diff --git a/examples/demo_adversarial_debiasing.ipynb b/examples/demo_adversarial_debiasing.ipynb
index 62477534..e5b0d8e4 100644
--- a/examples/demo_adversarial_debiasing.ipynb
+++ b/examples/demo_adversarial_debiasing.ipynb
@@ -10,18 +10,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/anaconda2/lib/python2.7/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
-      "  from ._conv import register_converters as _register_converters\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%matplotlib inline\n",
     "# Load all necessary packages\n",
@@ -57,7 +48,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import aif360\n",
+    "import urllib\n",
+    "\n",
+    "# Obtain the location where it is installed\n",
+    "LIB_PATH = aif360.__file__.rsplit(\"aif360\", 1)[0]\n",
+    "print(LIB_PATH)\n",
+    "\n",
+    "# check if the data got download properly\n",
+    "def check_data_or_download(destn, files, data_source_directory):\n",
+    "    check = all(item in os.listdir(destn) for item in files)\n",
+    "    if check:\n",
+    "        print(\"Adult dataset is available for us\")\n",
+    "    else:\n",
+    "        print(\"Some files are missing. Downloading now.\")\n",
+    "        for data_file in files:\n",
+    "            _ = urllib.request.urlretrieve(data_source_directory + data_file,\n",
+    "                                           os.path.join(destn, data_file))\n",
+    "\n",
+    "# Download adult dataset\n",
+    "data_source_directory = \"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/\"\n",
+    "destn = os.path.join(LIB_PATH, \"aif360\", \"data\", \"raw\", \"adult\")\n",
+    "files = [\"adult.data\", \"adult.test\", \"adult.names\"]\n",
+    "\n",
+    "check_data_or_download(destn, files, data_source_directory)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -72,105 +96,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/markdown": [
-       "#### Training Dataset shape"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(34189, 18)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/markdown": [
-       "#### Favorable and unfavorable labels"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(1.0, 0.0)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/markdown": [
-       "#### Protected attribute names"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['sex', 'race']\n"
-     ]
-    },
-    {
-     "data": {
-      "text/markdown": [
-       "#### Privileged and unprivileged protected attribute values"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "([array([1.]), array([1.])], [array([0.]), array([0.])])\n"
-     ]
-    },
-    {
-     "data": {
-      "text/markdown": [
-       "#### Dataset feature names"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['race', 'sex', 'Age (decade)=10', 'Age (decade)=20', 'Age (decade)=30', 'Age (decade)=40', 'Age (decade)=50', 'Age (decade)=60', 'Age (decade)=>=70', 'Education Years=6', 'Education Years=7', 'Education Years=8', 'Education Years=9', 'Education Years=10', 'Education Years=11', 'Education Years=12', 'Education Years=<6', 'Education Years=>12']\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# print out some labels, names, etc.\n",
     "display(Markdown(\"#### Training Dataset shape\"))\n",
@@ -195,30 +123,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/markdown": [
-       "#### Original training dataset"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Train set: Difference in mean outcomes between unprivileged and privileged groups = -0.192750\n",
-      "Test set: Difference in mean outcomes between unprivileged and privileged groups = -0.198626\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Metric for the original dataset\n",
     "metric_orig_train = BinaryLabelDatasetMetric(dataset_orig_train, \n",
@@ -234,30 +141,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/markdown": [
-       "#### Scaled dataset - Verify that the scaling does not affect the group label statistics"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Train set: Difference in mean outcomes between unprivileged and privileged groups = -0.192750\n",
