Assignment+2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assignment 2\n",
    "\n",
    "In this assignment you'll explore the relationship between model complexity and generalization performance, by adjusting key parameters of various supervised learning models. Part 1 of this assignment will look at regression and Part 2 will look at classification.\n",
    "\n",
    "## Part 1 - Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, run the following block to set up the variables needed for later sections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        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>"
      ]
     },
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     "output_type": "display_data"
    },
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "np.random.seed(0)\n",
    "n = 15\n",
    "x = np.linspace(0,10,n) + np.random.randn(n)/5\n",
    "y = np.sin(x)+x/6 + np.random.randn(n)/10\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(x, y, random_state=0)\n",
    "\n",
    "# You can use this function to help you visualize the dataset by plotting a scatterplot of the data points in the training and test sets.\n",
    "def part1_scatter():\n",
    "    import matplotlib.pyplot as plt\n",
    "    %matplotlib notebook\n",
    "    plt.figure()\n",
    "    plt.scatter(X_train, y_train, label='training data')\n",
    "    plt.scatter(X_test, y_test, label='test data')\n",
    "    plt.legend(loc=4);\n",
    "       \n",
    "# NOTE: Uncomment the function below to visualize the data, but be sure to **re-comment it before submitting this assignment to the autograder**.   \n",
    "part1_scatter()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 1\n",
    "\n",
    "Write a function that fits a polynomial LinearRegression model on the *training data* `X_train` for degrees 1, 3, 6, and 9. (Use PolynomialFeatures in sklearn.preprocessing to create the polynomial features and then fit a linear regression model) For each model, find 100 predicted values over the interval x = 0 to 10 (e.g. `np.linspace(0,10,100)`) and store this in a numpy array. The first row of this array should correspond to the output from the model trained on degree 1, the second row degree 3, the third row degree 6, and the fourth row degree 9.\n",
    "\n",
    "<img src=\"readonly/polynomialreg1.png\" style=\"width: 1000px;\"/>\n",
    "\n",
    "The figure above shows the fitted models plotted on top of the original data (using `plot_one()`).\n",
    "\n",
    "<br>\n",
    "*This function should return a numpy array with shape `(4, 100)`*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  2.53040195e-01,   2.69201547e-01,   2.85362899e-01,\n",
       "          3.01524251e-01,   3.17685603e-01,   3.33846955e-01,\n",
       "          3.50008306e-01,   3.66169658e-01,   3.82331010e-01,\n",
       "          3.98492362e-01,   4.14653714e-01,   4.30815066e-01,\n",
       "          4.46976417e-01,   4.63137769e-01,   4.79299121e-01,\n",
       "          4.95460473e-01,   5.11621825e-01,   5.27783177e-01,\n",
       "          5.43944529e-01,   5.60105880e-01,   5.76267232e-01,\n",
       "          5.92428584e-01,   6.08589936e-01,   6.24751288e-01,\n",
       "          6.40912640e-01,   6.57073992e-01,   6.73235343e-01,\n",
       "          6.89396695e-01,   7.05558047e-01,   7.21719399e-01,\n",
       "          7.37880751e-01,   7.54042103e-01,   7.70203454e-01,\n",
       "          7.86364806e-01,   8.02526158e-01,   8.18687510e-01,\n",
       "          8.34848862e-01,   8.51010214e-01,   8.67171566e-01,\n",
       "          8.83332917e-01,   8.99494269e-01,   9.15655621e-01,\n",
       "          9.31816973e-01,   9.47978325e-01,   9.64139677e-01,\n",
       "          9.80301028e-01,   9.96462380e-01,   1.01262373e+00,\n",
       "          1.02878508e+00,   1.04494644e+00,   1.06110779e+00,\n",
