PROJETO 2 (Chance of Admission).ipynb

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   "source": [
    "## Introdução"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O objetivo deste projeto é responder qual é a taxa de admição em uma pós graduação, em uma universidade americana, em função da pontuação de uma série de teste e estatísticas acadêmicas por meio de métodos preditivos de regressão."
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    "- **TOEFL**: Nota no exame de proficiência na língua inglesa\n",
    "- **GRE**: Nota no Exame de Registro de Graduação\n",
    "- **CGPA**: Valor do desempenho acadêmico ao longo de um determinado período de estudos, em geral, Ensino Médio ou Graduação\n",
    "- **Rank da Universidade**: Prestígio da universidade ranqueado de 1 a 5\n",
    "- **Statement of Purpose (SOP)**: Declaração pessoal de propósito que cita a razão de um candidato querer ingressar em uma universidade\n",
    "- **Letter of Recommendation**: Carta de recomendação \n",
    "- **Research**: Indivíduo já fez alguma pesquisa de cunho acadêmico"
   ]
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   "cell_type": "code",
   "execution_count": 23,
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   "source": [
    "#Importações\n",
    "\n",
    "%matplotlib notebook\n",
    "\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy.stats import norm, probplot\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#Regressão Linear\n",
    "from sklearn import datasets, svm\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "#Regression Tree\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn import tree\n",
    "#Random Forest Regression\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "#Importações para as predições\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "\n",
    "#Melhora a visualização\n",
    "from IPython.display import display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Função para Regressão\n",
    "#Faz a regressão para \n",
    "def regress(Y,X):\n",
    "    '''\n",
    "    Y: coluna do DataFrame utilizada como variável resposta (TARGET)\n",
    "    X: coluna(s) do DataFrame utilizadas como variável(is) explicativas (FEATURES)\n",
    "    '''\n",
    "    X_cp = sm.add_constant(X)\n",
    "    model = sm.OLS(Y,X_cp)\n",
    "    results = model.fit()\n",
    "    \n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"Admission_Predict.csv\")"
   ]
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   "source": [
    "## Análise Exploratória"
   ]
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   "execution_count": 26,
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    {
     "data": {
      "text/plain": [
       "Index(['Serial No.', 'GRE Score', 'TOEFL Score', 'University Rating', 'SOP',\n",
       "       'LOR ', 'CGPA', 'Research', 'Chance of Admit '],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
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   "execution_count": 27,
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GRE Score</th>\n",
       "      <th>TOEFL Score</th>\n",
       "      <th>University Rating</th>\n",
       "      <th>SOP</th>\n",
       "      <th>LOR</th>\n",
       "      <th>CGPA</th>\n",
       "      <th>Research</th>\n",
       "      <th>Chance of Admit</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>400.000000</td>\n",
       "      <td>400.000000</td>\n",
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       "      <td>400.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>316.807500</td>\n",
       "      <td>107.410000</td>\n",
       "      <td>3.087500</td>\n",
       "      <td>3.400000</td>\n",
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       "      <td>1.006869</td>\n",
       "      <td>0.898478</td>\n",
       "      <td>0.596317</td>\n",
       "      <td>0.498362</td>\n",
       "      <td>0.142609</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.800000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.340000</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>308.000000</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.170000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.640000</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>317.000000</td>\n",
       "      <td>107.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>8.610000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.730000</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>325.000000</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>9.062500</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.830000</td>\n",
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       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>340.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>9.920000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.970000</td>\n",
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      "text/plain": [
       "        GRE Score  TOEFL Score  University Rating         SOP        LOR   \\\n",
       "count  400.000000   400.000000         400.000000  400.000000  400.000000   \n",
       "mean   316.807500   107.410000           3.087500    3.400000    3.452500   \n",
       "std     11.473646     6.069514           1.143728    1.006869    0.898478   \n",
       "min    290.000000    92.000000           1.000000    1.000000    1.000000   \n",
       "25%    308.000000   103.000000           2.000000    2.500000    3.000000   \n",
       "50%    317.000000   107.000000           3.000000    3.500000    3.500000   \n",
       "75%    325.000000   112.000000           4.000000    4.000000    4.000000   \n",
       "max    340.000000   120.000000           5.000000    5.000000    5.000000   \n",
       "\n",
       "             CGPA    Research  Chance of Admit   \n",
       "count  400.000000  400.000000        400.000000  \n",
       "mean     8.598925    0.547500          0.724350  \n",
       "std      0.596317    0.498362          0.142609  \n",
       "min      6.800000    0.000000          0.340000  \n",
       "25%      8.170000    0.000000          0.640000  \n",
       "50%      8.610000    1.000000          0.730000  \n",
       "75%      9.062500    1.000000          0.830000  \n",
       "max      9.920000    1.000000          0.970000  "
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    "df = data[['GRE Score', 'TOEFL Score', 'University Rating', 'SOP',\n",
    "       'LOR ', 'CGPA', 'Research', 'Chance of Admit ']]\n",
    "df.describe()"
   ]
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   "execution_count": 28,
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    {
     "data": {
      "text/plain": [
       "GRE Score            0\n",
       "TOEFL Score          0\n",
       "University Rating    0\n",
       "SOP                  0\n",
       "LOR                  0\n",
       "CGPA                 0\n",
