coloured_mnist_2_channel.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6e0d252f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x2ab62bb7f30>"
      ]
     },
     "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": 2,
   "id": "106d2cbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = './data/'\n",
    "if not os.path.exists(data_path):\n",
    "    os.makedirs(data_path)\n",
    "train_mnist = datasets.mnist.MNIST(data_path, train=True, download=True)\n",
    "test_mnist = datasets.mnist.MNIST(data_path, train=False, download=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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),28,28,3))\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((28,28,1))\n",
    "        \n",
    "        im_arr = np.concatenate([np.array(arr),np.zeros((28,28,1)),np.array(arr)], axis=2)\n",
    "    \n",
    "        data[i] = im_arr\n",
    "        labels[i] = label\n",
    "        \n",
    "    for i, (img, label) in enumerate(test_mnist):\n",
    "        arr = rescale_values(np.array(img)).reshape((28,28,1))\n",
    "        \n",
    "        im_arr = np.concatenate([arr,np.zeros((28,28,1)),arr], axis=2)\n",
    "\n",
    "        data[len(train_mnist)+i] = im_arr\n",
    "        labels[len(train_mnist)+i] = label\n",
    "    \n",
    "    return data, labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 5,
   "id": "d59a4112",
   "metadata": {},
   "outputs": [],
   "source": [
    "# this number needs to be picked s.t. 50% is divisible by 10 and 9\n",
    "n_per_class = 3600\n",
    "\n",
    "sorted_data = np.zeros((n_per_class*10,28,28,3))\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": 6,
   "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": 7,
   "id": "a76b327d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "(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"
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       "(0.5, 0, 0.5)"
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     "execution_count": 7,
     "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": 9,
   "id": "8839dd42",
   "metadata": {},
   "outputs": [
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       "\n",
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       "\n",
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       "\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>"
      ]
     },
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     "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, '53.970588235294116')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# noise = random_noise(, mode='s&p')\n",
    "\n",
    "samp = data[0].copy()\n",
    "samp[:,:,0] *= 0.2\n",
    "samp[:,:,1] *= 0.0\n",
    "samp[:,:,2] *= 0.5\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=0.4))\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=0.25))\n",
    "axs[3].set_title(np.sum(samp[:,:,2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": 24,
   "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 = 0.4\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": 25,
   "id": "f3366c50",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-8820010.471320067 -8818231.964922024\n"
     ]
    }
   ],
   "source": [
    "print(np.sum(conf_train[:,:,:,0]), np.sum(conf_train[:,:,:,2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "dacfd9c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-8838385.134213367 -8826853.174463477\n"
     ]
    }
   ],
   "source": [
    "print(np.sum(sup_train[:,:,:,0]), np.sum(sup_train[:,:,:,2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1557de36",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-8810681.715053279 -8816256.789794656\n"
     ]
    }
   ],
   "source": [
    "print(np.sum(no_col_train[:,:,:,0]), np.sum(no_col_train[:,:,:,2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ad2cdc0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(no_col_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b38868bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(no_col_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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": 29,
   "id": "ed80b7c6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
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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",
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       "\n",
       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
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       "\n",
       "    this.ws = websocket;\n",
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       "        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",
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       "\n",
       "    this.context = undefined;\n",
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       "    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",
