Shallow Learning Models - Doc2Vec.ipynb

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      "Using TensorFlow backend.\n",
      "/anaconda3/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning:\n",
      "\n",
      "compiletime version 3.6 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.5\n",
      "\n",
      "/anaconda3/lib/python3.5/site-packages/sklearn/cross_validation.py:41: DeprecationWarning:\n",
      "\n",
      "This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "\n",
      "/anaconda3/lib/python3.5/site-packages/sklearn/ensemble/weight_boosting.py:29: DeprecationWarning:\n",
      "\n",
      "numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.\n",
      "\n"
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import gensim\n",
    "import os\n",
    "from pathlib import Path\n",
    "from gensim.models.doc2vec import *\n",
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
    "init_notebook_mode(connected=True)\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as colors\n",
    "import colorlover as cl\n",
    "from IPython.display import HTML\n",
    "from keras import regularizers\n",
    "from keras.layers import Dropout\n",
    "from keras import optimizers\n",
    "import ipywidgets as widgets\n",
    "from collections import Counter\n",
    "from sklearn import neighbors\n",
    "from sklearn.model_selection import train_test_split\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.metrics import accuracy_score, roc_auc_score, precision_score\n",
    "from sklearn.cross_validation import KFold\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
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       "      <th>author</th>\n",
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       "      <td>1.854182</td>\n",
       "      <td>MaryWollstonecraft</td>\n",
       "      <td>female</td>\n",
       "      <td>enlightenment</td>\n",
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       "      <td>MaryWollstonecraft</td>\n",
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       "      <td>enlightenment</td>\n",
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       "      <td>MaryWollstonecraft</td>\n",
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       "      <td>enlightenment</td>\n",
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       "          0         1         2         3         4         5         6  \\\n",
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       "\n",
       "          7         8         9      ...              13        14        15  \\\n",
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       "4 -2.305384  0.418952  1.774418      ...        2.602762 -0.780936  1.422385   \n",
       "\n",
       "         16        17        18        19              author     sex  \\\n",
       "0  0.347195  1.313847  2.680759 -0.379815  MaryWollstonecraft  female   \n",
       "1 -0.696870  0.742644  1.306955  2.271753  MaryWollstonecraft  female   \n",
       "2  0.577892 -2.256335 -0.870119  1.854182  MaryWollstonecraft  female   \n",
       "3  0.755759  1.035357  0.322843  1.942936  MaryWollstonecraft  female   \n",
       "4 -1.050799 -0.209620 -0.086764  1.308790  MaryWollstonecraft  female   \n",
       "\n",
       "          period  \n",
       "0  enlightenment  \n",
       "1  enlightenment  \n",
       "2  enlightenment  \n",
       "3  enlightenment  \n",
       "4  enlightenment  \n",
       "\n",
       "[5 rows x 23 columns]"
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     },
     "execution_count": 2,
     "metadata": {},
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    }
   ],
   "source": [
    "vectorized_df = pd.read_csv('document_vectors_df.csv', index_col = 0)\n",
    "vectorized_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "authors = list(set((vectorized_df['author'])))\n",
    "genders = list(set((vectorized_df['sex'])))\n",
    "periods = list(set((vectorized_df['period'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create author, gender, period dictionaries for mapping \n",
    "num = list(range(0,len(authors)))\n",
    "author_num = dict(zip(authors, num))\n",
    "\n",
    "num_gen = list(range(0,len(genders)))\n",
    "sex_num = dict(zip(genders, num_gen))\n",
    "\n",
    "num_period = list(range(0,len(periods)))\n",
    "period_num = dict(zip(periods, num_period))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorized_df['sex'] = vectorized_df['sex'].map(sex_num)\n",
    "vectorized_df['author'] = vectorized_df['author'].map(author_num)\n",
    "vectorized_df['period'] = vectorized_df['period'].map(period_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = vectorized_df.drop(['author', 'sex', 'period'], axis = 1)\n",
    "y_author = vectorized_df['author']\n",
