Adding correct version of notebook

Author: naoise-h

notebooks/attack_database_reconstruction.ipynb

@@ -111,7 +111,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Model accuracy (on the test dataset): 1.0\n"
+      "Model accuracy (on the test dataset): 0.9666666666666667\n"
      ]
     }
    ],
@@ -156,21 +156,9 @@
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "ImportError",
-     "evalue": "cannot import name 'DatabaseReconstruction'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-6-2dd2e9a7664c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mart\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mattacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minference\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDatabaseReconstruction\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mdbrecon\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDatabaseReconstruction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnon_private_art\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdbrecon\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreconstruct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_public\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_public\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mImportError\u001b[0m: cannot import name 'DatabaseReconstruction'"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "from art.attacks.inference import DatabaseReconstruction\n",
+    "from art.attacks.inference.reconstruction import DatabaseReconstruction\n",
     "\n",
     "dbrecon = DatabaseReconstruction(non_private_art)\n",
     "\n",
@@ -186,9 +174,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Inference RMSE: 5.789723287688911e-08\n"
+     ]
+    }
+   ],
    "source": [
     "print(\"Inference RMSE: {}\".format(\n",
     "    np.sqrt(((x_train[target_row] - x) ** 2).sum() / x_train.shape[1])))"
@@ -203,9 +199,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "np.argmax(y) == y_train[target_row]"
    ]
@@ -233,9 +240,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from diffprivlib import models\n",
     "\n",
@@ -261,7 +279,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -279,9 +297,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Inference RMSE (with differential privacy): 2.2594246979517965\n"
+     ]
+    }
+   ],
    "source": [
     "print(\"Inference RMSE (with differential privacy): {}\".format(\n",
     "    np.sqrt(((x_train[target_row] - x_dp) ** 2).sum() / x_train.shape[1])))"
@@ -296,11 +322,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {
     "scrolled": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([[4.80000094, 3.00000298, 1.39999864, 0.30000296]]),\n",
+       " array([6.4, 2.7, 5.3, 1.9]))"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "x_dp, x_train[target_row]"
    ]
@@ -314,9 +352,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0, 2)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "np.argmax(y_dp), y_train[target_row]"
    ]