{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
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   "source": [
    "#Rugby Analytics and FinTech - Tutorial\n",
    "* Peadar Coyle \n",
    "* Alternate title - Probabilistic Programming applied to Sports Analytics and FinTech\n",
    "* Alternate title - Why use PyMC and PyMC3?\n",
    "<img src='pydatalogo-generic.png'>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "* pip install patsy pandas\n",
    "* pip install pymc - PyMC2\n",
    "* pip install git+https://github.com/pymc-devs/pymc3 - PyMC3\n",
    "* Also need Scipy, NumPy, Seaborn - basically the PyData Stack\n",
    "* Anaconda is excellent to get up and running\n",
    "* Stop me and ask questions as we go along"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
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   },
   "source": [
    "\n",
    "# Newsflash\n",
    "* PyMC3 is Beta\n",
    "* Big shout out to John Salvatier, Thomas Wiecki and Chris Fonnesbeck - great work people :) \n",
    "* You are attending the first tutorial in the world on PyMC3 since it went Beta!\n",
    "* Welcome to the cutting-edge of Probabilistic Programming with the PyData stack :) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "* The next slide is just some code. Run it as a test for if you've installed the correct things.\n",
    "* The code is taken from Thomas Wiecki's tutorials on applying PyMC3 to Quantitative finance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
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    "slideshow": {
     "slide_type": "slide"
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   "source": [
    "%matplotlib inline\n",
    "#Most of this code is from @twiecki - used with attribution\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import itertools\n",
    "import scipy as sp\n",
    "import pymc3 as pm3\n",
    "import theano.tensor as T\n",
    "from scipy import stats\n",
    "from IPython.core.pylabtools import figsize\n",
    "\n",
    "figsize(12, 12)\n",
    "sns.set_style('darkgrid')\n",
    "import scipy\n",
    "data_0 = pd.read_csv('data0.csv', index_col=0, parse_dates=True, header=None)[1]\n",
    "data_1 = pd.read_csv('data1.csv', index_col=0, parse_dates=True, header=None)[1]\n",
    "def var_cov_var_t(P, c, nu=1, mu=0, sigma=1, **kwargs):\n",
    "    \"\"\"\n",
    "    Variance-Covariance calculation of daily Value-at-Risk\n",
    "    using confidence level c, with mean of returns mu\n",
    "    and standard deviation of returns sigma, on a portfolio\n",
    "    of value P.\n",
    "    \"\"\"\n",
    "    alpha = stats.t.ppf(1-c, nu, mu, sigma)\n",
    "    return P - P*(alpha + 1)\n",
    "\n",
    "def var_cov_var_normal(P, c, mu=0, sigma=1, **kwargs):\n",
    "    \"\"\"\n",
    "    Variance-Covariance calculation of daily Value-at-Risk\n",
    "    using confidence level c, with mean of returns mu\n",
    "    and standard deviation of returns sigma, on a portfolio\n",
    "    of value P.\n",
    "    \"\"\"\n",
    "    alpha = stats.norm.ppf(1-c, mu, sigma)\n",
    "    return P - P*(alpha + 1)\n",
    "\n",
    "def sample_normal(mu=0, sigma=1, **kwargs):\n",
    "    samples = stats.norm.rvs(mu, sigma, kwargs.get('size', 100))\n",
    "    return samples\n",
    "\n",
    "def sample_t(nu=1, mu=0, sigma=1, **kwargs):\n",
    "    samples = stats.t.rvs(nu, mu, sigma, kwargs.get('size', 100))\n",
    "    return samples\n",
    "\n",
    "def eval_normal(mu=0, sigma=1, **kwargs):\n",
    "    pdf = stats.norm(mu, sigma).pdf(kwargs.get('x', np.linspace(-0.05, 0.05, 500)))\n",
    "    return pdf\n",
    "\n",
    "def eval_t(nu=1, mu=0, sigma=1, **kwargs):\n",
    "    samples = stats.t(nu, mu, sigma).pdf(kwargs.get('x', np.linspace(-0.05, 0.05, 500)))\n",
    "    return samples\n",
    "\n",
    "def logp_normal(mu=0, sigma=1, **kwargs):\n",
    "    logp = np.sum(stats.norm(mu, sigma).logpdf(kwargs['data']))\n",
    "    return logp\n",
    "\n",
    "def logp_t(nu=1, mu=0, sigma=1, **kwargs):\n",
    "    logp = np.sum(stats.t(nu, mu, sigma).logpdf(kwargs['data']))\n",
    "    return logp\n",
    "\n",
    "# generate posterior predictive\n",
    "def post_pred(func, trace, *args, **kwargs):\n",
    "    samples = kwargs.pop('samples', 50)\n",
    "    ppc = []\n",