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       "\n",
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       "\n",
       "    this._init_header(this);\n",
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       "    this._init_toolbar(this);\n",
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       "    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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       "<img 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\" width=\"1000\">"
      ],
      "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",
      "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",
      "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",
      "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",
      "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": 32,
   "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": 2,
   "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(4 * 4 * 50, 500)\n",
    "        self.fc2 = nn.Linear(500, 10)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = F.relu(self.conv1(x))\n",
    "        x = F.max_pool2d(x, 2, 2)\n",
    "        x = F.relu(self.conv2(x))\n",
    "        x = F.max_pool2d(x, 2, 2)\n",
    "        x = x.view(-1, 4 * 4 * 50)\n",
    "        x = F.relu(self.fc1(x))\n",
    "        logits = self.fc2(x)\n",
    "        return logits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": 53,
   "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": 54,
   "id": "4d40a193",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = 0.005\n",
    "epochs = 50\n",
    "num_models=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "1857af32",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " epoch 1 in 50\n",
      "epoch train loss: 50.666155099868774 | epoch train acc 0.1000000001354651 | AUROC: 0.5172392129898071\n",
      "epoch val loss: 18.422764539718628 | epoch val acc 0.0988750010728836 | AUROC: 0.5222326517105103\n",
      "time elapsed: 8.16 s\n",
      " epoch 2 in 50\n",
      "epoch train loss: 50.657970666885376 | epoch train acc 0.10018181800842285 | AUROC: 0.5253221392631531\n",
      "epoch val loss: 18.422302961349487 | epoch val acc 0.09799999929964542 | AUROC: 0.528529703617096\n",
      "time elapsed: 11.05 s\n",
      " epoch 3 in 50\n",
      "epoch train loss: 50.65031027793884 | epoch train acc 0.10031818158247253 | AUROC: 0.5323665142059326\n",
      "epoch val loss: 18.419564723968506 | epoch val acc 0.09487499948590994 | AUROC: 0.5397389531135559\n",
      "time elapsed: 11.46 s\n",
      " epoch 4 in 50\n",
      "epoch train loss: 50.64471888542175 | epoch train acc 0.10010606016625058 | AUROC: 0.5392879843711853\n",
      "epoch val loss: 18.416330814361572 | epoch val acc 0.10237500071525574 | AUROC: 0.5463832020759583\n",
      "time elapsed: 20.98 s\n",
      " epoch 5 in 50\n",
      "epoch train loss: 50.63826537132263 | epoch train acc 0.09986363554542715 | AUROC: 0.5466981530189514\n",
      "epoch val loss: 18.415765523910522 | epoch val acc 0.09962499979883432 | AUROC: 0.5519963502883911\n",
      "time elapsed: 25.21 s\n",
      " epoch 6 in 50\n",
      "epoch train loss: 50.633113861083984 | epoch train acc 0.09975757619196718 | AUROC: 0.5542061924934387\n",
      "epoch val loss: 18.409207105636597 | epoch val acc 0.10200000088661909 | AUROC: 0.5588976740837097\n",
      "time elapsed: 11.83 s\n",
      " epoch 7 in 50\n",
      "epoch train loss: 50.62663507461548 | epoch train acc 0.10109090974385088 | AUROC: 0.5602412223815918\n",
      "epoch val loss: 18.408920764923096 | epoch val acc 0.09987499937415123 | AUROC: 0.5685672760009766\n",
      "time elapsed: 12.78 s\n",
      " epoch 8 in 50\n",
      "epoch train loss: 50.621742725372314 | epoch train acc 0.10377272828058763 | AUROC: 0.5660237073898315\n",
      "epoch val loss: 18.409031867980957 | epoch val acc 0.10562500078231096 | AUROC: 0.5684050917625427\n",
      "time elapsed: 13.53 s\n",
      " epoch 9 in 50\n",
      "epoch train loss: 50.61617684364319 | epoch train acc 0.10757575725967233 | AUROC: 0.5731071829795837\n",
      "epoch val loss: 18.408209800720215 | epoch val acc 0.1081250011920929 | AUROC: 0.5740258097648621\n",
      "time elapsed: 11.49 s\n",
      " epoch 10 in 50\n",
      "epoch train loss: 50.61072039604187 | epoch train acc 0.11506060578606346 | AUROC: 0.5791481137275696\n",
      "epoch val loss: 18.40298867225647 | epoch val acc 0.12025000154972076 | AUROC: 0.5837236642837524\n",
      "time elapsed: 11.61 s\n",
      " epoch 11 in 50\n",
      "epoch train loss: 50.606364011764526 | epoch train acc 0.12201515144922516 | AUROC: 0.5846666693687439\n",
      "epoch val loss: 18.40055513381958 | epoch val acc 0.12450000084936619 | AUROC: 0.5892292857170105\n",
      "time elapsed: 10.94 s\n",
      " epoch 12 in 50\n",
      "epoch train loss: 50.601755142211914 | epoch train acc 0.129348486661911 | AUROC: 0.590505838394165\n",
