cifar10.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6e0d252f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x2764ce16f30>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Datagen inspo taken from \n",
    "# https://colab.research.google.com/github/reiinakano/invariant-risk-minimization/blob/master/invariant_risk_minimization_colored_mnist.ipynb#scrollTo=sopHPgEhu4Jo\n",
    "\n",
    "%matplotlib notebook\n",
    "import os\n",
    "\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import torch\n",
    "from torch import nn\n",
    "from torchvision import datasets\n",
    "import torchvision.datasets.utils as dataset_utils\n",
    "\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import DataLoader, TensorDataset\n",
    "from torchmetrics import Accuracy, AUROC\n",
    "import time\n",
    "import torch.nn.functional as F\n",
    "\n",
    "import random\n",
    "\n",
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "from sklearn.preprocessing import minmax_scale\n",
    "\n",
    "import pickle \n",
    "import seaborn as sns\n",
    "\n",
    "import scipy\n",
    "from skimage.util import random_noise\n",
    "\n",
    "SEED = 1234\n",
    "DEVICE = 'cpu'\n",
    "np.random.seed(SEED)\n",
    "torch.manual_seed(SEED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "106d2cbb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "data_path = './data/'\n",
    "if not os.path.exists(data_path):\n",
    "    os.makedirs(data_path)\n",
    "train_mnist = datasets.CIFAR10(data_path, train=True, download=True)\n",
    "test_mnist = datasets.CIFAR10(data_path, train=False, download=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6e54c2cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<PIL.Image.Image image mode=RGB size=32x32>, 6)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_mnist[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1b80bf7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def rescale_values(image,max_val=1.0,min_val=0.0):\n",
    "    '''\n",
    "    image - numpy array\n",
    "    max_val/min_val - float\n",
    "    '''\n",
    "    return (image-image.min())/(image.max()-image.min())*(max_val-min_val)+min_val\n",
    "\n",
    "# Convert MNIST PIL images to 3 channel NP arrays and return separated data and label arrays\n",
    "def preprocess_data(train_mnist, test_mnist):\n",
    "    data = np.zeros((len(train_mnist)+len(test_mnist),32,32,3))\n",
    "    labels = np.zeros(len(train_mnist)+len(test_mnist))\n",
    "    \n",
    "    for i, (img, label) in enumerate(train_mnist):\n",
    "        arr = rescale_values(np.array(img)).reshape((32,32,3))\n",
    "        \n",
    "#         im_arr = np.concatenate([np.array(arr),np.zeros((32,32,1)),np.array(arr)], axis=2)\n",
    "    \n",
    "        data[i] = arr\n",
    "        labels[i] = label\n",
    "        \n",
    "    for i, (img, label) in enumerate(test_mnist):\n",
    "        arr = rescale_values(np.array(img)).reshape((32,32,3))\n",
    "        \n",
    "#         im_arr = np.concatenate([arr,np.zeros((32,32,1)),arr], axis=2)\n",
    "\n",
    "        data[len(train_mnist)+i] = arr\n",
    "        labels[len(train_mnist)+i] = label\n",
    "    \n",
    "    return data, labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f5fb4bb4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "0.0\n"
     ]
    }
   ],
   "source": [
    "data, labels = preprocess_data(train_mnist, test_mnist)\n",
    "print(data.max())\n",
    "print(data.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a48ec890",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 32, 32, 3)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "d59a4112",
   "metadata": {},
   "outputs": [],
   "source": [
    "# this number needs to be picked s.t. 50% is divisible by 10 and 9\n",
    "n_per_class = 1800\n",
    "\n",
    "sorted_data = np.zeros((n_per_class*10,32,32,3))\n",
    "sorted_labels = np.zeros((n_per_class*10,))\n",
    "\n",
    "ind = 0\n",
    "for i in range(10):\n",
    "    class_labels = np.where(labels==i)[0][:n_per_class]\n",
    "    sorted_data[ind:ind+n_per_class] = data[class_labels]\n",
    "    sorted_labels[ind:ind+n_per_class] = labels[class_labels]\n",
    "    \n",
    "    ind += n_per_class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "c7c428cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Shuffled 60/20/20 split\n",
    "# split 60/40 into train and val+test, then split val and test by half\n",
    "data_splitter = StratifiedShuffleSplit(n_splits=1, test_size=0.4, random_state=SEED)\n",
    "train_indices, val_indices = list(data_splitter.split(X=sorted_data, y=sorted_labels))[0]\n",
    "\n",
    "x_train = sorted_data[train_indices]\n",
    "y_train = sorted_labels[train_indices]\n",
    "x_val_test = sorted_data[val_indices]\n",
    "y_val_test = sorted_labels[val_indices]\n",
    "\n",
    "data_splitter = StratifiedShuffleSplit(n_splits=1, test_size=0.5, random_state=SEED)\n",
    "val_indices, test_indices = list(data_splitter.split(X=x_val_test, y=y_val_test))[0]\n",
    "\n",
    "x_val = x_val_test[val_indices]\n",
    "y_val = y_val_test[val_indices]\n",
    "\n",
    "x_test = x_val_test[test_indices]\n",
    "y_test = y_val_test[test_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "a76b327d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1.0, 0, 0.5)\n",
      "(0.9, 0, 0.5)\n",
      "(0.8, 0, 0.5)\n",
      "(0.7, 0, 0.5)\n",
      "(0.6, 0, 0.5)\n",
      "(0.5, 0, 0.5)\n",
      "(0.4, 0, 0.5)\n",
      "(0.3, 0, 0.5)\n",
      "(0.2, 0, 0.5)\n",
      "(0.1, 0, 0.5)\n",
      "(0.0, 0, 0.5)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.5, 0, 0.5)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def create_colours(neutral_scalar=0.5):\n",
    "    red = 1.0\n",
    "    colours = []\n",
    "    for i in range(11):\n",
    "        print((np.round(red,1),0,neutral_scalar))\n",
    "        colours.append((np.round(red,1),0,neutral_scalar))\n",
