "language_info": {
-            "codemirror_mode": {
-                "name": "ipython",
-                "version": 3
-            },
-            "file_extension": ".py",
-            "mimetype": "text/x-python",
-            "name": "python",
-            "nbconvert_exporter": "python",
-            "pygments_lexer": "ipython3",
-            "version": "3.9.18"
-        }
-    },
-    "nbformat": 4,
-    "nbformat_minor": 5
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "6105c66c",
+   "metadata": {},
+   "source": [
+    "# Safe Support Vector Machine Notebook \n",
+    "## A Quick Start Guide to implementing Safer Support Vector Machines\n",
+    "### Commands commented out for path manipulation are for developers only\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "82797236",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import os\n",
+    "\n",
+    "import numpy as np\n",
+    "from sklearn import datasets\n",
+    "\n",
+    "# next few commented out lines are for developers only\n",
+    "# ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(\"\")))\n",
+    "# sys.path.append(ROOT_DIR)\n",
+    "# home = expanduser(\"~\")\n",
+    "# sys.path.append(os.path.abspath(home + \"/AI-SDC\"))\n",
+    "# sys.path.insert(0, os.path.abspath(\"..\"))\n",
+    "\n",
+    "logging.basicConfig()\n",
+    "logger = logging.getLogger(\"wrapper_svm\")\n",
+    "logger.setLevel(logging.INFO)\n",
+    "# ROOT_PROJECT_FOLDER = os.path.dirname(os.path.dirname(__file__))\n",
+    "# sys.path.append(ROOT_PROJECT_FOLDER)\n",
+    "from aisdc.safemodel.classifiers import SafeSVC"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "190329f4",
+   "metadata": {},
+   "source": [
+    "## Use the sklearn Wisconsin breast cancer dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "85af89a0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cancer = datasets.load_breast_cancer()\n",
+    "x = np.asarray(cancer.data, dtype=np.float64)\n",
+    "y = np.asarray(cancer.target, dtype=np.float64)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1410e2f4",
+   "metadata": {},
+   "source": [
+    "## Kernel for approximator: equivalent to rbf."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "69481c5c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def rbf(x, y, gamma=1):\n",
+    "    return np.exp(-gamma * np.sum((x - y) ** 2))\n",
+    "\n",
+    "\n",
+    "def rbf_svm(x, y, gamma=1):\n",
+    "    r = np.zeros((x.shape[0], y.shape[0]))\n",
+    "    for i in range(x.shape[0]):\n",
+    "        for j in range(y.shape[0]):\n",
+    "            r[i, j] = rbf(x[i, :], y[j, :], gamma)\n",
+    "    return r"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f7bdc592",
+   "metadata": {},
+   "source": [
+    "## Set parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7cd6c7f2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gamma = 0.1  # Kernel width\n",
+    "C = 1  # Penalty term\n",
+    "dhat = 5  # Dimension of approximator\n",
+    "eps = 500  # DP level (not very private)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cab1eed3",
+   "metadata": {},
+   "source": [
+    "# Define Differentially Private version with DP level (approximate)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "1a7734dc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clf3 = SafeSVC(eps=eps, dhat=dhat, C=C, gamma=gamma)\n",
+    "clf3.fit(x, y)\n",
+    "c3 = clf3.predict(x)\n",
+    "p3 = clf3.predict_proba(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6c08d536",
+   "metadata": {},
+   "source": [
+    "## Define the model and fit it.\n",
+    "## Save and Request Release\n",
+    "### We are warned that dhat is too low."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "c3b7e21f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clf3 = SafeSVC(eps=eps, dhat=dhat, C=C, gamma=gamma)\n",
+    "clf3.fit(x, y)\n",
+    "clf3.save(name=\"testSaveSVC.pkl\")\n",
+    "clf3.request_release(path=\"testSaveSVC\", ext=\"pkl\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "9aa22802",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\n",
+      "    \"data_name\": \"\",\n",
+      "    \"n_samples\": 0,\n",
+      "    \"features\": {},\n",
+      "    \"n_features\": 0,\n",
+      "    \"n_samples_orig\": 0,\n",
+      "    \"generalisation_error\": \"unknown\",\n",
+      "    \"safemodel\": [\n",
+      "        {\n",
+      "            \"researcher\": \"j4-smith\",\n",
+      "            \"model_type\": \"SVC\",\n",
+      "            \"details\": \"WARNING: model parameters may present a disclosure risk:\\n- parameter dhat = 5 identified as less than the recommended min value of 1000.\",\n",
+      "            \"recommendation\": \"Do not allow release\",\n",
+      "            \"reason\": \"WARNING: model parameters may present a disclosure risk:\\n- parameter dhat = 5 identified as less than the recommended min value of 1000.\",\n",
+      "            \"timestamp\": \"2023-10-12 01:49:21\"\n",
+      "        }\n",
+      "    ],\n",
+      "    \"model_path\": \"model.pkl\",\n",
+      "    \"model_name\": \"SafeSVC\",\n",
+      "    \"model_params\": {}\n",
+      "}\n"
+     ]
+    }
+   ],
+   "source": [
+    "target_json = os.path.normpath(\"testSaveSVC/target.json\")\n",
+    "with open(target_json) as f:\n",
+    "    print(f.read())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cf32d434",
+   "metadata": {},
+   "source": [
+    "## Set Parameters to safe values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "87689404",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gamma = 0.1  # Kernel width\n",
+    "C = 1  # Penalty term\n",
+    "dhat = 1000  # Dimension of approximator\n",
+    "eps = 500  # DP level (not very private)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "75c8400a",
+   "metadata": {},
+   "source": [
+    "## Define the model and fit it.\n",
+    "## Save and Request Release\n",
+    "### Model parameters are within recommended ranges. The saved model can pass through next step of checking procedure"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "90369029",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clf3 = SafeSVC(eps=eps, dhat=dhat, C=C, gamma=gamma)\n",
+    "clf3.fit(x, y)\n",
+    "clf3.save(name=\"testSaveSVC.pkl\")\n",
+    "clf3.request_release(path=\"testSaveSVC\", ext=\"pkl\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f7ae65a4",
+   "metadata": {},
+   "source": [
+    "## Examine the checkfile"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "ca19340f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\n",
+      "    \"data_name\": \"\",\n",
+      "    \"n_samples\": 0,\n",
+      "    \"features\": {},\n",
+      "    \"n_features\": 0,\n",
+      "    \"n_samples_orig\": 0,\n",
+      "    \"generalisation_error\": \"unknown\",\n",
+      "    \"safemodel\": [\n",
+      "        {\n",
+      "            \"researcher\": \"j4-smith\",\n",
+      "            \"model_type\": \"SVC\",\n",
+      "            \"details\": \"Model parameters are within recommended ranges.\\n\",\n",
+      "            \"recommendation\": \"Proceed to next step of checking\",\n",
+      "            \"timestamp\": \"2023-10-12 01:49:21\"\n",
+      "        }\n",
+      "    ],\n",
+      "    \"model_path\": \"model.pkl\",\n",
+      "    \"model_name\": \"SafeSVC\",\n",
+      "    \"model_params\": {}\n",
+      "}\n"
+     ]
+    }
+   ],
+   "source": [
+    "target_json = os.path.normpath(\"testSaveSVC/target.json\")\n",
+    "with open(target_json) as f:\n",
+    "    print(f.read())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bc6c0dfe",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "aisdc-v1.1",
+   "language": "python",
+   "name": "aisdc-v1.1"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
 }
diff --git a/examples/notebooks/example-notebook-decisiontree.ipynb b/examples/notebooks/example-notebook-decisiontree.ipynb
index b9ffef9d..ea060408 100644
--- a/examples/notebooks/example-notebook-decisiontree.ipynb
+++ b/examples/notebooks/example-notebook-decisiontree.ipynb
@@ -1,931 +1,930 @@
 {
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "ba282cae",
-            "metadata": {},
-            "source": [
-                "# Safe Decision Tree Notebook \n",
-                "# Next cell is for developers only:\n",
-                "First set some path variables: this notebook expects to find the repository root in your home directory. You can change this by editing the last sys.path.append line to reflect with the repository root is found on your system"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 1,
-            "id": "166298a8",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import sys\n",
-                "import os\n",
-                "\n",
-                "# from os.path import expanduser\n",
-                "\n",
-                "# ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(\"\")))\n",
-                "# sys.path.append(ROOT_DIR)\n",
-                "# home = expanduser(\"~\")\n",
-                "# sys.path.append(os.path.abspath(home + \"/AI-SDC\"))\n",
-                "# sys.path.insert(0,os.path.abspath(\"..\"))"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "c0d92b96-a6bc-4b1b-9040-f81c095e8770",
-            "metadata": {},
-            "source": [
-                "## Some basic examples to explore what the wrapper class could look like and be implemented\n",
-                "\n",
-                "### Lets start by making some data with one disclosive case\n",
-                "- We'll do this by adding an example to the iris data and give it a new class to make things really obvious.\n",
-                "- The same risks exist for more complex data sets but _everyone knows iris_"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 2,
-            "id": "47e9809e-f778-48de-9857-a6a481d96cd3",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "feature 0 min 4.3, min 7.9\n",
-                        "feature 1 min 2.0, min 4.4\n",
-                        "feature 2 min 1.0, min 6.9\n",
-                        "feature 3 min 0.1, min 2.5\n"
-                    ]
-                }
-            ],
-            "source": [
-                "import numpy as np\n",
-                "from sklearn import datasets\n",
-                "\n",
-                "iris = datasets.load_iris()\n",
-                "X = iris.data\n",
-                "y = iris.target\n",
-                "\n",
-                "\n",
-                "# print the max and min values in each feature to help hand-craft the disclosive point\n",
-                "for feature in range(4):\n",
-                "    print(f\"feature {feature} min {np.min(X[:,feature])}, min {np.max(X[:,feature])}\")\n",
-                "\n",
-                "# now add a single disclosve point with features [7,2,4.5,1] and label 3\n",
-                "X = np.vstack([X, (7, 2.0, 4.5, 1)])\n",
-                "y = np.append(y, 4)"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "d5effc9f-f2b3-4e8c-b2c3-87633c7a76fb",
-            "metadata": {},
-            "source": [
-                "### and import some basic libraries to show our point"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 3,
-            "id": "828ba8d9-c78e-4259-bd49-802481b70ee5",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "from sklearn.tree import plot_tree\n",
-                "import matplotlib.pyplot as plt"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "9d657d29-6edb-4e50-813e-654b104c7f75",
-            "metadata": {},
-            "source": [
-                "## Here's the raw version\n",
-                "-  note I am setting random_state=1 to make it deterministic, just so you get the same reults as me\n",
-                " - the general point is not that someone always will, but that they could\n",
-                " - in practice I ran 10 times not setting random state and got the same tree 4/5 times"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 4,
-            "id": "0a2e85c9-5516-43f7-bb96-ddb48dc08d15",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Training set accuracy in this naive case is 1.0\n"
-                    ]
-                },
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 1500x1000 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "# example code with no safety\n",
-                "\n",
-                "from sklearn.tree import DecisionTreeClassifier\n",
-                "\n",
-                "rawDT = DecisionTreeClassifier(min_samples_leaf=1, criterion=\"gini\", random_state=1)\n",
-                "rawDT.fit(X, y)\n",
-                "\n",
-                "print(f\"Training set accuracy in this naive case is {rawDT.score(X,y)}\")\n",
-                "\n",
-                "fig, ax = plt.subplots(1, 1, figsize=(15, 10))\n",
-                "output = plot_tree(rawDT, filled=True, ax=ax, fontsize=10)"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "1dd44240-2ed4-4e94-9633-94057220d686",
-            "metadata": {},
-            "source": [
-                "### As we can see we have several disclosive nodes, one of which is our added point (in purple)\n",
-                "The exact values cannot be inferred but if we (reasonably) assume all features are non-negative we can get uper and lower bounds for the attribute values on that node:  \n",
-                "> (6.95,inf),  \\[0,2.6\\],  (0,4.95\\],  (0.8, 1.65\\]\n",
-                "\n",
-                "so this is disclosive to a certain degree.\n",
-                "\n",
-                "- In this case I spent 5 minutes manually tuning the values of the added point so that the tree included at least one decision node for each feature\n",
-                "\n",
-                "- It would be fairly trivial to use something like a Genetic Algorithm to automatically tune the feature values of the added point minimising the difference between the upper and lower bounds for each feature.\n",
-                "\n",
-                "- But that is not really the point of this exercise which was to show that allowing the user to set inappropriate values for a single parameter could produce a disclosive tree. \n"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "2bcf587c-fd29-4801-ba9b-51a342ac8aed",
-            "metadata": {},
-            "source": [
-                "### Diligent user realises problem, and changes their code to enforce at least n samples in each leaf\n",
-                "We'll use n=5 "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 5,
-            "id": "24bd26c3-6917-4493-b1c8-283d9fd8d4a2",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Training set accuracy with threshold rule enforced is 0.9668874172185431\n"
-                    ]
-                },
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 1500x1000 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "manualDT = DecisionTreeClassifier(min_samples_leaf=5, random_state=1)\n",
-                "manualDT.fit(X, y)\n",
-                "\n",
-                "print(f\"Training set accuracy with threshold rule enforced is {manualDT.score(X,y)}\")\n",
-                "\n",
-                "fig2, ax2 = plt.subplots(1, 1, figsize=(15, 10))\n",
-                "output = plot_tree(manualDT, filled=True, ax=ax2, fontsize=11)"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "968acece-746d-4b53-a20d-ef100e1b59a8",
-            "metadata": {},
-            "source": [
-                "### output is now non-disclosive (at least according to the threshold rule)\n",
-                "- You can easily see we don't get a node for the new class 3"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "ac1d51e4-a2ad-41e9-bd59-389741c1d996",
-            "metadata": {},
-            "source": [
-                "## So lets define a new class SafeDecisionTreeClassifier \n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 6,
-            "id": "a6c88914-790f-417c-89b7-6641f2ca6539",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "from aisdc.safemodel.safemodel import SafeModel\n",
-                "from aisdc.safemodel.classifiers import SafeDecisionTreeClassifier"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 7,
-            "id": "8993ca95-4e71-48e4-8b11-783fcffcdec9",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "None with parameters: {'model_type': 'None', 'model': None, 'saved_model': None, 'model_load_file': 'None', 'model_save_file': 'None', 'ignore_items': [], 'examine_seperately_items': [], 'basemodel_paramnames': [], 'filename': 'None', 'researcher': 'j4-smith', 'timestamp': 'None'}\n"
-                    ]
-                }
-            ],
-            "source": [
-                "noNameModel = SafeModel()\n",
-                "\n",
-                "try:\n",
-                "    print(noNameModel.__str__())\n",
-                "except:\n",
-                "    print(\"super class has no attributes to print\")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 8,
-            "id": "504c3c64-adcf-4796-b4ef-c574a6cfe1bb",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Preliminary checks: Model parameters are within recommended ranges.\n",
-                        "\n",
-                        "DecisionTreeClassifier with parameters: {'model_type': 'DecisionTreeClassifier', 'model': None, 'saved_model': None, 'model_load_file': 'None', 'model_save_file': 'None', 'ignore_items': ['model_save_file', 'basemodel_paramnames', 'ignore_items', 'timestamp'], 'examine_seperately_items': ['tree_'], 'basemodel_paramnames': ['criterion', 'splitter', 'max_depth', 'min_samples_split', 'min_samples_leaf', 'min_weight_fraction_leaf', 'max_features', 'random_state', 'max_leaf_nodes', 'min_impurity_decrease', 'class_weight', 'ccp_alpha'], 'filename': 'None', 'researcher': 'j4-smith', 'timestamp': 'None', 'criterion': 'gini', 'splitter': 'best', 'max_depth': None, 'min_samples_split': 2, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': None, 'max_leaf_nodes': None, 'random_state': None, 'min_impurity_decrease': 0.0, 'class_weight': None, 'ccp_alpha': 0.0, 'k_anonymity': 0}\n"
-                    ]
-                }
-            ],
-            "source": [
-                "safeDTModel = SafeDecisionTreeClassifier(min_samples_leaf=5)  # (criterion=\"entropy\")\n",
-                "print(safeDTModel.__str__())"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "2942eba1",
-            "metadata": {},
-            "source": [
-                "## Do the posthoc_check\n",
-                "In this cell the model has not run fit()\n",
-                "\n",
-                "posthoc_check detects that fit has not been run and reports a warning.\n",
-                "\n",
-                "Subsequently we run fit and report the accuracy."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 9,
-            "id": "de3c6317",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "disclosive currently True\n",
-                        "Because Error: user has not called fit() method or has deleted saved values.Recommendation: Do not release.\n",
-                        "Training set accuracy in this naive case is 0.9668874172185431\n"
-                    ]
-                }
-            ],
-            "source": [
-                "msg, disclosive = safeDTModel.posthoc_check()\n",
-                "print(f\"disclosive currently {disclosive}\")\n",
-                "print(\"Because \" + msg)\n",
-                "\n",
-                "safeDTModel.fit(X, y)\n",
-                "\n",
-                "print(f\"Training set accuracy in this naive case is {safeDTModel.score(X,y)}\")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "e24fedf0",
-            "metadata": {},
-            "source": [
-                "## We check our now fitted model from the previous step \n",
-                "posthoc_check reports it is not disclosive (safer)\n",
-                "\n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 10,
-            "id": "bfa6feb6",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "disclosive currently False\n"
-                    ]
-                }
-            ],
-            "source": [
-                "msg, disclosive = safeDTModel.posthoc_check()\n",
-                "print(f\"disclosive currently {disclosive}\")\n",
-                "if disclosive:\n",
-                "    print(\"Because \" + msg)"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "632ed8d0",
-            "metadata": {},
-            "source": [
-                "## We modify the parameters\n",
-                "* min_samples_leaf = 9\n",
-                "* min_samples_split = 1\n",
-                "\n",
-                "We use posthoc_check to detect whether model has been modified or interfered with since fit() was last run.\n",
-                "We are warned that the model may be disclosive because \n",
-                "the two parameters were changed after model was fitted."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 11,
-            "id": "cc1a87f1",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "disclosive currently True\n",
-                        "Because Warning: basic parameters differ in 2 places:\n",
-                        "parameter min_samples_split changed from 2 to 1 after model was fitted.\n",
-                        "parameter min_samples_leaf changed from 5 to 9 after model was fitted.\n",
-                        "\n"
-                    ]
-                }
-            ],
-            "source": [
-                "safeDTModel.min_samples_leaf = 9\n",
-                "safeDTModel.min_samples_split = 1\n",
-                "msg, disclosive = safeDTModel.posthoc_check()\n",
-                "print(f\"disclosive currently {disclosive}\")\n",
-                "if disclosive:\n",
-                "    print(\"Because \" + msg)"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "5067b094",
-            "metadata": {},
-            "source": [
-                "We modify the parameters, returning min_samples_leaf and min_samples split to their original values.\n",
-                "\n",
-                "    min_samples_leaf = 5\n",
-                "    min_samples_split = 2\n",
-                "\n",
-                "We use posthoc_check to detect whether model has been modified or interfered with since fit() was last run. Because the parameters are the same as the fit model, the model is not considered disclosive.\n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 12,
-            "id": "57e8061c",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "disclosive currently False\n"
-                    ]
-                }
-            ],
-            "source": [
-                "safeDTModel.min_samples_leaf = 5\n",
-                "safeDTModel.min_samples_split = 2\n",
-                "msg, disclosive = safeDTModel.posthoc_check()\n",
-                "print(f\"disclosive currently {disclosive}\")\n",
-                "if disclosive:\n",
-                "    print(\"Because \" + msg)"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "dbc720cd",
-            "metadata": {},
-            "source": [
-                "### We extract and plot the decision tree in order to visualize it."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 13,
-            "id": "e92b751a-5d23-4019-bc54-3986c072c9b1",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Preliminary checks: Model parameters are within recommended ranges.\n",
-                        "\n"
-                    ]
-                },
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 1500x1000 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "fig, ax = plt.subplots(1, 1, figsize=(15, 10))\n",
-                "# output = plot_tree(safeDTModel.model,filled=True, ax=ax,fontsize=11)\n",
-                "\n",
-                "output = plot_tree(safeDTModel, filled=True, ax=ax, fontsize=11)\n",
-                "\n",
-                "safeDTModel.save(name=\"testSave.pkl\")\n",
-                "safeDTModel.preliminary_check()\n",
-                "safeDTModel.request_release(path=\"testSave\", ext=\"pkl\")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "894919fa-17d4-41cc-b065-690e10cdc192",
-            "metadata": {},
-            "source": [
-                "## Demonstrate the save and reporting functionality\n",
-                "#### save the model\n",
-                "save(name=\"testSave.pkl\") - Writes model to file in appropriate format (.pkl).\n",
-                "#### perform a preliminary_check \n",
-                "safeDTModel.preliminary_check() - Checks whether current model parameters violate the safe rules.\n",
-                "Optionally automatically fixes any violations.\n",
-                "#### Request Release\n",
-                "safeDTModel.request_release(\"testSave.pkl\") - Saves model to filename specified and creates a report for the TRE output checkers."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 14,
-            "id": "2fec67c3-4b39-4d90-aa83-45ca576742ac",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Preliminary checks: Model parameters are within recommended ranges.\n",
-                        "\n"
-                    ]
-                }
-            ],
-            "source": [
-                "safeDTModel.save(name=\"testSave.pkl\")\n",
-                "safeDTModel.preliminary_check()\n",
-                "safeDTModel.request_release(path=\"testSave\", ext=\"pkl\")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "27211998-c809-4c20-96c7-49909983f1af",
-            "metadata": {},
-            "source": [
-                "## Now lets try to attack this approach\n",
-                "### starting with listing the params then trying to set the params manually after init"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 15,
-            "id": "6f393ab2-9237-43ed-8921-36ffd0044e5d",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "{'model_type': 'DecisionTreeClassifier', 'model': None, 'saved_model': {'model_type': 'DecisionTreeClassifier', 'model': None, 'saved_model': None, 'model_load_file': 'None', 'model_save_file': 'None', 'ignore_items': ['model_save_file', 'basemodel_paramnames', 'ignore_items', 'timestamp'], 'examine_seperately_items': ['tree_'], 'basemodel_paramnames': ['criterion', 'splitter', 'max_depth', 'min_samples_split', 'min_samples_leaf', 'min_weight_fraction_leaf', 'max_features', 'random_state', 'max_leaf_nodes', 'min_impurity_decrease', 'class_weight', 'ccp_alpha'], 'filename': 'None', 'researcher': 'j4-smith', 'timestamp': 'None', 'criterion': 'gini', 'splitter': 'best', 'max_depth': None, 'min_samples_split': 2, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': None, 'max_leaf_nodes': None, 'random_state': None, 'min_impurity_decrease': 0.0, 'class_weight': None, 'ccp_alpha': 0.0, 'k_anonymity': 5, 'n_features_in_': 4, 'n_outputs_': 1, 'classes_': array([0, 1, 2, 4]), 'n_classes_': 4, 'max_features_': 4, 'tree_': <sklearn.tree._tree.Tree object at 0x292909340>}, 'model_load_file': 'None', 'model_save_file': 'testSave/model.pkl', 'ignore_items': ['model_save_file', 'basemodel_paramnames', 'ignore_items', 'timestamp'], 'examine_seperately_items': ['tree_'], 'basemodel_paramnames': ['criterion', 'splitter', 'max_depth', 'min_samples_split', 'min_samples_leaf', 'min_weight_fraction_leaf', 'max_features', 'random_state', 'max_leaf_nodes', 'min_impurity_decrease', 'class_weight', 'ccp_alpha'], 'filename': 'None', 'researcher': 'j4-smith', 'timestamp': '2023-10-12 01:47:21', 'criterion': 'gini', 'splitter': 'best', 'max_depth': None, 'min_samples_split': 2, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': None, 'max_leaf_nodes': None, 'random_state': None, 'min_impurity_decrease': 0.0, 'class_weight': None, 'ccp_alpha': 0.0, 'k_anonymity': 5, 'n_features_in_': 4, 'n_outputs_': 1, 'classes_': array([0, 1, 2, 4]), 'n_classes_': 4, 'max_features_': 4, 'tree_': <sklearn.tree._tree.Tree object at 0x292909260>}\n"
-                    ]
-                }
-            ],
-            "source": [
-                "print(safeDTModel.__dict__)"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "023223f5",
-            "metadata": {},
-            "source": [
-                "## Train a model where the min_samples_leaf is 1 \n",
-                "### We extract and plot the decision tree.  \n",
-                "### Because min_samples_leaf is 1 this poses a disclosure risk.\n",
-                "### The prelimanary check reports this."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 16,
-            "id": "c060b2c1-9866-4eb4-93fd-083db423f393",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Training set accuracy in this naive case is 1.0\n",
-                        "Preliminary checks: WARNING: model parameters may present a disclosure risk:\n",
-                        "- parameter min_samples_leaf = 1 identified as less than the recommended min value of 5.\n"
-                    ]
-                },
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 1500x1000 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "safeDTModel.min_samples_leaf = 1\n",
-                "\n",
-                "safeDTModel.fit(X, y)\n",
-                "\n",
-                "print(f\"Training set accuracy in this naive case is {safeDTModel.score(X,y)}\")\n",
-                "\n",
-                "fig, ax = plt.subplots(1, 1, figsize=(15, 10))\n",
-                "output = plot_tree(safeDTModel, filled=True, ax=ax, fontsize=11)\n",
-                "\n",
-                "safeDTModel.save(name=\"testSave.pkl\")\n",
-                "safeDTModel.preliminary_check()\n",
-                "safeDTModel.request_release(path=\"testSave\", ext=\"pkl\")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "8bf0bb1a-1a5e-4702-9c4d-cdafa2bc1b8a",
-            "metadata": {},
-            "source": [
-                "## Example Implementation runs\n",
-                "### The researcher doesn't change recomended params"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 17,
-            "id": "51df3667",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "***researcher doesn't change recomended params\n",
-                        "Preliminary checks: WARNING: model parameters may present a disclosure risk:\n",
-                        "- parameter min_samples_leaf = 1 identified as less than the recommended min value of 5.\n",
-                        "Preliminary checks: WARNING: model parameters may present a disclosure risk:\n",
-                        "- parameter min_samples_leaf = 1 identified as less than the recommended min value of 5.\n"
-                    ]
-                }
-            ],
-            "source": [
-                "# create and fit using recomended params\n",
-                "print(\"***researcher doesn't change recomended params\")\n",
-                "safeDTModel2 = SafeDecisionTreeClassifier()\n",
-                "safeDTModel2.fit(X, y)\n",
-                "safeDTModel2.save(name=\"safe2.pkl\")\n",
-                "safeDTModel2.preliminary_check()\n",
-                "safeDTModel2.posthoc_check()\n",
-                "safeDTModel2.request_release(path=\"safe2\", ext=\"pkl\")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "d93ca2f1",
-            "metadata": {},
-            "source": [
-                "### The researcher changes params safely"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 18,
-            "id": "d919837f",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "\n",
-                        "***researcher changes params safely\n",
-                        "Preliminary checks: WARNING: model parameters may present a disclosure risk:\n",
-                        "- parameter min_samples_leaf = 1 identified as less than the recommended min value of 5.\n",
-                        "Preliminary checks: Model parameters are within recommended ranges.\n",
-                        "\n"
-                    ]
-                }
-            ],
-            "source": [
-                "# change model params to recommended values\n",
-                "print(\"\\n***researcher changes params safely\")\n",
-                "safeDTModel3 = SafeDecisionTreeClassifier()\n",
-                "safeDTModel3.min_samples_leaf = 5\n",
-                "safeDTModel3.fit(X, y)\n",
-                "safeDTModel3.save(name=\"safe3.pkl\")\n",
-                "safeDTModel3.preliminary_check()\n",
-                "safeDTModel3.posthoc_check()\n",
-                "safeDTModel3.request_release(path=\"safe3\", ext=\"pkl\")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "d8b3c8f1",
-            "metadata": {},
-            "source": [
-                "### The researcher changes params safely"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 19,
