Adversarial-Learning-on-MNIST.ipynb

{
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   "cell_type": "markdown",
   "metadata": {
    "id": "N_G0ddoulBej",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Load the Adult Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 952,
     "status": "ok",
     "timestamp": 1652819221821,
     "user": {
      "displayName": "Ian Hardy",
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     },
     "user_tz": 420
    },
    "id": "JaPRBx11-3Ye",
    "outputId": "fc8ee316-88f6-46b4-c20b-96a549e9ea7e"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
     ]
    }
   ],
   "source": [
    "## If Running in google colab\n",
    "from google.colab import drive\n",
    "drive.mount('/content/drive')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1652819221822,
     "user": {
      "displayName": "Ian Hardy",
      "userId": "01608928063074550753"
     },
     "user_tz": 420
    },
    "id": "vjaT7Kcg_DXB"
   },
   "outputs": [],
   "source": [
    "dir_path = '/content/drive/MyDrive/CSE_248/'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 4254,
     "status": "ok",
     "timestamp": 1652819233953,
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      "displayName": "Ian Hardy",
      "userId": "01608928063074550753"
     },
     "user_tz": 420
    },
    "id": "gPffwDCNlIv6",
    "outputId": "e01dc489-ec8c-4c68-fe22-578f50fa4885"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: torchattacks in /usr/local/lib/python3.7/dist-packages (3.2.6)\n"
     ]
    }
   ],
   "source": [
    "!pip install torchattacks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "executionInfo": {
     "elapsed": 168,
     "status": "ok",
     "timestamp": 1652819938630,
     "user": {
      "displayName": "Ian Hardy",
      "userId": "01608928063074550753"
     },
     "user_tz": 420
    },
    "id": "HouILEUYlBeo",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import torch\n",
    "import torchattacks\n",
    "from torch.utils.data import TensorDataset, DataLoader\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qwqg7d74lBep",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Load the MNIST dataset as numpy array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "executionInfo": {
     "elapsed": 216,
     "status": "ok",
     "timestamp": 1652819939460,
     "user": {
      "displayName": "Ian Hardy",
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     },
     "user_tz": 420
    },
    "id": "eTRlujiVpiOC"
   },
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "mnist = datasets.load_digits()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1652819939859,
     "user": {
      "displayName": "Ian Hardy",
      "userId": "01608928063074550753"
     },
     "user_tz": 420
    },
    "id": "0j2OMIFzplEI",
    "outputId": "fedc215d-ccfb-4c13-efd1-f1ed9ca79a89"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'frame', 'feature_names', 'target_names', 'images', 'DESCR'])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mnist.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1652819940390,
     "user": {
      "displayName": "Ian Hardy",
      "userId": "01608928063074550753"
     },
     "user_tz": 420
    },
    "id": "hstAzhGUpsuV",
    "outputId": "edea648e-ed0c-44f5-fbf4-53c54dd6032d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797, 64)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mnist['data'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1652819940988,
     "user": {
      "displayName": "Ian Hardy",
      "userId": "01608928063074550753"
     },
     "user_tz": 420
    },
    "id": "atASvhdBtcRv",
    "outputId": "5d309dbf-5139-4045-e1ea-399ce16d2806"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False,  True, False, ..., False, False, False])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mnist['target']==1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1652819941505,
     "user": {
      "displayName": "Ian Hardy",
      "userId": "01608928063074550753"
     },
     "user_tz": 420
    },
    "id": "BuZ_bzgnth3s",
    "outputId": "8940a35a-3995-4861-e24f-e7aae7dc3b4d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True, False, False, ..., False, False, False])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mnist['target']==0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "executionInfo": {
     "elapsed": 185,
     "status": "ok",
     "timestamp": 1652820034423,
     "user": {
      "displayName": "Ian Hardy",
      "userId": "01608928063074550753"
     },
     "user_tz": 420
    },
    "id": "bb5tEyySpxll"
   },
   "outputs": [],
   "source": [
    "z_targ = mnist['target']==0\n",
    "o_targ = mnist['target']==1\n",
    "features = mnist['data'][[a or b for a, b in zip(z_targ, o_targ)]]\n",
    "label = mnist['target'][[a or b for a, b in zip(z_targ, o_targ)]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "354lFgeXlBep",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Load the numpy array as torch tensors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1652820035184,
     "user": {
      "displayName": "Ian Hardy",
      "userId": "01608928063074550753"
     },
     "user_tz": 420
    },
    "id": "yZ4WiV0JlBeq",
    "outputId": "d0b4e5e1-2965-4951-a606-8f8e9e4c0ea2",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(360, 64) (360,)\n",
      "[0.00000000e+00 1.66666667e-02 3.31111111e+00 1.11305556e+01\n",
      " 1.08472222e+01 4.51944444e+00 5.19444444e-01 0.00000000e+00\n",
      " 0.00000000e+00 4.86111111e-01 8.27500000e+00 1.30638889e+01\n",
      " 1.27333333e+01 9.89444444e+00 1.08611111e+00 0.00000000e+00\n",
      " 5.55555556e-03 2.40277778e+00 1.06250000e+01 1.01138889e+01\n",
      " 8.20000000e+00 9.75277778e+00 2.09444444e+00 0.00000000e+00\n",
      " 2.77777778e-03 3.69166667e+00 1.08750000e+01 8.20833333e+00\n",
      " 7.07777778e+00 7.64166667e+00 3.37500000e+00 0.00000000e+00\n",
      " 0.00000000e+00 3.48611111e+00 9.16666667e+00 6.44166667e+00\n",
      " 6.94444444e+00 7.14444444e+00 3.70555556e+00 0.00000000e+00\n",
      " 0.00000000e+00 1.94722222e+00 9.18611111e+00 6.01666667e+00\n",
      " 7.53888889e+00 8.56388889e+00 3.07222222e+00 0.00000000e+00\n",
      " 0.00000000e+00 4.52777778e-01 8.85555556e+00 1.04583333e+01\n",
      " 1.19805556e+01 1.04416667e+01 2.23055556e+00 3.25000000e-01\n",
      " 0.00000000e+00 5.55555556e-03 3.18888889e+00 1.13250000e+01\n",
      " 1.31750000e+01 7.02500000e+00 1.67777778e+00 7.55555556e-01] [0.         0.16583124 3.31794597 4.88218527 4.08744596 4.91026812\n",
      " 1.57926133 0.         0.         1.33949675 5.8146785  4.16584344\n",
      " 4.42480382 5.33020452 2.23627329 0.         0.10526275 2.95738117\n",
      " 5.74707051 6.09834462 6.95413546 5.48356059 2.69711208 0.\n",
      " 0.05263138 3.35359467 4.92650851 7.12884886 7.31471542 5.12097403\n",
      " 3.56073299 0.         0.         3.19979624 5.3696679  6.93837457\n",
      " 7.26079443 5.11221003 3.95839084 0.         0.         2.3523022\n",
      " 6.196444   6.63825195 6.77566224 5.59675763 3.84567658 0.\n",
      " 0.         1.04774523 5.78707585 5.13954575 4.11192841 5.81969238\n",
      " 3.48323919 1.43466508 0.         0.07432827 3.08092499 4.93203108\n",
      " 2.95614943 5.43721104 4.11454811 2.94415093]\n"
     ]
    }
   ],
   "source": [
    "print(features.shape, label.shape)\n",
    "\n",
    "import torchvision\n",
    "mean = np.mean(features, axis=0)\n",
    "std = np.std(features, axis=0)\n",
    "\n",
    "transforms=torchvision.transforms.Normalize(mean=mean, std=std)\n",
    "print(mean, std)\n",
    "# torchvision.transforms.Normalize(\n",
    "#       [, meanOfChannel2, meanOfChannel3] \n",
    "#     , [stdOfChannel1, stdOfChannel2, stdOfChannel3] \n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1652820035365,
     "user": {
      "displayName": "Ian Hardy",
      "userId": "01608928063074550753"
     },
     "user_tz": 420
    },
    "id": "TizDYn50lBer",
    "outputId": "0a392534-56fb-4664-be31-d1d22c024b82",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The training data has the size of 288 and the val has the size of 72\n"
     ]
    }
   ],
   "source": [
    "SEED = 0\n",
    "BATCH_SIZE = 128 \n",
    "\n",
    "\n",
    "\n",
    "# generate the training data index and validation data index\n",
    "np.random.seed(SEED)\n",
    "random_index = np.random.rand(len(features))  \n",
    "random_index = np.argsort(random_index)\n",
    "train_index = random_index[: int(len(features)*0.8)]  # 80% data for training\n",
    "val_index = random_index[int(len(features)*0.8):]\n",
    "\n",
    "\n",
    "# generate the training data and validation data\n",
    "train_data = features[train_index]\n",
    "train_label = label[train_index]\n",
    "val_data = features[val_index]\n",
    "val_label = label[val_index]\n",
    "print('The training data has the size of {} and the val has the size of {}'.format(str(len(train_data)), str(len(val_data))))\n",
    "\n",
    "\n",
    "# prepare the dataloader\n",
    "train_dataset = TensorDataset(torch.Tensor(train_data).float(), torch.Tensor(train_label))\n",
    "val_dataset = TensorDataset(torch.Tensor(val_data).float(), torch.Tensor(val_label))\n",
    "train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True, pin_memory=True, num_workers=0) \n",
    "val_loader = DataLoader(val_dataset, batch_size=BATCH_SIZE, shuffle=False, pin_memory=True, num_workers=0, drop_last=False)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "executionInfo": {
     "elapsed": 223,
     "status": "ok",
     "timestamp": 1652820035808,
     "user": {
      "displayName": "Ian Hardy",
      "userId": "01608928063074550753"
     },
     "user_tz": 420
    },
    "id": "TXEiiHM9lBes",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "from torch.nn.functional import normalize\n",
    "\n",
    "# prepare our network\n",
    "class my_net(nn.Module):\n",
    "    def __init__(self, input_dim=64, emb_dim=64, mlp_ratio=4, num_classes=2): \n",
    "        super().__init__()  \n",
    "        \n",
    "        self.embedding = nn.Linear(input_dim, emb_dim)\n",
    "        self.block1 = nn.Sequential(\n",
    "            nn.Linear(emb_dim, emb_dim*mlp_ratio),\n",
    "             nn.GELU(),\n",
    "             nn.Linear(emb_dim*mlp_ratio, emb_dim),\n",
    "             )\n",
    "        self.block2 = nn.Sequential(\n",
    "            nn.Linear(emb_dim, emb_dim*mlp_ratio),\n",
    "             nn.GELU(),\n",
    "             nn.Linear(emb_dim*mlp_ratio, emb_dim),\n",
    "             )\n",
    "        self.cls = nn.Sequential(\n",
    "             nn.LayerNorm(emb_dim),\n",
    "             nn.Linear(emb_dim,  num_classes) \n",
    "        )\n",
    "    \n",
    "    def forward(self, x):\n",
    "        \n",
    "        x = self.embedding(x)\n",
    "        x = self.block1(x)             \n",
    "        x = self.cls(x)\n",
    "        \n",
    "        return x\n",
    "\n",
    "\n",
    "def topk(output, target, ks=(1,)):\n",
