Section 0. Introduction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bayesian Modelling in Python\n",
    "Mark Regan\n",
    "\n",
    "---\n",
    "\n",
    "### Section 0: Introduction\n",
    "Welcome to \"Bayesian Modelling in Python\" - a tutorial for those interested in learning Bayesian statistics in Python. You can find a list of all tutorial sections on the project's [homepage](https://github.com/markdregan/Hangout-with-PyMC3).\n",
    "\n",
    "Statistics is a topic that never resonated with me throughout my years in university. The frequentist techniques that we were taught (p-values, etc.) felt contrived and ultimately, I turned my back on statistics as a topic that I wasn't interested in.\n",
    "\n",
    "That was until I stumbled upon Bayesian statistics - a branch of statistics quite different from the traditional frequentist statistics that most universities teach. I was inspired by a number of different publications, blogs & videos that I would highly recommend any newbies to Bayesian stats to begin with. They include:\n",
    "- [Doing Bayesian Data Analysis](http://www.amazon.com/Doing-Bayesian-Analysis-Second-Edition/dp/0124058884/ref=dp_ob_title_bk) by John Kruschke\n",
    "- [Python port](https://github.com/aloctavodia/Doing_Bayesian_data_analysis) of John Kruschke's examples by Osvaldo Martin\n",
    "- [Bayesian Methods for Hackers](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers) provided me with a great source of inspiration to learn Bayesian stats. In recognition of this influence, I've adopted the same visual styles as BMH.\n",
    "- [While My MCMC Gently Samples](http://twiecki.github.io/) blog by Thomas Wiecki\n",
    "- [Healthy Algorithms](http://healthyalgorithms.com/tag/pymc/) blog by Abraham Flaxman\n",
    "- [Scipy Tutorial 2014](https://github.com/fonnesbeck/scipy2014_tutorial) by Chris Fonnesbeck\n",
    "\n",
    "I created this tutorial in the hope that others find it useful and it helps them learn Bayesian techniques just like the above resources helped me. I'd welcome any corrections/comments/contributions from the community.\n",
    "\n",
    "---\n",
    "\n",
    "### Loading your Google Hangout chat data\n",
    "Throughout this tutorial, we will use a dataset containing all of my Google Hangout chat messages. I've removed the messages content and anonymized my friends' names; the rest of the dataset is unaltered.\n",
    "\n",
    "If you'd like to use your Hangout chat data whilst working through this tutorial, you can download your Google Hangout data from [Google Takeout](https://www.google.com/settings/takeout/custom/chat). The Hangout data is downloadable in JSON format. After downloading, you can replace the `hangouts.json` file in the data folder.\n",
    "\n",
    "The json file is heavily nested and contains a lot of redundant information. Some of the key fields are summarized below:\n",
    "\n",
    "| Field           | Description                                                    | Example                                      |\n",
    "|-----------------|----------------------------------------------------------------|----------------------------------------------|\n",
    "| `conversation_id` | Conversation id representing the chat thread                   | Ugw5Xrm3ZO5mzAfKB7V4AaABAQ                   |\n",
    "| `participants`    | List of participants in the chat thread                        | [Mark, Peter, John]                          |\n",
    "| `event_id`        | Id representing an event such as chat message or video hangout | 7-H0Z7-FkyB7-H0au2avdw                       |\n",
    "| `timestamp`       | Timestamp                                                      | 2014-08-15 01:54:12                          |\n",
    "| `message`         | Content of the message sent                                    | Went to the local wedding photographer today |\n",
    "| `sender`          | Sender of the message                                          | Mark Regan                                   |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import json\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn.apionly as sns\n",
    "\n",
    "from datetime import datetime\n",
    "\n",
    "%matplotlib inline\n",
    "plt.style.use('bmh')\n",
    "colors = ['#348ABD', '#A60628', '#7A68A6', '#467821', '#D55E00', \n",
    "          '#CC79A7', '#56B4E9', '#009E73', '#F0E442', '#0072B2']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The below code loads the json data and parses each message into a single row in a pandas DataFrame.\n",
