Adversarial-Learning-on-FASHION.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "N_G0ddoulBej",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Load the Adult Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 17319,
     "status": "ok",
     "timestamp": 1653580847298,
     "user": {
      "displayName": "Ian Hardy",
      "userId": "01608928063074550753"
     },
     "user_tz": 420
    },
    "id": "JaPRBx11-3Ye",
    "outputId": "be00962f-3b43-41b9-f7c1-b1c2b166ec38"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mounted at /content/drive\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": 4,
   "metadata": {
    "executionInfo": {
     "elapsed": 165,
     "status": "ok",
     "timestamp": 1653580848990,
     "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": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 3426,
     "status": "ok",
     "timestamp": 1653580853250,
     "user": {
      "displayName": "Ian Hardy",
      "userId": "01608928063074550753"
     },
     "user_tz": 420
    },
    "id": "gPffwDCNlIv6",
    "outputId": "51504f5e-b8b4-4f23-f770-b0b29d903192"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
      "Collecting torchattacks\n",
      "  Using cached torchattacks-3.2.6-py3-none-any.whl (105 kB)\n",
      "Installing collected packages: torchattacks\n",
      "Successfully installed torchattacks-3.2.6\n"
     ]
    }
   ],
   "source": [
    "!pip install torchattacks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "executionInfo": {
     "elapsed": 406,
     "status": "ok",
     "timestamp": 1653580853652,
     "user": {
      "displayName": "Ian Hardy",
      "userId": "01608928063074550753"
     },
     "user_tz": 420
    },
    "id": "HouILEUYlBeo",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "import torchvision.models as models\n",
    "import torchvision.datasets as datasets\n",
    "from torch.utils.data import Subset\n",
    "import matplotlib.pyplot as plt\n",
    "from torch import unsqueeze\n",
    "import torchattacks\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qwqg7d74lBep",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Load the CIFAR dataset as numpy array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1653580867502,
     "user": {
      "displayName": "Ian Hardy",
      "userId": "01608928063074550753"
     },
     "user_tz": 420
    },
    "id": "2olJa1_MGYW8"
   },
   "outputs": [],
   "source": [
    "# Image preprocessing modules\n",
    "transform = transforms.Compose([\n",
    "        transforms.Resize(32),\n",
    "        transforms.ToTensor()\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 431,
     "referenced_widgets": [
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    },
    "executionInfo": {
     "elapsed": 7190,
     "status": "ok",
     "timestamp": 1653580875491,
     "user": {
      "displayName": "Ian Hardy",
      "userId": "01608928063074550753"
     },
     "user_tz": 420
    },
    "id": "eTRlujiVpiOC",
    "outputId": "fbafd48b-0c3b-4855-e8d0-905732f88eb7"
   },
   "outputs": [],
   "source": [
    "# Download data \n",
    "train_data = datasets.FashionMNIST(root = '.',\n",
    "                              train = True,\n",
    "                              download = True, \n",
    "                              transform=transform)\n",
    "test_data = datasets.FashionMNIST(root = '.',\n",
    "                             train = False, \n",
    "                             download = True, \n",
    "                             transform=transform)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "executionInfo": {
     "elapsed": 8492,
     "status": "ok",
     "timestamp": 1653582282943,
     "user": {
      "displayName": "Ian Hardy",
      "userId": "01608928063074550753"
     },
     "user_tz": 420
    },
    "id": "_W7SGBNGKiSo"
   },
   "outputs": [],
   "source": [
    "features_train = np.zeros((12000, 32, 32))\n",
    "label_train = np.zeros((12000,))\n",
    "count = 0\n",
    "for i in range(train_data.data.shape[0]):\n",
    "    features, label = train_data.__getitem__(i)\n",
    "    if label == 0 or label == 1:\n",
    "        features_train[count] = features.detach().numpy()\n",
    "        label_train[count] = label\n",
    "        count += 1\n",
    "\n",
    "features_train = np.asarray(features_train)\n",
    "label_train = np.asarray(label_train)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "executionInfo": {
     "elapsed": 1525,
     "status": "ok",
     "timestamp": 1653582438940,
     "user": {
      "displayName": "Ian Hardy",
      "userId": "01608928063074550753"
     },
     "user_tz": 420
    },
    "id": "t7ocYKKsMD5g"
   },
   "outputs": [],
   "source": [
    "features_test = np.zeros((2000, 32, 32))\n",
    "label_test = np.zeros((2000,))\n",
    "count = 0\n",
    "for i in range(test_data.data.shape[0]):\n",
    "    features, label = test_data.__getitem__(i)\n",
    "    if label == 0 or label == 1:\n",
    "        features_test[count] = features.detach().numpy()\n",
    "        label_test[count] = label\n",
    "        count += 1\n",
    "\n",
    "features_test = np.asarray(features_test)\n",
    "label_test = np.asarray(label_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "executionInfo": {
     "elapsed": 1221,
     "status": "ok",
     "timestamp": 1653582571442,
     "user": {
      "displayName": "Ian Hardy",
      "userId": "01608928063074550753"
     },
     "user_tz": 420
    },
    "id": "0pHi9TQnHvar",
    "outputId": "ecee5505-c393-464a-8ef6-0e7aa4467634"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32, 32)\n",
      "This is a T-shirt:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32, 32)\n",
      "This is a Trouser:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32, 32)\n",
      "This is a T-shirt:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32, 32)\n",
      "This is a Trouser:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32, 32)\n",
      "This is a T-shirt:\n"
     ]
    },
    {
     "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": [
    "# Preview a sample of the images\n",
    "def display_images(n, features_train, label_train, random = True):\n",
    "    label_names = {0: 'T-shirt',\n",
    "                   1:'Trouser'}\n",
    "    if random:\n",
    "        indices = np.random.choice(features_train.shape[0], size=n)\n",
    "    else:\n",
    "        indices = np.arange(n)\n",
    "    for i in indices:\n",
    "        image = features_train[i]\n",
    "        print(image.shape)\n",
    "        label = label_train[i]\n",
    "        print('This is a {}:'.format(label_names[label]))\n",
    "        plt.figure()\n",
    "        plt.imshow(image)\n",
    "        plt.show()\n",
    "display_images(5, features_train, label_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "354lFgeXlBep",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Load the numpy array as torch tensors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "executionInfo": {
     "elapsed": 152,
     "status": "ok",
     "timestamp": 1653587022250,
     "user": {
      "displayName": "Ian Hardy",
      "userId": "01608928063074550753"
     },
     "user_tz": 420
    },
    "id": "TizDYn50lBer",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from torch.utils.data import TensorDataset, DataLoader\n",
    "\n",
    "BATCH_SIZE = 128 \n",
    "# prepare the dataloader\n",
    "train_dataset = TensorDataset(torch.flatten(torch.Tensor(features_train).float(), start_dim=1), torch.Tensor(label_train))\n",
    "val_dataset = TensorDataset(torch.flatten(torch.Tensor(features_test).float(), start_dim=1), torch.Tensor(label_test))\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": 9,
   "metadata": {
    "executionInfo": {
     "elapsed": 157,
     "status": "ok",
     "timestamp": 1653587032905,
     "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=1024, 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": "markdown",
   "metadata": {},
   "source": [
    "## Natural training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 7780,
     "status": "ok",
     "timestamp": 1653587041662,
     "user": {
      "displayName": "Ian Hardy",
      "userId": "01608928063074550753"
     },
     "user_tz": 420
    },
    "id": "NZk7Sg2ZlBes",
    "outputId": "d96815aa-5b24-4c44-ec89-974cc219e0aa",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0 Current Step 0/94: loss is 0.630730\n",
      "------------------------------------------------\n",
      "Epoch 0 Validation acc is 0.980273\n",
      "Epoch 1 Current Step 0/94: loss is 0.022306\n",
      "------------------------------------------------\n",
      "Epoch 1 Validation acc is 0.980566\n",
      "Epoch 2 Current Step 0/94: loss is 0.020232\n",
