test_boneage_ensemble.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from torchvision import datasets, transforms, models\n",
    "from torch.utils.data.sampler import SubsetRandomSampler\n",
    "import matplotlib\n",
    "from tqdm import tqdm\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "from tqdm import tqdm\n",
    "from torch.utils.data.sampler import SubsetRandomSampler\n",
    "from data_generator_breast import BreastPathQDataset\n",
    "from data_generator_boneage import BoneAgeDataset\n",
    "from data_generator_endovis import EndoVisDataset\n",
    "from data_generator_oct import OCTDataset\n",
    "from models import BreastPathQModel\n",
    "from uce import uceloss\n",
    "from calibration_plots import plot_uncert, plot_frequency\n",
    "from utils import nll_criterion_gaussian\n",
    "from glob import glob\n",
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_model = 'densenet201'\n",
    "data_set_name = 'endovis' # 'breastpathq', 'boneage', 'endovis', 'oct'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert base_model in ['densenet201']\n",
    "assert data_set_name in ['breastpathq', 'boneage', 'endovis', 'oct']\n",
    "device = torch.device(\"cuda:0\")\n",
    "resize_to = (256, 256)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2159\n",
      "2232\n"
     ]
    }
   ],
   "source": [
    "if data_set_name == 'breastpathq':\n",
    "    in_channels = 3\n",
    "    out_channels = 1\n",
    "    batch_size = 16\n",
    "    resize_to = (384, 384)\n",
    "\n",
    "    data_set = BreastPathQDataset(data_dir='/media/fastdata/laves/breastpathq/', augment=False, resize_to=resize_to)\n",
    "    assert len(data_set) > 0\n",
    "\n",
    "    calib_indices = torch.load('./data_indices/breastpathq_valid_indices.pth')\n",
    "    test_indices = torch.load('./data_indices/breastpathq_test_indices.pth')\n",
    "\n",
    "    print(calib_indices.shape)\n",
    "    print(test_indices.shape)\n",
    "\n",
    "    calib_loader = torch.utils.data.DataLoader(data_set, batch_size=batch_size,\n",
    "                                               sampler=SubsetRandomSampler(calib_indices))\n",
    "    test_loader = torch.utils.data.DataLoader(data_set, batch_size=batch_size,\n",
    "                                              sampler=SubsetRandomSampler(test_indices))\n",
    "elif data_set_name == 'boneage':\n",
    "    in_channels = 1\n",
    "    out_channels = 1\n",
    "    batch_size = 16\n",
    "\n",
    "    data_set = BoneAgeDataset(data_dir='/media/fastdata/laves/rsna-bone-age/', augment=False, resize_to=resize_to)\n",
    "    assert len(data_set) > 0\n",
    "\n",
    "    calib_indices = torch.load('./data_indices/boneage_valid_indices.pth')\n",
    "    test_indices = torch.load('./data_indices/boneage_test_indices.pth')\n",
    "\n",
    "    print(calib_indices.shape)\n",
    "    print(test_indices.shape)\n",
    "\n",
    "    calib_loader = torch.utils.data.DataLoader(data_set, batch_size=batch_size,\n",
    "                                               sampler=SubsetRandomSampler(calib_indices))\n",
    "    test_loader = torch.utils.data.DataLoader(data_set, batch_size=batch_size,\n",
    "                                              sampler=SubsetRandomSampler(test_indices))\n",
    "elif data_set_name == 'endovis':\n",
    "    in_channels = 3\n",
    "    out_channels = 2\n",
    "    batch_size = 16\n",
    "\n",
    "    data_dir = '/media/fastdata/laves/EndoVis15_instrument_tracking'\n",
    "    data_set_valid = EndoVisDataset(data_dir=data_dir+'/valid', augment=False, scale=0.5)\n",
    "    data_set_test = EndoVisDataset(data_dir=data_dir+'/test', augment=False, scale=0.5)\n",
    "\n",
    "    assert len(data_set_valid) > 0\n",
    "    assert len(data_set_test) > 0\n",
    "    print(len(data_set_valid))\n",
    "    print(len(data_set_test))\n",
    "\n",
    "    calib_loader = torch.utils.data.DataLoader(data_set_valid, batch_size=batch_size, shuffle=False)\n",
    "    test_loader = torch.utils.data.DataLoader(data_set_test, batch_size=batch_size, shuffle=False)\n",
    "elif data_set_name == 'oct':\n",
    "    in_channels = 3\n",
    "    out_channels = 6\n",
    "    batch_size = 16\n",
    "    resize_to = (256, 256)\n",
    "\n",
    "    data_dir = '/media/fastdata/laves/oct_data_needle/data'\n",
    "    data_set = OCTDataset(data_dir=data_dir, augment=False, resize_to=resize_to)\n",
    "    assert len(data_set) > 0\n",
    "\n",