       "          1.07726914e+00,   1.09343049e+00,   1.10959184e+00,\n",
       "          1.12575320e+00,   1.14191455e+00,   1.15807590e+00,\n",
       "          1.17423725e+00,   1.19039860e+00,   1.20655995e+00,\n",
       "          1.22272131e+00,   1.23888266e+00,   1.25504401e+00,\n",
       "          1.27120536e+00,   1.28736671e+00,   1.30352807e+00,\n",
       "          1.31968942e+00,   1.33585077e+00,   1.35201212e+00,\n",
       "          1.36817347e+00,   1.38433482e+00,   1.40049618e+00,\n",
       "          1.41665753e+00,   1.43281888e+00,   1.44898023e+00,\n",
       "          1.46514158e+00,   1.48130294e+00,   1.49746429e+00,\n",
       "          1.51362564e+00,   1.52978699e+00,   1.54594834e+00,\n",
       "          1.56210969e+00,   1.57827105e+00,   1.59443240e+00,\n",
       "          1.61059375e+00,   1.62675510e+00,   1.64291645e+00,\n",
       "          1.65907781e+00,   1.67523916e+00,   1.69140051e+00,\n",
       "          1.70756186e+00,   1.72372321e+00,   1.73988457e+00,\n",
       "          1.75604592e+00,   1.77220727e+00,   1.78836862e+00,\n",
       "          1.80452997e+00,   1.82069132e+00,   1.83685268e+00,\n",
       "          1.85301403e+00],\n",
       "       [  1.22989539e+00,   1.15143628e+00,   1.07722393e+00,\n",
       "          1.00717881e+00,   9.41221419e-01,   8.79272234e-01,\n",
       "          8.21251741e-01,   7.67080426e-01,   7.16678772e-01,\n",
       "          6.69967266e-01,   6.26866391e-01,   5.87296632e-01,\n",
       "          5.51178474e-01,   5.18432402e-01,   4.88978901e-01,\n",
       "          4.62738455e-01,   4.39631549e-01,   4.19578668e-01,\n",
       "          4.02500297e-01,   3.88316920e-01,   3.76949022e-01,\n",
       "          3.68317088e-01,   3.62341603e-01,   3.58943051e-01,\n",
       "          3.58041918e-01,   3.59558687e-01,   3.63413845e-01,\n",
       "          3.69527874e-01,   3.77821261e-01,   3.88214491e-01,\n",
       "          4.00628046e-01,   4.14982414e-01,   4.31198078e-01,\n",
       "          4.49195522e-01,   4.68895233e-01,   4.90217694e-01,\n",
       "          5.13083391e-01,   5.37412808e-01,   5.63126429e-01,\n",
       "          5.90144741e-01,   6.18388226e-01,   6.47777371e-01,\n",
       "          6.78232660e-01,   7.09674578e-01,   7.42023609e-01,\n",
       "          7.75200238e-01,   8.09124950e-01,   8.43718230e-01,\n",
       "          8.78900563e-01,   9.14592432e-01,   9.50714324e-01,\n",
       "          9.87186723e-01,   1.02393011e+00,   1.06086498e+00,\n",
       "          1.09791181e+00,   1.13499108e+00,   1.17202328e+00,\n",
       "          1.20892890e+00,   1.24562842e+00,   1.28204233e+00,\n",
       "          1.31809110e+00,   1.35369523e+00,   1.38877520e+00,\n",
       "          1.42325149e+00,   1.45704459e+00,   1.49007498e+00,\n",
       "          1.52226316e+00,   1.55352959e+00,   1.58379478e+00,\n",
       "          1.61297919e+00,   1.64100332e+00,   1.66778766e+00,\n",
       "          1.69325268e+00,   1.71731887e+00,   1.73990672e+00,\n",
       "          1.76093671e+00,   1.78032933e+00,   1.79800506e+00,\n",
       "          1.81388438e+00,   1.82788778e+00,   1.83993575e+00,\n",
       "          1.84994877e+00,   1.85784732e+00,   1.86355189e+00,\n",
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       "          1.86284006e+00,   1.85638200e+00,   1.84725286e+00,\n",
       "          1.83537314e+00,   1.82066332e+00,   1.80304388e+00,\n",
       "          1.78243530e+00,   1.75875808e+00,   1.73193269e+00,\n",
       "          1.70187963e+00,   1.66851936e+00,   1.63177240e+00,\n",
       "          1.59155920e+00],\n",
       "       [ -1.99554310e-01,  -3.95192724e-03,   1.79851752e-01,\n",
       "          3.51005136e-01,   5.08831706e-01,   6.52819233e-01,\n",
       "          7.82609240e-01,   8.97986721e-01,   9.98870117e-01,\n",
