International Political Tele-Conferencing Equilibrium System

iptces.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#International Political Tele-Conferencing Equilibrium System (IPTCES) algorithm\n",
    "The following is a public-facing walkthrough of the code used to for the predictive tool that powers [@WorldLeaderTips](https://twitter.com/worldleadertips). The styling of the out graph has been changed for this context."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import json\n",
    "import random\n",
    "import datetime as dt\n",
    "from collections import deque\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import *\n",
    "from pandas.io.json import json_normalize\n",
    "\n",
    "import matplotlib.pylab\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.tsa import *\n",
    "\n",
    "from bigquery import get_client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tomorrow = str(dt.date.today())\n",
    "twoweeksago = str(dt.date.today() - dt.timedelta(days = 14))\n",
    "lastweek = dt.datetime.today() - dt.timedelta(days = 7)\n",
    "nextweek = dt.date.today() + dt.timedelta(days = 8)\n",
    "day_index = dt.datetime.today().weekday()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Initialize Google BigQuery client:\n",
    "with open('/PATH/TO/settings.json', 'rb') as json_data:\n",
    "    settings = json.load(json_data)\n",
    "\n",
    "client = get_client(settings['PROJECT_ID'], service_account=settings['SERVICE_ACCOUNT'], private_key=settings['KEY'], readonly=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Load the Goldstein suggstions table:\n",
    "gsCodes = pd.read_csv('assets/goldstein_suggestions.tsv', index_col='code', sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Search for: Pope Francis\n",
      "Query: SELECT GLOBALEVENTID, SQLDATE, Actor1Name, Actor1CountryCode, GoldsteinScale, NumMentions FROM [gdelt-bq:full.events] WHERE Actor1Name CONTAINS 'POPE' AND SQLDATE > 20130313 AND SQLDATE>19790101 AND SQLDATE<20150808 IGNORE CASE;\n"
     ]
    }
   ],
   "source": [
    "leader_display_name = 'Pope Francis'\n",
    "gdelt_search = \"WHERE Actor1Name CONTAINS 'POPE' AND SQLDATE > 20130313\"\n",
    "\n",
    "date_start = '19790101'\n",
    "date_end = ''.join(str(dt.date.today()).split('-'))\n",
    "\n",
    "query_template = 'SELECT GLOBALEVENTID, SQLDATE, Actor1Name, Actor1CountryCode, GoldsteinScale, NumMentions FROM [gdelt-bq:full.events] {} AND SQLDATE>{} AND SQLDATE<{} IGNORE CASE;'\n",
    "query = query_template.format(gdelt_search, date_start, date_end)\n",
    "\n",
    "print 'Search for: {}'.format(leader_display_name)\n",
    "print 'Query: {}'.format(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "filename = '{}.csv'.format(leader_display_name.replace(' ', '_'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "56788 results\n",
      "Saved: Pope_Francis.csv\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    job_id, results = client.query(query, timeout=2000)\n",
    "    print '{} results'.format(str(len(results)))\n",
    "\n",
    "    if len(results) == 0:\n",
    "        print \"NO RESULTS\"\n",
    "\n",
    "    df = json_normalize(results)\n",
    "    df = df.sort(columns='SQLDATE')\n",
    "    df.to_csv('{}'.format(filename), encoding='utf-8')\n",
    "    print 'Saved: {}'.format(filename)\n",
    "\n",
    "except:\n",
    "    print 'ERROR: Timed out'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>Unnamed: 0</th>\n",
       "      <th>Actor1CountryCode</th>\n",
       "      <th>Actor1Name</th>\n",
       "      <th>GLOBALEVENTID</th>\n",
       "      <th>GoldsteinScale</th>\n",
       "      <th>NumMentions</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SQLDATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-08-07</th>\n",
       "      <td> 39523</td>\n",
       "      <td> NaN</td>\n",
       "      <td> THE POPE</td>\n",
       "      <td> 455865092</td>\n",
       "      <td> 1.0</td>\n",
       "      <td>  1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-07</th>\n",
       "      <td> 39522</td>\n",
       "      <td> NaN</td>\n",
       "      <td> THE POPE</td>\n",
       "      <td> 455865091</td>\n",
       "      <td> 1.0</td>\n",
       "      <td>  1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-07</th>\n",
       "      <td> 39521</td>\n",
       "      <td> NaN</td>\n",
       "      <td> THE POPE</td>\n",
       "      <td> 455865090</td>\n",
       "      <td> 1.0</td>\n",
       "      <td>  7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-07</th>\n",
       "      <td> 39535</td>\n",