    "    \"\"\"Returns one boolean vector for each k, whether the target is within the output's top-k.\"\"\"\n",
    "    batch_size = output.size(0)\n",
    "    _, pred = output.topk(max(ks), 1, True, True)\n",
    "    pred = pred.t()\n",
    "    correct = pred.eq(target.view(1, -1).expand_as(pred))\n",
    "    return [correct[:k].max(0)[0].float().mean() for k in ks]\n",
    "\n",
    "def normalization(tensor):\n",
    "    return normalize(tensor, p=2.0, dim = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 834,
     "status": "ok",
     "timestamp": 1652820046585,
     "user": {
      "displayName": "Ian Hardy",
      "userId": "01608928063074550753"
     },
     "user_tz": 420
    },
    "id": "NZk7Sg2ZlBes",
    "outputId": "f1ef66d9-8118-4fb8-e00e-1bbfaac00ebe",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0 Current Step 0/3: loss is 0.920116\n",
      "------------------------------------------------\n",
      "Epoch 0 Validation acc is 0.416667\n",
      "Epoch 1 Current Step 0/3: loss is 3.498508\n",
      "------------------------------------------------\n",
      "Epoch 1 Validation acc is 0.583333\n",
      "Epoch 2 Current Step 0/3: loss is 2.351151\n",
      "------------------------------------------------\n",
      "Epoch 2 Validation acc is 0.416667\n",
      "Epoch 3 Current Step 0/3: loss is 1.635259\n",
      "------------------------------------------------\n",
      "Epoch 3 Validation acc is 0.583333\n",
      "Epoch 4 Current Step 0/3: loss is 0.921628\n",
      "------------------------------------------------\n",
      "Epoch 4 Validation acc is 0.416667\n",
      "Epoch 5 Current Step 0/3: loss is 0.734901\n",
      "------------------------------------------------\n",
      "Epoch 5 Validation acc is 0.416667\n",
      "Epoch 6 Current Step 0/3: loss is 0.651293\n",
      "------------------------------------------------\n",
      "Epoch 6 Validation acc is 0.583333\n",
      "Epoch 7 Current Step 0/3: loss is 0.757719\n",
      "------------------------------------------------\n",
      "Epoch 7 Validation acc is 0.416667\n",
      "Epoch 8 Current Step 0/3: loss is 0.632900\n",
      "------------------------------------------------\n",
      "Epoch 8 Validation acc is 0.986111\n",
      "Epoch 9 Current Step 0/3: loss is 0.433567\n",
      "------------------------------------------------\n",
      "Epoch 9 Validation acc is 1.000000\n",
      "Epoch 10 Current Step 0/3: loss is 0.276775\n",
      "------------------------------------------------\n",
      "Epoch 10 Validation acc is 0.888889\n",
      "Epoch 11 Current Step 0/3: loss is 0.121781\n",
      "------------------------------------------------\n",
      "Epoch 11 Validation acc is 1.000000\n",
      "Epoch 12 Current Step 0/3: loss is 0.046333\n",
      "------------------------------------------------\n",
      "Epoch 12 Validation acc is 1.000000\n",
      "Epoch 13 Current Step 0/3: loss is 0.034992\n",
      "------------------------------------------------\n",
      "Epoch 13 Validation acc is 1.000000\n",
      "Epoch 14 Current Step 0/3: loss is 0.001521\n",
      "------------------------------------------------\n",
      "Epoch 14 Validation acc is 0.944444\n",
      "Epoch 15 Current Step 0/3: loss is 0.114869\n",
      "------------------------------------------------\n",
      "Epoch 15 Validation acc is 1.000000\n",
      "Epoch 16 Current Step 0/3: loss is 0.015160\n",
      "------------------------------------------------\n",
      "Epoch 16 Validation acc is 1.000000\n",
      "Epoch 17 Current Step 0/3: loss is 0.040830\n",
      "------------------------------------------------\n",
      "Epoch 17 Validation acc is 1.000000\n",
      "Epoch 18 Current Step 0/3: loss is 0.016162\n",
      "------------------------------------------------\n",
      "Epoch 18 Validation acc is 0.958333\n",
      "Epoch 19 Current Step 0/3: loss is 0.030983\n",
      "------------------------------------------------\n",
      "Epoch 19 Validation acc is 0.972222\n",
      "Epoch 20 Current Step 0/3: loss is 0.017499\n",
      "------------------------------------------------\n",
      "Epoch 20 Validation acc is 1.000000\n",
      "Epoch 21 Current Step 0/3: loss is 0.000052\n",
      "------------------------------------------------\n",
      "Epoch 21 Validation acc is 1.000000\n",
      "Epoch 22 Current Step 0/3: loss is 0.000054\n",
      "------------------------------------------------\n",
      "Epoch 22 Validation acc is 1.000000\n",
      "Epoch 23 Current Step 0/3: loss is 0.000003\n",
      "------------------------------------------------\n",
      "Epoch 23 Validation acc is 1.000000\n",
      "Epoch 24 Current Step 0/3: loss is 0.000005\n",
      "------------------------------------------------\n",
      "Epoch 24 Validation acc is 1.000000\n",
      "Epoch 25 Current Step 0/3: loss is 0.000276\n",
      "------------------------------------------------\n",
      "Epoch 25 Validation acc is 1.000000\n",
      "Epoch 26 Current Step 0/3: loss is 0.000044\n",
      "------------------------------------------------\n",
      "Epoch 26 Validation acc is 1.000000\n",
      "Epoch 27 Current Step 0/3: loss is 0.000003\n",
      "------------------------------------------------\n",
      "Epoch 27 Validation acc is 1.000000\n",
      "Epoch 28 Current Step 0/3: loss is 0.000213\n",
      "------------------------------------------------\n",
      "Epoch 28 Validation acc is 1.000000\n",
      "Epoch 29 Current Step 0/3: loss is 0.000083\n",
      "------------------------------------------------\n",
      "Epoch 29 Validation acc is 1.000000\n",
      "Epoch 30 Current Step 0/3: loss is 0.000257\n",
      "------------------------------------------------\n",
      "Epoch 30 Validation acc is 1.000000\n",
      "Epoch 31 Current Step 0/3: loss is 0.000001\n",
      "------------------------------------------------\n",
      "Epoch 31 Validation acc is 1.000000\n",
      "Epoch 32 Current Step 0/3: loss is 0.000040\n",
      "------------------------------------------------\n",
      "Epoch 32 Validation acc is 1.000000\n",
      "Epoch 33 Current Step 0/3: loss is 0.000073\n",
      "------------------------------------------------\n",
      "Epoch 33 Validation acc is 1.000000\n",
      "Epoch 34 Current Step 0/3: loss is 0.000002\n",
      "------------------------------------------------\n",
      "Epoch 34 Validation acc is 1.000000\n",
      "Epoch 35 Current Step 0/3: loss is 0.000003\n",
      "------------------------------------------------\n",
      "Epoch 35 Validation acc is 1.000000\n",
      "Epoch 36 Current Step 0/3: loss is 0.000001\n",
      "------------------------------------------------\n",
      "Epoch 36 Validation acc is 1.000000\n",
      "Epoch 37 Current Step 0/3: loss is 0.000039\n",
      "------------------------------------------------\n",
      "Epoch 37 Validation acc is 1.000000\n",
      "Epoch 38 Current Step 0/3: loss is 0.000001\n",
      "------------------------------------------------\n",
      "Epoch 38 Validation acc is 1.000000\n",
      "Epoch 39 Current Step 0/3: loss is 0.000002\n",
      "------------------------------------------------\n",
      "Epoch 39 Validation acc is 1.000000\n"
     ]
    }
   ],
   "source": [
    "LR = 0.1\n",
    "NUM_EPOCH = 40\n",
    "LOG_STEP = 200\n",
    "\n",
    "\n",
    "our_net = my_net()\n",
    "criterion = torch.nn.CrossEntropyLoss()\n",
    "optim = torch.optim.SGD(our_net.parameters(), lr=LR, momentum=0.9)\n",
    "scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optim, T_max=NUM_EPOCH)\n",
    "\n",
    "train_all_loss = []\n",
    "val_all_loss = []\n",
    "train_acc = []\n",
    "val_acc = []\n",
    "\n",
    "for epoch in range(NUM_EPOCH):\n",
    "    our_net.train()\n",
    "    avg_top1 = []\n",
    "    for batch_id, (train_sample, train_labels) in enumerate(train_loader):   \n",
    "        \n",
    "        train_labels = train_labels.long()\n",
    "        # output = our_net(normalization(train_sample, mean=mean, std=std))\n",
    "        output = our_net(normalization(train_sample))\n",
    "        loss = criterion(output, train_labels)\n",
    "        optim.zero_grad()\n",
    "        loss.backward()\n",
    "        optim.step()\n",
    "        train_all_loss.append(loss.item())\n",
    "        top1 = topk(output, train_labels, ks=(1,))\n",
    "        avg_top1.extend(top1)\n",
    "        if batch_id % LOG_STEP ==0:\n",
    "            print('Epoch {} Current Step {}/{}: loss is {losses:.6f}'\n",
    "                  .format(str(epoch) ,str(batch_id), str(len(train_loader)), losses = loss.item()))\n",
    "    train_acc.append(np.mean(avg_top1))\n",
    "    our_net.eval()\n",
    "    avg_top1 = []\n",
    "    with torch.no_grad():\n",
    "        for  batch_id, (val_sample, val_labels) in enumerate(val_loader):\n",
    "            val_labels = val_labels.long()\n",
    "            # outputs = our_net(normalization(val_sample, mean=mean, std=std).float())\n",
    "            outputs = our_net(normalization(val_sample))\n",
    "            loss = criterion(outputs, val_labels)\n",
    "            val_all_loss.append(loss.item())\n",
    "            top1 = topk(outputs, val_labels, ks=(1,))\n",
    "            avg_top1.extend(top1)\n",
    "        print('------------------------------------------------')\n",
    "        print('Epoch {} Validation acc is {top1:.6f}'\n",
    "                  .format(str(epoch), top1 = np.mean(avg_top1)))\n",
    "    val_acc.append(np.mean(avg_top1))\n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 635
    },
    "executionInfo": {
     "elapsed": 572,
     "status": "ok",
     "timestamp": 1652820054839,
     "user": {
      "displayName": "Ian Hardy",
      "userId": "01608928063074550753"
     },
     "user_tz": 420
    },
    "id": "MgEC3qmKlBet",
    "outputId": "03f2d05d-792e-43c0-8467-410391349dad",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import matplotlib.pylab as pylab\n",
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import matplotlib.pylab as pylab\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "\n",
    "# plot the loss and acc\n",
    "def plot_loss_fig(train_loss, val_loss):\n",
    "    myparams = {\n",
    "\n",
    "   'axes.labelsize': '20',\n",
    "\n",
    "   'xtick.labelsize': '20',\n",
    "\n",
    "   'ytick.labelsize': '20',\n",
    "\n",
    "   'lines.linewidth': 2,\n",
    "\n",
    "   'legend.fontsize': '15',\n",
    "\n",
    "   'font.family': 'Times New Roman',\n",
    "}\n",
    "\n",
    "    pylab.rcParams.update(myparams) \n",
    "\n",
    "    plt.style.use('ggplot')\n",
    "    fig = plt.figure(figsize=(20,10))\n",
    "\n",
    "    plt.plot(range(len(train_loss)), train_loss, label = 'train loss')\n",
    "    plt.plot(range(len(val_loss)), val_loss, label = 'val loss')\n",
    "    \n",
    "    plt.grid(True)  # add grid\n",
    "    plt.legend()\n",
    "    plt.title(\"Train/Val Loss\")  \n",
    "    plt.ylabel('Loss') \n",
    "    plt.xlabel('Number of Steps ')\n",
    "plot_loss_fig(train_all_loss, val_all_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 635
