comparison_with_sota.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "import pickle\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 5)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def mi_schedule(n_iter):\n",
    "    mis = np.round(np.linspace(0.5, 5.5-1e-9, n_iter)) *2.0#0.1\n",
    "    return mis.astype(np.float32)\n",
    "\n",
    "def sigmoid(x):\n",
    "    return 1/(1. + np.exp(-x))\n",
    "\n",
    "estimators = [\n",
    "    'NWJ', 'iMINE', 'iMINE_j', 'SMILE', 'MINE', 'TUBA','InfoNCE', 'JS', 'TNCE', 'alpha'\n",
    "]\n",
    "estimates = {}\n",
    "\n",
    "for estimator in estimators:\n",
    "    fname = f'gaussian/{estimator}/64/0.pkl'\n",
    "    estimates[estimator] = pickle.load(open(fname, 'rb'))\n",
    "\n",
    "# Smooting span for Exponential Moving Average\n",
    "EMA_SPAN = 200\n",
    "\n",
    "# Ground truth MI\n",
    "mi_true = mi_schedule(20000)\n",
    "\n",
    "# Names specifies the key and ordering for plotting estimators\n",
    "names = np.sort(list(estimators))\n",
    "clean_names = {k: k for k in estimators}\n",
    "clean_names['SMILE'] = r'SMILE ($\\tau=100$)'\n",
    "clean_names['alpha'] = r'$I_{\\alpha}$ ($\\alpha=0.01$)'\n",
    "clean_names['iMINE'] = r'iMINE (C=0, $\\lambda=1$)'\n",
    "clean_names['iMINE_j'] = 'iMINE-J (C=0, $\\\\lambda=1$)'\n",
    "\n",
    "\n",
    "nrows = min(2, len(estimates))\n",
    "ncols = int(np.ceil(len(estimates) / float(nrows)))\n",
    "nrows, ncols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x504 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "#matplotlib.rc('font', size=16)\n",
    "\n",
    "fig, axs = plt.subplots(nrows, ncols, figsize=(16, 7)) \n",
    "if len(estimates) == 1:\n",
    "    axs = [axs]\n",
    "axs = np.ravel(axs)\n",
    "\n",
    "for i, name in enumerate(names):\n",
    "    plt.sca(axs[i])\n",
    "    plt.title(clean_names[names[i]])\n",
    "    # Plot estimated MI and smoothed MI\n",
    "    mis = estimates[name]  \n",
    "    mis_smooth = pd.Series(mis).ewm(span=EMA_SPAN).mean()\n",
    "    p1 = plt.plot(mis, alpha=0.3)[0]\n",
    "    plt.plot(mis_smooth, c=p1.get_color())\n",
    "    # Plot true MI and line for log(batch size)\n",
    "    plt.plot(mi_true, color='k', label='True MI')\n",
    "    if 'alpha' in name or 'NCE' in name:\n",
    "        # Add theoretical upper bound lines\n",
    "        if 'alpha' in name:\n",
    "            log_alpha = -np.log( 1+ np.exp(4.595))\n",
    "        else:\n",
    "            log_alpha = 1.\n",
    "        plt.axhline(1 + np.log(64) - log_alpha, c='k', linestyle='--', label=r'1 + log(K/$\\alpha$)' )\n",
    "    plt.ylim(-1, mi_true.max()+1)\n",
    "    plt.xlim(0, 20000)\n",
    "    if i == len(estimates) - ncols:\n",
    "        plt.xlabel('steps')\n",
    "        plt.ylabel('MI (nats)')\n",
    "    plt.tight_layout()\n",