      "------------------------------------------------\n",
      "Epoch 2 Validation acc is 0.985937\n",
      "Epoch 3 Current Step 0/94: loss is 0.009576\n",
      "------------------------------------------------\n",
      "Epoch 3 Validation acc is 0.985449\n",
      "Epoch 4 Current Step 0/94: loss is 0.015502\n",
      "------------------------------------------------\n",
      "Epoch 4 Validation acc is 0.986426\n",
      "Epoch 5 Current Step 0/94: loss is 0.002156\n",
      "------------------------------------------------\n",
      "Epoch 5 Validation acc is 0.984961\n",
      "Epoch 6 Current Step 0/94: loss is 0.006566\n",
      "------------------------------------------------\n",
      "Epoch 6 Validation acc is 0.984668\n",
      "Epoch 7 Current Step 0/94: loss is 0.037133\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/_c/8lzbqcz53c12ll3lxq05rr2c0000gn/T/ipykernel_25304/1995400715.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     25\u001b[0m         \u001b[0moptim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m         \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 27\u001b[0;31m         \u001b[0moptim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\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     28\u001b[0m         \u001b[0mtrain_all_loss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m         \u001b[0mtop1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtopk\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_labels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/myenv/lib/python3.7/site-packages/torch/optim/lr_scheduler.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     65\u001b[0m                 \u001b[0minstance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_step_count\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m                 \u001b[0mwrapped\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__get__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstance\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 67\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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     68\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m             \u001b[0;31m# Note that the returned function here is no longer a bound method,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/myenv/lib/python3.7/site-packages/torch/autograd/grad_mode.py\u001b[0m in \u001b[0;36mdecorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     24\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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     27\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mF\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/myenv/lib/python3.7/site-packages/torch/optim/sgd.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self, closure)\u001b[0m\n\u001b[1;32m     93\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mp\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mgroup\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'params'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 95\u001b[0;31m                 \u001b[0;32mif\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrad\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\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     96\u001b[0m                     \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     97\u001b[0m                 \u001b[0md_p\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrad\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/myenv/lib/python3.7/site-packages/torch/tensor.py\u001b[0m in \u001b[0;36mgrad\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    940\u001b[0m         \"\"\"\n\u001b[1;32m    941\u001b[0m         \u001b[0mrelevant_args\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 942\u001b[0;31m         \u001b[0;32mfrom\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moverrides\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mhas_torch_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle_torch_function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    943\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mTensor\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mhas_torch_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrelevant_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    944\u001b[0m             \u001b[0;31m# TODO mypy doesn't support @property, see: https://github.com/python/mypy/issues/6185\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/myenv/lib/python3.7/importlib/_bootstrap.py\u001b[0m in \u001b[0;36m_handle_fromlist\u001b[0;34m(module, fromlist, import_, recursive)\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "LR = 0.01\n",
    "NUM_EPOCH = 20\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(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(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": 82,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 706
    },
    "executionInfo": {
     "elapsed": 740,
     "status": "ok",
     "timestamp": 1653587046873,
     "user": {
      "displayName": "Ian Hardy",
      "userId": "01608928063074550753"
     },
     "user_tz": 420
    },
    "id": "MgEC3qmKlBet",
    "outputId": "36daa9da-8cda-4daa-8e86-ceee505be4a5",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['Times New Roman'] not found. Falling back to DejaVu Sans.\n",
      "findfont: Font family ['Times New Roman'] not found. Falling back to DejaVu Sans.\n",
      "findfont: Font family ['Times New Roman'] not found. Falling back to DejaVu Sans.\n",
      "findfont: Font family ['Times New Roman'] not found. Falling back to DejaVu Sans.\n"
     ]
    },
    {
     "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": 83,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 635
    },
    "executionInfo": {
     "elapsed": 672,
     "status": "ok",
     "timestamp": 1653587052417,
     "user": {
      "displayName": "Ian Hardy",
      "userId": "01608928063074550753"
     },
     "user_tz": 420
    },
    "id": "WxF7MWYqlBeu",
    "outputId": "19b09931-6236-4727-f5e9-038640e1f8df",
    "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": 84,
   "metadata": {
    "executionInfo": {
     "elapsed": 164,
     "status": "ok",
     "timestamp": 1653587061533,
     "user": {
      "displayName": "Ian Hardy",
      "userId": "01608928063074550753"
     },
     "user_tz": 420
    },
    "id": "rANc3skVlBew"
   },
   "outputs": [],
   "source": [
    "torch.save(our_net.state_dict(), dir_path+'naturally_trained_model_cifar.pt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0NejlU4smu-Q"
   },
   "source": [
    "# Adversarial Training Procedure\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 418635,
     "status": "ok",
     "timestamp": 1653588178214,
     "user": {
      "displayName": "Ian Hardy",
      "userId": "01608928063074550753"
     },
     "user_tz": 420
    },
    "id": "RF_w5cBSs84F",
    "outputId": "963c4d8d-2130-41af-ccaa-c587a0aa68b8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0 Current Step 0/94: loss is 1.291564\n",
      "------------------------------------------------\n",
      "Epoch 0 Validation acc is 0.975391\n",
      "Epoch 1 Current Step 0/94: loss is 0.146570\n",
      "------------------------------------------------\n",
      "Epoch 1 Validation acc is 0.980762\n",
      "Epoch 2 Current Step 0/94: loss is 0.084298\n",
      "------------------------------------------------\n",
      "Epoch 2 Validation acc is 0.983496\n",
      "Epoch 3 Current Step 0/94: loss is 0.082995\n",
      "------------------------------------------------\n",
      "Epoch 3 Validation acc is 0.984180\n",
      "Epoch 4 Current Step 0/94: loss is 0.084757\n",
      "------------------------------------------------\n",
      "Epoch 4 Validation acc is 0.982227\n",
      "Epoch 5 Current Step 0/94: loss is 0.063995\n",
      "------------------------------------------------\n",
      "Epoch 5 Validation acc is 0.983594\n",
      "Epoch 6 Current Step 0/94: loss is 0.121554\n",
      "------------------------------------------------\n",
      "Epoch 6 Validation acc is 0.985449\n",
      "Epoch 7 Current Step 0/94: loss is 0.057868\n",
      "------------------------------------------------\n",
      "Epoch 7 Validation acc is 0.983203\n",
      "Epoch 8 Current Step 0/94: loss is 0.064152\n",
      "------------------------------------------------\n",
      "Epoch 8 Validation acc is 0.986914\n",
      "Epoch 9 Current Step 0/94: loss is 0.036038\n",
      "------------------------------------------------\n",
      "Epoch 9 Validation acc is 0.986133\n",
      "Epoch 10 Current Step 0/94: loss is 0.040667\n",