-      "Test set: Difference in mean outcomes between unprivileged and privileged groups = -0.198626\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "min_max_scaler = MaxAbsScaler()\n",
     "dataset_orig_train.features = min_max_scaler.fit_transform(dataset_orig_train.features)\n",
@@ -282,7 +168,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -298,135 +184,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {
     "scrolled": true
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 0; iter: 0; batch classifier loss: 0.707587\n",
-      "epoch 0; iter: 200; batch classifier loss: 0.396519\n",
-      "epoch 1; iter: 0; batch classifier loss: 0.450665\n",
-      "epoch 1; iter: 200; batch classifier loss: 0.439213\n",
-      "epoch 2; iter: 0; batch classifier loss: 0.495045\n",
-      "epoch 2; iter: 200; batch classifier loss: 0.513713\n",
-      "epoch 3; iter: 0; batch classifier loss: 0.349774\n",
-      "epoch 3; iter: 200; batch classifier loss: 0.380733\n",
-      "epoch 4; iter: 0; batch classifier loss: 0.345100\n",
-      "epoch 4; iter: 200; batch classifier loss: 0.399097\n",
-      "epoch 5; iter: 0; batch classifier loss: 0.423275\n",
-      "epoch 5; iter: 200; batch classifier loss: 0.418846\n",
-      "epoch 6; iter: 0; batch classifier loss: 0.411661\n",
-      "epoch 6; iter: 200; batch classifier loss: 0.357504\n",
-      "epoch 7; iter: 0; batch classifier loss: 0.404039\n",
-      "epoch 7; iter: 200; batch classifier loss: 0.447010\n",
-      "epoch 8; iter: 0; batch classifier loss: 0.417079\n",
-      "epoch 8; iter: 200; batch classifier loss: 0.513713\n",
-      "epoch 9; iter: 0; batch classifier loss: 0.299503\n",
-      "epoch 9; iter: 200; batch classifier loss: 0.447425\n",
-      "epoch 10; iter: 0; batch classifier loss: 0.513632\n",
-      "epoch 10; iter: 200; batch classifier loss: 0.376522\n",
-      "epoch 11; iter: 0; batch classifier loss: 0.539716\n",
-      "epoch 11; iter: 200; batch classifier loss: 0.449963\n",
-      "epoch 12; iter: 0; batch classifier loss: 0.429474\n",
-      "epoch 12; iter: 200; batch classifier loss: 0.408618\n",
-      "epoch 13; iter: 0; batch classifier loss: 0.440729\n",
-      "epoch 13; iter: 200; batch classifier loss: 0.464946\n",
-      "epoch 14; iter: 0; batch classifier loss: 0.390707\n",
-      "epoch 14; iter: 200; batch classifier loss: 0.482682\n",
-      "epoch 15; iter: 0; batch classifier loss: 0.352653\n",
-      "epoch 15; iter: 200; batch classifier loss: 0.423660\n",
-      "epoch 16; iter: 0; batch classifier loss: 0.424234\n",
-      "epoch 16; iter: 200; batch classifier loss: 0.390729\n",
-      "epoch 17; iter: 0; batch classifier loss: 0.411589\n",
-      "epoch 17; iter: 200; batch classifier loss: 0.389220\n",
-      "epoch 18; iter: 0; batch classifier loss: 0.331668\n",
-      "epoch 18; iter: 200; batch classifier loss: 0.384711\n",
-      "epoch 19; iter: 0; batch classifier loss: 0.353290\n",
-      "epoch 19; iter: 200; batch classifier loss: 0.457664\n",
-      "epoch 20; iter: 0; batch classifier loss: 0.356439\n",
-      "epoch 20; iter: 200; batch classifier loss: 0.334217\n",
-      "epoch 21; iter: 0; batch classifier loss: 0.438827\n",
-      "epoch 21; iter: 200; batch classifier loss: 0.382024\n",
-      "epoch 22; iter: 0; batch classifier loss: 0.420756\n",
-      "epoch 22; iter: 200; batch classifier loss: 0.374907\n",
-      "epoch 23; iter: 0; batch classifier loss: 0.475280\n",
-      "epoch 23; iter: 200; batch classifier loss: 0.426664\n",
-      "epoch 24; iter: 0; batch classifier loss: 0.351704\n",
-      "epoch 24; iter: 200; batch classifier loss: 0.361529\n",
-      "epoch 25; iter: 0; batch classifier loss: 0.411303\n",
-      "epoch 25; iter: 200; batch classifier loss: 0.487325\n",
-      "epoch 26; iter: 0; batch classifier loss: 0.407306\n",
-      "epoch 26; iter: 200; batch classifier loss: 0.484252\n",
-      "epoch 27; iter: 0; batch classifier loss: 0.364663\n",
-      "epoch 27; iter: 200; batch classifier loss: 0.455063\n",
-      "epoch 28; iter: 0; batch classifier loss: 0.434696\n",
-      "epoch 28; iter: 200; batch classifier loss: 0.449683\n",