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       "          1.31599632e+00,   1.31089811e+00,   1.29504889e+00,\n",
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       "          9.50896441e-01,   8.78825970e-01,   8.03709344e-01,\n",
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       "          2.64926923e-01,   1.95718291e-01,   1.30608438e-01,\n",
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       "         -7.71657636e-02,  -1.13453547e-01,  -1.42666382e-01,\n",
       "         -1.64494044e-01,  -1.78683194e-01,  -1.85038228e-01,\n",
       "         -1.83421873e-01,  -1.73755533e-01,  -1.56019368e-01,\n",
       "         -1.30252132e-01,  -9.65507462e-02,  -5.50696232e-02,\n",
       "         -6.01973198e-03,   5.03325883e-02,   1.13667071e-01,\n",
       "          1.83611221e-01,   2.59742264e-01,   3.41589357e-01,\n",
       "          4.28636046e-01,   5.20322987e-01,   6.16050916e-01,\n",
       "          7.15183874e-01,   8.17052690e-01,   9.20958717e-01,\n",
       "          1.02617782e+00,   1.13196463e+00,   1.23755703e+00,\n",
       "          1.34218093e+00,   1.44505526e+00,   1.54539723e+00,\n",
       "          1.64242789e+00,   1.73537785e+00,   1.82349336e+00,\n",
       "          1.90604254e+00,   1.98232198e+00,   2.05166348e+00,\n",
       "          2.11344114e+00,   2.16707864e+00,   2.21205680e+00,\n",
       "          2.24792141e+00,   2.27429129e+00,   2.29086658e+00,\n",
       "          2.29743739e+00,   2.29389257e+00,   2.28022881e+00,\n",
       "          2.25656001e+00,   2.22312684e+00,   2.18030664e+00,\n",
       "          2.12862347e+00,   2.06875850e+00,   2.00156065e+00,\n",
       "          1.92805743e+00,   1.84946605e+00,   1.76720485e+00,\n",
       "          1.68290491e+00,   1.59842194e+00,   1.51584842e+00,\n",
       "          1.43752602e+00,   1.36605824e+00,   1.30432333e+00,\n",
       "          1.25548743e+00],\n",
       "       [  6.79502285e+00,   4.14319957e+00,   2.23123322e+00,\n",
       "          9.10495532e-01,   5.49803315e-02,  -4.41344457e-01,\n",
       "         -6.66950444e-01,  -6.94942887e-01,  -5.85049614e-01,\n",
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       "          1.08510202e+00,   1.23683979e+00,   1.34766069e+00,\n",
       "          1.41830632e+00,   1.45104724e+00,   1.44924694e+00,\n",
       "          1.41699534e+00,   1.35880444e+00,   1.27935985e+00,\n",
       "          1.18332182e+00,   1.07516995e+00,   9.59086410e-01,\n",
       "          8.38872457e-01,   7.17893658e-01,   5.99049596e-01,\n",
       "          4.84764051e-01,   3.76992063e-01,   2.77240599e-01,\n",
       "          1.86599822e-01,   1.05782272e-01,   3.51675757e-02,\n",
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       "         -1.68509141e-01,  -1.50055083e-01,  -1.26411638e-01,\n",
       "         -9.78053923e-02,  -6.43692604e-02,  -2.61485139e-02,\n",
       "          1.68888091e-02,   6.48376626e-02,   1.17838541e-01,\n",
       "          1.76057485e-01,   2.39664260e-01,   3.08809443e-01,\n",
       "          3.83601186e-01,   4.64082407e-01,   5.50209170e-01,\n",
       "          6.41830991e-01,   7.38673768e-01,   8.40326006e-01,\n",
       "          9.46228923e-01,   1.05567100e+00,   1.16778742e+00,\n",
       "          1.28156471e+00,   1.39585100e+00,   1.50937183e+00,\n",
       "          1.62075165e+00,   1.72854097e+00,   1.83124862e+00,\n",
       "          1.92737898e+00,   2.01547331e+00,   2.09415458e+00,\n",
       "          2.16217465e+00,   2.21846257e+00,   2.26217273e+00,\n",
       "          2.29273094e+00,   2.30987668e+00,   2.31369926e+00,\n",
       "          2.30466539e+00,   2.28363551e+00,   2.25186569e+00,\n",
       "          2.21099186e+00,   2.16299265e+00,   2.11012671e+00,\n",