    },
    "executionInfo": {
     "elapsed": 540,
     "status": "ok",
     "timestamp": 1652820060507,
     "user": {
      "displayName": "Ian Hardy",
      "userId": "01608928063074550753"
     },
     "user_tz": 420
    },
    "id": "WxF7MWYqlBeu",
    "outputId": "b3cb196d-d9a0-4f22-8ae3-d036fbfa02df",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import matplotlib.pylab as pylab\n",
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import matplotlib.pylab as pylab\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "\n",
    "# plot the acc and acc\n",
    "def plot_acc_fig(train_acc, val_acc):\n",
    "    myparams = {\n",
    "\n",
    "   'axes.labelsize': '20',\n",
    "\n",
    "   'xtick.labelsize': '20',\n",
    "\n",
    "   'ytick.labelsize': '20',\n",
    "\n",
    "   'lines.linewidth': 2,\n",
    "\n",
    "   'legend.fontsize': '15',\n",
    "\n",
    "   'font.family': 'Times New Roman',\n",
    "}\n",
    "\n",
    "    pylab.rcParams.update(myparams) \n",
    "\n",
    "    plt.style.use('ggplot')\n",
    "    fig = plt.figure(figsize=(20,10))\n",
    "\n",
    "    plt.plot(range(len(train_acc)), train_acc, label = 'train loss')\n",
    "    plt.plot(range(len(val_acc)), val_acc, label = 'val loss')\n",
    "    \n",
    "    plt.grid(True)  # add grid\n",
    "    plt.legend()\n",
    "    plt.title(\"Train/Val Acc\")  \n",
    "    plt.ylabel('Acc') \n",
    "    plt.xlabel('Number of Epochs ')\n",
    "plot_acc_fig(train_acc, val_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "executionInfo": {
     "elapsed": 141,
     "status": "ok",
     "timestamp": 1652820065941,
     "user": {
      "displayName": "Ian Hardy",
      "userId": "01608928063074550753"
     },
     "user_tz": 420
    },
    "id": "rANc3skVlBew"
   },
   "outputs": [],
   "source": [
    "torch.save(our_net.state_dict(), dir_path+'naturally_trained_model_mnist.pt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0NejlU4smu-Q"
   },
   "source": [
    "# Adversarial Training Procedure\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 16975,
     "status": "ok",
     "timestamp": 1652821017663,
     "user": {
      "displayName": "Ian Hardy",
      "userId": "01608928063074550753"
     },
     "user_tz": 420
    },
    "id": "RF_w5cBSs84F",
    "outputId": "3177a483-083c-40c7-bb1c-4cabff4bea19"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0 Current Step 0/3: loss is 1.187668\n",
      "------------------------------------------------\n",
      "Epoch 0 Validation acc is 0.819444\n",
      "Epoch 1 Current Step 0/3: loss is 0.789829\n",
      "------------------------------------------------\n",
      "Epoch 1 Validation acc is 0.583333\n",
      "Epoch 2 Current Step 0/3: loss is 0.793104\n",
      "------------------------------------------------\n",
      "Epoch 2 Validation acc is 0.416667\n",
      "Epoch 3 Current Step 0/3: loss is 0.787252\n",
      "------------------------------------------------\n",
      "Epoch 3 Validation acc is 0.847222\n",
      "Epoch 4 Current Step 0/3: loss is 0.742940\n",
      "------------------------------------------------\n",
      "Epoch 4 Validation acc is 0.777778\n",
      "Epoch 5 Current Step 0/3: loss is 0.738191\n",
      "------------------------------------------------\n",
      "Epoch 5 Validation acc is 0.416667\n",
      "Epoch 6 Current Step 0/3: loss is 0.714114\n",
      "------------------------------------------------\n",
      "Epoch 6 Validation acc is 0.694444\n",
      "Epoch 7 Current Step 0/3: loss is 0.715536\n",
      "------------------------------------------------\n",
      "Epoch 7 Validation acc is 0.583333\n",
      "Epoch 8 Current Step 0/3: loss is 0.726512\n",
      "------------------------------------------------\n",
      "Epoch 8 Validation acc is 0.416667\n",
      "Epoch 9 Current Step 0/3: loss is 0.709360\n",
      "------------------------------------------------\n",
      "Epoch 9 Validation acc is 0.416667\n",
      "Epoch 10 Current Step 0/3: loss is 0.714343\n",
      "------------------------------------------------\n",
      "Epoch 10 Validation acc is 0.583333\n",
      "Epoch 11 Current Step 0/3: loss is 0.730654\n",
      "------------------------------------------------\n",
      "Epoch 11 Validation acc is 0.416667\n",
      "Epoch 12 Current Step 0/3: loss is 0.702909\n",
      "------------------------------------------------\n",
      "Epoch 12 Validation acc is 0.416667\n",
      "Epoch 13 Current Step 0/3: loss is 0.710935\n",
      "------------------------------------------------\n",
      "Epoch 13 Validation acc is 0.583333\n",
      "Epoch 14 Current Step 0/3: loss is 0.696659\n",
      "------------------------------------------------\n",
      "Epoch 14 Validation acc is 0.416667\n",
      "Epoch 15 Current Step 0/3: loss is 0.702610\n",
      "------------------------------------------------\n",
      "Epoch 15 Validation acc is 0.416667\n",
      "Epoch 16 Current Step 0/3: loss is 0.719750\n",
      "------------------------------------------------\n",
      "Epoch 16 Validation acc is 1.000000\n",
      "Epoch 17 Current Step 0/3: loss is 0.697718\n",
      "------------------------------------------------\n",
      "Epoch 17 Validation acc is 0.583333\n",
      "Epoch 18 Current Step 0/3: loss is 0.698519\n",
      "------------------------------------------------\n",
      "Epoch 18 Validation acc is 0.416667\n",
      "Epoch 19 Current Step 0/3: loss is 0.726350\n",
      "------------------------------------------------\n",
      "Epoch 19 Validation acc is 0.416667\n",
      "Epoch 20 Current Step 0/3: loss is 0.708971\n",
      "------------------------------------------------\n",
      "Epoch 20 Validation acc is 0.583333\n",
      "Epoch 21 Current Step 0/3: loss is 0.698434\n",
      "------------------------------------------------\n",
      "Epoch 21 Validation acc is 0.416667\n",
      "Epoch 22 Current Step 0/3: loss is 0.697929\n",
      "------------------------------------------------\n",
      "Epoch 22 Validation acc is 0.416667\n",
      "Epoch 23 Current Step 0/3: loss is 0.699606\n",
      "------------------------------------------------\n",
      "Epoch 23 Validation acc is 0.416667\n",
      "Epoch 24 Current Step 0/3: loss is 0.706480\n",
      "------------------------------------------------\n",
      "Epoch 24 Validation acc is 0.416667\n",
      "Epoch 25 Current Step 0/3: loss is 0.683647\n",
      "------------------------------------------------\n",
      "Epoch 25 Validation acc is 0.694444\n",
      "Epoch 26 Current Step 0/3: loss is 0.698689\n",
      "------------------------------------------------\n",
      "Epoch 26 Validation acc is 0.583333\n",
      "Epoch 27 Current Step 0/3: loss is 0.695617\n",
      "------------------------------------------------\n",
      "Epoch 27 Validation acc is 0.416667\n",
      "Epoch 28 Current Step 0/3: loss is 0.721890\n",
      "------------------------------------------------\n",
      "Epoch 28 Validation acc is 0.416667\n",
      "Epoch 29 Current Step 0/3: loss is 0.706980\n",
      "------------------------------------------------\n",
      "Epoch 29 Validation acc is 0.652778\n",
      "Epoch 30 Current Step 0/3: loss is 0.698279\n",
      "------------------------------------------------\n",
      "Epoch 30 Validation acc is 0.583333\n",
      "Epoch 31 Current Step 0/3: loss is 0.707523\n",
      "------------------------------------------------\n",
      "Epoch 31 Validation acc is 0.416667\n",
      "Epoch 32 Current Step 0/3: loss is 0.694754\n",
      "------------------------------------------------\n",
      "Epoch 32 Validation acc is 0.416667\n",
      "Epoch 33 Current Step 0/3: loss is 0.704713\n",
      "------------------------------------------------\n",
      "Epoch 33 Validation acc is 0.583333\n",
      "Epoch 34 Current Step 0/3: loss is 0.700927\n",
      "------------------------------------------------\n",
      "Epoch 34 Validation acc is 0.416667\n",
      "Epoch 35 Current Step 0/3: loss is 0.693187\n",
      "------------------------------------------------\n",
      "Epoch 35 Validation acc is 0.416667\n",
      "Epoch 36 Current Step 0/3: loss is 0.710030\n",
      "------------------------------------------------\n",
      "Epoch 36 Validation acc is 0.958333\n",
      "Epoch 37 Current Step 0/3: loss is 0.697352\n",
      "------------------------------------------------\n",
      "Epoch 37 Validation acc is 0.416667\n",
      "Epoch 38 Current Step 0/3: loss is 0.701918\n",
      "------------------------------------------------\n",
      "Epoch 38 Validation acc is 0.416667\n",
      "Epoch 39 Current Step 0/3: loss is 0.695050\n",
      "------------------------------------------------\n",
      "Epoch 39 Validation acc is 0.916667\n",
      "Epoch 40 Current Step 0/3: loss is 0.699599\n",
      "------------------------------------------------\n",
      "Epoch 40 Validation acc is 0.583333\n",
      "Epoch 41 Current Step 0/3: loss is 0.701062\n",
      "------------------------------------------------\n",
      "Epoch 41 Validation acc is 0.416667\n",
      "Epoch 42 Current Step 0/3: loss is 0.721681\n",
      "------------------------------------------------\n",
      "Epoch 42 Validation acc is 0.416667\n",
      "Epoch 43 Current Step 0/3: loss is 0.700817\n",
      "------------------------------------------------\n",
      "Epoch 43 Validation acc is 1.000000\n",
      "Epoch 44 Current Step 0/3: loss is 0.697766\n",
      "------------------------------------------------\n",
      "Epoch 44 Validation acc is 0.652778\n",
      "Epoch 45 Current Step 0/3: loss is 0.699646\n",
      "------------------------------------------------\n",
      "Epoch 45 Validation acc is 0.416667\n",
      "Epoch 46 Current Step 0/3: loss is 0.702836\n",
      "------------------------------------------------\n",
      "Epoch 46 Validation acc is 0.416667\n",
      "Epoch 47 Current Step 0/3: loss is 0.694843\n",
      "------------------------------------------------\n",
      "Epoch 47 Validation acc is 0.583333\n",
      "Epoch 48 Current Step 0/3: loss is 0.697692\n",
      "------------------------------------------------\n",
      "Epoch 48 Validation acc is 0.583333\n",
      "Epoch 49 Current Step 0/3: loss is 0.703320\n",
      "------------------------------------------------\n",
      "Epoch 49 Validation acc is 0.416667\n",
      "Epoch 50 Current Step 0/3: loss is 0.719288\n",
      "------------------------------------------------\n",
      "Epoch 50 Validation acc is 0.625000\n",
      "Epoch 51 Current Step 0/3: loss is 0.694247\n",
      "------------------------------------------------\n",
      "Epoch 51 Validation acc is 0.583333\n",
      "Epoch 52 Current Step 0/3: loss is 0.715013\n",
      "------------------------------------------------\n",
      "Epoch 52 Validation acc is 0.416667\n",
      "Epoch 53 Current Step 0/3: loss is 0.701782\n",
      "------------------------------------------------\n",
      "Epoch 53 Validation acc is 0.416667\n",
      "Epoch 54 Current Step 0/3: loss is 0.687298\n",