      "------------------------------------------------\n",
      "Epoch 10 Validation acc is 0.986914\n",
      "Epoch 11 Current Step 0/94: loss is 0.069600\n",
      "------------------------------------------------\n",
      "Epoch 11 Validation acc is 0.985156\n",
      "Epoch 12 Current Step 0/94: loss is 0.025296\n",
      "------------------------------------------------\n",
      "Epoch 12 Validation acc is 0.985742\n",
      "Epoch 13 Current Step 0/94: loss is 0.020653\n",
      "------------------------------------------------\n",
      "Epoch 13 Validation acc is 0.985254\n",
      "Epoch 14 Current Step 0/94: loss is 0.007905\n",
      "------------------------------------------------\n",
      "Epoch 14 Validation acc is 0.986426\n",
      "Epoch 15 Current Step 0/94: loss is 0.060804\n",
      "------------------------------------------------\n",
      "Epoch 15 Validation acc is 0.986133\n",
      "Epoch 16 Current Step 0/94: loss is 0.040761\n",
      "------------------------------------------------\n",
      "Epoch 16 Validation acc is 0.986621\n",
      "Epoch 17 Current Step 0/94: loss is 0.045945\n",
      "------------------------------------------------\n",
      "Epoch 17 Validation acc is 0.986133\n",
      "Epoch 18 Current Step 0/94: loss is 0.033161\n",
      "------------------------------------------------\n",
      "Epoch 18 Validation acc is 0.985254\n",
      "Epoch 19 Current Step 0/94: loss is 0.038747\n",
      "------------------------------------------------\n",
      "Epoch 19 Validation acc is 0.987891\n",
      "Epoch 20 Current Step 0/94: loss is 0.084257\n",
      "------------------------------------------------\n",
      "Epoch 20 Validation acc is 0.986426\n",
      "Epoch 21 Current Step 0/94: loss is 0.078405\n",
      "------------------------------------------------\n",
      "Epoch 21 Validation acc is 0.987109\n",
      "Epoch 22 Current Step 0/94: loss is 0.026299\n",
      "------------------------------------------------\n",
      "Epoch 22 Validation acc is 0.986426\n",
      "Epoch 23 Current Step 0/94: loss is 0.040664\n",
      "------------------------------------------------\n",
      "Epoch 23 Validation acc is 0.988379\n",
      "Epoch 24 Current Step 0/94: loss is 0.034951\n",
      "------------------------------------------------\n",
      "Epoch 24 Validation acc is 0.986914\n",
      "Epoch 25 Current Step 0/94: loss is 0.030988\n",
      "------------------------------------------------\n",
      "Epoch 25 Validation acc is 0.987891\n",
      "Epoch 26 Current Step 0/94: loss is 0.030858\n",
      "------------------------------------------------\n",
      "Epoch 26 Validation acc is 0.987109\n",
      "Epoch 27 Current Step 0/94: loss is 0.114265\n",
      "------------------------------------------------\n",
      "Epoch 27 Validation acc is 0.985937\n",
      "Epoch 28 Current Step 0/94: loss is 0.008390\n",
      "------------------------------------------------\n",
      "Epoch 28 Validation acc is 0.987207\n",
      "Epoch 29 Current Step 0/94: loss is 0.053030\n",
      "------------------------------------------------\n",
      "Epoch 29 Validation acc is 0.986719\n",
      "Epoch 30 Current Step 0/94: loss is 0.033084\n",
      "------------------------------------------------\n",
      "Epoch 30 Validation acc is 0.987207\n",
      "Epoch 31 Current Step 0/94: loss is 0.009632\n",
      "------------------------------------------------\n",
      "Epoch 31 Validation acc is 0.987207\n",
      "Epoch 32 Current Step 0/94: loss is 0.044460\n",
      "------------------------------------------------\n",
      "Epoch 32 Validation acc is 0.987207\n",
      "Epoch 33 Current Step 0/94: loss is 0.015402\n",
      "------------------------------------------------\n",
      "Epoch 33 Validation acc is 0.986621\n",
      "Epoch 34 Current Step 0/94: loss is 0.026842\n",
      "------------------------------------------------\n",
      "Epoch 34 Validation acc is 0.987402\n",
      "Epoch 35 Current Step 0/94: loss is 0.089770\n",
      "------------------------------------------------\n",
      "Epoch 35 Validation acc is 0.986719\n",
      "Epoch 36 Current Step 0/94: loss is 0.022901\n",
      "------------------------------------------------\n",
      "Epoch 36 Validation acc is 0.986914\n",
      "Epoch 37 Current Step 0/94: loss is 0.021438\n",
      "------------------------------------------------\n",
      "Epoch 37 Validation acc is 0.986914\n",
      "Epoch 38 Current Step 0/94: loss is 0.031364\n",
      "------------------------------------------------\n",
      "Epoch 38 Validation acc is 0.988379\n",
      "Epoch 39 Current Step 0/94: loss is 0.011405\n",
      "------------------------------------------------\n",
      "Epoch 39 Validation acc is 0.987891\n"
     ]
    }
   ],
   "source": [
    "import copy \n",
    "\n",
    "LR = 0.01\n",
    "NUM_EPOCH = 40\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",
    "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",
    "        attacker = torchattacks.PGD(adv_net, eps=8/255, alpha=2/255, steps=32, random_start=True)\n",
    "        train_labels = train_labels.long()\n",
    "        # Generate adv. examples to mix in \n",
    "        atk_inds = np.random.choice(range(train_sample.shape[0])//2, 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(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(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(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": 88,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "executionInfo": {
     "elapsed": 413,
     "status": "ok",
     "timestamp": 1653588178612,
     "user": {
      "displayName": "Ian Hardy",
      "userId": "01608928063074550753"
     },
     "user_tz": 420
    },
    "id": "bbCxMV0OTEIU",
    "outputId": "cb54de5a-4e0d-488c-c11e-cecbd208b039"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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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": 89,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "executionInfo": {
     "elapsed": 442,
     "status": "ok",
     "timestamp": 1653588179050,
     "user": {
      "displayName": "Ian Hardy",
      "userId": "01608928063074550753"
     },
     "user_tz": 420
    },
    "id": "7HR1BaDlNEpT",
    "outputId": "ffb9e214-ab83-4abb-9b67-546c1388c066"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "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": 90,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 67291,
     "status": "ok",
     "timestamp": 1653588246337,
     "user": {
      "displayName": "Ian Hardy",
      "userId": "01608928063074550753"
     },
     "user_tz": 420
    },
    "id": "DGx1kSBaQyB0",
    "outputId": "d95535d5-f755-4b2a-f899-30155e09e9b0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number Incorrect (natural model): 878\n",
      "Number Attempted (natural model): 2000\n",
      "Number Incorrect (adversarially trained model): 77\n",
      "Number Attempted (adversarially trained model): 2000\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=16/255, alpha=2/255, steps=32, random_start=True)\n",
    "        adv_image = attacker_nat(instance.float(), label.long())\n",
    "        model_guess_nat = our_net(instance.float()).max(1).indices\n",
    "        adv_guess_nat = our_net(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=16/255, alpha=2/255, steps=32, random_start=True)\n",
    "        adv_image = attacker_adv(instance.float(), label.long())\n",
    "        model_guess_adv = adv_net(instance.float()).max(1).indices\n",
    "        adv_guess_adv = adv_net(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": 91,
   "metadata": {
    "executionInfo": {
     "elapsed": 8,
     "status": "ok",
     "timestamp": 1653588246337,
     "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_cifar.pt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "F_faTU9ca52W"
   },
   "source": [
    "# Recourse Generation: Setup\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "dcw4q9H5a5IH"
   },
   "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_fashion.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_fashion.pt')\n",
    "adv_model = my_net()\n",
    "adv_model.load_state_dict(adv_model_state)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_cols = []\n",
    "for i in range(32):\n",
    "    for j in range(32):\n",
    "        feature_cols.append('pixel_{}_{}'.format(i, j))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_train = torch.flatten(torch.Tensor(features_train).float(), start_dim=1).detach().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pixel_0_0</th>\n",
       "      <th>pixel_0_1</th>\n",
