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International Political Tele-Conferencing Equilibrium System
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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": {
"text/plain": [
"<matplotlib.axes.AxesSubplot at 0x11151a610>"
]
},
"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
}
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