notebooks/mnist.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prepare data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torchvision.datasets as dsets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load train set\n",
    "train = dsets.MNIST('../', train=True, download=True)\n",
    "X_train = train.data.reshape(-1, 784).float() / 255.0\n",
    "Y_train = train.targets.long()\n",
    "num_features = X_train.shape[1]\n",
    "\n",
    "# Create validation set\n",
    "ordering = torch.randperm(len(X_train))\n",
    "X_train = X_train[ordering]\n",
    "Y_train = Y_train[ordering]\n",
    "X_train, X_val = X_train[6000:], X_train[:6000]\n",
    "Y_train, Y_val = Y_train[6000:], Y_train[:6000]\n",
    "\n",
    "# Load test set\n",
    "test = dsets.MNIST('../', train=False, download=True)\n",
    "X_test = test.data.reshape(-1, 784).float() / 255.0\n",
    "Y_test = test.targets.long()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train model with missing data\n",
    "\n",
    "- Model will accommodate feature removal automatically."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import matplotlib.pyplot as plt\n",
    "from torch.utils.data import TensorDataset, DataLoader\n",
    "from rexplain.torch import MaskLayer2d\n",
    "from copy import deepcopy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare device\n",
    "device = torch.device('cuda')\n",
    "\n",
    "# Prepare validation data\n",
    "X_val_missing = X_val.repeat(100, 1)\n",
    "Y_val_missing = Y_val.repeat(100)\n",
    "\n",
    "# Random subsets\n",
    "S_val = torch.ones(X_val_missing.shape)\n",
    "num_included = np.random.choice(num_features + 1, size=len(S_val))\n",
    "for i in range(len(S_val)):\n",
    "    S_val[i, num_included[i]:] = 0\n",
    "    S_val[i] = S_val[i, torch.randperm(num_features)]\n",
    "\n",
    "# Create dataset iterator\n",
    "val_set = TensorDataset(X_val_missing, Y_val_missing, S_val)\n",
    "val_loader = DataLoader(val_set, batch_size=2056)\n",
    "\n",
    "def validate(model):\n",
    "    '''Measure performance on validation set.'''\n",
    "    with torch.no_grad():\n",
    "        # Setup\n",
    "        mean_loss = 0\n",
    "        N = 0\n",
    "\n",
    "        # Iterate over validation set\n",
    "        for x, y, S in val_loader:\n",
    "            x = x.to(device)\n",
    "            y = y.to(device)\n",
    "            S = S.to(device)\n",
    "            pred = model((x.view(-1, 1, 28, 28),\n",
    "                          S.view(-1, 1, 28, 28)))\n",
    "            loss = loss_fn(pred, y)\n",
    "            N += len(x)\n",
    "            mean_loss += len(x) * (loss - mean_loss) / N\n",
    "\n",
    "    return mean_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----- Epoch = 1 -----\n",
      "Val loss = 0.2724\n",
      "\n",
      "New best epoch, val loss = 0.2724\n",
      "\n",
      "----- Epoch = 2 -----\n",
      "Val loss = 0.2611\n",
      "\n",
      "New best epoch, val loss = 0.2611\n",
      "\n",
      "----- Epoch = 3 -----\n",
