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   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Click-Through Rate Prediction - AVAZU Kaggle Competition"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This is our ipython notebook which details our work in building a classifier on the avazu click through rate prediction."
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "We start by loading the usual libraries and define functions for improved plots."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "import pandas as pd\n",
      "import brewer2mpl\n",
      "from matplotlib import rcParams\n",
      "\n",
      "#colorbrewer2 Dark2 qualitative color table\n",
      "dark2_cmap = brewer2mpl.get_map('Dark2', 'Qualitative', 7)\n",
      "dark2_colors = dark2_cmap.mpl_colors\n",
      "\n",
      "rcParams['figure.figsize'] = (10, 6)\n",
      "rcParams['figure.dpi'] = 150\n",
      "rcParams['axes.color_cycle'] = dark2_colors\n",
      "rcParams['lines.linewidth'] = 2\n",
      "rcParams['axes.facecolor'] = 'white'\n",
      "rcParams['font.size'] = 14\n",
      "rcParams['patch.edgecolor'] = 'white'\n",
      "rcParams['patch.facecolor'] = dark2_colors[0]\n",
      "rcParams['font.family'] = 'StixGeneral'\n",
      "\n",
      "\n",
      "def remove_border(axes=None, top=False, right=False, left=True, bottom=True):\n",
      "    \"\"\"\n",
      "    Minimize chartjunk by stripping out unnecesasry plot borders and axis ticks\n",
      "    \n",
      "    The top/right/left/bottom keywords toggle whether the corresponding plot border is drawn\n",
      "    \"\"\"\n",
      "    ax = axes or plt.gca()\n",
      "    ax.spines['top'].set_visible(top)\n",
      "    ax.spines['right'].set_visible(right)\n",
      "    ax.spines['left'].set_visible(left)\n",
      "    ax.spines['bottom'].set_visible(bottom)\n",
      "    \n",
      "    #turn off all ticks\n",
      "    ax.yaxis.set_ticks_position('none')\n",
      "    ax.xaxis.set_ticks_position('none')\n",
      "    \n",
      "    #now re-enable visibles\n",
      "    if top:\n",
      "        ax.xaxis.tick_top()\n",
      "    if bottom:\n",
      "        ax.xaxis.tick_bottom()\n",
      "    if left:\n",
      "        ax.yaxis.tick_left()\n",
      "    if right:\n",
      "        ax.yaxis.tick_right()   "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 31
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "1. Load the training data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import csv\n",
      "csv_file_object = csv.reader(open(r\"train_rev2\",\"rb\"))\n",
      "header = csv_file_object.next()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "At this stage, we realised the size of the dataset (9Gb) was prohibitive to loading and manipulating into ipython notebook. Therefore we decided to explore a small sample of the dataset to try and understand the feature space."
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "We load consecutive 1000 rows of the dataset."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This allows us to subsequently plot the growth in feature space for each categorical variable as we are intending to use a boolean 0-1 hot encoding. We can therefore observe whether the features for that particular category grow with the number of examples of whether they tail off; this will help us determine whether that particular category is a useful feature to use in the model. (see Vowpal Wabbit section below)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "results = []\n",
      "results2 = []\n",
      "results3 = []\n",
      "results4 = []\n",
      "results5 = []\n",
      "counter = 0\n",
      "path  = \"train_rev2\"\n",
      "with open(path, \"r\") as data:\n",
      "    #Count the number of lines: 47686352\n",
      "#     for i, l in enumerate(data):\n",
      "#         pass\n",
      "#     print i + 1\n",
      "#     header = data.readline()\n",
      "    for line in data:\n",
      "        counter += 1\n",
      "        line = line.strip(\"\\n\")\n",
      "        line = line.strip()\n",
      "        if counter <= 1000:\n",
      "            results.append(line.split(\",\"))\n",
      "        if counter >= 1000 and counter <= 2000:\n",
      "            results2.append(line.split(\",\"))\n",
      "        if counter >= 2000 and counter <= 3000:\n",
      "            results3.append(line.split(\",\"))\n",
      "        if counter >= 3000 and counter <= 4000:\n",
      "            results4.append(line.split(\",\"))\n",
      "        if counter >= 4000 and counter <= 5000:\n",
      "            results5.append(line.split(\",\"))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We then load the first 1000 datapoints into a pandas dataframe to use as training set."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "testing  = pd.DataFrame(data=np.asarray(results[1:]), columns=header)\n",
      "testing.click = testing.click.astype(int)\n",
      "print testing.irow(0)\n",
      "testing.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "id                    10000222510487979663\n",
        "click                                    0\n",
        "hour                              14100100\n",
        "C1                                    1005\n",
        "banner_pos                               0\n",
        "site_id                           d41d8cd9\n",
        "site_domain                       d41d8cd9\n",
        "site_category                     d41d8cd9\n",
        "app_id                            ee72efa5\n",
        "app_domain                        85262c2b\n",
        "app_category                      7e5068fc\n",
        "device_id                         d41d8cd9\n",
        "device_ip                         22f9c6ba\n",
        "device_os                         c31b3236\n",
        "device_make                       3d517f89\n",
        "device_model                      3e238c9b\n",
        "device_type                              1\n",
        "device_conn_type                         0\n",
        "device_geo_country                fc9fdf08\n",
        "C17                                  11999\n",
        "C18                                    320\n",
        "C19                                     50\n",
        "C20                                   1248\n",
        "C21                                      2\n",
        "C22                                     39\n",
        "C23                                     -1\n",
        "C24                                     13\n",
        "Name: 0, dtype: object\n"
       ]
      },
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        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>id</th>\n",
        "      <th>click</th>\n",
        "      <th>hour</th>\n",
        "      <th>C1</th>\n",
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        "      <th>site_id</th>\n",
        "      <th>site_domain</th>\n",
        "      <th>site_category</th>\n",
        "      <th>app_id</th>\n",
        "      <th>app_domain</th>\n",
        "      <th>...</th>\n",
        "      <th>device_conn_type</th>\n",
        "      <th>device_geo_country</th>\n",
        "      <th>C17</th>\n",
        "      <th>C18</th>\n",
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        "      <th>C21</th>\n",
        "      <th>C22</th>\n",
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        "      <th>C24</th>\n",
        "    </tr>\n",
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        "      <td>  39</td>\n",
        "      <td>     -1</td>\n",
        "      <td> 13</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
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        "      <td> 0</td>\n",
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        "    <tr>\n",
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        "    <tr>\n",
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        "</table>\n",
        "<p>5 rows \u00d7 27 columns</p>\n",
        "</div>"
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       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "                     id  click      hour    C1 banner_pos   site_id  \\\n",
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        "4  10000488446663934007      1  14100100  1005          0  d41d8cd9   \n",
        "\n",
        "  site_domain site_category    app_id app_domain    ...     device_conn_type  \\\n",
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        "4    d41d8cd9      d41d8cd9  aa55fc10   85262c2b    ...                    2   \n",
        "\n",
        "  device_geo_country    C17  C18 C19   C20 C21  C22     C23 C24  \n",
        "0           fc9fdf08  11999  320  50  1248   2   39      -1  13  \n",