-            "id": "9021242f",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "\n",
-                        "***researcher changes params safely\n",
-                        "Preliminary checks: WARNING: model parameters may present a disclosure risk:\n",
-                        "- parameter min_samples_leaf = 1 identified as less than the recommended min value of 5.\n",
-                        "Preliminary checks: Model parameters are within recommended ranges.\n",
-                        "\n"
-                    ]
-                }
-            ],
-            "source": [
-                "# change model params in a safe way\n",
-                "print(\"\\n***researcher changes params safely\")\n",
-                "safeDTModel4 = SafeDecisionTreeClassifier()\n",
-                "safeDTModel4.min_samples_leaf = 10\n",
-                "safeDTModel4.fit(X, y)\n",
-                "safeDTModel4.save(name=\"safe4.pkl\")\n",
-                "safeDTModel4.preliminary_check()\n",
-                "safeDTModel4.posthoc_check()\n",
-                "safeDTModel4.request_release(path=\"safe4\", ext=\"pkl\")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "56c2a832",
-            "metadata": {},
-            "source": [
-                "### The researcher changes params unsafely"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 20,
-            "id": "efb66f5e",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "\n",
-                        "***researcher changes params unsafely\n",
-                        "Preliminary checks: WARNING: model parameters may present a disclosure risk:\n",
-                        "- parameter min_samples_leaf = 1 identified as less than the recommended min value of 5.\n",
-                        "Preliminary checks: WARNING: model parameters may present a disclosure risk:\n",
-                        "- parameter min_samples_leaf = 1 identified as less than the recommended min value of 5.\n"
-                    ]
-                }
-            ],
-            "source": [
-                "# change model params in an  unsafe way\n",
-                "print(\"\\n***researcher changes params unsafely\")\n",
-                "safeDTModel5 = SafeDecisionTreeClassifier()\n",
-                "safeDTModel5.min_samples_leaf = 1\n",
-                "safeDTModel5.save(name=\"unsafe.pkl\")\n",
-                "safeDTModel5.preliminary_check()\n",
-                "safeDTModel5.posthoc_check()\n",
-                "safeDTModel5.request_release(path=\"unsafe\", ext=\"pkl\")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "0002e88b",
-            "metadata": {},
-            "source": [
-                "### The researcher asks for a safe_decision tree but supplies with unsafe params"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 21,
-            "id": "0a89c1c7",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "\n",
-                        "***researcher asks for a safe_decision tree but supplies with unsafe params\n",
-                        "Preliminary checks: WARNING: model parameters may present a disclosure risk:\n",
-                        "- parameter min_samples_leaf = 1 identified as less than the recommended min value of 5.\n",
-                        "Preliminary checks: WARNING: model parameters may present a disclosure risk:\n",
-                        "- parameter min_samples_leaf = 1 identified as less than the recommended min value of 5.\n"
-                    ]
-                }
-            ],
-            "source": [
-                "# ask for a model with unsafe param values\n",
-                "print(\"\\n***researcher asks for a safe_decision tree but supplies with unsafe params\")\n",
-                "safeDTModel6 = SafeDecisionTreeClassifier(min_samples_leaf=1)\n",
-                "safeDTModel6.fit(X, y)\n",
-                "safeDTModel6.save(name=\"fixed-unsafe.pkl\")\n",
-                "safeDTModel6.preliminary_check()\n",
-                "safeDTModel6.posthoc_check()\n",
-                "safeDTModel6.request_release(path=\"fixed_unsafe\", ext=\"pkl\")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "985412f6",
-            "metadata": {},
-            "source": [
-                "### The researcher asks for a safe_decision tree, changes values to unsafe before fit() then back afterwards"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 22,
-            "id": "8b352bb2-3608-433e-b586-b235344ec58f",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "\n",
-                        "***researcher asks for a safe_decision tree, changes values to unsafe before fit() then back afterwards\n",
-                        "Preliminary checks: WARNING: model parameters may present a disclosure risk:\n",
-                        "- parameter min_samples_leaf = 1 identified as less than the recommended min value of 5.\n",
-                        "safeDTmodel7.score on training set is 1.0\n",
-                        "fit called. with min_samples_leaf = 1\n",
-                        "Preliminary checks: Model parameters are within recommended ranges.\n",
-                        "\n",
-                        "preliminary message is:Model parameters are within recommended ranges.\n",
-                        "\n",
-                        "preliminary disclosive is:False\n",
-                        "posthoc message is:Warning: basic parameters differ in 1 places:\n",
-                        "parameter min_samples_leaf changed from 1 to 5 after model was fitted.\n",
-                        "\n",
-                        "posthoc disclosive is:True\n"
-                    ]
-                }
-            ],
-            "source": [
-                "# trains a model with unsafe param values then  hacks values back to safe ones later\n",
-                "print(\n",
-                "    \"\\n***researcher asks for a safe_decision tree, changes values to unsafe before fit() then back afterwards\"\n",
-                ")\n",
-                "safeDTModel7 = SafeDecisionTreeClassifier(min_samples_leaf=1)\n",
-                "\n",
-                "safeDTModel7.min_samples_leaf = 1\n",
-                "safeDTModel7.fit(X, y)\n",
-                "print(f\"safeDTmodel7.score on training set is {safeDTModel7.score(X,y)}\")\n",
-                "print(f\"fit called. with min_samples_leaf = {safeDTModel7.min_samples_leaf}\")\n",
-                "safeDTModel7.min_samples_leaf = 5\n",
-                "\n",
-                "\n",
-                "safeDTModel7.save(name=\"hacked-unsafe.pkl\")\n",
-                "msg, disclosive = safeDTModel7.preliminary_check()\n",
-                "print(f\"preliminary message is:{msg}\")\n",
-                "print(f\"preliminary disclosive is:{disclosive}\")\n",
-                "msg2, disclosive2 = safeDTModel7.posthoc_check()\n",
-                "print(f\"posthoc message is:{msg2}\")\n",
-                "print(f\"posthoc disclosive is:{disclosive2}\")\n",
-                "safeDTModel7.request_release(path=\"hacked_unsafe\", ext=\"pkl\")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "381c05fe",
-            "metadata": {},
-            "source": [
-                "## Examine the contents of the checkfile.\n",
-                "### The checkfile is written in JSON format"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 23,
-            "id": "5c59ee3b-52fb-4aa8-a481-6ca879a14466",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "{\n",
-                        "    \"data_name\": \"\",\n",
-                        "    \"n_samples\": 0,\n",
-                        "    \"features\": {},\n",
-                        "    \"n_features\": 0,\n",
-                        "    \"n_samples_orig\": 0,\n",
-                        "    \"generalisation_error\": \"unknown\",\n",
-                        "    \"safemodel\": [\n",
-                        "        {\n",
-                        "            \"researcher\": \"j4-smith\",\n",
-                        "            \"model_type\": \"DecisionTreeClassifier\",\n",
-                        "            \"details\": \"Model parameters are within recommended ranges.\\n\",\n",
-                        "            \"k_anonymity\": \"1\",\n",
-                        "            \"recommendation\": \"Do not allow release\",\n",
-                        "            \"reason\": \"Model parameters are within recommended ranges.\\nWarning: basic parameters differ in 1 places:\\nparameter min_samples_leaf changed from 1 to 5 after model was fitted.\\n\",\n",
-                        "            \"timestamp\": \"2023-10-12 01:47:21\"\n",
-                        "        }\n",
-                        "    ],\n",
-                        "    \"model_path\": \"model.pkl\",\n",
-                        "    \"model_name\": \"SafeDecisionTreeClassifier\",\n",
-                        "    \"model_params\": {\n",
-                        "        \"criterion\": \"gini\",\n",
-                        "        \"splitter\": \"best\",\n",
-                        "        \"max_depth\": null,\n",
-                        "        \"min_samples_split\": 2,\n",
-                        "        \"min_samples_leaf\": 5,\n",
-                        "        \"min_weight_fraction_leaf\": 0.0,\n",
-                        "        \"max_features\": null,\n",
-                        "        \"max_leaf_nodes\": null,\n",
-                        "        \"random_state\": null,\n",
-                        "        \"min_impurity_decrease\": 0.0,\n",
-                        "        \"class_weight\": null,\n",
-                        "        \"ccp_alpha\": 0.0\n",
-                        "    }\n",
-                        "}\n"
-                    ]
-                }
-            ],
-            "source": [
-                "target_json = os.path.normpath(\"hacked_unsafe/target.json\")\n",
-                "with open(target_json, \"r\") as f:\n",
-                "    print(f.read())"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "aa4d24b6",
-            "metadata": {},
-            "outputs": [],
-            "source": []
-        }
-    ],
-    "metadata": {
-        "kernelspec": {
-            "display_name": "aisdc-v1.1",
-            "language": "python",
-            "name": "aisdc-v1.1"
-        },
-        "language_info": {
-            "codemirror_mode": {
-                "name": "ipython",
-                "version": 3
-            },
-            "file_extension": ".py",
-            "mimetype": "text/x-python",
-            "name": "python",
-            "nbconvert_exporter": "python",
-            "pygments_lexer": "ipython3",
-            "version": "3.9.18"
-        }
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "ba282cae",
+   "metadata": {},
+   "source": [
+    "# Safe Decision Tree Notebook \n",
+    "# Next cell is for developers only:\n",
+    "First set some path variables: this notebook expects to find the repository root in your home directory. You can change this by editing the last sys.path.append line to reflect with the repository root is found on your system"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "166298a8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "# from os.path import expanduser\n",
+    "\n",
+    "# ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(\"\")))\n",
+    "# sys.path.append(ROOT_DIR)\n",
+    "# home = expanduser(\"~\")\n",
+    "# sys.path.append(os.path.abspath(home + \"/AI-SDC\"))\n",
+    "# sys.path.insert(0,os.path.abspath(\"..\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c0d92b96-a6bc-4b1b-9040-f81c095e8770",
+   "metadata": {},
+   "source": [
+    "## Some basic examples to explore what the wrapper class could look like and be implemented\n",
+    "\n",
+    "### Lets start by making some data with one disclosive case\n",
+    "- We'll do this by adding an example to the iris data and give it a new class to make things really obvious.\n",
+    "- The same risks exist for more complex data sets but _everyone knows iris_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "47e9809e-f778-48de-9857-a6a481d96cd3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "feature 0 min 4.3, min 7.9\n",
+      "feature 1 min 2.0, min 4.4\n",
+      "feature 2 min 1.0, min 6.9\n",
+      "feature 3 min 0.1, min 2.5\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from sklearn import datasets\n",
+    "\n",
+    "iris = datasets.load_iris()\n",
+    "X = iris.data\n",
+    "y = iris.target\n",
+    "\n",
+    "\n",
+    "# print the max and min values in each feature to help hand-craft the disclosive point\n",
+    "for feature in range(4):\n",
+    "    print(f\"feature {feature} min {np.min(X[:,feature])}, min {np.max(X[:,feature])}\")\n",
+    "\n",
+    "# now add a single disclosve point with features [7,2,4.5,1] and label 3\n",
+    "X = np.vstack([X, (7, 2.0, 4.5, 1)])\n",
+    "y = np.append(y, 4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d5effc9f-f2b3-4e8c-b2c3-87633c7a76fb",
+   "metadata": {},
+   "source": [
+    "### and import some basic libraries to show our point"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "828ba8d9-c78e-4259-bd49-802481b70ee5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.tree import plot_tree"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9d657d29-6edb-4e50-813e-654b104c7f75",
+   "metadata": {},
+   "source": [
+    "## Here's the raw version\n",
+    "-  note I am setting random_state=1 to make it deterministic, just so you get the same reults as me\n",
+    " - the general point is not that someone always will, but that they could\n",
+    " - in practice I ran 10 times not setting random state and got the same tree 4/5 times"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0a2e85c9-5516-43f7-bb96-ddb48dc08d15",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training set accuracy in this naive case is 1.0\n"
+     ]
     },
-    "nbformat": 4,
-    "nbformat_minor": 5
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# example code with no safety\n",
+    "\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "\n",
+    "rawDT = DecisionTreeClassifier(min_samples_leaf=1, criterion=\"gini\", random_state=1)\n",
+    "rawDT.fit(X, y)\n",
+    "\n",
+    "print(f\"Training set accuracy in this naive case is {rawDT.score(X,y)}\")\n",
+    "\n",
+    "fig, ax = plt.subplots(1, 1, figsize=(15, 10))\n",
+    "output = plot_tree(rawDT, filled=True, ax=ax, fontsize=10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1dd44240-2ed4-4e94-9633-94057220d686",
+   "metadata": {},
+   "source": [
+    "### As we can see we have several disclosive nodes, one of which is our added point (in purple)\n",
+    "The exact values cannot be inferred but if we (reasonably) assume all features are non-negative we can get uper and lower bounds for the attribute values on that node:  \n",
+    "> (6.95,inf),  \\[0,2.6\\],  (0,4.95\\],  (0.8, 1.65\\]\n",
+    "\n",
+    "so this is disclosive to a certain degree.\n",
+    "\n",
+    "- In this case I spent 5 minutes manually tuning the values of the added point so that the tree included at least one decision node for each feature\n",
+    "\n",
+    "- It would be fairly trivial to use something like a Genetic Algorithm to automatically tune the feature values of the added point minimising the difference between the upper and lower bounds for each feature.\n",
+    "\n",
+    "- But that is not really the point of this exercise which was to show that allowing the user to set inappropriate values for a single parameter could produce a disclosive tree. \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2bcf587c-fd29-4801-ba9b-51a342ac8aed",
+   "metadata": {},
+   "source": [
+    "### Diligent user realises problem, and changes their code to enforce at least n samples in each leaf\n",
+    "We'll use n=5 "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "24bd26c3-6917-4493-b1c8-283d9fd8d4a2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training set accuracy with threshold rule enforced is 0.9668874172185431\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "manualDT = DecisionTreeClassifier(min_samples_leaf=5, random_state=1)\n",
+    "manualDT.fit(X, y)\n",
+    "\n",
+    "print(f\"Training set accuracy with threshold rule enforced is {manualDT.score(X,y)}\")\n",
+    "\n",
+    "fig2, ax2 = plt.subplots(1, 1, figsize=(15, 10))\n",
+    "output = plot_tree(manualDT, filled=True, ax=ax2, fontsize=11)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "968acece-746d-4b53-a20d-ef100e1b59a8",
+   "metadata": {},
+   "source": [
+    "### output is now non-disclosive (at least according to the threshold rule)\n",
+    "- You can easily see we don't get a node for the new class 3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ac1d51e4-a2ad-41e9-bd59-389741c1d996",
+   "metadata": {},
+   "source": [
+    "## So lets define a new class SafeDecisionTreeClassifier \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "a6c88914-790f-417c-89b7-6641f2ca6539",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from aisdc.safemodel.classifiers import SafeDecisionTreeClassifier\n",
+    "from aisdc.safemodel.safemodel import SafeModel"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "8993ca95-4e71-48e4-8b11-783fcffcdec9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "None with parameters: {'model_type': 'None', 'model': None, 'saved_model': None, 'model_load_file': 'None', 'model_save_file': 'None', 'ignore_items': [], 'examine_seperately_items': [], 'basemodel_paramnames': [], 'filename': 'None', 'researcher': 'j4-smith', 'timestamp': 'None'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "noNameModel = SafeModel()\n",
+    "\n",
+    "try:\n",
+    "    print(noNameModel.__str__())\n",
+    "except:\n",
+    "    print(\"super class has no attributes to print\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "504c3c64-adcf-4796-b4ef-c574a6cfe1bb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Preliminary checks: Model parameters are within recommended ranges.\n",
+      "\n",
+      "DecisionTreeClassifier with parameters: {'model_type': 'DecisionTreeClassifier', 'model': None, 'saved_model': None, 'model_load_file': 'None', 'model_save_file': 'None', 'ignore_items': ['model_save_file', 'basemodel_paramnames', 'ignore_items', 'timestamp'], 'examine_seperately_items': ['tree_'], 'basemodel_paramnames': ['criterion', 'splitter', 'max_depth', 'min_samples_split', 'min_samples_leaf', 'min_weight_fraction_leaf', 'max_features', 'random_state', 'max_leaf_nodes', 'min_impurity_decrease', 'class_weight', 'ccp_alpha'], 'filename': 'None', 'researcher': 'j4-smith', 'timestamp': 'None', 'criterion': 'gini', 'splitter': 'best', 'max_depth': None, 'min_samples_split': 2, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': None, 'max_leaf_nodes': None, 'random_state': None, 'min_impurity_decrease': 0.0, 'class_weight': None, 'ccp_alpha': 0.0, 'k_anonymity': 0}\n"
+     ]
+    }
+   ],
+   "source": [
+    "safeDTModel = SafeDecisionTreeClassifier(min_samples_leaf=5)  # (criterion=\"entropy\")\n",
+    "print(safeDTModel.__str__())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2942eba1",
+   "metadata": {},
+   "source": [
+    "## Do the posthoc_check\n",
+    "In this cell the model has not run fit()\n",
+    "\n",
+    "posthoc_check detects that fit has not been run and reports a warning.\n",
+    "\n",
+    "Subsequently we run fit and report the accuracy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "de3c6317",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "disclosive currently True\n",
+      "Because Error: user has not called fit() method or has deleted saved values.Recommendation: Do not release.\n",
+      "Training set accuracy in this naive case is 0.9668874172185431\n"
+     ]
+    }
+   ],
+   "source": [
+    "msg, disclosive = safeDTModel.posthoc_check()\n",
+    "print(f\"disclosive currently {disclosive}\")\n",
+    "print(\"Because \" + msg)\n",
+    "\n",
+    "safeDTModel.fit(X, y)\n",
+    "\n",
+    "print(f\"Training set accuracy in this naive case is {safeDTModel.score(X,y)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e24fedf0",
+   "metadata": {},
+   "source": [
+    "## We check our now fitted model from the previous step \n",
+    "posthoc_check reports it is not disclosive (safer)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "bfa6feb6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "disclosive currently False\n"
+     ]
+    }
+   ],
+   "source": [
+    "msg, disclosive = safeDTModel.posthoc_check()\n",
+    "print(f\"disclosive currently {disclosive}\")\n",
+    "if disclosive:\n",
+    "    print(\"Because \" + msg)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "632ed8d0",
+   "metadata": {},
+   "source": [
+    "## We modify the parameters\n",
+    "* min_samples_leaf = 9\n",
+    "* min_samples_split = 1\n",
+    "\n",
+    "We use posthoc_check to detect whether model has been modified or interfered with since fit() was last run.\n",
+    "We are warned that the model may be disclosive because \n",
+    "the two parameters were changed after model was fitted."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cc1a87f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "disclosive currently True\n",
+      "Because Warning: basic parameters differ in 2 places:\n",
+      "parameter min_samples_split changed from 2 to 1 after model was fitted.\n",
+      "parameter min_samples_leaf changed from 5 to 9 after model was fitted.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "safeDTModel.min_samples_leaf = 9\n",
+    "safeDTModel.min_samples_split = 1\n",
+    "msg, disclosive = safeDTModel.posthoc_check()\n",
+    "print(f\"disclosive currently {disclosive}\")\n",
+    "if disclosive:\n",
+    "    print(\"Because \" + msg)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5067b094",
+   "metadata": {},
+   "source": [
+    "We modify the parameters, returning min_samples_leaf and min_samples split to their original values.\n",
+    "\n",
+    "    min_samples_leaf = 5\n",
+    "    min_samples_split = 2\n",
+    "\n",
+    "We use posthoc_check to detect whether model has been modified or interfered with since fit() was last run. Because the parameters are the same as the fit model, the model is not considered disclosive.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "57e8061c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "disclosive currently False\n"
+     ]
+    }
+   ],
+   "source": [
+    "safeDTModel.min_samples_leaf = 5\n",
+    "safeDTModel.min_samples_split = 2\n",
+    "msg, disclosive = safeDTModel.posthoc_check()\n",
+    "print(f\"disclosive currently {disclosive}\")\n",
+    "if disclosive:\n",
+    "    print(\"Because \" + msg)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dbc720cd",
+   "metadata": {},
+   "source": [
+    "### We extract and plot the decision tree in order to visualize it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "e92b751a-5d23-4019-bc54-3986c072c9b1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Preliminary checks: Model parameters are within recommended ranges.\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 1, figsize=(15, 10))\n",
+    "# output = plot_tree(safeDTModel.model,filled=True, ax=ax,fontsize=11)\n",
+    "\n",
+    "output = plot_tree(safeDTModel, filled=True, ax=ax, fontsize=11)\n",
+    "\n",
+    "safeDTModel.save(name=\"testSave.pkl\")\n",
+    "safeDTModel.preliminary_check()\n",
+    "safeDTModel.request_release(path=\"testSave\", ext=\"pkl\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "894919fa-17d4-41cc-b065-690e10cdc192",
+   "metadata": {},
+   "source": [
+    "## Demonstrate the save and reporting functionality\n",
+    "#### save the model\n",
+    "save(name=\"testSave.pkl\") - Writes model to file in appropriate format (.pkl).\n",
+    "#### perform a preliminary_check \n",
+    "safeDTModel.preliminary_check() - Checks whether current model parameters violate the safe rules.\n",
+    "Optionally automatically fixes any violations.\n",
+    "#### Request Release\n",
+    "safeDTModel.request_release(\"testSave.pkl\") - Saves model to filename specified and creates a report for the TRE output checkers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "2fec67c3-4b39-4d90-aa83-45ca576742ac",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Preliminary checks: Model parameters are within recommended ranges.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "safeDTModel.save(name=\"testSave.pkl\")\n",
+    "safeDTModel.preliminary_check()\n",
+    "safeDTModel.request_release(path=\"testSave\", ext=\"pkl\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "27211998-c809-4c20-96c7-49909983f1af",
+   "metadata": {},
+   "source": [
+    "## Now lets try to attack this approach\n",
+    "### starting with listing the params then trying to set the params manually after init"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "6f393ab2-9237-43ed-8921-36ffd0044e5d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'model_type': 'DecisionTreeClassifier', 'model': None, 'saved_model': {'model_type': 'DecisionTreeClassifier', 'model': None, 'saved_model': None, 'model_load_file': 'None', 'model_save_file': 'None', 'ignore_items': ['model_save_file', 'basemodel_paramnames', 'ignore_items', 'timestamp'], 'examine_seperately_items': ['tree_'], 'basemodel_paramnames': ['criterion', 'splitter', 'max_depth', 'min_samples_split', 'min_samples_leaf', 'min_weight_fraction_leaf', 'max_features', 'random_state', 'max_leaf_nodes', 'min_impurity_decrease', 'class_weight', 'ccp_alpha'], 'filename': 'None', 'researcher': 'j4-smith', 'timestamp': 'None', 'criterion': 'gini', 'splitter': 'best', 'max_depth': None, 'min_samples_split': 2, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': None, 'max_leaf_nodes': None, 'random_state': None, 'min_impurity_decrease': 0.0, 'class_weight': None, 'ccp_alpha': 0.0, 'k_anonymity': 5, 'n_features_in_': 4, 'n_outputs_': 1, 'classes_': array([0, 1, 2, 4]), 'n_classes_': 4, 'max_features_': 4, 'tree_': <sklearn.tree._tree.Tree object at 0x292909340>}, 'model_load_file': 'None', 'model_save_file': 'testSave/model.pkl', 'ignore_items': ['model_save_file', 'basemodel_paramnames', 'ignore_items', 'timestamp'], 'examine_seperately_items': ['tree_'], 'basemodel_paramnames': ['criterion', 'splitter', 'max_depth', 'min_samples_split', 'min_samples_leaf', 'min_weight_fraction_leaf', 'max_features', 'random_state', 'max_leaf_nodes', 'min_impurity_decrease', 'class_weight', 'ccp_alpha'], 'filename': 'None', 'researcher': 'j4-smith', 'timestamp': '2023-10-12 01:47:21', 'criterion': 'gini', 'splitter': 'best', 'max_depth': None, 'min_samples_split': 2, 'min_samples_leaf': 5, 'min_weight_fraction_leaf': 0.0, 'max_features': None, 'max_leaf_nodes': None, 'random_state': None, 'min_impurity_decrease': 0.0, 'class_weight': None, 'ccp_alpha': 0.0, 'k_anonymity': 5, 'n_features_in_': 4, 'n_outputs_': 1, 'classes_': array([0, 1, 2, 4]), 'n_classes_': 4, 'max_features_': 4, 'tree_': <sklearn.tree._tree.Tree object at 0x292909260>}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(safeDTModel.__dict__)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "023223f5",
+   "metadata": {},
+   "source": [
+    "## Train a model where the min_samples_leaf is 1 \n",
+    "### We extract and plot the decision tree.  \n",
+    "### Because min_samples_leaf is 1 this poses a disclosure risk.\n",
+    "### The prelimanary check reports this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "c060b2c1-9866-4eb4-93fd-083db423f393",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training set accuracy in this naive case is 1.0\n",
+      "Preliminary checks: WARNING: model parameters may present a disclosure risk:\n",
+      "- parameter min_samples_leaf = 1 identified as less than the recommended min value of 5.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "safeDTModel.min_samples_leaf = 1\n",
+    "\n",
+    "safeDTModel.fit(X, y)\n",
+    "\n",
+    "print(f\"Training set accuracy in this naive case is {safeDTModel.score(X,y)}\")\n",
+    "\n",
+    "fig, ax = plt.subplots(1, 1, figsize=(15, 10))\n",
+    "output = plot_tree(safeDTModel, filled=True, ax=ax, fontsize=11)\n",
+    "\n",
+    "safeDTModel.save(name=\"testSave.pkl\")\n",
+    "safeDTModel.preliminary_check()\n",
+    "safeDTModel.request_release(path=\"testSave\", ext=\"pkl\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8bf0bb1a-1a5e-4702-9c4d-cdafa2bc1b8a",
+   "metadata": {},
+   "source": [
+    "## Example Implementation runs\n",
+    "### The researcher doesn't change recomended params"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "51df3667",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "***researcher doesn't change recomended params\n",
+      "Preliminary checks: WARNING: model parameters may present a disclosure risk:\n",
+      "- parameter min_samples_leaf = 1 identified as less than the recommended min value of 5.\n",
+      "Preliminary checks: WARNING: model parameters may present a disclosure risk:\n",
+      "- parameter min_samples_leaf = 1 identified as less than the recommended min value of 5.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# create and fit using recomended params\n",
+    "print(\"***researcher doesn't change recomended params\")\n",
+    "safeDTModel2 = SafeDecisionTreeClassifier()\n",
+    "safeDTModel2.fit(X, y)\n",
+    "safeDTModel2.save(name=\"safe2.pkl\")\n",
+    "safeDTModel2.preliminary_check()\n",
+    "safeDTModel2.posthoc_check()\n",
+    "safeDTModel2.request_release(path=\"safe2\", ext=\"pkl\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d93ca2f1",
+   "metadata": {},
+   "source": [
+    "### The researcher changes params safely"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "d919837f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "***researcher changes params safely\n",
+      "Preliminary checks: WARNING: model parameters may present a disclosure risk:\n",
+      "- parameter min_samples_leaf = 1 identified as less than the recommended min value of 5.\n",
+      "Preliminary checks: Model parameters are within recommended ranges.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# change model params to recommended values\n",
+    "print(\"\\n***researcher changes params safely\")\n",
+    "safeDTModel3 = SafeDecisionTreeClassifier()\n",
+    "safeDTModel3.min_samples_leaf = 5\n",
+    "safeDTModel3.fit(X, y)\n",
+    "safeDTModel3.save(name=\"safe3.pkl\")\n",
+    "safeDTModel3.preliminary_check()\n",
+    "safeDTModel3.posthoc_check()\n",
+    "safeDTModel3.request_release(path=\"safe3\", ext=\"pkl\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d8b3c8f1",
+   "metadata": {},
+   "source": [
+    "### The researcher changes params safely"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "9021242f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "***researcher changes params safely\n",
+      "Preliminary checks: WARNING: model parameters may present a disclosure risk:\n",
+      "- parameter min_samples_leaf = 1 identified as less than the recommended min value of 5.\n",
+      "Preliminary checks: Model parameters are within recommended ranges.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# change model params in a safe way\n",
+    "print(\"\\n***researcher changes params safely\")\n",
+    "safeDTModel4 = SafeDecisionTreeClassifier()\n",
+    "safeDTModel4.min_samples_leaf = 10\n",
+    "safeDTModel4.fit(X, y)\n",
+    "safeDTModel4.save(name=\"safe4.pkl\")\n",
+    "safeDTModel4.preliminary_check()\n",
+    "safeDTModel4.posthoc_check()\n",
+    "safeDTModel4.request_release(path=\"safe4\", ext=\"pkl\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "56c2a832",
+   "metadata": {},
+   "source": [
+    "### The researcher changes params unsafely"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "efb66f5e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "***researcher changes params unsafely\n",
+      "Preliminary checks: WARNING: model parameters may present a disclosure risk:\n",
+      "- parameter min_samples_leaf = 1 identified as less than the recommended min value of 5.\n",
+      "Preliminary checks: WARNING: model parameters may present a disclosure risk:\n",
+      "- parameter min_samples_leaf = 1 identified as less than the recommended min value of 5.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# change model params in an  unsafe way\n",
+    "print(\"\\n***researcher changes params unsafely\")\n",
+    "safeDTModel5 = SafeDecisionTreeClassifier()\n",
+    "safeDTModel5.min_samples_leaf = 1\n",
+    "safeDTModel5.save(name=\"unsafe.pkl\")\n",
+    "safeDTModel5.preliminary_check()\n",
+    "safeDTModel5.posthoc_check()\n",