      "epoch val loss: 18.400323390960693 | epoch val acc 0.13299999944865704 | AUROC: 0.5918514132499695\n",
      "time elapsed: 11.21 s\n",
      " epoch 13 in 50\n",
      "epoch train loss: 50.596925497055054 | epoch train acc 0.1413030309433287 | AUROC: 0.596295952796936\n",
      "epoch val loss: 18.39706039428711 | epoch val acc 0.15437499806284904 | AUROC: 0.6020808815956116\n",
      "time elapsed: 13.21 s\n",
      " epoch 14 in 50\n",
      "epoch train loss: 50.593013048172 | epoch train acc 0.15443939580158753 | AUROC: 0.6018562316894531\n",
      "epoch val loss: 18.39500856399536 | epoch val acc 0.1646249983459711 | AUROC: 0.6084522008895874\n",
      "time elapsed: 14.8 s\n",
      " epoch 15 in 50\n",
      "epoch train loss: 50.58770513534546 | epoch train acc 0.16724242405457931 | AUROC: 0.6070091724395752\n",
      "epoch val loss: 18.394310235977173 | epoch val acc 0.17662499845027924 | AUROC: 0.6096858978271484\n",
      "time elapsed: 14.75 s\n",
      " epoch 16 in 50\n",
      "epoch train loss: 50.58354473114014 | epoch train acc 0.17316666584123264 | AUROC: 0.6120981574058533\n",
      "epoch val loss: 18.391610860824585 | epoch val acc 0.1841249968856573 | AUROC: 0.6166081428527832\n",
      "time elapsed: 12.2 s\n",
      " epoch 17 in 50\n",
      "epoch train loss: 50.57861042022705 | epoch train acc 0.182424243200909 | AUROC: 0.6170491576194763\n",
      "epoch val loss: 18.391895055770874 | epoch val acc 0.18912500143051147 | AUROC: 0.6194890141487122\n",
      "time elapsed: 11.74 s\n",
      " epoch 18 in 50\n",
      "epoch train loss: 50.573994874954224 | epoch train acc 0.18590909174897455 | AUROC: 0.622069776058197\n",
      "epoch val loss: 18.38824224472046 | epoch val acc 0.19712499901652336 | AUROC: 0.6220729947090149\n",
      "time elapsed: 13.4 s\n",
      " epoch 19 in 50\n",
      "epoch train loss: 50.57033085823059 | epoch train acc 0.18962121145291763 | AUROC: 0.6267642378807068\n",
      "epoch val loss: 18.387474298477173 | epoch val acc 0.19412499852478504 | AUROC: 0.6340063810348511\n",
      "time elapsed: 12.48 s\n",
      " epoch 20 in 50\n",
      "epoch train loss: 50.56509828567505 | epoch train acc 0.1902727267958901 | AUROC: 0.6318953037261963\n",
      "epoch val loss: 18.386247873306274 | epoch val acc 0.196000000461936 | AUROC: 0.637377917766571\n",
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      " epoch 21 in 50\n",
      "epoch train loss: 50.561665296554565 | epoch train acc 0.18969696895642715 | AUROC: 0.6362504363059998\n",
      "epoch val loss: 18.384947776794434 | epoch val acc 0.19449999928474426 | AUROC: 0.6410404443740845\n",
      "time elapsed: 13.34 s\n",
      " epoch 22 in 50\n",
      "epoch train loss: 50.55627632141113 | epoch train acc 0.1922727261077274 | AUROC: 0.6410912871360779\n",
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      " epoch 23 in 50\n",
      "epoch train loss: 50.55202126502991 | epoch train acc 0.18815151466564697 | AUROC: 0.6457487344741821\n",
      "epoch val loss: 18.382960081100464 | epoch val acc 0.1854999978095293 | AUROC: 0.6468958258628845\n",
      "time elapsed: 12.22 s\n",
      " epoch 24 in 50\n",
      "epoch train loss: 50.546042680740356 | epoch train acc 0.1898484839634462 | AUROC: 0.6502358317375183\n",
      "epoch val loss: 18.37967276573181 | epoch val acc 0.18325000070035458 | AUROC: 0.6541212797164917\n",
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      " epoch 25 in 50\n",
      "epoch train loss: 50.542031049728394 | epoch train acc 0.1860606067559936 | AUROC: 0.6545854210853577\n",
      "epoch val loss: 18.377708911895752 | epoch val acc 0.1834999993443489 | AUROC: 0.6582990884780884\n",
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      " epoch 26 in 50\n",
      "epoch train loss: 50.53712821006775 | epoch train acc 0.18586363575675272 | AUROC: 0.6588736176490784\n",
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      " epoch 27 in 50\n",
      "epoch train loss: 50.53222179412842 | epoch train acc 0.18530303172089838 | AUROC: 0.6632384657859802\n",
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      " epoch 28 in 50\n",
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      "time elapsed: 11.91 s\n",
      " epoch 29 in 50\n",
      "epoch train loss: 50.522095680236816 | epoch train acc 0.18962121145291763 | AUROC: 0.6715583205223083\n",
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      " epoch 30 in 50\n",
      "epoch train loss: 50.51690649986267 | epoch train acc 0.18931818279353055 | AUROC: 0.6763309836387634\n",
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      "time elapsed: 13.72 s\n",
      " epoch 31 in 50\n",
      "epoch train loss: 50.51225233078003 | epoch train acc 0.18836363743651996 | AUROC: 0.6801890730857849\n",
      "epoch val loss: 18.36576223373413 | epoch val acc 0.18649999983608723 | AUROC: 0.6852742433547974\n",