      "Val loss = 0.2254\n",
      "\n",
      "New best epoch, val loss = 0.2254\n",
      "\n",
      "----- Epoch = 4 -----\n",
      "Val loss = 0.2206\n",
      "\n",
      "New best epoch, val loss = 0.2206\n",
      "\n",
      "----- Epoch = 5 -----\n",
      "Val loss = 0.2150\n",
      "\n",
      "New best epoch, val loss = 0.2150\n",
      "\n",
      "----- Epoch = 6 -----\n",
      "Val loss = 0.2085\n",
      "\n",
      "New best epoch, val loss = 0.2085\n",
      "\n",
      "----- Epoch = 7 -----\n",
      "Val loss = 0.2004\n",
      "\n",
      "New best epoch, val loss = 0.2004\n",
      "\n",
      "----- Epoch = 8 -----\n",
      "Val loss = 0.2039\n",
      "\n",
      "----- Epoch = 9 -----\n",
      "Val loss = 0.2021\n",
      "\n",
      "----- Epoch = 10 -----\n",
      "Val loss = 0.2012\n",
      "\n",
      "----- Epoch = 11 -----\n",
      "Val loss = 0.2043\n",
      "\n",
      "----- Epoch = 12 -----\n",
      "Val loss = 0.1955\n",
      "\n",
      "New best epoch, val loss = 0.1955\n",
      "\n",
      "----- Epoch = 13 -----\n",
      "Val loss = 0.1898\n",
      "\n",
      "New best epoch, val loss = 0.1898\n",
      "\n",
      "----- Epoch = 14 -----\n",
      "Val loss = 0.1947\n",
      "\n",
      "----- Epoch = 15 -----\n",
      "Val loss = 0.2062\n",
      "\n",
      "----- Epoch = 16 -----\n",
      "Val loss = 0.1948\n",
      "\n",
      "----- Epoch = 17 -----\n",
      "Val loss = 0.1863\n",
      "\n",
      "New best epoch, val loss = 0.1863\n",
      "\n",
      "----- Epoch = 18 -----\n",
      "Val loss = 0.1909\n",
      "\n",
      "----- Epoch = 19 -----\n",
      "Val loss = 0.1853\n",
      "\n",
      "New best epoch, val loss = 0.1853\n",
      "\n",
      "----- Epoch = 20 -----\n",
      "Val loss = 0.1904\n",
      "\n",
      "----- Epoch = 21 -----\n",
      "Val loss = 0.1850\n",
      "\n",
      "New best epoch, val loss = 0.1850\n",
      "\n",
      "----- Epoch = 22 -----\n",
      "Val loss = 0.1993\n",
      "\n",
      "----- Epoch = 23 -----\n",
      "Val loss = 0.1963\n",
      "\n",
      "----- Epoch = 24 -----\n",
      "Val loss = 0.1914\n",
      "\n",
      "----- Epoch = 25 -----\n",
      "Val loss = 0.1785\n",
      "\n",
      "New best epoch, val loss = 0.1785\n",
      "\n",
      "----- Epoch = 26 -----\n",
      "Val loss = 0.1831\n",
      "\n",
      "----- Epoch = 27 -----\n",
      "Val loss = 0.1898\n",
      "\n",
      "----- Epoch = 28 -----\n",
      "Val loss = 0.1842\n",
      "\n",
      "----- Epoch = 29 -----\n",
      "Val loss = 0.1914\n",
      "\n",
      "----- Epoch = 30 -----\n",
      "Val loss = 0.1847\n",
      "\n",
      "----- Epoch = 31 -----\n",
      "Val loss = 0.1862\n",
      "\n",
      "----- Epoch = 32 -----\n",
      "Val loss = 0.1790\n",
      "\n",
      "----- Epoch = 33 -----\n",
      "Val loss = 0.1916\n",
      "\n",
      "----- Epoch = 34 -----\n",
      "Val loss = 0.2021\n",
      "\n",
      "----- Epoch = 35 -----\n",
      "Val loss = 0.1886\n",
      "\n",
      "Stopping early\n",
      "----- Epoch = 1 -----\n",