       "Research             0\n",
       "Chance of Admit      0\n",
       "dtype: int64"
      ]
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   "source": [
    "df.isnull().sum()"
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   "cell_type": "code",
   "execution_count": 29,
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       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\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",
       "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",
       "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], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.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,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\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",
       "        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.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\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",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\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(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\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(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\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",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\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",
       "        }\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",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\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",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\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, 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\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"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\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data']);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\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;\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",
       "    fig.cell_info[0].output_area.element.one(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\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.innerHTML =\n",
       "        '<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 / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<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 () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('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",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\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",
       "\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",
       "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(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
    {
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\" width=\"1200\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1d4740d6f70>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.pairplot(df, height = 1.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regressão Linear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>Chance of Admit </td> <th>  R-squared:         </th> <td>   0.796</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.790</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   151.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 07 Jun 2022</td> <th>  Prob (F-statistic):</th> <td>5.92e-90</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>07:42:29</td>     <th>  Log-Likelihood:    </th> <td>  378.69</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   280</td>      <th>  AIC:               </th> <td>  -741.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   272</td>      <th>  BIC:               </th> <td>  -712.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     7</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "          <td></td>             <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>             <td>   -1.0547</td> <td>    0.149</td> <td>   -7.100</td> <td> 0.000</td> <td>   -1.347</td> <td>   -0.762</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>GRE Score</th>         <td>    0.0014</td> <td>    0.001</td> <td>    1.988</td> <td> 0.048</td> <td> 1.36e-05</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TOEFL Score</th>       <td>    0.0027</td> <td>    0.001</td> <td>    2.067</td> <td> 0.040</td> <td>    0.000</td> <td>    0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>University Rating</th> <td>    0.0089</td> <td>    0.006</td> <td>    1.487</td> <td> 0.138</td> <td>   -0.003</td> <td>    0.021</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SOP</th>               <td>   -0.0001</td> <td>    0.007</td> <td>   -0.019</td> <td> 0.985</td> <td>   -0.014</td> <td>    0.013</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>LOR </th>              <td>    0.0210</td> <td>    0.006</td> <td>    3.238</td> <td> 0.001</td> <td>    0.008</td> <td>    0.034</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CGPA</th>              <td>    0.1079</td> <td>    0.014</td> <td>    7.444</td> <td> 0.000</td> <td>    0.079</td> <td>    0.136</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Research</th>          <td>    0.0302</td> <td>    0.010</td> <td>    3.155</td> <td> 0.002</td> <td>    0.011</td> <td>    0.049</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>68.032</td> <th>  Durbin-Watson:     </th> <td>   2.044</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td> 144.011</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-1.207</td> <th>  Prob(JB):          </th> <td>5.35e-32</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 5.553</td> <th>  Cond. No.          </th> <td>1.31e+04</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.31e+04. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:       Chance of Admit    R-squared:                       0.796\n",
       "Model:                            OLS   Adj. R-squared:                  0.790\n",
       "Method:                 Least Squares   F-statistic:                     151.4\n",
       "Date:                Tue, 07 Jun 2022   Prob (F-statistic):           5.92e-90\n",
       "Time:                        07:42:29   Log-Likelihood:                 378.69\n",
       "No. Observations:                 280   AIC:                            -741.4\n",
       "Df Residuals:                     272   BIC:                            -712.3\n",
       "Df Model:                           7                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=====================================================================================\n",
       "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------------\n",
       "const                -1.0547      0.149     -7.100      0.000      -1.347      -0.762\n",
       "GRE Score             0.0014      0.001      1.988      0.048    1.36e-05       0.003\n",
