forest_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Requirements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload\n",
    "\n",
    "import itertools\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from data_utils import get_training, get_testing, HEADER\n",
    "from sklearn.ensemble import RandomForestClassifier as Forest\n",
    "\n",
    "from validation import Validation\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn import preprocessing\n",
    "from my_pca import my_pca\n",
    "\n",
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forests - finding best PCA Component"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "X, y = get_training()\n",
    "val = Validation(X, y, k = 5, bootstrap=-1)\n",
    "\n",
    "pca_comp = [3,5,9,11,13,15,20,25,30]\n",
    "\n",
    "pca_results = {\n",
    "    'accuracy' : [],\n",
    "    'recall' : [],\n",
    "    'precision' : []\n",
    "}\n",
    "\n",
    "bsf = (0,-1,-1)\n",
    "mdl = Forest(n_estimators=50, max_features=\"sqrt\")\n",
    "\n",
    "for comp in pca_comp:\n",
    "    val.update(pca=comp)\n",
    "    acc = val.cross_val_accuracy(mdl)\n",
    "    r = val.get_detailed_results()\n",
    "    pca_results['accuracy'].append(np.array(r['accuracy']).mean())\n",
    "    pca_results['recall'].append(np.array(r['recall']).mean())\n",
    "    pca_results['precision'].append(np.array(r['precision']).mean())\n",
    "    \n",
    "    if bsf[0] < acc:\n",
    "        bsf = (acc, comp)\n",
    "        \n",
    "best_pca_comp = bsf[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Searching for best number of trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X, y = get_training()\n",
    "val = Validation(X, y, pca = best_pca_comp, k = 5, bootstrap=-1)\n",
    "\n",
    "t_list = range(1,500,15)\n",
    "\n",
    "t_results = {\n",
    "    'accuracy' : [],\n",
    "    'recall' : [],\n",
    "    'precision' : []\n",
    "}\n",
    "\n",
    "bsf = (0,-1,3)\n",
    "\n",
    "for t in t_list:\n",
    "    mdl = mdl = Forest(n_estimators=t, max_features=\"sqrt\")\n",
    "    acc = val.cross_val_accuracy(mdl)\n",
    "    r = val.get_detailed_results()\n",
    "    t_results['accuracy'].append(np.array(r['accuracy']).mean())\n",
    "    t_results['recall'].append(np.array(r['recall']).mean())\n",
    "    t_results['precision'].append(np.array(r['precision']).mean())\n",
    "    \n",
    "    if bsf[0] < acc:\n",
    "        bsf = (acc, best_pca_comp, t)\n",
    "        \n",
    "best_t = bsf[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "/* 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",
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       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
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       "\n",
       "    $(parent_element).append(this.root);\n",
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       "            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",
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       "\n",
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       "            if (fig.image_mode == 'full') {\n",
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       "\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",
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       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
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       "    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",
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       "\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\", \"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 overriden (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=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.close()\n",
    "lines = plt.plot(pca_comp, pca_results['accuracy'], 'rd-', pca_comp, pca_results['recall'], 'bs-', pca_comp, pca_results['precision'], 'g^-')\n",
    "plt.legend(lines, ('accuracy','recall','precision'),loc=' right')\n",
    "plt.title('Random Forests vs Varying PCA Components (learners = 50)')\n",
    "plt.xlabel('Number of Components')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": 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",
       "        this.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\", \"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 overriden (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"
    },
    {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.close()\n",
    "lines = plt.plot(t_list, t_results['accuracy'], 'rd-', t_list, t_results['recall'], 'bs-', t_list, t_results['precision'], 'g^-')\n",
    "plt.legend(lines, ('accuracy','recall','precision'),loc='lower right')\n",
    "plt.title('Random Forests')\n",
    "plt.xlabel('Number of Learners')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.94006191950464379"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# choosing best k & pca\n",
    "# pca = 5, learners = 496\n",
    "# pca = 5, learners = 250 (more regularized)\n",
    "chosen_best_pca_comp = 5\n",
    "chosen_best_learners = 250\n",
    "bsf[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Confusion matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, '%.2f'%cm[i, j],\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.gcf().subplots_adjust(bottom=0.2)\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\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",
       "            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",
       "        this.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 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);\n",
       "        canvas.attr('height', height);\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'];\n",
       "    var y0 = fig.canvas.height - msg['y0'];\n",
       "    var x1 = msg['x1'];\n",
       "    var y1 = fig.canvas.height - msg['y1'];\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;\n",
       "    var y = canvas_pos.y;\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 overriden (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",
       "    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 + '\">');\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 dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\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"
    },
    {
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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized confusion matrix\n"
     ]
    }
   ],
   "source": [
    "plt.figure(figsize=(6,5))\n",
    "X_reduced = my_pca(X, chosen_best_pca_comp)\n",
    "mdl = Forest(n_estimators=chosen_best_learners, max_features=\"sqrt\")\n",
    "y_pred = mdl.fit(X_reduced, y).predict(X_reduced)\n",
    "cm_pca = confusion_matrix(y, y_pred)\n",
    "plot_confusion_matrix(cm_pca, classes=['Benign', 'Malignant'], normalize=True)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}