       "      <th>pixel_0_2</th>\n",
       "      <th>pixel_0_3</th>\n",
       "      <th>pixel_0_4</th>\n",
       "      <th>pixel_0_5</th>\n",
       "      <th>pixel_0_6</th>\n",
       "      <th>pixel_0_7</th>\n",
       "      <th>pixel_0_8</th>\n",
       "      <th>pixel_0_9</th>\n",
       "      <th>...</th>\n",
       "      <th>pixel_31_23</th>\n",
       "      <th>pixel_31_24</th>\n",
       "      <th>pixel_31_25</th>\n",
       "      <th>pixel_31_26</th>\n",
       "      <th>pixel_31_27</th>\n",
       "      <th>pixel_31_28</th>\n",
       "      <th>pixel_31_29</th>\n",
       "      <th>pixel_31_30</th>\n",
       "      <th>pixel_31_31</th>\n",
       "      <th>Income_Greater_50k</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>0.003922</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.498039</td>\n",
       "      <td>0.309804</td>\n",
       "      <td>0.054902</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003922</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003922</td>\n",
       "      <td>0.039216</td>\n",
       "      <td>0.458824</td>\n",
       "      <td>...</td>\n",
       "      <td>0.584314</td>\n",
       "      <td>0.105882</td>\n",
       "      <td>0.011765</td>\n",
       "      <td>0.007843</td>\n",
       "      <td>0.003922</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.392157</td>\n",
       "      <td>0.027451</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1025 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   pixel_0_0  pixel_0_1  pixel_0_2  pixel_0_3  pixel_0_4  pixel_0_5  \\\n",
       "0        0.0        0.0        0.0        0.0        0.0        0.0   \n",
       "1        0.0        0.0        0.0        0.0        0.0        0.0   \n",
       "2        0.0        0.0        0.0        0.0        0.0        0.0   \n",
       "3        0.0        0.0        0.0        0.0        0.0        0.0   \n",
       "4        0.0        0.0        0.0        0.0        0.0        0.0   \n",
       "\n",
       "   pixel_0_6  pixel_0_7  pixel_0_8  pixel_0_9  ...  pixel_31_23  pixel_31_24  \\\n",
       "0   0.003922   0.000000   0.000000   0.000000  ...     0.498039     0.309804   \n",
       "1   0.000000   0.000000   0.000000   0.000000  ...     0.003922     0.000000   \n",
       "2   0.000000   0.000000   0.000000   0.000000  ...     0.000000     0.000000   \n",
       "3   0.000000   0.003922   0.039216   0.458824  ...     0.584314     0.105882   \n",
       "4   0.000000   0.000000   0.000000   0.000000  ...     0.392157     0.027451   \n",
       "\n",
       "   pixel_31_25  pixel_31_26  pixel_31_27  pixel_31_28  pixel_31_29  \\\n",
       "0     0.054902     0.000000     0.000000          0.0          0.0   \n",
       "1     0.000000     0.000000     0.000000          0.0          0.0   \n",
       "2     0.000000     0.000000     0.000000          0.0          0.0   \n",
       "3     0.011765     0.007843     0.003922          0.0          0.0   \n",
       "4     0.000000     0.000000     0.000000          0.0          0.0   \n",
       "\n",
       "   pixel_31_30  pixel_31_31  Income_Greater_50k  \n",
       "0          0.0          0.0                   0  \n",
       "1          0.0          0.0                   0  \n",
       "2          0.0          0.0                   0  \n",
       "3          0.0          0.0                   0  \n",
       "4          0.0          0.0                   1  \n",
       "\n",
       "[5 rows x 1025 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "feature_cols = []\n",
    "for i in range(32):\n",
    "    for j in range(32):\n",
    "        feature_cols.append('pixel_{}_{}'.format(i, j))\n",
    "\n",
    "features_train = torch.flatten(torch.Tensor(features_train).float(), start_dim=1).detach().numpy()\n",
    "df = pd.DataFrame(features_train, columns=feature_cols)\n",
    "df['Income_Greater_50k'] = label_train\n",
    "df['Income_Greater_50k'] = df['Income_Greater_50k'].astype(int)\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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_train))  \n",
    "random_index = np.argsort(random_index)\n",
    "train_index = random_index[: int(len(features_train)*0.8)]  # 80% data for training\n",
    "val_index = random_index[int(len(features_train)*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": 18,
   "metadata": {},
   "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 self.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": 68,
   "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.from_numpy(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": "markdown",
   "metadata": {},
   "source": [
    "## Recourse Generation-- Growing Spheres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/_c/8lzbqcz53c12ll3lxq05rr2c0000gn/T/ipykernel_24851/1245172015.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;31m# # generate counterfactual examples\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mcounterfactuals_1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgs_1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_counterfactuals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfactuals\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     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;31m# # load a recourse model and pass black box model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Documents/CSE_248/CSE_248_Adversarial_Recourse/carla/recourse_methods/catalog/growing_spheres/model.py\u001b[0m in \u001b[0;36mget_counterfactuals\u001b[0;34m(self, factuals)\u001b[0m\n\u001b[1;32m     67\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_categorical_enc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mlmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfeature_input_order\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 69\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mlmodel\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     70\u001b[0m             )\n\u001b[1;32m     71\u001b[0m             \u001b[0mlist_cfs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcounterfactual\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Documents/CSE_248/CSE_248_Adversarial_Recourse/carla/recourse_methods/catalog/growing_spheres/library/gs_counterfactuals.py\u001b[0m in \u001b[0;36mgrowing_spheres_search\u001b[0;34m(instance, keys_mutable, keys_immutable, continuous_cols, binary_cols, feature_order, model, n_search_samples, p_norm, step, max_iter)\u001b[0m\n\u001b[1;32m    108\u001b[0m         \u001b[0;31m# STEP 1 -- SAMPLE POINTS on hyper sphere around instance\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    109\u001b[0m         candidate_counterfactuals_continuous, _ = hyper_sphere_coordindates(\n\u001b[0;32m--> 110\u001b[0;31m             \u001b[0mn_search_samples\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minstance_mutable_replicated_continuous\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhigh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp_norm\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    111\u001b[0m         )\n\u001b[1;32m    112\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Documents/CSE_248/CSE_248_Adversarial_Recourse/carla/recourse_methods/catalog/growing_spheres/library/gs_counterfactuals.py\u001b[0m in \u001b[0;36mhyper_sphere_coordindates\u001b[0;34m(n_search_samples, instance, high, low, p_norm)\u001b[0m\n\u001b[1;32m     28\u001b[0m     \"\"\"\n\u001b[1;32m     29\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 30\u001b[0;31m     \u001b[0mdelta_instance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_search_samples\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minstance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\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     31\u001b[0m     \u001b[0mdist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrand\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_search_samples\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mhigh\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mlow\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mlow\u001b[0m  \u001b[0;31m# length range [l, h)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m     \u001b[0mnorm_p\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLA\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdelta_instance\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mord\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mp_norm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "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[:500]\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,
   "metadata": {},
   "outputs": [],
   "source": [
    "factuals = ds.df[:500]\n",