      "Val loss = 0.1679\n",
      "\n",
      "New best epoch, val loss = 0.1679\n",
      "\n",
      "----- Epoch = 2 -----\n",
      "Val loss = 0.1702\n",
      "\n",
      "----- Epoch = 3 -----\n",
      "Val loss = 0.1655\n",
      "\n",
      "New best epoch, val loss = 0.1655\n",
      "\n",
      "----- Epoch = 4 -----\n",
      "Val loss = 0.1655\n",
      "\n",
      "New best epoch, val loss = 0.1655\n",
      "\n",
      "----- Epoch = 5 -----\n",
      "Val loss = 0.1722\n",
      "\n",
      "----- Epoch = 6 -----\n",
      "Val loss = 0.1618\n",
      "\n",
      "New best epoch, val loss = 0.1618\n",
      "\n",
      "----- Epoch = 7 -----\n",
      "Val loss = 0.1680\n",
      "\n",
      "----- Epoch = 8 -----\n",
      "Val loss = 0.1610\n",
      "\n",
      "New best epoch, val loss = 0.1610\n",
      "\n",
      "----- Epoch = 9 -----\n",
      "Val loss = 0.1631\n",
      "\n",
      "----- Epoch = 10 -----\n",
      "Val loss = 0.1609\n",
      "\n",
      "New best epoch, val loss = 0.1609\n",
      "\n",
      "----- Epoch = 11 -----\n",
      "Val loss = 0.1657\n",
      "\n",
      "----- Epoch = 12 -----\n",
      "Val loss = 0.1664\n",
      "\n",
      "----- Epoch = 13 -----\n",
      "Val loss = 0.1629\n",
      "\n",
      "----- Epoch = 14 -----\n",
      "Val loss = 0.1597\n",
      "\n",
      "New best epoch, val loss = 0.1597\n",
      "\n",
      "----- Epoch = 15 -----\n",
      "Val loss = 0.1595\n",
      "\n",
      "New best epoch, val loss = 0.1595\n",
      "\n",
      "----- Epoch = 16 -----\n",
      "Val loss = 0.1676\n",
      "\n",
      "----- Epoch = 17 -----\n",
      "Val loss = 0.1623\n",
      "\n",
      "----- Epoch = 18 -----\n",
      "Val loss = 0.1631\n",
      "\n",
      "----- Epoch = 19 -----\n",
      "Val loss = 0.1595\n",
      "\n",
      "----- Epoch = 20 -----\n",
      "Val loss = 0.1640\n",
      "\n",
      "----- Epoch = 21 -----\n",
      "Val loss = 0.1637\n",
      "\n",
      "----- Epoch = 22 -----\n",
      "Val loss = 0.1643\n",
      "\n",
      "----- Epoch = 23 -----\n",
      "Val loss = 0.1587\n",
      "\n",
      "New best epoch, val loss = 0.1587\n",
      "\n",
      "----- Epoch = 24 -----\n",
      "Val loss = 0.1684\n",
      "\n",
      "----- Epoch = 25 -----\n",
      "Val loss = 0.1611\n",
      "\n",
      "----- Epoch = 26 -----\n",
      "Val loss = 0.1666\n",
      "\n",
      "----- Epoch = 27 -----\n",
      "Val loss = 0.1580\n",
      "\n",
      "New best epoch, val loss = 0.1580\n",
      "\n",
      "----- Epoch = 28 -----\n",
      "Val loss = 0.1712\n",
      "\n",
      "----- Epoch = 29 -----\n",
      "Val loss = 0.1674\n",
      "\n",
      "----- Epoch = 30 -----\n",
      "Val loss = 0.1600\n",
      "\n",
      "----- Epoch = 31 -----\n",
      "Val loss = 0.1622\n",
      "\n",
      "----- Epoch = 32 -----\n",
      "Val loss = 0.1620\n",
      "\n",
      "----- Epoch = 33 -----\n",
      "Val loss = 0.1673\n",
      "\n",
      "----- Epoch = 34 -----\n",
      "Val loss = 0.1666\n",
      "\n",