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     ],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "2. Data Processing"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "At this point, we choose to convert the time into a numerical variable from the categorical format that it is currently in. (hour: format is YYMMDDHH)\n",
      "We add this new column to the dataframe under the name \"timestamp\"."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "NB:, at a later stage we realised that all training examples came from the same month and year but vary according to hour and day of the week. Therefore for our feature selection we actually split the hour data into two separate categories, namely day of week and time of day instead."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from datetime import datetime, date, time\n",
      "testing['timestamp'] = testing['hour'].map(lambda x: int(datetime.strptime(x[4:6] + \"/\" + x[2:4] + \"/\" + x[0:2] + \" \" + x[6:8], \"%d/%m/%y %H\").strftime(\"%s\")))\n",
      "testing.tail()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
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        "      <td> 1005</td>\n",
        "      <td> 0</td>\n",
        "      <td> d41d8cd9</td>\n",
        "      <td> d41d8cd9</td>\n",
        "      <td> d41d8cd9</td>\n",
        "      <td> ca1502d1</td>\n",
        "      <td> 96b73ddc</td>\n",
        "      <td>...</td>\n",
        "      <td> 0d149b90</td>\n",
        "      <td> 16839</td>\n",
        "      <td> 320</td>\n",
        "      <td> 50</td>\n",
        "      <td> 1883</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1451</td>\n",
        "      <td> 100094</td>\n",
        "      <td> 17</td>\n",
        "      <td> 1412096400</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 28 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "                       id  click      hour    C1 banner_pos   site_id  \\\n",
        "994  10085753362715434105      0  14100100  1005          1  b01fd8c0   \n",
        "995  10085762866609622098      0  14100100  1005          0  d41d8cd9   \n",
        "996  10085798392965113138      0  14100100  1005          0  d41d8cd9   \n",
        "997  10085816395998880696      1  14100100  1005          0  d41d8cd9   \n",
        "998  10085854709161323225      0  14100100  1005          0  d41d8cd9   \n",
        "\n",
        "    site_domain site_category    app_id app_domain    ...      \\\n",
        "994    a56a5285      7e5068fc  d41d8cd9   d41d8cd9    ...       \n",
        "995    d41d8cd9      d41d8cd9  ca1502d1   96b73ddc    ...       \n",
        "996    d41d8cd9      d41d8cd9  ca1502d1   96b73ddc    ...       \n",
        "997    d41d8cd9      d41d8cd9  8374fbd9   4f04e5f8    ...       \n",
        "998    d41d8cd9      d41d8cd9  ca1502d1   96b73ddc    ...       \n",
        "\n",
        "    device_geo_country    C17  C18 C19   C20 C21   C22     C23 C24   timestamp  \n",
        "994           959848ca  14915  320  50  1623   3   175  100156  42  1412096400  \n",
        "995           3d26b0b1  16966  320  50  1919   0   169  100108  17  1412096400  \n",
        "996           3d26b0b1  16966  320  50  1919   0   169  100108  17  1412096400  \n",
        "997           e529a9ce  16615  320  50  1863   3    39  100248  23  1412096400  \n",
        "998           0d149b90  16839  320  50  1883   0  1451  100094  17  1412096400  \n",
        "\n",
        "[5 rows x 28 columns]"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We convert the categorical variables into indicator variables to use in the classifier algorithm using the get_dummies method. We will not require the index column, nor the click or hour columns."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trial_text = header[3:-1]\n",
      "df = pd.DataFrame()\n",
      "\n",
      "for elem in trial_text:\n",
      "    interim = pd.get_dummies(testing[elem])\n",
      "    if \"d41d8cd9\" in interim.columns.values:\n",
      "        interim.drop(\"d41d8cd9\", axis=1, inplace=True)\n",
      "    interim.rename(columns=lambda x: elem + \"_\" + x, inplace=True)\n",
      "    df = df.join(interim)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We populate the dataframe with a 0-1 hot encoding for each categorical column. The cell below adds the 1s."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for ix, value in enumerate(testing.ix[:,3:-1].values):\n",
      "    mydict = {}\n",
      "    indy = []\n",
      "    indy.append(ix)\n",
      "    for j, title in enumerate(value):\n",
      "        mydict[header[j+3] + \"_\" + title] = 1\n",
      "    df2 = pd.DataFrame(mydict, index = indy)\n",
      "    df = df.append(df2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This cell adds the 0s and the quantitative timestamps to the data_frame"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.fillna(0, inplace=True)\n",
      "\n",
      "df['timestamp'] = testing['timestamp']\n",
      "print df.shape\n",
      "df.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(999, 2486)\n"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>C17_10198</th>\n",
        "      <th>C17_10199</th>\n",
        "      <th>C17_10200</th>\n",
        "      <th>C17_10229</th>\n",
        "      <th>C17_10289</th>\n",
        "      <th>C17_1037</th>\n",
        "      <th>C17_1039</th>\n",
        "      <th>C17_10704</th>\n",
        "      <th>C17_10901</th>\n",
        "      <th>C17_10941</th>\n",
        "      <th>...</th>\n",
        "      <th>site_id_f3c495d0</th>\n",
        "      <th>site_id_f440e761</th>\n",
        "      <th>site_id_f4e01d44</th>\n",
        "      <th>site_id_f701c177</th>\n",
        "      <th>site_id_f91a85b6</th>\n",
        "      <th>site_id_fb5ff023</th>\n",
        "      <th>site_id_fe1972d4</th>\n",
        "      <th>site_id_ff2e3304</th>\n",
        "      <th>site_id_ffa60702</th>\n",
        "      <th>timestamp</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 0</td>\n",
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        "      <td> 1412096400</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
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        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
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        "      <td> 0</td>\n",
        "      <td> 1412096400</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
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        "      <td> 0</td>\n",
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        "      <td> 0</td>\n",
        "      <td> 1412096400</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
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        "      <td>...</td>\n",
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        "      <td> 0</td>\n",
        "      <td> 1412096400</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
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        "      <td> 1412096400</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 2486 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "   C17_10198  C17_10199  C17_10200  C17_10229  C17_10289  C17_1037  C17_1039  \\\n",
        "0          0          0          0          0          0         0         0   \n",
        "1          0          0          0          0          0         0         0   \n",
        "2          0          0          0          0          0         0         0   \n",
        "3          0          0          0          0          0         0         0   \n",
        "4          0          0          0          0          0         0         0   \n",
        "\n",
        "   C17_10704  C17_10901  C17_10941    ...      site_id_f3c495d0  \\\n",
        "0          0          0          0    ...                     0   \n",
        "1          0          0          0    ...                     0   \n",
        "2          0          0          0    ...                     0   \n",
        "3          0          0          0    ...                     0   \n",
        "4          0          0          0    ...                     0   \n",
        "\n",
        "   site_id_f440e761  site_id_f4e01d44  site_id_f701c177  site_id_f91a85b6  \\\n",
        "0                 0                 0                 0                 0   \n",
        "1                 0                 0                 0                 0   \n",
        "2                 0                 0                 0                 0   \n",
        "3                 0                 0                 0                 0   \n",
        "4                 0                 0                 0                 0   \n",
        "\n",
        "   site_id_fb5ff023  site_id_fe1972d4  site_id_ff2e3304  site_id_ffa60702  \\\n",
        "0                 0                 0                 0                 0   \n",