    "counterfactuals_1 = pd.read_csv('gs_cfs_natural_model_fashion.csv').drop('Unnamed: 0', axis=1)\n",
    "counterfactuals_2 = pd.read_csv('gs_cfs_adv_model_fashion.csv').drop('Unnamed: 0', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "from typing import List\n",
    "from carla import Data\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 connectedness(\n",
    "    factuals: Data,\n",
    "    counterfactuals: pd.DataFrame,\n",
    "    mlmodel: MLModel,\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",
    "    crosses = []\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=1).fit(positive_class.values)\n",
    "\n",
    "    for i, row in counterfactuals.iterrows():\n",
    "        if pd.isna(row).any():\n",
    "            crosses.append(np.nan)\n",
    "            continue\n",
    "        knn = nbrs.kneighbors(row.values.reshape((1, -1)), 1, return_distance=False)[0]\n",
    "        cross = 0\n",
    "        row = row.drop(mlmodel.data.target)\n",
    "        for idx in knn:\n",
    "            neighbour = positive_class.iloc[idx]\n",
    "            neighbour = neighbour.drop(mlmodel.data.target)\n",
    "            step = (neighbour-row)/100\n",
    "            step_df = pd.DataFrame(columns = row.index, index = np.arange(100)).astype(neighbour.dtypes)\n",
    "            for j in range(100):\n",
    "                step_df.iloc[j] = (row+step*(j+1)).astype(np.float64)\n",
    "            preds = []\n",
    "            preds = mlmodel.predict_proba(step_df).argmax(1)\n",
    "            for j, pred in enumerate(preds):\n",
    "                if j < len(preds)-1:\n",
    "                    if pred == 1 and preds[j+1] == 0:\n",
    "                        cross += 1\n",
    "        crosses.append(cross)\n",
    "    return crosses\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "263"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(counterfactuals_1['pixel_0_0'].isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "con_gs_1 = connectedness(ds, counterfactuals_1, nat_model_carla)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "con_gs_2 = connectedness(ds, counterfactuals_2, adv_model_carla)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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.hist(con_gs_1)\n",
    "plt.xlabel('Connectedness Of Natural Model- Fashion MNIST')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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": [
    "plt.figure()\n",
    "plt.hist(con_gs_2)\n",
    "plt.xlabel('Connectedness Of Adversarial Model- Fashion MNIST')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "con_ar_adult_nat = pd.read_csv('connectedness_natural_adult.csv').drop('Unnamed: 0', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "con_ar_adult_adv = pd.read_csv('connectedness_adv_adult.csv').drop('Unnamed: 0', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "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.hist(con_ar_adult_nat)\n",
    "plt.xlabel('Connectedness Of Natural Model- Adult')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "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.hist(con_ar_adult_adv)\n",
    "plt.xlabel('Connectedness Of Adversarial Model- Adult')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum([val for val in con_gs_2 if not pd.isnull(val)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "52"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum([val for val in con_gs_1 if not pd.isnull(val)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(con_gs_1[4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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     "execution_count": 21,
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  {
   "cell_type": "code",
   "execution_count": 22,
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     "execution_count": 22,
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   "source": [
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 1025 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     pixel_0_0  pixel_0_1  pixel_0_2  pixel_0_3  pixel_0_4  ...  pixel_31_28  \\\n",
       "0     1.116168   0.568203  -0.594107   0.384450  -0.101069  ...     1.517370   \n",
       "1    -0.062027   0.111667  -0.125122   0.178089   0.088399  ...    -0.056358   \n",
       "2     0.100965  -0.003192  -0.046114  -0.042365   0.060089  ...    -0.046386   \n",
       "3    -0.939348  -0.797049   0.318105   0.231940  -0.559515  ...     0.029473   \n",
       "4          NaN        NaN        NaN        NaN        NaN  ...          NaN   \n",
       "..         ...        ...        ...        ...        ...  ...          ...   \n",
       "495        NaN        NaN        NaN        NaN        NaN  ...          NaN   \n",
       "496  -0.089768  -0.461623   0.394211  -1.479154   0.183900  ...    -0.535693   \n",
       "497        NaN        NaN        NaN        NaN        NaN  ...          NaN   \n",
       "498        NaN        NaN        NaN        NaN        NaN  ...          NaN   \n",
       "499        NaN        NaN        NaN        NaN        NaN  ...          NaN   \n",
       "\n",
       "     pixel_31_29  pixel_31_30  pixel_31_31  Income_Greater_50k  \n",
       "0      -0.308193     0.255253     0.294801                 1.0  \n",
       "1       0.130624    -0.289321    -0.236128                 1.0  \n",
       "2       0.041966     0.038243     0.133604                 1.0  \n",
       "3      -0.773379    -0.205289    -0.084993                 1.0  \n",
       "4            NaN          NaN          NaN                 NaN  \n",
       "..           ...          ...          ...                 ...  \n",
       "495          NaN          NaN          NaN                 NaN  \n",
       "496     0.736844    -0.791135     0.685059                 1.0  \n",
       "497          NaN          NaN          NaN                 NaN  \n",
       "498          NaN          NaN          NaN                 NaN  \n",
       "499          NaN          NaN          NaN                 NaN  \n",
       "\n",
       "[500 rows x 1025 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counterfactuals_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "234"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "factuals.shape[0]-sum(factuals['Income_Greater_50k']==1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "237"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(counterfactuals_1['pixel_0_0'].notnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0128205128205128"
      ]
     },
     "execution_count": 30,
     "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": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0128205128205128"
      ]
     },
     "execution_count": 31,
     "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": 32,
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def epsilon_connectedness(\n",
    "    x1: pd.DataFrame,\n",
    "    x2: pd.DataFrame,\n",
    "    epsilon: float = 1.\n",
    ") -> pd.DataFrame:\n",
    "    \"\"\"\n",
    "    Parameters\n",
    "    ----------\n",
    "    x1, x2: feature vectors of size [N, d]\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    pd.DataFrame of size [N]\n",
    "    \"\"\"\n",
    "    x1, x2 = x1.to_numpy(), x2.to_numpy()\n",
    "    \n",
    "    # the default distance is L2 norm\n",
    "    distance = np.linalg.norm(x1 - x2, axis=1)\n",
    "    connectedness = distance / epsilon\n",
    "    return connectedness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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": 34,
   "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": 35,
   "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": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "322.9534111276031\n",
      "369.7706679788419\n"
     ]
    }
   ],
   "source": [
    "print(np.mean(ynn_1))\n",
    "print(np.mean(ynn_2))\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([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()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.144302852215045\n",
      "9.70110026234104\n"
     ]
    }
   ],
   "source": [
    "print(np.mean(sphere_1))\n",
    "print(np.mean(sphere_2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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": 41,
   "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": 42,