    "        red -= 0.1\n",
    "        \n",
    "    return colours\n",
    "\n",
    "colours = create_colours()\n",
    "colours.pop(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2628784",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "8839dd42",
   "metadata": {},
   "outputs": [
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       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    fig.rubberband_canvas.style.cursor = msg['cursor'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\n",
       "                /* FIXME: We get \"Resource interpreted as Image but\n",
       "                 * transferred with MIME type text/plain:\" errors on\n",
       "                 * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "                 * to be part of the websocket stream */\n",
       "                img.type = 'image/png';\n",
       "            }\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * https://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(data);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '231.43764705882353')"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# noise = random_noise(, mode='s&p')\n",
    "\n",
    "samp = data[0].copy()\n",
    "samp[:,:,0] *= 0.1\n",
    "samp[:,:,1] *= 0.5\n",
    "samp[:,:,2] *= 0.9\n",
    "\n",
    "\n",
    "fig, axs = plt.subplots(1,4)\n",
    "axs[0].imshow(samp)\n",
    "axs[1].imshow(random_noise(samp[:,:,0], mode='s&p', amount=0.0))\n",
    "axs[1].set_title(np.sum(samp[:,:,0]))\n",
    "\n",
    "axs[2].imshow(samp[:,:,1])\n",
    "axs[2].set_title(np.sum(samp[:,:,1]))\n",
    "\n",
    "axs[3].imshow(random_noise(samp[:,:,2], mode='s&p', amount=0.0))\n",
    "axs[3].set_title(np.sum(samp[:,:,2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "5eac4449",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create suppressor set where 50% of samples of all classes have colour c6=(0.5,0.5,0.5), \n",
    "# and the rest are split evenly 5% for each colour [c0,...,c10]\\{c6}\n",
    "\n",
    "lower = 0\n",
    "upper = 1\n",
    "mu = 0.5\n",
    "sigma = 0.3\n",
    "truncnorm = scipy.stats.truncnorm((lower-mu)/sigma,(upper-mu)/sigma,loc=mu,scale=sigma)\n",
    "\n",
    "def scale_to_sum(arr, arrsum):\n",
    "    return (arrsum / np.sum(np.abs(arr))) * arr\n",
    "\n",
    "def create_suppressor(data, colours, c6=(0.5,0.0,0.5), noise_scale=0.5):\n",
    "    num_samples = len(data)\n",
    "    suppressor_data = np.zeros(data.shape)\n",
    "    colour_labels = np.zeros((data.shape[0],3))\n",
    "    \n",
    "    c6_chunk = data[:int(num_samples/2)]\n",
    "    c6_chunk[:,:,:,0] *= c6[0]\n",
    "    c6_chunk[:,:,:,1] *= c6[1]\n",
    "    c6_chunk[:,:,:,2] *= c6[2]\n",
    "    \n",
    "#     noise = truncnorm.rvs(size=c6_chunk.shape)\n",
    "#     noise[:,:,:,1] *= 0\n",
    "    \n",
    "#     noise[:,:,:,0] = scale_to_sum(noise[:,:,:,0], np.sum(c6_chunk[:,:,:,0]))\n",
    "\n",
    "#     noise[:,:,:,1] = scale_to_sum(noise[:,:,:,1], np.sum(noise[:,:,:,0]*0.5))\n",
    "#     noise[:,:,:,2] = scale_to_sum(noise[:,:,:,2], np.sum(noise[:,:,:,0]*0.5))\n",
    "\n",
    "#     noise[:,:,:,1:] = scale_to_sum(noise[:,:,:,1:], np.sum(noise[:,:,:,0]))\n",
    "    \n",
    "#     c6_chunk = (1-noise_scale)*c6_chunk + abs(noise_scale*noise)\n",
    "    c6_chunk = random_noise(c6_chunk, mode='s&p', amount=noise_scale, seed=SEED)\n",
    "    c6_chunk[:,:,:,1] = 0\n",
    "    \n",
    "    colour_labels[:int(num_samples/2)] = np.array([c6]*int(num_samples/2))\n",
    "    \n",
    "    suppressor_data[:int(num_samples/2)] =  c6_chunk\n",
    "    \n",
    "    for i, data_chunk in enumerate(np.split(data[int(num_samples/2):],10,axis=0)):\n",
    "        chunk_size = len(data_chunk)\n",
    "\n",
    "        data_chunk[:,:,:,0] *= colours[i][0]\n",
    "        data_chunk[:,:,:,1] *= colours[i][1]\n",
    "        data_chunk[:,:,:,2] *= colours[i][2]\n",
    "        \n",
    "#         noise = truncnorm.rvs(size=data_chunk.shape)\n",
    "#         noise[:,:,:,1] *= 0\n",
    "\n",
    "#         data_chunk = (1-noise_scale)*data_chunk + abs(noise_scale*noise)\n",
    "        data_chunk = random_noise(data_chunk, mode='s&p', amount=noise_scale, seed=SEED)\n",
    "        data_chunk[:,:,:,1] = 0\n",
    "        \n",
    "        suppressor_data[int(num_samples/2)+chunk_size*i:int(num_samples/2)+chunk_size*(i+1)] = data_chunk\n",
    "        \n",
    "        colour_labels[int(num_samples/2)+chunk_size*i:int(num_samples/2)+chunk_size*(i+1)] = [colours[i]]*int(data_chunk.shape[0])\n",
    "    return suppressor_data, colour_labels\n",
    "\n",
    "\n",
    "# create confounder set where 50% of samples of all classes have colour c6=(0.5,0.5,0.5), \n",
    "# 25% have the class' colour and the rest are split evenly for each colour [c0,...,c10]\\{c6}\n",
    "def create_confounder(data, class_colour, colours,confounder_portion=0.25, c6=(0.5,0.0,0.5), noise_scale=0.5):\n",
    "    num_samples = len(data)\n",
    "    confounder_data = np.zeros(data.shape)\n",
    "    colour_labels = np.zeros((data.shape[0],3))\n",
    "\n",
    "    c6_chunk = data[:int(num_samples/2)]\n",
    "    c6_chunk[:,:,:,0] *= c6[0]\n",
    "    c6_chunk[:,:,:,1] *= c6[1]\n",
    "    c6_chunk[:,:,:,2] *= c6[2]\n",
    "    \n",
    "#     noise = truncnorm.rvs(size=c6_chunk.shape)\n",
    "#     noise[:,:,:,1] *= 0\n",
    "    \n",
    "#     c6_chunk = (1-noise_scale)*c6_chunk + abs(noise_scale*noise)\n",
    "\n",
    "    c6_chunk = random_noise(c6_chunk, mode='s&p', amount=noise_scale, seed=SEED)\n",
    "    c6_chunk[:,:,:,1] = 0\n",
    "    \n",
    "\n",
    "    colour_labels[:int(num_samples/2)] = np.array([c6]*int(num_samples/2))\n",
    "    confounder_data[:int(num_samples/2)] = c6_chunk\n",
    "    \n",
    "    n_confounded = int(num_samples/2)+int(num_samples*confounder_portion) \n",
    "    \n",
    "    confounder_data[int(num_samples/2):n_confounded] = data[int(num_samples/2):n_confounded]\n",