        "1                 0                 0                 0                 0   \n",
        "2                 0                 0                 0                 0   \n",
        "3                 0                 0                 0                 0   \n",
        "4                 0                 0                 0                 0   \n",
        "\n",
        "    timestamp  \n",
        "0  1412096400  \n",
        "1  1412096400  \n",
        "2  1412096400  \n",
        "3  1412096400  \n",
        "4  1412096400  \n",
        "\n",
        "[5 rows x 2486 columns]"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "3. Data Exploration"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We plot the different variables to explore the significance of the features and their relation to leading to a click event. Deployed in blue is a simple sequential bar chart of all the unique features in each of the different categories; in red, the percentage of that category who clicked, as a line chart."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Samping the scatter plot functionality\n",
      "fig, axes=plt.subplots(figsize=(20, 15), nrows=6, ncols=5)\n",
      "fig.subplots_adjust(hspace = 0.8)\n",
      "fig.subplots_adjust(wspace = 0.8)\n",
      "bar_width = 1.\n",
      "\n",
      "for ix in np.arange(6):\n",
      "    for ix2 in np.arange(5):\n",
      "        if (ix*5 + ix2) < len(header):\n",
      "            count_data = testing.groupby(header[ix*5 + ix2]).click.count() #Count plot of clicks\n",
      "            sum_data = testing.groupby(header[ix*5 + ix2]).click.sum() #Sum plot of clicks\n",
      "            percentage_data = sum_data / count_data\n",
      "            index = np.arange(len(count_data))\n",
      "            axes[ix][ix2].bar(index, count_data, bar_width, color='b', label='category count')\n",
      "            axes[ix][ix2].set_ylabel(\"Category count\")\n",
      "            axes[ix][ix2].set_xlabel(\"Sequential Categories\")\n",
      "            axes[ix][ix2].set_title(header[ix*5 + ix2])\n",
      "            remove_border(axes[ix][ix2], top=False, right=True, left=True, bottom=True)\n",
      "            secondAxis = axes[ix][ix2].twinx()\n",
      "            secondAxis.plot(index + bar_width / 2., percentage_data, color='r', label='percentage from category who clicked')\n",
      "            secondAxis.set_ylabel(\"Percentage Click\")\n",
      "        else:\n",
      "            fig.delaxes(axes[ix][ix2])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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0WA54CPXT/w7s22FNQ5vvcyuFnsKNh0MLP+Q6+jKXnbifh9iRbZjG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s5FuVD8xJkm9C2KxrN+yPp4NpqqdBhwOPACcA3gppbfCLkFBnXO42EGtOxprxH2J9CU1G+iRJ5/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Ah5O9pr+Ht/EMdPM6DO+OAOSKOrLYHxGCsJrui24CzkO5G45BnWF//POqdPdYl86AmaSKDWgB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gRlEJJ3ZR3EDfV4pDpJDyQh7CdI6dPrjH0lj2JeKLQBTQjWF5Wc2PpERiUnnoYI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a4mJ6MDAO91CXMNQiVX2OQHIrgM5q+3PTTfDZZ7DZZnBWZVTtWcwqXMjVdOd13qAbG/AN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\nsSc4/rjcj7DyvP3IP577Q+wC1c/65J4QNRvSdCW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sSNOiWBSq6TosL9K5mBfX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\nRK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x111677450>"
       ]
      }
     ],
     "prompt_number": 70
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "From the above plots, we can get an overview of the different categories and their variability as well as how they influence clicking.\n",
      "For example, we can observe that in category C1, type \"0\", although not many examples are present, those available seem to consistently lead to a click event suggesting that the feature C1-0 might be a good predictor of clicking."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "4. Model Fitting in scikit-learn (first experimentation)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We import the required libraries to experiment with the Naive Bayes and Logistic Regression classifiers."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.cross_validation import train_test_split\n",
      "from sklearn.naive_bayes import MultinomialNB\n",
      "from sklearn.linear_model import LogisticRegression"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We define the supervised learning vector y."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y = testing.click"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We split the data into a training and test set so that we can validate our model."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "xtrain, xtest, ytrain, ytest = train_test_split(df, y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "4.1 Naive Bayes"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We build a Naive Bayes model and fit it to our training set."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf = MultinomialNB().fit(xtrain, ytrain)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print \"Training accuracy: %0.2f%%\" % (100 * clf.score(xtrain, ytrain))\n",
      "print \"Test accuracy: %0.2f%%\" % (100 * clf.score(xtest, ytest))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Training accuracy: 92.26%\n",
        "Test accuracy: 90.40%\n"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "prob = clf.predict_proba(xtest)\n",
      "plt.hist(prob[:,1])\n",
      "remove_border()\n",
      "plt.ylabel(\"Count\")\n",
      "plt.xlabel(\"Probability\")\n",
      "plt.title(\"Naive Bayes Probability Histogram\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 48,
       "text": [
        "<matplotlib.text.Text at 0x11e2a5710>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ScjqAu4EDs8dTgUeBjQqrSJIkqQXaoeuzixiLdiZxmo69gMOBNUUWNQSNAUbR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\n1uEkAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x113241890>"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The model shows a high level accuracy which is misleading because due to the low number of examples corresponding to a click, even if you were to always predict no click, you would perform to a high level of accuracy, only misclassifying those few examples where a click actually had occurred.\n",
      "Also our model suffers from overfitting due to the large number of features and in our case the low number of training examples because we could only upload 1000 examples at a time."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "4.2 Logistic Regression"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We now experiment with fitting a logistic regression model, this does not suffer from the same limitations as the naive bayes with regards to independance of features. However we would expect this to suffer from similar over-fitting problems"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf2 = LogisticRegression().fit(xtrain, ytrain)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print \"Training accuracy: %0.2f%%\" % (100 * clf2.score(xtrain, ytrain))\n",
      "print \"Test accuracy: %0.2f%%\" % (100 * clf2.score(xtest, ytest))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Training accuracy: 92.26%\n",
        "Test accuracy: 90.40%\n"
       ]
      }
     ],
     "prompt_number": 83
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "prob = clf2.predict_proba(xtest)\n",
      "plt.hist(prob[:,1])\n",
      "remove_border()\n",
      "plt.ylabel(\"Count\")\n",
      "plt.xlabel(\"Probability\")\n",
      "plt.title(\"Logistic Regression Probability Histogram\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 50,
       "text": [
        "<matplotlib.text.Text at 0x116120690>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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DnCRJUqYMcpIkSZkyyEmSJGXKICdJkpQpg5wkSVKmDHKSJEmZMshJkiRlyiAn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\nSZIkSZIkSZJa6P8DH7YjRLfj2tAAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x114057850>"
       ]
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This model suffers from the same problems as the Naive Bayes above, the model is over-fit and training on a much larger dataset is required"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "5. Visualise the Feature Space"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In order to better understand the feature space and potentially reduce it, or remove problems with the data, we aim to visualise the features. \n",
      "\n",
      "- The plot below attempts to look at the growth of the feature space as training data is added. This allows us to see how the feature space grows as the dataset increases and suggests categories we may want to remove."