    "> Note: the data/ directory is missing the hangouts.json file. You must download and add your own JSON file as described above. Alternatively, you can skip to the next section where I import an anonymized dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Import json data\n",
    "with open('data/hangouts.json') as json_file:\n",
    "    json_data = json.load(json_file)\n",
    "\n",
    "# Generate map from gaia_id to real name\n",
    "def user_name_mapping(data):\n",
    "    user_map = {'gaia_id': ''}\n",
    "    for state in data['conversation_state']:\n",
    "        participants = state['conversation_state']['conversation']['participant_data']\n",
    "        for participant in participants:\n",
    "            if 'fallback_name' in participant:\n",
    "                user_map[participant['id']['gaia_id']] = participant['fallback_name']\n",
    "\n",
    "    return user_map\n",
    "\n",
    "user_dict = user_name_mapping(json_data)\n",
    "\n",
    "# Parse data into flat list\n",
    "def fetch_messages(data):\n",
    "    messages = []\n",
    "    for state in data['conversation_state']:\n",
    "        conversation_state = state['conversation_state']\n",
    "        conversation = conversation_state['conversation']\n",
    "        conversation_id = conversation_state['conversation']['id']['id']\n",
    "        participants = conversation['participant_data']\n",
    "\n",
    "        all_participants = []\n",
    "        for participant in participants:\n",
    "            if 'fallback_name' in participant:\n",
    "                user = participant['fallback_name']\n",
    "            else:\n",
    "                # Scope to call G+ API to get name\n",
    "                user = participant['id']['gaia_id']\n",
    "            all_participants.append(user)\n",
    "            num_participants = len(all_participants)\n",
    "        \n",
    "        for event in conversation_state['event']:\n",
    "            try:\n",
    "                sender = user_dict[event['sender_id']['gaia_id']]\n",
    "            except:\n",
    "                sender = event['sender_id']['gaia_id']\n",
    "            \n",
    "            timestamp = datetime.fromtimestamp(float(float(event['timestamp'])/10**6.))\n",
    "            event_id = event['event_id']\n",
    "\n",
    "            if 'chat_message' in event:\n",
    "                content = event['chat_message']['message_content']\n",
    "                if 'segment' in content:\n",
    "                    segments = content['segment']\n",
    "                    for segment in segments:\n",
    "                        if 'text' in segment:\n",
    "                            message = segment['text']\n",
    "                            message_length = len(message)\n",
    "                            message_type = segment['type']\n",
    "                            if len(message) > 0:\n",
    "                                messages.append((conversation_id,\n",
    "                                                 event_id, \n",
    "                                                 timestamp, \n",
    "                                                 sender, \n",
    "                                                 message,\n",
    "                                                 message_length,\n",
    "                                                 all_participants,\n",
    "                                                 ', '.join(all_participants),\n",
    "                                                 num_participants,\n",
    "                                                 message_type))\n",
    "\n",
    "    messages.sort(key=lambda x: x[0])\n",
    "    return messages\n",
    "\n",
    "# Parse data into data frame\n",
    "cols = ['conversation_id', 'event_id', 'timestamp', 'sender', \n",
    "        'message', 'message_length', 'participants', 'participants_str', \n",
    "        'num_participants', 'message_type']\n",
    "\n",
    "messages = pd.DataFrame(fetch_messages(json_data), columns=cols).sort(['conversation_id', 'timestamp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>conversation_id</th>\n",
       "      <th>event_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>sender</th>\n",
       "      <th>message</th>\n",
       "      <th>message_length</th>\n",
       "      <th>participants</th>\n",
       "      <th>participants_str</th>\n",
       "      <th>num_participants</th>\n",
       "      <th>message_type</th>\n",
       "      <th>prev_timestamp</th>\n",
       "      <th>prev_sender</th>\n",
       "      <th>time_delay_seconds</th>\n",
       "      <th>time_delay_mins</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>year_month</th>\n",