       "          2.05484041e+00,   1.99964089e+00,   1.94692956e+00,\n",
       "          1.89879060e+00,   1.85672836e+00,   1.82134774e+00,\n",
       "          1.79197049e+00,   1.76618058e+00,   1.73929091e+00,\n",
       "          1.70372341e+00,   1.64829405e+00,   1.55739372e+00,\n",
       "          1.41005558e+00]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def answer_one():\n",
    "    from sklearn.linear_model import LinearRegression\n",
    "    from sklearn.preprocessing import PolynomialFeatures\n",
    "\n",
    "    # Your code here\n",
    "    result = np.zeros([4, 100])\n",
    "    predict = np.linspace(0, 10, 100).reshape(-1, 1)\n",
    "    X_t = X_train.reshape(-1, 1)  \n",
    "    \n",
    "    poly1 = PolynomialFeatures(degree=1)\n",
    "    X_train_poly1 = poly1.fit_transform(X_t)\n",
    "    predict_ = poly1.fit_transform(predict)\n",
    "    reg = LinearRegression()\n",
    "    reg.fit(X_train_poly1, y_train)\n",
    "    result[0, :] = reg.predict(predict_)\n",
    " \n",
    "    poly2 = PolynomialFeatures(degree=3)\n",
    "    X_train_poly2 = poly2.fit_transform(X_t)\n",
    "    predict_ = poly2.fit_transform(predict)\n",
    "    reg = LinearRegression()\n",
    "    reg.fit(X_train_poly2, y_train)\n",
    "    result[1, :] = reg.predict(predict_)\n",
    "    \n",
    "    poly3 = PolynomialFeatures(degree=6)\n",
    "    X_train_poly3 = poly3.fit_transform(X_t)\n",
    "    predict_ = poly3.fit_transform(predict)\n",
    "    reg = LinearRegression()\n",
    "    reg.fit(X_train_poly3, y_train)\n",
    "    result[2, :] = reg.predict(predict_)\n",
    "    \n",
    "    poly4 = PolynomialFeatures(degree=9)\n",
    "    X_train_poly4 = poly4.fit_transform(X_t)\n",
    "    predict_ = poly4.fit_transform(predict)\n",
    "    reg = LinearRegression()\n",
    "    reg.fit(X_train_poly4, y_train)\n",
    "    result[3, :] = reg.predict(predict_)\n",
    "    \n",
    "    return result\n",
    "\n",
    "answer_one()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": {
      "text/html": [
       "<img 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\" width=\"1000\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# feel free to use the function plot_one() to replicate the figure from the prompt once you have completed question one\n",
    "def plot_one(degree_predictions):\n",
    "    import matplotlib.pyplot as plt\n",
    "    %matplotlib notebook\n",
    "    plt.figure(figsize=(10,5))\n",
    "    plt.plot(X_train, y_train, 'o', label='training data', markersize=10)\n",
    "    plt.plot(X_test, y_test, 'o', label='test data', markersize=10)\n",
    "    for i,degree in enumerate([1,3,6,9]):\n",
    "        plt.plot(np.linspace(0,10,100), degree_predictions[i], alpha=0.8, lw=2, label='degree={}'.format(degree))\n",
    "    plt.ylim(-1,2.5)\n",
    "    plt.legend(loc=4)\n",
    "\n",
    "plot_one(answer_one())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2\n",
    "\n",
    "Write a function that fits a polynomial LinearRegression model on the training data `X_train` for degrees 0 through 9. For each model compute the $R^2$ (coefficient of determination) regression score on the training data as well as the the test data, and return both of these arrays in a tuple.\n",
    "\n",
    "*This function should return one tuple of numpy arrays `(r2_train, r2_test)`. Both arrays should have shape `(10,)`*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0.        ,  0.42924578,  0.4510998 ,  0.58719954,  0.91941945,\n",
       "         0.97578641,  0.99018233,  0.99352509,  0.99637545,  0.99803706]),\n",
       " array([-0.47808642, -0.45237104, -0.06856984,  0.00533105,  0.73004943,\n",
       "         0.87708301,  0.9214094 ,  0.92021504,  0.63247951, -0.64525377]))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def answer_two():\n",
    "    from sklearn.linear_model import LinearRegression\n",