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Features Scaling Plot"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In order to check how the feature space grows, we plot a progressive bar chart of the cumulative number of unique category variables on adding 1000 additional data rows at a time. If this plot grows linearly, then we can assume there is not much generalisable information in this category and potentially remove it from our feature space."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "testing2  = pd.DataFrame(data=np.asarray(results2[:]), columns=header)\n",
      "testing2.click = testing2.click.astype(int)\n",
      "testing2['timestamp'] = testing2['hour'].map(lambda x: int(datetime.strptime(x[4:6] + \"/\" + x[2:4] + \"/\" + x[0:2] + \" \" + x[6:8], \"%d/%m/%y %H\").strftime(\"%s\")))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 51
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, axes=plt.subplots(figsize=(20,15), nrows=6, ncols=5)\n",
      "fig.subplots_adjust(hspace = 0.8)\n",
      "fig.subplots_adjust(wspace = 0.6)\n",
      "\n",
      "for ix in np.arange(6):\n",
      "    for ix2 in np.arange(5):\n",
      "        if (ix*5 + ix2) < len(header):\n",
      "            first_count = testing[header[ix*5 + ix2]].unique()\n",
      "            first_count_len = len(first_count)\n",
      "            first_count_set = set(first_count)\n",
      "            second_count_set = set(testing2[header[ix*5 + ix2]].unique())\n",
      "            second_count_len = len(second_count_set.difference(first_count_set))\n",
      "            index = np.arange(2)\n",
      "            axes[ix][ix2].bar(index, [first_count_len, first_count_len+second_count_len], color='b', label='count')\n",
      "            axes[ix][ix2].set_title(header[ix*5 + ix2])\n",
      "            axes[ix][ix2].set_ylabel(\"Count\")\n",
      "            axes[ix][ix2].set_xlabel(\"Index\")\n",
      "            remove_border(axes[ix][ix2])\n",
      "        else:\n",
      "            fig.delaxes(axes[ix][ix2])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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TxxPEiKk/Zl47EvnNU8PJY3IyZ1SZBvZKlF0XryA6mS5tcMwGwOnAK4HnA3fn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\nXEb+D3pNX548oEUpAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1183b3110>"
       ]
      }
     ],
     "prompt_number": 73
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We now do this a few more times for feature exploring purposes."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "testing3  = pd.DataFrame(data=np.asarray(results3[:]), columns=header)\n",
      "testing3.click = testing3.click.astype(int)\n",
      "testing3['timestamp'] = testing3['hour'].map(lambda x: int(datetime.strptime(x[4:6] + \"/\" + x[2:4] + \"/\" + x[0:2] + \" \" + x[6:8], \"%d/%m/%y %H\").strftime(\"%s\")))\n",
      "\n",
      "testing4  = pd.DataFrame(data=np.asarray(results4[:]), columns=header)\n",
      "testing4.click = testing4.click.astype(int)\n",
      "testing4['timestamp'] = testing4['hour'].map(lambda x: int(datetime.strptime(x[4:6] + \"/\" + x[2:4] + \"/\" + x[0:2] + \" \" + x[6:8], \"%d/%m/%y %H\").strftime(\"%s\")))\n",
      "\n",
      "testing5  = pd.DataFrame(data=np.asarray(results5[:]), columns=header)\n",
      "testing5.click = testing5.click.astype(int)\n",
      "testing5['timestamp'] = testing5['hour'].map(lambda x: int(datetime.strptime(x[4:6] + \"/\" + x[2:4] + \"/\" + x[0:2] + \" \" + x[6:8], \"%d/%m/%y %H\").strftime(\"%s\")))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 71
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, axes=plt.subplots(figsize=(20,15), nrows=6, ncols=5)\n",
      "fig.subplots_adjust(hspace = 0.8)\n",
      "fig.subplots_adjust(wspace = 0.6)\n",
      "\n",
      "for ix in np.arange(6):\n",
      "    for ix2 in np.arange(5):\n",
      "        if (ix*5 + ix2) < len(header):\n",
      "            first_count = testing[header[ix*5 + ix2]].unique()\n",
      "            first_count_len = len(first_count)\n",
      "            first_count_set = set(first_count)\n",
      "            second_count_set = set(testing2[header[ix*5 + ix2]].unique())\n",
      "            second_count_set = second_count_set.union(first_count_set)\n",
      "            second_count_len = len(second_count_set)\n",
      "            third_count_set = set(testing3[header[ix*5 + ix2]].unique())\n",
      "            third_count_set = third_count_set.union(second_count_set)\n",
      "            third_count_len = len(third_count_set)\n",
      "            fourth_count_set = set(testing4[header[ix*5 + ix2]].unique())\n",
      "            fourth_count_set = fourth_count_set.union(third_count_set)\n",
      "            fourth_count_len = len(fourth_count_set)\n",
      "            fifth_count_set = set(testing5[header[ix*5 + ix2]].unique())\n",
      "            fifth_count_set = fifth_count_set.union(fourth_count_set)\n",
      "            fifth_count_len = len(fifth_count_set)        \n",
      "            index = np.arange(5)\n",