    "    calib_indices = torch.load('./data_indices/oct_valid_indices.pth')\n",
    "    test_indices = torch.load('./data_indices/oct_test_indices.pth')\n",
    "\n",
    "    print(calib_indices.shape)\n",
    "    print(test_indices.shape)\n",
    "\n",
    "    calib_loader = torch.utils.data.DataLoader(data_set, batch_size=batch_size,\n",
    "                                               sampler=SubsetRandomSampler(calib_indices))\n",
    "    test_loader = torch.utils.data.DataLoader(data_set, batch_size=batch_size,\n",
    "                                              sampler=SubsetRandomSampler(test_indices))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading previous weights at epoch 199\n",
      "Loading previous weights at epoch 199\n",
      "Loading previous weights at epoch 199\n"
     ]
    }
   ],
   "source": [
    "num_ensembles = 3\n",
    "ensembles = []\n",
    "\n",
    "for i in range(num_ensembles):\n",
    "    model = BreastPathQModel(base_model, in_channels=in_channels, out_channels=out_channels).to(device)\n",
    "\n",
    "    checkpoint_path = glob(f\"/media/fastdata/laves/regression_snapshots/{base_model}_gaussian_{data_set_name}_ensemble_{i}.pth.tar\")[0]\n",
    "\n",
    "    checkpoint = torch.load(checkpoint_path, map_location=device)\n",
    "    model.load_state_dict(checkpoint['state_dict'])\n",
    "    print(\"Loading previous weights at epoch \" + str(checkpoint['epoch']))\n",
    "    model.eval()\n",
    "    ensembles.append(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 140/140 [01:06<00:00,  2.11it/s]\n"
     ]
    }
   ],
   "source": [
    "y_ps_test = []\n",
    "vars_test = []\n",
    "logvars_test = []\n",
    "targets_test = []\n",
    "\n",
    "with torch.no_grad():\n",
    "    for batch_idx, (data, target) in enumerate(tqdm(test_loader)):\n",
    "        data, target = data.to(device), target.to(device)\n",
    "\n",
    "        y_ps = []\n",
    "        logvars = []\n",
    "        for net in ensembles:\n",
    "            y_p, logvar, _ = net(data, dropout=False, mc_dropout=False)\n",
    "            y_ps.append(y_p)\n",
    "            logvars.append(logvar)\n",
    "\n",
    "        y_ps = torch.stack(y_ps)\n",
    "        var = y_ps.var(dim=0)\n",
    "        logvars = torch.stack(logvars).mean(dim=0)\n",
    "\n",
    "        y_ps_test.append(y_ps.detach())\n",
    "        vars_test.append(var.detach())\n",
    "        logvars_test.append(logvar.detach())\n",
    "        targets_test.append(target.detach())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_p_test = torch.cat(y_ps_test, dim=1).clamp(0, 1).permute(1,0,2)\n",
    "mu_test = y_p_test.mean(dim=1)\n",
    "var_test = torch.cat(vars_test, dim=0)\n",
    "logvar_test = torch.cat(logvars_test, dim=0)\n",
    "target_test = torch.cat(targets_test, dim=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "err_test = (target_test-mu_test).pow(2).mean(dim=1, keepdim=True).sqrt()\n",
    "errvar_test = (y_p_test-target_test.unsqueeze(1).repeat(1,num_ensembles,1)).pow(2).mean(dim=(1,2)).unsqueeze(-1)\n",
    "\n",
    "uncertainty = 'total'\n",
    "\n",
    "uncert_aleatoric_test = logvar_test.exp().mean(dim=1, keepdim=True)\n",
    "uncert_epistemic_test = var_test.mean(dim=1, keepdim=True)\n",
    "\n",
    "if uncertainty == 'aleatoric':\n",
    "    uncert_test = uncert_aleatoric_test.sqrt().clamp(0, 1)\n",
    "elif uncertainty == 'epistemic':\n",
    "    uncert_test = uncert_epistemic_test.sqrt().clamp(0, 1)\n",
    "else:\n",
    "    uncert_test = (uncert_aleatoric_test + uncert_epistemic_test).sqrt().clamp(0, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0.0018, device='cuda:0')\n",
      "tensor(0.0024, device='cuda:0')\n",
      "tensor(0.0016, device='cuda:0')\n",
      "tensor(0.0008, device='cuda:0')\n",
      "tensor(0.0008, device='cuda:0')\n"
     ]
    }
   ],
   "source": [
    "print((err_test**2).mean())\n",
    "print(errvar_test.mean())\n",
    "print((uncert_test**2).mean())\n",
    "print(uncert_aleatoric_test.mean())\n",
    "print(uncert_epistemic_test.mean())\n",
    "\n",
    "err_test = errvar_test.sqrt()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-5.378011226654053\n"
     ]
    }
   ],
   "source": [
    "print(nll_criterion_gaussian(mu_test, uncert_test.pow(2).log(), target_test).item())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.03583833575248718\n"
     ]
    }
   ],
   "source": [
    "print(torch.nn.functional.mse_loss(uncert_test**2, err_test**2, reduction='sum').item())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.009865633212029934\n"