      "----- Epoch = 35 -----\n",
      "Val loss = 0.1650\n",
      "\n",
      "----- Epoch = 36 -----\n",
      "Val loss = 0.1636\n",
      "\n",
      "----- Epoch = 37 -----\n",
      "Val loss = 0.1669\n",
      "\n",
      "Stopping early\n",
      "----- Epoch = 1 -----\n",
      "Val loss = 0.1557\n",
      "\n",
      "New best epoch, val loss = 0.1557\n",
      "\n",
      "----- Epoch = 2 -----\n",
      "Val loss = 0.1548\n",
      "\n",
      "New best epoch, val loss = 0.1548\n",
      "\n",
      "----- Epoch = 3 -----\n",
      "Val loss = 0.1504\n",
      "\n",
      "New best epoch, val loss = 0.1504\n",
      "\n",
      "----- Epoch = 4 -----\n",
      "Val loss = 0.1543\n",
      "\n",
      "----- Epoch = 5 -----\n",
      "Val loss = 0.1549\n",
      "\n",
      "----- Epoch = 6 -----\n",
      "Val loss = 0.1569\n",
      "\n",
      "----- Epoch = 7 -----\n",
      "Val loss = 0.1540\n",
      "\n",
      "----- Epoch = 8 -----\n",
      "Val loss = 0.1538\n",
      "\n",
      "----- Epoch = 9 -----\n",
      "Val loss = 0.1524\n",
      "\n",
      "----- Epoch = 10 -----\n",
      "Val loss = 0.1574\n",
      "\n",
      "----- Epoch = 11 -----\n",
      "Val loss = 0.1494\n",
      "\n",
      "New best epoch, val loss = 0.1494\n",
      "\n",
      "----- Epoch = 12 -----\n",
      "Val loss = 0.1533\n",
      "\n",
      "----- Epoch = 13 -----\n",
      "Val loss = 0.1539\n",
      "\n",
      "----- Epoch = 14 -----\n",
      "Val loss = 0.1533\n",
      "\n",
      "----- Epoch = 15 -----\n",
      "Val loss = 0.1518\n",
      "\n",
      "----- Epoch = 16 -----\n",
      "Val loss = 0.1534\n",
      "\n",
      "----- Epoch = 17 -----\n",
      "Val loss = 0.1497\n",
      "\n",
      "----- Epoch = 18 -----\n",
      "Val loss = 0.1490\n",
      "\n",
      "New best epoch, val loss = 0.1490\n",
      "\n",
      "----- Epoch = 19 -----\n",
      "Val loss = 0.1522\n",
      "\n",
      "----- Epoch = 20 -----\n",
      "Val loss = 0.1535\n",
      "\n",
      "----- Epoch = 21 -----\n",
      "Val loss = 0.1508\n",
      "\n",
      "----- Epoch = 22 -----\n",
      "Val loss = 0.1523\n",
      "\n",
      "----- Epoch = 23 -----\n",
      "Val loss = 0.1511\n",
      "\n",
      "----- Epoch = 24 -----\n",
      "Val loss = 0.1531\n",
      "\n",
      "----- Epoch = 25 -----\n",
      "Val loss = 0.1511\n",
      "\n",
      "----- Epoch = 26 -----\n",
      "Val loss = 0.1567\n",
      "\n",
      "----- Epoch = 27 -----\n",
      "Val loss = 0.1562\n",
      "\n",
      "----- Epoch = 28 -----\n",
      "Val loss = 0.1511\n",
      "\n",
      "Stopping early\n",
      "----- Epoch = 1 -----\n",
      "Val loss = 0.1517\n",
      "\n",
      "----- Epoch = 2 -----\n",
      "Val loss = 0.1509\n",
      "\n",
      "----- Epoch = 3 -----\n",
      "Val loss = 0.1509\n",
      "\n",
      "----- Epoch = 4 -----\n",
      "Val loss = 0.1509\n",
      "\n",
      "----- Epoch = 5 -----\n",
      "Val loss = 0.1460\n",
      "\n",
      "New best epoch, val loss = 0.1460\n",
      "\n",
      "----- Epoch = 6 -----\n",