      "            axes[ix][ix2].bar(index, [first_count_len, second_count_len, third_count_len, fourth_count_len, fifth_count_len], color='b', label='count')\n",
      "            axes[ix][ix2].set_title(header[ix*5 + ix2])\n",
      "            axes[ix][ix2].set_ylabel(\"Count\")\n",
      "            axes[ix][ix2].set_xlabel(\"Index\")\n",
      "            remove_border(axes[ix][ix2])\n",
      "        else:\n",
      "            fig.delaxes(axes[ix][ix2])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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AFxAnyJ/KsY6SGvc00en06BjHTQZuqrVjcHDwuecDAwMMDAy0qGqSalhQ9foB4M4ax2Xz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AYN8TPLjTLMhH8icEHWB2FmzADuQz0hbgI2CV6fDFwEHI3ml9k68j3ttplZepot1fwCGp43CQ0Z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\nAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x109dc3c50>"
       ]
      }
     ],
     "prompt_number": 75
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "From the plots we can see that a few of the categories seem to scale with the size of the dataset. In this initial analysis, we decide to remove these features to reduce the feature space and try to reduce the time required to create models.\n",
      "\n",
      "We decide to re-run the model but not include the following features:\n",
      "    - device_ip\n",
      "    - device_model\n",
      "    - device_id"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "6. Re-experiment with Model Fitting in scikit-learn"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trial_text_new = header[3:]\n",
      "df_new = pd.DataFrame()\n",
      "\n",
      "for elem_new in trial_text_new:\n",
      "    if elem_new != \"device_ip\" and elem_new != \"device_id\" and elem_new != \"device_model\":\n",
      "        interim_new = pd.get_dummies(testing[elem_new])\n",
      "        if \"d41d8cd9\" in interim_new.columns.values:\n",
      "            interim_new.drop(\"d41d8cd9\", axis=1, inplace=True)\n",
      "        interim_new.rename(columns=lambda x: elem_new + \"_\" + x, inplace=True)\n",
      "        df_new = df_new.join(interim_new)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 76
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "COLnames = df_new.columns.values\n",
      "df_new.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>C1_1002</th>\n",
        "      <th>C1_1005</th>\n",
        "      <th>C1_1010</th>\n",
        "      <th>banner_pos_0</th>\n",
        "      <th>banner_pos_1</th>\n",
        "      <th>site_id_02e31e62</th>\n",
        "      <th>site_id_05222bb6</th>\n",
        "      <th>site_id_060f567a</th>\n",
        "      <th>site_id_09ab1430</th>\n",
        "      <th>site_id_0be51948</th>\n",
        "      <th>...</th>\n",
        "      <th>C24_32</th>\n",
        "      <th>C24_33</th>\n",
        "      <th>C24_42</th>\n",
        "      <th>C24_46</th>\n",
        "      <th>C24_48</th>\n",
        "      <th>C24_52</th>\n",
        "      <th>C24_62</th>\n",
        "      <th>C24_79</th>\n",
        "      <th>C24_82</th>\n",
        "      <th>C24_95</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>0 rows \u00d7 949 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 77,
       "text": [
        "Empty DataFrame\n",
        "Columns: [C1_1002, C1_1005, C1_1010, banner_pos_0, banner_pos_1, site_id_02e31e62, site_id_05222bb6, site_id_060f567a, site_id_09ab1430, site_id_0be51948, site_id_0c54586d, site_id_0c54f200, site_id_0dbffacc, site_id_0f80c291, site_id_14676398, site_id_18825971, site_id_188e0b6c, site_id_18b0fd8c, site_id_19521507, site_id_19d4634c, site_id_1a4c6a8a, site_id_1b0831c0, site_id_1b8fa17c, site_id_1d99d27f, site_id_2015bca5, site_id_21116e76, site_id_286c820c, site_id_2a42cbee, site_id_2af6df5e, site_id_2c6158c9, site_id_2d313d37, site_id_334346c8, site_id_367b3181, site_id_36cd0d4e, site_id_39876b13, site_id_39f5db2b, site_id_3c7a209e, site_id_3d69cd1a, site_id_3f88b6d1, site_id_404c1afb, site_id_42aa2e70, site_id_433bc11c, site_id_477bac29, site_id_49615e68, site_id_4a41431e, site_id_4daf6bc9, site_id_4db1c6ba, site_id_50541e3c, site_id_523ef887, site_id_52402f77, site_id_52534301, site_id_52a020a3, site_id_54a5f909, site_id_572b5d37, site_id_5744bee7, site_id_5872a33e, site_id_587549b8, site_id_58cb4eca, site_id_5bca826c, site_id_5cf05681, site_id_5e6ad0f9, site_id_5fa09cb7, site_id_60ab002c, site_id_60b71708, site_id_61b26182, site_id_61b86513, site_id_6f68870f, site_id_70911d3e, site_id_788a425d, site_id_7e13b022, site_id_7fd05f59, site_id_83cce40c, site_id_855241a8, site_id_861a6501, site_id_86e7ce37, site_id_8c4d6cf2, site_id_8fe599a5, site_id_90cc0939, site_id_91fd0528, site_id_943cad72, site_id_949afc75, site_id_953bc3ae, site_id_96e4556b, site_id_96f0f834, site_id_9e37d61f, site_id_9ec6c3bb, site_id_a0e3c3bb, site_id_a200fc60, site_id_a211f418, site_id_a27311e2, site_id_a3cfee0f, site_id_a4adce6d, site_id_a70a17e5, site_id_ab4190bf, site_id_ac6378fa, site_id_ae312467, site_id_afe18a38, site_id_b002504e, site_id_b01fd8c0, site_id_b0c67a9c, ...]\n",
        "Index: []\n",
        "\n",
        "[0 rows x 949 columns]"
       ]
      }
     ],