    "    from sklearn.preprocessing import PolynomialFeatures\n",
    "    from sklearn.metrics.regression import r2_score\n",
    "\n",
    "    # Your code here\n",
    "    degrees = np.arange(0, 10)\n",
    "    r2_train = np.zeros([len(degrees)])\n",
    "    r2_test = np.zeros([len(degrees)])\n",
    "    \n",
    "    X_tr = X_train.reshape(-1, 1)\n",
    "    X_tst = X_test.reshape(-1, 1)\n",
    "    \n",
    "    for i, d in enumerate(degrees):\n",
    "        poly = PolynomialFeatures(degree=d)\n",
    "        X_train_poly = poly.fit_transform(X_tr)\n",
    "        X_test_poly = poly.transform(X_tst)\n",
    "        reg = LinearRegression()\n",
    "        reg.fit(X_train_poly, y_train)\n",
    "        r2_train[i] = r2_score(y_train, reg.predict(X_train_poly))\n",
    "        r2_test[i] = r2_score(y_test, reg.predict(X_test_poly))      \n",
    "    \n",
    "    return r2_train, r2_test\n",
    "\n",
    "answer_two()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 3\n",
    "\n",
    "Based on the $R^2$ scores from question 2 (degree levels 0 through 9), what degree level corresponds to a model that is underfitting? What degree level corresponds to a model that is overfitting? What choice of degree level would provide a model with good generalization performance on this dataset? \n",
    "\n",
    "Hint: Try plotting the $R^2$ scores from question 2 to visualize the relationship between degree level and $R^2$. Remember to comment out the import matplotlib line before submission.\n",
    "\n",
    "*This function should return one tuple with the degree values in this order: `(Underfitting, Overfitting, Good_Generalization)`. There might be multiple correct solutions, however, you only need to return one possible solution, for example, (1,2,3).* "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 9, 7)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def answer_three():\n",
    "    \n",
    "    # Your code here\n",
    "    r2_train, r2_test = answer_two()\n",
    "    \n",
    "    r2_train_sorted = np.sort(r2_train)\n",
    "    r2_test_sorted = np.sort(r2_test)\n",
    "    \n",
    "    Underfitting = 0\n",
    "    Overfitting = 0\n",
    "    Good_Generalization = 0 \n",
    "    \n",
    "    for deg, d in enumerate(zip(r2_train, r2_test)):\n",
    "        if d[0] < r2_train_sorted[5] and d[1] < r2_test_sorted[5]:\n",
    "            Underfitting = deg\n",
    "        if d[0] >= r2_train_sorted[5] and d[1] < r2_test_sorted[5]:\n",
    "            Overfitting = deg\n",
    "        if d[0] >= r2_train_sorted[5] and d[1] >= r2_test_sorted[7]:\n",
    "            Good_Generalization = deg\n",
    "    \n",
    "    return Underfitting, Overfitting, Good_Generalization\n",
    "\n",
    "answer_three()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 4\n",
    "\n",
    "Training models on high degree polynomial features can result in overly complex models that overfit, so we often use regularized versions of the model to constrain model complexity, as we saw with Ridge and Lasso linear regression.\n",
    "\n",
    "For this question, train two models: a non-regularized LinearRegression model (default parameters) and a regularized Lasso Regression model (with parameters `alpha=0.01`, `max_iter=10000`) both on polynomial features of degree 12. Return the $R^2$ score for both the LinearRegression and Lasso model's test sets.\n",
    "\n",
    "*This function should return one tuple `(LinearRegression_R2_test_score, Lasso_R2_test_score)`*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:484: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(-4.3120017974975458, 0.84066256147502361)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def answer_four():\n",
    "    from sklearn.preprocessing import PolynomialFeatures\n",