-      "epoch 29; iter: 0; batch classifier loss: 0.418321\n",
-      "epoch 29; iter: 200; batch classifier loss: 0.434468\n",
-      "epoch 30; iter: 0; batch classifier loss: 0.409858\n",
-      "epoch 30; iter: 200; batch classifier loss: 0.466626\n",
-      "epoch 31; iter: 0; batch classifier loss: 0.450511\n",
-      "epoch 31; iter: 200; batch classifier loss: 0.450152\n",
-      "epoch 32; iter: 0; batch classifier loss: 0.465642\n",
-      "epoch 32; iter: 200; batch classifier loss: 0.428328\n",
-      "epoch 33; iter: 0; batch classifier loss: 0.392987\n",
-      "epoch 33; iter: 200; batch classifier loss: 0.373837\n",
-      "epoch 34; iter: 0; batch classifier loss: 0.448555\n",
-      "epoch 34; iter: 200; batch classifier loss: 0.485128\n",
-      "epoch 35; iter: 0; batch classifier loss: 0.344462\n",
-      "epoch 35; iter: 200; batch classifier loss: 0.388613\n",
-      "epoch 36; iter: 0; batch classifier loss: 0.466822\n",
-      "epoch 36; iter: 200; batch classifier loss: 0.363230\n",
-      "epoch 37; iter: 0; batch classifier loss: 0.440089\n",
-      "epoch 37; iter: 200; batch classifier loss: 0.382196\n",
-      "epoch 38; iter: 0; batch classifier loss: 0.386720\n",
-      "epoch 38; iter: 200; batch classifier loss: 0.447435\n",
-      "epoch 39; iter: 0; batch classifier loss: 0.384074\n",
-      "epoch 39; iter: 200; batch classifier loss: 0.394575\n",
-      "epoch 40; iter: 0; batch classifier loss: 0.378215\n",
-      "epoch 40; iter: 200; batch classifier loss: 0.421163\n",
-      "epoch 41; iter: 0; batch classifier loss: 0.387049\n",
-      "epoch 41; iter: 200; batch classifier loss: 0.392461\n",
-      "epoch 42; iter: 0; batch classifier loss: 0.392354\n",
-      "epoch 42; iter: 200; batch classifier loss: 0.413999\n",
-      "epoch 43; iter: 0; batch classifier loss: 0.447966\n",
-      "epoch 43; iter: 200; batch classifier loss: 0.417566\n",
-      "epoch 44; iter: 0; batch classifier loss: 0.507449\n",
-      "epoch 44; iter: 200; batch classifier loss: 0.407887\n",
-      "epoch 45; iter: 0; batch classifier loss: 0.396286\n",
-      "epoch 45; iter: 200; batch classifier loss: 0.390399\n",
-      "epoch 46; iter: 0; batch classifier loss: 0.418439\n",
-      "epoch 46; iter: 200; batch classifier loss: 0.380013\n",
-      "epoch 47; iter: 0; batch classifier loss: 0.407893\n",
-      "epoch 47; iter: 200; batch classifier loss: 0.433631\n",
-      "epoch 48; iter: 0; batch classifier loss: 0.461974\n",
-      "epoch 48; iter: 200; batch classifier loss: 0.447301\n",
-      "epoch 49; iter: 0; batch classifier loss: 0.356089\n",
-      "epoch 49; iter: 200; batch classifier loss: 0.467275\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<aif360.algorithms.inprocessing.adversarial_debiasing.AdversarialDebiasing at 0x106cc2f10>"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plain_model.fit(dataset_orig_train)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -437,54 +206,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/markdown": [
-       "#### Plain model - without debiasing - dataset metrics"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Train set: Difference in mean outcomes between unprivileged and privileged groups = -0.217876\n",
-      "Test set: Difference in mean outcomes between unprivileged and privileged groups = -0.221187\n"
-     ]
-    },
-    {
-     "data": {
-      "text/markdown": [
-       "#### Plain model - without debiasing - classification metrics"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Test set: Classification accuracy = 0.804955\n",
-      "Test set: Balanced classification accuracy = 0.666400\n",
-      "Test set: Disparate impact = 0.000000\n",
-      "Test set: Equal opportunity difference = -0.470687\n",
-      "Test set: Average odds difference = -0.291055\n",
-      "Test set: Theil_index = 0.175113\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Metrics for the dataset from plain model (without debiasing)\n",
     "display(Markdown(\"#### Plain model - without debiasing - dataset metrics\"))\n",
@@ -525,7 +249,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -536,7 +260,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -550,141 +274,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {
     "scrolled": true
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 0; iter: 0; batch classifier loss: 0.721611; batch adversarial loss: 0.630777\n",
-      "epoch 0; iter: 200; batch classifier loss: 0.442980; batch adversarial loss: 0.656542\n",
-      "epoch 1; iter: 0; batch classifier loss: 0.453149; batch adversarial loss: 0.657557\n",
-      "epoch 1; iter: 200; batch classifier loss: 0.496931; batch adversarial loss: 0.617686\n",
-      "epoch 2; iter: 0; batch classifier loss: 0.547117; batch adversarial loss: 0.653103\n",
-      "epoch 2; iter: 200; batch classifier loss: 0.331452; batch adversarial loss: 0.617297\n",
-      "epoch 3; iter: 0; batch classifier loss: 0.407935; batch adversarial loss: 0.627860\n",
-      "epoch 3; iter: 200; batch classifier loss: 0.413469; batch adversarial loss: 0.616086\n",
-      "epoch 4; iter: 0; batch classifier loss: 0.370982; batch adversarial loss: 0.604738\n",
-      "epoch 4; iter: 200; batch classifier loss: 0.469453; batch adversarial loss: 0.617892\n",
-      "epoch 5; iter: 0; batch classifier loss: 0.502638; batch adversarial loss: 0.595247\n",
-      "epoch 5; iter: 200; batch classifier loss: 0.379807; batch adversarial loss: 0.635309\n",
-      "epoch 6; iter: 0; batch classifier loss: 0.484228; batch adversarial loss: 0.591971\n",
-      "epoch 6; iter: 200; batch classifier loss: 0.421526; batch adversarial loss: 0.612220\n",
-      "epoch 7; iter: 0; batch classifier loss: 0.392731; batch adversarial loss: 0.636230\n",
-      "epoch 7; iter: 200; batch classifier loss: 0.391191; batch adversarial loss: 0.614543\n",
-      "epoch 8; iter: 0; batch classifier loss: 0.481106; batch adversarial loss: 0.665277\n",
-      "epoch 8; iter: 200; batch classifier loss: 0.465566; batch adversarial loss: 0.638007\n",
-      "epoch 9; iter: 0; batch classifier loss: 0.418696; batch adversarial loss: 0.575338\n",
-      "epoch 9; iter: 200; batch classifier loss: 0.450467; batch adversarial loss: 0.611760\n",
-      "epoch 10; iter: 0; batch classifier loss: 0.347429; batch adversarial loss: 0.652913\n",
-      "epoch 10; iter: 200; batch classifier loss: 0.398043; batch adversarial loss: 0.601349\n",
-      "epoch 11; iter: 0; batch classifier loss: 0.478137; batch adversarial loss: 0.630736\n",
-      "epoch 11; iter: 200; batch classifier loss: 0.479216; batch adversarial loss: 0.552848\n",
-      "epoch 12; iter: 0; batch classifier loss: 0.473339; batch adversarial loss: 0.597487\n",
-      "epoch 12; iter: 200; batch classifier loss: 0.378719; batch adversarial loss: 0.630144\n",
-      "epoch 13; iter: 0; batch classifier loss: 0.461751; batch adversarial loss: 0.583154\n",
-      "epoch 13; iter: 200; batch classifier loss: 0.427811; batch adversarial loss: 0.594790\n",
-      "epoch 14; iter: 0; batch classifier loss: 0.520254; batch adversarial loss: 0.586920\n",
-      "epoch 14; iter: 200; batch classifier loss: 0.375389; batch adversarial loss: 0.622141\n",
-      "epoch 15; iter: 0; batch classifier loss: 0.358494; batch adversarial loss: 0.610482\n",
-      "epoch 15; iter: 200; batch classifier loss: 0.377246; batch adversarial loss: 0.600464\n",
-      "epoch 16; iter: 0; batch classifier loss: 0.330568; batch adversarial loss: 0.631124\n",
-      "epoch 16; iter: 200; batch classifier loss: 0.493238; batch adversarial loss: 0.602217\n",
-      "epoch 17; iter: 0; batch classifier loss: 0.430809; batch adversarial loss: 0.622507\n",
-      "epoch 17; iter: 200; batch classifier loss: 0.420727; batch adversarial loss: 0.631383\n",
-      "epoch 18; iter: 0; batch classifier loss: 0.463418; batch adversarial loss: 0.613122\n",
-      "epoch 18; iter: 200; batch classifier loss: 0.407586; batch adversarial loss: 0.583201\n",
-      "epoch 19; iter: 0; batch classifier loss: 0.438854; batch adversarial loss: 0.588028\n",
-      "epoch 19; iter: 200; batch classifier loss: 0.468554; batch adversarial loss: 0.586143\n",
-      "epoch 20; iter: 0; batch classifier loss: 0.491485; batch adversarial loss: 0.627042\n",