      "time elapsed: 12.96 s\n",
      " epoch 32 in 50\n",
      "epoch train loss: 50.50681447982788 | epoch train acc 0.19065151566808874 | AUROC: 0.6839190721511841\n",
      "epoch val loss: 18.366921186447144 | epoch val acc 0.17962499894201756 | AUROC: 0.6827343106269836\n",
      "time elapsed: 12.73 s\n",
      " epoch 33 in 50\n",
      "epoch train loss: 50.50141453742981 | epoch train acc 0.18830303034999155 | AUROC: 0.6885038614273071\n",
      "epoch val loss: 18.363490343093872 | epoch val acc 0.18324999883770943 | AUROC: 0.6896699666976929\n",
      "time elapsed: 12.73 s\n",
      " epoch 34 in 50\n",
      "epoch train loss: 50.4963161945343 | epoch train acc 0.18898484923622824 | AUROC: 0.6918835639953613\n",
      "epoch val loss: 18.36140513420105 | epoch val acc 0.18649999983608723 | AUROC: 0.6924124956130981\n",
      "time elapsed: 12.73 s\n",
      " epoch 35 in 50\n",
      "epoch train loss: 50.49011564254761 | epoch train acc 0.1896363618698987 | AUROC: 0.6959790587425232\n",
      "epoch val loss: 18.35929775238037 | epoch val acc 0.18587500043213367 | AUROC: 0.701565146446228\n",
      "time elapsed: 12.68 s\n",
      " epoch 36 in 50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch train loss: 50.484376668930054 | epoch train acc 0.19743939556858756 | AUROC: 0.6998476386070251\n",
      "epoch val loss: 18.35688591003418 | epoch val acc 0.18925000168383121 | AUROC: 0.702932596206665\n",
      "time elapsed: 11.95 s\n",
      " epoch 37 in 50\n",
      "epoch train loss: 50.47857928276062 | epoch train acc 0.19434848563237625 | AUROC: 0.7039567232131958\n",
      "epoch val loss: 18.35721254348755 | epoch val acc 0.18487500213086605 | AUROC: 0.7023242712020874\n",
      "time elapsed: 17.9 s\n",
      " epoch 38 in 50\n",
      "epoch train loss: 50.47245955467224 | epoch train acc 0.19715151393955405 | AUROC: 0.7082496881484985\n",
      "epoch val loss: 18.351463079452515 | epoch val acc 0.19100000150501728 | AUROC: 0.7085252404212952\n",
      "time elapsed: 13.94 s\n",
      " epoch 39 in 50\n",
      "epoch train loss: 50.46601629257202 | epoch train acc 0.19842424311421133 | AUROC: 0.7115002870559692\n",
      "epoch val loss: 18.350722312927246 | epoch val acc 0.18712500296533108 | AUROC: 0.7143307328224182\n",
      "time elapsed: 12.46 s\n",
      " epoch 40 in 50\n",
      "epoch train loss: 50.460477113723755 | epoch train acc 0.18963636525652625 | AUROC: 0.7153386473655701\n",
      "epoch val loss: 18.346773386001587 | epoch val acc 0.1933750007301569 | AUROC: 0.720278263092041\n",
      "time elapsed: 14.43 s\n",
      " epoch 41 in 50\n",
      "epoch train loss: 50.453566789627075 | epoch train acc 0.20253030278465964 | AUROC: 0.7190797328948975\n",
      "epoch val loss: 18.34676432609558 | epoch val acc 0.19499999657273293 | AUROC: 0.7204327583312988\n",
      "time elapsed: 12.0 s\n",
      " epoch 42 in 50\n",
      "epoch train loss: 50.44750928878784 | epoch train acc 0.20087878812443127 | AUROC: 0.7229918241500854\n",
      "epoch val loss: 18.345194578170776 | epoch val acc 0.19887499883770943 | AUROC: 0.7227559089660645\n",
      "time elapsed: 11.76 s\n",
      " epoch 43 in 50\n",
      "epoch train loss: 50.44054341316223 | epoch train acc 0.20207575844092804 | AUROC: 0.7267557382583618\n",
      "epoch val loss: 18.341872453689575 | epoch val acc 0.20312500186264515 | AUROC: 0.7290339469909668\n",
      "time elapsed: 11.63 s\n",
      " epoch 44 in 50\n",
      "epoch train loss: 50.43351340293884 | epoch train acc 0.20422727275978436 | AUROC: 0.7306856513023376\n",
      "epoch val loss: 18.339129209518433 | epoch val acc 0.2003750018775463 | AUROC: 0.7308255434036255\n",
      "time elapsed: 12.31 s\n",
      " epoch 45 in 50\n",
      "epoch train loss: 50.42626714706421 | epoch train acc 0.2090606059540402 | AUROC: 0.7339519262313843\n",
      "epoch val loss: 18.33565902709961 | epoch val acc 0.2071250006556511 | AUROC: 0.7365063428878784\n",
      "time elapsed: 11.89 s\n",
      " epoch 46 in 50\n",
      "epoch train loss: 50.41959810256958 | epoch train acc 0.2070303030989387 | AUROC: 0.7373075485229492\n",
      "epoch val loss: 18.333155870437622 | epoch val acc 0.20662499964237213 | AUROC: 0.743301272392273\n",
      "time elapsed: 11.61 s\n",
      " epoch 47 in 50\n",
      "epoch train loss: 50.41074728965759 | epoch train acc 0.2161818194118413 | AUROC: 0.7413213849067688\n",
      "epoch val loss: 18.32984232902527 | epoch val acc 0.21087499894201756 | AUROC: 0.7416365146636963\n",
      "time elapsed: 12.15 s\n",
      " epoch 48 in 50\n",
      "epoch train loss: 50.40402317047119 | epoch train acc 0.21227272803133185 | AUROC: 0.7442786693572998\n",
      "epoch val loss: 18.32774591445923 | epoch val acc 0.21200000308454037 | AUROC: 0.7477110028266907\n",
      "time elapsed: 12.75 s\n",
      " epoch 49 in 50\n"
     ]
    },
    {
     "ename": "RuntimeError",