      "------------------------------------------------\n",
      "Epoch 54 Validation acc is 0.583333\n",
      "Epoch 55 Current Step 0/3: loss is 0.711684\n",
      "------------------------------------------------\n",
      "Epoch 55 Validation acc is 0.416667\n",
      "Epoch 56 Current Step 0/3: loss is 0.698413\n",
      "------------------------------------------------\n",
      "Epoch 56 Validation acc is 0.416667\n",
      "Epoch 57 Current Step 0/3: loss is 0.699765\n",
      "------------------------------------------------\n",
      "Epoch 57 Validation acc is 0.583333\n",
      "Epoch 58 Current Step 0/3: loss is 0.705965\n",
      "------------------------------------------------\n",
      "Epoch 58 Validation acc is 0.902778\n",
      "Epoch 59 Current Step 0/3: loss is 0.703820\n",
      "------------------------------------------------\n",
      "Epoch 59 Validation acc is 0.416667\n",
      "Epoch 60 Current Step 0/3: loss is 0.719347\n",
      "------------------------------------------------\n",
      "Epoch 60 Validation acc is 0.930556\n",
      "Epoch 61 Current Step 0/3: loss is 0.700736\n",
      "------------------------------------------------\n",
      "Epoch 61 Validation acc is 0.583333\n",
      "Epoch 62 Current Step 0/3: loss is 0.706090\n",
      "------------------------------------------------\n",
      "Epoch 62 Validation acc is 0.416667\n",
      "Epoch 63 Current Step 0/3: loss is 0.702752\n",
      "------------------------------------------------\n",
      "Epoch 63 Validation acc is 0.416667\n",
      "Epoch 64 Current Step 0/3: loss is 0.706334\n",
      "------------------------------------------------\n",
      "Epoch 64 Validation acc is 0.930556\n",
      "Epoch 65 Current Step 0/3: loss is 0.698814\n",
      "------------------------------------------------\n",
      "Epoch 65 Validation acc is 1.000000\n",
      "Epoch 66 Current Step 0/3: loss is 0.704442\n",
      "------------------------------------------------\n",
      "Epoch 66 Validation acc is 0.722222\n",
      "Epoch 67 Current Step 0/3: loss is 0.702551\n",
      "------------------------------------------------\n",
      "Epoch 67 Validation acc is 0.416667\n",
      "Epoch 68 Current Step 0/3: loss is 0.712099\n",
      "------------------------------------------------\n",
      "Epoch 68 Validation acc is 0.583333\n",
      "Epoch 69 Current Step 0/3: loss is 0.741512\n",
      "------------------------------------------------\n",
      "Epoch 69 Validation acc is 0.416667\n",
      "Epoch 70 Current Step 0/3: loss is 0.754079\n",
      "------------------------------------------------\n",
      "Epoch 70 Validation acc is 0.791667\n",
      "Epoch 71 Current Step 0/3: loss is 0.703784\n",
      "------------------------------------------------\n",
      "Epoch 71 Validation acc is 0.638889\n",
      "Epoch 72 Current Step 0/3: loss is 0.723616\n",
      "------------------------------------------------\n",
      "Epoch 72 Validation acc is 0.958333\n",
      "Epoch 73 Current Step 0/3: loss is 0.703138\n",
      "------------------------------------------------\n",
      "Epoch 73 Validation acc is 0.416667\n",
      "Epoch 74 Current Step 0/3: loss is 0.718119\n",
      "------------------------------------------------\n",
      "Epoch 74 Validation acc is 0.930556\n",
      "Epoch 75 Current Step 0/3: loss is 0.707265\n",
      "------------------------------------------------\n",
      "Epoch 75 Validation acc is 0.986111\n",
      "Epoch 76 Current Step 0/3: loss is 0.705924\n",
      "------------------------------------------------\n",
      "Epoch 76 Validation acc is 0.902778\n",
      "Epoch 77 Current Step 0/3: loss is 0.713508\n",
      "------------------------------------------------\n",
      "Epoch 77 Validation acc is 0.819444\n",
      "Epoch 78 Current Step 0/3: loss is 0.707234\n",
      "------------------------------------------------\n",
      "Epoch 78 Validation acc is 0.750000\n",
      "Epoch 79 Current Step 0/3: loss is 0.702962\n",
      "------------------------------------------------\n",
      "Epoch 79 Validation acc is 0.972222\n",
      "Epoch 80 Current Step 0/3: loss is 0.721806\n",
      "------------------------------------------------\n",
      "Epoch 80 Validation acc is 0.958333\n",
      "Epoch 81 Current Step 0/3: loss is 0.709078\n",
      "------------------------------------------------\n",
      "Epoch 81 Validation acc is 0.750000\n",
      "Epoch 82 Current Step 0/3: loss is 0.707325\n",
      "------------------------------------------------\n",
      "Epoch 82 Validation acc is 0.972222\n",
      "Epoch 83 Current Step 0/3: loss is 0.713485\n",
      "------------------------------------------------\n",
      "Epoch 83 Validation acc is 0.958333\n",
      "Epoch 84 Current Step 0/3: loss is 0.709053\n",
      "------------------------------------------------\n",
      "Epoch 84 Validation acc is 0.902778\n",
      "Epoch 85 Current Step 0/3: loss is 0.724131\n",
      "------------------------------------------------\n",
      "Epoch 85 Validation acc is 0.986111\n",
      "Epoch 86 Current Step 0/3: loss is 0.733475\n",
      "------------------------------------------------\n",
      "Epoch 86 Validation acc is 0.930556\n",
      "Epoch 87 Current Step 0/3: loss is 0.717972\n",
      "------------------------------------------------\n",
      "Epoch 87 Validation acc is 0.930556\n",
      "Epoch 88 Current Step 0/3: loss is 0.707074\n",
      "------------------------------------------------\n",
      "Epoch 88 Validation acc is 1.000000\n",
      "Epoch 89 Current Step 0/3: loss is 0.730005\n",
      "------------------------------------------------\n",
      "Epoch 89 Validation acc is 0.958333\n",
      "Epoch 90 Current Step 0/3: loss is 0.724475\n",
      "------------------------------------------------\n",
      "Epoch 90 Validation acc is 0.944444\n",
      "Epoch 91 Current Step 0/3: loss is 0.741300\n",
      "------------------------------------------------\n",
      "Epoch 91 Validation acc is 0.944444\n",
      "Epoch 92 Current Step 0/3: loss is 0.719128\n",
      "------------------------------------------------\n",
      "Epoch 92 Validation acc is 0.944444\n",
      "Epoch 93 Current Step 0/3: loss is 0.742824\n",
      "------------------------------------------------\n",
      "Epoch 93 Validation acc is 0.944444\n",
      "Epoch 94 Current Step 0/3: loss is 0.722999\n",
      "------------------------------------------------\n",
      "Epoch 94 Validation acc is 0.930556\n",
      "Epoch 95 Current Step 0/3: loss is 0.767046\n",
      "------------------------------------------------\n",
      "Epoch 95 Validation acc is 0.986111\n",
      "Epoch 96 Current Step 0/3: loss is 0.744504\n",
      "------------------------------------------------\n",
      "Epoch 96 Validation acc is 0.944444\n",
      "Epoch 97 Current Step 0/3: loss is 0.738373\n",
      "------------------------------------------------\n",
      "Epoch 97 Validation acc is 0.944444\n",
      "Epoch 98 Current Step 0/3: loss is 0.734115\n",
      "------------------------------------------------\n",
      "Epoch 98 Validation acc is 0.986111\n",
      "Epoch 99 Current Step 0/3: loss is 0.748392\n",
      "------------------------------------------------\n",
      "Epoch 99 Validation acc is 0.958333\n",
      "Epoch 100 Current Step 0/3: loss is 0.735158\n",
      "------------------------------------------------\n",
      "Epoch 100 Validation acc is 0.944444\n",
      "Epoch 101 Current Step 0/3: loss is 0.792370\n",
      "------------------------------------------------\n",
      "Epoch 101 Validation acc is 0.930556\n",
      "Epoch 102 Current Step 0/3: loss is 0.763088\n",
      "------------------------------------------------\n",
      "Epoch 102 Validation acc is 0.930556\n",
      "Epoch 103 Current Step 0/3: loss is 0.764866\n",
      "------------------------------------------------\n",
      "Epoch 103 Validation acc is 0.958333\n",
      "Epoch 104 Current Step 0/3: loss is 0.734513\n",
      "------------------------------------------------\n",
      "Epoch 104 Validation acc is 0.958333\n",
      "Epoch 105 Current Step 0/3: loss is 0.747745\n",
      "------------------------------------------------\n",
      "Epoch 105 Validation acc is 0.986111\n",
      "Epoch 106 Current Step 0/3: loss is 0.767243\n",
      "------------------------------------------------\n",
      "Epoch 106 Validation acc is 0.888889\n",
      "Epoch 107 Current Step 0/3: loss is 0.806696\n",
      "------------------------------------------------\n",
      "Epoch 107 Validation acc is 1.000000\n",
      "Epoch 108 Current Step 0/3: loss is 0.807817\n",
      "------------------------------------------------\n",
      "Epoch 108 Validation acc is 0.888889\n",
      "Epoch 109 Current Step 0/3: loss is 0.817888\n",
      "------------------------------------------------\n",
      "Epoch 109 Validation acc is 0.944444\n",
      "Epoch 110 Current Step 0/3: loss is 0.740260\n",
      "------------------------------------------------\n",
      "Epoch 110 Validation acc is 0.958333\n",
      "Epoch 111 Current Step 0/3: loss is 0.739512\n",
      "------------------------------------------------\n",
      "Epoch 111 Validation acc is 0.944444\n",
      "Epoch 112 Current Step 0/3: loss is 0.774775\n",
      "------------------------------------------------\n",
      "Epoch 112 Validation acc is 0.986111\n",
      "Epoch 113 Current Step 0/3: loss is 0.745467\n",
      "------------------------------------------------\n",
      "Epoch 113 Validation acc is 0.930556\n",
      "Epoch 114 Current Step 0/3: loss is 0.748317\n",
      "------------------------------------------------\n",
      "Epoch 114 Validation acc is 0.958333\n",
      "Epoch 115 Current Step 0/3: loss is 0.755635\n",
      "------------------------------------------------\n",
      "Epoch 115 Validation acc is 0.958333\n",
      "Epoch 116 Current Step 0/3: loss is 0.735765\n",
      "------------------------------------------------\n",
      "Epoch 116 Validation acc is 0.930556\n",
      "Epoch 117 Current Step 0/3: loss is 0.805917\n",
      "------------------------------------------------\n",
      "Epoch 117 Validation acc is 0.944444\n",
      "Epoch 118 Current Step 0/3: loss is 0.772474\n",
      "------------------------------------------------\n",
      "Epoch 118 Validation acc is 0.930556\n",
      "Epoch 119 Current Step 0/3: loss is 0.782029\n",
      "------------------------------------------------\n",
      "Epoch 119 Validation acc is 0.972222\n",
      "Epoch 120 Current Step 0/3: loss is 0.762610\n",
      "------------------------------------------------\n",
      "Epoch 120 Validation acc is 0.930556\n",
      "Epoch 121 Current Step 0/3: loss is 0.779558\n",
      "------------------------------------------------\n",
      "Epoch 121 Validation acc is 0.958333\n",
      "Epoch 122 Current Step 0/3: loss is 0.750101\n",
      "------------------------------------------------\n",
      "Epoch 122 Validation acc is 0.958333\n",