    "#plt.legend(loc='best', fontsize=8, framealpha=0.0)\n",
    "plt.gcf().tight_layout()\n",
    "#plt.savefig('test1.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    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",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\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 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) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        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 / mpl.ratio, fig.canvas.height / mpl.ratio);\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",
       "        // 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",
       "\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=\"800\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\\multicolumn{1}{c|}{\\multirow{10}{*}{ NAME }} & InfoNCE & -0.30 & -1.00 & -2.31 & -4.00 & -5.89 \\\\\n",
      "\\multicolumn{1}{c|}{} & JS & -0.57 & -0.72 & -0.94 & -1.21 & -1.61 \\\\\n",
      "\\multicolumn{1}{c|}{} & MINE & -0.15 & -0.24 & 0.32 & 33.81 &  \\\\\n",
      "\\multicolumn{1}{c|}{} & NWJ & -0.20 & -0.36 & -0.74 & -1.47 & -2.70 \\\\\n",
      "\\multicolumn{1}{c|}{} & SMILE ($\\tau=100$) & -0.16 & -0.24 & 0.10 &  &  \\\\\n",
      "\\multicolumn{1}{c|}{} & TNCE & -0.19 & -0.93 & -2.26 & -3.98 & -5.88 \\\\\n",
      "\\multicolumn{1}{c|}{} & TUBA & -0.17 & -0.33 & -0.74 & -1.42 & -2.85 \\\\\n",
      "\\multicolumn{1}{c|}{} & $I_{\\alpha}$ ($\\alpha=0.01$) & -0.16 & -0.28 & -0.53 & -1.01 & -1.91 \\\\\n",
      "\\multicolumn{1}{c|}{} & iMINE (C=0, $\\lambda=1$) & -0.15 & -0.27 & -0.50 & -0.66 & -0.58 \\\\\n",
      "\\multicolumn{1}{c|}{} & iMINE-J (C=0, $\\lambda=1$) & -0.16 & -0.29 & -0.51 & -0.64 & -0.39 \\\\\n",
      "\n",
      "\\multicolumn{1}{c|}{\\multirow{10}{*}{ NAME }} & InfoNCE & 0.09 & 0.03 & 0.02 & 0.01 & 0.00 \\\\\n",
      "\\multicolumn{1}{c|}{} & JS & 0.12 & 0.10 & 0.15 & 0.22 & 0.57 \\\\\n",
      "\\multicolumn{1}{c|}{} & MINE & 0.12 & 0.27 & 7.16 &  &  \\\\\n",
      "\\multicolumn{1}{c|}{} & NWJ & 0.15 & 0.26 & 1.54 & 6.85 & 23.42 \\\\\n",
      "\\multicolumn{1}{c|}{} & SMILE ($\\tau=100$) & 0.12 & 0.25 & 5.74 &  &  \\\\\n",
      "\\multicolumn{1}{c|}{} & TNCE & 0.04 & 0.03 & 0.02 & 0.01 & 0.00 \\\\\n",
      "\\multicolumn{1}{c|}{} & TUBA & 0.12 & 0.21 & 2.06 & 21.97 & 35.54 \\\\\n",
      "\\multicolumn{1}{c|}{} & $I_{\\alpha}$ ($\\alpha=0.01$) & 0.12 & 0.16 & 0.31 & 0.38 & 0.38 \\\\\n",
      "\\multicolumn{1}{c|}{} & iMINE (C=0, $\\lambda=1$) & 0.11 & 0.15 & 0.35 & 0.69 & 1.33 \\\\\n",
      "\\multicolumn{1}{c|}{} & iMINE-J (C=0, $\\lambda=1$) & 0.12 & 0.13 & 0.20 & 0.31 & 0.56 \\\\\n",
      "\n",
      "\\multicolumn{1}{c|}{\\multirow{10}{*}{ NAME }} & InfoNCE & 0.17 & 1.03 & 5.33 & 16.02 & 34.68 \\\\\n",
      "\\multicolumn{1}{c|}{} & JS & 0.45 & 0.62 & 1.03 & 1.67 & 3.17 \\\\\n",
      "\\multicolumn{1}{c|}{} & MINE & 0.14 & 0.33 & 7.27 &  &  \\\\\n",