       "      <th>is_weekend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Ugw5Xrm3ZO5mzAfKB7V4AaABAQ</td>\n",
       "      <td>7-H0Z7-FkyB7-HDBYj4KKh</td>\n",
       "      <td>2014-08-15 03:44:12.840014</td>\n",
       "      <td>Mark Regan</td>\n",
       "      <td>Thanks guys!!!</td>\n",
       "      <td>14</td>\n",
       "      <td>[Keir Alexander, Louise Alexander Regan, Mark ...</td>\n",
       "      <td>Keir Alexander, Louise Alexander Regan, Mark R...</td>\n",
       "      <td>3</td>\n",
       "      <td>TEXT</td>\n",
       "      <td>2014-08-15 03:44:00.781652</td>\n",
       "      <td>Keir Alexander</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2014-08</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               conversation_id                event_id  \\\n",
       "10  Ugw5Xrm3ZO5mzAfKB7V4AaABAQ  7-H0Z7-FkyB7-HDBYj4KKh   \n",
       "\n",
       "                    timestamp      sender         message  message_length  \\\n",
       "10 2014-08-15 03:44:12.840014  Mark Regan  Thanks guys!!!              14   \n",
       "\n",
       "                                         participants  \\\n",
       "10  [Keir Alexander, Louise Alexander Regan, Mark ...   \n",
       "\n",
       "                                     participants_str  num_participants  \\\n",
       "10  Keir Alexander, Louise Alexander Regan, Mark R...                 3   \n",
       "\n",
       "   message_type             prev_timestamp     prev_sender  \\\n",
       "10         TEXT 2014-08-15 03:44:00.781652  Keir Alexander   \n",
       "\n",
       "    time_delay_seconds  time_delay_mins  day_of_week year_month  is_weekend  \n",
       "10                  12                1            4    2014-08           0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Engineer features\n",
    "messages['prev_timestamp'] = messages.groupby(['conversation_id'])['timestamp'].shift(1)\n",
    "messages['prev_sender'] = messages.groupby(['conversation_id'])['sender'].shift(1)\n",
    "\n",
    "# Exclude messages are are replies to oneself (not first reply)\n",
    "messages = messages[messages['sender'] != messages['prev_sender']]\n",
    "\n",
    "# Time delay\n",
    "messages['time_delay_seconds'] = (messages['timestamp'] - messages['prev_timestamp']).astype('timedelta64[s]')\n",
    "messages = messages[messages['time_delay_seconds'].notnull()]\n",
    "messages['time_delay_mins'] = np.ceil(messages['time_delay_seconds'].astype(int)/60.0)\n",
    "\n",
    "# Time attributes\n",
    "messages['day_of_week'] = messages['timestamp'].apply(lambda x: x.dayofweek)\n",
    "messages['year_month'] = messages['timestamp'].apply(lambda x: x.strftime(\"%Y-%m\"))\n",
    "messages['is_weekend'] = messages['day_of_week'].isin([5,6]).apply(lambda x: 1 if x == True else 0)\n",
    "\n",
    "# Limit to messages sent by me and exclude all messages between me and Alison\n",
    "messages = messages[(messages['sender'] == 'Mark Regan') & (messages['participants_str'] != 'Alison Darcy, Mark Regan')]\n",
    "\n",
    "# Remove messages not responded within 60 seconds\n",
    "# This introduces an issue by right censoring the data (might return to address)\n",
    "messages = messages[messages['time_delay_seconds'] < 60]\n",
    "\n",
    "messages.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have a data model that we can work with more easily. The above table shows a single row in the pandas DataFrame. I'm interested in how long it takes me to respond to messages. Let's create some plots that describe my typical response times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsgAAAHzCAYAAAA9/uuFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cbd6630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,7))\n",
    "ax = fig.add_subplot(211)\n",
    "\n",
    "order = np.sort(messages['year_month'].unique())\n",
    "sns.boxplot(x=messages['year_month'], y=messages['time_delay_seconds'], order=order, orient=\"v\", color=colors[5], linewidth=1, ax=ax)\n",
    "_ = ax.set_title('Response time distribution by month')\n",
    "_ = ax.set_xlabel('Month-Year')\n",
    "_ = ax.set_ylabel('Response time')\n",
    "_ = plt.xticks(rotation=30)\n",
    "\n",
    "ax = fig.add_subplot(212)\n",
    "plt.hist(messages['time_delay_seconds'].values, range=[0, 60], bins=60, histtype='stepfilled', color=colors[0])\n",
    "_ = ax.set_title('Response time distribution')\n",
    "_ = ax.set_xlabel('Response time (seconds)')\n",
    "_ = ax.set_ylabel('Number of messages')\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above plots give a monthly and an overall perspective of the length of time (in seconds) that it takes me to respond to messages. At this point I have a lot of questions that I want to ask of the data. For example:\n",