    "    for i, idx in enumerate(np.linspace(0, len(trace), samples)):\n",
    "        t = trace[int(i)]\n",
    "        try:\n",
    "            kwargs['nu'] = t['nu_minus_one']+1\n",
    "        except KeyError:\n",
    "            pass\n",
    "        mu = t['mean returns']\n",
    "        sigma = t['volatility']\n",
    "        ppc.append(func(*args, mu=mu, sigma=sigma, **kwargs))\n",
    "\n",
    "    return ppc\n",
    "\n",
    "def plot_strats(sharpe=False):\n",
    "    figsize(12, 6)\n",
    "    f, (ax1, ax2) = plt.subplots(1, 2)\n",
    "    if sharpe:\n",
    "        label = 'etrade\\nn=%i\\nSharpe=%.2f' % (len(data_0), (data_0.mean() / data_0.std() * np.sqrt(252)))\n",
    "    else:\n",
    "        label = 'etrade\\nn=%i\\n' % (len(data_0))\n",
    "    sns.distplot(data_0, kde=False, ax=ax1, label=label, color='b')\n",
    "    ax1.set_xlabel('daily returns'); ax1.legend(loc=0)\n",
    "    if sharpe:\n",
    "        label = 'IB\\nn=%i\\nSharpe=%.2f' % (len(data_1), (data_1.mean() / data_1.std() * np.sqrt(252)))\n",
    "    else:\n",
    "        label = 'IB\\nn=%i\\n' % (len(data_1))\n",
    "    sns.distplot(data_1, kde=False, ax=ax2, label=label, color='g')\n",
    "    ax2.set_xlabel('daily returns'); ax2.legend(loc=0);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [-----------------100%-----------------] 5000 of 5000 complete in 11.8 sec"
     ]
    }
   ],
   "source": [
    "def model_returns_normal(data):\n",
    "    with pm3.Model() as model:\n",
    "        mu = pm3.Normal('mean returns', mu=0, sd=.01, testval=data.mean())\n",
    "        sigma, log_sigma = model.TransformedVar('volatility', \n",
    "                                                pm3.HalfCauchy.dist(beta=1, testval=data.std()), \n",
    "                                                pm3.transforms.logtransform)\n",
    "        #sigma = pm.HalfCauchy('volatility', beta=.1, testval=data.std())\n",
    "        returns = pm3.Normal('returns', mu=mu, sd=sigma, observed=data)\n",
    "        ann_vol = pm3.Deterministic('annual volatility', returns.distribution.variance**.5 * np.sqrt(252))\n",
    "        sharpe = pm3.Deterministic('sharpe', \n",
    "                                  returns.distribution.mean / returns.distribution.variance**.5 * np.sqrt(252))\n",
    "        start = pm3.find_MAP(fmin=scipy.optimize.fmin_powell)\n",
    "        step = pm3.NUTS(scaling=start)\n",
    "        trace_normal = pm3.sample(5000, step, start=start)\n",
    "    return trace_normal\n",
    "\n",
    "def model_returns_t(data):\n",
    "    with pm3.Model() as model:\n",
    "        mu = pm3.Normal('mean returns', mu=0, sd=.01, testval=data.mean())\n",
    "        sigma, log_sigma = model.TransformedVar('volatility', \n",
    "                                                pm3.HalfCauchy.dist(beta=1, testval=data.std()), \n",
    "                                                pm3.transforms.logtransform)\n",
    "        nu, log_nu = model.TransformedVar('nu_minus_one',\n",
    "                                          pm3.Exponential.dist(1./10., testval=3.),\n",
    "                                          pm3.transforms.logtransform)\n",
    "\n",
    "        returns = pm3.T('returns', nu=nu+2, mu=mu, sd=sigma, observed=data)\n",
    "        ann_vol = pm3.Deterministic('annual volatility', returns.distribution.variance**.5 * np.sqrt(252))\n",
    "        sharpe = pm3.Deterministic('sharpe', \n",
    "                                  returns.distribution.mean / returns.distribution.variance**.5 * np.sqrt(252))\n",
    "\n",
    "        start = pm3.find_MAP(fmin=scipy.optimize.fmin_powell)\n",
    "        step = pm3.NUTS(scaling=start)\n",
    "        trace = pm3.sample(5000, step, start=start)\n",
    "\n",
    "    return trace\n",
    "\n",
    "def model_returns_t_stoch_vol(data):\n",
    "    from pymc3.distributions.timeseries import GaussianRandomWalk\n",
    "\n",
    "    with pm3.Model() as model:\n",
    "        mu = pm3.Normal('mean returns', mu=0, sd=.01, testval=data.mean())\n",
    "        step_size, log_step_size = model.TransformedVar('step size', \n",
    "                                                pm3.Exponential.dist(1./.02, testval=.06), \n",
    "                                                pm3.transforms.logtransform)\n",
    "        \n",