    "    from sklearn.linear_model import Lasso, LinearRegression\n",
    "    from sklearn.metrics.regression import r2_score\n",
    "\n",
    "    # Your code here\n",
    "    a = 0.01\n",
    "    maxIter = 10000\n",
    "    deg = 12\n",
    "    \n",
    "    X_tr = X_train.reshape(-1, 1)\n",
    "    X_tst = X_test.reshape(-1, 1)\n",
    "    poly = PolynomialFeatures(degree=deg)\n",
    "    X_train_poly = poly.fit_transform(X_tr)\n",
    "    X_test_poly = poly.fit_transform(X_tst)\n",
    "    \n",
    "    lr = LinearRegression()\n",
    "    lr.fit(X_train_poly, y_train)\n",
    "    LinearRegression_R2_test_score = r2_score(y_test, lr.predict(X_test_poly))\n",
    "    \n",
    "    lasso = Lasso(alpha = a, max_iter = maxIter).fit(X_train_poly, y_train)\n",
    "    Lasso_R2_test_score = r2_score(y_test, lasso.predict(X_test_poly))\n",
    "        \n",
    "    return LinearRegression_R2_test_score, Lasso_R2_test_score\n",
    "\n",
    "answer_four()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 2 - Classification\n",
    "\n",
    "Here's an application of machine learning that could save your life! For this section of the assignment we will be working with the [UCI Mushroom Data Set](http://archive.ics.uci.edu/ml/datasets/Mushroom?ref=datanews.io) stored in `mushrooms.csv`. The data will be used to train a model to predict whether or not a mushroom is poisonous. The following attributes are provided:\n",
    "\n",
    "*Attribute Information:*\n",
    "\n",
    "1. cap-shape: bell=b, conical=c, convex=x, flat=f, knobbed=k, sunken=s \n",
    "2. cap-surface: fibrous=f, grooves=g, scaly=y, smooth=s \n",
    "3. cap-color: brown=n, buff=b, cinnamon=c, gray=g, green=r, pink=p, purple=u, red=e, white=w, yellow=y \n",
    "4. bruises?: bruises=t, no=f \n",
    "5. odor: almond=a, anise=l, creosote=c, fishy=y, foul=f, musty=m, none=n, pungent=p, spicy=s \n",
    "6. gill-attachment: attached=a, descending=d, free=f, notched=n \n",
    "7. gill-spacing: close=c, crowded=w, distant=d \n",
    "8. gill-size: broad=b, narrow=n \n",
    "9. gill-color: black=k, brown=n, buff=b, chocolate=h, gray=g, green=r, orange=o, pink=p, purple=u, red=e, white=w, yellow=y \n",
    "10. stalk-shape: enlarging=e, tapering=t \n",
    "11. stalk-root: bulbous=b, club=c, cup=u, equal=e, rhizomorphs=z, rooted=r, missing=? \n",
    "12. stalk-surface-above-ring: fibrous=f, scaly=y, silky=k, smooth=s \n",
    "13. stalk-surface-below-ring: fibrous=f, scaly=y, silky=k, smooth=s \n",
    "14. stalk-color-above-ring: brown=n, buff=b, cinnamon=c, gray=g, orange=o, pink=p, red=e, white=w, yellow=y \n",
    "15. stalk-color-below-ring: brown=n, buff=b, cinnamon=c, gray=g, orange=o, pink=p, red=e, white=w, yellow=y \n",
    "16. veil-type: partial=p, universal=u \n",
    "17. veil-color: brown=n, orange=o, white=w, yellow=y \n",
    "18. ring-number: none=n, one=o, two=t \n",
    "19. ring-type: cobwebby=c, evanescent=e, flaring=f, large=l, none=n, pendant=p, sheathing=s, zone=z \n",
    "20. spore-print-color: black=k, brown=n, buff=b, chocolate=h, green=r, orange=o, purple=u, white=w, yellow=y \n",
    "21. population: abundant=a, clustered=c, numerous=n, scattered=s, several=v, solitary=y \n",
    "22. habitat: grasses=g, leaves=l, meadows=m, paths=p, urban=u, waste=w, woods=d\n",
    "\n",
    "<br>\n",
    "\n",
    "The data in the mushrooms dataset is currently encoded with strings. These values will need to be encoded to numeric to work with sklearn. We'll use pd.get_dummies to convert the categorical variables into indicator variables. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "mush_df = pd.read_csv('mushrooms.csv')\n",
    "mush_df2 = pd.get_dummies(mush_df)\n",
    "\n",
    "X_mush = mush_df2.iloc[:,2:]\n",
    "y_mush = mush_df2.iloc[:,1]\n",
    "\n",
    "# use the variables X_train2, y_train2 for Question 5\n",