    "\n",
    "    confounder_data[int(num_samples/2):n_confounded][:,:,:,0] *= class_colour[0]\n",
    "    confounder_data[int(num_samples/2):n_confounded][:,:,:,1] *= class_colour[1]\n",
    "    confounder_data[int(num_samples/2):n_confounded][:,:,:,2] *= class_colour[2]\n",
    "    \n",
    "#     noise = truncnorm.rvs(size=confounder_data[int(num_samples/2):n_confounded].shape)\n",
    "#     noise[:,:,:,1] *= 0\n",
    "    \n",
    "#     confounder_data[int(num_samples/2):n_confounded] = (1-noise_scale)*confounder_data[int(num_samples/2):n_confounded] + abs(noise_scale*noise)\n",
    "    confounder_data[int(num_samples/2):n_confounded] = random_noise(confounder_data[int(num_samples/2):n_confounded], mode='s&p', amount=noise_scale, seed=SEED)\n",
    "    confounder_data[int(num_samples/2):n_confounded][:,:,:,1] = 0\n",
    "    \n",
    "    colour_labels[int(num_samples/2):n_confounded] = np.array([class_colour]*(n_confounded - int(num_samples/2)))\n",
    "    \n",
    "    other_colours = [x for x in colours if x != class_colour] \n",
    "    \n",
    "    for i, data_chunk in enumerate(np.split(data[n_confounded:],9,axis=0)):\n",
    "        chunk_size = len(data_chunk)\n",
    "\n",
    "        data_chunk[:,:,:,0] *= other_colours[i][0]\n",
    "        data_chunk[:,:,:,1] *= other_colours[i][1]\n",
    "        data_chunk[:,:,:,2] *= other_colours[i][2]\n",
    "        \n",
    "#         noise = truncnorm.rvs(size=data_chunk.shape)\n",
    "#         noise[:,:,:,1] *= 0\n",
    "        \n",
    "#         data_chunk = (1-noise_scale)*data_chunk + abs(noise_scale*noise)\n",
    "        data_chunk = random_noise(data_chunk, mode='s&p', amount=noise_scale, seed=SEED)\n",
    "        data_chunk[:,:,:,1] = 0\n",
    "        \n",
    "        confounder_data[n_confounded+chunk_size*i:n_confounded+chunk_size*(i+1)] = data_chunk\n",
    "        \n",
    "        colour_labels[n_confounded+chunk_size*i:n_confounded+chunk_size*(i+1)] = [other_colours[i]]*int(data_chunk.shape[0])\n",
    "        \n",
    "    return confounder_data, colour_labels\n",
    "\n",
    "def create_no_col(data, c6=(0.5,0.0,0.5), noise_scale=0.5):\n",
    "    no_col_data = np.zeros((data.shape))\n",
    "    \n",
    "    c6_chunk = data\n",
    "    c6_chunk[:,:,:,0] *= c6[0]\n",
    "    c6_chunk[:,:,:,1] *= c6[1]\n",
    "    c6_chunk[:,:,:,2] *= c6[2]\n",
    "    \n",
    "#     noise = truncnorm.rvs(size=data.shape)\n",
    "#     noise[:,:,:,1] *= 0\n",
    "    \n",
    "#     c6_chunk = (1-noise_scale)*c6_chunk + abs(noise_scale*noise)\n",
    "    c6_chunk = random_noise(c6_chunk, mode='s&p', amount=noise_scale, seed=SEED)\n",
    "    c6_chunk[:,:,:,1] = 0\n",
    "    \n",
    "    return c6_chunk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "28264be6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fig, axs = plt.subplots(1,4)\n",
    "\n",
    "# for i in range(4):\n",
    "#     mask = x_train[i].copy()\n",
    "#     mask[mask != 0] = 1\n",
    "#     axs[i].imshow(mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "aef555f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def scale_to_bound(row, scale):\n",
    "    # scale = bound / np.max(np.abs(row))\n",
    "    return row * scale\n",
    "\n",
    "noise_scale = 0.0\n",
    "confounder_portion = 0.4\n",
    "c6_portion = 0.5\n",
    "\n",
    "conf_train = np.zeros(x_train.shape)\n",
    "conf_val = np.zeros(x_val.shape)\n",
    "conf_test = np.zeros(x_test.shape)\n",
    "\n",
    "sup_train = np.zeros(x_train.shape)\n",
    "sup_val = np.zeros(x_val.shape)\n",
    "sup_test = np.zeros(x_test.shape)\n",
    "\n",
    "no_col_train = np.zeros(x_train.shape)\n",
    "no_col_val = np.zeros(x_val.shape)\n",
    "no_col_test = np.zeros(x_test.shape)\n",
    "\n",
    "conf_train_colours = np.zeros((x_train.shape[0], 3))\n",
    "conf_val_colours = np.zeros((x_val.shape[0], 3))\n",
    "conf_test_colours = np.zeros((x_test.shape[0], 3))\n",
    "\n",
    "sup_train_colours = np.zeros((x_train.shape[0], 3))\n",
    "sup_val_colours = np.zeros((x_val.shape[0], 3))\n",
    "sup_test_colours = np.zeros((x_test.shape[0], 3))\n",
    "\n",
    "train_ind = 0\n",
    "val_ind = 0\n",
    "\n",
    "for i in range(10):\n",
    "    train_inds = np.where(y_train==i)[0]\n",
    "    val_inds = np.where(y_val==i)[0]\n",
    "    test_inds = np.where(y_test==i)[0]  \n",
    "\n",
    "    conf_train[train_inds], conf_train_colours[train_inds] = create_confounder(x_train[train_inds].copy(), colours[i], colours, confounder_portion=confounder_portion, noise_scale=noise_scale)\n",
    "    conf_val[val_inds], conf_val_colours[val_inds] = create_confounder(x_val[val_inds].copy(), colours[i], colours, confounder_portion=confounder_portion, noise_scale=noise_scale)\n",
    "    conf_test[test_inds], conf_test_colours[test_inds] = create_confounder(x_test[test_inds].copy(), colours[i], colours, confounder_portion=confounder_portion, noise_scale=noise_scale)\n",
    "    \n",
    "    sup_train[train_inds], sup_train_colours[train_inds] = create_suppressor(x_train[train_inds].copy(), colours, noise_scale=noise_scale)\n",
    "    sup_val[val_inds], sup_val_colours[val_inds] = create_suppressor(x_val[val_inds].copy(), colours, noise_scale=noise_scale)\n",
    "    sup_test[test_inds], sup_test_colours[test_inds] = create_suppressor(x_test[test_inds].copy(), colours, noise_scale=noise_scale)  \n",
    "\n",
    "    no_col_train[train_inds] = create_no_col(x_train[train_inds].copy(), noise_scale=noise_scale)\n",
    "    no_col_val[val_inds] = create_no_col(x_val[val_inds].copy(), noise_scale=noise_scale)\n",
    "    no_col_test[test_inds] = create_no_col(x_test[test_inds].copy(), noise_scale=noise_scale)\n",
    "\n",
    "#     no_col_train[train_inds] = (1.0-noise_scale)*(x_train[train_inds].copy()) + abs(noise_scale*truncnorm.rvs(size=no_col_train[train_inds].shape))\n",