    "        vol = GaussianRandomWalk('volatility', step_size**-2, shape=len(data))\n",
    "        \n",
    "        nu, log_nu = model.TransformedVar('nu_minus_one',\n",
    "                                          pm3.Exponential.dist(1./10., testval=3.),\n",
    "                                          pm3.transforms.logtransform)\n",
    "\n",
    "        returns = pm3.T('returns', nu=nu+2, mu=mu, lam=pm.exp(-2*vol), observed=data)\n",
    "        #ann_vol = pm.Deterministic('annual volatility', returns.distribution.variance**.5 * np.sqrt(252))\n",
    "        #sharpe = pm.Deterministic('sharpe', \n",
    "        #                          returns.distribution.mean / ann_vol)\n",
    "\n",
    "        start = pm3.find_MAP(vars=[vol], fmin=sp.optimize.fmin_l_bfgs_b)\n",
    "        #start = pm.find_MAP(fmin=scipy.optimize.fmin_powell, start=start)\n",
    "        step = pm3.NUTS(scaling=start)\n",
    "        trace = pm3.sample(5000, step, start=start)\n",
    "\n",
    "    return trace\n",
    "\n",
    "results_normal = {0: model_returns_normal(data_0),\n",
    "                  1: model_returns_normal(data_1)}\n",
    "results_t = {0: model_returns_t(data_0),\n",
    "             1: model_returns_t(data_1)}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "##Who am I?\n",
    "* I'm a Data Analytics Professional based in Luxembourg\n",
    "* I currently work for Vodafone\n",
    "* My intellectual background is in Physics, Philosophy and Mathematics - including some Financial Mathematics\n",
    "* I worked at Amazon in Supply Chain Analytics\n",
    "* I interned at http://import.io - where I got introduced to Data Science\n",
    "* I've made open source contributions to Pandas and Probabilistic Programming and Bayesian Methods for Hackers. \n",
    "* All opinions are my own!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
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   "source": [
    "<p>\n",
    "\n",
    "<table style=\"border-collapse: collapse; border-top-color: rgb(255, 255, 255); border-right-color: rgb(255, 255, 255); border-bottom-color: rgb(255, 255, 255); border-left-color: rgb(255, 255, 255); border-top-width: 1px; border-right-width: 1px; border-bottom-width: 1px; border-left-width: 1px; \" border=\"0\" bordercolor=\"#888\" cellspacing=\"0\" align=\"left\">\n",
    "  <tr>\n",
    "    <td>\n",
    "<a href=\"mailto: peadarcoyle@gmail.com\"><svg width=\"40px\" height=\"40px\" viewBox=\"0 0 60 60\" version=\"1.1\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:sketch=\"http://www.bohemiancoding.com/sketch/ns\">\n",
    "    <path d=\"M0.224580688,30 C0.224580688,13.4314567 13.454941,0 29.7754193,0 C46.0958976,0 59.3262579,13.4314567 59.3262579,30 C59.3262579,46.5685433 46.0958976,60 29.7754193,60 C13.454941,60 0.224580688,46.5685433 0.224580688,30 Z M0.224580688,30\" fill=\"#FFFFFF\" sketch:type=\"MSShapeGroup\"></path>\n",
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    "</svg></a> \n",
    "</td> \n",
    "<td>\n",
    "<a href=\"mailto: peadarcoyle@gmail.com\" target=\"_blank\">peadarcoyle@gmail.com</a>  \n",
    "</td> </tr><tr> <td>\n",
    "                 \n",
    "<a href=\"http://github.com/springcoil\"><svg width=\"40px\" height=\"40px\" viewBox=\"0 0 60 60\" version=\"1.1\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:sketch=\"http://www.bohemiancoding.com/sketch/ns\">\n",
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    "</svg></a>\n",
    "\n",
    "</td><td>\n",
    "<a href=\"http://github.com/springcoil\" target=\"_blank\">http://github.com/springcoil</a>  \n",
    "\n",
    "</td> </tr><tr> <td>\n",
    "\n",
    "<a href=\"https://www.linkedin.com/in/peadarcoyle\"><svg width=\"40px\" height=\"40px\" viewBox=\"0 0 60 60\" version=\"1.1\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:sketch=\"http://www.bohemiancoding.com/sketch/ns\">\n",
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    "</svg></a> \n",
    "</td> <td>\n",
    "<a href=\"https://www.linkedin.com/in/peadarcoyle\" target=\"_blank\">https://www.linkedin.com/in/peadarcoyle</a>  \n",
    "\n",
    "</td> </tr><tr> <td>\n",
    "\n",
    "<a href=\"http://twitter.com/springcoil\"><svg width=\"40px\" height=\"40px\" viewBox=\"0 0 60 60\" version=\"1.1\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:sketch=\"http://www.bohemiancoding.com/sketch/ns\">\n",