    "X_train2, X_test2, y_train2, y_test2 = train_test_split(X_mush, y_mush, random_state=0)\n",
    "\n",
    "# For performance reasons in Questions 6 and 7, we will create a smaller version of the \n",
    "# entire mushroom dataset for use in those questions.  For simplicity we'll just re-use\n",
    "# the 25% test split created above as the representative subset.\n",
    "#\n",
    "# Use the variables X_subset, y_subset for Questions 6 and 7.\n",
    "X_subset = X_test2\n",
    "y_subset = y_test2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 5\n",
    "\n",
    "Using `X_train2` and `y_train2` from the preceeding cell, train a DecisionTreeClassifier with default parameters and random_state=0. What are the 5 most important features found by the decision tree?\n",
    "\n",
    "As a reminder, the feature names are available in the `X_train2.columns` property, and the order of the features in `X_train2.columns` matches the order of the feature importance values in the classifier's `feature_importances_` property. \n",
    "\n",
    "*This function should return a list of length 5 containing the feature names in descending order of importance.*\n",
    "\n",
    "*Note: remember that you also need to set random_state in the DecisionTreeClassifier.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['odor_n', 'stalk-root_c', 'stalk-root_r', 'spore-print-color_r', 'odor_l']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def answer_five():\n",
    "    from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "    # Your code here\n",
    "    clf = DecisionTreeClassifier(random_state=0).fit(X_train2, y_train2)\n",
    "    top_five = clf.feature_importances_.argsort()[::-1][:5]\n",
    "    result = list(X_train2.columns[top_five])\n",
    "\n",
    "    return result\n",
    "\n",
    "answer_five()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 6\n",
    "\n",
    "For this question, we're going to use the `validation_curve` function in `sklearn.model_selection` to determine training and test scores for a Support Vector Classifier (`SVC`) with varying parameter values.  Recall that the validation_curve function, in addition to taking an initialized unfitted classifier object, takes a dataset as input and does its own internal train-test splits to compute results.\n",
    "\n",
    "**Because creating a validation curve requires fitting multiple models, for performance reasons this question will use just a subset of the original mushroom dataset: please use the variables X_subset and y_subset as input to the validation curve function (instead of X_mush and y_mush) to reduce computation time.**\n",
    "\n",
    "The initialized unfitted classifier object we'll be using is a Support Vector Classifier with radial basis kernel.  So your first step is to create an `SVC` object with default parameters (i.e. `kernel='rbf', C=1`) and `random_state=0`. Recall that the kernel width of the RBF kernel is controlled using the `gamma` parameter.  \n",
    "\n",
    "With this classifier, and the dataset in X_subset, y_subset, explore the effect of `gamma` on classifier accuracy by using the `validation_curve` function to find the training and test scores for 6 values of `gamma` from `0.0001` to `10` (i.e. `np.logspace(-4,1,6)`). Recall that you can specify what scoring metric you want validation_curve to use by setting the \"scoring\" parameter.  In this case, we want to use \"accuracy\" as the scoring metric.\n",
    "\n",
    "For each level of `gamma`, `validation_curve` will fit 3 models on different subsets of the data, returning two 6x3 (6 levels of gamma x 3 fits per level) arrays of the scores for the training and test sets.\n",
    "\n",
    "Find the mean score across the three models for each level of `gamma` for both arrays, creating two arrays of length 6, and return a tuple with the two arrays.\n",
    "\n",