    "#     no_col_val[val_inds] = (1.0-noise_scale)*(x_val[val_inds].copy()) + abs(noise_scale*truncnorm.rvs(size=no_col_val[val_inds].shape))\n",
    "#     no_col_test[test_inds] = (1.0-noise_scale)*(x_test[test_inds].copy()) + abs(noise_scale*truncnorm.rvs(size=no_col_test[test_inds].shape))\n",
    "\n",
    "\n",
    "scale = 1 / np.max(np.abs(np.vstack([conf_train, conf_val, conf_test])))\n",
    "\n",
    "conf_train = np.apply_along_axis(scale_to_bound, 1, conf_train, scale) * 2 - 1\n",
    "conf_val = np.apply_along_axis(scale_to_bound, 1, conf_val, scale) * 2 - 1\n",
    "conf_test = np.apply_along_axis(scale_to_bound, 1, conf_test, scale) * 2 - 1\n",
    "\n",
    "##############\n",
    "\n",
    "scale = 1 / np.max(np.abs(np.vstack([sup_train, sup_val, sup_test])))\n",
    "\n",
    "sup_train = np.apply_along_axis(scale_to_bound, 1, sup_train, scale) * 2 - 1\n",
    "sup_val = np.apply_along_axis(scale_to_bound, 1, sup_val, scale) * 2 - 1\n",
    "sup_test = np.apply_along_axis(scale_to_bound, 1, sup_test, scale) * 2 - 1\n",
    "\n",
    "##############\n",
    "\n",
    "scale = 1 / np.max(np.abs(np.vstack([no_col_train, no_col_val, no_col_test])))\n",
    "\n",
    "no_col_train = np.apply_along_axis(scale_to_bound, 1, no_col_train, scale) * 2 - 1\n",
    "no_col_val = np.apply_along_axis(scale_to_bound, 1, no_col_val, scale) * 2 - 1\n",
    "no_col_test = np.apply_along_axis(scale_to_bound, 1, no_col_test, scale) * 2 - 1\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "f3366c50",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-5524149.589699072 -6111928.211136567\n"
     ]
    }
   ],
   "source": [
    "print(np.sum(conf_train[:,:,:,0]), np.sum(conf_train[:,:,:,2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "dacfd9c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-5523187.941091554 -6111928.211136567\n"
     ]
    }
   ],
   "source": [
    "print(np.sum(sup_train[:,:,:,0]), np.sum(sup_train[:,:,:,2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "1557de36",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-3277.464096843929 -1164656.4222731388\n"
     ]
    }
   ],
   "source": [
    "print(np.sum(no_col_train[:,:,:,0]), np.sum(no_col_train[:,:,:,2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "ad2cdc0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.0"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(no_col_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "b38868bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(no_col_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "a0ef3380",
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_class_data(x_conf, x_sup, x_no_col, y, class_ind=0, n_cols=10):\n",
    "    class_inds = np.where(y==class_ind)[0]\n",
    "    \n",
    "    sample = random.sample(list(class_inds), n_cols)\n",
    "    fig, axs = plt.subplots(3,n_cols, figsize=(10,4))\n",
    "    \n",
    "    dataset_labels = ['Confounder', 'Suppressor', 'No\\nColour']\n",
    "    \n",
    "    for i, dataset in enumerate([x_conf, x_sup, x_no_col]):\n",
    "        for j in range(n_cols):\n",
    "            if i == 0:\n",
    "                axs[i,j].set_title(f'Sample\\n{sample[j]}')\n",
    "            axs[i,j].imshow(dataset[sample[j]])\n",
    "            axs[i,j].set_xticks([])\n",
    "            axs[i,j].set_yticks([])\n",
    "            \n",
    "        axs[i,0].set_ylabel(dataset_labels[i])\n",
    "            \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "ed80b7c6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
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       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
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       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById('mpl-warnings');\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen = function () {\n",
       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
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       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    fig.rubberband_canvas.style.cursor = msg['cursor'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\n",
       "                /* FIXME: We get \"Resource interpreted as Image but\n",
       "                 * transferred with MIME type text/plain:\" errors on\n",
       "                 * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "                 * to be part of the websocket stream */\n",
       "                img.type = 'image/png';\n",
       "            }\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * https://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(data);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" 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      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
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      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
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      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    }
   ],
   "source": [
    "show_class_data(conf_train, sup_train, no_col_train, y_train, class_ind=0, n_cols=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "1d069248",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle as pkl\n",
    "\n",
    "conf_save = [(conf_train, y_train, conf_train_colours), (conf_val, y_val, conf_val_colours), (conf_test, y_test, conf_test_colours)]\n",
    "sup_save = [(sup_train, y_train, sup_train_colours), (sup_val, y_val, sup_val_colours), (sup_test, y_test, sup_test_colours)]\n",
    "no_col_save = [(no_col_train, y_train), (no_col_val, y_val), (no_col_test, y_test)]\n",
    "\n",
    "\n",
    "with open('mnist_conf_data.pkl', 'wb') as f:\n",
    "    pkl.dump(conf_save, f)\n",
    "    \n",
    "with open('mnist_sup_data.pkl', 'wb') as f:\n",
    "    pkl.dump(sup_save, f)\n",
    "    \n",
    "with open('mnist_no_col_data.pkl', 'wb') as f:\n",
    "    pkl.dump(no_col_save, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6452747f",