       "TOEFL Score           0.0027      0.001      2.067      0.040       0.000       0.005\n",
       "University Rating     0.0089      0.006      1.487      0.138      -0.003       0.021\n",
       "SOP                  -0.0001      0.007     -0.019      0.985      -0.014       0.013\n",
       "LOR                   0.0210      0.006      3.238      0.001       0.008       0.034\n",
       "CGPA                  0.1079      0.014      7.444      0.000       0.079       0.136\n",
       "Research              0.0302      0.010      3.155      0.002       0.011       0.049\n",
       "==============================================================================\n",
       "Omnibus:                       68.032   Durbin-Watson:                   2.044\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              144.011\n",
       "Skew:                          -1.207   Prob(JB):                     5.35e-32\n",
       "Kurtosis:                       5.553   Cond. No.                     1.31e+04\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.31e+04. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = df['Chance of Admit ']\n",
    "X = df[['GRE Score', 'TOEFL Score', 'University Rating', 'SOP',\n",
    "       'LOR ', 'CGPA', 'Research']]\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3, random_state=1) \n",
    "results = regress(Y_train,X_train)\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retirando SOP - Statement of purpose "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Foi tomado como alfa um valor de 5% após observar os valores de P>|t|. Com isso, a primeira variável que foi eliminada foi o SOP, que apresenta na célula anterior um valor de 98,5%. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>Chance of Admit </td> <th>  R-squared:         </th> <td>   0.796</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.791</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   177.2</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 07 Jun 2022</td> <th>  Prob (F-statistic):</th> <td>4.26e-91</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>07:42:29</td>     <th>  Log-Likelihood:    </th> <td>  378.69</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   280</td>      <th>  AIC:               </th> <td>  -743.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   273</td>      <th>  BIC:               </th> <td>  -717.9</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     6</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "          <td></td>             <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>             <td>   -1.0545</td> <td>    0.148</td> <td>   -7.120</td> <td> 0.000</td> <td>   -1.346</td> <td>   -0.763</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>GRE Score</th>         <td>    0.0014</td> <td>    0.001</td> <td>    2.004</td> <td> 0.046</td> <td> 2.51e-05</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TOEFL Score</th>       <td>    0.0027</td> <td>    0.001</td> <td>    2.093</td> <td> 0.037</td> <td>    0.000</td> <td>    0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>University Rating</th> <td>    0.0089</td> <td>    0.006</td> <td>    1.574</td> <td> 0.117</td> <td>   -0.002</td> <td>    0.020</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>LOR </th>              <td>    0.0210</td> <td>    0.006</td> <td>    3.505</td> <td> 0.001</td> <td>    0.009</td> <td>    0.033</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CGPA</th>              <td>    0.1079</td> <td>    0.014</td> <td>    7.527</td> <td> 0.000</td> <td>    0.080</td> <td>    0.136</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Research</th>          <td>    0.0302</td> <td>    0.010</td> <td>    3.165</td> <td> 0.002</td> <td>    0.011</td> <td>    0.049</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>68.041</td> <th>  Durbin-Watson:     </th> <td>   2.044</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td> 144.023</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-1.207</td> <th>  Prob(JB):          </th> <td>5.32e-32</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 5.553</td> <th>  Cond. No.          </th> <td>1.31e+04</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.31e+04. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:       Chance of Admit    R-squared:                       0.796\n",
       "Model:                            OLS   Adj. R-squared:                  0.791\n",
       "Method:                 Least Squares   F-statistic:                     177.2\n",
       "Date:                Tue, 07 Jun 2022   Prob (F-statistic):           4.26e-91\n",
       "Time:                        07:42:29   Log-Likelihood:                 378.69\n",
       "No. Observations:                 280   AIC:                            -743.4\n",
       "Df Residuals:                     273   BIC:                            -717.9\n",
       "Df Model:                           6                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=====================================================================================\n",
       "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------------\n",
       "const                -1.0545      0.148     -7.120      0.000      -1.346      -0.763\n",
       "GRE Score             0.0014      0.001      2.004      0.046    2.51e-05       0.003\n",
       "TOEFL Score           0.0027      0.001      2.093      0.037       0.000       0.005\n",
       "University Rating     0.0089      0.006      1.574      0.117      -0.002       0.020\n",
       "LOR                   0.0210      0.006      3.505      0.001       0.009       0.033\n",
       "CGPA                  0.1079      0.014      7.527      0.000       0.080       0.136\n",
       "Research              0.0302      0.010      3.165      0.002       0.011       0.049\n",
       "==============================================================================\n",
       "Omnibus:                       68.041   Durbin-Watson:                   2.044\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              144.023\n",
       "Skew:                          -1.207   Prob(JB):                     5.32e-32\n",
       "Kurtosis:                       5.553   Cond. No.                     1.31e+04\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.31e+04. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "    #Retirando a variavel SOP - Statement of purpose\n",
    "X1 = X_train[['GRE Score', 'TOEFL Score', 'University Rating',\n",
    "       'LOR ', 'CGPA', 'Research']]\n",
    "results1 = regress(Y_train,X1)\n",
