sklearn/5. Measuring Prediction Performance.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(Source: http://www.scipy-lectures.org/packages/scikit-learn/index.html#measuring-prediction-performance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quick test on KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[178   0   0   0   0   0   0   0   0   0]\n",
      " [  0 182   0   0   0   0   0   0   0   0]\n",
      " [  0   0 177   0   0   0   0   0   0   0]\n",
      " [  0   0   0 183   0   0   0   0   0   0]\n",
      " [  0   0   0   0 181   0   0   0   0   0]\n",
      " [  0   0   0   0   0 182   0   0   0   0]\n",
      " [  0   0   0   0   0   0 181   0   0   0]\n",
      " [  0   0   0   0   0   0   0 179   0   0]\n",
      " [  0   0   0   0   0   0   0   0 174   0]\n",
      " [  0   0   0   0   0   0   0   0   0 180]]\n"
     ]
    }
   ],
   "source": [
    "# Get the data\n",
    "from sklearn.datasets import load_digits\n",
    "digits = load_digits()\n",
    "X = digits.data\n",
    "y = digits.target\n",
    "\n",
    "# Instantiate and train the classifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "clf = KNeighborsClassifier(n_neighbors=1)\n",
    "clf.fit(X, y) \n",
    "\n",
    "\n",
    "# Check the results using metrics\n",
    "from sklearn import metrics\n",
    "y_pred = clf.predict(X)\n",
    "\n",
    "print(metrics.confusion_matrix(y_pred, y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "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, 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\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\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": {
      "text/html": [
       "<img src=\"data:image/png;base64,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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import load_boston\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "data = load_boston()\n",
    "clf = DecisionTreeRegressor().fit(data.data, data.target)\n",
    "predicted = clf.predict(data.data)\n",
    "expected = data.target\n",
    "\n",
    "plt.scatter(expected, predicted) \n",
    "\n",
    "plt.plot([0, 50], [0, 50], '--k')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Validation Sets\n",
    "\n",
    "Learning the parameters of a prediction function and testing it on the same data is a methodological mistake: a model that would just repeat the labels of the samples that it has just seen would have a perfect score but would fail to predict anything useful on yet-unseen data.\n",
    "\n",
    "To avoid over-fitting, we have to define two different sets:\n",
    "\n",
    " - a training set X_train, y_train which is used for learning the parameters of a predictive model\n",
    " - a testing set X_test, y_test which is used for evaluating the fitted predictive model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1797, 64), (1347, 64), (450, 64)\n"
     ]
    }
   ],
   "source": [
    "from sklearn import model_selection\n",
    "X = digits.data\n",
    "y = digits.target\n",
    "\n",
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y,\n",
    "                                        test_size=0.25, random_state=0)\n",
    "\n",
    "print(\"%r, %r, %r\" % (X.shape, X_train.shape, X_test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[37  0  0  0  0  0  0  0  0  0]\n",
      " [ 0 43  0  0  0  0  0  0  0  0]\n",
      " [ 0  0 43  1  0  0  0  0  0  0]\n",
      " [ 0  0  0 45  0  0  0  0  0  0]\n",
      " [ 0  0  0  0 38  0  0  0  0  0]\n",
      " [ 0  0  0  0  0 47  0  0  0  1]\n",
      " [ 0  0  0  0  0  0 52  0  0  0]\n",
      " [ 0  0  0  0  0  0  0 48  0  0]\n",
      " [ 0  0  0  0  0  0  0  0 48  0]\n",
      " [ 0  0  0  1  0  1  0  0  0 45]]\n"
     ]
    }
   ],
   "source": [
    "clf = KNeighborsClassifier(n_neighbors=1).fit(X_train, y_train)\n",
    "y_pred = clf.predict(X_test)\n",
    "\n",
    "print(metrics.confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        37\n",
      "           1       1.00      1.00      1.00        43\n",
      "           2       1.00      0.98      0.99        44\n",
      "           3       0.96      1.00      0.98        45\n",
      "           4       1.00      1.00      1.00        38\n",
      "           5       0.98      0.98      0.98        48\n",
      "           6       1.00      1.00      1.00        52\n",
      "           7       1.00      1.00      1.00        48\n",
      "           8       1.00      1.00      1.00        48\n",
      "           9       0.98      0.96      0.97        47\n",
      "\n",
      "   micro avg       0.99      0.99      0.99       450\n",
      "   macro avg       0.99      0.99      0.99       450\n",