   "metadata": {},
   "source": [
    "## Model Training\n",
    "Following the implementation of Anders et. al (2022): https://www.sciencedirect.com/science/article/pii/S1566253521001573\n",
    "and https://colab.research.google.com/github/reiinakano/invariant-risk-minimization/blob/master/invariant_risk_minimization_colored_mnist.ipynb#scrollTo=9hYJRewnv80x\n",
    "\n",
    "\n",
    "All models on colored MNIST in Sections 3.2 and 4.1 are trained\n",
    "using the AdaDelta algorithm with a learning rate of 1.0, which is\n",
    "multiplied by 0.7 after each epoch, for 10 epochs. The a posteriori ClArC\n",
    "is trained for 10 epochs on top of the baseline model, which has also\n",
    "been trained for 10 epochs. The network consists of 2 convolutional\n",
    "layers, followed by a max-pooling, and finally 2 fully-connected layers.\n",
    "Dropout is used after the max pooling and after the first fully-connected\n",
    "layer, with 25 percent and 50 percent dropout probabilities respectively. ReLU activations follow all fully-connected layers except the\n",
    "final one. The model used for 5.1 is trained with SGD, with a learning\n",
    "rate of 0.001 for 5 epochs. The architecture, however, is the same as\n",
    "for the other colored MNIST models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "b5631e99",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Net, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(3, 20, 5, 1)\n",
    "        self.conv2 = nn.Conv2d(20, 50, 5, 1)\n",
    "        self.fc1 = nn.Linear(5 * 5 * 50, 1250)\n",
    "        self.fc2 = nn.Linear(1250, 10)\n",
    "\n",
    "    def forward(self, x):\n",
    "\n",
    "        x = F.relu(self.conv1(x))\n",
    "\n",
    "        x = F.max_pool2d(x, 2, 2)\n",
    "\n",
    "        x = F.relu(self.conv2(x))\n",
    "\n",
    "        x = F.max_pool2d(x, 2, 2)\n",
    "\n",
    "        x = x.view(-1, 5 * 5 * 50)\n",
    "\n",
    "        x = F.relu(self.fc1(x))\n",
    "\n",
    "        logits = self.fc2(x)\n",
    "\n",
    "        return logits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "a3a5d77d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(lr,epochs,train_loader,val_loader,name_model,momentum=0.9,weight_decay=1e-3,num_models=10):\n",
    "    metric = AUROC(task='multiclass', num_classes=10) \n",
    "    softmax = nn.Softmax(dim=1)\n",
    "    \n",
    "    training_accuracy = Accuracy(task='multiclass', num_classes=10).to(DEVICE)\n",
    "    val_accuracy = Accuracy(task='multiclass', num_classes=10).to(DEVICE)\n",
    "\n",
    "    torch.manual_seed(SEED)\n",
    "\n",
    "    trained_models = []\n",
    "    training_logs = []\n",
    "\n",
    "    for training_ind in range(num_models):\n",
    "        cnn_conf=Net()\n",
    "        cnn_conf.to(DEVICE)\n",
    "        criterion = nn.CrossEntropyLoss()\n",
    "        \n",
    "        # Anders et. al uses mainly AdaDelta but also SGD in one section\n",
    "#         optimizer = optim.SGD(cnn_conf.parameters(), lr=lr, momentum=momentum, weight_decay=weight_decay)\n",
    "        optimizer = optim.Adadelta(cnn_conf.parameters(), lr=lr, weight_decay=weight_decay)\n",
    "\n",
    "\n",
    "        loss_list_train=[]\n",
    "        loss_list_val=[]\n",
    "        acc_list_train=[]\n",
    "        acc_list_val=[]\n",
    "        \n",
    "\n",
    "        min_loss = 100000000\n",
    "    #     last_loss = 1000000000\n",
    "\n",
    "        for epoch in range(epochs):  # loop over the dataset multiple times\n",
    "            print(f' epoch {epoch+1} in {epochs}')\n",
    "            t0=time.time()\n",
    "            epoch_loss = 0.0\n",
    "            epoch_loss_val= 0.0\n",
    "            batch_acc=[]\n",
    "            batch_acc_val=[]\n",
    "\n",
    "            auroc_train=[]\n",
    "            auroc_val=[]\n",
    "\n",
    "            pred_train=[]\n",
    "            pred_val=[]\n",
    "            true_labels_train=[]\n",
    "            true_labels_val=[]\n",
    "\n",
    "            for i, data in enumerate(train_loader):\n",
    "                inputs, labels = data\n",
    "\n",
    "                inputs = inputs.to(DEVICE,dtype=torch.float)\n",
    "                labels = labels.type(torch.LongTensor)\n",
    "                labels=labels.to(DEVICE)\n",
    "\n",
    "                # zero the parameter gradients\n",
    "                optimizer.zero_grad()\n",
    "\n",
    "                # forward + backward + optimize\n",
    "                outputs = cnn_conf(inputs).squeeze()\n",
    "                _, predicted = torch.max(outputs.data,1)\n",
    "                loss = criterion(outputs, labels)\n",
    "                loss.backward()\n",
    "                optimizer.step()\n",
    "\n",
    "                epoch_loss += loss.item()        \n",
    "                batch_acc.append(training_accuracy(predicted, labels).item())\n",
    "\n",
    "#                 print(softmax(outputs)[:,1].detach().numpy().shape)\n",
    "#                 pred_train=np.concatenate((pred_train, softmax(outputs)[:,1].detach().numpy()))\n",
    "#                 true_labels_train=np.concatenate((true_labels_train, labels.cpu().numpy()))\n",
    "                \n",
    "                auroc_train.append(metric(outputs,labels.to(torch.int32)))\n",
    "\n",
    "            auroc_train = sum(auroc_train)/len(auroc_train)\n",
    "\n",
    "            for i_v, data_val in enumerate(val_loader):\n",
    "                inputs_val, labels_val = data_val\n",
    "\n",
    "                inputs_val = inputs_val.to(DEVICE,dtype=torch.float)\n",
    "                labels_val = labels_val.type(torch.LongTensor)\n",
    "                labels_val=labels_val.to(DEVICE)\n",
    "\n",
    "                outputs_val = cnn_conf(inputs_val).squeeze()\n",
    "                _, predicted_val = torch.max(outputs_val.data, 1)\n",
    "\n",