    "results1.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Com novos valores obtidos notou-se que a variável University Rating também apresentava um valor de alfa de 11,7%, ou seja, superior ao valor que se deseja."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>Chance of Admit </td> <th>  R-squared:         </th> <td>   0.794</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.790</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   211.0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 07 Jun 2022</td> <th>  Prob (F-statistic):</th> <td>9.52e-92</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>07:42:29</td>     <th>  Log-Likelihood:    </th> <td>  377.43</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   280</td>      <th>  AIC:               </th> <td>  -742.9</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   274</td>      <th>  BIC:               </th> <td>  -721.0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     5</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>          <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>       <td>   -1.1376</td> <td>    0.139</td> <td>   -8.197</td> <td> 0.000</td> <td>   -1.411</td> <td>   -0.864</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>GRE Score</th>   <td>    0.0014</td> <td>    0.001</td> <td>    2.018</td> <td> 0.045</td> <td> 3.51e-05</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TOEFL Score</th> <td>    0.0031</td> <td>    0.001</td> <td>    2.520</td> <td> 0.012</td> <td>    0.001</td> <td>    0.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>LOR </th>        <td>    0.0247</td> <td>    0.006</td> <td>    4.462</td> <td> 0.000</td> <td>    0.014</td> <td>    0.036</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CGPA</th>        <td>    0.1129</td> <td>    0.014</td> <td>    8.057</td> <td> 0.000</td> <td>    0.085</td> <td>    0.140</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Research</th>    <td>    0.0303</td> <td>    0.010</td> <td>    3.163</td> <td> 0.002</td> <td>    0.011</td> <td>    0.049</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>66.172</td> <th>  Durbin-Watson:     </th> <td>   2.065</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td> 136.678</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-1.186</td> <th>  Prob(JB):          </th> <td>2.09e-30</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 5.467</td> <th>  Cond. No.          </th> <td>1.23e+04</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.23e+04. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:       Chance of Admit    R-squared:                       0.794\n",
       "Model:                            OLS   Adj. R-squared:                  0.790\n",
       "Method:                 Least Squares   F-statistic:                     211.0\n",
       "Date:                Tue, 07 Jun 2022   Prob (F-statistic):           9.52e-92\n",
       "Time:                        07:42:29   Log-Likelihood:                 377.43\n",
       "No. Observations:                 280   AIC:                            -742.9\n",
       "Df Residuals:                     274   BIC:                            -721.0\n",
       "Df Model:                           5                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===============================================================================\n",
       "                  coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------\n",
       "const          -1.1376      0.139     -8.197      0.000      -1.411      -0.864\n",
       "GRE Score       0.0014      0.001      2.018      0.045    3.51e-05       0.003\n",
       "TOEFL Score     0.0031      0.001      2.520      0.012       0.001       0.006\n",
       "LOR             0.0247      0.006      4.462      0.000       0.014       0.036\n",
       "CGPA            0.1129      0.014      8.057      0.000       0.085       0.140\n",
       "Research        0.0303      0.010      3.163      0.002       0.011       0.049\n",
       "==============================================================================\n",
       "Omnibus:                       66.172   Durbin-Watson:                   2.065\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              136.678\n",
       "Skew:                          -1.186   Prob(JB):                     2.09e-30\n",
       "Kurtosis:                       5.467   Cond. No.                     1.23e+04\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.23e+04. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Retirando a variavel University Rating\n",
    "X2 = X_train[['GRE Score', 'TOEFL Score',\n",
    "       'LOR ', 'CGPA', 'Research']]\n",
    "results2 = regress(Y_train,X2)\n",
    "results2.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como visto, nenhuma outra variável apresenta valor igual ou superior ao alfa adotado. Desse modo, foi gerado um gráfico comparando os valores ordenados e teóricos para descobrir se é uma distribuição normal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "admit = results2.params[0]\n",
    "gre = results2.params[1]\n",
    "toefl = results2.params[2]\n",
    "lore = results2.params[3]\n",
    "cgpa = results2.params[4]\n",
    "rese = results2.params[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            '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",
       "\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 = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(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 (fig.ratio !== 1) {\n",
       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n",
       "        }\n",
       "        fig.send_message('refresh', {});\n",
       "    };\n",
       "\n",
       "    this.imageObj.onload = function () {\n",
       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
       "            // almost always do), so we need to clear the canvas so that\n",
       "            // there is no ghosting.\n",
       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "        }\n",
       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "    };\n",
       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "    if (this.ratio !== 1) {\n",
       "        fig.send_message('set_dpi_ratio', { dpi_ratio: this.ratio });\n",
       "    }\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    var resizeObserver = new ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    resizeObserver.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\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 toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\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",
       "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",
       "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], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.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,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\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",