     "evalue": "[enforce fail at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\c10\\core\\impl\\alloc_cpu.cpp:72] data. DefaultCPUAllocator: not enough memory: you tried to allocate 46080000 bytes.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\1\\ipykernel_3556\\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_3556\\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     81\u001b[0m                 \u001b[0mlabels_val\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlabels_val\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mDEVICE\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     82\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 83\u001b[1;33m                 \u001b[0moutputs_val\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcnn_conf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs_val\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msqueeze\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     84\u001b[0m                 \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpredicted_val\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_val\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     85\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\.virtualenvs\\debugging_xai-9luXJGIR\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m   1192\u001b[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[0;32m   1193\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[1;32m-> 1194\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\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   1195\u001b[0m         \u001b[1;31m# Do not call functions when jit is used\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1196\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\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[1;32m~\\AppData\\Local\\Temp\\1\\ipykernel_3556\\1189412014.py\u001b[0m in \u001b[0;36mforward\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\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[1;32m---> 10\u001b[1;33m         \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mF\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconv1\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\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     11\u001b[0m         \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mF\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax_pool2d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m         \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mF\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconv2\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\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[1;32m~\\.virtualenvs\\debugging_xai-9luXJGIR\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m   1192\u001b[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[0;32m   1193\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[1;32m-> 1194\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\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   1195\u001b[0m         \u001b[1;31m# Do not call functions when jit is used\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1196\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\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[1;32m~\\.virtualenvs\\debugging_xai-9luXJGIR\\lib\\site-packages\\torch\\nn\\modules\\conv.py\u001b[0m in \u001b[0;36mforward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m    461\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    462\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 463\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_conv_forward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbias\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    464\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    465\u001b[0m \u001b[1;32mclass\u001b[0m \u001b[0mConv3d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_ConvNd\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[1;32m~\\.virtualenvs\\debugging_xai-9luXJGIR\\lib\\site-packages\\torch\\nn\\modules\\conv.py\u001b[0m in \u001b[0;36m_conv_forward\u001b[1;34m(self, input, weight, bias)\u001b[0m\n\u001b[0;32m    458\u001b[0m                             _pair(0), self.dilation, self.groups)\n\u001b[0;32m    459\u001b[0m         return F.conv2d(input, weight, bias, self.stride,\n\u001b[1;32m--> 460\u001b[1;33m                         self.padding, self.dilation, self.groups)\n\u001b[0m\u001b[0;32m    461\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    462\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mRuntimeError\u001b[0m: [enforce fail at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\c10\\core\\impl\\alloc_cpu.cpp:72] data. DefaultCPUAllocator: not enough memory: you tried to allocate 46080000 bytes."
     ]
    }
   ],
   "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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