    "                loss_val = criterion(outputs_val, labels_val)\n",
    "\n",
    "                epoch_loss_val += loss_val.item()        \n",
    "                batch_acc_val.append(val_accuracy(predicted_val, labels_val).item())\n",
    "\n",
    "                auroc_val.append(metric(outputs_val,labels_val.to(torch.int32)))\n",
    "\n",
    "#                 pred_val=np.concatenate((pred_val, softmax(outputs_val)[:,1].cpu().detach().numpy()))\n",
    "#                 true_labels_val=np.concatenate((true_labels_val, labels_val.cpu().detach().numpy()))\n",
    "            auroc_val = sum(auroc_val)/len(auroc_val)\n",
    "\n",
    "            if epoch_loss_val < min_loss:\n",
    "                min_loss = epoch_loss_val\n",
    "                save_model = type(cnn_conf)() # get a new instance\n",
    "                save_model.load_state_dict(cnn_conf.state_dict()) # copy weights and stuff\n",
    "\n",
    "                torch.save(save_model.state_dict(), f'{str(name_model)}_{training_ind}.pt')\n",
    "\n",
    "#             auroc_val=metric(torch.tensor(pred_val),torch.tensor(true_labels_val).to(torch.int32))\n",
    "\n",
    "            epoch_acc=sum(batch_acc)/len(batch_acc)\n",
    "            epoch_acc_val=sum(batch_acc_val)/len(batch_acc_val)\n",
    "\n",
    "            print(f'epoch train loss: {epoch_loss} | epoch train acc {epoch_acc} | AUROC: {auroc_train}')\n",
    "            print(f'epoch val loss: {epoch_loss_val} | epoch val acc {epoch_acc_val} | AUROC: {auroc_val}')\n",
    "            print(f'time elapsed: {round(time.time()-t0,2)} s')\n",
    "\n",
    "            loss_list_train.append(epoch_loss)\n",
    "            loss_list_val.append(epoch_loss_val)\n",
    "            acc_list_train.append(epoch_acc)\n",
    "            acc_list_val.append(epoch_acc_val)\n",
    "\n",
    "            epoch_loss = 0.0\n",
    "            epoch_loss_val=0.0\n",
    "\n",
    "        print('Finished Training')\n",
    "        print()\n",
    "        print('\\t \\t *******************')\n",
    "        print()\n",
    "        \n",
    "        torch.save(save_model.state_dict(), f'{str(name_model)}_{training_ind}.pt')\n",
    "\n",
    "        trained_models.append(save_model)\n",
    "        training_logs.append([acc_list_train, loss_list_train, acc_list_val, loss_list_val])\n",
    "#     plt.plot(loss_list_train)\n",
    "#     plt.plot(loss_list_val)\n",
    "#     plt.plot([i*100 for i in acc_list_train])\n",
    "#     plt.plot([i*100 for i in acc_list_va])\n",
    "    return trained_models, [acc_list_train, loss_list_train, acc_list_val, loss_list_val]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "9415ca55",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_loader = DataLoader(TensorDataset(torch.tensor(conf_train.transpose(0,3,1,2)), torch.tensor(y_train)), batch_size=1000, shuffle=True)\n",
    "val_loader = DataLoader(TensorDataset(torch.tensor(conf_val.transpose(0,3,1,2)), torch.tensor(y_val)), batch_size=1000, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "4d40a193",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = 0.005\n",
    "epochs = 20\n",
    "num_models=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "1857af32",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " epoch 1 in 20\n",
      "epoch train loss: 50.64732766151428 | epoch train acc 0.105530303648927 | AUROC: 0.5316533446311951\n",
      "epoch val loss: 18.41024112701416 | epoch val acc 0.10900000017136335 | AUROC: 0.5564974546432495\n",
      "time elapsed: 11.64 s\n",
      " epoch 2 in 20\n",
      "epoch train loss: 50.611862897872925 | epoch train acc 0.11254545504396612 | AUROC: 0.5752776861190796\n",
      "epoch val loss: 18.398555517196655 | epoch val acc 0.11499999929219484 | AUROC: 0.5979584455490112\n",
      "time elapsed: 18.14 s\n",
      " epoch 3 in 20\n",
      "epoch train loss: 50.57416248321533 | epoch train acc 0.1163333339447325 | AUROC: 0.6149024367332458\n",
      "epoch val loss: 18.381505966186523 | epoch val acc 0.11687499936670065 | AUROC: 0.6369352340698242\n",
      "time elapsed: 12.57 s\n",
      " epoch 4 in 20\n",
      "epoch train loss: 50.53849387168884 | epoch train acc 0.12028787962415001 | AUROC: 0.6452692747116089\n",
      "epoch val loss: 18.36642289161682 | epoch val acc 0.12512499932199717 | AUROC: 0.659362256526947\n",
      "time elapsed: 13.63 s\n",
      " epoch 5 in 20\n",
      "epoch train loss: 50.501516342163086 | epoch train acc 0.1265151524408297 | AUROC: 0.667536199092865\n",
      "epoch val loss: 18.35441303253174 | epoch val acc 0.13062500022351742 | AUROC: 0.6761701107025146\n",
      "time elapsed: 14.98 s\n",
      " epoch 6 in 20\n",
      "epoch train loss: 50.46385669708252 | epoch train acc 0.13966666703874414 | AUROC: 0.6831632256507874\n",
      "epoch val loss: 18.343544483184814 | epoch val acc 0.14875000156462193 | AUROC: 0.6871853470802307\n",
      "time elapsed: 15.53 s\n",
      " epoch 7 in 20\n",
      "epoch train loss: 50.42383599281311 | epoch train acc 0.16678787767887115 | AUROC: 0.6928735971450806\n",
      "epoch val loss: 18.32955574989319 | epoch val acc 0.17024999856948853 | AUROC: 0.6962041854858398\n",
      "time elapsed: 16.57 s\n",
      " epoch 8 in 20\n",
      "epoch train loss: 50.38061261177063 | epoch train acc 0.18118181960149246 | AUROC: 0.7007434964179993\n",
      "epoch val loss: 18.31107187271118 | epoch val acc 0.1906249988824129 | AUROC: 0.7020617723464966\n",
      "time elapsed: 15.17 s\n",
      " epoch 9 in 20\n",
      "epoch train loss: 50.333781242370605 | epoch train acc 0.19112121246077798 | AUROC: 0.7061882019042969\n",
      "epoch val loss: 18.293110847473145 | epoch val acc 0.19099999964237213 | AUROC: 0.7060748934745789\n",
      "time elapsed: 20.69 s\n",
      " epoch 10 in 20\n",
      "epoch train loss: 50.281246185302734 | epoch train acc 0.1997575732794675 | AUROC: 0.7097887396812439\n",