      "Epoch 123 Current Step 0/3: loss is 0.747648\n",
      "------------------------------------------------\n",
      "Epoch 123 Validation acc is 0.930556\n",
      "Epoch 124 Current Step 0/3: loss is 0.775644\n",
      "------------------------------------------------\n",
      "Epoch 124 Validation acc is 0.958333\n",
      "Epoch 125 Current Step 0/3: loss is 0.741157\n",
      "------------------------------------------------\n",
      "Epoch 125 Validation acc is 0.958333\n",
      "Epoch 126 Current Step 0/3: loss is 0.751921\n",
      "------------------------------------------------\n",
      "Epoch 126 Validation acc is 0.958333\n",
      "Epoch 127 Current Step 0/3: loss is 0.752764\n",
      "------------------------------------------------\n",
      "Epoch 127 Validation acc is 0.958333\n",
      "Epoch 128 Current Step 0/3: loss is 0.759930\n",
      "------------------------------------------------\n",
      "Epoch 128 Validation acc is 0.958333\n",
      "Epoch 129 Current Step 0/3: loss is 0.741400\n",
      "------------------------------------------------\n",
      "Epoch 129 Validation acc is 0.958333\n",
      "Epoch 130 Current Step 0/3: loss is 0.785329\n",
      "------------------------------------------------\n",
      "Epoch 130 Validation acc is 0.958333\n",
      "Epoch 131 Current Step 0/3: loss is 0.756099\n",
      "------------------------------------------------\n",
      "Epoch 131 Validation acc is 0.944444\n",
      "Epoch 132 Current Step 0/3: loss is 0.732486\n",
      "------------------------------------------------\n",
      "Epoch 132 Validation acc is 0.958333\n",
      "Epoch 133 Current Step 0/3: loss is 0.732087\n",
      "------------------------------------------------\n",
      "Epoch 133 Validation acc is 0.958333\n",
      "Epoch 134 Current Step 0/3: loss is 0.731777\n",
      "------------------------------------------------\n",
      "Epoch 134 Validation acc is 0.958333\n",
      "Epoch 135 Current Step 0/3: loss is 0.733153\n",
      "------------------------------------------------\n",
      "Epoch 135 Validation acc is 0.958333\n",
      "Epoch 136 Current Step 0/3: loss is 0.766415\n",
      "------------------------------------------------\n",
      "Epoch 136 Validation acc is 0.930556\n",
      "Epoch 137 Current Step 0/3: loss is 0.844886\n",
      "------------------------------------------------\n",
      "Epoch 137 Validation acc is 0.944444\n",
      "Epoch 138 Current Step 0/3: loss is 0.732809\n",
      "------------------------------------------------\n",
      "Epoch 138 Validation acc is 0.958333\n",
      "Epoch 139 Current Step 0/3: loss is 0.765609\n",
      "------------------------------------------------\n",
      "Epoch 139 Validation acc is 0.944444\n",
      "Epoch 140 Current Step 0/3: loss is 0.792044\n",
      "------------------------------------------------\n",
      "Epoch 140 Validation acc is 0.958333\n",
      "Epoch 141 Current Step 0/3: loss is 0.728438\n",
      "------------------------------------------------\n",
      "Epoch 141 Validation acc is 0.958333\n",
      "Epoch 142 Current Step 0/3: loss is 0.772924\n",
      "------------------------------------------------\n",
      "Epoch 142 Validation acc is 0.930556\n",
      "Epoch 143 Current Step 0/3: loss is 0.795641\n",
      "------------------------------------------------\n",
      "Epoch 143 Validation acc is 0.958333\n",
      "Epoch 144 Current Step 0/3: loss is 0.765974\n",
      "------------------------------------------------\n",
      "Epoch 144 Validation acc is 0.958333\n",
      "Epoch 145 Current Step 0/3: loss is 0.778884\n",
      "------------------------------------------------\n",
      "Epoch 145 Validation acc is 0.916667\n",
      "Epoch 146 Current Step 0/3: loss is 0.811962\n",
      "------------------------------------------------\n",
      "Epoch 146 Validation acc is 0.972222\n",
      "Epoch 147 Current Step 0/3: loss is 0.773194\n",
      "------------------------------------------------\n",
      "Epoch 147 Validation acc is 0.944444\n",
      "Epoch 148 Current Step 0/3: loss is 0.829305\n",
      "------------------------------------------------\n",
      "Epoch 148 Validation acc is 0.958333\n",
      "Epoch 149 Current Step 0/3: loss is 0.760714\n",
      "------------------------------------------------\n",
      "Epoch 149 Validation acc is 0.972222\n",
      "Epoch 150 Current Step 0/3: loss is 0.755925\n",
      "------------------------------------------------\n",
      "Epoch 150 Validation acc is 0.958333\n",
      "Epoch 151 Current Step 0/3: loss is 0.768484\n",
      "------------------------------------------------\n",
      "Epoch 151 Validation acc is 0.958333\n",
      "Epoch 152 Current Step 0/3: loss is 0.775649\n",
      "------------------------------------------------\n",
      "Epoch 152 Validation acc is 0.958333\n",
      "Epoch 153 Current Step 0/3: loss is 0.767693\n",
      "------------------------------------------------\n",
      "Epoch 153 Validation acc is 0.958333\n",
      "Epoch 154 Current Step 0/3: loss is 0.736798\n",
      "------------------------------------------------\n",
      "Epoch 154 Validation acc is 0.958333\n",
      "Epoch 155 Current Step 0/3: loss is 0.752790\n",
      "------------------------------------------------\n",
      "Epoch 155 Validation acc is 0.958333\n",
      "Epoch 156 Current Step 0/3: loss is 0.744744\n",
      "------------------------------------------------\n",
      "Epoch 156 Validation acc is 0.958333\n",
      "Epoch 157 Current Step 0/3: loss is 0.729490\n",
      "------------------------------------------------\n",
      "Epoch 157 Validation acc is 0.958333\n",
      "Epoch 158 Current Step 0/3: loss is 0.748303\n",
      "------------------------------------------------\n",
      "Epoch 158 Validation acc is 0.944444\n",
      "Epoch 159 Current Step 0/3: loss is 0.726694\n",
      "------------------------------------------------\n",
      "Epoch 159 Validation acc is 0.972222\n",
      "Epoch 160 Current Step 0/3: loss is 0.743430\n",
      "------------------------------------------------\n",
      "Epoch 160 Validation acc is 0.958333\n",
      "Epoch 161 Current Step 0/3: loss is 0.725306\n",
      "------------------------------------------------\n",
      "Epoch 161 Validation acc is 0.944444\n",
      "Epoch 162 Current Step 0/3: loss is 0.782550\n",
      "------------------------------------------------\n",
      "Epoch 162 Validation acc is 0.972222\n",
      "Epoch 163 Current Step 0/3: loss is 0.761528\n",
      "------------------------------------------------\n",
      "Epoch 163 Validation acc is 0.944444\n",
      "Epoch 164 Current Step 0/3: loss is 0.768743\n",
      "------------------------------------------------\n",
      "Epoch 164 Validation acc is 0.958333\n",
      "Epoch 165 Current Step 0/3: loss is 0.717342\n",
      "------------------------------------------------\n",
      "Epoch 165 Validation acc is 0.958333\n",
      "Epoch 166 Current Step 0/3: loss is 0.759646\n",
      "------------------------------------------------\n",
      "Epoch 166 Validation acc is 0.958333\n",
      "Epoch 167 Current Step 0/3: loss is 0.773468\n",
      "------------------------------------------------\n",
      "Epoch 167 Validation acc is 0.958333\n",
      "Epoch 168 Current Step 0/3: loss is 0.730995\n",
      "------------------------------------------------\n",
      "Epoch 168 Validation acc is 0.958333\n",
      "Epoch 169 Current Step 0/3: loss is 0.751106\n",
      "------------------------------------------------\n",
      "Epoch 169 Validation acc is 0.958333\n",
      "Epoch 170 Current Step 0/3: loss is 0.746804\n",
      "------------------------------------------------\n",
      "Epoch 170 Validation acc is 0.958333\n",
      "Epoch 171 Current Step 0/3: loss is 0.792177\n",
      "------------------------------------------------\n",
      "Epoch 171 Validation acc is 0.958333\n",
      "Epoch 172 Current Step 0/3: loss is 0.778558\n",
      "------------------------------------------------\n",
      "Epoch 172 Validation acc is 0.958333\n",
      "Epoch 173 Current Step 0/3: loss is 0.724678\n",
      "------------------------------------------------\n",
      "Epoch 173 Validation acc is 0.958333\n",
      "Epoch 174 Current Step 0/3: loss is 0.763662\n",
      "------------------------------------------------\n",
      "Epoch 174 Validation acc is 0.944444\n",
      "Epoch 175 Current Step 0/3: loss is 0.785937\n",
      "------------------------------------------------\n",
      "Epoch 175 Validation acc is 0.958333\n",
      "Epoch 176 Current Step 0/3: loss is 0.748168\n",
      "------------------------------------------------\n",
      "Epoch 176 Validation acc is 0.958333\n",
      "Epoch 177 Current Step 0/3: loss is 0.758869\n",
      "------------------------------------------------\n",
      "Epoch 177 Validation acc is 0.958333\n",
      "Epoch 178 Current Step 0/3: loss is 0.725847\n",
      "------------------------------------------------\n",
      "Epoch 178 Validation acc is 0.958333\n",
      "Epoch 179 Current Step 0/3: loss is 0.779301\n",
      "------------------------------------------------\n",
      "Epoch 179 Validation acc is 0.958333\n",
      "Epoch 180 Current Step 0/3: loss is 0.746638\n",
      "------------------------------------------------\n",
      "Epoch 180 Validation acc is 0.958333\n",
      "Epoch 181 Current Step 0/3: loss is 0.790017\n",
      "------------------------------------------------\n",
      "Epoch 181 Validation acc is 0.958333\n",
      "Epoch 182 Current Step 0/3: loss is 0.750428\n",
      "------------------------------------------------\n",
      "Epoch 182 Validation acc is 0.944444\n",
      "Epoch 183 Current Step 0/3: loss is 0.785315\n",
      "------------------------------------------------\n",
      "Epoch 183 Validation acc is 0.958333\n",
      "Epoch 184 Current Step 0/3: loss is 0.724964\n",
      "------------------------------------------------\n",
      "Epoch 184 Validation acc is 0.958333\n",
      "Epoch 185 Current Step 0/3: loss is 0.771996\n",
      "------------------------------------------------\n",
      "Epoch 185 Validation acc is 0.958333\n",
      "Epoch 186 Current Step 0/3: loss is 0.737837\n",
      "------------------------------------------------\n",
      "Epoch 186 Validation acc is 0.958333\n",
      "Epoch 187 Current Step 0/3: loss is 0.732914\n",
      "------------------------------------------------\n",
      "Epoch 187 Validation acc is 0.958333\n",
      "Epoch 188 Current Step 0/3: loss is 0.754661\n",
      "------------------------------------------------\n",
      "Epoch 188 Validation acc is 0.958333\n",
      "Epoch 189 Current Step 0/3: loss is 0.741961\n",
      "------------------------------------------------\n",
      "Epoch 189 Validation acc is 0.972222\n",
      "Epoch 190 Current Step 0/3: loss is 0.763310\n",
      "------------------------------------------------\n",
      "Epoch 190 Validation acc is 0.958333\n",