      "Val loss = 0.1442\n",
      "\n",
      "New best epoch, val loss = 0.1442\n",
      "\n",
      "----- Epoch = 7 -----\n",
      "Val loss = 0.1463\n",
      "\n",
      "----- Epoch = 8 -----\n",
      "Val loss = 0.1475\n",
      "\n",
      "----- Epoch = 9 -----\n",
      "Val loss = 0.1459\n",
      "\n",
      "----- Epoch = 10 -----\n",
      "Val loss = 0.1462\n",
      "\n",
      "----- Epoch = 11 -----\n",
      "Val loss = 0.1467\n",
      "\n",
      "----- Epoch = 12 -----\n",
      "Val loss = 0.1492\n",
      "\n",
      "----- Epoch = 13 -----\n",
      "Val loss = 0.1496\n",
      "\n",
      "----- Epoch = 14 -----\n",
      "Val loss = 0.1475\n",
      "\n",
      "----- Epoch = 15 -----\n",
      "Val loss = 0.1477\n",
      "\n",
      "----- Epoch = 16 -----\n",
      "Val loss = 0.1476\n",
      "\n",
      "Stopping early\n",
      "----- Epoch = 1 -----\n",
      "Val loss = 0.1465\n",
      "\n",
      "----- Epoch = 2 -----\n",
      "Val loss = 0.1470\n",
      "\n",
      "----- Epoch = 3 -----\n",
      "Val loss = 0.1473\n",
      "\n",
      "----- Epoch = 4 -----\n",
      "Val loss = 0.1486\n",
      "\n",
      "----- Epoch = 5 -----\n",
      "Val loss = 0.1490\n",
      "\n",
      "----- Epoch = 6 -----\n",
      "Val loss = 0.1470\n",
      "\n",
      "----- Epoch = 7 -----\n",
      "Val loss = 0.1464\n",
      "\n",
      "----- Epoch = 8 -----\n",
      "Val loss = 0.1454\n",
      "\n",
      "----- Epoch = 9 -----\n",
      "Val loss = 0.1491\n",
      "\n",
      "----- Epoch = 10 -----\n",
      "Val loss = 0.1467\n",
      "\n",
      "----- Epoch = 11 -----\n",
      "Val loss = 0.1468\n",
      "\n",
      "Stopping early\n"
     ]
    }
   ],
   "source": [
    "# Set up model\n",
    "model = nn.Sequential(\n",
    "    MaskLayer2d(),\n",
    "    nn.Conv2d(2, 16, 3, 1),\n",
    "    nn.ELU(inplace=True),\n",
    "    nn.Conv2d(16, 32, 3, 1),\n",
    "    nn.ELU(inplace=True),\n",
    "    nn.MaxPool2d(2, 2),\n",
    "    nn.Conv2d(32, 64, 3, 1),\n",
    "    nn.ELU(inplace=True),\n",
    "    nn.Conv2d(64, 128, 3, 1),\n",
    "    nn.ELU(inplace=True),\n",
    "    nn.MaxPool2d(2, 2),\n",
    "    nn.Flatten(start_dim=1),\n",
    "    nn.Linear(2048, 256),\n",
    "    nn.ELU(inplace=True),\n",
    "    nn.Linear(256, 10)).to(device)\n",
    "\n",
    "# Training parameters\n",
    "lr = 1e-3\n",
    "nepochs = 1000\n",
    "early_stop_epochs = 10\n",
    "\n",
    "# Loss function\n",
    "loss_fn = nn.CrossEntropyLoss()\n",
    "loss_list = []\n",
    "\n",
    "for mbsize in (128, 512, 2048, 5096, 10192):\n",
    "    # Optimizer\n",
    "    optimizer = optim.Adam(model.parameters(), lr=lr)\n",
    "\n",
    "    # Set up data loaders\n",
    "    train_set = TensorDataset(X_train, Y_train)\n",
    "    train_loader = DataLoader(train_set, batch_size=mbsize,\n",
    "                              shuffle=True, drop_last=True)\n",
    "\n",
    "    # For saving best model\n",
    "    min_epoch = 0\n",
    "    best_model = deepcopy(model)\n",