       "      <td> NaN</td>\n",
       "      <td> THE POPE</td>\n",
       "      <td> 455885123</td>\n",
       "      <td> 1.9</td>\n",
       "      <td> 10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-07</th>\n",
       "      <td> 39619</td>\n",
       "      <td> NaN</td>\n",
       "      <td> THE POPE</td>\n",
       "      <td> 456037032</td>\n",
       "      <td> 0.0</td>\n",
       "      <td> 10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Unnamed: 0 Actor1CountryCode Actor1Name  GLOBALEVENTID  \\\n",
       "SQLDATE                                                              \n",
       "2015-08-07       39523               NaN   THE POPE      455865092   \n",
       "2015-08-07       39522               NaN   THE POPE      455865091   \n",
       "2015-08-07       39521               NaN   THE POPE      455865090   \n",
       "2015-08-07       39535               NaN   THE POPE      455885123   \n",
       "2015-08-07       39619               NaN   THE POPE      456037032   \n",
       "\n",
       "            GoldsteinScale  NumMentions  \n",
       "SQLDATE                                  \n",
       "2015-08-07             1.0            1  \n",
       "2015-08-07             1.0            1  \n",
       "2015-08-07             1.0            7  \n",
       "2015-08-07             1.9           10  \n",
       "2015-08-07             0.0           10  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(filename, index_col='SQLDATE', parse_dates=True)\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# These steps incorporate the number of mentions a Goldstein score is associated with, reducing the impact of error in the event encoding, making the average better reflect the event's presence in the GDELT.\n",
    "df['GoldMentions'] = df['GoldsteinScale'] * df['NumMentions']\n",
    "goldstein = df.groupby([df.index.date]).agg({'GoldMentions': np.sum, 'NumMentions': np.sum})\n",
    "goldstein['GoldAverage'] = goldstein['GoldMentions'] / goldstein['NumMentions']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "full_daterange = pd.date_range(start=min(df.index), end=max(df.index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# interpolate() takes care of days that do not have a entry / Goldstein score in GDELT:\n",
    "goldstein = goldstein.reindex(full_daterange).interpolate(method='linear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sufficient data, backfilling was avoided.\n"
     ]
    }
   ],
   "source": [
    "# In case there is not enough historical data for the world leader:\n",
    "if len(goldstein['GoldAverage']) < 230:\n",
    "    day_difference = 230 - len(goldstein['GoldAverage']) + 1\n",
    "\n",
    "    new_dates = [goldstein.index[0] - dt.timedelta(days=x) for x in range(1, day_difference)]\n",
    "    new_dates = reversed(new_dates)\n",
    "\n",
    "    columns = ['NumMentions', 'GoldMentions', 'GoldAverage']\n",
    "    temp_data = {col: [None for x in range(1, day_difference)] for col in columns}\n",
    "    missing = pd.DataFrame(temp_data, index=new_dates, columns=columns)\n",
    "\n",
    "    temp_goldstein = missing.append(goldstein)\n",
    "    goldstein = temp_goldstein.fillna(method='bfill')\n",
    "\n",
    "    print 'Insufficient data, missing entries were backfilled.'\n",
    "else:\n",
    "    print 'Sufficient data, backfilling was avoided.'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Sample             Goldstein daily average\n",
      "2015-08-03                 1.599440\n",
      "2015-08-04                 2.368912\n",
      "2015-08-05                -1.088742\n",
      "2015-08-06                 1.525023\n",
      "2015-08-07                 2.479502\n",
      "\n",
      "Plot Sample             Goldstein daily average\n",
      "2015-08-03                 1.599440\n",
      "2015-08-04                 2.368912\n",
      "2015-08-05                -1.088742\n",
      "2015-08-06                 1.525023\n",
      "2015-08-07                 2.479502\n"
     ]
    }
   ],
   "source": [
    "#Create two instances of the data, one to train the model...\n",
    "training = pd.DataFrame(goldstein['GoldAverage'])\n",
    "training.index = pd.to_datetime(training.index)\n",
    "training.columns = ['Goldstein daily average']\n",
    "print 'Test Sample {}\\n'.format(training.tail())\n",
    "\n",
    "# ... the other to create the plot:\n",
    "plot_sample = pd.DataFrame(goldstein['GoldAverage'][-230:])\n",
    "plot_sample.index = pd.to_datetime(plot_sample.index)\n",
    "plot_sample.columns = ['Goldstein daily average']\n",