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    "</svg></a>\n",
    "</td> <td>\n",
    "<a href=\"http://twitter.com/springcoil\" target=\"_blank\">@springcoil</a>  \n",
    "\n",
    "</td> </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#Contents: Probabilistic Programming applied to not only Rugby\n",
    "* I'll discuss what probabilistic programming is, why should you care and how to use PyMC from Python to implement these methods. \n",
    "* I'll be applying these methods to studying the problem of 'rugby sports analytics' particularly how to model the winning team in the recent Six Nations in Rugby. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "* I will discuss the framework and how I was able to quickly and easily produce an innovative and powerful model as a non-expert.\n",
    "* I'll include examples from Finance (FinTech), toy examples from Cameron Davidson-Pilon (for Football or Soccer) and a defence of Bayesian Statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#All Sports Commentary!\n",
    "<img src=\"http://imgs.xkcd.com/comics/sports.png\" align=\"center\">\n",
    "* Attribution: Xkcd\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# How can statistics help with sports?\n",
    "* Well fundamentally a Rugby game is a simulatable event.\n",
    "* How do we generate a model to predict the outcome of a tournament?\n",
    "* How do we quantify our uncertainty in our model?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#What influenced me on this?\n",
    "<img src=\"http://blog.quantopian.com/wp-content/uploads/2014/12/GraphicalModel.jpg\">\n",
    "Attribution: Quantopian blog"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#What's wrong with statistics\n",
    "* Models should not be built for mathematical convenience (e.g. normality assumption), but to most accurately model the data.\n",
    "\n",
    "* Pre-specified models, like frequentist statistics, make many assumptions that are all to easily violated.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#\"The purpose of computation is insight, not numbers.\" -- Richard Hamming"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# What is Bayesian Statistics?\n",
    "* At the core: formula to update our beliefs after having observed data (Bayes formula)\n",
    "* Implies that we have a prior belief about the world.\n",
    "* Updated beliefs after observing data is called posterior.\n",
    "* Beliefs are represented using random variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Who uses Bayesian Statistics?\n",
    "* In the **real** world?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/0/0b/Zalando_logo.svg/1000px-Zalando_logo.svg.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## What they did?\n",
    "* Deriving our new measuring model and verifying that it works took some effort! But it was all worth it, because now we have:\n",
    "\n",
    "* Automatic weight estimations for each Zalando article, which saves workers time\n",
    "* A reliable way to know the accuracy of our estimations\n",
    "* And most importantly: our warehouse workers can now focus on getting your fashion to you as quickly as possible. That’s isn’t just saving money–that’s priceless.\n",
    "- Attribution: The Zalando tech blog\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## We will have a quick detour\n",
    "###Short primer on random variables (hattip: @twiecki)\n",
    "* Represents our beliefs about an unknown state.\n",
    "* Probability distribution assigns a probability to each possible state.\n",
    "* Not a single number (e.g. most likely state).\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## We already know what a variable is\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "coin = 0 # 0 for tails\n",
    "coin = 1 # 1 for heads"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "##A random variable assigns all possible values a certain probability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "#coin = {0: 50%,\n",
    "#        1: 50%}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Alternatively:\n",
    "* coin ~ Bernoulli(p=0.5)\n",
    "* coin is a random variable\n",
    "* Bernoulli is a probability distribution\n",
    "* ~ reads as \"is distributed as\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Binary cases are not so interesting - the world is continuous\n",