-      "epoch 20; iter: 200; batch classifier loss: 0.434700; batch adversarial loss: 0.629269\n",
-      "epoch 21; iter: 0; batch classifier loss: 0.445875; batch adversarial loss: 0.589738\n",
-      "epoch 21; iter: 200; batch classifier loss: 0.435593; batch adversarial loss: 0.629081\n",
-      "epoch 22; iter: 0; batch classifier loss: 0.364423; batch adversarial loss: 0.610640\n",
-      "epoch 22; iter: 200; batch classifier loss: 0.389425; batch adversarial loss: 0.605668\n",
-      "epoch 23; iter: 0; batch classifier loss: 0.562680; batch adversarial loss: 0.634945\n",
-      "epoch 23; iter: 200; batch classifier loss: 0.473808; batch adversarial loss: 0.566636\n",
-      "epoch 24; iter: 0; batch classifier loss: 0.424366; batch adversarial loss: 0.585584\n",
-      "epoch 24; iter: 200; batch classifier loss: 0.359588; batch adversarial loss: 0.609465\n",
-      "epoch 25; iter: 0; batch classifier loss: 0.519477; batch adversarial loss: 0.564588\n",
-      "epoch 25; iter: 200; batch classifier loss: 0.449761; batch adversarial loss: 0.571238\n",
-      "epoch 26; iter: 0; batch classifier loss: 0.447675; batch adversarial loss: 0.591839\n",
-      "epoch 26; iter: 200; batch classifier loss: 0.369251; batch adversarial loss: 0.580864\n",
-      "epoch 27; iter: 0; batch classifier loss: 0.384472; batch adversarial loss: 0.661156\n",
-      "epoch 27; iter: 200; batch classifier loss: 0.393334; batch adversarial loss: 0.638825\n",
-      "epoch 28; iter: 0; batch classifier loss: 0.451982; batch adversarial loss: 0.552013\n",
-      "epoch 28; iter: 200; batch classifier loss: 0.399544; batch adversarial loss: 0.612651\n",
-      "epoch 29; iter: 0; batch classifier loss: 0.390971; batch adversarial loss: 0.580380\n",
-      "epoch 29; iter: 200; batch classifier loss: 0.401580; batch adversarial loss: 0.582367\n",
-      "epoch 30; iter: 0; batch classifier loss: 0.297665; batch adversarial loss: 0.547717\n",
-      "epoch 30; iter: 200; batch classifier loss: 0.470934; batch adversarial loss: 0.625385\n",
-      "epoch 31; iter: 0; batch classifier loss: 0.418402; batch adversarial loss: 0.622812\n",
-      "epoch 31; iter: 200; batch classifier loss: 0.385281; batch adversarial loss: 0.603873\n",
-      "epoch 32; iter: 0; batch classifier loss: 0.418848; batch adversarial loss: 0.573049\n",
-      "epoch 32; iter: 200; batch classifier loss: 0.443066; batch adversarial loss: 0.621068\n",
-      "epoch 33; iter: 0; batch classifier loss: 0.461614; batch adversarial loss: 0.606992\n",
-      "epoch 33; iter: 200; batch classifier loss: 0.451093; batch adversarial loss: 0.621659\n",
-      "epoch 34; iter: 0; batch classifier loss: 0.407544; batch adversarial loss: 0.646782\n",
-      "epoch 34; iter: 200; batch classifier loss: 0.441481; batch adversarial loss: 0.645866\n",
-      "epoch 35; iter: 0; batch classifier loss: 0.344949; batch adversarial loss: 0.589151\n",
-      "epoch 35; iter: 200; batch classifier loss: 0.387160; batch adversarial loss: 0.549727\n",
-      "epoch 36; iter: 0; batch classifier loss: 0.432171; batch adversarial loss: 0.675994\n",
-      "epoch 36; iter: 200; batch classifier loss: 0.388955; batch adversarial loss: 0.621595\n",
-      "epoch 37; iter: 0; batch classifier loss: 0.443978; batch adversarial loss: 0.658480\n",
-      "epoch 37; iter: 200; batch classifier loss: 0.422210; batch adversarial loss: 0.617039\n",
-      "epoch 38; iter: 0; batch classifier loss: 0.381281; batch adversarial loss: 0.588504\n",
-      "epoch 38; iter: 200; batch classifier loss: 0.323892; batch adversarial loss: 0.596638\n",
-      "epoch 39; iter: 0; batch classifier loss: 0.396359; batch adversarial loss: 0.614882\n",
-      "epoch 39; iter: 200; batch classifier loss: 0.473418; batch adversarial loss: 0.562516\n",
-      "epoch 40; iter: 0; batch classifier loss: 0.415690; batch adversarial loss: 0.617672\n",
-      "epoch 40; iter: 200; batch classifier loss: 0.472975; batch adversarial loss: 0.537192\n",
-      "epoch 41; iter: 0; batch classifier loss: 0.473487; batch adversarial loss: 0.591801\n",
-      "epoch 41; iter: 200; batch classifier loss: 0.379132; batch adversarial loss: 0.602665\n",