     ]
    },
    {
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       "        return fig.key_event(event, event['data']);\n",
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       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
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       "\n",
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       "\n",
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       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\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",
       "\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",
       "\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]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.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, 0, fig.canvas.width, fig.canvas.height);\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",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = 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.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",
       "            /* 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",
       "            evt.data.type = \"image/png\";\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",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\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(\"No handler for the '\" + msg_type + \"' message type: \", msg);\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(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://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",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\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",
       " * http://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",
       "    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",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\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",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             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",
       "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\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";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.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",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['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 = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\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.get(0);\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",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\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).html('<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/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<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 () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\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) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('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",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\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('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"250\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.08341568754985929\n"
     ]
    }
   ],
   "source": [
    "if data_set_name == \"breastpathq\":\n",
    "    uce_range = [0, 0.01]\n",
    "elif data_set_name == \"boneage\":\n",
    "    uce_range = [0, 0.01]\n",
    "elif data_set_name == \"endovis\":\n",
    "    uce_range = [0, 0.01]\n",
    "elif data_set_name == \"oct\":\n",
    "    uce_range = [0, 0.01]\n",
    "\n",
    "print((uncert_test**2).max().item())\n",
    "\n",
    "n_bins = 15\n",
    "uce_uncal, _, _, _ = uceloss(err_test**2, uncert_test**2, n_bins=n_bins, )\n",
    "_, err_uncal, sigma_uncal, _ = uceloss(err_test**2, uncert_test**2, n_bins=n_bins, range=uce_range)\n",
    "fig, ax = plot_uncert(err_uncal.cpu(), sigma_uncal.cpu())\n",
    "ax.set_title(data_set_name + \" \" + base_model + \" \" + str(uce_uncal.item()*100), size=6)\n",
    "print(uce_uncal.item()*100)\n",
    "fig.savefig(f\"ensemble_{base_model}_{data_set_name}.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.03916516900062561\n"
     ]
    }
   ],
   "source": [
    "nll = nll_criterion_gaussian(mu_test, uncert_test, target_test).item()\n",
    "print(nll)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}