     "prompt_number": 77
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for ix, value in enumerate(testing.ix[:,3:-1].values):\n",
      "    mydict = {}\n",
      "    indy = []\n",
      "    indy.append(ix)\n",
      "    for j, title in enumerate(value):\n",
      "        if j != 8 and j != 9 and j != 12 and title != \"d41d8cd9\":\n",
      "            mydict[header[j+3] + \"_\" + title] = 1\n",
      "    df2_new = pd.DataFrame(mydict, index = indy)\n",
      "    df_new = df_new.append(df2_new)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 78
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_new['timestamp'] = testing['timestamp']\n",
      "df_new.fillna(0, inplace=True)\n",
      "y=testing.click\n",
      "xtrain, xtest, ytrain, ytest = train_test_split(df_new, y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 99
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf = MultinomialNB().fit(xtrain, ytrain)\n",
      "print \"Testing Accuracy: %0.2f%%\" % (100 * clf.score(xtest, ytest))\n",
      "print \"Training Accuracy: %0.2f%%\" % (100 * clf.score(xtrain, ytrain))\n",
      "prob = clf.predict_proba(xtest)\n",
      "plt.hist(prob[:,1])\n",
      "remove_border()\n",
      "plt.ylabel(\"Count\")\n",
      "plt.xlabel(\"Probability\")\n",
      "plt.title(\"Naive Bayes Probability Histogram\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Testing Accuracy: 90.40%\n",
        "Training Accuracy: 92.26%\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 80,
       "text": [
        "<matplotlib.text.Text at 0x10b815f10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ScjqAu4EDs8dTgUeBjQqrSJIkqQXaoeuzixiLdiZxmo69gMOBNUUWNQSNAUbR\nvfVSkjYEmwHPAauLLqSNTCF6tx4HbgBWFlqNkuVVCwamgxjM+CDwhmJLaTszgD8Q4fYmYNtiy2k7\nuwG/Av4O/AzYvNhy2tIwYBGVk61UvyVAZ3a7p+Ba2s07gFuBVxRdSJvZFlhP+XVXuu1QZFGDqQP4\nLXFSXIgWt+V41YJGbEmc6qQTz03XiK2IDwY7A28mJrf8rMiC2swo4GxiCMM44Dbg84VW1J5OBJ4E\nDii6kDY0DfgUsHt226rYctrKTKIVbZuC62hHJxKNIpOz2/bAnwqtaJC9kWimznfh3gscWUw5bc2g\n1ph3AS/JPZ6H3fGNeBkR1krOBT5bUC3tajpwKHA/BrX++A7wUeDVRRfSZjqIKwR9suhC2tSEqseH\nAl/qbYd2uDJBb7xqgYpyJfBs7vFjwAMF1dKOHgNeyO6PJoLbecWV03Y2B/YFbiy6kDY1nBiTdgrx\n4f5KYGShFbWPfYhuuinA1URoO7HIgtrM36oezyGuZz5kXUT0keddDlxXQC3tzha1gTkDOLnoItrQ\nW4DfAw8B+xdcSzs5i3KLpC1q/dcBHE186PpiwbW0i5OIqwNtkT3enWgs2buwitrXMOAv9NFo1u4t\nauuIC7TntfvPpPYzjrh6xleLLqQNLSQ+Ud5CfMhS3+YD36XcIgntcZWZFHURr7uPEIFNfduYaIV8\nInt8F/Ab4mwMaszewO+IhpIetXuoeQTYpGrZpsBfC6hFG65TiU+Zvf6xqUcrgPcTn9Cd+dm3+cQ/\n9zXZ7eXAT4nuO/XPdcR7h/r2N+LDad5DwEsLqKXdzWED6AH0qgXNY9dn/8yn8hqzjnPpvwexZag/\n7PocuAlEF7z6NpXoKs7/r/sRMd5PjfkjlZPSamr3FrXbiQHc+asWjCW6U1S/0uvAN8nGzCNaNEYS\nr70ZwNwiC2ojmxHj00pmAJcRXVHSYNsTOJby/76T8PQw9bqHOC1WqatzFDH0w6ELjdmRmFT1bF8b\nDgXbAd8GTsi+TiuymDa0JXA6cQK+S4jAob4dTIyPzJ+wcD3wqiKLaiN7EF0ovyTeJN9bbDltzRa1\nxr2FuBThYuATwFsLrab9TAK+D3wcuAB4U7HltKXTgA8WXYQkSZIkSZIkSZIkSZIkSZIkSZIkSZIk\nSZLUuDHA+KKLKGn3E95KkiQ1QwdxIvNlxEmRm2E6lefb7CSu07txvU9gUJO0oZmS3RoxFtirge23\nJ04mLamaTcKXAAAEKUlEQVR9bAHcTJzQt1lXSZkJvJ7y/53pxIm+/9Gk55ekJLyZuBboU8BFxOVq\nbqDxT70HAE8C72lgny2BG4FFPazfCFgO7Js9Pp64akXpagEvJy5aPaXBWiUVo5nXvp5Q9fgE4ENN\nem5JSsp3gF/kHh8HPE9cM68Ri2ksqEF0h/QU1ABOJK5fWrKCclAbDZycfS05vsHvL6l1qoPaHOBz\nxIfDrzOw3sif0OCHNrs+JbWL9cQYkpLvAyNp/DqDg3Hh968RrX21vsfzwJezrwDzgXcOQg2Smm8y\n8Abgk8ARwJHAMf18rk2IFvoVjexkUJPUTvIBaFz2dQ1wF3AI8DhwKNG6dQ7RcvVdunc1bA/cSYSr\nrwEjsuUHAP9BhKmrgU1z+wwjul2fAW7LnoPse/0bMKOHmrcBTgF2JlrV3kh0h54ObAXcDvyZeEMA\nmAsszNUkqThzga2Ji6h/hGhZf0k/n+tQ4MdNqkuSkvNtyt2Po4BvAfdTnlV1IOUQdCPlrotRxPi2\nudnjxcAVRGjaE1hFdE0C3Ar8S3b/e8BJ2f15wN+IcPYS4KfA77J1hxIDg9+Xq/V+IvQNA46isivl\nGCq7UWdlNZSC53xgj16PhKTBlP97XUAMs6jlLmBlD7elNba/ksYmJQG2qElqL9sDFwNXAauB/YCH\ns3WLgJ8RA/kPBu7Ilr9AhK5js8ddxDiR54lWte8R3RkQYetHwFSiJSzforaUmLb/LPBp4LXAdkQo\nXNlDvZ1Ei15eR9Xjm4EHKHenTAN+08PzSWqtJ4kPgXmvzb7uTnRl1rpVj50dBewG/LrRAgxqktrJ\nvUQ342xiAP8jNbaZlH0dm1v2ABG8avkLMXYE4GngLOIf7XK6h6r8PuT2G6ivELPBNie6byUVo5SL\nSn/71wNvJ/7fvIz4UNefFu83EKfl6HdBkjRUrMi+bp9bNhq4r4ftRxFjxCBax34E/Dc9h7TSPuuJ\nFrZmuJx4E/gKMTZOUuttCXycaHWfS7Ss30kMjfgEcDfRKnZJP577rUToa5iDVSW1ixHELM9q1YHq\nceAa4P3Ar7JlM4Hzc9sMz92fRfxz3hx4XfY9NgJeQ0w2KG2b/2D75uz5/pl7vvxzDsvVNSy3Ddk+\nW2T3t8rqfQ74BnEagD/W+BklDb6VwNnZLe+C7DYQH+jvjsP73kSSCvcmomvw5cBfia7HTqKl7MPA\n/sAT2fK1wE3A4cDe2brfEwN5IWaJvhvYiZgIcDUxwWANsCvwMWIG5i+IWaN3Ar/NaphOdHtMIKbr\nd1I5XX8J8BbiPG2riAkHc7NaniOC40PEJIUDiBlgpTOUv0CMu+tX94gkSZIGzyeJyQmS9CK7PiWp\nOB3E+dQeJMbULS+2HEmSJJVsTIS0uyif8FaSJEmSJEmSJEmSJEmSJEmSJEmSJEmSlJT/A/KW3XXN\n1uEkAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11b763810>"
       ]
      }
     ],
     "prompt_number": 80
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf2 = LogisticRegression().fit(xtrain, ytrain)\n",
      "print \"Testing Accuracy: %0.2f%%\" % (100 * clf2.score(xtest, ytest))\n",
      "print \"Training Accuracy: %0.2f%%\" % (100 * clf2.score(xtrain, ytrain))\n",
      "prob = clf2.predict_proba(xtest)\n",
      "plt.hist(prob[:,1])\n",
      "remove_border()\n",
      "plt.ylabel(\"Count\")\n",
      "plt.xlabel(\"Probability\")\n",
      "plt.title(\"Logistic Regression Probability Histogram\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Testing Accuracy: 90.40%\n",