      "weighted avg       0.99      0.99      0.99       450\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(metrics.classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9913675218842191"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.f1_score(y_test, y_pred, average=\"macro\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.f1_score(y_train, clf.predict(X_train), average=\"macro\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Validation-led Model Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GaussianNB: 0.8332741681010101\n",
      "KNeighborsClassifier: 0.9804562804949924\n",
      "LinearSVC: 0.9283849479091444\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/greg/code/gkiar/sklearn-tutorials/env/sklearn/lib/python3.6/site-packages/sklearn/svm/base.py:922: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "X = digits.data\n",
    "y = digits.target\n",
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y,\n",
    "                            test_size=0.25, random_state=0)\n",
    "\n",
    "for Model in [GaussianNB, KNeighborsClassifier, LinearSVC]:\n",
    "    clf = Model().fit(X_train, y_train)\n",
    "    y_pred = clf.predict(X_test)\n",
    "    print('%s: %s' % (Model.__name__, metrics.f1_score(y_test, y_pred, average=\"macro\")))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise: Which hyper parameters to choose for each classifier?\n",
    "\n",
    "(Source: http://www.scipy-lectures.org/packages/scikit-learn/index.html#model-selection-via-validation)\n",
    "\n",
    "(Solution: http://www.scipy-lectures.org/packages/scikit-learn/auto_examples/plot_compare_classifiers.html#sphx-glr-packages-scikit-learn-auto-examples-plot-compare-classifiers-py)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.9478022 , 0.9558011 , 0.96657382, 0.98039216, 0.96338028])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = KNeighborsClassifier()\n",
    "from sklearn.model_selection import cross_val_score\n",
    "cross_val_score(clf, X, y, cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.98888889, 0.99444444, 0.98888889, 0.98888889, 0.98888889])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import ShuffleSplit\n",
    "cv = ShuffleSplit(n_splits=5)\n",
    "cross_val_score(clf, X, y, cv=cv) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyper Parameter Optimization with CV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(442, 10)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_diabetes\n",
    "data = load_diabetes()\n",
    "X, y = data.data, data.target\n",
    "print(X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ridge: 0.40942743830329875\n",
      "Lasso: 0.35380008329932017\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge, Lasso\n",
    "\n",
    "for Model in [Ridge, Lasso]:\n",
    "    model = Model()\n",
    "    print('%s: %s' % (Model.__name__, cross_val_score(model, X, y, cv=3).mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "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, 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\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\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": {
      "text/html": [
       "<img src=\"data:image/png;base64,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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "alphas = np.logspace(-3, -1, 30)\n",
    "plt.figure()\n",
    "for Model in [Lasso, Ridge]:\n",
    "    scores = [cross_val_score(Model(alpha), X, y, cv=3).mean()\n",
    "              for alpha in alphas]\n",
    "    plt.plot(alphas, scores, label=Model.__name__)\n",
    "\n",
    "plt.legend(loc='lower left')\n",
    "plt.xlabel('alpha')\n",
    "plt.ylabel('cross validation score')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But, are these results actually useful?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grid search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ridge: {'alpha': 0.06210169418915616}\n",
      "Lasso: {'alpha': 0.01268961003167922}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "for Model in [Ridge, Lasso]:\n",
    "    gscv = GridSearchCV(Model(), dict(alpha=alphas), cv=3).fit(X, y)\n",
    "    print('%s: %s' % (Model.__name__, gscv.best_params_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RidgeCV: 0.06210169418915616\n",
      "LassoCV: 0.01268961003167922\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import RidgeCV, LassoCV\n",
    "for Model in [RidgeCV, LassoCV]:\n",
    "    model = Model(alphas=alphas, cv=3).fit(X, y)\n",
    "    print('%s: %s' % (Model.__name__, model.alpha_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RidgeCV 0.48916142454965544\n",
      "LassoCV 0.4854913966864775\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/greg/code/gkiar/sklearn-tutorials/env/sklearn/lib/python3.6/site-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n",
      "/Users/greg/code/gkiar/sklearn-tutorials/env/sklearn/lib/python3.6/site-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "for Model in [RidgeCV, LassoCV]:\n",
    "    scores = cross_val_score(Model(alphas=alphas, cv=3), X, y, cv=3)\n",
    "    print(Model.__name__, np.mean(scores))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}