      "Epoch 191 Current Step 0/3: loss is 0.776672\n",
      "------------------------------------------------\n",
      "Epoch 191 Validation acc is 0.958333\n",
      "Epoch 192 Current Step 0/3: loss is 0.744088\n",
      "------------------------------------------------\n",
      "Epoch 192 Validation acc is 0.958333\n",
      "Epoch 193 Current Step 0/3: loss is 0.785430\n",
      "------------------------------------------------\n",
      "Epoch 193 Validation acc is 0.958333\n",
      "Epoch 194 Current Step 0/3: loss is 0.723643\n",
      "------------------------------------------------\n",
      "Epoch 194 Validation acc is 0.958333\n",
      "Epoch 195 Current Step 0/3: loss is 0.746752\n",
      "------------------------------------------------\n",
      "Epoch 195 Validation acc is 0.958333\n",
      "Epoch 196 Current Step 0/3: loss is 0.762141\n",
      "------------------------------------------------\n",
      "Epoch 196 Validation acc is 0.958333\n",
      "Epoch 197 Current Step 0/3: loss is 0.785208\n",
      "------------------------------------------------\n",
      "Epoch 197 Validation acc is 0.958333\n",
      "Epoch 198 Current Step 0/3: loss is 0.747541\n",
      "------------------------------------------------\n",
      "Epoch 198 Validation acc is 0.958333\n",
      "Epoch 199 Current Step 0/3: loss is 0.755337\n",
      "------------------------------------------------\n",
      "Epoch 199 Validation acc is 0.958333\n"
     ]
    }
   ],
   "source": [
    "import copy \n",
    "\n",
    "LR = 0.01\n",
    "NUM_EPOCH = 200\n",
    "LOG_STEP = 200\n",
    "\n",
    "adv_net = my_net()\n",
    "criterion = torch.nn.CrossEntropyLoss()\n",
    "optim = torch.optim.SGD(adv_net.parameters(), lr=LR, momentum=0.9)\n",
    "scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optim, T_max=NUM_EPOCH)\n",
    "\n",
    "train_all_loss = []\n",
    "val_all_loss = []\n",
    "train_acc = []\n",
    "val_acc = []\n",
    "\n",
    "attacker = torchattacks.PGD(adv_net, eps=16/255, alpha=2/255, steps=20, random_start=True)\n",
    "for epoch in range(NUM_EPOCH):\n",
    "    adv_net.train()\n",
    "    avg_top1 = []\n",
    "    for batch_id, (train_sample, train_labels) in enumerate(train_loader): \n",
    "        train_labels = train_labels.long()\n",
    "        # Generate adv. examples to mix in \n",
    "        atk_inds = np.random.choice(range(train_sample.shape[0]), size=train_sample.shape[0], replace=False)\n",
    "        orig_inds = [ind for ind in range(train_sample.shape[0]) if ind not in atk_inds]\n",
    "        #adv_samples = attacker(normalization(train_sample[atk_inds], mean=mean, std=std).float(), train_labels[atk_inds])\n",
    "        adv_samples = attacker(normalization(train_sample[atk_inds]), train_labels[atk_inds])\n",
    "        orig_samples = train_sample[orig_inds]\n",
    "        mixed_set = torch.cat((adv_samples, orig_samples))\n",
    "        mixed_labels = torch.cat((train_labels[atk_inds], train_labels[orig_inds]))\n",
    "        \n",
    "        # output = adv_net(normalization(mixed_set, mean=mean, std=std).float())\n",
    "        output = adv_net(normalization(mixed_set).float())\n",
    "        loss = criterion(output, mixed_labels)\n",
    "        optim.zero_grad()\n",
    "        loss.backward()\n",
    "        optim.step()\n",
    "        train_all_loss.append(loss.item())\n",
    "        top1 = topk(output, train_labels, ks=(1,))\n",
    "        avg_top1.extend(top1)\n",
    "        if batch_id % LOG_STEP ==0:\n",
    "            print('Epoch {} Current Step {}/{}: loss is {losses:.6f}'\n",
    "                  .format(str(epoch) ,str(batch_id), str(len(train_loader)), losses = loss.item()))\n",
    "    train_acc.append(np.mean(avg_top1))\n",
    "    adv_net.eval()\n",
    "    avg_top1 = []\n",
    "    with torch.no_grad():\n",
    "        for  batch_id, (val_sample, val_labels) in enumerate(val_loader):\n",
    "            val_labels = val_labels.long()\n",
    "            # outputs = adv_net(normalization(val_sample, mean=mean, std=std).float())\n",
    "            outputs = adv_net(normalization(val_sample).float())\n",
    "            loss = criterion(outputs, val_labels)\n",
    "            val_all_loss.append(loss.item())\n",
    "            top1 = topk(outputs, val_labels, ks=(1,))\n",
    "            avg_top1.extend(top1)\n",
    "        print('------------------------------------------------')\n",
    "        print('Epoch {} Validation acc is {top1:.6f}'\n",
    "                  .format(str(epoch), top1 = np.mean(avg_top1)))\n",
    "    val_acc.append(np.mean(avg_top1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 635
    },
    "executionInfo": {
     "elapsed": 764,
     "status": "ok",
     "timestamp": 1652821020003,
     "user": {
      "displayName": "Ian Hardy",
      "userId": "01608928063074550753"
     },
     "user_tz": 420
    },
    "id": "bbCxMV0OTEIU",
    "outputId": "d5a485f4-f377-42bd-f888-7d2da24f67e6"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_loss_fig(train_all_loss, val_all_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 635
    },
    "executionInfo": {
     "elapsed": 724,
     "status": "ok",
     "timestamp": 1652821025123,
     "user": {
      "displayName": "Ian Hardy",
      "userId": "01608928063074550753"
     },
     "user_tz": 420
    },
    "id": "7HR1BaDlNEpT",
    "outputId": "7b33649a-cc66-4ee0-b8bb-26370cfb27f5"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_acc_fig(train_acc, val_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1733,
     "status": "ok",
     "timestamp": 1652821030707,
     "user": {
      "displayName": "Ian Hardy",
      "userId": "01608928063074550753"
     },
     "user_tz": 420
    },
    "id": "DGx1kSBaQyB0",
    "outputId": "067682af-b289-495a-9983-17ef5c53dd38"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number Incorrect (natural model): 40\n",
      "Number Attempted (natural model): 72\n",
      "Number Incorrect (adversarially trained model): 28\n",
      "Number Attempted (adversarially trained model): 72\n"
     ]
    }
   ],
   "source": [
    "from torch import unsqueeze\n",
    "num_wrong_nat = 0\n",
    "num_attempt_nat = 0\n",
    "num_wrong_adv = 0\n",
    "num_attempt_adv = 0\n",
    "for batch_id, (train_sample, train_labels) in enumerate(val_loader):\n",
    "    for j in range(train_sample.shape[0]):\n",
    "        instance = unsqueeze(train_sample[j], 0)\n",
    "        label = unsqueeze(train_labels[j], 0)\n",
    "\n",
    "        attacker_nat = torchattacks.PGD(our_net, eps=8/255, alpha=2/255, steps=20, random_start=True)\n",
    "        adv_image = attacker_nat(normalization(instance).float(), label.long())\n",
    "        model_guess_nat = our_net(normalization(instance).float()).max(1).indices\n",
    "        adv_guess_nat = our_net(normalization(adv_image).float()).max(1).indices\n",
    "        if model_guess_nat !=  adv_guess_nat:\n",
    "            num_wrong_nat += 1\n",
    "        num_attempt_nat += 1\n",
    "\n",
    "        attacker_adv = torchattacks.PGD(adv_net, eps=8/255, alpha=2/255, steps=20, random_start=True)\n",
    "        adv_image = attacker_adv(normalization(instance).float(), label.long())\n",
    "        model_guess_adv = adv_net(normalization(instance).float()).max(1).indices\n",
    "        adv_guess_adv = adv_net(normalization(adv_image).float()).max(1).indices\n",
    "        if model_guess_adv !=  adv_guess_adv:\n",
    "            num_wrong_adv += 1\n",
    "        num_attempt_adv += 1\n",
    "\n",
    "print('Number Incorrect (natural model): {}'.format(num_wrong_nat))\n",
    "print('Number Attempted (natural model): {}'.format(num_attempt_nat))\n",
    "\n",
    "print('Number Incorrect (adversarially trained model): {}'.format(num_wrong_adv))\n",
    "print('Number Attempted (adversarially trained model): {}'.format(num_attempt_adv))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "executionInfo": {
     "elapsed": 153,
     "status": "ok",
     "timestamp": 1652821056715,
     "user": {
      "displayName": "Ian Hardy",
      "userId": "01608928063074550753"
     },
     "user_tz": 420
    },
    "id": "sz_m3ysB_Vtg"
   },
   "outputs": [],
   "source": [
    "torch.save(adv_net.state_dict(), dir_path+'adversarially_trained_model_mnist.pt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "F_faTU9ca52W"
   },
   "source": [
    "# Recourse Generation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "WzOkuTPuUsC3"
   },
   "outputs": [],
   "source": [
    "! pip install carla-recourse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nat_model_state = torch.load('naturally_trained_model_mnist.pt')\n",
    "nat_model = my_net()\n",
    "nat_model.load_state_dict(nat_model_state)\n",
    "\n",
    "adv_model_state = torch.load('adversarially_trained_model_mnist.pt')\n",
    "adv_model = my_net()\n",
    "adv_model.load_state_dict(adv_model_state)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 168
    },
    "executionInfo": {
     "elapsed": 222,
     "status": "error",
     "timestamp": 1651777532410,
     "user": {
      "displayName": "Ian Hardy",
      "userId": "01608928063074550753"
     },
     "user_tz": 420
    },
    "id": "TT1Au4QVnOKN",
    "outputId": "8c925f20-1f7a-48a8-dda4-a59a91d4878a"
   },
   "outputs": [],
   "source": [
    "feature_cols = mnist['feature_names']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "   pixel_0_0  pixel_0_1  pixel_0_2  pixel_0_3  pixel_0_4  pixel_0_5  \\\n",
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       "\n",
       "   pixel_7_1  pixel_7_2  pixel_7_3  pixel_7_4  pixel_7_5  pixel_7_6  \\\n",
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       "4        0.0   0.023772   0.051201   0.050743   0.023732   0.000000   \n",
       "\n",
       "   pixel_7_7  Income_Greater_50k  \n",
       "0        0.0                   0  \n",
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       "2        0.0                   0  \n",
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       "\n",
       "[5 rows x 65 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.DataFrame(normalization(torch.tensor(features)).detach().numpy(), columns=feature_cols)\n",
    "df['Income_Greater_50k'] = label\n",
    "df['Income_Greater_50k'] = df['Income_Greater_50k'].astype(int)\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "SEED = 0\n",
    "BATCH_SIZE = 128 \n",
    "\n",
    "\n",
    "\n",
    "# generate the training data index and validation data index\n",
    "np.random.seed(SEED)\n",
    "random_index = np.random.rand(len(features))  \n",
    "random_index = np.argsort(random_index)\n",
    "train_index = random_index[: int(len(features)*0.8)]  # 80% data for training\n",
    "val_index = random_index[int(len(features)*0.8):]\n",
    "\n",
    "\n",
    "# generate the training data and validation data\n",
    "train_data_df = df.iloc[train_index]\n",