    "y_period = vectorized_df['period']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_author, X_test_author, y_train_author, y_test_author = train_test_split(X, y_author)\n",
    "X_train_period, X_test_period, y_train_period, y_test_period = train_test_split(X, y_period)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "freq_train_author = [sum(y_train_author == i) for i in range(0, len(authors))]\n",
    "freq_test_author = [sum(y_test_author == i) for i in range(0, len(authors))]\n",
    "freq_train_period = [sum(y_train_period == i) for i in range(0, len(periods))]\n",
    "freq_test_period = [sum(y_test_period == i) for i in range(0, len(periods))]"
   ]
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       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"800\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking the class inbalance\n",
    "%matplotlib notebook\n",
    "plt.figure(figsize = (8,2.5))\n",
    "plt.subplot(121)\n",
    "plt.bar(list(range(0,len(authors))), freq_train_author)\n",
    "plt.show()\n",
    "plt.subplot(122)\n",
    "plt.bar(list(range(0,len(authors))), freq_test_author)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"800\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking the class inbalance\n",
    "%matplotlib notebook\n",
    "plt.figure(figsize = (8,2.5))\n",
    "plt.subplot(121)\n",
    "plt.bar(list(range(0,len(periods))), freq_train_period)\n",
    "plt.show()\n",
    "plt.subplot(122)\n",
    "plt.bar(list(range(0,len(periods))), freq_test_period)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Shallow Learning Classifiers\n",
    "#### K - Nearest Neighbors\n",
    "- Author Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting & Processing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(Path.cwd().joinpath('250Paragraphs_150Words.csv'), index_col = 0)\n",
    "\n",
    "sliced_df = df.loc[:, 'text']\n",
    "labels_sex = df.loc[:, 'sex']\n",
    "labels_period = df.loc[:, 'period']\n",
    "labels_author = df.loc[:, 'author']\n",
    "\n",
    "#note that we can call all_labels and see our names, should we forget them later\n",
    "all_labels = {'sex': labels_sex, 'period': labels_period, 'author': labels_author}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bag of Words & TF-IDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer, TfidfTransformer\n",
    "from nltk.tokenize import word_tokenize\n",
    "from sklearn.metrics.pairwise import cosine_similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# max_df and min_df work interestingly\n",
    "# they can either be a float value between 0.0 and 1.0\n",
    "# in which case they represent a proportion of the documents - i.e. .98 = 98% of the docs\n",
    "# or they can be an integer\n",
    "# in which case they can be an absolute count - i.e. 2 = 2 out of all documents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# I'm creating this dictionary so the transformed vectors can be easily referenced throughout the doc\n",
    "transformed_vectors = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Bag of Words without n-grams\n",
    "\n",
    "count_vec = CountVectorizer(min_df = 2, max_df = .98, stop_words=\"english\")\n",
    "count_vec.fit(sliced_df)\n",
    "count_vec_transformed = count_vec.transform(sliced_df)\n",
    "transformed_vectors['count_vec_transformed'] = count_vec_transformed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<3500x14125 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 184397 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Taking a look at our transformed data - a sparse matrix \n",
    "count_vec_transformed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Bag of Words - bigrams\n",
    "\n",
    "count_vec_bigrams = CountVectorizer(min_df = 2, max_df = .98, stop_words=\"english\", ngram_range=(1,2))\n",
    "count_vec_bigrams.fit(sliced_df)\n",
    "count_vec_bigrams_transformed = count_vec_bigrams.transform(sliced_df)\n",
    "transformed_vectors['count_vec_bigrams_transformed'] = count_vec_bigrams_transformed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Bag of Words - trigrams\n",
    "\n",
    "count_vec_trigrams = CountVectorizer(min_df = 2, max_df = .98, stop_words=\"english\", ngram_range=(1,3))\n",
    "count_vec_trigrams.fit(sliced_df)\n",
    "count_vec_trigrams_transformed = count_vec_trigrams.transform(sliced_df)\n",
    "transformed_vectors['count_vec_trigrams_transformed'] = count_vec_trigrams_transformed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#TFIDF without n-grams\n",
    "\n",
    "tfidf_vec = TfidfVectorizer(min_df = 2, max_df = .98, stop_words='english')\n",
    "tfidf_vec.fit(sliced_df)\n",
    "tfidf_vec_transformed = tfidf_vec.transform(sliced_df)\n",