   "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": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEICAYAAACktLTqAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAAbzUlEQVR4nO3df5xcVX3/8debBDAIJCxZKCaBUMlXxLakuFJQVCpIgVaDLUYpLSGGplaKiFrFylfgW/iK/VZRilIpP5IgChEKxHyBApGUAgbYyG+CJSBIUhIWCOGXqIFP/zhnyM0yszszu5tdTt7Px2Mee++559577szd95w5M3NHEYGZmZVls+FugJmZDT6Hu5lZgRzuZmYFcribmRXI4W5mViCHu5lZgRzu1jJJ10iaMdztaJek0yQ9JWnVcLelWZJ2lvSCpFGDWbeJbc2RdNpAt9Nrm0dKum4wt2mv53Af4SQtlrRG0pYbaX8h6cUcDk9LWiTpY9U6EXFIRMxtclu7DV1rWydpZ+BzwB4R8VvD1IY9JC2QtFbS85JulPTuvtaJiF9ExNYR8Up/22+l7kBIOlrSK/lceU7SXZL+pIn2XRwRB7Wwj5sH3tpNj8N9BJM0GXgvEMCHN+Ku94yIrYG3AXOAsyWdvBH3P5R2Bp6OiCfrLZQ0eih3LumtwC3AvcCuwFuAK4DrJO07HG0aoJ/kc2UccD4wX9J2w9skAyAifBuhN+ArpCD4BrAwl20JPAv8TqVeJ/BLYIc8/wXgCeC/gWNITw67NbnP19UFDgdeBrbP84uBY/L0bsB/AGuBp4BLc/lNeVsvAi8AHwO2AxYCPcCaPD2xsp/FwD/kY34euA4YX1m+H3BrPv7HgaMr98k/Ab8AVgP/Aoypc2wH5vvp1dymOcDk3M5Zef2bSJ2ek4DHgCeBecDYvI1a/Zm5DWuATwLvAu7JbTu7j/v3IuDqOuXnADf12ke1TbWy0bnOrrn8eeAG4NvA93qtP7rJ+/WHwKr8GN4EvKOybA5wWoNjORq4uTL/5rzfLmBsvt968v14ErBZg/Ui34cP5fvv24CAt5POu1fy4/XscP9PvpFuw94A3/p4cGA58CngncBvgB1z+QXA6ZV6xwLX5umD8z/qO4CtgO8x8HDfHFgHHJLnF7M+3H8AfJkUiG8C9mu0LWB74M9yu7bJoXJlZfli4GHgfwFj8vwZedkuOZiOyO3ZHpial50JLAA68nZ/BHy1wfHtD6yozNeCcF4OpzHAJ/J9/9vA1sC/ARf1qv8v+XgPygF0JbADMIH0hPD+BvtfBcysU/6HOcTGNGhTrawW2D8hPaFtQXrSe46+w73u/ZqXfyLfb1sC3wTuqiybQxPhDowGjs+PUS3Yr8rbnQz8FzCr93qV82Qhqfe/M+kJ4eB6dX1rIT+GuwG+NXhg0j/sb8g9LOBB4IQ8fSDwcKXuLcBRefqCarCRetYDCvdcvgo4Mk8vZn24zwPOpdID729bleVTgTWV+cXASZX5T7H+SetLwBV1tiHSq4O3Vsr2BX7eYJ/7Uz/cf7tStgj4VGX+bfmxGF2pP6Gy/GngY5X5y4HPNNj/ulpw9SrfvbbdBm2qlY3OAbgO2Kqy/Hv0He5179c67RiX1x2b5+fQd7ivI/W2nwKW5HNzFPBr0vsatbp/DSyurNc73KudgvnAifXq+tb8zWPuI9cM4LqIeCrPfz+XAdwIbCXpD/K4/FTSuC2kMdzHK9upTrdF0uakoZ9n6iz+Ailgb5d0v6RP9LGdrSR9V9Jjkp4jDQGM6/WpjuonWF4i9ZwBJpF6n711kl4JLJX0rKRngWtzeSuq99NbSEMJNY+RQnXHStnqyvQv68xvTX1PATvVKd+JNFy0pkGbqt4CPBMRLzVRt6bu/SpplKQzJD2cH5NHc53x/WyvZklEjIuI8RGxT0TckNfdnNffhxNabZ+1byS/UbPJkjQGmA6Mqnxcb0tSEO4ZEXdLmk8aolhNGo9/Ptd7AphY2dykQWjSNFIP7fbeCyJiFfBXud37ATdIuikiltfZzudIveA/iIhVkqYCd5KeHPrzOLB3nfKnSGH6johY2cR2GonK9H+ThoFqaj3l1Wx437bjBuCjwIW9yqeT3px8SXrt7gjqewLokLRVJeDbfZz/nPT4HkgK9rGkJ5hmHpNGniK90tkFeCCX7Qy08/g0ug+sH+65j0yHkcZf9yD1yqeS3lz6T+CoXOf7pDcpj8zTNfOBmZLeLmkr4H+32whJHZKOJL3B9bWIeLpOnY9KqgXeGtI/46t5fjVp3LpmG1IQPyupAzi5heZcDBwoabqk0ZK2lzQ1Il4F/hU4U9IOuU0TJP1RC9vu7QfACZJ2lbQ18H9JbxSvG8A2a04F3i3p9Hz/biPpONLj+sVmNhARjwHdwCmStsifsvlQm+3ZBvgVaWhpK9KxDkikj2DOB07Px7cL8FnS0FGrVgMTJW0x0HZtahzuI9MM4MJIn1deVbsBZwNHShodEbeRxprfAlxTWzEirgHOIg3dLCeNg0L6B0bS30u6hr7dLemFvP4xpLH+rzSo+y7gtlx/AXB8RDySl50CzM3DJdNJb9aNYf347LXN3R3ps9vAoaTe/zPAXcCeefEXa8eahxZuIL1CaNcFpE+13AT8nPSG6XED2N5rIuIh0vspe5J6yk+Q3mT+o4i4pYVNHUl6b+Fp4DTgUvJj3KJ5pCGTlaRe9pK+qzftONL5+QhwM6kDckEb2/kxcD+wStJT/VW29ZTftLBCSXo7cB+w5SD1PG0EknQp8GBEtPJqyArmnnuBJH1E0pb5yyRfA37kYC+LpHdJequkzSQdTBo3v3KYm2UjiMO9TH9N+qz1w6Sx+78Z3ubYEPgt0kccXyANw/1NRNw5rC2yEcXDMmZmBXLP3cysQCPic+7jx4+PyZMnD3czzMzeUJYuXfpURNT9wt6ICPfJkyfT3d093M0wM3tDkfRYo2UeljEzK5DD3cysQE2Fu6Rxki6T9KCkZZL2zV+dvl7SQ/nvdrmuJJ0labmkeyTtNbSHYGZmvTXbc/8W6RKhu5O+Nr0MOBFYFBFTSJdIPTHXPQSYkm+zST9CYGZmG1G/4S5pLPA+0k9oERG/johnSd+Iq/2O5lzSxa7I5fMiWUK6kmG9S5yamdkQaabnvivpl1EulHSnpPMkvZn0q0BP5DqrWH+t6wlseG3pFdS5jrOk2ZK6JXX39PS0fwRmZvY6zYT7aGAv4JyI+H3Sld5OrFaI9DXXlr7qGhHnRkRXRHR1drb6uwpmZtaXZsJ9BelnyW7L85eRwn51bbgl/639mvxKNvzhgIm0d5F+MzNrU7/hnq8j/rik2vWxDyBd93kB63/2bQbpx3DJ5UflT83sA6ytDN+Y2SZAUss3G1zNfkP1OODi/GsojwAzSU8M8yXNIl3sf3quezXpRxWWk34LceagttjMRrxGFySU1HCZDa6mwj0i7gK66iw6oE7dAI4dWLPMzGwg/A1VM7MCOdzNzArkcDczK5DD3cysQA53M7MCOdzNzArkcDczK5DD3cysQA53M7MCOdzNzArkcDczK5DD3cysQA53M7MCOdzNzArkcDczK5DD3cysQA53M7MCOdzNzArkcDczK5DD3cysQA53M7MCOdzNzAo0ergbYO2R1NZ6ETHILTGzkcjh/gbVV0hLcoibbeI8LGNmVqCmwl3So5LulXSXpO5c1iHpekkP5b/b5XJJOkvSckn3SNprKA/AzMxer5We+x9GxNSI6MrzJwKLImIKsCjPAxwCTMm32cA5g9VYMzNrzkCGZaYBc/P0XOCwSvm8SJYA4yTtNID9mJlZi5oN9wCuk7RU0uxctmNEPJGnVwE75ukJwOOVdVfksg1Imi2pW1J3T09PG003M7NGmv20zH4RsVLSDsD1kh6sLoyIkNTSxzMi4lzgXICuri5/tMPMbBA11XOPiJX575PAFcDewOracEv++2SuvhKYVFl9Yi4zM7ONpN9wl/RmSdvUpoGDgPuABcCMXG0GcFWeXgAclT81sw+wtjJ8Y2ZmG0EzwzI7Alfkb0SOBr4fEddKugOYL2kW8BgwPde/GjgUWA68BMwc9FabmVmf+g33iHgE2LNO+dPAAXXKAzh2UFpnZmZt8TdUzcwK5HA3MyuQw93MrEAOdzOzAjnczcwK5HA3MyuQw93MrEAOdzOzAjnczcwK5HA3MyuQw93MrEAOdzOzAjnczcwK5HA3MyuQw93MrEAOdzOzAjnczcwK5HA3MyuQw93M2tbR0YGkpm9AS/Ul0dHRMcxH+cbUzA9km5nVtWbNGtLPJg+d2pOCtcY9dzOzAjnczcwK5HA3MyuQw93MrEAOdzOzAjUd7pJGSbpT0sI8v6uk2yQtl3SppC1y+ZZ5fnlePnmI2m5mZg200nM/HlhWmf8acGZE7AasAWbl8lnAmlx+Zq5nZmYbUVPhLmki8MfAeXlewAeAy3KVucBheXpanicvP0D+oKqZ2UbVbM/9m8AXgFfz/PbAsxGxLs+vACbk6QnA4wB5+dpcfwOSZkvqltTd09PTXuvNzKyufsNd0p8AT0bE0sHccUScGxFdEdHV2dk5mJs2M9vkNXP5gfcAH5Z0KPAmYFvgW8A4SaNz73wisDLXXwlMAlZIGg2MBZ4e9JabmVlD/fbcI+JLETExIiYDHwd+HBFHAjcCh+dqM4Cr8vSCPE9e/uMY6otPmJnZBgbyOfcvAp+VtJw0pn5+Lj8f2D6XfxY4cWBNNDOzVrV0VciIWAwsztOPAHvXqfMy8NFBaJuZmbXJ31A1MyuQw93MrEAOdzOzAjnczcwK5HA3MyuQw93MrEAOdzOzAjncR7iOjg4ktXQDWqrf0dExzEdpZoOtpS8x2ca3Zs0ahvrqDb4is1l53HM3MyuQw93MrEAOdzOzAjnczcwK5HA3MyuQw93MrEAOdzOzAvlz7mbWtjh5Wzhl7NDvw1rmcDeztunU5zbKl+zilCHdRZE8LGNmViCHu5lZgRzuZmYFcribmRXI4W5mViCHu5lZgRzuZmYF6jfcJb1J0u2S7pZ0v6RTc/mukm6TtFzSpZK2yOVb5vnlefnkIT4GMzPrpZme+6+AD0TEnsBU4GBJ+wBfA86MiN2ANcCsXH8WsCaXn5nrmZnZRtRvuEfyQp7dPN8C+ABwWS6fCxyWp6flefLyA+TfcTMz26iaGnOXNErSXcCTwPXAw8CzEbEuV1kBTMjTE4DHAfLytcD2dbY5W1K3pO6enp4BHYSZmW2oqXCPiFciYiowEdgb2H2gO46IcyOiKyK6Ojs7B7o5MzOraOnTMhHxLHAjsC8wTlLtwmMTgZV5eiUwCSAvHws8PRiNNTOz5jTzaZlOSePy9Bjgg8AyUsgfnqvNAK7K0wvyPHn5j2OoLxtnZmYbaOaSvzsBcyWNIj0ZzI+IhZIeAC6RdBpwJ3B+rn8+cJGk5cAzwMeHoN2bDF8v28zaoZHQqe7q6oru7u7hbsaIJGnjXC97BJwH9sbj83N4SVoaEV31lvkbqmZmBXK4m5kVyOFuZlYgh7uZWYEc7mZmBXK4m5kVyOFuZlYgh7uZWYEc7mZmBXK4m5kVyOFuZlagZi4cZmbW0FD/0Np22203pNsvlcPdzNrW6gW9fBGwjcfDMmZmBXK4m5kVyOFuZlYgh7uZWYEc7mZmBXK4m5kVyOFuZlYgh7uZWYEc7mZmBXK4m5kVyOFuZlagfsNd0iRJN0p6QNL9ko7P5R2Srpf0UP67XS6XpLMkLZd0j6S9hvogzMxsQ8303NcBn4uIPYB9gGMl7QGcCCyKiCnAojwPcAgwJd9mA+cMeqvNzKxP/YZ7RDwRET/N088Dy4AJwDRgbq42FzgsT08D5kWyBBgnaafBbriZmTXW0pi7pMnA7wO3ATtGxBN50Spgxzw9AXi8stqKXNZ7W7MldUvq7unpabXdZmbWh6bDXdLWwOXAZyLiueqySBdobukizRFxbkR0RURXZ2dnK6uamVk/mvqxDkmbk4L94oj4t1y8WtJOEfFEHnZ5MpevBCZVVp+Yy6xN/qUbM2tVM5+WEXA+sCwivlFZtACYkadnAFdVyo/Kn5rZB1hbGb6xFkVEy7dW13vmmWeG+SjNbLA103N/D/CXwL2S7splfw+cAcyXNAt4DJiel10NHAosB14CZg5mg83MrH/9hntE3Aw0Ghc4oE79AI4dYLvMzGwA/A1VM7MCOdzNzArkcDczK5DD3cysQA53M7MCOdzNzArkcDczK5DD3cysQA53M7MCOdzNzArkcDczK5DD3cysQA53M7MCOdzNzArkcDczK5DD3cysQA53M7MCOdzNzArkcDczK5DD3cysQA53M7MCOdzNzArkcDczK5DD3cysQA53M7MC9Rvuki6Q9KSk+yplHZKul/RQ/rtdLpeksyQtl3SPpL2GsvFmZlZfMz33OcDBvcpOBBZFxBRgUZ4HOASYkm+zgXMGp5lmZtaKfsM9Im4CnulVPA2Ym6fnAodVyudFsgQYJ2mnQWqrmZk1qd0x9x0j4ok8vQrYMU9PAB6v1FuRy15H0mxJ3ZK6e3p62myGmZnVM+A3VCMigGhjvXMjoisiujo7OwfaDDMzq2g33FfXhlvy3ydz+UpgUqXexFxmZmYbUbvhvgCYkadnAFdVyo/Kn5rZB1hbGb4xM7ONZHR/FST9ANgfGC9pBXAycAYwX9Is4DFgeq5+NXAosBx4CZg5BG02M7N+9BvuEXFEg0UH1KkbwLEDbZSZmQ2Mv6FqZlYgh7uZWYEc7mZmBXK4m5kVyOFuZlYgh7uZWYEc7mZmBXK4m5kVyOFuZlYgh7uZWYEc7mZmBXK4m5kVyOFuZlYgh7uZWYEc7mZmBXK4m5kVyOFuZlYgh7uZWYEc7mZmBXK4m5kVyOFuZlYgh7uZWYEc7mZmBRo93A0ws/JIanlZRAxVczZJDnczG3QO6uE3JMMykg6W9DNJyyWdOBT7MDOzxga95y5pFPBt4IPACuAOSQsi4oHB3temrK+XvX0td4/KbNMwFMMyewPLI+IRAEmXANMAh/sgckibWV+GYlhmAvB4ZX5FLtuApNmSuiV19/T0DEEzzMw2XcP2UciIODciuiKiq7Ozc7iaYWZWpKEI95XApMr8xFxmZmYbyVCE+x3AFEm7StoC+DiwYAj2Y2ZmDQz6G6oRsU7S3wL/DowCLoiI+wd7P2Zm1tiQfIkpIq4Grh6KbZuZWf98bRkzswI53M3MCqSR8GUYST3AY8PdjoKMB54a7kaY1eFzc3DtEhF1P0s+IsLdBpek7ojoGu52mPXmc3Pj8bCMmVmBHO5mZgVyuJfp3OFugFkDPjc3Eo+5m5kVyD13M7MCOdzNzArkcG+BpJD09cr85yWd0s86+0t69yC341FJ4/P0C02u87uS7sq3ZyT9PE/f0OT6Hx6sn0yUdIqkzw/GtjYVkg7L59/ufdRZLGnYP2YoqUvSWf3U2V/Swjx9tKSzm9z2lyvn8SuV6U83uf55kvZopm4T23rt/3Ak8g9kt+ZXwJ9K+mpENPtFjP2BF4Bbm92JpNERsa6N9jUUEfcCU/P25wALI+KyZvcbEQvw1T2H0xHAzfnvyUO5I0mjIuKVNtcdHRHdQPcgNwuAiDgdOD3v64WImNpr/yK9l/hqg/WPGYp2jUTuubdmHend/hN6L5D0IUm3SbpT0g2SdpQ0GfgkcELuXbxX0hxJh1fWeyH/3V/Sf0paQP5JQklXSloq6X5Js/tqmKR5kg6rzF8saVp/B5R7e9+U1A0cX+84cr3Xelf5GM6SdKukR3odz99JukPSPZJOrZR/WdJ/SboZeFt/7bL1JG0N7AfMIl1Cu1Y+RtIlkpZJugIYk8s/Ken/VepVH7u/kHR7Ph+/m3/zGEkvSPq6pLuBfSWdIemB/Dj+U67T6Nw4RdJFkm4BLurVK99b0k/yOrdKavjYS9omv6LcPM9vW53vY73Jkn4maR5wHzBJ0jlKv/R2f6/z8LVXN/mYT5d0t6QllePplHR5Po/vkPSeXL69pOvyNs8D+v4h4+EWEb41eSP1wLcFHgXGAp8HTsnLtmP9p4+OAb6ep08BPl/Zxhzg8Oo289/9gReBXSvLOvLfMaSTdvs8/ygwvtf67weuzNNjgZ8Doxscx2ttABYD36ksa3QcRwNnV9b/IalzsAfpN3MBDiI9+SkvWwi8D3gncC+wVb7/llfvE9/6Pe+OBM7P07cC78zTnyVdUhvg90idjy6gs/aY5GXXkJ4c3g78CNg8l38HOCpPBzA9T28P/KxyHoxr4hxfCoypnMsL8/S2tfMQOBC4vE6d6rl1IXBYnp5d20ej/8f8dzLwKrBPnf+dUfkc/73K+d5VOeYP5el/BE7K098H9svTOwPL8vRZwFfy9B/n9ccP9/nR6OZhmRZFxHO5h/Bp4JeVRROBSyXtBGxBCtdW3R4R1fU+LekjeXoSMAV4ukG7/kPSdyR1An9G+idqdmjn0sp0s8dxZaSXvg/UejykcD8IuDPPb53bvA1wRUS8BJBfnVjzjgC+lacvyfNLSU+cZwFExD2S7snTPfkV1T7AQ8DuwC3AsaQn2jvS6AVjgCfzdl8BLs/Ta4GXgfNzD3xhLu/r3FgQEdX/h5qxwFxJU0hh2GcvHDgP+AJwJTAT+Kt+6tc8FhFLKvPT86vd0cBOpE7IPb3W+TXrj20p8ME8fSCwR76PALbNr57eB/wpQET8f0lrmmzbsHC4t+ebwE9JvYyafwa+ERELJO1P6s3Us448HCZpM9I/Sc2LtYm8jQOBfSPiJUmLgTf10655wF+QXrrPbOI4Xrdfmj+OX1WmVfn71Yj4brWipM+00BarkNQBfAD4XUlB6omGpL/rZ9VLgOnAg6Qn1lBKq7kR8aU69V+OPM4e6Qd39gYOAA4H/ja3oa9z40Xq+wfgxoj4iNIw5eK+Gh0Rt+Rhlv2BURFxXz/H+br9S9qV9Kr6XRGxRuk9pnr/O7+J3A0nPbnV8nAz0quAl6uVK2H/huAx9zZExDPAfNIYaM1Y1v9W7IxK+fOknmvNo6TeE8CHadyTGQusycG+O7BPE02bA3wmt/GBJuo32m+942jGvwOfyL0cJE2QtANwE3BYHiPeBvhQm23bFB0OXBQRu0TE5IiYROoxv5d0v/45gKTfIQ3N1FwBTCP18i/JZYuAw/NjgqQOSbv03mF+/MZG+tGdE4A986J2zo3qOkc3uc480tDIhf1VbGBbUtivza8qD2lx/euA42ozkqbmyer9fQhpmGrEcri37+uky5fWnAL8UNJSNryk6Y+Aj+Q3sN4L/Cvw/tobVzTu8VwLjJa0DDgDWNKg3msiYjWwjPb/KaDxcfQrIq4j/VP+RNK9wGXANhHxU9LQz92k8d87BtC+Tc0RpKCuujyXnwNsnc+R/0MaWgAgItaQzoVdIuL2XPYAcBJwXR7CuZ40ZNHbNsDCXOdm0tg+tHdu/CPwVUl30vxIwcWk4PxBk/U3EBF3k4YGHySdj7e0uIlPA135zeQHSB+KADgVeJ+k+0nDM79op30biy8/UBBJW5HeuNwrItYOd3vM2qH06atpEfGXw92WNzKPuRdC0oHA+cCZDnZ7o5L0z6RhlEOHuy1vdO65m5kVyGPuZmYFcribmRXI4W5mViCHu5lZgRzuZmYF+h/r1IC5ENTQ6QAAAABJRU5ErkJggg==\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()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Proximity to Positive Manifold')"