       "        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.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\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",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\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(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\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(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\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",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\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",
       "        }\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",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\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",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\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, 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\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"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\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data']);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\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;\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",
       "    fig.cell_info[0].output_area.element.one(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\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.innerHTML =\n",
       "        '<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 / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<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 () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('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",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\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",
       "\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",
       "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(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "probplot(results2.resid,dist=\"norm\",plot=plt)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    Contudo, como observado nas extremidades do gráfico nota-se que ele se mantém distante da linha teórica, já no meio \n",
    "    aparenta estar um pouco mais condizente com os quantis teóricos.\n",
    "\n",
    "    Recapitulando as variáveis removidas, o que mais marcou logicamente foi a retirada do University Rating, pois seguindo o raciocínio quanto maior o rank de uma universidade menor deveria ser sua porcentagem de alunos aceitos. No entanto, \n",
    "    pelos gráficos pairplot gerados, ao analisar o de University Rating X Chance of Admit, nota-se que este pensamento está aparentemente errado, e que na verdade as universidades de maior rank são as com maiores índices de admissão. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8103743116598131\n",
      "0.06578381051086298\n"
     ]
    }
   ],
   "source": [
    "clf = LinearRegression()\n",
    "clf.fit(X_train, Y_train)\n",
    "prediction = clf.predict(X_test) #Y^\n",
    "RMSE = np.mean((Y_test-prediction)**2)**0.5\n",
    "R1 = metrics.r2_score(Y_test, prediction) # Avalia R2 da árvore de decisão\n",
    "\n",
    "print(R1)\n",
    "print(RMSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Árvore de Decisão"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A Árvore de Decisão nada mais é do que um mapa dos possíveis resultados de uma série de escolhas relacionanadas. Ela permite que um indivíduo ou organização compare possíveis ações com base em seus custos, probabilidades, e benefícios. Neste caso, ela será usada para prever a taxa de admição para uma pós graduação em uma universidade americanas separando os fatores que podem ser usados para avaliação, bem como as interferências. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Monta a árvore de decisão\n",
    "\n",
    "Adr = DecisionTreeRegressor(max_depth = 3)\n",
    "Adr = Adr.fit(X_train, Y_train)\n",
    "\n",
    "Y_Ar = Adr.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Após a criação dessa árvore em dados, a mesma foi representada como uma figura para melhor compreenção."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            '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",
       "\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 = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(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 (fig.ratio !== 1) {\n",
       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n",
       "        }\n",
       "        fig.send_message('refresh', {});\n",
       "    };\n",
       "\n",
       "    this.imageObj.onload = function () {\n",
       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
       "            // almost always do), so we need to clear the canvas so that\n",
       "            // there is no ghosting.\n",
       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "        }\n",
       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "    };\n",
       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "    if (this.ratio !== 1) {\n",
       "        fig.send_message('set_dpi_ratio', { dpi_ratio: this.ratio });\n",
       "    }\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    var resizeObserver = new ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    resizeObserver.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\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 toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\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",
       "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",
       "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], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.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,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\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",
       "        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.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\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",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\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(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\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(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\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",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\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",
       "        }\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",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\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",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\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, 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\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"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\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data']);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\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;\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",
