lda_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data"
   ]
  },
  {
   "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.linear_model import LogisticRegression\n",
    "\n",
    "from validation import Validation\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn import preprocessing\n",
    "from my_pca import my_pca\n",
    "\n",
    "%matplotlib notebook\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LinearDiscriminantAnalysis - PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Logistic Regression\n",
    "X, y = get_training()\n",
    "mdl = LinearDiscriminantAnalysis()\n",
    "val = Validation(X, y, k = 10)\n",
    "pca_comp = [3,5,9,11,13,15,20,25,30]\n",
    "\n",
    "pca_results = {\n",
    "    'accuracy' : [],\n",
    "    'recall' : [],\n",
    "    'precision' : []\n",
    "}\n",
    "bsf = (0,-1)\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",
    "best_pca_comp = bsf[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LinearDiscriminantAnalysis - Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-1\n",
      "0.1\n",
      "0.2\n",
      "0.25\n"
     ]
    }
   ],
   "source": [
    "# Logistic Regression\n",
    "X, y = get_training()\n",
    "mdl = LinearDiscriminantAnalysis()\n",
    "val = Validation(X, y, k = 10, pca=-1)\n",
    "lasso_alphas = [-1, 0.1, 0.2, 0.25]\n",
    "\n",
    "lasso_results = {\n",
    "    'accuracy' : [],\n",
    "    'recall' : [],\n",
    "    'precision' : []\n",
    "}\n",
    "bsf = (0,best_pca_comp,-1)\n",
    "for alpha in lasso_alphas:\n",
    "    print alpha\n",
    "    val.update(lasso=alpha)\n",
    "    acc = val.cross_val_accuracy(mdl)\n",
    "    r = val.get_detailed_results()\n",
    "    lasso_results['accuracy'].append(np.array(r['accuracy']).mean())\n",
    "    lasso_results['recall'].append(np.array(r['recall']).mean())\n",
    "    lasso_results['precision'].append(np.array(r['precision']).mean())\n",
    "    \n",
    "    if bsf[0] < acc:\n",
    "        bsf = (acc, best_pca_comp, alpha)\n",
    "\n",
    "best_alpha = bsf[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LinearDiscriminantAnalysis - Lasso + PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Logistic Regression\n",
    "X, y = get_training()\n",
    "mdl = LinearDiscriminantAnalysis()\n",
    "val = Validation(X, y, k = 10, pca=15)\n",
    "lasso_alphas = [-1, 0.1, 0.2, 0.25]\n",
    "\n",
    "lasso_results_pca = {\n",
    "    'accuracy' : [],\n",
    "    'recall' : [],\n",
    "    'precision' : []\n",
    "}\n",
    "bsf = (0,best_pca_comp,-1)\n",
    "for alpha in lasso_alphas:\n",
    "    val.update(lasso=alpha)\n",
    "    acc = val.cross_val_accuracy(mdl)\n",
    "    r = val.get_detailed_results()\n",
    "    lasso_results_pca['accuracy'].append(np.array(r['accuracy']).mean())\n",
    "    lasso_results_pca['recall'].append(np.array(r['recall']).mean())\n",
    "    lasso_results_pca['precision'].append(np.array(r['precision']).mean())\n",
    "    \n",
    "    if bsf[0] < acc:\n",
    "        bsf = (acc, best_pca_comp, alpha)\n",
    "\n",
    "best_alpha_pca = bsf[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Scores - PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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",
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       "    } 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",
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       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
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       "\n",
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       "\n",
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       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
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       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\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",
       "    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"
    },
    {
     "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='lower right')\n",
    "plt.title('LDA vs Varying PCA Components')\n",
    "plt.xlabel('Number of Components')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Scores - Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.close()\n",
    "x = range(4)\n",
    "x_labels = ['No Lasso', 0.1, 0.2, 0.25]\n",
    "lines = plt.plot(x, lasso_results['accuracy'], 'rd-', x, lasso_results['recall'], 'bs-', x, lasso_results['precision'], 'g^-')\n",
    "plt.legend(lines, ('accuracy','recall','precision'),loc='lower left')\n",
    "plt.title('LDA vs Varying Lasso Alphas (no PCA)')\n",
    "plt.xlabel('Alpha')\n",
    "plt.xticks(x, x_labels)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Scores - Lasso + PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.close()\n",
    "x = range(4)\n",
    "x_labels = ['No Lasso', 0.1, 0.2, 0.25]\n",
    "lines = plt.plot(x, lasso_results_pca['accuracy'], 'rd-', x, lasso_results_pca['recall'], 'bs-', x, lasso_results_pca['precision'], 'g^-')\n",
    "plt.legend(lines, ('accuracy','recall','precision'),loc='lower left')\n",
    "plt.title('LDA vs Varying Lasso Alphas (w/ PCA = 30)')\n",
    "plt.xlabel('Alpha')\n",
    "plt.xticks(x, x_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_pca_comp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Confusion matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.95834853801169573"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bsf[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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"
    },
    {
     "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,bsf[1])\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
}