      ]
     },
     "execution_count": 44,
     "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')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "id": "QvOQjKATa5LF"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Proximity to Positive Manifold')"
      ]
     },
     "execution_count": 45,
     "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')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "counterfactuals_1.to_csv('gs_cfs_natural_model_fashion.csv')\n",
    "counterfactuals_2.to_csv('gs_cfs_adv_model_fashion.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Recourse Generation: Actionable Recourse\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "custom_bounds = {}\n",
    "for col in ds.df[nat_model_carla.feature_input_order].columns:\n",
    "    custom_bounds[col] = (0, 1, \"percentile\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.49186937683796467"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "a = [-2.2,-0.2,0.8,-0.2]\n",
    "a = [0.6,1.6,-1.4,1.6]\n",
    "b= [-1.5,-0.5,1.5,0.5]\n",
    "from numpy import dot\n",
    "from numpy.linalg import norm\n",
    "\n",
    "cos_sim = dot(a, b)/(norm(a)*norm(b))\n",
    "cos_sim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Start generating LIME coefficients [model.py get_counterfactuals]\n",
      "[INFO] Finished generating LIME coefficients [model.py get_counterfactuals]\n"
     ]
    },
    {
     "ename": "AssertionError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n",
      "\u001b[0;32m~/Documents/CSE_248/CSE_248_Adversarial_Recourse/carla/recourse_methods/catalog/actionable_recourse/model.py\u001b[0m in \u001b[0;36mget_counterfactuals\u001b[0;34m(self, factuals)\u001b[0m\n\u001b[1;32m    217\u001b[0m             )\n\u001b[1;32m    218\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 219\u001b[0;31m                 \u001b[0mfs_pop\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpopulate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtotal_items\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fs_size\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    220\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mValueError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    221\u001b[0m                 log.warning(\n",
      "\u001b[0;32m/opt/anaconda3/envs/myenv/lib/python3.7/site-packages/recourse/flipset.py\u001b[0m in \u001b[0;36mpopulate\u001b[0;34m(self, total_items, enumeration_type, cost_type, time_limit, node_limit, display_flag)\u001b[0m\n\u001b[1;32m    172\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    173\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_builder\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 174\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_builder\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRecourseBuilder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maction_set\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maction_set\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcoefficients\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_coefs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mintercept\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_intercept\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmip_cost_type\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcost_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msolver\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_solver\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    175\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    176\u001b[0m         \u001b[0mitems\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_builder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpopulate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtotal_items\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtotal_items\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0menumeration_type\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0menumeration_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtime_limit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime_limit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnode_limit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnode_limit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdisplay_flag\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdisplay_flag\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/myenv/lib/python3.7/site-packages/recourse/builder.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, action_set, x, **kwargs)\u001b[0m\n\u001b[1;32m    630\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    631\u001b[0m         \u001b[0;31m## initialize base class\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 632\u001b[0;31m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maction_set\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maction_set\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    633\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    634\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/myenv/lib/python3.7/site-packages/recourse/builder.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, action_set, x, **kwargs)\u001b[0m\n\u001b[1;32m     85\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_x\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     86\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 87\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     88\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     89\u001b[0m         \u001b[0;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_rep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/myenv/lib/python3.7/site-packages/recourse/builder.py\u001b[0m in \u001b[0;36mx\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m    180\u001b[0m         \u001b[0;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_variables\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    181\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_x\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 182\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild_mip\u001b[0m\u001b[0;34m(\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    183\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    184\u001b[0m     \u001b[0;31m#### model ####\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/myenv/lib/python3.7/site-packages/recourse/builder.py\u001b[0m in \u001b[0;36mbuild_mip\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    646\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    647\u001b[0m         \u001b[0;31m# cost/action information\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 648\u001b[0;31m         \u001b[0mbuild_info\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_mip_build_info\u001b[0m\u001b[0;34m(\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    649\u001b[0m         \u001b[0;31m## TODO: note: if actiongrid is empty, build_info, indices == {}. Correct handling?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    650\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/myenv/lib/python3.7/site-packages/recourse/builder.py\u001b[0m in \u001b[0;36m_get_mip_build_info\u001b[0;34m(self, cost_function_type, validate)\u001b[0m\n\u001b[1;32m    420\u001b[0m         \u001b[0;31m# get names of variables associated with constraints\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    421\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mvalidate\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 422\u001b[0;31m             \u001b[0;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_mip_build_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbuild_info\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    423\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    424\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mbuild_info\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindices\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/myenv/lib/python3.7/site-packages/recourse/builder.py\u001b[0m in \u001b[0;36m_check_mip_build_info\u001b[0;34m(self, build_info)\u001b[0m\n\u001b[1;32m    431\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mbuild_info\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    432\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 433\u001b[0;31m             \u001b[0;32massert\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'coef'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.0\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    434\u001b[0m             \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'actions'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    435\u001b[0m             \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'costs'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAssertionError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "%%time\n",