    "    best_epoch_loss = validate(model).item()\n",
    "    best_epoch = 0\n",
    "\n",
    "    # Begin training\n",
    "    for epoch in range(nepochs):\n",
    "        for i, (x, y) in enumerate(train_loader):\n",
    "            # Prepare data\n",
    "            x = x.to(device)\n",
    "            y = y.to(device)\n",
    "\n",
    "            # Generate subset\n",
    "            S = torch.ones(mbsize, num_features, dtype=torch.float32, device=device)\n",
    "            num_included = np.random.choice(num_features + 1, size=mbsize)\n",
    "            for j in range(mbsize):\n",
    "                S[j, num_included[j]:] = 0\n",
    "                S[j] = S[j, torch.randperm(num_features)]\n",
    "\n",
    "            # Make predictions\n",
    "            pred = model((x.view(-1, 1, 28, 28), S.view(-1, 1, 28, 28)))\n",
    "            loss = loss_fn(pred, y)\n",
    "\n",
    "            # Optimizer step\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            model.zero_grad()\n",
    "\n",
    "        # End of epoch progress message\n",
    "        val_loss = validate(model).item()\n",
    "        loss_list.append(val_loss)\n",
    "        print('----- Epoch = {} -----'.format(epoch + 1))\n",
    "        print('Val loss = {:.4f}'.format(val_loss))\n",
    "        print('')\n",
    "\n",
    "        # Check if best model\n",
    "        if epoch >= min_epoch:\n",
    "            if val_loss < best_epoch_loss:\n",
    "                best_epoch_loss = val_loss\n",
    "                best_model = deepcopy(model)\n",
    "                best_epoch = epoch\n",
    "                print('New best epoch, val loss = {:.4f}'.format(val_loss))\n",
    "                print('')\n",
    "            else:\n",
    "                # Check for early stopping\n",
    "                if epoch - best_epoch == early_stop_epochs:\n",
    "                    print('Stopping early')\n",
    "                    break\n",
    "\n",
    "    model = best_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot loss during training\n",
    "plt.figure(figsize=(9, 6))\n",
    "plt.plot(loss_list)\n",
    "plt.xlabel('Epochs', fontsize=18)\n",
    "plt.ylabel('Cross entropy loss', fontsize=18)\n",
    "plt.tick_params(labelsize=16)\n",
    "plt.title('Surrogate training', fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prediction loss explanations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from rexplain import removal, behavior, summary\n",
    "from rexplain.utils import crossentropyloss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOsAAADrCAYAAACICmHVAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAAAGfUlEQVR4nO3dX6jfdR3H8e9v52zn4HE5pwP/rE2w2dSUIcM5vLEtUjLCbpIC7UK8CfWiiyDwIhQECQILS41oxJIorC4KIpJWobXSHBT+Q3CKojTdH5ets3nOt6tCct/3+fP7nT+v83s8bl/7/vZh8Nx37MM5p9e2bQMsf6uW+gDA7IgVQogVQogVQogVQogVQozO5Rev6Y21483EQp0Fht6/m3ebk+1k73TbnGIdbyaaHb3dgzkV8AH728c7N/8MhhBihRBihRBihRBihRBihRBihRBihRBihRBihRBihRBihRBihRBihRBihRBihRBihRBihRBihRBihRBihRBihRBihRBihRBihRBihRBihRBihRBihRBihRBihRBihRBihRBihRBihRBihRCjS32AYTD18avK/Y5Hfty5fWfLRwZ9nGXj+M3XlPu6A291blMvvDTo4yx73qwQQqwQQqwQQqwQQqwQQqwQQqwQwj3rInjl+rFyXz/yz0U6yfLy5o0ny/3ULd3vkvWfHvRplj9vVgghVgghVgghVgghVgghVgjh6mYAeqvXlPuuXQcW5yBh1j4zXu6fu+13ndtv120sn506emxeZ1rOvFkhhFghhFghhFghhFghhFghhFghhHvWATj+2fpbjX7zwm+V+6U/v6Nz29Lsn9eZEkye3Zb7XWc/37ntW3tp/eHuWYGlIlYIIVYIIVYIIVYIIVYIIVYI4Z51Ftprt5X7g/c/UO5739lc7lvvfrFzmyqfzLbzk39f6iNE8WaFEGKFEGKFEGKFEGKFEGKFEGKFEO5ZZ+HIV/9V7htH3yv3L995Y7mvPvL0nM+UYPT888r9+5t+Ve6nWu+S9/OnASHECiHECiHECiHECiHECiHECiHcszZN8/btO8v9J1d8vdx/cOzKcl/9m5V5jzqTZ+/5cLmfauuv1v3iwU90blP/ODSvMyXzZoUQYoUQYoUQYoUQYoUQYoUQrm6apll101vlfsHoWLl/79Ebyn1j8+Scz5Rg5PKPlvve3Q+X+2R7qtxf/cYlndvE5Mr9UZhdvFkhhFghhFghhFghhFghhFghhFghxNDcs45s2NC53X3JL/v67I33rcx71Jk8/6V15b59rP4SuAePXFbuE48N311qxZsVQogVQogVQogVQogVQogVQogVQgzNPWvvjPHO7fozjpXPXv2XW8v9vOa5eZ0p3bkXHe7r+R++vL3+/ObFvj5/pfFmhRBihRBihRBihRBihRBihRBihRBDc886ffho53bvoavKZ79w8VPl/vvzLy739954s9yXs9HN3T+28YltP5rh6fpdcOJP587wvHvW9/NmhRBihRBihRBihRBihRBihRBihRDDc896/Hjn9uvXt5bP/mHbo+X+xi/Oqp9/eGe5L6Sjl7XlfuZF9dfyXnPBwc5tupmez5H+p1cfjf/jzQohxAohxAohxAohxAohxAohem07+/8//1Bvfbujt3sBj7NErr6inI997US5/+xje8p9/cjYXE80ME9NjpT71Ax/X29fc7JzG+n15nWm/7pp665yr67bVqr97ePNO+3h0/7BerNCCLFCCLFCCLFCCLFCCLFCCLFCiKH5ErnSn/9Wzmd9qn78luvuKvejW5bunvWc7/6xr+df/+nlndvTO/b09dnDeI/aD29WCCFWCCFWCCFWCCFWCCFWCCFWCOGedQBG9v213M/ZtxinWBgnDq7tHnf099nttdvKvffEgf5+gxXGmxVCiBVCiBVCiBVCiBVCiBVCiBVCuGelVnxr4FV9/l3vHnVuvFkhhFghhFghhFghhFghhFghhKsbasVPBJ1uphfvHHizQgqxQgixQgixQgixQgixQgixQgj3rJSmx+d/l3poanKAJ8GbFUKIFUKIFUKIFUKIFUKIFUKIFUK4Z6W094aHOrfnTtZ3sJ/f85Vy39Q8Oa8zDStvVgghVgghVgghVgghVgghVgghVgjhnpXSPS9/pnN799sXls9uesw96iB5s0IIsUIIsUIIsUIIsUIIsUIIsUII96zUdr/WOU003RuD580KIcQKIcQKIcQKIcQKIcQKIcQKIcQKIcQKIcQKIcQKIcQKIcQKIcQKIcQKIcQKIcQKIcQKIcQKIcQKIcQKIcQKIXpt287+F/d6h5qmeWXhjgNDb3PbthtON8wpVmDp+GcwhBArhBArhBArhBArhBArhBArhBArhBArhPgPOZ7Y74mHu4UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Find a good digit\n",
    "ind = 4\n",
    "plt.imshow(X_test[ind].cpu().data.numpy().reshape((28, 28)))\n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make model callable with numpy input\n",
    "model_lam = lambda x, S: torch.softmax(\n",
    "    model((torch.tensor(x, dtype=torch.float32, device=device).reshape(-1, 1, 28, 28),\n",
    "           torch.tensor(S, dtype=torch.float32, device=device).reshape(-1, 1, 28, 28))),\n",
    "    dim=1).cpu().data.numpy()\n",
    "\n",
    "# Model extension\n",
    "conditional_extension = removal.ConditionalSupervisedExtension(model_lam)\n",
    "\n",
    "# Cooperative game\n",
    "game = behavior.PredictionLossGame(conditional_extension, X_test[ind].cpu().numpy(),\n",
    "                                   Y_test[ind].cpu().numpy(), crossentropyloss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 78.4 ms, sys: 2.96 ms, total: 81.4 ms\n",
      "Wall time: 10.4 ms\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Summary technique\n",
    "%time attr = summary.IncludeIndividual(game)\n",
    "\n",
    "plt.figure(figsize=(5, 5))\n",
    "m = np.max(np.abs(attr))\n",