+    "safeDTModel5.request_release(path=\"unsafe\", ext=\"pkl\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0002e88b",
+   "metadata": {},
+   "source": [
+    "### The researcher asks for a safe_decision tree but supplies with unsafe params"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "0a89c1c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "***researcher asks for a safe_decision tree but supplies with unsafe params\n",
+      "Preliminary checks: WARNING: model parameters may present a disclosure risk:\n",
+      "- parameter min_samples_leaf = 1 identified as less than the recommended min value of 5.\n",
+      "Preliminary checks: WARNING: model parameters may present a disclosure risk:\n",
+      "- parameter min_samples_leaf = 1 identified as less than the recommended min value of 5.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ask for a model with unsafe param values\n",
+    "print(\"\\n***researcher asks for a safe_decision tree but supplies with unsafe params\")\n",
+    "safeDTModel6 = SafeDecisionTreeClassifier(min_samples_leaf=1)\n",
+    "safeDTModel6.fit(X, y)\n",
+    "safeDTModel6.save(name=\"fixed-unsafe.pkl\")\n",
+    "safeDTModel6.preliminary_check()\n",
+    "safeDTModel6.posthoc_check()\n",
+    "safeDTModel6.request_release(path=\"fixed_unsafe\", ext=\"pkl\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "985412f6",
+   "metadata": {},
+   "source": [
+    "### The researcher asks for a safe_decision tree, changes values to unsafe before fit() then back afterwards"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "8b352bb2-3608-433e-b586-b235344ec58f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "***researcher asks for a safe_decision tree, changes values to unsafe before fit() then back afterwards\n",
+      "Preliminary checks: WARNING: model parameters may present a disclosure risk:\n",
+      "- parameter min_samples_leaf = 1 identified as less than the recommended min value of 5.\n",
+      "safeDTmodel7.score on training set is 1.0\n",
+      "fit called. with min_samples_leaf = 1\n",
+      "Preliminary checks: Model parameters are within recommended ranges.\n",
+      "\n",
+      "preliminary message is:Model parameters are within recommended ranges.\n",
+      "\n",
+      "preliminary disclosive is:False\n",
+      "posthoc message is:Warning: basic parameters differ in 1 places:\n",
+      "parameter min_samples_leaf changed from 1 to 5 after model was fitted.\n",
+      "\n",
+      "posthoc disclosive is:True\n"
+     ]
+    }
+   ],
+   "source": [
+    "# trains a model with unsafe param values then  hacks values back to safe ones later\n",
+    "print(\n",
+    "    \"\\n***researcher asks for a safe_decision tree, changes values to unsafe before fit() then back afterwards\"\n",
+    ")\n",
+    "safeDTModel7 = SafeDecisionTreeClassifier(min_samples_leaf=1)\n",
+    "\n",
+    "safeDTModel7.min_samples_leaf = 1\n",
+    "safeDTModel7.fit(X, y)\n",
+    "print(f\"safeDTmodel7.score on training set is {safeDTModel7.score(X,y)}\")\n",
+    "print(f\"fit called. with min_samples_leaf = {safeDTModel7.min_samples_leaf}\")\n",
+    "safeDTModel7.min_samples_leaf = 5\n",
+    "\n",
+    "\n",
+    "safeDTModel7.save(name=\"hacked-unsafe.pkl\")\n",
+    "msg, disclosive = safeDTModel7.preliminary_check()\n",
+    "print(f\"preliminary message is:{msg}\")\n",
+    "print(f\"preliminary disclosive is:{disclosive}\")\n",
+    "msg2, disclosive2 = safeDTModel7.posthoc_check()\n",
+    "print(f\"posthoc message is:{msg2}\")\n",
+    "print(f\"posthoc disclosive is:{disclosive2}\")\n",
+    "safeDTModel7.request_release(path=\"hacked_unsafe\", ext=\"pkl\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "381c05fe",
+   "metadata": {},
+   "source": [
+    "## Examine the contents of the checkfile.\n",
+    "### The checkfile is written in JSON format"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "5c59ee3b-52fb-4aa8-a481-6ca879a14466",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\n",
+      "    \"data_name\": \"\",\n",
+      "    \"n_samples\": 0,\n",
+      "    \"features\": {},\n",
+      "    \"n_features\": 0,\n",
+      "    \"n_samples_orig\": 0,\n",
+      "    \"generalisation_error\": \"unknown\",\n",
+      "    \"safemodel\": [\n",
+      "        {\n",
+      "            \"researcher\": \"j4-smith\",\n",
+      "            \"model_type\": \"DecisionTreeClassifier\",\n",
+      "            \"details\": \"Model parameters are within recommended ranges.\\n\",\n",
+      "            \"k_anonymity\": \"1\",\n",
+      "            \"recommendation\": \"Do not allow release\",\n",
+      "            \"reason\": \"Model parameters are within recommended ranges.\\nWarning: basic parameters differ in 1 places:\\nparameter min_samples_leaf changed from 1 to 5 after model was fitted.\\n\",\n",
+      "            \"timestamp\": \"2023-10-12 01:47:21\"\n",
+      "        }\n",
+      "    ],\n",
+      "    \"model_path\": \"model.pkl\",\n",
+      "    \"model_name\": \"SafeDecisionTreeClassifier\",\n",
+      "    \"model_params\": {\n",
+      "        \"criterion\": \"gini\",\n",
+      "        \"splitter\": \"best\",\n",
+      "        \"max_depth\": null,\n",
+      "        \"min_samples_split\": 2,\n",
+      "        \"min_samples_leaf\": 5,\n",
+      "        \"min_weight_fraction_leaf\": 0.0,\n",
+      "        \"max_features\": null,\n",
+      "        \"max_leaf_nodes\": null,\n",
+      "        \"random_state\": null,\n",
+      "        \"min_impurity_decrease\": 0.0,\n",
+      "        \"class_weight\": null,\n",
+      "        \"ccp_alpha\": 0.0\n",
+      "    }\n",
+      "}\n"
+     ]
+    }
+   ],
+   "source": [
+    "target_json = os.path.normpath(\"hacked_unsafe/target.json\")\n",
+    "with open(target_json) as f:\n",
+    "    print(f.read())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aa4d24b6",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "aisdc-v1.1",
+   "language": "python",
+   "name": "aisdc-v1.1"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
 }
diff --git a/examples/notebooks/example-notebook-keras.ipynb b/examples/notebooks/example-notebook-keras.ipynb
index f03479eb..8f14425f 100644
--- a/examples/notebooks/example-notebook-keras.ipynb
+++ b/examples/notebooks/example-notebook-keras.ipynb
@@ -1,758 +1,753 @@
 {
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "6d9366ac",
-            "metadata": {},
-            "source": [
-                "# Safe Keras Notebook \n",
-                "  "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 1,
-            "id": "f96259b1",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import sys\n",
-                "import os\n",
-                "\n",
-                "\n",
-                "### Lines below set some path variables: only developers should need to  change this\n",
-                "# from os.path import expanduser\n",
-                "\n",
-                "# ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(\"\")))\n",
-                "# sys.path.append(ROOT_DIR)\n",
-                "# home = expanduser(\"~\")\n",
-                "# sys.path.append(os.path.abspath(home + \"/GitHub/AI-SDC/SACRO-ML\"))\n",
-                "\n",
-                "# sys.path.insert(0, os.path.abspath(\"..\"))"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 2,
-            "id": "594e2527",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import numpy as np\n",
-                "import matplotlib.pyplot as plt\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",
-                "\n",
-                "# set tensorflow messages to warning level\n",
-                "tf.get_logger().setLevel(\"WARNING\")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "b0599f07",
-            "metadata": {},
-            "source": [
-                "## A Quick Start Guide to implementing Safer Keras Models\n",
-                "### Definition of the datasets\n",
-                "1. We draw data points from a distribution.\n",
-                "2. We split these data points into the target dataset and a shadow dataset drawn from the same distribution.\n",
-                "3. We also draw a dataset from a different distribution.\n",
-                "\n",
-                "**NOTE**. ***we make datasets with few samples but with many features to force the target model to overfit.***\n",
-                "\n",
-                "\n",
-                "**NOTE**: batch_size 25 so DP optimizer would run with same hyperparams\n",
-                "\n",
-                "**NOTE**: Next cell determines which dataset is used"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 3,
-            "id": "d2bde25d",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "simple_data_for_pytests = False"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 4,
-            "id": "488fa7af",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "if not simple_data_for_pytests:\n",
-                "    n_classes = 2\n",
-                "\n",
-                "    # (X,y): Original distribution\n",
-                "    X, y = make_classification(\n",
-                "        n_samples=1000,\n",
-                "        n_classes=n_classes,\n",
-                "        n_features=300,\n",
-                "        n_informative=300,\n",
-                "        n_redundant=0,\n",
-                "        n_repeated=0,\n",
-                "        random_state=15,\n",
-                "    )\n",
-                "    # One-hot encoding of the label\n",
-                "    y = np.eye(n_classes)[y]\n",
-                "\n",
-                "    # (Xt, yt) is the target dataset, owned by the TRE and drawn from the (X,y) distribution\n",
-                "    # (Xs, ys) is a shadow dataset drawn from the (X,y) distribution\n",
-                "    Xt, Xs, yt, ys = train_test_split(X, y, test_size=0.50, random_state=15)\n",
-                "\n",
-                "    # (Xd, yd) is a shadow dataset, drawn from a different distribution (different seed)\n",
-                "    Xd, yd = make_classification(\n",
-                "        n_samples=1000,\n",
-                "        n_classes=n_classes,\n",
-                "        n_features=300,\n",
-                "        n_informative=300,\n",
-                "        n_redundant=0,\n",
-                "        n_repeated=0,\n",
-                "        random_state=42,\n",
-                "    )\n",
-                "    yd = np.eye(n_classes)[yd]\n",
-                "\n",
-                "    # Split into train (member) and test (non-member) datasets\n",
-                "    # Set shuffle to False so that Xt_membership is consistent with Xt, otherwise\n",
-                "    # we need to stack Xt_member and Xt_nonmember again to get a consistent Xt.\n",
-                "    Xt_member, Xt_nonmember, yt_member, yt_nonmember = train_test_split(\n",
-                "        Xt, yt, test_size=0.5, shuffle=False\n",
-                "    )\n",
-                "\n",
-                "    # Set membership status for future tests\n",
-                "    Xt_membership = np.vstack(\n",
-                "        (\n",
-                "            np.ones((Xt_member.shape[0], 1), np.uint8),\n",
-                "            np.zeros((Xt_nonmember.shape[0], 1), np.uint8),\n",
-                "        )\n",
-                "    ).flatten()\n",
-                "\n",
-                "    X = Xt_member\n",
-                "    y = yt_member\n",
-                "    Xval = Xt_nonmember\n",
-                "    yval = yt_nonmember"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 5,
-            "id": "f8845b4b",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "if simple_data_for_pytests:\n",
-                "    from sklearn import datasets\n",
-                "\n",
-                "    def get_data():\n",
-                "        iris = datasets.load_iris()\n",
-                "        x = np.asarray(iris.data, dtype=np.float64)\n",
-                "        y = np.asarray(iris.target, dtype=np.float64)\n",
-                "        return x, y\n",
-                "\n",
-                "    xall, yall = get_data()\n",
-                "    n_classes = 4\n",
-                "    X, Xval, y, yval = train_test_split(\n",
-                "        xall, yall, test_size=0.2, shuffle=True, random_state=12345\n",
-                "    )\n",
-                "\n",
-                "    y = tf.one_hot(y, n_classes)\n",
-                "    yval = tf.one_hot(yval, n_classes)\n",
-                "# yval"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "7f2b7816",
-            "metadata": {},
-            "source": [
-                "## Define the target model architecture\n",
-                "\n",
-                "*Again, we use a rather big model (for the classification task) to favour overfitting.*"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 6,
-            "id": "d20787df",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "2023-10-12 01:54:15.775124: I metal_plugin/src/device/metal_device.cc:1154] Metal device set to: Apple M1 Pro\n",
-                        "2023-10-12 01:54:15.775149: I metal_plugin/src/device/metal_device.cc:296] systemMemory: 32.00 GB\n",
-                        "2023-10-12 01:54:15.775155: I metal_plugin/src/device/metal_device.cc:313] maxCacheSize: 10.67 GB\n",
-                        "2023-10-12 01:54:15.775217: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:303] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n",
-                        "2023-10-12 01:54:15.775317: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:269] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\n",
-                        "/Users/j4-smith/miniforge3/envs/aisdc-v1.1/lib/python3.9/site-packages/keras/src/initializers/initializers.py:120: UserWarning: The initializer GlorotUniform is unseeded and being called multiple times, which will return identical values each time (even if the initializer is unseeded). Please update your code to provide a seed to the initializer, or avoid using the same initializer instance more than once.\n",
-                        "  warnings.warn(\n"
-                    ]
-                }
-            ],
-            "source": [
-                "# Define target model\n",
-                "# Tensorflow model (MLP) (making it big to make it overfit)\n",
-                "\n",
-                "# make results repeatable\n",
-                "tf.random.set_seed(12345)\n",
-                "initializer = tf.keras.initializers.GlorotUniform()\n",
-                "\n",
-                "input_data = Input(shape=X[0].shape)\n",
-                "x = Dense(128, activation=\"relu\", kernel_initializer=initializer)(input_data)\n",
-                "x = Dense(128, activation=\"relu\", kernel_initializer=initializer)(x)\n",
-                "x = Dense(64, activation=\"relu\", kernel_initializer=initializer)(x)\n",
-                "output = Dense(n_classes, activation=\"softmax\", kernel_initializer=initializer)(x)"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "7bbed581",
-            "metadata": {},
-            "source": [
-                "### Define the SafeModel"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 7,
-            "id": "944288d2",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Preliminary checks: WARNING: model parameters may present a disclosure risk:\n",
-                        "- parameter noise_multiplier = 0.5 identified as less than the recommended min value of 0.7.\n",
-                        "Changed parameter noise_multiplier = 0.7.\n",
-                        "\n"
-                    ]
-                }
-            ],
-            "source": [
-                "safeModel = SafeKerasModel(\n",
-                "    inputs=input_data,\n",
-                "    outputs=output,\n",
-                "    name=\"safekeras-test\",\n",
-                "    num_samples=X.shape[0],\n",
-                "    epochs=10,\n",
-                ")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "6534c8b7",
-            "metadata": {},
-            "source": [
-                "### Set loss and compile"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 8,
-            "id": "b76b10b9",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "During compilation: Changed parameter optimizer = 'DPKerasSGDOptimizer'\n"
-                    ]
-                }
-            ],
-            "source": [
-                "loss = tf.keras.losses.CategoricalCrossentropy(\n",
-                "    from_logits=False, reduction=tf.losses.Reduction.NONE\n",
-                ")\n",
-                "\n",
-                "\n",
-                "safeModel.compile(loss=loss, optimizer=None)"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "d0e4a371",
-            "metadata": {},
-            "source": [
-                "### Fit the model"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 9,
-            "id": "b2e4da84",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "WARNING:absl:`compute_dp_sgd_privacy` is deprecated. It does not account for doubling of sensitivity with microbatching, and assumes Poisson subsampling, which is rarely used in practice. Please use `compute_dp_sgd_privacy_statement`, which provides appropriate context for the guarantee. To compute epsilon under different assumptions than those in `compute_dp_sgd_privacy_statement`, call the `dp_accounting` libraries directly.\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "\n",
-                        "\n",
-                        "Epoch 1/10\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "2023-10-12 01:54:29.826997: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "10/10 [==============================] - 10s 674ms/step - loss: 9.0033 - accuracy: 0.4600 - val_loss: 8.6313 - val_accuracy: 0.5080\n",
-                        "Epoch 2/10\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "2023-10-12 01:54:39.346510: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "10/10 [==============================] - 6s 609ms/step - loss: 8.9409 - accuracy: 0.5280 - val_loss: 12.8332 - val_accuracy: 0.4920\n",
-                        "Epoch 3/10\n",
-                        "10/10 [==============================] - 7s 657ms/step - loss: 12.6006 - accuracy: 0.5440 - val_loss: 22.3344 - val_accuracy: 0.5200\n",
-                        "Epoch 4/10\n",
-                        "10/10 [==============================] - 8s 770ms/step - loss: 23.2052 - accuracy: 0.5720 - val_loss: 39.2820 - val_accuracy: 0.5320\n",
-                        "Epoch 5/10\n",
-                        "10/10 [==============================] - 7s 721ms/step - loss: 40.8724 - accuracy: 0.5680 - val_loss: 67.2007 - val_accuracy: 0.5240\n",
-                        "Epoch 6/10\n",
-                        "10/10 [==============================] - 7s 711ms/step - loss: 65.2681 - accuracy: 0.5720 - val_loss: 110.5973 - val_accuracy: 0.5040\n",
-                        "Epoch 7/10\n",
-                        "10/10 [==============================] - 7s 665ms/step - loss: 109.5257 - accuracy: 0.5320 - val_loss: 174.7665 - val_accuracy: 0.5040\n",
-                        "Epoch 8/10\n",
-                        "10/10 [==============================] - 7s 647ms/step - loss: 170.2266 - accuracy: 0.5600 - val_loss: 265.9124 - val_accuracy: 0.5080\n",
-                        "Epoch 9/10\n",
-                        "10/10 [==============================] - 6s 598ms/step - loss: 252.6672 - accuracy: 0.5520 - val_loss: 391.0987 - val_accuracy: 0.5200\n",
-                        "Epoch 10/10\n",
-                        "10/10 [==============================] - 6s 612ms/step - loss: 372.4639 - accuracy: 0.5600 - val_loss: 559.9529 - val_accuracy: 0.5280\n"
-                    ]
-                },
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 640x480 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "8/8 [==============================] - 0s 4ms/step\n",
-                        "model training  report:\n",
-                        "               precision    recall  f1-score   support\n",
-                        "\n",
-                        "           0       0.53      0.56      0.54       117\n",
-                        "           1       0.59      0.57      0.58       133\n",
-                        "\n",
-                        "    accuracy                           0.56       250\n",
-                        "   macro avg       0.56      0.56      0.56       250\n",
-                        "weighted avg       0.57      0.56      0.56       250\n",
-                        "\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "2023-10-12 01:55:39.572624: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n"
-                    ]
-                }
-            ],
-            "source": [
-                "epochs = 10\n",
-                "batch_size = 25\n",
-                "\n",
-                "r_DP = safeModel.fit(\n",
-                "    X,\n",
-                "    y,\n",
-                "    validation_data=(Xval, yval),\n",
-                "    epochs=epochs,\n",
-                "    batch_size=batch_size,\n",
-                ")\n",
-                "if r_DP == None:\n",
-                "    print(\"You have chosen to exit. Reset relevant parameter values then re-run fit().\")\n",
-                "else:\n",
-                "    plt.plot(r_DP.history[\"accuracy\"], label=\"accuracy\")\n",
-                "    plt.plot(r_DP.history[\"val_accuracy\"], label=\"validation accuracy\")\n",
-                "    plt.legend()\n",
-                "    plt.show()\n",
-                "    ypred = safeModel.predict(X)\n",
-                "    ylabels = np.argmax(y, axis=1)\n",
-                "    ypredlabels = np.argmax(ypred, axis=1)\n",
-                "    print(f\"model training  report:\\n {classification_report(ylabels,ypredlabels)}\")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "b059f431",
-            "metadata": {},
-            "source": [
-                "### Compute privacy and check if requirements for Differential Privacy are met"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 10,
-            "id": "55805bad",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "WARNING:absl:`compute_dp_sgd_privacy` is deprecated. It does not account for doubling of sensitivity with microbatching, and assumes Poisson subsampling, which is rarely used in practice. Please use `compute_dp_sgd_privacy_statement`, which provides appropriate context for the guarantee. To compute epsilon under different assumptions than those in `compute_dp_sgd_privacy_statement`, call the `dp_accounting` libraries directly.\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "with these settings privacy = 23.09099569905857\n"
-                    ]
-                }
-            ],
-            "source": [
-                "num_samples = X.shape[0]\n",
-                "batch_size = safeModel.batch_size\n",
-                "epochs = 20\n",
-                "\n",
-                "dp_met, privacy = safeModel.dp_epsilon_met(\n",
-                "    num_examples=num_samples, batch_size=batch_size, epochs=epochs\n",
-                ")\n",
-                "\n",
-                "print(f\"with these settings privacy = {privacy}\")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 12,
-            "id": "88413967",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "WARNING:absl:`compute_dp_sgd_privacy` is deprecated. It does not account for doubling of sensitivity with microbatching, and assumes Poisson subsampling, which is rarely used in practice. Please use `compute_dp_sgd_privacy_statement`, which provides appropriate context for the guarantee. To compute epsilon under different assumptions than those in `compute_dp_sgd_privacy_statement`, call the `dp_accounting` libraries directly.\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "\n",
-                        "Satisfies DP: False\n",
-                        "\n"
-                    ]
-                }
-            ],
-            "source": [
-                "dp_met, msg = safeModel.check_epsilon(\n",
-                "    X.shape[0], safeModel.batch_size, safeModel.epochs\n",
-                ")\n",
-                "print(f\"Satisfies DP: {dp_met}\")\n",
-                "print(f\"{msg}\")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "21813930",
-            "metadata": {},
-            "source": [
-                "### Check model and request release\n",
-                "\n",
-                "Note that the request_release() process will fail if the version checkpointed\n",
-                "during the fit() call, is not present\n",
-                "- it should be in  \"tfsaves/fit_model.tf\" "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 13,
-            "id": "62a9a6d1",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Preliminary checks: Model parameters are within recommended ranges.\n",
-                        "\n",
-                        "WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "/Users/j4-smith/miniforge3/envs/aisdc-v1.1/lib/python3.9/site-packages/keras/src/initializers/initializers.py:120: UserWarning: The initializer GlorotUniform is unseeded and being called multiple times, which will return identical values each time (even if the initializer is unseeded). Please update your code to provide a seed to the initializer, or avoid using the same initializer instance more than once.\n",
-                        "  warnings.warn(\n",
-                        "WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "/Users/j4-smith/miniforge3/envs/aisdc-v1.1/lib/python3.9/site-packages/keras/src/initializers/initializers.py:120: UserWarning: The initializer GlorotUniform is unseeded and being called multiple times, which will return identical values each time (even if the initializer is unseeded). Please update your code to provide a seed to the initializer, or avoid using the same initializer instance more than once.\n",
-                        "  warnings.warn(\n",
-                        "WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n",
-                        "WARNING:absl:`compute_dp_sgd_privacy` is deprecated. It does not account for doubling of sensitivity with microbatching, and assumes Poisson subsampling, which is rarely used in practice. Please use `compute_dp_sgd_privacy_statement`, which provides appropriate context for the guarantee. To compute epsilon under different assumptions than those in `compute_dp_sgd_privacy_statement`, call the `dp_accounting` libraries directly.\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Recommendation is further discussion needed WARNING: epsilon 16.608813401454455 is above normal max recommended value.\n",
-                        "Discussion with researcher needed.\n",
-                        ".\n",
-                        "\n"
-                    ]
-                }
-            ],
-            "source": [
-                "safeModel.save(\"safe1.tf\")\n",
-                "safeModel.preliminary_check()\n",
-                "safeModel.request_release(path=\"safe1\", ext=\"tf\")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "7f2e46a5",
-            "metadata": {},
-            "source": [
-                "### Examine Checkfile\n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 14,
-            "id": "9cccce2f",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "{\n",
-                        "    \"data_name\": \"\",\n",
-                        "    \"n_samples\": 0,\n",
-                        "    \"features\": {},\n",
-                        "    \"n_features\": 0,\n",
-                        "    \"n_samples_orig\": 0,\n",
-                        "    \"generalisation_error\": \"unknown\",\n",
-                        "    \"safemodel\": [\n",
-                        "        {\n",
-                        "            \"researcher\": \"j4-smith\",\n",
-                        "            \"model_type\": \"KerasModel\",\n",
-                        "            \"details\": \"Model parameters are within recommended ranges.\\n\",\n",
-                        "            \"recommendation\": \"Do not allow release\",\n",
-                        "            \"reason\": \"Model parameters are within recommended ranges.\\nRecommendation is not to release because None.\\nWARNING: epsilon 16.608813401454455 is above normal max recommended value.\\nDiscussion with researcher needed.\\n\",\n",
-                        "            \"timestamp\": \"2023-10-12 01:56:19\"\n",
-                        "        }\n",
-                        "    ],\n",
-                        "    \"model_path\": \"model.tf\",\n",
-                        "    \"model_name\": \"SafeKerasModel\",\n",
-                        "    \"model_params\": {}\n",
-                        "}\n"
-                    ]
-                }
-            ],
-            "source": [
-                "target_json = os.path.normpath(\"safe1/target.json\")\n",
-                "with open(target_json, \"r\") as f:\n",
-                "    print(f.read())"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "3ac98139-306b-4b52-83e8-021cd87789c1",
-            "metadata": {},
-            "source": [
-                "## load saved model\n",
-                "\n",
-                "### note that for security reasons we do not include the optimizer in the saved version\n",
-                "- This means that tensorflow will assume it has not beeen compiled\n",
-                "- so the loss and accuracy functions are not defined. \n",
-                "- really urgent users can define losses if they want to use built-in evaluate functions\n",
-                "- or they can use standard methods to assess accuracy\n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 15,
-            "id": "7ea50179-8943-4845-a6e9-f584603f7314",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "/Users/j4-smith/miniforge3/envs/aisdc-v1.1/lib/python3.9/site-packages/keras/src/initializers/initializers.py:120: UserWarning: The initializer GlorotUniform is unseeded and being called multiple times, which will return identical values each time (even if the initializer is unseeded). Please update your code to provide a seed to the initializer, or avoid using the same initializer instance more than once.\n",
-                        "  warnings.warn(\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "/Users/j4-smith/miniforge3/envs/aisdc-v1.1/lib/python3.9/site-packages/keras/src/initializers/initializers.py:120: UserWarning: The initializer GlorotUniform is unseeded and being called multiple times, which will return identical values each time (even if the initializer is unseeded). Please update your code to provide a seed to the initializer, or avoid using the same initializer instance more than once.\n",
-                        "  warnings.warn(\n",
-                        "WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n"
-                    ]
-                }
-            ],
-            "source": [
-                "from aisdc.safemodel.classifiers.safekeras import load_safe_keras_model\n",
-                "\n",
-                "model_path = os.path.normpath(\"safe1/model.tf\")\n",
-                "status, reloaded_model = load_safe_keras_model(model_path)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 16,
-            "id": "32c0e0df-eeec-4e16-b0d8-b8bc9107267e",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "8/8 [==============================] - 0s 2ms/step\n",
-                        "reloaded model training  accuracy:\n",
-                        "               precision    recall  f1-score   support\n",
-                        "\n",
-                        "           0       0.53      0.56      0.54       117\n",
-                        "           1       0.59      0.57      0.58       133\n",
-                        "\n",
-                        "    accuracy                           0.56       250\n",
-                        "   macro avg       0.56      0.56      0.56       250\n",
-                        "weighted avg       0.57      0.56      0.56       250\n",
-                        "\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "2023-10-12 01:56:39.307876: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n"
-                    ]
-                }
-            ],
-            "source": [
-                "ypred = reloaded_model.predict(X)\n",
-                "ylabels = np.argmax(y, axis=1)\n",
-                "ypredlabels = np.argmax(ypred, axis=1)\n",
-                "reloaded_cm = confusion_matrix(ylabels, ypredlabels)\n",