      "epoch val loss: 18.275744438171387 | epoch val acc 0.2058749981224537 | AUROC: 0.7093531489372253\n",
      "time elapsed: 21.19 s\n",
      " epoch 11 in 20\n",
      "epoch train loss: 50.22543716430664 | epoch train acc 0.20689393783157523 | AUROC: 0.7123419046401978\n",
      "epoch val loss: 18.252445697784424 | epoch val acc 0.20412499830126762 | AUROC: 0.7120485305786133\n",
      "time elapsed: 15.6 s\n",
      " epoch 12 in 20\n",
      "epoch train loss: 50.161032915115356 | epoch train acc 0.21143939413807608 | AUROC: 0.7140297293663025\n",
      "epoch val loss: 18.22587513923645 | epoch val acc 0.2166249994188547 | AUROC: 0.7145392894744873\n",
      "time elapsed: 15.05 s\n",
      " epoch 13 in 20\n",
      "epoch train loss: 50.089587688446045 | epoch train acc 0.21678787876259198 | AUROC: 0.715077817440033\n",
      "epoch val loss: 18.20102024078369 | epoch val acc 0.21749999932944775 | AUROC: 0.714411199092865\n",
      "time elapsed: 15.09 s\n",
      " epoch 14 in 20\n",
      "epoch train loss: 50.01269173622131 | epoch train acc 0.2223333330316977 | AUROC: 0.7146503925323486\n",
      "epoch val loss: 18.168320894241333 | epoch val acc 0.2251249961555004 | AUROC: 0.7133476138114929\n",
      "time elapsed: 15.15 s\n",
      " epoch 15 in 20\n",
      "epoch train loss: 49.921823263168335 | epoch train acc 0.22453030266545035 | AUROC: 0.7148599028587341\n",
      "epoch val loss: 18.139012813568115 | epoch val acc 0.22174999862909317 | AUROC: 0.7081193327903748\n",
      "time elapsed: 21.18 s\n",
      " epoch 16 in 20\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\1\\ipykernel_19636\\1339579275.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtrain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlr\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mepochs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtrain_loader\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mval_loader\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'mnist_conf'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmomentum\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.9\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mweight_decay\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1e-3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mnum_models\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnum_models\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\1\\ipykernel_19636\\377032434.py\u001b[0m in \u001b[0;36mtrain\u001b[1;34m(lr, epochs, train_loader, val_loader, name_model, momentum, weight_decay, num_models)\u001b[0m\n\u001b[0;32m     60\u001b[0m                 \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpredicted\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     61\u001b[0m                 \u001b[0mloss\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 62\u001b[1;33m                 \u001b[0mloss\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     63\u001b[0m                 \u001b[0moptimizer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     64\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\.virtualenvs\\debugging_xai-9luXJGIR\\lib\\site-packages\\torch\\_tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[1;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[0;32m    487\u001b[0m             )\n\u001b[0;32m    488\u001b[0m         torch.autograd.backward(\n\u001b[1;32m--> 489\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    490\u001b[0m         )\n\u001b[0;32m    491\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\.virtualenvs\\debugging_xai-9luXJGIR\\lib\\site-packages\\torch\\autograd\\__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[1;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[0;32m    197\u001b[0m     Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass\n\u001b[0;32m    198\u001b[0m         \u001b[0mtensors\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgrad_tensors_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 199\u001b[1;33m         allow_unreachable=True, accumulate_grad=True)  # Calls into the C++ engine to run the backward pass\n\u001b[0m\u001b[0;32m    200\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    201\u001b[0m def grad(\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "train(lr,epochs,train_loader,val_loader,'mnist_conf',momentum=0.9,weight_decay=1e-3,num_models=num_models)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "59fffd1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_loader = DataLoader(TensorDataset(torch.tensor(sup_train.transpose(0,3,1,2)), torch.tensor(y_train)), batch_size=1000, shuffle=True)\n",
    "val_loader = DataLoader(TensorDataset(torch.tensor(sup_val.transpose(0,3,1,2)), torch.tensor(y_val)), batch_size=1000, shuffle=True)\n",
    "\n",
    "train(lr,epochs,train_loader,val_loader,'mnist_sup',momentum=0.9,weight_decay=1e-3,num_models=num_models)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7bf2bac",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_loader = DataLoader(TensorDataset(torch.tensor(no_col_train.transpose(0,3,1,2)), torch.tensor(y_train)), batch_size=1000, shuffle=True)\n",
    "val_loader = DataLoader(TensorDataset(torch.tensor(no_col_val.transpose(0,3,1,2)), torch.tensor(y_val)), batch_size=1000, shuffle=True)\n",
    "\n",
    "train(lr,epochs,train_loader,val_loader,'mnist_no_col',momentum=0.9,weight_decay=1e-3,num_models=num_models)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "12cef5c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_trained(path, device='cpu'):\n",
    "    model = Net()\n",
    "    model.load_state_dict(torch.load(path,map_location=device))\n",
    "    return model\n",
    "\n",
    "def print_AUC(loader, model_ind=0, device='cpu'):\n",
    "\n",
    "    cnn_conf=load_trained(f'./mnist_conf_{model_ind}.pt').to(device)\n",
    "    cnn_sup=load_trained(f'./mnist_sup_{model_ind}.pt').to(device)\n",
    "    cnn_no=load_trained(f'./mnist_no_col_{model_ind}.pt').to(device)\n",