    "plt.imshow(attr.reshape(28, 28), vmin=-m, vmax=m, cmap='seismic')\n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    "plt.title('Include individual', fontsize=20)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 50s, sys: 1.77 s, total: 1min 52s\n",
      "Wall time: 11.3 s\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Summary technique\n",
    "%time attr = summary.ShapleyValue(game)\n",
    "\n",
    "plt.figure(figsize=(5, 5))\n",
    "m = np.max(np.abs(attr))\n",
    "plt.imshow(attr.reshape(28, 28), vmin=-m, vmax=m, cmap='seismic')\n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    "plt.title('Shapley value', fontsize=20)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prediction explanations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Find a good digit\n",
    "ind = 32\n",
    "actual_digit = 3\n",
    "contrast_digit = 8\n",
    "plt.imshow(X_test[ind].cpu().data.numpy().reshape((28, 28)))\n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cooperative game\n",
    "game = behavior.PredictionGame(conditional_extension, X_test[ind].cpu().numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 75.9 ms, sys: 0 ns, total: 75.9 ms\n",
      "Wall time: 9.26 ms\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Summary technique\n",
    "%time attr = summary.IncludeIndividual(game)\n",
    "\n",
    "fig, (ax0, ax1) = plt.subplots(1, 2, figsize=(10, 5))\n",
    "\n",
    "m = np.max(np.abs(attr[:, actual_digit]))\n",
    "ax0.imshow(attr[:, actual_digit].reshape(28, 28),\n",
    "           vmin=-m, vmax=m, cmap='seismic')\n",
    "ax0.set_xticks([])\n",
    "ax0.set_yticks([])\n",
    "ax0.set_xlabel('Actual digit ({})'.format(actual_digit), fontsize=16)\n",
    "\n",
    "m = np.max(np.abs(attr[:, contrast_digit]))\n",
    "ax1.imshow(attr[:, contrast_digit].reshape(28, 28),\n",
    "           vmin=-m, vmax=m, cmap='seismic')\n",
    "ax1.set_xticks([])\n",
    "ax1.set_yticks([])\n",
    "ax1.set_xlabel('Contrast digit ({})'.format(contrast_digit), fontsize=16)\n",
    "\n",
    "plt.suptitle('Include individual', fontsize=20)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 9min 43s, sys: 8.85 s, total: 9min 52s\n",
      "Wall time: 60 s\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Summary technique\n",
    "%time attr = summary.ShapleyValue(game)\n",
    "\n",
    "fig, (ax0, ax1) = plt.subplots(1, 2, figsize=(10, 5))\n",
    "\n",
    "m = np.max(np.abs(attr[:, actual_digit]))\n",
    "ax0.imshow(attr[:, actual_digit].reshape(28, 28),\n",
    "           vmin=-m, vmax=m, cmap='seismic')\n",
    "ax0.set_xticks([])\n",
    "ax0.set_yticks([])\n",
    "ax0.set_xlabel('Actual digit ({})'.format(actual_digit), fontsize=16)\n",
    "\n",
    "m = np.max(np.abs(attr[:, contrast_digit]))\n",
    "ax1.imshow(attr[:, contrast_digit].reshape(28, 28),\n",
    "           vmin=-m, vmax=m, cmap='seismic')\n",
    "ax1.set_xticks([])\n",
    "ax1.set_yticks([])\n",
    "ax1.set_xlabel('Contrast digit ({})'.format(contrast_digit), fontsize=16)\n",
    "\n",
    "plt.suptitle('Shapley value', fontsize=20)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}