-                "print(\n",
-                "    f\"reloaded model training  accuracy:\\n {classification_report(ylabels,ypredlabels)}\"\n",
-                ")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "840bf7ff",
-            "metadata": {},
-            "outputs": [],
-            "source": []
-        }
-    ],
-    "metadata": {
-        "kernelspec": {
-            "display_name": "aisdc-v1.1",
-            "language": "python",
-            "name": "aisdc-v1.1"
-        },
-        "language_info": {
-            "codemirror_mode": {
-                "name": "ipython",
-                "version": 3
-            },
-            "file_extension": ".py",
-            "mimetype": "text/x-python",
-            "name": "python",
-            "nbconvert_exporter": "python",
-            "pygments_lexer": "ipython3",
-            "version": "3.9.18"
-        }
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "6d9366ac",
+   "metadata": {},
+   "source": [
+    "# Safe Keras Notebook \n",
+    "  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "f96259b1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "\n",
+    "### Lines below set some path variables: only developers should need to  change this\n",
+    "# from os.path import expanduser\n",
+    "\n",
+    "# ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(\"\")))\n",
+    "# sys.path.append(ROOT_DIR)\n",
+    "# home = expanduser(\"~\")\n",
+    "# sys.path.append(os.path.abspath(home + \"/GitHub/AI-SDC/SACRO-ML\"))\n",
+    "\n",
+    "# sys.path.insert(0, os.path.abspath(\"..\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "594e2527",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# Scikit-learn utils\n",
+    "# Tensorflow imports\n",
+    "import tensorflow as tf\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\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b0599f07",
+   "metadata": {},
+   "source": [
+    "## A Quick Start Guide to implementing Safer Keras Models\n",
+    "### Definition of the datasets\n",
+    "1. We draw data points from a distribution.\n",
+    "2. We split these data points into the target dataset and a shadow dataset drawn from the same distribution.\n",
+    "3. We also draw a dataset from a different distribution.\n",
+    "\n",
+    "**NOTE**. ***we make datasets with few samples but with many features to force the target model to overfit.***\n",
+    "\n",
+    "\n",
+    "**NOTE**: batch_size 25 so DP optimizer would run with same hyperparams\n",
+    "\n",
+    "**NOTE**: Next cell determines which dataset is used"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "d2bde25d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "simple_data_for_pytests = False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "488fa7af",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if not simple_data_for_pytests:\n",
+    "    n_classes = 2\n",
+    "\n",
+    "    # (X,y): Original distribution\n",
+    "    X, y = make_classification(\n",
+    "        n_samples=1000,\n",
+    "        n_classes=n_classes,\n",
+    "        n_features=300,\n",
+    "        n_informative=300,\n",
+    "        n_redundant=0,\n",
+    "        n_repeated=0,\n",
+    "        random_state=15,\n",
+    "    )\n",
+    "    # One-hot encoding of the label\n",
+    "    y = np.eye(n_classes)[y]\n",
+    "\n",
+    "    # (Xt, yt) is the target dataset, owned by the TRE and drawn from the (X,y) distribution\n",
+    "    # (Xs, ys) is a shadow dataset drawn from the (X,y) distribution\n",
+    "    Xt, Xs, yt, ys = train_test_split(X, y, test_size=0.50, random_state=15)\n",
+    "\n",
+    "    # (Xd, yd) is a shadow dataset, drawn from a different distribution (different seed)\n",
+    "    Xd, yd = make_classification(\n",
+    "        n_samples=1000,\n",
+    "        n_classes=n_classes,\n",
+    "        n_features=300,\n",
+    "        n_informative=300,\n",
+    "        n_redundant=0,\n",
+    "        n_repeated=0,\n",
+    "        random_state=42,\n",
+    "    )\n",
+    "    yd = np.eye(n_classes)[yd]\n",
+    "\n",
+    "    # Split into train (member) and test (non-member) datasets\n",
+    "    # Set shuffle to False so that Xt_membership is consistent with Xt, otherwise\n",
+    "    # we need to stack Xt_member and Xt_nonmember again to get a consistent Xt.\n",
+    "    Xt_member, Xt_nonmember, yt_member, yt_nonmember = train_test_split(\n",
+    "        Xt, yt, test_size=0.5, shuffle=False\n",
+    "    )\n",
+    "\n",
+    "    # Set membership status for future tests\n",
+    "    Xt_membership = np.vstack(\n",
+    "        (\n",
+    "            np.ones((Xt_member.shape[0], 1), np.uint8),\n",
+    "            np.zeros((Xt_nonmember.shape[0], 1), np.uint8),\n",
+    "        )\n",
+    "    ).flatten()\n",
+    "\n",
+    "    X = Xt_member\n",
+    "    y = yt_member\n",
+    "    Xval = Xt_nonmember\n",
+    "    yval = yt_nonmember"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "f8845b4b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if simple_data_for_pytests:\n",
+    "    from sklearn import datasets\n",
+    "\n",
+    "    def get_data():\n",
+    "        iris = datasets.load_iris()\n",
+    "        x = np.asarray(iris.data, dtype=np.float64)\n",
+    "        y = np.asarray(iris.target, dtype=np.float64)\n",
+    "        return x, y\n",
+    "\n",
+    "    xall, yall = get_data()\n",
+    "    n_classes = 4\n",
+    "    X, Xval, y, yval = train_test_split(\n",
+    "        xall, yall, test_size=0.2, shuffle=True, random_state=12345\n",
+    "    )\n",
+    "\n",
+    "    y = tf.one_hot(y, n_classes)\n",
+    "    yval = tf.one_hot(yval, n_classes)\n",
+    "# yval"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f2b7816",
+   "metadata": {},
+   "source": [
+    "## Define the target model architecture\n",
+    "\n",
+    "*Again, we use a rather big model (for the classification task) to favour overfitting.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d20787df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2023-10-12 01:54:15.775124: I metal_plugin/src/device/metal_device.cc:1154] Metal device set to: Apple M1 Pro\n",
+      "2023-10-12 01:54:15.775149: I metal_plugin/src/device/metal_device.cc:296] systemMemory: 32.00 GB\n",
+      "2023-10-12 01:54:15.775155: I metal_plugin/src/device/metal_device.cc:313] maxCacheSize: 10.67 GB\n",
+      "2023-10-12 01:54:15.775217: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:303] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n",
+      "2023-10-12 01:54:15.775317: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:269] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\n",
+      "/Users/j4-smith/miniforge3/envs/aisdc-v1.1/lib/python3.9/site-packages/keras/src/initializers/initializers.py:120: UserWarning: The initializer GlorotUniform is unseeded and being called multiple times, which will return identical values each time (even if the initializer is unseeded). Please update your code to provide a seed to the initializer, or avoid using the same initializer instance more than once.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Define target model\n",
+    "# Tensorflow model (MLP) (making it big to make it overfit)\n",
+    "\n",
+    "# make results repeatable\n",
+    "tf.random.set_seed(12345)\n",
+    "initializer = tf.keras.initializers.GlorotUniform()\n",
+    "\n",
+    "input_data = Input(shape=X[0].shape)\n",
+    "x = Dense(128, activation=\"relu\", kernel_initializer=initializer)(input_data)\n",
+    "x = Dense(128, activation=\"relu\", kernel_initializer=initializer)(x)\n",
+    "x = Dense(64, activation=\"relu\", kernel_initializer=initializer)(x)\n",
+    "output = Dense(n_classes, activation=\"softmax\", kernel_initializer=initializer)(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7bbed581",
+   "metadata": {},
+   "source": [
+    "### Define the SafeModel"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "944288d2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Preliminary checks: WARNING: model parameters may present a disclosure risk:\n",
+      "- parameter noise_multiplier = 0.5 identified as less than the recommended min value of 0.7.\n",
+      "Changed parameter noise_multiplier = 0.7.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "safeModel = SafeKerasModel(\n",
+    "    inputs=input_data,\n",
+    "    outputs=output,\n",
+    "    name=\"safekeras-test\",\n",
+    "    num_samples=X.shape[0],\n",
+    "    epochs=10,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6534c8b7",
+   "metadata": {},
+   "source": [
+    "### Set loss and compile"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "b76b10b9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "During compilation: Changed parameter optimizer = 'DPKerasSGDOptimizer'\n"
+     ]
+    }
+   ],
+   "source": [
+    "loss = tf.keras.losses.CategoricalCrossentropy(\n",
+    "    from_logits=False, reduction=tf.losses.Reduction.NONE\n",
+    ")\n",
+    "\n",
+    "\n",
+    "safeModel.compile(loss=loss, optimizer=None)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d0e4a371",
+   "metadata": {},
+   "source": [
+    "### Fit the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "b2e4da84",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:absl:`compute_dp_sgd_privacy` is deprecated. It does not account for doubling of sensitivity with microbatching, and assumes Poisson subsampling, which is rarely used in practice. Please use `compute_dp_sgd_privacy_statement`, which provides appropriate context for the guarantee. To compute epsilon under different assumptions than those in `compute_dp_sgd_privacy_statement`, call the `dp_accounting` libraries directly.\n"
+     ]
     },
-    "nbformat": 4,
-    "nbformat_minor": 5
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "Epoch 1/10\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2023-10-12 01:54:29.826997: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10/10 [==============================] - 10s 674ms/step - loss: 9.0033 - accuracy: 0.4600 - val_loss: 8.6313 - val_accuracy: 0.5080\n",
+      "Epoch 2/10\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2023-10-12 01:54:39.346510: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10/10 [==============================] - 6s 609ms/step - loss: 8.9409 - accuracy: 0.5280 - val_loss: 12.8332 - val_accuracy: 0.4920\n",
+      "Epoch 3/10\n",
+      "10/10 [==============================] - 7s 657ms/step - loss: 12.6006 - accuracy: 0.5440 - val_loss: 22.3344 - val_accuracy: 0.5200\n",
+      "Epoch 4/10\n",
+      "10/10 [==============================] - 8s 770ms/step - loss: 23.2052 - accuracy: 0.5720 - val_loss: 39.2820 - val_accuracy: 0.5320\n",
+      "Epoch 5/10\n",
+      "10/10 [==============================] - 7s 721ms/step - loss: 40.8724 - accuracy: 0.5680 - val_loss: 67.2007 - val_accuracy: 0.5240\n",
+      "Epoch 6/10\n",
+      "10/10 [==============================] - 7s 711ms/step - loss: 65.2681 - accuracy: 0.5720 - val_loss: 110.5973 - val_accuracy: 0.5040\n",
+      "Epoch 7/10\n",
+      "10/10 [==============================] - 7s 665ms/step - loss: 109.5257 - accuracy: 0.5320 - val_loss: 174.7665 - val_accuracy: 0.5040\n",
+      "Epoch 8/10\n",
+      "10/10 [==============================] - 7s 647ms/step - loss: 170.2266 - accuracy: 0.5600 - val_loss: 265.9124 - val_accuracy: 0.5080\n",
+      "Epoch 9/10\n",
+      "10/10 [==============================] - 6s 598ms/step - loss: 252.6672 - accuracy: 0.5520 - val_loss: 391.0987 - val_accuracy: 0.5200\n",
+      "Epoch 10/10\n",
+      "10/10 [==============================] - 6s 612ms/step - loss: 372.4639 - accuracy: 0.5600 - val_loss: 559.9529 - val_accuracy: 0.5280\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8/8 [==============================] - 0s 4ms/step\n",
+      "model training  report:\n",
+      "               precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.53      0.56      0.54       117\n",
+      "           1       0.59      0.57      0.58       133\n",
+      "\n",
+      "    accuracy                           0.56       250\n",
+      "   macro avg       0.56      0.56      0.56       250\n",
+      "weighted avg       0.57      0.56      0.56       250\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2023-10-12 01:55:39.572624: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n"
+     ]
+    }
+   ],
+   "source": [
+    "epochs = 10\n",
+    "batch_size = 25\n",
+    "\n",
+    "r_DP = safeModel.fit(\n",
+    "    X,\n",
+    "    y,\n",
+    "    validation_data=(Xval, yval),\n",
+    "    epochs=epochs,\n",
+    "    batch_size=batch_size,\n",
+    ")\n",
+    "if r_DP == None:\n",
+    "    print(\"You have chosen to exit. Reset relevant parameter values then re-run fit().\")\n",
+    "else:\n",
+    "    plt.plot(r_DP.history[\"accuracy\"], label=\"accuracy\")\n",
+    "    plt.plot(r_DP.history[\"val_accuracy\"], label=\"validation accuracy\")\n",
+    "    plt.legend()\n",
+    "    plt.show()\n",
+    "    ypred = safeModel.predict(X)\n",
+    "    ylabels = np.argmax(y, axis=1)\n",
+    "    ypredlabels = np.argmax(ypred, axis=1)\n",
+    "    print(f\"model training  report:\\n {classification_report(ylabels,ypredlabels)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b059f431",
+   "metadata": {},
+   "source": [
+    "### Compute privacy and check if requirements for Differential Privacy are met"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "55805bad",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:absl:`compute_dp_sgd_privacy` is deprecated. It does not account for doubling of sensitivity with microbatching, and assumes Poisson subsampling, which is rarely used in practice. Please use `compute_dp_sgd_privacy_statement`, which provides appropriate context for the guarantee. To compute epsilon under different assumptions than those in `compute_dp_sgd_privacy_statement`, call the `dp_accounting` libraries directly.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "with these settings privacy = 23.09099569905857\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_samples = X.shape[0]\n",
+    "batch_size = safeModel.batch_size\n",
+    "epochs = 20\n",
+    "\n",
+    "dp_met, privacy = safeModel.dp_epsilon_met(\n",
+    "    num_examples=num_samples, batch_size=batch_size, epochs=epochs\n",
+    ")\n",
+    "\n",
+    "print(f\"with these settings privacy = {privacy}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "88413967",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:absl:`compute_dp_sgd_privacy` is deprecated. It does not account for doubling of sensitivity with microbatching, and assumes Poisson subsampling, which is rarely used in practice. Please use `compute_dp_sgd_privacy_statement`, which provides appropriate context for the guarantee. To compute epsilon under different assumptions than those in `compute_dp_sgd_privacy_statement`, call the `dp_accounting` libraries directly.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Satisfies DP: False\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "dp_met, msg = safeModel.check_epsilon(\n",
+    "    X.shape[0], safeModel.batch_size, safeModel.epochs\n",
+    ")\n",
+    "print(f\"Satisfies DP: {dp_met}\")\n",
+    "print(f\"{msg}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21813930",
+   "metadata": {},
+   "source": [
+    "### Check model and request release\n",
+    "\n",
+    "Note that the request_release() process will fail if the version checkpointed\n",
+    "during the fit() call, is not present\n",
+    "- it should be in  \"tfsaves/fit_model.tf\" "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "62a9a6d1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Preliminary checks: Model parameters are within recommended ranges.\n",
+      "\n",
+      "WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/j4-smith/miniforge3/envs/aisdc-v1.1/lib/python3.9/site-packages/keras/src/initializers/initializers.py:120: UserWarning: The initializer GlorotUniform is unseeded and being called multiple times, which will return identical values each time (even if the initializer is unseeded). Please update your code to provide a seed to the initializer, or avoid using the same initializer instance more than once.\n",
+      "  warnings.warn(\n",
+      "WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/j4-smith/miniforge3/envs/aisdc-v1.1/lib/python3.9/site-packages/keras/src/initializers/initializers.py:120: UserWarning: The initializer GlorotUniform is unseeded and being called multiple times, which will return identical values each time (even if the initializer is unseeded). Please update your code to provide a seed to the initializer, or avoid using the same initializer instance more than once.\n",
+      "  warnings.warn(\n",
+      "WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n",
+      "WARNING:absl:`compute_dp_sgd_privacy` is deprecated. It does not account for doubling of sensitivity with microbatching, and assumes Poisson subsampling, which is rarely used in practice. Please use `compute_dp_sgd_privacy_statement`, which provides appropriate context for the guarantee. To compute epsilon under different assumptions than those in `compute_dp_sgd_privacy_statement`, call the `dp_accounting` libraries directly.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Recommendation is further discussion needed WARNING: epsilon 16.608813401454455 is above normal max recommended value.\n",
+      "Discussion with researcher needed.\n",
+      ".\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "safeModel.save(\"safe1.tf\")\n",
+    "safeModel.preliminary_check()\n",
+    "safeModel.request_release(path=\"safe1\", ext=\"tf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f2e46a5",
+   "metadata": {},
+   "source": [
+    "### Examine Checkfile\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "9cccce2f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\n",
+      "    \"data_name\": \"\",\n",
+      "    \"n_samples\": 0,\n",
+      "    \"features\": {},\n",
+      "    \"n_features\": 0,\n",
+      "    \"n_samples_orig\": 0,\n",
+      "    \"generalisation_error\": \"unknown\",\n",
+      "    \"safemodel\": [\n",
+      "        {\n",
+      "            \"researcher\": \"j4-smith\",\n",
+      "            \"model_type\": \"KerasModel\",\n",
+      "            \"details\": \"Model parameters are within recommended ranges.\\n\",\n",
+      "            \"recommendation\": \"Do not allow release\",\n",
+      "            \"reason\": \"Model parameters are within recommended ranges.\\nRecommendation is not to release because None.\\nWARNING: epsilon 16.608813401454455 is above normal max recommended value.\\nDiscussion with researcher needed.\\n\",\n",
+      "            \"timestamp\": \"2023-10-12 01:56:19\"\n",
+      "        }\n",
+      "    ],\n",
+      "    \"model_path\": \"model.tf\",\n",
+      "    \"model_name\": \"SafeKerasModel\",\n",
+      "    \"model_params\": {}\n",
+      "}\n"
+     ]
+    }
+   ],
+   "source": [
+    "target_json = os.path.normpath(\"safe1/target.json\")\n",
+    "with open(target_json) as f:\n",
+    "    print(f.read())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3ac98139-306b-4b52-83e8-021cd87789c1",
+   "metadata": {},
+   "source": [
+    "## load saved model\n",
+    "\n",
+    "### note that for security reasons we do not include the optimizer in the saved version\n",
+    "- This means that tensorflow will assume it has not beeen compiled\n",
+    "- so the loss and accuracy functions are not defined. \n",
+    "- really urgent users can define losses if they want to use built-in evaluate functions\n",
+    "- or they can use standard methods to assess accuracy\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "7ea50179-8943-4845-a6e9-f584603f7314",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/j4-smith/miniforge3/envs/aisdc-v1.1/lib/python3.9/site-packages/keras/src/initializers/initializers.py:120: UserWarning: The initializer GlorotUniform is unseeded and being called multiple times, which will return identical values each time (even if the initializer is unseeded). Please update your code to provide a seed to the initializer, or avoid using the same initializer instance more than once.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/j4-smith/miniforge3/envs/aisdc-v1.1/lib/python3.9/site-packages/keras/src/initializers/initializers.py:120: UserWarning: The initializer GlorotUniform is unseeded and being called multiple times, which will return identical values each time (even if the initializer is unseeded). Please update your code to provide a seed to the initializer, or avoid using the same initializer instance more than once.\n",
+      "  warnings.warn(\n",
+      "WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from aisdc.safemodel.classifiers.safekeras import load_safe_keras_model\n",
+    "\n",
+    "model_path = os.path.normpath(\"safe1/model.tf\")\n",
+    "status, reloaded_model = load_safe_keras_model(model_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "32c0e0df-eeec-4e16-b0d8-b8bc9107267e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8/8 [==============================] - 0s 2ms/step\n",
+      "reloaded model training  accuracy:\n",
+      "               precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.53      0.56      0.54       117\n",
+      "           1       0.59      0.57      0.58       133\n",
+      "\n",
+      "    accuracy                           0.56       250\n",
+      "   macro avg       0.56      0.56      0.56       250\n",
+      "weighted avg       0.57      0.56      0.56       250\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2023-10-12 01:56:39.307876: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n"
+     ]
+    }
+   ],
+   "source": [
+    "ypred = reloaded_model.predict(X)\n",
+    "ylabels = np.argmax(y, axis=1)\n",
+    "ypredlabels = np.argmax(ypred, axis=1)\n",
+    "reloaded_cm = confusion_matrix(ylabels, ypredlabels)\n",
+    "print(\n",
+    "    f\"reloaded model training  accuracy:\\n {classification_report(ylabels,ypredlabels)}\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "840bf7ff",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "aisdc-v1.1",
+   "language": "python",
+   "name": "aisdc-v1.1"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
 }
diff --git a/examples/notebooks/example-notebook-randomforest.ipynb b/examples/notebooks/example-notebook-randomforest.ipynb
index 9fc8bc7c..4de62c5b 100644
--- a/examples/notebooks/example-notebook-randomforest.ipynb
+++ b/examples/notebooks/example-notebook-randomforest.ipynb
@@ -1,389 +1,386 @@
 {
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "2eedb8a2",
-            "metadata": {},
-            "source": [
-                "# Safe Random Forest Notebook \n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 1,
-            "id": "f96259b1",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import sys\n",
-                "import os\n",
-                "\n",
-                "##Commented out lines below applies to developers only\n",
-                "# from os.path import expanduser\n",
-                "\n",
-                "# ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(\"\")))\n",
-                "# sys.path.append(ROOT_DIR)\n",
-                "# home = expanduser(\"~\")\n",
-                "# sys.path.append(os.path.abspath(home + \"/AI-SDC\"))\n",
-                "# sys.path.insert(0, os.path.abspath(\"..\"))"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "347ea4fc",
-            "metadata": {},
-            "source": [
-                "## A Quick Start Guide to implementing Safer Random Forests\n",
-                "\n",
-                "### Lets start by making some data with one disclosive case\n",
-                "- We'll do this by adding an example to the iris data and give it a new class to make things really obvious.\n",
-                "- The same risks exist for more complex data sets but _everyone knows iris_"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 2,
-            "id": "6fa372c0",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "feature 0 min 4.3, min 7.9\n",
-                        "feature 1 min 2.0, min 4.4\n",
-                        "feature 2 min 1.0, min 6.9\n",
-                        "feature 3 min 0.1, min 2.5\n"
-                    ]
-                }
-            ],
-            "source": [
-                "import numpy as np\n",
-                "from sklearn import datasets\n",
-                "\n",
-                "iris = datasets.load_iris()\n",
-                "X = iris.data\n",
-                "y = iris.target\n",
-                "\n",
-                "\n",
-                "# print the max and min values in each feature to help hand-craft the disclosive point\n",
-                "for feature in range(4):\n",
-                "    print(f\"feature {feature} min {np.min(X[:,feature])}, min {np.max(X[:,feature])}\")\n",
-                "\n",
-                "# now add a single disclosve point with features [7,2,4.5,1] and label 3\n",
-                "X = np.vstack([X, (7, 2.0, 4.5, 1)])\n",
-                "y = np.append(y, 4)"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "597b5d8b",
-            "metadata": {},
-            "source": [
-                "## Some basic Libraries for visualization"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 3,
-            "id": "fb391f42",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "from sklearn.tree import plot_tree\n",
-                "import matplotlib.pyplot as plt"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "4a847115",
-            "metadata": {},
-            "source": [
-                "## Defining a new class SafeRandomForestClassifier\u00b6\n",
-                "-Don't forget to import the SafeModel classes."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 4,
-            "id": "02c6ffd6",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "from aisdc.safemodel.safemodel import SafeModel\n",
-                "from aisdc.safemodel.classifiers import SafeRandomForestClassifier"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 5,
-            "id": "6d617df5",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Preliminary checks: WARNING: model parameters may present a disclosure risk:\n",
-                        "- parameter min_samples_leaf = 1 identified as less than the recommended min value of 5.\n",
-                        "Training set accuracy in this safe case is 1.0\n"
-                    ]
-                },
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 1500x1500 with 100 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "safeRFModel = SafeRandomForestClassifier(n_estimators=100)  # (criterion=\"entropy\")\n",
-                "\n",
-                "safeRFModel.fit(X, y)\n",
-                "\n",
-                "print(f\"Training set accuracy in this safe case is {safeRFModel.score(X,y)}\")\n",
-                "fig, ax = plt.subplots(10, 10, figsize=(15, 15))\n",
-                "for row in range(10):\n",
-                "    for column in range(10):\n",
-                "        whichTree = 10 * row + column\n",
-                "        treeRowCol = safeRFModel.estimators_[whichTree]\n",
-                "        _ = plot_tree(treeRowCol, filled=True, ax=ax[row][column], fontsize=1)"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "64195133",
-            "metadata": {},
-            "source": [
-                "## Using the save and reporting functionality\u00b6"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 6,
-            "id": "d0936f2e",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Preliminary checks: WARNING: model parameters may present a disclosure risk:\n",
-                        "- parameter min_samples_leaf = 1 identified as less than the recommended min value of 5.\n"
-                    ]
-                }
-            ],
-            "source": [
-                "safeRFModel.save(name=\"testSaveRF.pkl\")\n",
-                "safeRFModel.preliminary_check()\n",
-                "safeRFModel.request_release(path=\"testSaveRF\", ext=\"pkl\")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "cc069f60",
-            "metadata": {},
-            "source": [
-                "## The checkfile reports any warnings and recomendations in JSON format"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 7,
-            "id": "d1cdf236",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "{\n",
-                        "    \"data_name\": \"\",\n",
-                        "    \"n_samples\": 0,\n",
-                        "    \"features\": {},\n",
-                        "    \"n_features\": 0,\n",
-                        "    \"n_samples_orig\": 0,\n",
-                        "    \"generalisation_error\": \"unknown\",\n",
-                        "    \"safemodel\": [\n",
-                        "        {\n",
-                        "            \"researcher\": \"j4-smith\",\n",
-                        "            \"model_type\": \"RandomForestClassifier\",\n",
-                        "            \"details\": \"WARNING: model parameters may present a disclosure risk:\\n- parameter min_samples_leaf = 1 identified as less than the recommended min value of 5.\",\n",
-                        "            \"k_anonymity\": \"1\",\n",
-                        "            \"recommendation\": \"Do not allow release\",\n",
-                        "            \"reason\": \"WARNING: model parameters may present a disclosure risk:\\n- parameter min_samples_leaf = 1 identified as less than the recommended min value of 5.\",\n",
-                        "            \"timestamp\": \"2023-10-12 01:51:15\"\n",
-                        "        }\n",
-                        "    ],\n",
-                        "    \"model_path\": \"model.pkl\",\n",
-                        "    \"model_name\": \"SafeRandomForestClassifier\",\n",
-                        "    \"model_params\": {\n",
-                        "        \"n_estimators\": 100,\n",
-                        "        \"bootstrap\": true,\n",
-                        "        \"oob_score\": false,\n",
-                        "        \"n_jobs\": null,\n",
-                        "        \"random_state\": null,\n",
-                        "        \"verbose\": 0,\n",
-                        "        \"warm_start\": false,\n",
-                        "        \"class_weight\": null,\n",
-                        "        \"max_samples\": null,\n",
-                        "        \"criterion\": \"gini\",\n",
-                        "        \"max_depth\": null,\n",
-                        "        \"min_samples_split\": 2,\n",
-                        "        \"min_samples_leaf\": 1,\n",
-                        "        \"min_weight_fraction_leaf\": 0.0,\n",
-                        "        \"max_features\": \"sqrt\",\n",
-                        "        \"max_leaf_nodes\": null,\n",
-                        "        \"min_impurity_decrease\": 0.0,\n",
-                        "        \"ccp_alpha\": 0.0\n",
-                        "    }\n",
-                        "}\n"
-                    ]
-                }
-            ],
-            "source": [
-                "target_json = os.path.normpath(\"testSaveRF/target.json\")\n",
-                "with open(target_json, \"r\") as f:\n",
-                "    print(f.read())"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "7139bae4",
-            "metadata": {},
-            "source": [
-                "## Putting it all together\n",
-                "-Don't forget to import the SafeModel classes."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 8,
-            "id": "548d74de",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Preliminary checks: WARNING: model parameters may present a disclosure risk:\n",
-                        "- parameter min_samples_leaf = 1 identified as less than the recommended min value of 5.\n",
-                        "Preliminary checks: WARNING: model parameters may present a disclosure risk:\n",
-                        "- parameter min_samples_leaf = 1 identified as less than the recommended min value of 5.\n"
-                    ]
-                }
-            ],
-            "source": [
-                "from aisdc.safemodel.safemodel import SafeModel\n",
-                "from aisdc.safemodel.classifiers import SafeRandomForestClassifier\n",
-                "\n",
-                "safeRFModel = SafeRandomForestClassifier(n_estimators=100)  # (criterion=\"entropy\")\n",
-                "safeRFModel.fit(X, y)\n",
-                "safeRFModel.save(name=\"testSaveRF.pkl\")\n",