      "\\multicolumn{1}{c|}{} & NWJ & 0.19 & 0.39 & 2.09 & 9.01 & 30.68 \\\\\n",
      "\\multicolumn{1}{c|}{} & SMILE ($\\tau=100$) & 0.14 & 0.31 & 5.75 &  &  \\\\\n",
      "\\multicolumn{1}{c|}{} & TNCE & 0.08 & 0.89 & 5.14 & 15.82 & 34.55 \\\\\n",
      "\\multicolumn{1}{c|}{} & TUBA & 0.14 & 0.32 & 2.60 & 23.97 & 43.67 \\\\\n",
      "\\multicolumn{1}{c|}{} & $I_{\\alpha}$ ($\\alpha=0.01$) & 0.14 & 0.23 & 0.60 & 1.39 & 4.03 \\\\\n",
      "\\multicolumn{1}{c|}{} & iMINE (C=0, $\\lambda=1$) & 0.14 & 0.22 & 0.60 & 1.13 & 1.67 \\\\\n",
      "\\multicolumn{1}{c|}{} & iMINE-J (C=0, $\\lambda=1$) & 0.14 & 0.21 & 0.45 & 0.72 & 0.72 \\\\\n"
     ]
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "import matplotlib\n",
    "matplotlib.rc('font', size=10)\n",
    "\n",
    "from matplotlib.lines import Line2D\n",
    "\n",
    "def bias(arr, true_mi):\n",
    "    return np.mean(arr) - true_mi\n",
    "\n",
    "def variance(arr, true_mi):\n",
    "    return np.var(arr)\n",
    "\n",
    "def mse(arr, true_mi):\n",
    "    return np.mean(np.square(arr-true_mi))\n",
    "\n",
    "\n",
    "fig, axs = plt.subplots(1, 3, figsize=(8, 4)) \n",
    "axs = np.ravel(axs)\n",
    "\n",
    "for i, f in enumerate([bias, variance, mse]):\n",
    "    plt.sca(axs[i])\n",
    "    print('')\n",
    "    for name_index, name in enumerate(names):\n",
    "        mis = estimates[name]\n",
    "        points = []\n",
    "        for j in range(5):\n",
    "            true_mi = (j+1)*2\n",
    "            points.append(f(mis[j*4000:(j+1)*4000], true_mi))\n",
    "        axs[i].plot(list(range(5)), points, 'o-', label=name, c=f'C{name_index}')\n",
    "        \n",
    "        if name_index == 0:\n",
    "            print(r'\\multicolumn{1}{c|}{\\multirow{10}{*}{ NAME }}', end='')\n",
    "        else:\n",
    "            print(r'\\multicolumn{1}{c|}{}', end='')\n",
    "        print(f' & {clean_names[name]} & ' + ' & '.join(['%.2f'%f if f < 100 else '' for f in points]) + r' \\\\',)\n",
    "\n",
    "    axs[i].axhline(0, linestyle='--', color='black', alpha=0.4)\n",
    "    if i == 0:\n",
    "        axs[i].set_ylim((-5, 5))\n",
    "        axs[i].set_title('Bias')        \n",
    "    elif i == 1:\n",
    "        axs[i].set_ylim((-0.5, 4.5))\n",
    "        axs[i].set_title('Variance')\n",
    "    else:\n",
    "        axs[i].set_ylim((-0.5, 4.5))\n",
    "        axs[i].set_title('MSE')\n",
    "\n",
    "lines = [\n",
    "    Line2D([0], [0], color=f'C{i}', lw=4)\n",
    "    for i, name in enumerate(names)\n",
    "]\n",
    "        \n",
    "plt.gcf().tight_layout()\n",
    "plt.gcf().legend(lines, [clean_names[n] for n in names], ncol=5, loc='center', bbox_to_anchor=(0.5, 0.07))\n",
    "plt.gcf().subplots_adjust(bottom=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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