    "* Returns ~ Normal($\\mu, \\sigma^{2}$)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1240c8f28>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MprS/v6/l5SXTpQCA5wjHAOoe/cbH467T3FzGcCUA4D3CMYC65+4cM8atMoRj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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11db25f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "sns.distplot(data_0, kde=False, fit=stats.norm)\n",
    "plt.xlabel('returns')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# How to estimate μ and σ?\n",
    "* Naive: point estimate\n",
    "* Set mu = mean(data) and sigma = std(data)\n",
    "* Maximum Likelihood Estimate\n",
    "* Correct answer as n→∞\n",
    "# Bayesian analysis\n",
    "* Most of the time n $\\neq \\infty$...\n",
    "* Uncertainty about μ and σ\n",
    "* Turn μ and σ into random variables\n",
    "* How to estimate?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Some terminology\n",
    "<img src=\"http://www.lancs.ac.uk/~jamest/Group/images/bayesthm.JPG\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# A Toy Problem\n",
    "* A 'toy' problem is a notion from Physics, that you use a simple problem first to derive insight.\n",
    "* We will use one of Allen Downeys examples - the code is in PyMC2 and comes from Cameron Davidson-Pilon\n",
    "* It is hard to find examples online that aren't influenced by either of these two heavyweights :) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# First build the model then Infer. \n",
    "* Our first step is to build the model. \n",
    "* The second step is to infer. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "In the final match of the 2014 FIFA World Cup, Germany defeated Argentina 1-0. How much evidence does this victory provide that Germany had the better team? What is the probability that Germany would win a rematch?\n",
    "- From Allen Downey"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Our first step is that we need to make some Modelling decisions\n",
    "* What kind of Model suits Football (Soocer)?\n",
    "* Well we know empirically that scoring follows a Poisson process\n",
    "* This means that a *Poisson* distribution will be our likelihood function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "\"Scoring in games like soccer and hockey can be well modeled by a Poisson process, which assumes that each team, \n",
    "against a given opponent, will score goals at some goal-scoring rate, $\\lambda$, and that this rate is stationary; \n",
    "in other words, the probability of scoring a goal is about the same at any point during the game.\" - Allen Downey\n",
    "\n",
    "* A good framework for this is to use Bayesian Statistics\n",
    "* We will use this in this example - and the code comes from Cameron Davidson-Pilon\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "import pymc as pm\n",
    "%matplotlib inline\n",
    "sns.set_style(\"darkgrid\")\n",
    "from matplotlib.pyplot import *\n",
    "figsize(12,8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "avg_goals_per_team = 1.34\n",
    "duration_of_game = 93.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hxsbGxo4dvsUFAIBtdYsC5xEF4+VX69xjzvmHy6s2fwfj7uryQ22/Y8eO9u698lCrsE32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\n/wBCZX4MMSa97wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x133071828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "german_prior = pm.Exponential('german_prior', duration_of_game/avg_goals_per_team)\n",
    "arg_prior = pm.Exponential('arg_prior', duration_of_game/avg_goals_per_team)\n",
    "\n",
    "sample = np.array([german_prior.random() for i in range(10000)])\n",
    "hist(sample, bins=30, normed=True, histtype='stepfilled');\n",
    "plt.title('Prior distribution: Exponential with mean equal to observed mean');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "* So now that we've done our Prior Distribution. We need to add some observed data and infer our Posterior.\n",
    "* Our posterior is 'the probability of winning the final' or the 'what is the most likely result'\n",
    "* We model the scoring function as a Poisson taking into account the Prior of the team and the observed data\n",