-                "safeRFModel.preliminary_check()\n",
-                "safeRFModel.request_release(path=\"testSaveRF\", ext=\"pkl\")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "f7dc6f64",
-            "metadata": {},
-            "source": [
-                "## Examine the checkfile contents\n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 9,
-            "id": "377aa265",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "{\n",
-                        "    \"data_name\": \"\",\n",
-                        "    \"n_samples\": 0,\n",
-                        "    \"features\": {},\n",
-                        "    \"n_features\": 0,\n",
-                        "    \"n_samples_orig\": 0,\n",
-                        "    \"generalisation_error\": \"unknown\",\n",
-                        "    \"safemodel\": [\n",
-                        "        {\n",
-                        "            \"researcher\": \"j4-smith\",\n",
-                        "            \"model_type\": \"RandomForestClassifier\",\n",
-                        "            \"details\": \"WARNING: model parameters may present a disclosure risk:\\n- parameter min_samples_leaf = 1 identified as less than the recommended min value of 5.\",\n",
-                        "            \"k_anonymity\": \"1\",\n",
-                        "            \"recommendation\": \"Do not allow release\",\n",
-                        "            \"reason\": \"WARNING: model parameters may present a disclosure risk:\\n- parameter min_samples_leaf = 1 identified as less than the recommended min value of 5.\",\n",
-                        "            \"timestamp\": \"2023-10-12 01:51:35\"\n",
-                        "        }\n",
-                        "    ],\n",
-                        "    \"model_path\": \"model.pkl\",\n",
-                        "    \"model_name\": \"SafeRandomForestClassifier\",\n",
-                        "    \"model_params\": {\n",
-                        "        \"n_estimators\": 100,\n",
-                        "        \"bootstrap\": true,\n",
-                        "        \"oob_score\": false,\n",
-                        "        \"n_jobs\": null,\n",
-                        "        \"random_state\": null,\n",
-                        "        \"verbose\": 0,\n",
-                        "        \"warm_start\": false,\n",
-                        "        \"class_weight\": null,\n",
-                        "        \"max_samples\": null,\n",
-                        "        \"criterion\": \"gini\",\n",
-                        "        \"max_depth\": null,\n",
-                        "        \"min_samples_split\": 2,\n",
-                        "        \"min_samples_leaf\": 1,\n",
-                        "        \"min_weight_fraction_leaf\": 0.0,\n",
-                        "        \"max_features\": \"sqrt\",\n",
-                        "        \"max_leaf_nodes\": null,\n",
-                        "        \"min_impurity_decrease\": 0.0,\n",
-                        "        \"ccp_alpha\": 0.0\n",
-                        "    }\n",
-                        "}\n"
-                    ]
-                }
-            ],
-            "source": [
-                "target_json = os.path.normpath(\"testSaveRF/target.json\")\n",
-                "with open(target_json, \"r\") as f:\n",
-                "    print(f.read())"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "id": "7e6e5dbf-e181-486c-9c48-23e1daf93fd2",
-            "metadata": {},
-            "outputs": [],
-            "source": []
-        }
-    ],
-    "metadata": {
-        "kernelspec": {
-            "display_name": "aisdc-v1.1",
-            "language": "python",
-            "name": "aisdc-v1.1"
-        },
-        "language_info": {
-            "codemirror_mode": {
-                "name": "ipython",
-                "version": 3
-            },
-            "file_extension": ".py",
-            "mimetype": "text/x-python",
-            "name": "python",
-            "nbconvert_exporter": "python",
-            "pygments_lexer": "ipython3",
-            "version": "3.9.18"
-        }
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "2eedb8a2",
+   "metadata": {},
+   "source": [
+    "# Safe Random Forest Notebook \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "f96259b1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "##Commented out lines below applies to developers only\n",
+    "# from os.path import expanduser\n",
+    "\n",
+    "# ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(\"\")))\n",
+    "# sys.path.append(ROOT_DIR)\n",
+    "# home = expanduser(\"~\")\n",
+    "# sys.path.append(os.path.abspath(home + \"/AI-SDC\"))\n",
+    "# sys.path.insert(0, os.path.abspath(\"..\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "347ea4fc",
+   "metadata": {},
+   "source": [
+    "## A Quick Start Guide to implementing Safer Random Forests\n",
+    "\n",
+    "### Lets start by making some data with one disclosive case\n",
+    "- We'll do this by adding an example to the iris data and give it a new class to make things really obvious.\n",
+    "- The same risks exist for more complex data sets but _everyone knows iris_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "6fa372c0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "feature 0 min 4.3, min 7.9\n",
+      "feature 1 min 2.0, min 4.4\n",
+      "feature 2 min 1.0, min 6.9\n",
+      "feature 3 min 0.1, min 2.5\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from sklearn import datasets\n",
+    "\n",
+    "iris = datasets.load_iris()\n",
+    "X = iris.data\n",
+    "y = iris.target\n",
+    "\n",
+    "\n",
+    "# print the max and min values in each feature to help hand-craft the disclosive point\n",
+    "for feature in range(4):\n",
+    "    print(f\"feature {feature} min {np.min(X[:,feature])}, min {np.max(X[:,feature])}\")\n",
+    "\n",
+    "# now add a single disclosve point with features [7,2,4.5,1] and label 3\n",
+    "X = np.vstack([X, (7, 2.0, 4.5, 1)])\n",
+    "y = np.append(y, 4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "597b5d8b",
+   "metadata": {},
+   "source": [
+    "## Some basic Libraries for visualization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "fb391f42",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.tree import plot_tree"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4a847115",
+   "metadata": {},
+   "source": [
+    "## Defining a new class SafeRandomForestClassifier¶\n",
+    "-Don't forget to import the SafeModel classes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "02c6ffd6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from aisdc.safemodel.classifiers import SafeRandomForestClassifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "6d617df5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Preliminary checks: WARNING: model parameters may present a disclosure risk:\n",
+      "- parameter min_samples_leaf = 1 identified as less than the recommended min value of 5.\n",
+      "Training set accuracy in this safe case is 1.0\n"
+     ]
     },
-    "nbformat": 4,
-    "nbformat_minor": 5
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1500 with 100 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "safeRFModel = SafeRandomForestClassifier(n_estimators=100)  # (criterion=\"entropy\")\n",
+    "\n",
+    "safeRFModel.fit(X, y)\n",
+    "\n",
+    "print(f\"Training set accuracy in this safe case is {safeRFModel.score(X,y)}\")\n",
+    "fig, ax = plt.subplots(10, 10, figsize=(15, 15))\n",
+    "for row in range(10):\n",
+    "    for column in range(10):\n",
+    "        whichTree = 10 * row + column\n",
+    "        treeRowCol = safeRFModel.estimators_[whichTree]\n",
+    "        _ = plot_tree(treeRowCol, filled=True, ax=ax[row][column], fontsize=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "64195133",
+   "metadata": {},
+   "source": [
+    "## Using the save and reporting functionality¶"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d0936f2e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Preliminary checks: WARNING: model parameters may present a disclosure risk:\n",
+      "- parameter min_samples_leaf = 1 identified as less than the recommended min value of 5.\n"
+     ]
+    }
+   ],
+   "source": [
+    "safeRFModel.save(name=\"testSaveRF.pkl\")\n",
+    "safeRFModel.preliminary_check()\n",
+    "safeRFModel.request_release(path=\"testSaveRF\", ext=\"pkl\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cc069f60",
+   "metadata": {},
+   "source": [
+    "## The checkfile reports any warnings and recomendations in JSON format"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "d1cdf236",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\n",
+      "    \"data_name\": \"\",\n",
+      "    \"n_samples\": 0,\n",
+      "    \"features\": {},\n",
+      "    \"n_features\": 0,\n",
+      "    \"n_samples_orig\": 0,\n",
+      "    \"generalisation_error\": \"unknown\",\n",
+      "    \"safemodel\": [\n",
+      "        {\n",
+      "            \"researcher\": \"j4-smith\",\n",
+      "            \"model_type\": \"RandomForestClassifier\",\n",
+      "            \"details\": \"WARNING: model parameters may present a disclosure risk:\\n- parameter min_samples_leaf = 1 identified as less than the recommended min value of 5.\",\n",
+      "            \"k_anonymity\": \"1\",\n",
+      "            \"recommendation\": \"Do not allow release\",\n",
+      "            \"reason\": \"WARNING: model parameters may present a disclosure risk:\\n- parameter min_samples_leaf = 1 identified as less than the recommended min value of 5.\",\n",
+      "            \"timestamp\": \"2023-10-12 01:51:15\"\n",
+      "        }\n",
+      "    ],\n",
+      "    \"model_path\": \"model.pkl\",\n",
+      "    \"model_name\": \"SafeRandomForestClassifier\",\n",
+      "    \"model_params\": {\n",
+      "        \"n_estimators\": 100,\n",
+      "        \"bootstrap\": true,\n",
+      "        \"oob_score\": false,\n",
+      "        \"n_jobs\": null,\n",
+      "        \"random_state\": null,\n",
+      "        \"verbose\": 0,\n",
+      "        \"warm_start\": false,\n",
+      "        \"class_weight\": null,\n",
+      "        \"max_samples\": null,\n",
+      "        \"criterion\": \"gini\",\n",
+      "        \"max_depth\": null,\n",
+      "        \"min_samples_split\": 2,\n",
+      "        \"min_samples_leaf\": 1,\n",
+      "        \"min_weight_fraction_leaf\": 0.0,\n",
+      "        \"max_features\": \"sqrt\",\n",
+      "        \"max_leaf_nodes\": null,\n",
+      "        \"min_impurity_decrease\": 0.0,\n",
+      "        \"ccp_alpha\": 0.0\n",
+      "    }\n",
+      "}\n"
+     ]
+    }
+   ],
+   "source": [
+    "target_json = os.path.normpath(\"testSaveRF/target.json\")\n",
+    "with open(target_json) as f:\n",
+    "    print(f.read())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7139bae4",
+   "metadata": {},
+   "source": [
+    "## Putting it all together\n",
+    "-Don't forget to import the SafeModel classes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "548d74de",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Preliminary checks: WARNING: model parameters may present a disclosure risk:\n",
+      "- parameter min_samples_leaf = 1 identified as less than the recommended min value of 5.\n",
+      "Preliminary checks: WARNING: model parameters may present a disclosure risk:\n",
+      "- parameter min_samples_leaf = 1 identified as less than the recommended min value of 5.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from aisdc.safemodel.classifiers import SafeRandomForestClassifier\n",
+    "\n",
+    "safeRFModel = SafeRandomForestClassifier(n_estimators=100)  # (criterion=\"entropy\")\n",
+    "safeRFModel.fit(X, y)\n",
+    "safeRFModel.save(name=\"testSaveRF.pkl\")\n",
+    "safeRFModel.preliminary_check()\n",
+    "safeRFModel.request_release(path=\"testSaveRF\", ext=\"pkl\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f7dc6f64",
+   "metadata": {},
+   "source": [
+    "## Examine the checkfile contents\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "377aa265",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\n",
+      "    \"data_name\": \"\",\n",
+      "    \"n_samples\": 0,\n",
+      "    \"features\": {},\n",
+      "    \"n_features\": 0,\n",
+      "    \"n_samples_orig\": 0,\n",
+      "    \"generalisation_error\": \"unknown\",\n",
+      "    \"safemodel\": [\n",
+      "        {\n",
+      "            \"researcher\": \"j4-smith\",\n",
+      "            \"model_type\": \"RandomForestClassifier\",\n",
+      "            \"details\": \"WARNING: model parameters may present a disclosure risk:\\n- parameter min_samples_leaf = 1 identified as less than the recommended min value of 5.\",\n",
+      "            \"k_anonymity\": \"1\",\n",
+      "            \"recommendation\": \"Do not allow release\",\n",
+      "            \"reason\": \"WARNING: model parameters may present a disclosure risk:\\n- parameter min_samples_leaf = 1 identified as less than the recommended min value of 5.\",\n",
+      "            \"timestamp\": \"2023-10-12 01:51:35\"\n",
+      "        }\n",
+      "    ],\n",
+      "    \"model_path\": \"model.pkl\",\n",
+      "    \"model_name\": \"SafeRandomForestClassifier\",\n",
+      "    \"model_params\": {\n",
+      "        \"n_estimators\": 100,\n",
+      "        \"bootstrap\": true,\n",
+      "        \"oob_score\": false,\n",
+      "        \"n_jobs\": null,\n",
+      "        \"random_state\": null,\n",
+      "        \"verbose\": 0,\n",
+      "        \"warm_start\": false,\n",
+      "        \"class_weight\": null,\n",
+      "        \"max_samples\": null,\n",
+      "        \"criterion\": \"gini\",\n",
+      "        \"max_depth\": null,\n",
+      "        \"min_samples_split\": 2,\n",
+      "        \"min_samples_leaf\": 1,\n",
+      "        \"min_weight_fraction_leaf\": 0.0,\n",
+      "        \"max_features\": \"sqrt\",\n",
+      "        \"max_leaf_nodes\": null,\n",
+      "        \"min_impurity_decrease\": 0.0,\n",
+      "        \"ccp_alpha\": 0.0\n",
+      "    }\n",
+      "}\n"
+     ]
+    }
+   ],
+   "source": [
+    "target_json = os.path.normpath(\"testSaveRF/target.json\")\n",
+    "with open(target_json) as f:\n",
+    "    print(f.read())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7e6e5dbf-e181-486c-9c48-23e1daf93fd2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "aisdc-v1.1",
+   "language": "python",
+   "name": "aisdc-v1.1"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
 }
diff --git a/examples/risk_examples/python/attribute_inference_cancer.ipynb b/examples/risk_examples/python/attribute_inference_cancer.ipynb
index 6a712788..5d1b1d6b 100644
--- a/examples/risk_examples/python/attribute_inference_cancer.ipynb
+++ b/examples/risk_examples/python/attribute_inference_cancer.ipynb
@@ -1,506 +1,507 @@
 {
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "b0feddf2-7437-4548-8add-f07822e2792d",
-            "metadata": {
-                "tags": []
-            },
-            "source": [
-                "# Risk of cancer prediction for patients with multiple disorders\n",
-                "\n",
-                "Scientists have created a model to predict the risk of suffering cancer for people who suffer multiple disorders. This model is expected to save lives.\n",
-                "\n",
-                "The same scientist published the details of their research, how the model was built and a detailed description of the data (e.g., the health conditions investigated), the NHS board where the data was collected. The data was deidentified and was not released as it is confidential patient information, and any leak might break existing legislation.\n",
-                "\n",
-                "The researchers balanced the benefits and potential risks of the model realease, and it was decided that overall, there is a clear benefit for the population for the model to be made public.\n",
-                "\n",
-                "What they didn\u2019t realise, is that the NHS board in question is home to a famous Member of Parliament (MP). This famous MP is a former Prime Minister, and it is of public knowledge he suffered from cancer. Also, it is straightforward for anyone to do an online search and find some other details for this individual (age etc).\n",
-                "\n",
-                "## Attribute Inference\n",
-                "\n",
-                "We will use this example to demonstrate an _attribute inference_ attack. In such an attack, an attacker has access to some information about a particular individual but not all, and attempts to use the model to fill in the gaps in their knowledge. In this particular example, some aspects of the MPs health are public knowledgey. We will use that information, and a trained model, to find out information particular to this individual that is not in the public domain, and should remain in the TRE."
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "33ffc813-c9e5-43c7-8a32-2bb2aed3b5f5",
-            "metadata": {},
-            "source": [
-                "## Let's get hands on with this example.\n",
-                "\n",
-                "The following code imports some standard libraries that we will need."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 1,
-            "id": "63720e84-26f9-4763-9f20-18bad396ba4b",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import random\n",
-                "from itertools import product\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"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "9c7acbc2-1970-4259-90b9-dda459bf85a4",
-            "metadata": {
-                "tags": []
-            },
-            "source": [
-                "## Create the original model\n",
-                "\n",
-                "We are assuming that a model is trained within a TRE on real data. However, we do not have access to real data, so we will randomly generate some realistic looking data.\n",
-                "\n",
-                "In particular, we will generate data for 200 people: 100 cancer patients, and 100 non-cancer patients. Our MP will be one of the patients in the cancer set.\n",
-                "\n",
-                "For each patient, we generate six values that in reality would be extracted from their electronic health records:\n",
-                "1. `diabetes` -- whether or not the patient suffers from diabetes (1 = yes, 0 = no)\n",
-                "1. `asthma` -- whether or not the patient suffers from asthma (1 = yes, 0 = no)\n",
-                "1. `bmi_group` -- the BMI group in which the patient falls (1, 2, 3, or 4)\n",
-                "1. `blood_pressure` -- the blood pressure group in which the patient falls (0, 1, 2, 3, 4, or 5)\n",
-                "1. `smoker` -- whether or not the patient is a smoker (1 = yes, 0 = no)\n",
-                "1. `age` -- the patient's age\n",
-                "\n",
-                "Each patient is also associated with a value to indicate whether they are in the cancer group (1) or non-cancer (0).\n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 2,
-            "id": "2f94ec62-eefa-4859-acba-6ccb29f4ebb2",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# 1 is cancer, 0 is no cancer, this is our label and what we want to predict.\n",
-                "cancer = [1] * 99 + [0] * 100\n",
-                "\n",
-                "df = pd.DataFrame()\n",
-                "\n",
-                "# diabetes 0 no, 1 yes\n",
-                "df[\"diabetes\"] = [[1, 0][random.random() > 0.7] for n in range(99)] + [\n",
-                "    [1, 0][random.random() > 0.2] for n in range(100)\n",
-                "]\n",
-                "\n",
-                "# asthma 0 no, 1 yes\n",
-                "df[\"asthma\"] = [[1, 0][random.random() > 0.7] for n in range(99)] + [\n",
-                "    [1, 0][random.random() > 0.5] for n in range(100)\n",
-                "]\n",
-                "\n",
-                "# bmi group 1 under, 2 normal, 3 overweight, 4 obese\n",
-                "df[\"bmi_group\"] = [\n",
-                "    random.choices([1, 2, 3, 4], weights=[0.5, 5, 7, 5], k=1)[0] for n in range(99)\n",
-                "] + [random.choices([1, 2, 3, 4], weights=[1, 7, 4, 1], k=1)[0] for n in range(100)]\n",
-                "\n",
-                "# blood pressure 0 is low, 1 is normal, 5 is extremly high\n",
-                "df[\"blood_pressure\"] = [\n",
-                "    random.choices([0, 1, 2, 3, 4, 5], weights=[0.5, 1, 5, 6, 1, 0.5], k=1)[0]\n",
-                "    for n in range(99)\n",
-                "] + [\n",
-                "    random.choices([0, 1, 2, 3, 4, 5], weights=[0.5, 5, 5, 1, 1, 0.5], k=1)[0]\n",
-                "    for n in range(100)\n",
-                "]\n",
-                "\n",
-                "# smoker 0 is non smoker, 1 is smoker\n",
-                "df[\"smoker\"] = [[1, 0][random.random() > 0.8] for n in range(99)] + [\n",
-                "    [1, 0][random.random() > 0.2] for n in range(100)\n",
-                "]\n",
-                "\n",
-                "# age\n",
-                "x = np.arange(20, 90)\n",
-                "pmf = poisson.pmf(x, 72)\n",
-                "age = [random.choices(x, weights=pmf, k=1)[0] for n in range(99)]\n",
-                "x = np.arange(20, 90)\n",
-                "pmf = poisson.pmf(x, 55)\n",
-                "age2 = [random.choices(x, weights=pmf, k=1)[0] for n in range(100)]\n",
-                "df[\"age\"] = age + age2\n",
-                "\n",
-                "# Add the data of your MP\n",
-                "cancer = cancer + [1]\n",
-                "\n",
-                "# add new row to end of DataFrame\n",
-                "# the order of the list indicates in order diabetes, asthma, bmi_group, blood_pressure, smoker, age\n",
-                "df.loc[len(df.index)] = [1, 1, 3, 2, 1, 62]"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "011f7237",
-            "metadata": {},
-            "source": [
-                "This looks like the kind of data that might exist within a TRE. Here's the first few rows:"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 3,
-            "id": "978b6754",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "   diabetes  asthma  bmi_group  blood_pressure  smoker  age\n",
-                        "0         1       0          4               3       1   72\n",
-                        "1         1       1          2               3       0   83\n",
-                        "2         0       0          4               3       1   63\n",
-                        "3         1       1          4               3       0   77\n",
-                        "4         1       1          4               2       1   87\n"
-                    ]
-                }
-            ],
-            "source": [
-                "print(df.head())"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "65a2663c",
-            "metadata": {},
-            "source": [
-                "Our MP is the final row of the data, here are their values:"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 4,
-            "id": "34992ca0",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "diabetes           1\n",
-                        "asthma             1\n",
-                        "bmi_group          3\n",
-                        "blood_pressure     2\n",
-                        "smoker             1\n",
-                        "age               62\n",
-                        "Name: 199, dtype: int64\n"
-                    ]
-                }
-            ],
-            "source": [
-                "print(df.iloc[199, :])"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "dace3d79-78bc-4fb8-a1ae-2d8587c663b2",
-            "metadata": {},
-            "source": [
-                "## Model training\n",
-                "\n",
-                "The researcher trained a particular machine learning model called a Support Vector Machine (SVM). This is a very popular model for tasks in which we want to assign things (in this case patients) to groups (in this case cancer v non-cancer). The attribute inference attack we will perform is not unique to SVMs, we just use them as a popular example.\n",
-                "\n",
-                "Training the model is very straightforward -- just a couple of lines of code (the details are not important)."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 5,
-            "id": "d5500d7b",
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "SVC(C=1, gamma=3, probability=True,\n",
-                            "    random_state=RandomState(MT19937) at 0x2C05FDD5240)"
-                        ]
-                    },
-                    "execution_count": 5,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "# Train a model\n",
-                "prng = np.random.RandomState(12)\n",
-                "svc = SVC(C=1, gamma=3, probability=True, random_state=prng)\n",
-                "svc.fit(df, cancer)"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "418f7fa2",
-            "metadata": {},
-            "source": [
-                "The trained model can be used to make predictions about new individuals. Given data for an individual, it will produce two scores (probabilities). The first is how likely they are to belong to the non-cancer group (higher = more likely) and the second how likely they are to belong to the cancer group. The scores are always positive, and sum to 1.\n",
-                "\n",
-                "For example, if we have an individual who has diabetes, has asthma, has a bmi group of 1, blood pressure of 5. is a non-smoker and is 42 years old, we can use the model to predict whether or not they should belong in the cancer or non-cancer groups:"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 6,
-            "id": "e21f2890",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "non-cancer score = 0.49\n",
-                        "cancer score = 0.51\n"
-                    ]
-                }
-            ],
-            "source": [
-                "test_example = pd.DataFrame(\n",
-                "    {\n",
-                "        \"diabetes\": 1,\n",
-                "        \"asthma\": 1,\n",
-                "        \"bmi_group\": 1,\n",
-                "        \"blood_pressure\": 5,\n",
-                "        \"smoker\": 1,\n",
-                "        \"age\": 72,\n",
-                "    },\n",
-                "    index=[1],\n",
-                ")\n",
-                "predictions = svc.predict_proba(test_example)\n",
-                "print(f\"non-cancer score = {predictions[0][0]:.2f}\")\n",
-                "print(f\"cancer score = {predictions[0][1]:.2f}\")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "79712b01",
-            "metadata": {},
-            "source": [
-                "## The attack\n",
-                "\n",
-                "We now assume the role of the attacker. The attacker is allowed to make predictions as we have just done.\n",
-                "\n",
-                "As we are interested in a famous individual, some information is available in the public domain. In particular, the attacker knows the following:\n",
-                "- The MP is a smoker\n",
-                "- The MP is aged 62\n",
-                "- The MP has diabetes\n",
-                "- The MP has asthma\n",
-                "\n",
-                "The attacker does not know the MP's `bmi_group` or `blood_pressure` and it is those that they are trying to determine through the attack.\n",
-                "\n",
-                "The attacker does know the possible values that these two variables can take -- `bmi_group` is 1, 2, 3, or 4 and `blood_pressure` is 0, 1, 2, 3, 4, or 5.\n",
-                "\n",
-                "### How does the attack work?\n",
-                "\n",
-                "Recall that when we used the model to make predictions, the model provided two scores -- the cancer and non-cancer scores. The more extreme these scores become (e.g one is close to 1 and the other to 0 (recall that they have to add up to 1)), the more _confident_ the model is in assigning that example. It is not uncommon for models to have higher confidence for examples that they were trained on than examples that they haven't seen before. It is this property that the attacker will make use of.\n",
-                "\n",
-                "In particular, the attacker will query the model with the known values and all combinations of the unknown values (i.e. in total, they will make $4\\times 6 = 24$ queries for the four bmi groups and 6 blood pressure values). For each query, the attacker will record the score for the cancer group. The attacker will assume that the higher this score (i.e. the more confident the model), the more likely that the values are correct. Note that we used the confidence in the cancer group (rather than non-cancer) because the attacker _knows_ that the MP had cancer and would therefore have been in the cancer group.\n",
-                "\n",
-                "The following code goes through all values and computes the model's predictions.\n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 7,
-            "id": "dc88f314",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "<itertools.product object at 0x000002C060E51240>\n"
-                    ]
-                }
-            ],
-            "source": [
-                "feature_vals = {\n",
-                "    \"diabetes\": [1],\n",
-                "    \"asthma\": [1],\n",
-                "    \"bmi_group\": [1, 2, 3, 4],\n",
-                "    \"blood_pressure\": [0, 1, 2, 3, 4, 5],\n",
-                "    \"smoker\": [1],\n",
-                "    \"age\": [62],\n",
-                "}\n",
-                "\n",
-                "all_combinations = product(*feature_vals.values())\n",
-                "print(all_combinations)\n",
-                "g = {}\n",
-                "for _, combination in enumerate(all_combinations):\n",
-                "    # Turn this particular combination into a dictionary\n",
-                "    g[_] = {n: v for n, v in zip(feature_vals.keys(), combination)}\n",
-                "attack_inputs = pd.DataFrame(g).T\n",
-                "\n",
-                "probs = svc.predict_proba(attack_inputs)\n",
-                "\n",
-                "# Add the prob cancer to the dataframe\n",
-                "attack_values = attack_inputs.copy()\n",
-                "attack_values[\"confidence\"] = probs[:, 1]\n",
-                "sorted_attack_values = attack_values.sort_values(by=\"confidence\", ascending=False)[\n",
-                "    [\"bmi_group\", \"blood_pressure\", \"confidence\"]\n",
-                "]"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "50564216",
-            "metadata": {},
-            "source": [
-                "The attacker now has a handy table of all possible values of the unknown variables for the MP and the confidence that the model gives each one. Here are the five values with the highest confidence:"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 8,
-            "id": "12a1a069",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "    bmi_group  blood_pressure  confidence\n",
-                        "14          3               2    0.938385\n",
-                        "8           2               2    0.577007\n",
-                        "9           2               3    0.545445\n",
-                        "15          3               3    0.542361\n",
-                        "20          4               2    0.542322\n"
-                    ]
-                }
-            ],
-            "source": [
-                "print(sorted_attack_values.head())"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "df7d15d1",
-            "metadata": {},
-            "source": [
-                "The attacker can see that there is a combination that has a _much_ higher confidence that the others -- when `bmi_group = 3` and `blood_pressure = 2`, the model places the example in the cancer group with a very high score (0.95), whereas the next highest score is 0.53. To the attacker, this is very strong evidence that the first example were the values that the model was trained on (i.e. are the correct values) and the others have not been seen by the model before.\n",
-                "\n",
-                "The attacker can therefore confidently predict that these are the correct values. And, if they did so, they would be correct. This represents a breah from the TRE -- the MPs bmi group and blood pressure constitute personal information that shouldn't be allowed out of the TRE."