    "transformed_vectors['tfidf_vec_transformed'] = tfidf_vec_transformed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#TFIDF - bigrams\n",
    "\n",
    "tfidf_vec_bigrams = TfidfVectorizer(min_df = 2, max_df = .98, stop_words='english', ngram_range=(1,2))\n",
    "tfidf_vec_bigrams.fit(sliced_df)\n",
    "tfidf_vec_bigrams_transformed = tfidf_vec.transform(sliced_df)\n",
    "transformed_vectors['tfidf_vec_bigrams_transformed'] = tfidf_vec_bigrams_transformed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#TFIDF - trigrams\n",
    "\n",
    "tfidf_vec_trigrams = TfidfVectorizer(min_df = 2, max_df = .98, stop_words='english', ngram_range=(1,3))\n",
    "tfidf_vec_trigrams.fit(sliced_df)\n",
    "tfidf_vec_trigrams_transformed = tfidf_vec.transform(sliced_df)\n",
    "transformed_vectors['tfidf_vec_trigrams_transformed'] = tfidf_vec_trigrams_transformed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "transformed_vectors_all = transformed_vectors\n",
    "\n",
    "# trigrams didn't add much to our accuracy and added a lot of computational power / time, so we deleted them\n",
    "# but obviously they could also be left in, and other matrices could be added. \n",
    "\n",
    "del transformed_vectors['tfidf_vec_trigrams_transformed']\n",
    "del transformed_vectors['count_vec_trigrams_transformed']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<3500x14125 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 184397 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfidf_vec_bigrams_transformed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pipeline Implementation - Classifiers for Author & Period"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Functions \n",
    "\n",
    "def test_classifiers(transformed_vectors, grid, labels):\n",
    "    results_dict = {}\n",
    "    \n",
    "    for named_matrix, sparse_matrix in transformed_vectors.items():\n",
    "        X_train, X_test, y_train, y_test = train_test_split(sparse_matrix, labels)\n",
    "        grid.fit(X_train, y_train)\n",
    "        results_dict[named_matrix + ' results'] = [grid.best_score_, grid.best_params_, grid.score(X_train, y_train), grid.score(X_test, y_test)]\n",
    "    \n",
    "    return results_dict\n",
    "\n",
    "def run_multi_naive_bayes(transformed_vectors, labels):\n",
    "    results_dict = {}\n",
    "    \n",
    "    mnb_clf = MultinomialNB()\n",
    "    \n",
    "    for named_matrix, sparse_matrix in transformed_vectors.items():\n",
    "        X_train, X_test, y_train, y_test = train_test_split(sparse_matrix, labels)\n",
    "        mnb_clf.fit(X_train, y_train)\n",
    "        mnb_clf_train_preds = mnb_clf.predict(X_train)\n",
    "        mnb_clf_test_preds = mnb_clf.predict(X_test)\n",
    "        mnb_clf_train_accuracy = accuracy_score(y_train, mnb_clf_train_preds)\n",
    "        mnb_clf_test_accuracy = accuracy_score(y_test, mnb_clf_test_preds)\n",
    "        results_dict[named_matrix + ' results'] = [mnb_clf_train_accuracy, mnb_clf_test_accuracy]\n",
    "        \n",
    "    return results_dict\n",
    "\n",
    "def run_bern_naive_bayes(transformed_vectors, labels):\n",
    "    results_dict = {}\n",
    "    \n",
    "    bernoulli_clf = BernoulliNB()\n",
    "    \n",
    "    for named_matrix, sparse_matrix in transformed_vectors.items():\n",
    "        X_train, X_test, y_train, y_test = train_test_split(sparse_matrix, labels)\n",
    "        bernoulli_clf.fit(X_train, y_train)\n",
    "        bernoulli_train_preds = bernoulli_clf.predict(X_train)\n",
    "        bernoulli_test_preds = bernoulli_clf.predict(X_test)\n",
    "        bernoulli_train_accuracy = accuracy_score(y_train, bernoulli_train_preds)\n",
    "        bernoulli_test_accuracy = accuracy_score(y_test, bernoulli_test_preds)\n",
    "        results_dict[named_matrix + ' results'] = [bernoulli_train_accuracy, bernoulli_test_accuracy]\n",
    "        \n",
    "    return results_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Naive Bayes\n",
    "\n",
    "Ran multiple versions. All testing was classification on sex (binary). Multi-categorical classification (period and author) was done with ensemble methdods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import BernoulliNB, MultinomialNB"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### NB Function Calls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "mulitnomial_nb_results = run_multi_naive_bayes(transformed_vectors, all_labels['sex'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "bernoulli_nb_results = run_bern_naive_bayes(transformed_vectors, all_labels['sex'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# RandomForestClassifier??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_param_grid = {'randomforestclassifier__n_estimators': [150, 250, 350], \n",
    "                'randomforestclassifier__max_depth': [5, 10, 15]}\n",