    "#from carla import OnlineCatalog, MLModelCatalog\n",
    "from carla.recourse_methods import ActionableRecourse\n",
    "\n",
    "# # get factuals from the data to generate counterfactual examples\n",
    "factuals = ds.df[:2]\n",
    "\n",
    "DEFAULT_HYPERPARAMS = {\n",
    "        \"fs_size\": 500,\n",
    "        \"discretize\": True,\n",
    "        \"sample\": True,\n",
    "    }\n",
    "\n",
    "# # load a recourse model and pass black box model\n",
    "ar_1 = ActionableRecourse(nat_model_carla, DEFAULT_HYPERPARAMS, custom_bounds = custom_bounds)\n",
    "\n",
    "# # generate counterfactual examples\n",
    "counterfactuals_ar_1 = ar_1.get_counterfactuals(factuals)\n",
    "\n",
    "# # load a recourse model and pass black box model\n",
    "ar_2 = ActionableRecourse(adv_model_carla, DEFAULT_HYPERPARAMS)\n",
    "\n",
    "# # generate counterfactual examples\n",
    "counterfactuals_ar_2 = gs_2.get_counterfactuals(factuals)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([], dtype=float64)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(0,0,0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "list_ = []\n",
    "for col in ds.df[nat_model_carla.feature_input_order].columns:\n",
    "    list_.append(sum(np.isinf((ds.df[nat_model_carla.feature_input_order][col].values))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "mins = []\n",
    "maxs = []\n",
    "for col in ds.df[nat_model_carla.feature_input_order].columns:\n",
    "    mins.append(min(ds.df[nat_model_carla.feature_input_order][col]))\n",
    "    maxs.append(max(ds.df[nat_model_carla.feature_input_order][col]))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.027450980618596077,\n",
       " 0.0784313753247261,\n",
       " 0.3529411852359772,\n",
       " 0.34117648005485535,\n",
       " 0.4431372582912445,\n",
       " 0.6980392336845398,\n",
       " 0.729411780834198,\n",
       " 0.8313725590705872,\n",
       " 0.8549019694328308,\n",
       " 0.9215686321258545,\n",
       " 0.9686274528503418,\n",
       " 0.9607843160629272,\n",
       " 0.9921568632125854,\n",
       " 0.9843137264251709,\n",
       " 0.9960784316062927,\n",
       " 1.0,\n",
       " 1.0,\n",
       " 1.0,\n",
       " 0.9960784316062927,\n",
       " 0.9764705896377563,\n",
       " 0.9725490212440491,\n",
       " 0.9725490212440491,\n",
       " 0.9647058844566345,\n",
       " 0.9411764740943909,\n",
       " 0.9411764740943909,\n",
       " 0.7647058963775635,\n",
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       " ...]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maxs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "cols_to_adjust = []\n",
    "for col in ds.df[nat_model_carla.feature_input_order].columns:\n",
    "    if all(ds.df[nat_model_carla.feature_input_order][col]==0):\n",
    "        cols_to_adjust.append(col)\n",
    "cols_to_adjust\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
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     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "sum(list_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
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       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</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>11995</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003922</td>\n",
       "      <td>0.003922</td>\n",
       "      <td>0.003922</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003922</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11996</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11997</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11998</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11999</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12000 rows × 1025 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       pixel_0_0  pixel_0_1  pixel_0_2  pixel_0_3  pixel_0_4  ...  \\\n",
       "0            0.0        0.0   0.000000   0.000000   0.000000  ...   \n",
       "1            0.0        0.0   0.000000   0.000000   0.000000  ...   \n",
       "2            0.0        0.0   0.000000   0.000000   0.000000  ...   \n",
       "3            0.0        0.0   0.000000   0.000000   0.000000  ...   \n",
       "4            0.0        0.0   0.000000   0.000000   0.000000  ...   \n",
       "...          ...        ...        ...        ...        ...  ...   \n",
       "11995        0.0        0.0   0.003922   0.003922   0.003922  ...   \n",
       "11996        0.0        0.0   0.000000   0.000000   0.000000  ...   \n",
       "11997        0.0        0.0   0.000000   0.000000   0.000000  ...   \n",
       "11998        0.0        0.0   0.000000   0.000000   0.000000  ...   \n",
       "11999        0.0        0.0   0.000000   0.000000   0.000000  ...   \n",
       "\n",
       "       pixel_31_28  pixel_31_29  pixel_31_30  pixel_31_31  Income_Greater_50k  \n",
       "0         0.000000          0.0          0.0          0.0                   0  \n",
       "1         0.000000          0.0          0.0          0.0                   0  \n",
       "2         0.000000          0.0          0.0          0.0                   0  \n",
       "3         0.000000          0.0          0.0          0.0                   0  \n",
       "4         0.000000          0.0          0.0          0.0                   1  \n",
       "...            ...          ...          ...          ...                 ...  \n",
       "11995     0.003922          0.0          0.0          0.0                   0  \n",
       "11996     0.000000          0.0          0.0          0.0                   1  \n",
       "11997     0.000000          0.0          0.0          0.0                   1  \n",
       "11998     0.000000          0.0          0.0          0.0                   1  \n",
       "11999     0.000000          0.0          0.0          0.0                   0  \n",
       "\n",
       "[12000 rows x 1025 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.14907119849998596"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from numpy import dot\n",
    "from numpy.linalg import norm\n",
    "\n",
    "a = np.asarray([0,0,1.5,0,0.75,0])\n",
    "b = np.asarray([3,3,0,0,1.5,0])\n",
    "cos_sim = dot(a, b)/(norm(a)*norm(b))\n",
    "cos_sim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7745966692414834"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy import spatial\n",
    "\n",
    "result = 1 - spatial.distance.cosine(a, b)\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.3571062644833445"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(1.5**2+0.75**2)*np.sqrt(1.5**2+1.5**2+1.5**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.375"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(1.5*1.5+1.5*0.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7745966692414833"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3.375/4.3571062644833445"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.1188618555710315"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "4.875/4.3571062644833445"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid character in identifier (614721979.py, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"/var/folders/_c/8lzbqcz53c12ll3lxq05rr2c0000gn/T/ipykernel_25304/614721979.py\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    a = np.asarray([0, 0, 1.5, 0, 0.75, 0])\u001b[0m\n\u001b[0m                          ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid character in identifier\n"
     ]
    }
   ],
   "source": [
    "a = np.asarray([0, 0, 1.5, 0, 0.75, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 0, 1.5, 0, 0.75, 0]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[0,0,1.5,0,0.75,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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