    "print 'Plot Sample {}'.format(plot_sample.tail())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<statsmodels.tsa.arima_model.ARMAResultsWrapper object at 0x110ef2cd0>\n"
     ]
    }
   ],
   "source": [
    "# Creates the forcasting model using Autoregressive Moving Average (ARMA):\n",
    "tries = 0\n",
    "success = False\n",
    "while tries < 6 and success is False:\n",
    "    try:\n",
    "        model = sm.tsa.ARMA(training,(12, tries)).fit()\n",
    "        success = True\n",
    "    except:\n",
    "        tries += 1\n",
    "\n",
    "if success is False:\n",
    "    print \"NOT SUCCESSFUL\"\n",
    "    \n",
    "print model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Creates the prediction based on the proceeding two weeks through one week into the future:\n",
    "prediction = model.predict(twoweeksago, str(nextweek), dynamic = False)\n",
    "\n",
    "# The fitting of the prediction to the model:\n",
    "prediction = prediction.shift(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def stir_the_pot(sample_range, st_dev):\n",
    "    mean = sample_range.mean()\n",
    "    added_range = mean + st_dev\n",
    "    random_suggestion = None\n",
    "\n",
    "    # Randomly suggest something either above or below the stagnant Goldstein range:\n",
    "    if random.randint(0,1) == 1:\n",
    "        random_suggestion = \"{0:.1f}\".format(random.uniform(float(added_range),10))\n",
    "    else:\n",
    "        random_suggestion = \"{0:.1f}\".format(random.uniform(-10,-float(added_range)))\n",
    "\n",
    "    print 'new suggestion: {}'.format(random_suggestion)\n",
    "    return float(random_suggestion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your actions are too stagnant!\n",
      "new suggestion: -5.4\n",
      "suggestion: -5.4\n"
     ]
    }
   ],
   "source": [
    "# Based on the Prediction, determine the Suggestion:\n",
    "sample_window = training[-60:]\n",
    "standard_deviation = float(sample_window.std())\n",
    "suggestion = None\n",
    "\n",
    "if standard_deviation < 2.1 and random.randint(1,5) > 2:\n",
    "    # The leader has been stuck in a rut and is not honing in on Goldstein Score of '1', 3/5 chance of getting a new suggestion:\n",
    "    print 'Your actions are too stagnant!'\n",
    "    suggestion = stir_the_pot(sample_window, standard_deviation)\n",
    "\n",
    "    predicts = round(prediction.ix[tomorrow:str(nextweek)].mean(), 1)\n",
    "    \n",
    "else:\n",
    "    # Finds the average of the Goldstein scores for the coming week:\n",
    "    predicts = round(prediction.ix[tomorrow:str(nextweek)].mean(), 1)\n",
    "    suggestion = round(((predicts - 1) * -1), 1)\n",
    "\n",
    "print 'suggestion: {}'.format(suggestion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# From the possible suggestions for the score, find one at random:\n",
    "prediction_text = random.choice(gsCodes.loc[predicts].values[0].split(';')).strip()\n",
    "gsDescription   = random.choice(gsCodes.loc[suggestion].values[0].split(';')).strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==================================================\n",
      "Pope Francis's Forecast: 1.7 Make an apology \n",
      "Pope Francis's Suggested score: -5.4 \n",
      "Suggested actions for the coming week:\n",
      "Perform a non-military demonstration or walk out on something\n",
      "==================================================\n"
     ]
    }
   ],
   "source": [
    "print '=================================================='\n",
    "print \"{}'s Forecast: {} {} \".format(leader_display_name, predicts, prediction_text)\n",
    "print \"{}'s Suggested score: {} \".format(leader_display_name, suggestion)\n",
    "\n",
    "if predicts == 1.0:\n",
    "    gsDescription = \"You're doing great. Stay the course.\"\n",
    "else:\n",
    "    print \"Suggested actions for the coming week:\\n{}\".format(gsDescription)\n",
    "\n",
    "print '=================================================='"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110ef2790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_sample.plot(kind='line',\n",
    "                 title='{}: Goldstein Trend and Prediction'.format(leader_display_name),\n",
    "                 ylim=(-10,10),\n",
    "                 figsize=(16,8),\n",
    "                 color='lightblue')\n",
    "prediction.plot(kind='line', \n",
    "                label='prediction',\n",
    "                legend=True,\n",
    "                color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}