       "    fig.cell_info[0].output_area.element.one(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\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.innerHTML =\n",
       "        '<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 / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<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 () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('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",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\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",
       "\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",
       "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(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"800\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Árvore de decisão\n",
    "\n",
    "plt.figure(figsize=(8,8))\n",
    "tree.plot_tree(Adr, feature_names=['GRE Score', 'TOEFL Score', 'University Rating', 'SOP', 'LOR', 'CGPA', 'Research'], filled=True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Com isso, o modelo foi validado com os dados reais."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            '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",
       "\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 = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(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 (fig.ratio !== 1) {\n",
       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n",
       "        }\n",
       "        fig.send_message('refresh', {});\n",
       "    };\n",
       "\n",
       "    this.imageObj.onload = function () {\n",
       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
       "            // almost always do), so we need to clear the canvas so that\n",
       "            // there is no ghosting.\n",
       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "        }\n",
       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "    };\n",
       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "    if (this.ratio !== 1) {\n",
       "        fig.send_message('set_dpi_ratio', { dpi_ratio: this.ratio });\n",
       "    }\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    var resizeObserver = new ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    resizeObserver.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\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 toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\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",
       "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",
       "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], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.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,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\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",
       "        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.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\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",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\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(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\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(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\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",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\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",
       "        }\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",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\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",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\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, 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\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"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\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data']);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\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;\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",
       "    fig.cell_info[0].output_area.element.one(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\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.innerHTML =\n",
       "        '<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 / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<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 () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('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",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\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",
       "\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",
       "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(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "\n",
    "plt.scatter(Y_Ar, Y_test, label='valores')\n",
    "plt.plot([0,1],[0,1],'r',label='identidade')\n",
    "plt.xlim([0,1])\n",
    "plt.ylim([0,1])\n",
    "plt.xlabel('Ground Truth')\n",
    "plt.ylabel('Predição')\n",
    "plt.legend()\n",
    "plt.title('Análise Regression Tree')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7315331943881513\n",
      "0.07827367753881494\n"
     ]
    }
   ],
   "source": [
    "rmse = np.mean((Y_test-Y_Ar)**2)**0.5\n",
    "R2 = metrics.r2_score(Y_test, Y_Ar) # Avalia R2 da árvore de decisão\n",
    "\n",
    "print(R2)\n",
    "print(rmse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Neste algoritmo vários pontos de decisão serão criado. Estes pontos são os \"nós\" da árvore e em cada um deles o resultado da decisão será seguir por um caminho, ou por outro.Os caminhos existentes são os \"Ramos\". Os nós são responsáveis pelas conferências que irão indicar um ramo ou outro para sequência do fluxo. Neste caso é que cada nó é uma forma de avaliar difentes metodos avaliativos para passar em uma pós graduação americana."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Montagem da Random Forest\n",
    "rf = RandomForestRegressor()\n",
    "\n",
    "rf = rf.fit(X_train, Y_train)\n",
    "\n",
    "y_pred = rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Com essa random forest feita conseguimos fazer o grafico da random forest, assim podendo verificar sua qualidade."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            '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",
       "\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 = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(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 (fig.ratio !== 1) {\n",
       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n",
       "        }\n",
       "        fig.send_message('refresh', {});\n",