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "5c3692e3",
-            "metadata": {},
-            "source": [
-                "## Mitigation\n",
-                "\n",
-                "An important question for TREs is how can an attack like this be mitigated?\n",
-                "\n",
-                "When our researcher trained the SVM, they configured it by setting a particular parameter (`gamma`) to the value 3. Tuning this parameter can lead to a model that is much less susceptible to attack. For example, let's re-train the model, but this time with `gamma = 0.1` and try the attack again:"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 9,
-            "id": "41927b12",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "    bmi_group  blood_pressure  confidence\n",
-                        "15          3               3    0.863876\n",
-                        "21          4               3    0.847483\n",
-                        "9           2               3    0.835714\n",
-                        "14          3               2    0.834142\n",
-                        "20          4               2    0.812857\n",
-                        "8           2               2    0.803217\n",
-                        "22          4               4    0.798077\n",
-                        "16          3               4    0.797924\n",
-                        "3           1               3    0.767166\n",
-                        "10          2               4    0.754672\n",
-                        "2           1               2    0.727526\n",
-                        "23          4               5    0.686306\n",
-                        "4           1               4    0.684278\n",
-                        "13          3               1    0.683948\n",
-                        "19          4               1    0.676913\n",
-                        "17          3               5    0.654893\n",
-                        "7           2               1    0.638978\n",
-                        "11          2               5    0.593411\n",
-                        "1           1               1    0.559613\n",
-                        "5           1               5    0.534109\n",
-                        "18          4               0    0.500000\n",
-                        "12          3               0    0.473850\n",
-                        "6           2               0    0.421279\n",
-                        "0           1               0    0.378039\n"
-                    ]
-                }
-            ],
-            "source": [
-                "svc = SVC(C=1, gamma=0.1, probability=True)\n",
-                "svc.fit(df, cancer)\n",
-                "probs = svc.predict_proba(attack_inputs)\n",
-                "\n",
-                "# Add the prob cancer to the dataframe\n",
-                "attack_values = attack_inputs.copy()\n",
-                "attack_values[\"confidence\"] = probs[:, 1]\n",
-                "sorted_attack_values = attack_values.sort_values(by=\"confidence\", ascending=False)[\n",
-                "    [\"bmi_group\", \"blood_pressure\", \"confidence\"]\n",
-                "]\n",
-                "print(sorted_attack_values.head(n=24))"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "5e6a7823",
-            "metadata": {},
-            "source": [
-                "The attacker now sees a very different picture - there is no single combination that for which the model is much more confident than others. The attacker can therefore not make any clear prediction about these values -- the model is much safer. "
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "67474576-080f-4f35-ad14-f7f8067541d0",
-            "metadata": {},
-            "source": [
-                "## Conclusions\n",
-                "\n",
-                "With this example, we have demonstrated how an attacker who has some information about an individual in the model's training set and is allowed to query the model can potentially learn information about that individual. This is known as an _attribute inference_ attack, and it makes use of the fact that under some configurations, models can be more confident on examples they've seen during training than on examples that they haven't.\n",
-                "\n",
-                "The susceptibility of a model to attack depends, to a significant degree, on their configuration. In the case of an SVM, changing the `gamma` parameter can control how safe the model is. Although the details of an SVM's `gamma` parameter are not important, it's important to see how small changes in a model's configuration can have dramatic changes in their vulnerability. "
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "bd973a30",
-            "metadata": {},
-            "source": []
-        }
-    ],
-    "metadata": {
-        "kernelspec": {
-            "display_name": "Python 3.9.4 ('venv': venv)",
-            "language": "python",
-            "name": "python3"
-        },
-        "language_info": {
-            "codemirror_mode": {
-                "name": "ipython",
-                "version": 3
-            },
-            "file_extension": ".py",
-            "mimetype": "text/x-python",
-            "name": "python",
-            "nbconvert_exporter": "python",
-            "pygments_lexer": "ipython3",
-            "version": "3.9.4"
-        },
-        "vscode": {
-            "interpreter": {
-                "hash": "fcca1ce0a591990538c4a1a2cbe16853d718e2332b5914ea18ddb1937a418955"
-            }
-        }
-    },
-    "nbformat": 4,
-    "nbformat_minor": 5
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b0feddf2-7437-4548-8add-f07822e2792d",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# Risk of cancer prediction for patients with multiple disorders\n",
+    "\n",
+    "Scientists have created a model to predict the risk of suffering cancer for people who suffer multiple disorders. This model is expected to save lives.\n",
+    "\n",
+    "The same scientist published the details of their research, how the model was built and a detailed description of the data (e.g., the health conditions investigated), the NHS board where the data was collected. The data was deidentified and was not released as it is confidential patient information, and any leak might break existing legislation.\n",
+    "\n",
+    "The researchers balanced the benefits and potential risks of the model realease, and it was decided that overall, there is a clear benefit for the population for the model to be made public.\n",
+    "\n",
+    "What they didn’t realise, is that the NHS board in question is home to a famous Member of Parliament (MP). This famous MP is a former Prime Minister, and it is of public knowledge he suffered from cancer. Also, it is straightforward for anyone to do an online search and find some other details for this individual (age etc).\n",
+    "\n",
+    "## Attribute Inference\n",
+    "\n",
+    "We will use this example to demonstrate an _attribute inference_ attack. In such an attack, an attacker has access to some information about a particular individual but not all, and attempts to use the model to fill in the gaps in their knowledge. In this particular example, some aspects of the MPs health are public knowledgey. We will use that information, and a trained model, to find out information particular to this individual that is not in the public domain, and should remain in the TRE."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "33ffc813-c9e5-43c7-8a32-2bb2aed3b5f5",
+   "metadata": {},
+   "source": [
+    "## Let's get hands on with this example.\n",
+    "\n",
+    "The following code imports some standard libraries that we will need."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "63720e84-26f9-4763-9f20-18bad396ba4b",
+   "metadata": {},
+   "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",
+    "import pandas as pd\n",
+    "from scipy.stats import poisson\n",
+    "from sklearn.svm import SVC"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9c7acbc2-1970-4259-90b9-dda459bf85a4",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Create the original model\n",
+    "\n",
+    "We are assuming that a model is trained within a TRE on real data. However, we do not have access to real data, so we will randomly generate some realistic looking data.\n",
+    "\n",
+    "In particular, we will generate data for 200 people: 100 cancer patients, and 100 non-cancer patients. Our MP will be one of the patients in the cancer set.\n",
+    "\n",
+    "For each patient, we generate six values that in reality would be extracted from their electronic health records:\n",
+    "1. `diabetes` -- whether or not the patient suffers from diabetes (1 = yes, 0 = no)\n",
+    "1. `asthma` -- whether or not the patient suffers from asthma (1 = yes, 0 = no)\n",
+    "1. `bmi_group` -- the BMI group in which the patient falls (1, 2, 3, or 4)\n",
+    "1. `blood_pressure` -- the blood pressure group in which the patient falls (0, 1, 2, 3, 4, or 5)\n",
+    "1. `smoker` -- whether or not the patient is a smoker (1 = yes, 0 = no)\n",
+    "1. `age` -- the patient's age\n",
+    "\n",
+    "Each patient is also associated with a value to indicate whether they are in the cancer group (1) or non-cancer (0).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2f94ec62-eefa-4859-acba-6ccb29f4ebb2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 1 is cancer, 0 is no cancer, this is our label and what we want to predict.\n",
+    "cancer = [1] * 99 + [0] * 100\n",
+    "\n",
+    "df = pd.DataFrame()\n",
+    "\n",
+    "# diabetes 0 no, 1 yes\n",
+    "df[\"diabetes\"] = [[1, 0][random.random() > 0.7] for n in range(99)] + [\n",
+    "    [1, 0][random.random() > 0.2] for n in range(100)\n",
+    "]\n",
+    "\n",
+    "# asthma 0 no, 1 yes\n",
+    "df[\"asthma\"] = [[1, 0][random.random() > 0.7] for n in range(99)] + [\n",
+    "    [1, 0][random.random() > 0.5] for n in range(100)\n",
+    "]\n",
+    "\n",
+    "# bmi group 1 under, 2 normal, 3 overweight, 4 obese\n",
+    "df[\"bmi_group\"] = [\n",
+    "    random.choices([1, 2, 3, 4], weights=[0.5, 5, 7, 5], k=1)[0] for n in range(99)\n",
+    "] + [random.choices([1, 2, 3, 4], weights=[1, 7, 4, 1], k=1)[0] for n in range(100)]\n",
+    "\n",
+    "# blood pressure 0 is low, 1 is normal, 5 is extremly high\n",
+    "df[\"blood_pressure\"] = [\n",
+    "    random.choices([0, 1, 2, 3, 4, 5], weights=[0.5, 1, 5, 6, 1, 0.5], k=1)[0]\n",
+    "    for n in range(99)\n",
+    "] + [\n",
+    "    random.choices([0, 1, 2, 3, 4, 5], weights=[0.5, 5, 5, 1, 1, 0.5], k=1)[0]\n",
+    "    for n in range(100)\n",
+    "]\n",
+    "\n",
+    "# smoker 0 is non smoker, 1 is smoker\n",
+    "df[\"smoker\"] = [[1, 0][random.random() > 0.8] for n in range(99)] + [\n",
+    "    [1, 0][random.random() > 0.2] for n in range(100)\n",
+    "]\n",
+    "\n",
+    "# age\n",
+    "x = np.arange(20, 90)\n",
+    "pmf = poisson.pmf(x, 72)\n",
+    "age = [random.choices(x, weights=pmf, k=1)[0] for n in range(99)]\n",
+    "x = np.arange(20, 90)\n",
+    "pmf = poisson.pmf(x, 55)\n",
+    "age2 = [random.choices(x, weights=pmf, k=1)[0] for n in range(100)]\n",
+    "df[\"age\"] = age + age2\n",
+    "\n",
+    "# Add the data of your MP\n",
+    "cancer = cancer + [1]\n",
+    "\n",
+    "# add new row to end of DataFrame\n",
+    "# the order of the list indicates in order diabetes, asthma, bmi_group, blood_pressure, smoker, age\n",
+    "df.loc[len(df.index)] = [1, 1, 3, 2, 1, 62]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "011f7237",
+   "metadata": {},
+   "source": [
+    "This looks like the kind of data that might exist within a TRE. Here's the first few rows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "978b6754",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   diabetes  asthma  bmi_group  blood_pressure  smoker  age\n",
+      "0         1       0          4               3       1   72\n",
+      "1         1       1          2               3       0   83\n",
+      "2         0       0          4               3       1   63\n",
+      "3         1       1          4               3       0   77\n",
+      "4         1       1          4               2       1   87\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "65a2663c",
+   "metadata": {},
+   "source": [
+    "Our MP is the final row of the data, here are their values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "34992ca0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "diabetes           1\n",
+      "asthma             1\n",
+      "bmi_group          3\n",
+      "blood_pressure     2\n",
+      "smoker             1\n",
+      "age               62\n",
+      "Name: 199, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.iloc[199, :])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dace3d79-78bc-4fb8-a1ae-2d8587c663b2",
+   "metadata": {},
+   "source": [
+    "## Model training\n",
+    "\n",
+    "The researcher trained a particular machine learning model called a Support Vector Machine (SVM). This is a very popular model for tasks in which we want to assign things (in this case patients) to groups (in this case cancer v non-cancer). The attribute inference attack we will perform is not unique to SVMs, we just use them as a popular example.\n",
+    "\n",
+    "Training the model is very straightforward -- just a couple of lines of code (the details are not important)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "d5500d7b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SVC(C=1, gamma=3, probability=True,\n",
+       "    random_state=RandomState(MT19937) at 0x2C05FDD5240)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Train a model\n",
+    "prng = np.random.RandomState(12)\n",
+    "svc = SVC(C=1, gamma=3, probability=True, random_state=prng)\n",
+    "svc.fit(df, cancer)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "418f7fa2",
+   "metadata": {},
+   "source": [
+    "The trained model can be used to make predictions about new individuals. Given data for an individual, it will produce two scores (probabilities). The first is how likely they are to belong to the non-cancer group (higher = more likely) and the second how likely they are to belong to the cancer group. The scores are always positive, and sum to 1.\n",
+    "\n",
+    "For example, if we have an individual who has diabetes, has asthma, has a bmi group of 1, blood pressure of 5. is a non-smoker and is 42 years old, we can use the model to predict whether or not they should belong in the cancer or non-cancer groups:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e21f2890",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "non-cancer score = 0.49\n",
+      "cancer score = 0.51\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_example = pd.DataFrame(\n",
+    "    {\n",
+    "        \"diabetes\": 1,\n",
+    "        \"asthma\": 1,\n",
+    "        \"bmi_group\": 1,\n",
+    "        \"blood_pressure\": 5,\n",
+    "        \"smoker\": 1,\n",
+    "        \"age\": 72,\n",
+    "    },\n",
+    "    index=[1],\n",
+    ")\n",
+    "predictions = svc.predict_proba(test_example)\n",
+    "print(f\"non-cancer score = {predictions[0][0]:.2f}\")\n",
+    "print(f\"cancer score = {predictions[0][1]:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "79712b01",
+   "metadata": {},
+   "source": [
+    "## The attack\n",
+    "\n",
+    "We now assume the role of the attacker. The attacker is allowed to make predictions as we have just done.\n",
+    "\n",
+    "As we are interested in a famous individual, some information is available in the public domain. In particular, the attacker knows the following:\n",
+    "- The MP is a smoker\n",
+    "- The MP is aged 62\n",
+    "- The MP has diabetes\n",
+    "- The MP has asthma\n",
+    "\n",
+    "The attacker does not know the MP's `bmi_group` or `blood_pressure` and it is those that they are trying to determine through the attack.\n",
+    "\n",
+    "The attacker does know the possible values that these two variables can take -- `bmi_group` is 1, 2, 3, or 4 and `blood_pressure` is 0, 1, 2, 3, 4, or 5.\n",
+    "\n",
+    "### How does the attack work?\n",
+    "\n",
+    "Recall that when we used the model to make predictions, the model provided two scores -- the cancer and non-cancer scores. The more extreme these scores become (e.g one is close to 1 and the other to 0 (recall that they have to add up to 1)), the more _confident_ the model is in assigning that example. It is not uncommon for models to have higher confidence for examples that they were trained on than examples that they haven't seen before. It is this property that the attacker will make use of.\n",
+    "\n",
+    "In particular, the attacker will query the model with the known values and all combinations of the unknown values (i.e. in total, they will make $4\\times 6 = 24$ queries for the four bmi groups and 6 blood pressure values). For each query, the attacker will record the score for the cancer group. The attacker will assume that the higher this score (i.e. the more confident the model), the more likely that the values are correct. Note that we used the confidence in the cancer group (rather than non-cancer) because the attacker _knows_ that the MP had cancer and would therefore have been in the cancer group.\n",
+    "\n",
+    "The following code goes through all values and computes the model's predictions.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "dc88f314",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<itertools.product object at 0x000002C060E51240>\n"
+     ]
+    }
+   ],
+   "source": [
+    "feature_vals = {\n",
+    "    \"diabetes\": [1],\n",
+    "    \"asthma\": [1],\n",
+    "    \"bmi_group\": [1, 2, 3, 4],\n",
+    "    \"blood_pressure\": [0, 1, 2, 3, 4, 5],\n",
+    "    \"smoker\": [1],\n",
+    "    \"age\": [62],\n",
+    "}\n",
+    "\n",
+    "all_combinations = product(*feature_vals.values())\n",
+    "print(all_combinations)\n",
+    "g = {}\n",
+    "for _, combination in enumerate(all_combinations):\n",
+    "    # Turn this particular combination into a dictionary\n",
+    "    g[_] = {n: v for n, v in zip(feature_vals.keys(), combination)}\n",
+    "attack_inputs = pd.DataFrame(g).T\n",
+    "\n",
+    "probs = svc.predict_proba(attack_inputs)\n",
+    "\n",
+    "# Add the prob cancer to the dataframe\n",
+    "attack_values = attack_inputs.copy()\n",
+    "attack_values[\"confidence\"] = probs[:, 1]\n",
+    "sorted_attack_values = attack_values.sort_values(by=\"confidence\", ascending=False)[\n",
+    "    [\"bmi_group\", \"blood_pressure\", \"confidence\"]\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "50564216",
+   "metadata": {},
+   "source": [
+    "The attacker now has a handy table of all possible values of the unknown variables for the MP and the confidence that the model gives each one. Here are the five values with the highest confidence:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "12a1a069",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    bmi_group  blood_pressure  confidence\n",
+      "14          3               2    0.938385\n",
+      "8           2               2    0.577007\n",
+      "9           2               3    0.545445\n",
+      "15          3               3    0.542361\n",
+      "20          4               2    0.542322\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(sorted_attack_values.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "df7d15d1",
+   "metadata": {},
+   "source": [
+    "The attacker can see that there is a combination that has a _much_ higher confidence that the others -- when `bmi_group = 3` and `blood_pressure = 2`, the model places the example in the cancer group with a very high score (0.95), whereas the next highest score is 0.53. To the attacker, this is very strong evidence that the first example were the values that the model was trained on (i.e. are the correct values) and the others have not been seen by the model before.\n",
+    "\n",
+    "The attacker can therefore confidently predict that these are the correct values. And, if they did so, they would be correct. This represents a breah from the TRE -- the MPs bmi group and blood pressure constitute personal information that shouldn't be allowed out of the TRE."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c3692e3",
+   "metadata": {},
+   "source": [
+    "## Mitigation\n",
+    "\n",
+    "An important question for TREs is how can an attack like this be mitigated?\n",
+    "\n",
+    "When our researcher trained the SVM, they configured it by setting a particular parameter (`gamma`) to the value 3. Tuning this parameter can lead to a model that is much less susceptible to attack. For example, let's re-train the model, but this time with `gamma = 0.1` and try the attack again:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "41927b12",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    bmi_group  blood_pressure  confidence\n",
+      "15          3               3    0.863876\n",
+      "21          4               3    0.847483\n",
+      "9           2               3    0.835714\n",
+      "14          3               2    0.834142\n",
+      "20          4               2    0.812857\n",
+      "8           2               2    0.803217\n",
+      "22          4               4    0.798077\n",
+      "16          3               4    0.797924\n",
+      "3           1               3    0.767166\n",
+      "10          2               4    0.754672\n",
+      "2           1               2    0.727526\n",
+      "23          4               5    0.686306\n",
+      "4           1               4    0.684278\n",
+      "13          3               1    0.683948\n",
+      "19          4               1    0.676913\n",
+      "17          3               5    0.654893\n",
+      "7           2               1    0.638978\n",
+      "11          2               5    0.593411\n",
+      "1           1               1    0.559613\n",
+      "5           1               5    0.534109\n",
+      "18          4               0    0.500000\n",
+      "12          3               0    0.473850\n",
+      "6           2               0    0.421279\n",
+      "0           1               0    0.378039\n"
+     ]
+    }
+   ],
+   "source": [
+    "svc = SVC(C=1, gamma=0.1, probability=True)\n",
+    "svc.fit(df, cancer)\n",
+    "probs = svc.predict_proba(attack_inputs)\n",
+    "\n",
+    "# Add the prob cancer to the dataframe\n",
+    "attack_values = attack_inputs.copy()\n",
+    "attack_values[\"confidence\"] = probs[:, 1]\n",
+    "sorted_attack_values = attack_values.sort_values(by=\"confidence\", ascending=False)[\n",
+    "    [\"bmi_group\", \"blood_pressure\", \"confidence\"]\n",
+    "]\n",
+    "print(sorted_attack_values.head(n=24))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5e6a7823",
+   "metadata": {},
+   "source": [
+    "The attacker now sees a very different picture - there is no single combination that for which the model is much more confident than others. The attacker can therefore not make any clear prediction about these values -- the model is much safer. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "67474576-080f-4f35-ad14-f7f8067541d0",
+   "metadata": {},
+   "source": [
+    "## Conclusions\n",
+    "\n",
+    "With this example, we have demonstrated how an attacker who has some information about an individual in the model's training set and is allowed to query the model can potentially learn information about that individual. This is known as an _attribute inference_ attack, and it makes use of the fact that under some configurations, models can be more confident on examples they've seen during training than on examples that they haven't.\n",
+    "\n",
+    "The susceptibility of a model to attack depends, to a significant degree, on their configuration. In the case of an SVM, changing the `gamma` parameter can control how safe the model is. Although the details of an SVM's `gamma` parameter are not important, it's important to see how small changes in a model's configuration can have dramatic changes in their vulnerability. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd973a30",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.4 ('venv': venv)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.4"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "fcca1ce0a591990538c4a1a2cbe16853d718e2332b5914ea18ddb1937a418955"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
 }
diff --git a/examples/risk_examples/python/instance_based_mimic.ipynb b/examples/risk_examples/python/instance_based_mimic.ipynb
index 7fbd308f..7cb87d04 100644
--- a/examples/risk_examples/python/instance_based_mimic.ipynb
+++ b/examples/risk_examples/python/instance_based_mimic.ipynb
@@ -1,260 +1,260 @@
 {
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "# Instance based attacks\n",
-                "\n",
-                "This notebook demonstrates additional issues that are unique to instance-based models.\n",
-                "\n",
-                "Instance-based models are popular within Machine Learning -- common examples are K-Nearest-Neighbours and the Support Vector Machine. All machine learning models (instance-based or otherwise) require access to data during the training phase. What makes instance-based models distinct is that they also require access to training data to make predictions and therefore need to store some of the training data within the model file.\n",
-                "\n",
-                "As it is this model file that researchers wish to export from the TRE, this constitutes a problem.\n",
-                "\n",
-                "We will illustrate this with an example."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 1,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "INFO:c:\\Users\\simonr04\\git\\GRAIMatter\\data_preprocessing\\data_interface.py:ROOT PROJECT FOLDER = c:\\Users\\simonr04\\git\\GRAIMatter\n"
-                    ]
-                }
-            ],
-            "source": [
-                "import os\n",
-                "import logging\n",
-                "import numpy as np\n",
-                "import pylab as plt\n",
-                "\n",
-                "%matplotlib inline\n",
-                "\n",
-                "logging.getLogger(\"matplotlib.font_manager\").disabled = True\n",
-                "\n",
-                "os.chdir(\"c:\\\\Users\\\\simonr04\\\\git\\\\GRAIMatter\")\n",
-                "from data_preprocessing.data_interface import get_data_sklearn\n",
-                "\n",
-                "logging.basicConfig(level=logging.DEBUG)"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Here we are using an open source dataset as we cannot show an example with data from the TRE."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 2,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "INFO:c:\\Users\\simonr04\\git\\GRAIMatter\\data_preprocessing\\data_interface.py:DATASET FOLDER = c:\\Users\\simonr04\\git\\GRAIMatter\\data\n",
-                        "INFO:c:\\Users\\simonr04\\git\\GRAIMatter\\data_preprocessing\\data_interface.py:Loading mimic2-iaccd\n",
-                        "INFO:c:\\Users\\simonr04\\git\\GRAIMatter\\data_preprocessing\\data_interface.py:Preprocessing\n"
-                    ]
-                }
-            ],
-            "source": [
-                "DATASET_NAME = \"mimic2-iaccd\"\n",
-                "X, y = get_data_sklearn(DATASET_NAME)\n",
-                "# Choose some features (we don't need all of them)\n",
-                "FEATURES = [\"age\", \"gender_num\", \"bmi\", \"day_icu_intime_num\", \"liver_flg\", \"copd_flg\"]\n",
-                "subX = X[FEATURES].copy()\n",
-                "\n",
-                "# Round bmi to an integer\n",
-                "subX[\"bmi\"] = subX[\"bmi\"].astype(int)\n",
-                "subX[\"age\"] = subX[\"age\"].astype(int)"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Training the model\n",
-                "\n",
-                "We now train an instance-based model (a Support Verctor Machine; SVM). In this case, we are predicting whether an individual admitted to hospital died or not."