    "\n",
    "rf_pipe = make_pipeline(RandomForestClassifier())\n",
    "\n",
    "rf_grid = GridSearchCV(rf_pipe, rf_param_grid, cv = 5, scoring = \"accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4min 27s, sys: 1.58 s, total: 4min 28s\n",
      "Wall time: 4min 33s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "rf_author_results = test_classifiers(transformed_vectors, rf_grid, all_labels['author'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4min 18s, sys: 1.13 s, total: 4min 19s\n",
      "Wall time: 4min 22s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "rf_period_results = test_classifiers(transformed_vectors, rf_grid, all_labels['period'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4min 9s, sys: 1.88 s, total: 4min 11s\n",
      "Wall time: 4min 15s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "rf_sex_results = test_classifiers(transformed_vectors, rf_grid, all_labels['sex'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adaboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "ada = AdaBoostClassifier()\n",
    "\n",
    "ada_param_grid = {'adaboostclassifier__n_estimators': [50, 150, 250], \n",
    "                 'adaboostclassifier__learning_rate': [0.0001, 0.001, 0.01, 0.1, 1]}\n",
    "\n",
    "ada_pipe = make_pipeline(ada)\n",
    "\n",
    "ada_grid = GridSearchCV(ada_pipe, ada_param_grid, cv = 5, scoring = \"accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3 µs, sys: 1 µs, total: 4 µs\n",
      "Wall time: 7.87 µs\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "ada_author_results = test_classifiers(transformed_vectors, ada_grid, all_labels['author'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4 µs, sys: 1e+03 ns, total: 5 µs\n",
      "Wall time: 16 µs\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "ada_period_results = test_classifiers(transformed_vectors, ada_grid, all_labels['period'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3 µs, sys: 1e+03 ns, total: 4 µs\n",
      "Wall time: 6.91 µs\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "ada_sex_results = test_classifiers(transformed_vectors, ada_grid, all_labels['sex'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K-Neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_neighbors = 100\n",
    "\n",
    "scores = []\n",
    "for weight in ['uniform', 'distance']:\n",
    "    clf = neighbors.KNeighborsClassifier(n_neighbors, weights=weight)\n",
    "    clf.fit(X_train_author, y_train_author)\n",
    "    y_hat = clf.score(X_train_author, y_train_author)\n",
    "    scores.append(y_hat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.5264017419706043, 1.0]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Examining Results "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'count_vec_bigrams_transformed results': [0.9935238095238095,\n",
       "  0.8754285714285714],\n",
       " 'count_vec_transformed results': [0.9836190476190476, 0.8845714285714286],\n",
       " 'tfidf_vec_bigrams_transformed results': [0.9767619047619047,\n",
       "  0.8674285714285714],\n",
       " 'tfidf_vec_transformed results': [0.976, 0.8708571428571429]}"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mulitnomial_nb_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'count_vec_bigrams_transformed results': [0.9969523809523809, 0.904],\n",
       " 'count_vec_transformed results': [0.9870476190476191, 0.8914285714285715],\n",
       " 'tfidf_vec_bigrams_transformed results': [0.9897142857142858,\n",
       "  0.8765714285714286],\n",
       " 'tfidf_vec_transformed results': [0.9900952380952381, 0.864]}"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bernoulli_nb_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'count_vec_bigrams_transformed results': [0.7687619047619048,\n",
       "  {'adaboostclassifier__learning_rate': 1,\n",
       "   'adaboostclassifier__n_estimators': 250},\n",
       "  0.7794285714285715],\n",
       " 'count_vec_transformed results': [0.7828571428571428,\n",
       "  {'adaboostclassifier__learning_rate': 1,\n",
       "   'adaboostclassifier__n_estimators': 250},\n",
       "  0.7508571428571429],\n",
       " 'tfidf_vec_bigrams_transformed results': [0.7390476190476191,\n",
       "  {'adaboostclassifier__learning_rate': 1,\n",
       "   'adaboostclassifier__n_estimators': 250},\n",
       "  0.7828571428571428],\n",
       " 'tfidf_vec_transformed results': [0.7573333333333333,\n",
       "  {'adaboostclassifier__learning_rate': 1,\n",
       "   'adaboostclassifier__n_estimators': 250},\n",
       "  0.7805714285714286]}"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ada_sex_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'count_vec_bigrams_transformed results': [0.43923809523809526,\n",
       "  {'adaboostclassifier__learning_rate': 0.1,\n",