    "val_data_df = df.iloc[val_index]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "VpMiiZtdl_tP"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/myenv/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/opt/anaconda3/envs/myenv/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/opt/anaconda3/envs/myenv/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/opt/anaconda3/envs/myenv/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/opt/anaconda3/envs/myenv/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/opt/anaconda3/envs/myenv/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
      "/opt/anaconda3/envs/myenv/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/opt/anaconda3/envs/myenv/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/opt/anaconda3/envs/myenv/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/opt/anaconda3/envs/myenv/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/opt/anaconda3/envs/myenv/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/opt/anaconda3/envs/myenv/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Using Python-MIP package version 1.12.0 [model.py <module>]\n"
     ]
    }
   ],
   "source": [
    "from carla import Data\n",
    "\n",
    "# Custom data set implementations need to inherit from the Data interface\n",
    "class IncomeDataset(Data):\n",
    "\n",
    "    def __init__(self, data, train, val, feature_cols):\n",
    "        # The data set can e.g. be loaded in the constructor\n",
    "        self._dataset = data\n",
    "        self._train = train\n",
    "        self._val = val\n",
    "        self.feature_cols = feature_cols\n",
    "\n",
    "    # List of all categorical features\n",
    "    @property\n",
    "    def categorical(self):\n",
    "        return []\n",
    "\n",
    "    # List of all continuous features\n",
    "    @property\n",
    "    def continuous(self):\n",
    "        return feature_cols\n",
    "\n",
    "    # List of all immutable features which\n",
    "    # should not be changed by the recourse method\n",
    "    @property\n",
    "    def immutables(self):\n",
    "        return []\n",
    "\n",
    "    # Feature name of the target column\n",
    "    @property\n",
    "    def target(self):\n",
    "        return 'Income_Greater_50k'\n",
    "    \n",
    "    @property\n",
    "    def df(self):\n",
    "        return self._dataset\n",
    "        pass\n",
    "\n",
    "    @property\n",
    "    def df_train(self):\n",
    "        return self._train\n",
    "\n",
    "    @property\n",
    "    def df_test(self):\n",
    "        return self._val\n",
    "\n",
    "    def transform(self, df):\n",
    "        new_vals = normalization(df.values, self._mean, self._std)\n",
    "        return pd.DataFrame(new_vals, columns=self._dataset.columns)\n",
    "\n",
    "    def inverse_transform(self, df):\n",
    "        new_vals = inverse_normalization(df.values, self._mean, self._std)\n",
    "        return pd.DataFrame(new_vals, columns=self._dataset.columns)\n",
    "    \n",
    "    def normalization(tensor, mean, std):\n",
    "        return ((tensor - mean) / std).float()\n",
    "    \n",
    "    def inverse_normalization(x, mean, std):\n",
    "        return (x*std)+mean\n",
    "    \n",
    "ds = IncomeDataset(df, train_data_df, val_data_df, feature_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from carla import MLModel\n",
    "\n",
    "# Custom black-box models need to inherit from\n",
    "# the MLModel interface\n",
    "class MyOwnModel(MLModel):\n",
    "    def __init__(self, data, model, feature_cols):\n",
    "        super().__init__(data)\n",
    "        # The constructor can be used to load or build an\n",
    "        # arbitrary black-box-model\n",
    "        self._mymodel = model\n",
    "        self.feature_cols = feature_cols\n",
    "\n",
    "    # List of the feature order the ml model was trained on\n",
    "    @property\n",
    "    def feature_input_order(self):\n",
    "        return self.feature_cols\n",
    "    # The ML framework the model was trained on\n",
    "    @property\n",
    "    def backend(self):\n",
    "        return \"pytorch\"\n",
    "\n",
    "    # The black-box model object\n",
    "    @property\n",
    "    def raw_model(self):\n",
    "        return self._mymodel\n",
    "\n",
    "    # The predict function outputs\n",
    "    # the continuous prediction of the model\n",
    "    def predict(self, x):\n",
    "        if isinstance(x, np.ndarray):\n",
    "            x = torch.Tensor(x)\n",
    "        elif isinstance(x, pd.DataFrame):\n",
    "            x = torch.Tensor(x.values)\n",
    "        return self._mymodel(x).max(1).indices\n",
    "\n",
    "    # The predict_proba method outputs\n",
    "    # the prediction as class probabilities\n",
    "    def predict_proba(self, x):\n",
    "        if isinstance(x, np.ndarray):\n",
    "            x = torch.Tensor(x)\n",
    "        elif isinstance(x, pd.DataFrame):\n",
    "            x = torch.Tensor(x.values)\n",
    "        logits = self._mymodel(x)\n",
    "        probs = torch.nn.functional.softmax(logits, dim=1)\n",
    "        return probs.detach().numpy()\n",
    "    \n",
    "nat_model_carla = MyOwnModel(ds, nat_model, feature_cols)\n",
    "adv_model_carla = MyOwnModel(ds, adv_model, feature_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from carla import OnlineCatalog, MLModelCatalog\n",
    "from carla.recourse_methods import GrowingSpheres\n",
    "\n",
    "# # get factuals from the data to generate counterfactual examples\n",
    "factuals = ds.df\n",
    "\n",
    "# # load a recourse model and pass black box model\n",
    "gs_1 = GrowingSpheres(nat_model_carla)\n",
    "\n",
    "# # generate counterfactual examples\n",
    "counterfactuals_1 = gs_1.get_counterfactuals(factuals)\n",
    "\n",
    "# # load a recourse model and pass black box model\n",
    "gs_2 = GrowingSpheres(adv_model_carla)\n",
    "\n",
    "# # generate counterfactual examples\n",
    "counterfactuals_2 = gs_2.get_counterfactuals(factuals)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
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     "execution_count": 22,
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       "      <th>pixel_7_4</th>\n",
       "      <th>pixel_7_5</th>\n",
       "      <th>pixel_7_6</th>\n",
       "      <th>pixel_7_7</th>\n",
       "      <th>Income_Greater_50k</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.016625</td>\n",
       "      <td>0.002213</td>\n",
       "      <td>0.053804</td>\n",
       "      <td>0.031427</td>\n",
       "      <td>0.061996</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009914</td>\n",
       "      <td>-0.053396</td>\n",
       "      <td>0.047499</td>\n",
       "      <td>0.085197</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.001740</td>\n",
       "      <td>0.005977</td>\n",
       "      <td>-0.068165</td>\n",
       "      <td>0.053351</td>\n",
       "      <td>-0.048530</td>\n",
       "      <td>...</td>\n",
       "      <td>0.112356</td>\n",
       "      <td>0.011885</td>\n",
       "      <td>-0.049018</td>\n",
       "      <td>0.080475</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.024893</td>\n",
       "      <td>0.110888</td>\n",
       "      <td>0.022472</td>\n",
       "      <td>0.044603</td>\n",
       "      <td>0.019754</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000861</td>\n",
       "      <td>-0.009192</td>\n",
       "      <td>0.037574</td>\n",
       "      <td>0.017920</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>0.100079</td>\n",
       "      <td>0.060570</td>\n",
       "      <td>0.136893</td>\n",
       "      <td>0.014068</td>\n",
       "      <td>0.069958</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.003357</td>\n",
       "      <td>0.073205</td>\n",
       "      <td>0.028445</td>\n",
       "      <td>0.035249</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>-0.133177</td>\n",
       "      <td>0.059716</td>\n",
       "      <td>-0.011684</td>\n",
       "      <td>0.167413</td>\n",
       "      <td>-0.012876</td>\n",
       "      <td>...</td>\n",
       "      <td>0.018249</td>\n",
       "      <td>0.081628</td>\n",
       "      <td>-0.009265</td>\n",
       "      <td>0.062737</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>360 rows × 65 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     pixel_0_0  pixel_0_1  pixel_0_2  pixel_0_3  pixel_0_4  ...  pixel_7_4  \\\n",
       "0     0.016625   0.002213   0.053804   0.031427   0.061996  ...  -0.009914   \n",
       "1          NaN        NaN        NaN        NaN        NaN  ...        NaN   \n",
       "2    -0.001740   0.005977  -0.068165   0.053351  -0.048530  ...   0.112356   \n",
       "3          NaN        NaN        NaN        NaN        NaN  ...        NaN   \n",
       "4     0.024893   0.110888   0.022472   0.044603   0.019754  ...  -0.000861   \n",
       "..         ...        ...        ...        ...        ...  ...        ...   \n",
       "355        NaN        NaN        NaN        NaN        NaN  ...        NaN   \n",
       "356        NaN        NaN        NaN        NaN        NaN  ...        NaN   \n",
       "357   0.100079   0.060570   0.136893   0.014068   0.069958  ...  -0.003357   \n",
       "358        NaN        NaN        NaN        NaN        NaN  ...        NaN   \n",
       "359  -0.133177   0.059716  -0.011684   0.167413  -0.012876  ...   0.018249   \n",
       "\n",
       "     pixel_7_5  pixel_7_6  pixel_7_7  Income_Greater_50k  \n",
       "0    -0.053396   0.047499   0.085197                 1.0  \n",
       "1          NaN        NaN        NaN                 NaN  \n",
       "2     0.011885  -0.049018   0.080475                 1.0  \n",
       "3          NaN        NaN        NaN                 NaN  \n",
       "4    -0.009192   0.037574   0.017920                 1.0  \n",
       "..         ...        ...        ...                 ...  \n",
       "355        NaN        NaN        NaN                 NaN  \n",
       "356        NaN        NaN        NaN                 NaN  \n",
       "357   0.073205   0.028445   0.035249                 1.0  \n",
       "358        NaN        NaN        NaN                 NaN  \n",
       "359   0.081628  -0.009265   0.062737                 1.0  \n",
       "\n",
       "[360 rows x 65 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counterfactuals_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "178"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "factuals.shape[0]-sum(factuals['Income_Greater_50k']==1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "178"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(counterfactuals_1['pixel_0_0'].notnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(counterfactuals_1['pixel_0_0'].notnull())/(factuals.shape[0]-sum(factuals['Income_Greater_50k']==1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.3202247191011236"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(counterfactuals_2['pixel_0_0'].notnull())/(factuals.shape[0]-sum(factuals['Income_Greater_50k']==1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "from typing import List\n",