        "Training Accuracy: 92.26%\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 82,
       "text": [
        "<matplotlib.text.Text at 0x1117bc6d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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DnCRJUqYMcpIkSZkyyEmSJGXKICdJkpQpg5wkSVKmDHKSJEmZMshJkiRlyiAn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\nSZIkSZIkSZJa6P8DH7YjRLfj2tAAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x117f40bd0>"
       ]
      }
     ],
     "prompt_number": 82
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The accuracy of both models has not changed and remains very high. Although the above was a useful exploration and practise exercise, we need to train the models on much larger datasets with such a large number of features in order to avoid overfitting, particularly as data is available. Therefore we decide to learn about Vowpal Wabbit which seems to be the current first choice for large data analysis on Kaggle. It is an online learner which we will aim to introduce below and detail the steps that we took to train it."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "7. Vowpal Wabbit Learning"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Due to the size of the dataset ~9GB, it is very difficult to train models using approaches best suited for smaller datasets in Scikit Learn, as the computer is not able to fit the dataset into memory. After frustratingly attempting to read the dataset into ipython notebook, we explored methods for machine learning (ML) on 'Big Data':\n",
      "\n",
      "1. Sampling and Statistical Methods\n",
      "2. Online Learners\n",
      "3. Parallelisation\n",
      "\n",
      "After exploring the Kaggle forums and approaches on large datasets, we were introduced to the Vowpal Wabbit tool. This is an online learner which is able to deal with very large datasets on local machines by iteratively training a model using each new example.\n"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Vowpal Wabbit Strategy"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Our strategy for using Vowpal Wabbit was as follows:\n",
      "1. 'Hot Encoding' of categorical variables\n",
      "2. Exploration of validation error and its causes\n",
      "3. Exploration of feature space with vw-varinfo\n",
      "4. Fitting of hyperparameters\n",
      "5. Experimentation of bootstrapping and multiple passes"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "7.1 Hot Encoding of Variables"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The variables are encoded as 'hot encoding': which means each unique value for a categorical variable is used as a boolean predictor."
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Data processing"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "1. From our previous analysis (see section 2.), we discovered that the training data came from the same month and year, therefore decided to extract day of week and hour of the day information from the \"hour\" variable in the dataset. As a result the categorical variable \"hour\" was not included as a feature in the model.\n",
      "\n",
      "2. Also from previous analysis, we observed that the categories labelled as \"device_id\" and \"device_ip\" grow linearly with the number of examples and therefore decided to remove those from our features.\n",
      "\n",
      "3. The categorical variable \"id\" was not used as a feature either as it consisted of a unique id for each row.\n",
      " \n",
      "4. The entire input file was then turned into vw encoding using another python script [github_link - adapted from Triskelion <info@mlwave.com>]"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "7.2 Validation Error Problem"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The loss function used was a logistic loss function as defined by the Kaggle competition and also this makes sense for evaluating probabilities as it is derived from a Bernoulli distribution."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The logarithmic loss (interpreted as the validation error) from our trained model showed initial decrease (as expected from convergence) but then started to increase again. Looking into how to debug the model, we expect this could be due to one of the following:\n",
      "    1. Data is not shuffled but instead ordered according to a systematic component meaning the learner is trained first on a subset of the data then experiences another subset etc... the most obvious candidate category for this is the time 'seasonality'.\n",
      "    2. The learning rate is too high and the algorithm is not converging properly. In section 7.4 we explain how to optmise the learning rate to help the algorithm converge."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "7.3 Exploration of feature space with vw-varinfo"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In order to reduce the feature space further, we need to examine the contribution the features make to our Vowpal Wabbit logistic regression model. In order to do this we us the vw-varinfo function to output the weighting of each feature within the logistic regression."