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "We start by splitting the data into two sets, and then training the model with one of the sets. We show the model performance via a ROC curve. This is just to show that the model is able to do something (lines above the dashed line show performance better than guessing)."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 3,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "[<matplotlib.lines.Line2D at 0x2c91c5636d0>]"
-                        ]
-                    },
-                    "execution_count": 3,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                },
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 720x720 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {
-                        "needs_background": "light"
-                    },
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "from sklearn.svm import SVC\n",
-                "from sklearn.model_selection import train_test_split\n",
-                "from sklearn.metrics import roc_curve\n",
-                "\n",
-                "font = {\"size\": 14}\n",
-                "plt.rc(\"font\", **font)\n",
-                "train_X, test_X, train_y, test_y = train_test_split(subX.values, y.values.flatten())\n",
-                "svm = SVC(probability=True, gamma=0.01)\n",
-                "svm.fit(train_X, train_y)\n",
-                "train_probs = svm.predict_proba(train_X)\n",
-                "test_probs = svm.predict_proba(test_X)\n",
-                "plt.figure(figsize=(10, 10))\n",
-                "fpr, tpr, _ = roc_curve(test_y, test_probs[:, 1])\n",
-                "plt.plot(fpr, tpr, label=\"test\")\n",
-                "fpr, tpr, _ = roc_curve(train_y, train_probs[:, 1])\n",
-                "plt.plot(fpr, tpr, label=\"train\")\n",
-                "plt.legend()\n",
-                "plt.xlabel(\"fpr\")\n",
-                "plt.ylabel(\"tpr\")\n",
-                "plt.plot([0, 1], [0, 1], \"k--\")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "The python object `svm` is the model. This is what a researcher would want to save and export from the TRE.\n",
-                "\n",
-                "Unfortunately, it includes _exact_ copies of some of the data examples. Details in the next cell."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 5,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "In this example, the SVM has stored exact copies of 441 of the original data rows (out of 798 total rows)\n"
-                    ]
-                }
-            ],
-            "source": [
-                "n_support_vectors = len(svm.support_vectors_)\n",
-                "n_total = len(train_X)\n",
-                "print(\n",
-                "    f\"In this example, the SVM has stored exact copies of {n_support_vectors} of the original data rows (out of {n_total} total rows)\"\n",
-                ")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Without these, the SVM won't work. They are immediately accessible with access to the `svm` object. For example, here are the top 5, and the same rows from the training data for comparison:"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 6,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "EXAMPLE 0\n",
-                        "\t Original:\t [46.  1. 39.  5.  0.  0.]\n",
-                        "\t Stored:\t [46.  1. 39.  5.  0.  0.]\n",
-                        "EXAMPLE 1\n",
-                        "\t Original:\t [36.  1. 26.  5.  0.  0.]\n",
-                        "\t Stored:\t [36.  1. 26.  5.  0.  0.]\n",
-                        "EXAMPLE 2\n",
-                        "\t Original:\t [67.  1. 19.  4.  0.  0.]\n",
-                        "\t Stored:\t [67.  1. 19.  4.  0.  0.]\n",
-                        "EXAMPLE 3\n",
-                        "\t Original:\t [55.  0. 19.  6.  0.  0.]\n",
-                        "\t Stored:\t [55.  0. 19.  6.  0.  0.]\n",
-                        "EXAMPLE 4\n",
-                        "\t Original:\t [87.  0. 24.  7.  0.  0.]\n",
-                        "\t Stored:\t [87.  0. 24.  7.  0.  0.]\n"
-                    ]
-                }
-            ],
-            "source": [
-                "NTOP = 5\n",
-                "for i in range(NTOP):\n",
-                "    sv_idx = svm.support_[i]\n",
-                "    sv = svm.support_vectors_[i]\n",
-                "    original = train_X[sv_idx, :]\n",
-                "    print(f\"EXAMPLE {i}\")\n",
-                "    print(\"\\t Original:\\t\", original)\n",
-                "    print(\"\\t Stored:\\t\", sv)"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "It is immediately clear that exporting the model would, in effect export exact copies of some individual level data.\n",
-                "\n",
-                "This is an issue with all instance-based models where an attacker has direct access to the contents of the model (or the model file). It is not an issue if the attacker is only able to query the model and not have access to its inner workings."
-            ]
-        }
-    ],
-    "metadata": {
-        "kernelspec": {
-            "display_name": "Python 3.9.4 ('venv': venv)",
-            "language": "python",
-            "name": "python3"
-        },
-        "language_info": {
-            "codemirror_mode": {
-                "name": "ipython",
-                "version": 3
-            },
-            "file_extension": ".py",
-            "mimetype": "text/x-python",
-            "name": "python",
-            "nbconvert_exporter": "python",
-            "pygments_lexer": "ipython3",
-            "version": "3.9.4"
-        },
-        "orig_nbformat": 4,
-        "vscode": {
-            "interpreter": {
-                "hash": "fcca1ce0a591990538c4a1a2cbe16853d718e2332b5914ea18ddb1937a418955"
-            }
-        }
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Instance based attacks\n",
+    "\n",
+    "This notebook demonstrates additional issues that are unique to instance-based models.\n",
+    "\n",
+    "Instance-based models are popular within Machine Learning -- common examples are K-Nearest-Neighbours and the Support Vector Machine. All machine learning models (instance-based or otherwise) require access to data during the training phase. What makes instance-based models distinct is that they also require access to training data to make predictions and therefore need to store some of the training data within the model file.\n",
+    "\n",
+    "As it is this model file that researchers wish to export from the TRE, this constitutes a problem.\n",
+    "\n",
+    "We will illustrate this with an example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:c:\\Users\\simonr04\\git\\GRAIMatter\\data_preprocessing\\data_interface.py:ROOT PROJECT FOLDER = c:\\Users\\simonr04\\git\\GRAIMatter\n"
+     ]
+    }
+   ],
+   "source": [
+    "import logging\n",
+    "import os\n",
+    "\n",
+    "import pylab as plt\n",
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "logging.getLogger(\"matplotlib.font_manager\").disabled = True\n",
+    "\n",
+    "os.chdir(\"c:\\\\Users\\\\simonr04\\\\git\\\\GRAIMatter\")\n",
+    "from data_preprocessing.data_interface import get_data_sklearn\n",
+    "\n",
+    "logging.basicConfig(level=logging.DEBUG)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we are using an open source dataset as we cannot show an example with data from the TRE."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:c:\\Users\\simonr04\\git\\GRAIMatter\\data_preprocessing\\data_interface.py:DATASET FOLDER = c:\\Users\\simonr04\\git\\GRAIMatter\\data\n",
+      "INFO:c:\\Users\\simonr04\\git\\GRAIMatter\\data_preprocessing\\data_interface.py:Loading mimic2-iaccd\n",
+      "INFO:c:\\Users\\simonr04\\git\\GRAIMatter\\data_preprocessing\\data_interface.py:Preprocessing\n"
+     ]
+    }
+   ],
+   "source": [
+    "DATASET_NAME = \"mimic2-iaccd\"\n",
+    "X, y = get_data_sklearn(DATASET_NAME)\n",
+    "# Choose some features (we don't need all of them)\n",
+    "FEATURES = [\"age\", \"gender_num\", \"bmi\", \"day_icu_intime_num\", \"liver_flg\", \"copd_flg\"]\n",
+    "subX = X[FEATURES].copy()\n",
+    "\n",
+    "# Round bmi to an integer\n",
+    "subX[\"bmi\"] = subX[\"bmi\"].astype(int)\n",
+    "subX[\"age\"] = subX[\"age\"].astype(int)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Training the model\n",
+    "\n",
+    "We now train an instance-based model (a Support Verctor Machine; SVM). In this case, we are predicting whether an individual admitted to hospital died or not."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We start by splitting the data into two sets, and then training the model with one of the sets. We show the model performance via a ROC curve. This is just to show that the model is able to do something (lines above the dashed line show performance better than guessing)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2c91c5636d0>]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
     },
-    "nbformat": 4,
-    "nbformat_minor": 2
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "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",
+    "train_X, test_X, train_y, test_y = train_test_split(subX.values, y.values.flatten())\n",
+    "svm = SVC(probability=True, gamma=0.01)\n",
+    "svm.fit(train_X, train_y)\n",
+    "train_probs = svm.predict_proba(train_X)\n",
+    "test_probs = svm.predict_proba(test_X)\n",
+    "plt.figure(figsize=(10, 10))\n",
+    "fpr, tpr, _ = roc_curve(test_y, test_probs[:, 1])\n",
+    "plt.plot(fpr, tpr, label=\"test\")\n",
+    "fpr, tpr, _ = roc_curve(train_y, train_probs[:, 1])\n",
+    "plt.plot(fpr, tpr, label=\"train\")\n",
+    "plt.legend()\n",
+    "plt.xlabel(\"fpr\")\n",
+    "plt.ylabel(\"tpr\")\n",
+    "plt.plot([0, 1], [0, 1], \"k--\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The python object `svm` is the model. This is what a researcher would want to save and export from the TRE.\n",
+    "\n",
+    "Unfortunately, it includes _exact_ copies of some of the data examples. Details in the next cell."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "In this example, the SVM has stored exact copies of 441 of the original data rows (out of 798 total rows)\n"
+     ]
+    }
+   ],
+   "source": [
+    "n_support_vectors = len(svm.support_vectors_)\n",
+    "n_total = len(train_X)\n",
+    "print(\n",
+    "    f\"In this example, the SVM has stored exact copies of {n_support_vectors} of the original data rows (out of {n_total} total rows)\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Without these, the SVM won't work. They are immediately accessible with access to the `svm` object. For example, here are the top 5, and the same rows from the training data for comparison:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "EXAMPLE 0\n",
+      "\t Original:\t [46.  1. 39.  5.  0.  0.]\n",
+      "\t Stored:\t [46.  1. 39.  5.  0.  0.]\n",
+      "EXAMPLE 1\n",
+      "\t Original:\t [36.  1. 26.  5.  0.  0.]\n",
+      "\t Stored:\t [36.  1. 26.  5.  0.  0.]\n",
+      "EXAMPLE 2\n",
+      "\t Original:\t [67.  1. 19.  4.  0.  0.]\n",
+      "\t Stored:\t [67.  1. 19.  4.  0.  0.]\n",
+      "EXAMPLE 3\n",
+      "\t Original:\t [55.  0. 19.  6.  0.  0.]\n",
+      "\t Stored:\t [55.  0. 19.  6.  0.  0.]\n",
+      "EXAMPLE 4\n",
+      "\t Original:\t [87.  0. 24.  7.  0.  0.]\n",
+      "\t Stored:\t [87.  0. 24.  7.  0.  0.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "NTOP = 5\n",
+    "for i in range(NTOP):\n",
+    "    sv_idx = svm.support_[i]\n",
+    "    sv = svm.support_vectors_[i]\n",
+    "    original = train_X[sv_idx, :]\n",
+    "    print(f\"EXAMPLE {i}\")\n",
+    "    print(\"\\t Original:\\t\", original)\n",
+    "    print(\"\\t Stored:\\t\", sv)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is immediately clear that exporting the model would, in effect export exact copies of some individual level data.\n",
+    "\n",
+    "This is an issue with all instance-based models where an attacker has direct access to the contents of the model (or the model file). It is not an issue if the attacker is only able to query the model and not have access to its inner workings."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.4 ('venv': venv)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.4"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "fcca1ce0a591990538c4a1a2cbe16853d718e2332b5914ea18ddb1937a418955"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
 }
diff --git a/examples/risk_examples/python/membership_inference_cancer.ipynb b/examples/risk_examples/python/membership_inference_cancer.ipynb
index fe81ae24..38bb26b6 100644
--- a/examples/risk_examples/python/membership_inference_cancer.ipynb
+++ b/examples/risk_examples/python/membership_inference_cancer.ipynb
@@ -1,692 +1,692 @@
 {
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "b0feddf2-7437-4548-8add-f07822e2792d",
-            "metadata": {
-                "tags": []
-            },
-            "source": [
-                "# Drug response prediction for cancer patients\n",
-                "\n",
-                "Scientists have created a model to predict whether or not cancer patients will respond to a drug.\n",
-                "\n",
-                "The same scientist published the details of their research, how the model was built and a detailed description of the data (e.g., the health conditions investigated), the NHS board where the data was collected. The data was deidentified and was not released as it is confidential patient information, and any leak might break existing legislation.\n",
-                "\n",
-                "The researchers balanced the benefits and potential risks of the model realease, and it was decided that overall, there is a clear benefit for the population for the model to be made public.\n",
-                "\n",
-                "What they didn\u2019t realise, is that the NHS board in question is home to a famous Member of Parliament (MP). This famous MP is a former Prime Minister. There had been some speculation that the MP had cancer, but it is not in the public domain.\n",
-                "\n",
-                "## Membership Inference\n",
-                "\n",
-                "We will use this example to demonstrate a _membership inference_ attack. In such an attack, an attacker has access to information about a particular individual (maybe they are famous), and attempts to find out if their data was used to train the model. In this case, knowing if they were in the training set for the model would be disclosive as it would reveal that they had indeed suffered from cancer (all people in the training set had cancer)"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "33ffc813-c9e5-43c7-8a32-2bb2aed3b5f5",
-            "metadata": {},
-            "source": [
-                "## Let's get hands on with this example.\n",
-                "\n",
-                "The following code imports some standard libraries that we will need."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 1,
-            "id": "63720e84-26f9-4763-9f20-18bad396ba4b",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import random\n",
-                "from itertools import product\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"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "9c7acbc2-1970-4259-90b9-dda459bf85a4",
-            "metadata": {
-                "tags": []
-            },
-            "source": [
-                "## Create the original model\n",
-                "\n",
-                "We are assuming that a model is trained within a TRE on real data. However, we do not have access to real data, so we will randomly generate some realistic looking data.\n",
-                "\n",
-                "In particular, we will generate data for 200 people: all are cancer patients, 100 responded well to the drug, and 100 did not. Our MP will be one of the patients in the good responders set.\n",
-                "\n",
-                "For each patient, we generate six values that in reality would be extracted from their electronic health records:\n",
-                "1. `diabetes` -- whether or not the patient suffers from diabetes (1 = yes, 0 = no)\n",
-                "1. `asthma` -- whether or not the patient suffers from asthma (1 = yes, 0 = no)\n",
-                "1. `bmi_group` -- the BMI group in which the patient falls (1, 2, 3, or 4)\n",
-                "1. `blood_pressure` -- the blood pressure group in which the patient falls (0, 1, 2, 3, 4, or 5)\n",
-                "1. `smoker` -- whether or not the patient is a smoker (1 = yes, 0 = no)\n",
-                "1. `age` -- the patient's age\n",
-                "\n",
-                "Each patient is also associated with a value to indicate whether they responded well to the drug (1) or not (0).\n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 2,
-            "id": "2f94ec62-eefa-4859-acba-6ccb29f4ebb2",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# 1 is cancer, 0 is no cancer, this is our label and what we want to predict.\n",
-                "response = [1] * 99 + [0] * 100\n",
-                "\n",
-                "df = pd.DataFrame()\n",
-                "\n",
-                "# diabetes 0 no, 1 yes\n",
-                "df[\"diabetes\"] = [[1, 0][random.random() > 0.7] for n in range(99)] + [\n",
-                "    [1, 0][random.random() > 0.2] for n in range(100)\n",
-                "]\n",
-                "\n",
-                "# asthma 0 no, 1 yes\n",
-                "df[\"asthma\"] = [[1, 0][random.random() > 0.7] for n in range(99)] + [\n",
-                "    [1, 0][random.random() > 0.5] for n in range(100)\n",
-                "]\n",
-                "\n",
-                "# bmi group 1 under, 2 normal, 3 overweight, 4 obese\n",
-                "df[\"bmi_group\"] = [\n",
-                "    random.choices([1, 2, 3, 4], weights=[0.5, 5, 7, 5], k=1)[0] for n in range(99)\n",
-                "] + [random.choices([1, 2, 3, 4], weights=[1, 7, 4, 1], k=1)[0] for n in range(100)]\n",
-                "\n",
-                "# blood pressure 0 is low, 1 is normal, 5 is extremly high\n",
-                "df[\"blood_pressure\"] = [\n",
-                "    random.choices([0, 1, 2, 3, 4, 5], weights=[0.5, 1, 5, 6, 1, 0.5], k=1)[0]\n",
-                "    for n in range(99)\n",
-                "] + [\n",
-                "    random.choices([0, 1, 2, 3, 4, 5], weights=[0.5, 5, 5, 1, 1, 0.5], k=1)[0]\n",
-                "    for n in range(100)\n",
-                "]\n",
-                "\n",
-                "# smoker 0 is non smoker, 1 is smoker\n",
-                "df[\"smoker\"] = [[1, 0][random.random() > 0.8] for n in range(99)] + [\n",
-                "    [1, 0][random.random() > 0.2] for n in range(100)\n",
-                "]\n",
-                "\n",
-                "# age\n",
-                "x = np.arange(20, 90)\n",
-                "pmf = poisson.pmf(x, 72)\n",
-                "age = [random.choices(x, weights=pmf, k=1)[0] for n in range(99)]\n",
-                "x = np.arange(20, 90)\n",
-                "pmf = poisson.pmf(x, 55)\n",
-                "age2 = [random.choices(x, weights=pmf, k=1)[0] for n in range(100)]\n",
-                "df[\"age\"] = age + age2\n",
-                "\n",
-                "# Add the data of your MP\n",
-                "response = response + [1]\n",
-                "\n",
-                "# add new row to end of DataFrame\n",
-                "# the order of the list indicates in order diabetes, asthma, bmi_group, blood_pressure, smoker, age\n",
-                "df.loc[len(df.index)] = [1, 1, 3, 2, 1, 62]"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "011f7237",
-            "metadata": {},
-            "source": [
-                "This looks like the kind of data that might exist within a TRE. Here's the first few rows:"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 3,
-            "id": "978b6754",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "   diabetes  asthma  bmi_group  blood_pressure  smoker  age\n",
-                        "0         1       0          4               3       1   72\n",
-                        "1         1       1          2               3       0   83\n",
-                        "2         0       0          4               3       1   63\n",
-                        "3         1       1          4               3       0   77\n",
-                        "4         1       1          4               2       1   87\n"
-                    ]
-                }
-            ],
-            "source": [
-                "print(df.head())"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "65a2663c",
-            "metadata": {},
-            "source": [
-                "Our MP is the final row of the data, here are their values:"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 4,
-            "id": "34992ca0",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "diabetes           1\n",
-                        "asthma             1\n",
-                        "bmi_group          3\n",
-                        "blood_pressure     2\n",
-                        "smoker             1\n",
-                        "age               62\n",
-                        "Name: 199, dtype: int64\n"
-                    ]
-                }
-            ],
-            "source": [
-                "print(df.iloc[199, :])"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "dace3d79-78bc-4fb8-a1ae-2d8587c663b2",
-            "metadata": {},
-            "source": [
-                "## Model training\n",
-                "\n",
-                "The researcher trained a particular machine learning model called a Support Vector Machine (SVM). This is a very popular model for tasks in which we want to assign things (in this case patients) to groups (in this case cancer v non-cancer). The attribute inference attack we will perform is not unique to SVMs, we just use them as a popular example.\n",
-                "\n",
-                "Training the model is very straightforward -- just a couple of lines of code (the details are not important)."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 5,
-            "id": "d5500d7b",
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "SVC(C=1, gamma=3, probability=True,\n",
-                            "    random_state=RandomState(MT19937) at 0x159E5341140)"
-                        ]
-                    },
-                    "execution_count": 5,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "# Train a model\n",
-                "prng = np.random.RandomState(12)\n",
-                "svc = SVC(C=1, gamma=3, probability=True, random_state=prng)\n",
-                "svc.fit(df, response)"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "418f7fa2",
-            "metadata": {},
-            "source": [
-                "The trained model can be used to make predictions about new individuals. Given data for an individual, it will produce two scores (probabilities). The first is how likely they are to belong to the non-responders group (higher = more likely) and the second how likely they are to belong to the responders group. The scores are always positive, and sum to 1.\n",
-                "\n",
-                "For example, if we have an individual who has diabetes, has asthma, has a bmi group of 1, blood pressure of 5. is a non-smoker and is 42 years old, we can use the model to predict whether or not they should belong in the cancer or non-cancer groups:"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 6,
-            "id": "e21f2890",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "non-responders score = 0.49\n",
-                        "responders score = 0.51\n"
-                    ]
-                }
-            ],
-            "source": [
-                "test_example = pd.DataFrame(\n",
-                "    {\n",
-                "        \"diabetes\": 1,\n",
-                "        \"asthma\": 1,\n",
-                "        \"bmi_group\": 1,\n",
-                "        \"blood_pressure\": 5,\n",
-                "        \"smoker\": 1,\n",
-                "        \"age\": 72,\n",
-                "    },\n",
-                "    index=[1],\n",
-                ")\n",
-                "predictions = svc.predict_proba(test_example)\n",
-                "print(f\"non-responders score = {predictions[0][0]:.2f}\")\n",
-                "print(f\"responders score = {predictions[0][1]:.2f}\")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "01608373",
-            "metadata": {},
-            "source": [
-                "## The attack\n",
-                "\n",
-                "We now assume the role of the attacker. The attacker knows some general properties about the data -- for example, they know the range of values each variable can take. They also know the configuration of the classifier that was trained (that it was an SVM and any parameters that were used to define it (more on this later)). Finally, they know (or can make a good guess at) the input data for the MP (they are famous and this information is perhaps in the public domain). The attacker is going to try and determine, from this information, and with access to the trained model, whether or not the MP was in the dataset and hence determine if they had cancer or not.\n",
-                "\n",
-                "## How does the attack work?\n",
-                "\n",
-                "Recall that when we used the model to make predictions, the model provided two scores -- the cancer and non-cancer scores. The more extreme these scores become (e.g one is close to 1 and the other to 0 (recall that they have to add up to 1)), the more _confident_ the model is in assigning that example. It is not uncommon for models to have higher confidence for examples that they were trained on than examples that they haven't seen before. It is this property that the attacker will make use of.\n",
-                "\n",
-                "In particular, the attacker will generate their own dataset (known as _shadow_ data) that has similar properties to the original. They can do this randomly -- it doesn't matter that it won't be quite right -- all they need to know is the rough ranges of the variables. They will then use some of this data to train their own model (a _shadow_ model). This allows them to see roughly what kind of confidence their model gives to examples it was trained on, and examples it wasn't trained on. This gives them an idea about how confident the original model is likely to be on data it was trained on, and data it wasn't trained on. Comparing this to the actual confidence obtained when the MPs data is given to the original model will allow them to infer if the MP was in the training data or not.\n",
-                "\n",
-                "Let's look at that step-by-step...\n",
-                "\n",
-                "Firstly, the attacker presents the MPs data to the original model to see what the model's predictions are...\n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 7,
-            "id": "ddbad0bf",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "[[0.06161495 0.93838505]]\n"
-                    ]
-                }
-            ],
-            "source": [
-                "mp_data = pd.DataFrame(\n",
-                "    {\n",
-                "        \"diabetes\": 1,\n",
-                "        \"asthma\": 1,\n",
-                "        \"bmi_group\": 3,\n",
-                "        \"blood_pressure\": 2,\n",
-                "        \"smoker\": 1,\n",
-                "        \"age\": 62,\n",
-                "    },\n",
-                "    index=[1],\n",
-                ")\n",
-                "mp_preds = svc.predict_proba(mp_data)\n",
-                "print(mp_preds)"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "710bbec4",
-            "metadata": {},
-            "source": [
-                "The model stronly predicts that the MP is in the reponder class. This in itself doesn't tell the attacker that the model was in the training set. What the attacker needs is to estimate how confident the model is when presented with examples from the training set, and when not. This is where the _shadow_ model comes in -- they hope that their shadow model is similar enough to the original that the confidences it gives can be used as a proxy against which to compare these values for the MP."
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "3879fa3a",
-            "metadata": {},
-            "source": [
-                "The attacker generates their _shadow_ data. There are lots of ways they could do this, in this case they use the same process we used above."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 8,
-            "id": "71f854d0",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# 1 is cancer, 0 is no response, this is our label and what we want to predict.\n",
-                "shadow_response = [1] * 100 + [0] * 100\n",
-                "\n",
-                "shadow_df = pd.DataFrame()\n",
-                "\n",
-                "# diabetes 0 no, 1 yes\n",
-                "shadow_df[\"diabetes\"] = [[1, 0][random.random() > 0.7] for n in range(100)] + [\n",
-                "    [1, 0][random.random() > 0.2] for n in range(100)\n",
-                "]\n",
-                "\n",
-                "# asthma 0 no, 1 yes\n",
-                "shadow_df[\"asthma\"] = [[1, 0][random.random() > 0.7] for n in range(100)] + [\n",
-                "    [1, 0][random.random() > 0.5] for n in range(100)\n",
-                "]\n",
-                "\n",
-                "# bmi group 1 under, 2 normal, 3 overweight, 4 obese\n",
-                "shadow_df[\"bmi_group\"] = [\n",
-                "    random.choices([1, 2, 3, 4], weights=[0.5, 5, 7, 5], k=1)[0] for n in range(100)\n",
-                "] + [random.choices([1, 2, 3, 4], weights=[1, 7, 4, 1], k=1)[0] for n in range(100)]\n",
-                "\n",
-                "# blood pressure 0 is low, 1 is normal, 5 is extremly high\n",
-                "shadow_df[\"blood_pressure\"] = [\n",
-                "    random.choices([0, 1, 2, 3, 4, 5], weights=[0.5, 1, 5, 6, 1, 0.5], k=1)[0]\n",
-                "    for n in range(100)\n",
-                "] + [\n",
-                "    random.choices([0, 1, 2, 3, 4, 5], weights=[0.5, 5, 5, 1, 1, 0.5], k=1)[0]\n",
-                "    for n in range(100)\n",
-                "]\n",
-                "\n",
-                "# smoker 0 is non smoker, 1 is smoker\n",
-                "shadow_df[\"smoker\"] = [[1, 0][random.random() > 0.8] for n in range(100)] + [\n",
-                "    [1, 0][random.random() > 0.2] for n in range(100)\n",
-                "]\n",
-                "\n",
-                "# age\n",
-                "x = np.arange(20, 90)\n",
-                "pmf = poisson.pmf(x, 72)\n",
-                "age = [random.choices(x, weights=pmf, k=1)[0] for n in range(100)]\n",
-                "x = np.arange(20, 90)\n",
-                "pmf = poisson.pmf(x, 55)\n",
-                "age2 = [random.choices(x, weights=pmf, k=1)[0] for n in range(100)]\n",
-                "shadow_df[\"age\"] = age + age2"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "1d376b8d",
-            "metadata": {},
-            "source": [
-                "Now, we split the shadow data into two. We will use one set to train the shadow model"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 9,
-            "id": "38d7126d",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "shadow_train_x, shadow_test_x, shadow_train_y, shadow_test_y = train_test_split(\n",
-                "    shadow_df, shadow_response, test_size=0.5\n",
-                ")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "e770da87",
-            "metadata": {},
-            "source": [
-                "And train the shadow model..."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 10,
-            "id": "22a4e547",
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "SVC(C=1, gamma=3, probability=True,\n",
-                            "    random_state=RandomState(MT19937) at 0x159E5341840)"
-                        ]
-                    },
-                    "execution_count": 10,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "# Train a model\n",
-                "prng = np.random.RandomState(12)\n",
-                "shadow_svc = SVC(C=1, gamma=3, probability=True, random_state=prng)\n",
-                "shadow_svc.fit(shadow_train_x, shadow_train_y)"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "afca4893",
-            "metadata": {},
-            "source": [
-                "The attacker now passes the portion of shadow data used for training, and the portion used for testing through the trained shadow model to extract the model's confidence. For each example, the attacker just needs the highest of the two values (reponse confidence or non-response confidence, whichever is larger). A quick look at the average of these values for the two sets tells us that the shadow model assigns much higher confidence to training examples than non-training examples"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 11,
-            "id": "5c4e82be",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Mean of confidence for training examples = 0.92\n",
-                        "Mean of confidence for non-training examples = 0.54\n"
-                    ]
-                }
-            ],
-            "source": [
-                "train_preds = shadow_svc.predict_proba(shadow_train_x).max(axis=1)\n",
-                "test_preds = shadow_svc.predict_proba(shadow_test_x).max(axis=1)\n",
-                "print(f\"Mean of confidence for training examples = {train_preds.mean():.2f}\")\n",
-                "print(f\"Mean of confidence for non-training examples = {test_preds.mean():.2f}\")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "c79b9928",
-            "metadata": {},
-            "source": [
-                "The attacker now knows the kind of confidence values that their shadow model gives to training and non-training examples. They're confident that the data they generated is similar enough to the original data, and that their model is confifgured similarly to the original model (remember that the researcher released this information) to assume that these confidence values are similar to those that the original model would give on training and non-training data. They can therefore use them as a baseline against which to compare the the value they got when they presented the MP's data to the original model."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 16,
-            "id": "45dfa8e0",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Maximum confidence for MP in original model: 0.84\n"
-                    ]
-                }
-            ],
-            "source": [
-                "print(f\"Maximum confidence for MP in original model: {mp_preds.max():.2f}\")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "a34463fb",
-            "metadata": {},
-            "source": [
-                "The attacker can do this comparison in a number of ways. Here we will assume that the attacker trains another ML model (the _attack_ model) to distinguish between these two sets of confidences. We use a LogisticRegression model (a very simple classifier), but the attacker could use anything."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 13,
-            "id": "7b45f764",
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "LogisticRegression()"
-                        ]
-                    },
-                    "execution_count": 13,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "from sklearn.linear_model import LogisticRegression\n",
-                "\n",
-                "lr = LogisticRegression()\n",
-                "train_x = np.vstack((train_preds[:, None], test_preds[:, None]))\n",
-                "train_y = np.hstack((np.ones(len(train_preds)), np.zeros(len(test_preds))))\n",
-                "lr.fit(train_x, train_y)"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "d87b27c9",
-            "metadata": {},
-            "source": [
-                "The attacker now passes the maximum confidence value they got from the original model with the MPs data to this new model to obtain a prediction as to whether or not it was in the training data. Note that the prediction takes the same form as previous predictions: two confidence values. One the confidence of it not having been in the training set, another the confidence that it was:"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 14,
-            "id": "309dd568",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Confidence of non-membership = 0.20\n",
-                        "Confidence of membership = 0.80\n"
-                    ]
-                }
-            ],
-            "source": [
-                "input_array = np.array([[mp_preds.max()]])\n",
-                "prob_membership = lr.predict_proba(input_array)\n",
-                "print(f\"Confidence of non-membership = {prob_membership[0][0]:.2f}\")\n",
-                "print(f\"Confidence of membership = {prob_membership[0][1]:.2f}\")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "1d98a425",
-            "metadata": {},
-            "source": [
-                "The model is making a strong prediction that the MP _was_ in the training data, and therefore the attacker concludes that they _did_ have Cancer. This prediction is correct."