-      "epoch 42; iter: 0; batch classifier loss: 0.418546; batch adversarial loss: 0.568511\n",
-      "epoch 42; iter: 200; batch classifier loss: 0.366345; batch adversarial loss: 0.603213\n",
-      "epoch 43; iter: 0; batch classifier loss: 0.364993; batch adversarial loss: 0.596730\n",
-      "epoch 43; iter: 200; batch classifier loss: 0.436417; batch adversarial loss: 0.611999\n",
-      "epoch 44; iter: 0; batch classifier loss: 0.419406; batch adversarial loss: 0.602352\n",
-      "epoch 44; iter: 200; batch classifier loss: 0.472369; batch adversarial loss: 0.592246\n",
-      "epoch 45; iter: 0; batch classifier loss: 0.479547; batch adversarial loss: 0.564802\n",
-      "epoch 45; iter: 200; batch classifier loss: 0.476123; batch adversarial loss: 0.603599\n",
-      "epoch 46; iter: 0; batch classifier loss: 0.546357; batch adversarial loss: 0.631894\n",
-      "epoch 46; iter: 200; batch classifier loss: 0.389170; batch adversarial loss: 0.576345\n",
-      "epoch 47; iter: 0; batch classifier loss: 0.480703; batch adversarial loss: 0.603182\n",
-      "epoch 47; iter: 200; batch classifier loss: 0.586694; batch adversarial loss: 0.635715\n",
-      "epoch 48; iter: 0; batch classifier loss: 0.394101; batch adversarial loss: 0.558852\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch 48; iter: 200; batch classifier loss: 0.453874; batch adversarial loss: 0.602889\n",
-      "epoch 49; iter: 0; batch classifier loss: 0.506737; batch adversarial loss: 0.624289\n",
-      "epoch 49; iter: 200; batch classifier loss: 0.359482; batch adversarial loss: 0.618086\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<aif360.algorithms.inprocessing.adversarial_debiasing.AdversarialDebiasing at 0x1c32efcf10>"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "debiased_model.fit(dataset_orig_train)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -695,98 +296,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/markdown": [
-       "#### Plain model - without debiasing - dataset metrics"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Train set: Difference in mean outcomes between unprivileged and privileged groups = -0.217876\n",
-      "Test set: Difference in mean outcomes between unprivileged and privileged groups = -0.221187\n"
-     ]
-    },
-    {
-     "data": {
-      "text/markdown": [
-       "#### Model - with debiasing - dataset metrics"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Train set: Difference in mean outcomes between unprivileged and privileged groups = -0.090157\n",
-      "Test set: Difference in mean outcomes between unprivileged and privileged groups = -0.094732\n"
-     ]
-    },
-    {
-     "data": {
-      "text/markdown": [
-       "#### Plain model - without debiasing - classification metrics"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Test set: Classification accuracy = 0.804955\n",
-      "Test set: Balanced classification accuracy = 0.666400\n",
-      "Test set: Disparate impact = 0.000000\n",
-      "Test set: Equal opportunity difference = -0.470687\n",
-      "Test set: Average odds difference = -0.291055\n",
-      "Test set: Theil_index = 0.175113\n"
-     ]
-    },
-    {
-     "data": {
-      "text/markdown": [
-       "#### Model - with debiasing - classification metrics"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Test set: Classification accuracy = 0.792056\n",
-      "Test set: Balanced classification accuracy = 0.672481\n",
-      "Test set: Disparate impact = 0.553746\n",
-      "Test set: Equal opportunity difference = -0.090716\n",
-      "Test set: Average odds difference = -0.053841\n",
-      "Test set: Theil_index = 0.170358\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Metrics for the dataset from plain model (without debiasing)\n",
     "display(Markdown(\"#### Plain model - without debiasing - dataset metrics\"))\n",
@@ -858,21 +370,21 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "firstEnv",
    "language": "python",
-   "name": "python2"
+   "name": "firstenv"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.15"
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,