     ]
    },
    {
     "cell_type": "heading",
     "level": 6,
     "metadata": {},
     "source": [
      "Note: You can use varinfo in the same way as vw as detailed in https://github.com/JohnLangford/vowpal_wabbit/wiki/using-vw-varinfo but remember to adjust the command line path (that confused us for a while...)  :"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "ie. if you were running:\n",
      "#### vw data.train\n",
      "you probably now need to run:\n",
      "#### ../utl/vw-varinfo data.train"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We read in the varinfo text file to explore the features and their weights."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tester = []\n",
      "path  = \"varinfo.txt\"\n",
      "with open(path,\"r\") as infile:\n",
      "    for row in infile:\n",
      "        row = row.strip(\"c^\").split(\"\\n\")\n",
      "        tester.append(row[0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 88
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "the_brain = {}\n",
      "feature_set = []\n",
      "totalScores = []\n",
      "for value in tester[1:]:\n",
      "    totalScores.append((value.split()[0], value.split()[5]))\n",
      "    feature = value.split()[0].rsplit('_', 1)[0]\n",
      "    score = float(value.split()[5].strip(\"%\"))\n",
      "    if feature not in the_brain:\n",
      "        feature_set.append(feature)\n",
      "        the_brain[feature] = [score]\n",
      "    else:\n",
      "        the_brain[feature].append(score)\n",
      "print len(feature_set)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "26\n"
       ]
      }
     ],
     "prompt_number": 91
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, axes=plt.subplots(figsize=(23,15), nrows=6, ncols=5)\n",
      "fig.subplots_adjust(hspace = 0.8)\n",
      "fig.subplots_adjust(wspace = 0.6)\n",
      "\n",
      "for ix in np.arange(6):\n",
      "    for ix2 in np.arange(5):\n",
      "        if (ix*6 + ix2) < len(feature_set):\n",
      "            axes[ix][ix2].hist(the_brain[feature_set[ix*6+ix2]], bins = 10)\n",
      "            axes[ix][ix2].set_title(feature_set[ix*6+ix2])\n",
      "            axes[ix][ix2].set_ylabel(\"Count\")\n",
      "            axes[ix][ix2].set_xlabel(\"Weight\")\n",
      "            remove_border(axes[ix][ix2])\n",
      "        else:\n",
      "            fig.delaxes(axes[ix][ix2])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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vsm8zYgqaJBWprHhzH7BNbt9yxDS4+4Ftq5QtrnWyRYsW/evn4eFhhoeHW7w8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\nraH29pQmAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11358ac10>"
       ]
      }
     ],
     "prompt_number": 102
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We create an array that collects all the features that are weighted less than |1%| with the aim to remove those features from our training set to reduce the feature space."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lessThanAbsOne = [e1[0] for e1 in totalScores if e1[1] < 1 and e1[1] > -1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 99
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We then create a csv file with these features that we will then read into the python script to create the vw encoding with the removed features."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "with open('featureRemove.csv', 'w') as fp:\n",
      "    a = csv.writer(fp, delimiter='\\n')\n",
      "    data = lessThanAbsOne\n",
      "    a.writerow(data)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 100
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "7.4 Fitting of hyperparameters"
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "7.4.1. Learning rate"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The learning rate for our model may be too high, preventing convergence causing the logarithmic loss to be increase. Vowpal Wabbit provides a golden search function to optimise the learning rate within a user defined range to yield the lowest validation error and improve convergence."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We run the following command in Vowpal Wabbit to search for the best learning rate parameter (on our training set with reduced features):\n",
      "#### ../utl/vw-hypersearch 0.1 10 vw --loss_function logistic -l % reducedTrain.vw"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In our case, the optimised learning rate turned out to be 4.5, which is quite high compared to the default value of 0.5. However when we trained our model with this new parameter, the log loss did decrease as expected but the effect was negligeable."
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "7.4.2. Regularisation"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Vowpal Wabbit provides two parameters to add regularization to the model, namely L1 and L2: as described in the very useful post: http://fastml.com/large-scale-l1-feature-selection-with-vowpal-wabbit/"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We run the following command in Vowpal Wabbit to search for the best regularization parameter l1 (on our training set with reduced features):\n",
      "#### ../utl/vw-hypersearch -L 1e-20 1 vw --loss_function logistic --l1 % reducedTrain.vw\n",
      "\n",
      "The -L prompt does a log-space search, which we chose to increase speed."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In our case, the optimised l1 regularization parameter was of the order 10-8, which being so close to zero, we chose to leave off and train our dataset without regularization."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "7.5 Experimentation of bootstrapping and multiple passes"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Considering the possible bias of the model to a sub-section of the training data (observed due to a bump in the error rate), bootstrapping and mutiple passes through the training data were explored to improve the prediction. Below is a graph of the log loss error with our initial model and our model using boostrapping and multiple passes.\n",
      "Vowal Wabbit allows multiple passes of the dataset to be made in training which allows each row to be used multiple times; however there is then a risk of overfitting to the training data and reducing the error rate at the cost of making a non generalisable model."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "path  = \"log_loss_.csv\"\n",
      "counter = 0\n",
      "boot_res_x = []\n",
      "boot_res_y = []\n",
      "first_res_x = []\n",
      "first_res_y = []\n",
      "with open(path, \"r\") as data:\n",
      "    for line in data:\n",
      "        line = line.replace(\" \", \",\")\n",
      "        line = line.split(\",\")\n",
      "        line = [e.strip(\"\\n\") for e in line if e != \"\"]\n",
      "        if counter > 1 and line[6]!='26':\n",
      "            boot_res_y.append(line[0])\n",
      "            boot_res_x.append(line[3])\n",
      "        if counter > 1 and line[6]=='26':\n",
      "            first_res_y.append(line[0])\n",
      "            first_res_x.append(line[3])\n",
      "        counter = counter + 1\n",
      "        \n",
      "#PLOTs\n",
      "plt.plot(boot_res_x, boot_res_y, color = \"b\", label = \"bootstrap samples\")\n",
      "plt.plot(first_res_x, first_res_y, color = \"r\", label = \"simple model\")\n",
      "plt.ylim(0.2, 0.5)\n",
      "remove_border()\n",
      "plt.title(\"Log loss versus number of training examples\")\n",
      "plt.ylabel(\"Log loss\")\n",
      "plt.xlabel(\"Number of training examples\")\n",
      "plt.legend()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 195,
       "text": [
        "<matplotlib.legend.Legend at 0x11e1f1910>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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vzTv+6quvdsnJyW7z5s3OOeeef/55FwgEyvwziYWSXs8VCTyqkRMRkfgWiygX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\ngDOBS9GMzEY3AXVddQE/Q4EGuN2IVar+3G70Lfb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       "text": [
        "<matplotlib.figure.Figure at 0x119488210>"
       ]
      }
     ],
     "prompt_number": 195
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Note: blue plot shows many more training examples as running multiple passes on the model."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The above plot shows that even with bootstrapping and running multiple passes, we observe the \"bump\" showing increase in log loss error before stabilisation; this seems to indicate that the data would still benefit from being shuffled and probably suffers from some seasonality effects.\n",
      "However the bump has been reduced so there seems to have been some advantage to running bootstrap and multiple passes."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Some technical considerations which we did not find to be easily documented around bootstrap and running multiple passes.\n",
      "\n",
      "1. Bootstrap (--bs): The documentation states boostrapping prompt should be -B or --bootstrap. We found it had to be: --bs\n",
      "2. Caching (--c): In order to use mutiple passes you need to cache data to increase the algorithms speed. However if you encounter an error saying that no cache file exists this is due to the fact you have to kill (-k) existing cache files. This seems to be the case even though there are no existing cache files that you can see in your directory.\n",
      "2. Passes (--passes): Like all vowpal wabbit parameters you need to determine the optimal argument and the risk of over-fitting increases with the number of passes.\n",
      "3. Holdout (--holdout_period): This details the number of rows from the training data to 'hold-out' in order to calculate the cross-validation error and is introduced for 'over-fit avoidance'. Performance is evaluated on the held-out-data.\n",
      "4. Early Terminate (--early_terminate): Again introduced for 'over-fit avoidance', this is used to specify the number of passes to allow even though the loss is increasing before terminating."
     ]
    }
   ],
   "metadata": {}
  }
 ]
}