    "\n",
    "from carla.models.api import MLModel\n",
    "from carla.recourse_methods.api import RecourseMethod\n",
    "from carla.evaluation.distances import get_distances\n",
    "\n",
    "\n",
    "def yNN_manifold(\n",
    "    factuals: Data,\n",
    "    counterfactuals: pd.DataFrame,\n",
    "    recourse_method: RecourseMethod,\n",
    "    mlmodel: MLModel,\n",
    "    y: int = 10,\n",
    "    dist_type: int =1,\n",
    ")-> List[float]:\n",
    "    \"\"\"\n",
    "    TODO\n",
    "    Parameters\n",
    "    ----------\n",
    "    counterfactuals: Generated counterfactual examples\n",
    "    recourse_method: Method we want to benchmark\n",
    "    y: Number of\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    List[List[float]]\n",
    "    \"\"\"\n",
    "    distances = []\n",
    "    N = counterfactuals.shape[0]\n",
    "\n",
    "    positive_class = factuals.df.loc[factuals.df.index[factuals.df[factuals.target]==1]]\n",
    "    \n",
    "    nbrs = NearestNeighbors(n_neighbors=y).fit(positive_class.values)\n",
    "\n",
    "    for i, row in counterfactuals.iterrows():\n",
    "        if pd.isna(row).any():\n",
    "            distances.append(np.nan)\n",
    "            continue\n",
    "        knn = nbrs.kneighbors(row.values.reshape((1, -1)), y, return_distance=False)[0]\n",
    "        distances_local = 0\n",
    "        \n",
    "        for idx in knn:\n",
    "            neighbour = positive_class.iloc[idx]\n",
    "            neighbour = neighbour.drop(mlmodel.data.target)\n",
    "            neighbour = neighbour.values.reshape((1, -1))\n",
    "            row_copy = row.drop(mlmodel.data.target)\n",
    "            row_copy = row_copy.values.reshape((1, -1))\n",
    "            \n",
    "            distances_local += get_distances(row_copy, neighbour)[0][dist_type]\n",
    "        distances.append((1 / y) * distances_local)\n",
    "    return distances\n",
    "\n",
    "def sphere_manifold(\n",
    "    factuals: Data,\n",
    "    counterfactuals: pd.DataFrame,\n",
    "    recourse_method: RecourseMethod,\n",
    "    mlmodel: MLModel,\n",
    "    sphere_factor: float = 0.8,\n",
    "    dist_type: int = 2,\n",
    ")-> List[List[float]]:\n",
    "    \"\"\"\n",
    "    TODO\n",
    "    Parameters\n",
    "    ----------\n",
    "    counterfactuals: Generated counterfactual examples\n",
    "    recourse_method: Method we want to benchmark\n",
    "    y: Number of\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    List[List[float]]\n",
    "    \"\"\"\n",
    "    distances = []\n",
    "    N = counterfactuals.shape[0]\n",
    "\n",
    "    positive_class = factuals.df.loc[factuals.df.index[factuals.df[factuals.target]==1]]\n",
    "    \n",
    "    for i, row in counterfactuals.iterrows():\n",
    "        if pd.isna(row).any():\n",
    "            distances.append(np.nan)\n",
    "            continue\n",
    "        all_deltas = np.asarray(factuals.df.drop(mlmodel.data.target, axis=1) - row.drop(mlmodel.data.target))\n",
    "        all_dists = np.sum(np.square(np.abs(all_deltas)), axis=1, dtype=np.float)\n",
    "        all_euc_dist = np.sqrt(all_dists)\n",
    "        radius = np.mean(all_euc_dist)*sphere_factor\n",
    "\n",
    "        pos_deltas = np.asarray(positive_class.drop(mlmodel.data.target, axis=1) - row.drop(mlmodel.data.target))\n",
    "        pos_dists = np.sum(np.square(np.abs(pos_deltas)), axis=1, dtype=np.float)\n",
    "        pos_euc_dist = np.sqrt(pos_dists)\n",
    "        relevant_neighbors = np.where(pos_euc_dist < radius)[0]\n",
    "        #print(relevant_neighbors)\n",
    "        if len(relevant_neighbors) == 0:\n",
    "            distances.append(np.nan)\n",
    "        else:\n",
    "            relevant_distances = pos_euc_dist[relevant_neighbors]\n",
    "            distances.append(np.mean(relevant_distances))\n",
    "        \n",
    "    return distances\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "ynn_1 = yNN_manifold(ds, counterfactuals_1, gs_1, nat_model_carla)\n",
    "ynn_2 = yNN_manifold(ds, counterfactuals_2, gs_2, adv_model_carla)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "sphere_1 = sphere_manifold(ds, counterfactuals_1, gs_1, nat_model_carla)\n",
    "sphere_2 = sphere_manifold(ds, counterfactuals_2, gs_2, adv_model_carla)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "ynn_1 = [val for val in ynn_1 if not pd.isnull(val)]\n",
    "ynn_2 = [val for val in ynn_2 if not pd.isnull(val)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.353357767815421"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(ynn_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.6282805039590342"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(ynn_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure()\n",
    "plt.boxplot([ynn_1, ynn_2])\n",
    "plt.title('Avg. Distance to 5 Nearest Neighbors- MNIST')\n",
    "plt.xticks([1, 2], ['Naturally Trained', 'Adversarially Trained'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "sphere_1 = [val for val in sphere_1 if not pd.isnull(val)]\n",
    "sphere_2 = [val for val in sphere_2 if not pd.isnull(val)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.29254440784846625"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(sphere_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2760134906213657"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(sphere_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure()\n",
    "plt.boxplot([sphere_1, sphere_2])\n",
    "plt.title('Avg. Distance to Nearest Neighbors in L2 Sphere- MNIST')\n",
    "plt.xticks([1, 2], ['Naturally Trained', 'Adversarially Trained'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dist(\n",
    "    factuals: Data,\n",
    "    counterfactuals: pd.DataFrame,\n",
    "    recourse_method: RecourseMethod,\n",
    "    mlmodel: MLModel,\n",
    "    y: int = 5,\n",
    "    dist_type: int = 1,\n",
    ")-> List[float]:\n",
    "    \"\"\"\n",
    "    TODO\n",
    "    Parameters\n",
    "    ----------\n",
    "    counterfactuals: Generated counterfactual examples\n",
    "    recourse_method: Method we want to benchmark\n",
    "    y: Number of\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    List[List[float]]\n",
    "    \"\"\"\n",
    "    distances = []\n",
    "    N = counterfactuals.shape[0]\n",
    "\n",
    "    for i, row in counterfactuals.iterrows():\n",
    "        if pd.isna(row).any():\n",
    "            distances.append(np.nan)\n",
    "            continue\n",
    "            \n",
    "        original = factuals.df.drop(mlmodel.data.target, axis=1).iloc[i].values.reshape((1,-1))\n",
    "        \n",
    "        row_copy = row.drop(mlmodel.data.target)\n",
    "        row_copy = row_copy.values.reshape((1, -1))\n",
    "        distances.append(get_distances(row_copy, original)[0][dist_type])\n",
    "    return distances\n",
    "\n",
    "dist_1 = dist(ds, counterfactuals_1, gs_1, nat_model_carla)\n",
    "dist_2 = dist(ds, counterfactuals_2, gs_2, adv_model_carla)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "dist_1 = [val for val in dist_1 if not pd.isnull(val)]\n",
    "dist_2 = [val for val in dist_2 if not pd.isnull(val)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure()\n",
    "plt.boxplot([dist_1, dist_2])\n",
    "plt.title('Avg. Distance from Original Point')\n",
    "plt.xticks([1, 2], ['Naturally Trained', 'Adversarially Trained'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "360"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dist_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "360"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ynn_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Proximity to Positive Manifold')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(dist_1, ynn_1)\n",
    "plt.title('Natural Model')\n",
    "plt.xlabel('Distance from Original Point')\n",
    "plt.ylabel('Proximity to Positive Manifold')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Proximity to Positive Manifold')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(dist_2, ynn_2)\n",
    "plt.title('Adversarially Trained Model')\n",
    "plt.xlabel('Distance from Original Point')\n",
    "plt.ylabel('Proximity to Positive Manifold')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Proximity to Positive Manifold')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(dist_1, sphere_1)\n",
    "plt.title('Natural Model')\n",
    "plt.xlabel('Distance from Original Point')\n",
    "plt.ylabel('Proximity to Positive Manifold')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Proximity to Positive Manifold')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(dist_2, sphere_2)\n",
    "plt.title('Natural Model')\n",
    "plt.xlabel('Distance from Original Point')\n",
    "plt.ylabel('Proximity to Positive Manifold')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 240
    },
    "executionInfo": {
     "elapsed": 258,
     "status": "error",
     "timestamp": 1651776890106,
     "user": {
      "displayName": "Ian Hardy",
      "userId": "01608928063074550753"
     },
     "user_tz": 420
    },
    "id": "E9oxOpuDh1DG",
    "outputId": "48222a67-79d6-4431-d876-a24b4c41c75a"
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "ignored",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-5e5e199643c9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# load a catalog dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mdata_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"adult\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mdataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mOnlineCatalog\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraw\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'OnlineCatalog' is not defined"
     ]
    }
   ],
   "source": [
    "#from carla import OnlineCatalog, MLModelCatalog\n",
    "from carla.recourse_methods import GrowingSpheres\n",
    "\n",
    "# load a catalog dataset\n",
    "data_name = \"adult\"\n",
    "dataset = OnlineCatalog(data_name)\n",
    "\n",
    "dataset.raw\n",
    "\n",
    "# load artificial neural network from catalog\n",
    "model = MLModelCatalog(dataset, \"ann\")\n",
    "\n",
    "# get factuals from the data to generate counterfactual examples\n",
    "factuals = dataset.raw.iloc[:10]\n",
    "\n",
    "# load a recourse model and pass black box model\n",
    "gs = GrowingSpheres(model)\n",
    "\n",
    "# generate counterfactual examples\n",
    "counterfactuals = gs.get_counterfactuals(factuals)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "o2M1TMGLa5El"
   },
   "outputs": [],
   "source": [
    "factuals = \n",
    "gs = GrowingSpheres(our_net)\n",
    "\n",
    "# generate counterfactual examples\n",
    "counterfactuals = gs.get_counterfactuals(factuals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "dcw4q9H5a5IH"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "QvOQjKATa5LF"
   },
   "outputs": [],
   "source": []
  }
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