    "e.g.\n",
    "\n",
    "if one of your array of scores is\n",
    "\n",
    "    array([[ 0.5,  0.4,  0.6],\n",
    "           [ 0.7,  0.8,  0.7],\n",
    "           [ 0.9,  0.8,  0.8],\n",
    "           [ 0.8,  0.7,  0.8],\n",
    "           [ 0.7,  0.6,  0.6],\n",
    "           [ 0.4,  0.6,  0.5]])\n",
    "       \n",
    "it should then become\n",
    "\n",
    "    array([ 0.5,  0.73333333,  0.83333333,  0.76666667,  0.63333333, 0.5])\n",
    "\n",
    "*This function should return one tuple of numpy arrays `(training_scores, test_scores)` where each array in the tuple has shape `(6,)`.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0.56647847,  0.93155951,  0.99039881,  1.        ,  1.        ,  1.        ]),\n",
       " array([ 0.56768547,  0.92959558,  0.98965952,  1.        ,  0.99507994,\n",
       "         0.52240279]))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def answer_six():\n",
    "    from sklearn.svm import SVC\n",
    "    from sklearn.model_selection import validation_curve\n",
    "\n",
    "    # Your code here\n",
    "    param_range = np.logspace(-4, 1, 6)\n",
    "    clf = SVC(kernel = 'rbf', C = 1, random_state = 0)\n",
    "    \n",
    "    train_scores, test_scores = validation_curve(clf, X_subset, y_subset, param_name = 'gamma', param_range = param_range, cv = 3, scoring = \"accuracy\")\n",
    "\n",
    "    training_scores = np.mean(train_scores, axis = 1)\n",
    "    test_scores = np.mean(test_scores, axis = 1)\n",
    "    \n",
    "    return training_scores, test_scores\n",
    "\n",
    "answer_six()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 7\n",
    "\n",
    "Based on the scores from question 6, what gamma value corresponds to a model that is underfitting (and has the worst test set accuracy)? What gamma value corresponds to a model that is overfitting (and has the worst test set accuracy)? What choice of gamma would be the best choice for a model with good generalization performance on this dataset (high accuracy on both training and test set)? \n",
    "\n",
    "Hint: Try plotting the scores from question 6 to visualize the relationship between gamma and accuracy. Remember to comment out the import matplotlib line before submission.\n",
    "\n",
    "*This function should return one tuple with the degree values in this order: `(Underfitting, Overfitting, Good_Generalization)` Please note there is only one correct solution.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0001, 10.0, 0.10000000000000001)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def answer_seven():\n",
    "    \n",
    "    # Your code here\n",
    "    param_range = np.logspace(-4, 1, 6)\n",
    "    \n",
    "    training_scores, test_scores = answer_six()\n",
    "    \n",
    "    training_scores_sorted = np.sort(training_scores)\n",
    "    test_scores_sorted = np.sort(test_scores)\n",
    "    \n",
    "    Underfitting = 0\n",
    "    Overfitting = 0\n",
    "    Good_Generalization = 0 \n",
    "    min_training_scores = np.min(training_scores)\n",
    "    max_training_scores = np.max(training_scores)\n",
    "    min_test_scores = np.max(test_scores)\n",
    "    max_test_scores = np.max(test_scores)  \n",
    "    \n",
    "    for gam, d in zip(param_range, zip(training_scores, test_scores)):\n",
    "        if d[0] <= training_scores_sorted[1] and d[1] <= test_scores_sorted[1]:\n",
    "            Underfitting = gam\n",
    "        if d[0] > training_scores_sorted[1] and d[1] <= test_scores_sorted[1]:\n",
    "            Overfitting = gam\n",
    "        if d[0] == max_training_scores and d[1] == max_test_scores:\n",
    "            Good_Generalization = gam\n",
    "    \n",
    "    return Underfitting, Overfitting, Good_Generalization\n",
    "    \n",
    "answer_seven()"
   ]
  }
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