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "f6d2860b",
-            "metadata": {},
-            "source": [
-                "## Mitigation\n",
-                "\n",
-                "Mitigating this kind of attack involves configuring the classifier to not give different confidences to examples that it has been trained upon. In this case, decreasing the SVMs `gamma` parameter will have a strong effect. For example, here is what happens in the attack if the original model's `gamma` is reduced from 3 to 0.1"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 17,
-            "id": "921d437f",
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "[[0.15803859 0.84196141]]\n",
-                        "Mean of confidence for training examples = 0.93\n",
-                        "Mean of confidence for non-training examples = 0.90\n",
-                        "Confidence of non-membership = 0.52\n",
-                        "Confidence of membership = 0.48\n"
-                    ]
-                }
-            ],
-            "source": [
-                "prng = np.random.RandomState(12)\n",
-                "svc = SVC(C=1, gamma=0.1, probability=True, random_state=prng)\n",
-                "svc.fit(df, response)\n",
-                "\n",
-                "mp_preds = svc.predict_proba(mp_data)\n",
-                "print(mp_preds)\n",
-                "\n",
-                "shadow_svc = SVC(C=1, gamma=0.1, probability=True, random_state=prng)\n",
-                "shadow_svc.fit(shadow_train_x, shadow_train_y)\n",
-                "\n",
-                "train_preds = shadow_svc.predict_proba(shadow_train_x).max(axis=1)\n",
-                "test_preds = shadow_svc.predict_proba(shadow_test_x).max(axis=1)\n",
-                "print(f\"Mean of confidence for training examples = {train_preds.mean():.2f}\")\n",
-                "print(f\"Mean of confidence for non-training examples = {test_preds.mean():.2f}\")\n",
-                "\n",
-                "lr = LogisticRegression()\n",
-                "train_x = np.vstack((train_preds[:, None], test_preds[:, None]))\n",
-                "train_y = np.hstack((np.ones(len(train_preds)), np.zeros(len(test_preds))))\n",
-                "lr.fit(train_x, train_y)\n",
-                "\n",
-                "\n",
-                "input_array = np.array([[mp_preds.max()]])\n",
-                "prob_membership = lr.predict_proba(input_array)\n",
-                "print(f\"Confidence of non-membership = {prob_membership[0][0]:.2f}\")\n",
-                "print(f\"Confidence of membership = {prob_membership[0][1]:.2f}\")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "94d3f196",
-            "metadata": {},
-            "source": [
-                "The attack is now almost completely ambiguous, providing the attacker with no information as to whether or not the MP was in the training set.\n",
-                "\n",
-                "The technical effect `gamma` has on the SVM is unimportant -- the important point is that changes to the model's configuration can play a significant role in its vulnerability."
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "bf4518cd",
-            "metadata": {},
-            "source": [
-                "## Conclusions\n",
-                "\n",
-                "This example has shown how an attacker can perform a membership inference attack to determine that a well-known individual was in a model's training data.\n",
-                "\n",
-                "It is hopefully clear that this is non-trivial -- the attacker has to put in quite a lot of effort. Their success is also contingent on them knowing certain things about the problem. In particular:\n",
-                "1. Enough information about the original data that they can generate a shadow dataset. This will be things like: types of variables, ranges of variables, distributions of variables. Such information is often available at population levels (e.g. average age, proportion of population with diabetes etc).\n",
-                "1. Configuration information about the original model. In this case, it was the parameters that define the model and, in particular a parameter called `gamma` that is used in the SVM. It is quite common for researchers to publish this information.\n",
-                "1. The input values for the individual in question. This is harder to come by, but for famous individuals, it's conceivable that a lot of this information might be in the public domain.\n",
-                "\n"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "f8b9da84",
-            "metadata": {},
-            "source": []
-        }
-    ],
-    "metadata": {
-        "kernelspec": {
-            "display_name": "Python 3 (ipykernel)",
-            "language": "python",
-            "name": "python3"
-        },
-        "language_info": {
-            "codemirror_mode": {
-                "name": "ipython",
-                "version": 3
-            },
-            "file_extension": ".py",
-            "mimetype": "text/x-python",
-            "name": "python",
-            "nbconvert_exporter": "python",
-            "pygments_lexer": "ipython3",
-            "version": "3.9.13"
-        },
-        "vscode": {
-            "interpreter": {
-                "hash": "fcca1ce0a591990538c4a1a2cbe16853d718e2332b5914ea18ddb1937a418955"
-            }
-        }
-    },
-    "nbformat": 4,
-    "nbformat_minor": 5
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b0feddf2-7437-4548-8add-f07822e2792d",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# Drug response prediction for cancer patients\n",
+    "\n",
+    "Scientists have created a model to predict whether or not cancer patients will respond to a drug.\n",
+    "\n",
+    "The same scientist published the details of their research, how the model was built and a detailed description of the data (e.g., the health conditions investigated), the NHS board where the data was collected. The data was deidentified and was not released as it is confidential patient information, and any leak might break existing legislation.\n",
+    "\n",
+    "The researchers balanced the benefits and potential risks of the model realease, and it was decided that overall, there is a clear benefit for the population for the model to be made public.\n",
+    "\n",
+    "What they didn’t realise, is that the NHS board in question is home to a famous Member of Parliament (MP). This famous MP is a former Prime Minister. There had been some speculation that the MP had cancer, but it is not in the public domain.\n",
+    "\n",
+    "## Membership Inference\n",
+    "\n",
+    "We will use this example to demonstrate a _membership inference_ attack. In such an attack, an attacker has access to information about a particular individual (maybe they are famous), and attempts to find out if their data was used to train the model. In this case, knowing if they were in the training set for the model would be disclosive as it would reveal that they had indeed suffered from cancer (all people in the training set had cancer)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "33ffc813-c9e5-43c7-8a32-2bb2aed3b5f5",
+   "metadata": {},
+   "source": [
+    "## Let's get hands on with this example.\n",
+    "\n",
+    "The following code imports some standard libraries that we will need."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "63720e84-26f9-4763-9f20-18bad396ba4b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import random\n",
+    "\n",
+    "import numpy as np\n",
+    "\n",
+    "np.random.seed(1234)\n",
+    "random.seed(12345)\n",
+    "\n",
+    "import pandas as pd\n",
+    "from scipy.stats import poisson\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.svm import SVC"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9c7acbc2-1970-4259-90b9-dda459bf85a4",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Create the original model\n",
+    "\n",
+    "We are assuming that a model is trained within a TRE on real data. However, we do not have access to real data, so we will randomly generate some realistic looking data.\n",
+    "\n",
+    "In particular, we will generate data for 200 people: all are cancer patients, 100 responded well to the drug, and 100 did not. Our MP will be one of the patients in the good responders set.\n",
+    "\n",
+    "For each patient, we generate six values that in reality would be extracted from their electronic health records:\n",
+    "1. `diabetes` -- whether or not the patient suffers from diabetes (1 = yes, 0 = no)\n",
+    "1. `asthma` -- whether or not the patient suffers from asthma (1 = yes, 0 = no)\n",
+    "1. `bmi_group` -- the BMI group in which the patient falls (1, 2, 3, or 4)\n",
+    "1. `blood_pressure` -- the blood pressure group in which the patient falls (0, 1, 2, 3, 4, or 5)\n",
+    "1. `smoker` -- whether or not the patient is a smoker (1 = yes, 0 = no)\n",
+    "1. `age` -- the patient's age\n",
+    "\n",
+    "Each patient is also associated with a value to indicate whether they responded well to the drug (1) or not (0).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2f94ec62-eefa-4859-acba-6ccb29f4ebb2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 1 is cancer, 0 is no cancer, this is our label and what we want to predict.\n",
+    "response = [1] * 99 + [0] * 100\n",
+    "\n",
+    "df = pd.DataFrame()\n",
+    "\n",
+    "# diabetes 0 no, 1 yes\n",
+    "df[\"diabetes\"] = [[1, 0][random.random() > 0.7] for n in range(99)] + [\n",
+    "    [1, 0][random.random() > 0.2] for n in range(100)\n",
+    "]\n",
+    "\n",
+    "# asthma 0 no, 1 yes\n",
+    "df[\"asthma\"] = [[1, 0][random.random() > 0.7] for n in range(99)] + [\n",
+    "    [1, 0][random.random() > 0.5] for n in range(100)\n",
+    "]\n",
+    "\n",
+    "# bmi group 1 under, 2 normal, 3 overweight, 4 obese\n",
+    "df[\"bmi_group\"] = [\n",
+    "    random.choices([1, 2, 3, 4], weights=[0.5, 5, 7, 5], k=1)[0] for n in range(99)\n",
+    "] + [random.choices([1, 2, 3, 4], weights=[1, 7, 4, 1], k=1)[0] for n in range(100)]\n",
+    "\n",
+    "# blood pressure 0 is low, 1 is normal, 5 is extremly high\n",
+    "df[\"blood_pressure\"] = [\n",
+    "    random.choices([0, 1, 2, 3, 4, 5], weights=[0.5, 1, 5, 6, 1, 0.5], k=1)[0]\n",
+    "    for n in range(99)\n",
+    "] + [\n",
+    "    random.choices([0, 1, 2, 3, 4, 5], weights=[0.5, 5, 5, 1, 1, 0.5], k=1)[0]\n",
+    "    for n in range(100)\n",
+    "]\n",
+    "\n",
+    "# smoker 0 is non smoker, 1 is smoker\n",
+    "df[\"smoker\"] = [[1, 0][random.random() > 0.8] for n in range(99)] + [\n",
+    "    [1, 0][random.random() > 0.2] for n in range(100)\n",
+    "]\n",
+    "\n",
+    "# age\n",
+    "x = np.arange(20, 90)\n",
+    "pmf = poisson.pmf(x, 72)\n",
+    "age = [random.choices(x, weights=pmf, k=1)[0] for n in range(99)]\n",
+    "x = np.arange(20, 90)\n",
+    "pmf = poisson.pmf(x, 55)\n",
+    "age2 = [random.choices(x, weights=pmf, k=1)[0] for n in range(100)]\n",
+    "df[\"age\"] = age + age2\n",
+    "\n",
+    "# Add the data of your MP\n",
+    "response = response + [1]\n",
+    "\n",
+    "# add new row to end of DataFrame\n",
+    "# the order of the list indicates in order diabetes, asthma, bmi_group, blood_pressure, smoker, age\n",
+    "df.loc[len(df.index)] = [1, 1, 3, 2, 1, 62]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "011f7237",
+   "metadata": {},
+   "source": [
+    "This looks like the kind of data that might exist within a TRE. Here's the first few rows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "978b6754",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   diabetes  asthma  bmi_group  blood_pressure  smoker  age\n",
+      "0         1       0          4               3       1   72\n",
+      "1         1       1          2               3       0   83\n",
+      "2         0       0          4               3       1   63\n",
+      "3         1       1          4               3       0   77\n",
+      "4         1       1          4               2       1   87\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "65a2663c",
+   "metadata": {},
+   "source": [
+    "Our MP is the final row of the data, here are their values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "34992ca0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "diabetes           1\n",
+      "asthma             1\n",
+      "bmi_group          3\n",
+      "blood_pressure     2\n",
+      "smoker             1\n",
+      "age               62\n",
+      "Name: 199, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.iloc[199, :])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dace3d79-78bc-4fb8-a1ae-2d8587c663b2",
+   "metadata": {},
+   "source": [
+    "## Model training\n",
+    "\n",
+    "The researcher trained a particular machine learning model called a Support Vector Machine (SVM). This is a very popular model for tasks in which we want to assign things (in this case patients) to groups (in this case cancer v non-cancer). The attribute inference attack we will perform is not unique to SVMs, we just use them as a popular example.\n",
+    "\n",
+    "Training the model is very straightforward -- just a couple of lines of code (the details are not important)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "d5500d7b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SVC(C=1, gamma=3, probability=True,\n",
+       "    random_state=RandomState(MT19937) at 0x159E5341140)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Train a model\n",
+    "prng = np.random.RandomState(12)\n",
+    "svc = SVC(C=1, gamma=3, probability=True, random_state=prng)\n",
+    "svc.fit(df, response)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "418f7fa2",
+   "metadata": {},
+   "source": [
+    "The trained model can be used to make predictions about new individuals. Given data for an individual, it will produce two scores (probabilities). The first is how likely they are to belong to the non-responders group (higher = more likely) and the second how likely they are to belong to the responders group. The scores are always positive, and sum to 1.\n",
+    "\n",
+    "For example, if we have an individual who has diabetes, has asthma, has a bmi group of 1, blood pressure of 5. is a non-smoker and is 42 years old, we can use the model to predict whether or not they should belong in the cancer or non-cancer groups:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e21f2890",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "non-responders score = 0.49\n",
+      "responders score = 0.51\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_example = pd.DataFrame(\n",
+    "    {\n",
+    "        \"diabetes\": 1,\n",
+    "        \"asthma\": 1,\n",
+    "        \"bmi_group\": 1,\n",
+    "        \"blood_pressure\": 5,\n",
+    "        \"smoker\": 1,\n",
+    "        \"age\": 72,\n",
+    "    },\n",
+    "    index=[1],\n",
+    ")\n",
+    "predictions = svc.predict_proba(test_example)\n",
+    "print(f\"non-responders score = {predictions[0][0]:.2f}\")\n",
+    "print(f\"responders score = {predictions[0][1]:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "01608373",
+   "metadata": {},
+   "source": [
+    "## The attack\n",
+    "\n",
+    "We now assume the role of the attacker. The attacker knows some general properties about the data -- for example, they know the range of values each variable can take. They also know the configuration of the classifier that was trained (that it was an SVM and any parameters that were used to define it (more on this later)). Finally, they know (or can make a good guess at) the input data for the MP (they are famous and this information is perhaps in the public domain). The attacker is going to try and determine, from this information, and with access to the trained model, whether or not the MP was in the dataset and hence determine if they had cancer or not.\n",
+    "\n",
+    "## How does the attack work?\n",
+    "\n",
+    "Recall that when we used the model to make predictions, the model provided two scores -- the cancer and non-cancer scores. The more extreme these scores become (e.g one is close to 1 and the other to 0 (recall that they have to add up to 1)), the more _confident_ the model is in assigning that example. It is not uncommon for models to have higher confidence for examples that they were trained on than examples that they haven't seen before. It is this property that the attacker will make use of.\n",
+    "\n",
+    "In particular, the attacker will generate their own dataset (known as _shadow_ data) that has similar properties to the original. They can do this randomly -- it doesn't matter that it won't be quite right -- all they need to know is the rough ranges of the variables. They will then use some of this data to train their own model (a _shadow_ model). This allows them to see roughly what kind of confidence their model gives to examples it was trained on, and examples it wasn't trained on. This gives them an idea about how confident the original model is likely to be on data it was trained on, and data it wasn't trained on. Comparing this to the actual confidence obtained when the MPs data is given to the original model will allow them to infer if the MP was in the training data or not.\n",
+    "\n",
+    "Let's look at that step-by-step...\n",
+    "\n",
+    "Firstly, the attacker presents the MPs data to the original model to see what the model's predictions are...\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ddbad0bf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0.06161495 0.93838505]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "mp_data = pd.DataFrame(\n",
+    "    {\n",
+    "        \"diabetes\": 1,\n",
+    "        \"asthma\": 1,\n",
+    "        \"bmi_group\": 3,\n",
+    "        \"blood_pressure\": 2,\n",
+    "        \"smoker\": 1,\n",
+    "        \"age\": 62,\n",
+    "    },\n",
+    "    index=[1],\n",
+    ")\n",
+    "mp_preds = svc.predict_proba(mp_data)\n",
+    "print(mp_preds)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "710bbec4",
+   "metadata": {},
+   "source": [
+    "The model stronly predicts that the MP is in the reponder class. This in itself doesn't tell the attacker that the model was in the training set. What the attacker needs is to estimate how confident the model is when presented with examples from the training set, and when not. This is where the _shadow_ model comes in -- they hope that their shadow model is similar enough to the original that the confidences it gives can be used as a proxy against which to compare these values for the MP."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3879fa3a",
+   "metadata": {},
+   "source": [
+    "The attacker generates their _shadow_ data. There are lots of ways they could do this, in this case they use the same process we used above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "71f854d0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 1 is cancer, 0 is no response, this is our label and what we want to predict.\n",
+    "shadow_response = [1] * 100 + [0] * 100\n",
+    "\n",
+    "shadow_df = pd.DataFrame()\n",
+    "\n",
+    "# diabetes 0 no, 1 yes\n",
+    "shadow_df[\"diabetes\"] = [[1, 0][random.random() > 0.7] for n in range(100)] + [\n",
+    "    [1, 0][random.random() > 0.2] for n in range(100)\n",
+    "]\n",
+    "\n",
+    "# asthma 0 no, 1 yes\n",
+    "shadow_df[\"asthma\"] = [[1, 0][random.random() > 0.7] for n in range(100)] + [\n",
+    "    [1, 0][random.random() > 0.5] for n in range(100)\n",
+    "]\n",
+    "\n",
+    "# bmi group 1 under, 2 normal, 3 overweight, 4 obese\n",
+    "shadow_df[\"bmi_group\"] = [\n",
+    "    random.choices([1, 2, 3, 4], weights=[0.5, 5, 7, 5], k=1)[0] for n in range(100)\n",
+    "] + [random.choices([1, 2, 3, 4], weights=[1, 7, 4, 1], k=1)[0] for n in range(100)]\n",
+    "\n",
+    "# blood pressure 0 is low, 1 is normal, 5 is extremly high\n",
+    "shadow_df[\"blood_pressure\"] = [\n",
+    "    random.choices([0, 1, 2, 3, 4, 5], weights=[0.5, 1, 5, 6, 1, 0.5], k=1)[0]\n",
+    "    for n in range(100)\n",
+    "] + [\n",
+    "    random.choices([0, 1, 2, 3, 4, 5], weights=[0.5, 5, 5, 1, 1, 0.5], k=1)[0]\n",
+    "    for n in range(100)\n",
+    "]\n",
+    "\n",
+    "# smoker 0 is non smoker, 1 is smoker\n",
+    "shadow_df[\"smoker\"] = [[1, 0][random.random() > 0.8] for n in range(100)] + [\n",
+    "    [1, 0][random.random() > 0.2] for n in range(100)\n",
+    "]\n",
+    "\n",
+    "# age\n",
+    "x = np.arange(20, 90)\n",
+    "pmf = poisson.pmf(x, 72)\n",
+    "age = [random.choices(x, weights=pmf, k=1)[0] for n in range(100)]\n",
+    "x = np.arange(20, 90)\n",
+    "pmf = poisson.pmf(x, 55)\n",
+    "age2 = [random.choices(x, weights=pmf, k=1)[0] for n in range(100)]\n",
+    "shadow_df[\"age\"] = age + age2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1d376b8d",
+   "metadata": {},
+   "source": [
+    "Now, we split the shadow data into two. We will use one set to train the shadow model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "38d7126d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "shadow_train_x, shadow_test_x, shadow_train_y, shadow_test_y = train_test_split(\n",
+    "    shadow_df, shadow_response, test_size=0.5\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e770da87",
+   "metadata": {},
+   "source": [
+    "And train the shadow model..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "22a4e547",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SVC(C=1, gamma=3, probability=True,\n",
+       "    random_state=RandomState(MT19937) at 0x159E5341840)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Train a model\n",
+    "prng = np.random.RandomState(12)\n",
+    "shadow_svc = SVC(C=1, gamma=3, probability=True, random_state=prng)\n",
+    "shadow_svc.fit(shadow_train_x, shadow_train_y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "afca4893",
+   "metadata": {},
+   "source": [
+    "The attacker now passes the portion of shadow data used for training, and the portion used for testing through the trained shadow model to extract the model's confidence. For each example, the attacker just needs the highest of the two values (reponse confidence or non-response confidence, whichever is larger). A quick look at the average of these values for the two sets tells us that the shadow model assigns much higher confidence to training examples than non-training examples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "5c4e82be",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean of confidence for training examples = 0.92\n",
+      "Mean of confidence for non-training examples = 0.54\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_preds = shadow_svc.predict_proba(shadow_train_x).max(axis=1)\n",
+    "test_preds = shadow_svc.predict_proba(shadow_test_x).max(axis=1)\n",
+    "print(f\"Mean of confidence for training examples = {train_preds.mean():.2f}\")\n",
+    "print(f\"Mean of confidence for non-training examples = {test_preds.mean():.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c79b9928",
+   "metadata": {},
+   "source": [
+    "The attacker now knows the kind of confidence values that their shadow model gives to training and non-training examples. They're confident that the data they generated is similar enough to the original data, and that their model is confifgured similarly to the original model (remember that the researcher released this information) to assume that these confidence values are similar to those that the original model would give on training and non-training data. They can therefore use them as a baseline against which to compare the the value they got when they presented the MP's data to the original model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "45dfa8e0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Maximum confidence for MP in original model: 0.84\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Maximum confidence for MP in original model: {mp_preds.max():.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a34463fb",
+   "metadata": {},
+   "source": [
+    "The attacker can do this comparison in a number of ways. Here we will assume that the attacker trains another ML model (the _attack_ model) to distinguish between these two sets of confidences. We use a LogisticRegression model (a very simple classifier), but the attacker could use anything."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "7b45f764",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LogisticRegression()"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "\n",
+    "lr = LogisticRegression()\n",
+    "train_x = np.vstack((train_preds[:, None], test_preds[:, None]))\n",
+    "train_y = np.hstack((np.ones(len(train_preds)), np.zeros(len(test_preds))))\n",
+    "lr.fit(train_x, train_y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d87b27c9",
+   "metadata": {},
+   "source": [
+    "The attacker now passes the maximum confidence value they got from the original model with the MPs data to this new model to obtain a prediction as to whether or not it was in the training data. Note that the prediction takes the same form as previous predictions: two confidence values. One the confidence of it not having been in the training set, another the confidence that it was:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "309dd568",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Confidence of non-membership = 0.20\n",
+      "Confidence of membership = 0.80\n"
+     ]
+    }
+   ],
+   "source": [
+    "input_array = np.array([[mp_preds.max()]])\n",
+    "prob_membership = lr.predict_proba(input_array)\n",
+    "print(f\"Confidence of non-membership = {prob_membership[0][0]:.2f}\")\n",
+    "print(f\"Confidence of membership = {prob_membership[0][1]:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1d98a425",
+   "metadata": {},
+   "source": [
+    "The model is making a strong prediction that the MP _was_ in the training data, and therefore the attacker concludes that they _did_ have Cancer. This prediction is correct."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f6d2860b",
+   "metadata": {},
+   "source": [
+    "## Mitigation\n",
+    "\n",
+    "Mitigating this kind of attack involves configuring the classifier to not give different confidences to examples that it has been trained upon. In this case, decreasing the SVMs `gamma` parameter will have a strong effect. For example, here is what happens in the attack if the original model's `gamma` is reduced from 3 to 0.1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "921d437f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0.15803859 0.84196141]]\n",
+      "Mean of confidence for training examples = 0.93\n",
+      "Mean of confidence for non-training examples = 0.90\n",
+      "Confidence of non-membership = 0.52\n",
+      "Confidence of membership = 0.48\n"
+     ]
+    }
+   ],
+   "source": [
+    "prng = np.random.RandomState(12)\n",
+    "svc = SVC(C=1, gamma=0.1, probability=True, random_state=prng)\n",
+    "svc.fit(df, response)\n",
+    "\n",
+    "mp_preds = svc.predict_proba(mp_data)\n",
+    "print(mp_preds)\n",
+    "\n",
+    "shadow_svc = SVC(C=1, gamma=0.1, probability=True, random_state=prng)\n",
+    "shadow_svc.fit(shadow_train_x, shadow_train_y)\n",
+    "\n",
+    "train_preds = shadow_svc.predict_proba(shadow_train_x).max(axis=1)\n",
+    "test_preds = shadow_svc.predict_proba(shadow_test_x).max(axis=1)\n",
+    "print(f\"Mean of confidence for training examples = {train_preds.mean():.2f}\")\n",
+    "print(f\"Mean of confidence for non-training examples = {test_preds.mean():.2f}\")\n",
+    "\n",
+    "lr = LogisticRegression()\n",
+    "train_x = np.vstack((train_preds[:, None], test_preds[:, None]))\n",
+    "train_y = np.hstack((np.ones(len(train_preds)), np.zeros(len(test_preds))))\n",
+    "lr.fit(train_x, train_y)\n",
+    "\n",
+    "\n",
+    "input_array = np.array([[mp_preds.max()]])\n",
+    "prob_membership = lr.predict_proba(input_array)\n",
+    "print(f\"Confidence of non-membership = {prob_membership[0][0]:.2f}\")\n",
+    "print(f\"Confidence of membership = {prob_membership[0][1]:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "94d3f196",
+   "metadata": {},
+   "source": [
+    "The attack is now almost completely ambiguous, providing the attacker with no information as to whether or not the MP was in the training set.\n",
+    "\n",
+    "The technical effect `gamma` has on the SVM is unimportant -- the important point is that changes to the model's configuration can play a significant role in its vulnerability."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bf4518cd",
+   "metadata": {},
+   "source": [
+    "## Conclusions\n",
+    "\n",
+    "This example has shown how an attacker can perform a membership inference attack to determine that a well-known individual was in a model's training data.\n",
+    "\n",
+    "It is hopefully clear that this is non-trivial -- the attacker has to put in quite a lot of effort. Their success is also contingent on them knowing certain things about the problem. In particular:\n",
+    "1. Enough information about the original data that they can generate a shadow dataset. This will be things like: types of variables, ranges of variables, distributions of variables. Such information is often available at population levels (e.g. average age, proportion of population with diabetes etc).\n",
+    "1. Configuration information about the original model. In this case, it was the parameters that define the model and, in particular a parameter called `gamma` that is used in the SVM. It is quite common for researchers to publish this information.\n",
+    "1. The input values for the individual in question. This is harder to come by, but for famous individuals, it's conceivable that a lot of this information might be in the public domain.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f8b9da84",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "fcca1ce0a591990538c4a1a2cbe16853d718e2332b5914ea18ddb1937a418955"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
 }
diff --git a/tests/conftest.py b/tests/conftest.py
index de2e19f2..1bfd4883 100644
--- 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.