    "    \n",
    "    #models -> cnn_no; cnn_sup; cnn_conf\n",
    "\n",
    "    softmax = nn.Softmax(dim=1)\n",
    "    metric = AUROC(num_classes=10, task='multiclass')\n",
    "\n",
    "    model_conf_test_conf_pred=np.array([])\n",
    "    model_sup_test_conf_pred=np.array([])\n",
    "    model_no_test_conf_pred=np.array([])\n",
    "    true_labels=np.array([])\n",
    "    \n",
    "    model_conf_auroc_test = []\n",
    "    model_sup_auroc_test = []\n",
    "    model_no_col_auroc_test = []\n",
    "\n",
    "    for i_v, data_val in enumerate(loader):\n",
    "        inputs_val, labels_val = data_val\n",
    "        inputs_val = inputs_val.to(DEVICE,dtype=torch.float)\n",
    "        labels_val = labels_val.type(torch.LongTensor)\n",
    "        labels_val=labels_val.to(DEVICE)\n",
    "\n",
    "        outputs_conf = cnn_conf(inputs_val).squeeze()\n",
    "        outputs_sup = cnn_sup(inputs_val).squeeze()\n",
    "        outputs_no = cnn_no(inputs_val).squeeze()\n",
    "\n",
    "        out_pred_conf=softmax(outputs_conf)\n",
    "        out_pred_sup=softmax(outputs_sup)\n",
    "        out_pred_no=softmax(outputs_no)\n",
    "\n",
    "#         model_conf_test_conf_pred=np.concatenate((model_conf_test_conf_pred, out_pred_conf[:,1].cpu().detach().numpy()))\n",
    "#         model_sup_test_conf_pred=np.concatenate((model_sup_test_conf_pred, out_pred_sup[:,1].cpu().detach().numpy()))\n",
    "#         model_no_test_conf_pred=np.concatenate((model_no_test_conf_pred, out_pred_no[:,1].cpu().detach().numpy()))\n",
    "\n",
    "#         print(labels_val.shape)\n",
    "#         print(model_conf_test_conf_pred)\n",
    "\n",
    "        model_conf_auroc_test.append(metric(out_pred_conf, labels_val))\n",
    "        model_sup_auroc_test.append(metric(out_pred_sup, labels_val))\n",
    "        model_no_col_auroc_test.append(metric(out_pred_no, labels_val))\n",
    "        \n",
    "#         true_labels=np.concatenate((true_labels, labels_val.cpu().numpy()))\n",
    "\n",
    "    print('model: confounder', sum(model_conf_auroc_test)/len(model_conf_auroc_test))\n",
    "    print('model: suppressor',sum(model_sup_auroc_test)/len(model_sup_auroc_test))\n",
    "    print('model: no colour',sum(model_no_col_auroc_test)/len(model_no_col_auroc_test))\n",
    "    print()\n",
    "\n",
    "    l=[sum(model_conf_auroc_test)/len(model_conf_auroc_test),\n",
    "       sum(model_sup_auroc_test)/len(model_sup_auroc_test),\n",
    "       sum(model_no_col_auroc_test)/len(model_no_col_auroc_test)]\n",
    "    return l\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "baad3499",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle as pkl\n",
    "with open('mnist_conf_data.pkl', 'rb') as f:\n",
    "    conf_data = pkl.load(f)\n",
    "    \n",
    "with open('mnist_sup_data.pkl', 'rb') as f:\n",
    "    sup_data = pkl.load(f)\n",
    "    \n",
    "with open('mnist_no_col_data.pkl', 'rb') as f:\n",
    "    no_col_data = pkl.load(f)\n",
    "\n",
    "batch_size = 64\n",
    "\n",
    "confounder_test_loader = DataLoader(TensorDataset(torch.tensor(conf_data[2][0].transpose(0,3,1,2)), torch.tensor(conf_data[2][1])),batch_size=batch_size, shuffle=True)\n",
    "suppressor_test_loader = DataLoader(TensorDataset(torch.tensor(sup_data[2][0].transpose(0,3,1,2)), torch.tensor(sup_data[2][1])),batch_size=batch_size, shuffle=True)\n",
    "no_col_test_loader = DataLoader(TensorDataset(torch.tensor(no_col_data[2][0].transpose(0,3,1,2)), torch.tensor(no_col_data[2][1])),batch_size=batch_size, shuffle=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9c4b2f38",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MODEL 0\n",
      "confounder data:\n",
      "model: confounder tensor(0.9720)\n",
      "model: suppressor tensor(0.9687)\n",
      "model: no colour tensor(0.9689)\n",
      "\n",
      "\n",
      "suppressor data:\n",
      "model: confounder tensor(0.9624)\n",
      "model: suppressor tensor(0.9683)\n",
      "model: no colour tensor(0.9696)\n",
      "\n",
      "\n",
      "no colour data:\n",
      "model: confounder tensor(0.9700)\n",
      "model: suppressor tensor(0.9722)\n",
      "model: no colour tensor(0.9745)\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "batch_size=64\n",
    "# confounder_test_loader = DataLoader(TensorDataset(torch.tensor(conf_test.transpose(0,3,1,2)), torch.tensor(y_test)),batch_size=batch_size, shuffle=False)\n",
    "# supressor_test_loader = DataLoader(TensorDataset(torch.tensor(sup_test.transpose(0,3,1,2)), torch.tensor(y_test)),batch_size=batch_size, shuffle=False)\n",
    "# no_col_test_loader = DataLoader(TensorDataset(torch.tensor(no_col_test.transpose(0,3,1,2)), torch.tensor(y_test)),batch_size=batch_size, shuffle=False)\n",
    "\n",
    "confounder_data_results = []\n",
    "suppressor_data_results = []\n",
    "no_col_data_results = []\n",
    "\n",
    "for i in range(1):\n",
    "    print(f'MODEL {i}')\n",
    "    print('confounder data:')\n",
    "    confounder_data_results.append(print_AUC(confounder_test_loader, i))\n",
    "    print()\n",
    "    print('suppressor data:')\n",
    "    suppressor_data_results.append(print_AUC(suppressor_test_loader, i))\n",
    "    print()\n",
    "    print('no colour data:')\n",
    "    no_col_data_results.append(print_AUC(no_col_test_loader, i))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a135d8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# current problem\n",
    "# scaling channel noise by sum of that channel will colour background in accordance with pattern colour\n",
    "# alternative: scale channel noise according to another factor, i.e. sum(channel_0) = 2*sum(channel_1 or _2)\n",
    "# key aim: keep total mass in red = green + blue\n",
    "# another problem: we have the c6_chunk = (1.0, 1.0, 1.0) where r = g + b; should we just make this 1.0, 0.5, 0.5? it's "
   ]
  }
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