    "1. Is my response time affected by who I am talking to?\n",
    "2. Are there environmental factors that affect my response time (day of week, location, etc.)?\n",
    "3. What is the best and worst day to get in touch with me?\n",
    "\n",
    "Before we try and answer some of these questions, lets take some baby steps by estimating some parameters of a model that describes the above data. That'll make it easier for us to understand the data and inquire further.\n",
    "\n",
    "In the next section, we'll estimate parameters that describe the above distribution.\n",
    "\n",
    "### Export data for usage throughout tutorial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# excluded some colums from csv output\n",
    "messages.drop(['participants', 'message', 'participants_str'], axis=1, inplace=True)\n",
    "\n",
    "# Save csv to data folder\n",
    "messages.to_csv('data/hangout_chat_data.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### References\n",
    "1. [Hangout reader](https://bitbucket.org/dotcs/hangouts-log-reader/) by Fabian Mueller"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       "    @font-face {\n",
       "        font-family: \"Computer Modern\";\n",
       "        src: url('http://9dbb143991406a7c655e-aa5fcb0a5a4ec34cff238a2d56ca4144.r56.cf5.rackcdn.com/cmunss.otf');\n",
       "    }\n",
       "    @font-face {\n",
       "        font-family: \"Computer Modern\";\n",
       "        font-weight: bold;\n",
       "        src: url('http://9dbb143991406a7c655e-aa5fcb0a5a4ec34cff238a2d56ca4144.r56.cf5.rackcdn.com/cmunsx.otf');\n",
       "    }\n",
       "    @font-face {\n",
       "        font-family: \"Computer Modern\";\n",
       "        font-style: oblique;\n",
       "        src: url('http://9dbb143991406a7c655e-aa5fcb0a5a4ec34cff238a2d56ca4144.r56.cf5.rackcdn.com/cmunsi.otf');\n",
       "    }\n",
       "    @font-face {\n",
       "        font-family: \"Computer Modern\";\n",
       "        font-weight: bold;\n",
       "        font-style: oblique;\n",
       "        src: url('http://9dbb143991406a7c655e-aa5fcb0a5a4ec34cff238a2d56ca4144.r56.cf5.rackcdn.com/cmunso.otf');\n",
       "    }\n",
       "    div.cell{\n",
       "        width:800px;\n",
       "        margin-left:16% !important;\n",
       "        margin-right:auto;\n",
       "    }\n",
       "    h1 {\n",
       "        font-family: Helvetica, serif;\n",
       "    }\n",
       "    h4{\n",
       "        margin-top:12px;\n",
       "        margin-bottom: 3px;\n",
       "       }\n",
       "    div.text_cell_render{\n",
       "        font-family: Computer Modern, \"Helvetica Neue\", Arial, Helvetica, Geneva, sans-serif;\n",
       "        line-height: 145%;\n",
       "        font-size: 130%;\n",
       "        width:800px;\n",
       "        margin-left:auto;\n",
       "        margin-right:auto;\n",
       "    }\n",
       "    .CodeMirror{\n",
       "            font-family: \"Source Code Pro\", source-code-pro,Consolas, monospace;\n",
       "    }\n",
       "    .prompt{\n",
       "        display: None;\n",
       "    }\n",
       "    .text_cell_render h5 {\n",
       "        font-weight: 300;\n",
       "        font-size: 22pt;\n",
       "        color: #4057A1;\n",
       "        font-style: italic;\n",
       "        margin-bottom: .5em;\n",
       "        margin-top: 0.5em;\n",
       "        display: block;\n",
       "    }\n",
       "    \n",
       "    .warning{\n",
       "        color: rgb( 240, 20, 20 )\n",
       "        }  \n",
       "</style>\n",
       "<script>\n",
       "    MathJax.Hub.Config({\n",
       "                        TeX: {\n",
       "                           extensions: [\"AMSmath.js\"]\n",
       "                           },\n",
       "                tex2jax: {\n",
       "                    inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
       "                    displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n",
       "                },\n",
       "                displayAlign: 'center', // Change this to 'center' to center equations.\n",
       "                \"HTML-CSS\": {\n",
       "                    styles: {'.MathJax_Display': {\"margin\": 4}}\n",
       "                }\n",
       "        });\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Apply pretty styles\n",
    "from IPython.core.display import HTML\n",
    "\n",
    "def css_styling():\n",
    "    styles = open(\"styles/custom.css\", \"r\").read()\n",
    "    return HTML(styles)\n",
    "css_styling()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}