       "    };\n",
       "\n",
       "    this.imageObj.onload = function () {\n",
       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
       "            // almost always do), so we need to clear the canvas so that\n",
       "            // there is no ghosting.\n",
       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "        }\n",
       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "    };\n",
       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "    if (this.ratio !== 1) {\n",
       "        fig.send_message('set_dpi_ratio', { dpi_ratio: this.ratio });\n",
       "    }\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    var resizeObserver = new ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    resizeObserver.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\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 toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\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",
       "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",
       "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], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.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,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\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",
       "        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.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\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",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\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(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\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(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\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",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\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",
       "        }\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",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\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",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\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, 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\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"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\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data']);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\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;\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",
       "    fig.cell_info[0].output_area.element.one(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\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.innerHTML =\n",
       "        '<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 / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<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 () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('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",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\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",
       "\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",
       "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(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\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": [
    "#Gráfico da Random Forest\n",
    "plt.figure()\n",
    "\n",
    "plt.scatter(y_pred, Y_test, color = 'g')\n",
    "plt.plot([0,1],[0,1],'r',label='identidade')\n",
    "plt.xlim([0,1])\n",
    "plt.ylim([0,1])\n",
    "plt.xlabel('Ground Truth')\n",
    "plt.ylabel('Predição')\n",
    "plt.title('Análise Random Forest')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Com esses valores podemos avaliar difentes formas, como pelo R2 e o rmse."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7879944581563721\n",
      "0.06955751337322712\n"
     ]
    }
   ],
   "source": [
    "rmse = np.mean((Y_test-y_pred)**2)**0.5\n",
    "R3 = metrics.r2_score(Y_test, y_pred) # Avalia R2 da Random Forest\n",
    "\n",
    "print(R3)\n",
    "print(rmse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusão"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Os métodos usaods pelo grupo (regressão linear, árvore de decisão, e random forest) foram validados de suas formas diferentes para verificar qual deles tem um ajuste melhor à amostra. Tais formas foram a probabilidade calculada por R², e pelo RMSE (root mean squared error).\n",
    "\n",
    "O R² diz com quanta precisão o modelo pode explicar os valores observados. Assim, modelos com um maior valor de R² são aqueles que se ajustam melhor à amostra, variando esse valor de 0 a 1.\n",
    "\n",
    "O RMSE é a medida que calcula a raiz quadrada média dos erros entre valores observados (reais) e predições (hipóteses). Assim, quanto menor ele é melhor o modelo.\n",
    "\n",
    "Por meio da Regressão Linear, foi obtido o valor de R² de 0,81 e o valor do RMSE de 0,06. Ccomparando o nosso modelo com os valores reais.\n",
    "\n",
    "Por meio da Árvore de Decisão, foi obtido o valor de R² de 0,73 e o valor do RMSE de 0,08. Comparando o nosso modelo com os valores reais\n",
    "\n",
    "Por meio da Random Forest, foi obtido o valor de R² de aproximadamente 0,79  e o valor do RMSE de 0,07. Comparando o nosso modelo com os valores reais"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Com esses valores calculados e com os graficos feitos, podemos comparar esses modelos e dizer entre eles qual é o melhor. Após discussões entre o grupo, e comparado os modelos foi escolhido que a Regressão Linear é o melhor modelo entre eles, pois foi o que teve os melhores valores de R²(mais próximo de 1) e do RMSE(mais próximo de zero).\n",
    " Por tanto o modelo escolhido para prever valores futuros de pessoas com notas para entrar em uma pós graduação americana foi a Regressão linear."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Futuras interações"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para futuras iterações, uma possibilidade seria o cálculo de outros métodos para previsão da amostra, e entâo calcular seus R² e RMSE. Para garantir um maior número de acertos, e assim pode-se garantir uma maior precisão."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Referencias"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://pt.linkedin.com/pulse/rmse-ou-mae-como-avaliar-meu-modelo-de-machine-learning-rezende\n",
    "\n",
    "https://didatica.tech/como-funciona-o-algoritmo-arvore-de-decisao/\n",
    "\n",
    "https://scikit-learn.org/stable/modules/generated/sklearn.tree.plot_tree.html\n",
    "\n",
    "https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeRegressor.html\n",
    "\n",
    "https://scikit-learn.org/stable/index.html"
   ]
  }
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