       "   'adaboostclassifier__n_estimators': 150},\n",
       "  0.4297142857142857],\n",
       " 'count_vec_transformed results': [0.43047619047619046,\n",
       "  {'adaboostclassifier__learning_rate': 0.1,\n",
       "   'adaboostclassifier__n_estimators': 250},\n",
       "  0.45371428571428574],\n",
       " 'tfidf_vec_bigrams_transformed results': [0.42438095238095236,\n",
       "  {'adaboostclassifier__learning_rate': 0.1,\n",
       "   'adaboostclassifier__n_estimators': 150},\n",
       "  0.424],\n",
       " 'tfidf_vec_transformed results': [0.41409523809523807,\n",
       "  {'adaboostclassifier__learning_rate': 0.1,\n",
       "   'adaboostclassifier__n_estimators': 150},\n",
       "  0.4297142857142857]}"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ada_period_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'count_vec_bigrams_transformed results': [0.3264761904761905,\n",
       "  {'adaboostclassifier__learning_rate': 0.1,\n",
       "   'adaboostclassifier__n_estimators': 250},\n",
       "  0.3291428571428571],\n",
       " 'count_vec_transformed results': [0.3139047619047619,\n",
       "  {'adaboostclassifier__learning_rate': 0.01,\n",
       "   'adaboostclassifier__n_estimators': 250},\n",
       "  0.3017142857142857],\n",
       " 'tfidf_vec_bigrams_transformed results': [0.33485714285714285,\n",
       "  {'adaboostclassifier__learning_rate': 0.01,\n",
       "   'adaboostclassifier__n_estimators': 250},\n",
       "  0.32],\n",
       " 'tfidf_vec_transformed results': [0.3222857142857143,\n",
       "  {'adaboostclassifier__learning_rate': 0.01,\n",
       "   'adaboostclassifier__n_estimators': 250},\n",
       "  0.3062857142857143]}"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ada_author_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'count_vec_bigrams_transformed results': [0.8053333333333333,\n",
       "  {'randomforestclassifier__max_depth': 15,\n",
       "   'randomforestclassifier__n_estimators': 350},\n",
       "  0.8125714285714286],\n",
       " 'count_vec_transformed results': [0.8091428571428572,\n",
       "  {'randomforestclassifier__max_depth': 15,\n",
       "   'randomforestclassifier__n_estimators': 350},\n",
       "  0.7862857142857143],\n",
       " 'tfidf_vec_bigrams_transformed results': [0.772952380952381,\n",
       "  {'randomforestclassifier__max_depth': 15,\n",
       "   'randomforestclassifier__n_estimators': 250},\n",
       "  0.7977142857142857],\n",
       " 'tfidf_vec_transformed results': [0.7782857142857142,\n",
       "  {'randomforestclassifier__max_depth': 15,\n",
       "   'randomforestclassifier__n_estimators': 250},\n",
       "  0.7611428571428571]}"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_sex_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'count_vec_bigrams_transformed results': [0.6346666666666667,\n",
       "  {'randomforestclassifier__max_depth': 15,\n",
       "   'randomforestclassifier__n_estimators': 350},\n",
       "  0.6525714285714286],\n",
       " 'count_vec_transformed results': [0.6209523809523809,\n",
       "  {'randomforestclassifier__max_depth': 15,\n",
       "   'randomforestclassifier__n_estimators': 250},\n",
       "  0.64],\n",
       " 'tfidf_vec_bigrams_transformed results': [0.6076190476190476,\n",
       "  {'randomforestclassifier__max_depth': 15,\n",
       "   'randomforestclassifier__n_estimators': 350},\n",
       "  0.5897142857142857],\n",
       " 'tfidf_vec_transformed results': [0.6102857142857143,\n",
       "  {'randomforestclassifier__max_depth': 15,\n",
       "   'randomforestclassifier__n_estimators': 350},\n",
       "  0.6137142857142858]}"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_period_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'count_vec_bigrams_transformed results': [0.5874285714285714,\n",
       "  {'randomforestclassifier__max_depth': 15,\n",
       "   'randomforestclassifier__n_estimators': 250},\n",
       "  0.6034285714285714],\n",
       " 'count_vec_transformed results': [0.5805714285714285,\n",
       "  {'randomforestclassifier__max_depth': 15,\n",
       "   'randomforestclassifier__n_estimators': 350},\n",
       "  0.5737142857142857],\n",
       " 'tfidf_vec_bigrams_transformed results': [0.5691428571428572,\n",
       "  {'randomforestclassifier__max_depth': 15,\n",
       "   'randomforestclassifier__n_estimators': 250},\n",
       "  0.5828571428571429],\n",
       " 'tfidf_vec_transformed results': [0.5748571428571428,\n",
       "  {'randomforestclassifier__max_depth': 15,\n",
       "   'randomforestclassifier__n_estimators': 350},\n",
       "  0.5988571428571429]}"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_author_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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