    "* Finally we create a predictive model - which consists of the duration of the game*the prior "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "germany = pm.Poisson('germany_obs', german_prior, observed=True, value=[1])\n",
    "argentina = pm.Poisson('arg_obs', arg_prior, observed=True, value=[0])\n",
    " \n",
    "germany_predictive = pm.Poisson('germany_predictive', duration_of_game*german_prior)\n",
    "arg_predictive = pm.Poisson('arg_predictive', duration_of_game*arg_prior)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "mcmc = pm.MCMC([germany, argentina, german_prior, \n",
    "                arg_prior, germany_predictive, arg_predictive])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "* Now we need to run the model :) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [-----------------100%-----------------] 20000 of 20000 complete in 2.7 sec"
     ]
    }
   ],
   "source": [
    "mcmc.sample(20000, 5000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "german_lambda_trace = mcmc.trace('german_prior')[:]\n",
    "arg_lambda_trace = mcmc.trace('arg_prior')[:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6CXWZxm09bSsO3OfAMlUlSZKkoTbug/HEaZNYM62DNXRkGle9bEmZKpIkSdJw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\nLEmSJAHw/wFQEMyCJKB4pwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x133799a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist(german_lambda_trace, bins=45, histtype='stepfilled', label='Germany', \n",
    "     alpha=0.9, normed=True);\n",
    "hist(arg_lambda_trace, bins=45, histtype='stepfilled', label='Argentina', \n",
    "     alpha=0.8, normed=True);\n",
    "plt.legend();\n",
    "plt.title('Posteriors of average goals/minute of the two teams');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "german_post_trace = mcmc.trace('germany_predictive')[:]\n",
    "arg_post_trace = mcmc.trace('arg_predictive')[:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1337b6a58>"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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55z848sh309/fz2GHvZ4vf/krjB8/vi3P3auLSJIkqS3uvvsuttpqq0duH3fce3nXu97B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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x133694550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "hist(german_post_trace, bins=10, histtype='stepfilled', label='Germany', alpha=0.9, normed=True);\n",
    "hist(arg_post_trace, bins=10, histtype='stepfilled', label='Argentina', alpha=0.8, normed=True);\n",
    "plt.legend();\n",
    "plt.title('Posteriors of Goals per Team for a 93 minute game');\n",
    "plt.ylabel('probability')\n",
    "plt.xlabel('Number of goals')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probability of Germany winning: 0.604\n",
      "Probability of Argentina winning: 0.223\n",
      "Probability of tie: 0.173\n"
     ]
    }
   ],
   "source": [
    "print(\"Probability of Germany winning: %.3f\"%(german_post_trace > arg_post_trace).mean())\n",
    "print(\"Probability of Argentina winning: %.3f\"%(german_post_trace < arg_post_trace).mean())\n",
    "print(\"Probability of tie: %.3f\"%(german_post_trace == arg_post_trace).mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Wrap up \n",
    "* We built a model of the 'teams strength' from our priors and our knowledge of scoring functions in Soccer.\n",
    "* We can definitely apply this kind of formulation to OTHER problems\n",
    "* Such as Rugby!\n",
    "* Well done you just built a Probabilistic Programming Model :) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "###So what problem could I apply Bayesian models to?\n",
    "* Rugby Analysis!\n",
    "<img src=\"rugbyball.jpg\", width=\"50%\" height=\"50%\"/>\n",
    "Attribution: http://www.sportsballshop.co.uk"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#Bayesian Rugby\n",
    " I came across the following blog post on http://danielweitzenfeld.github.io/passtheroc/blog/2014/10/28/bayes-premier-league/ \n",
    "\n",
    "\n",
    "\n",
    "* Based on the work of [Baio and Blangiardo](http://www.statistica.it/gianluca/Research/BaioBlangiardo.pdf)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "* In this talk, I'm going to reproduce the first model described in the paper using pymc.\n",
    "* Since I am a rugby fan I decide to apply the results of the paper Bayesian Football to the Six Nations.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#So why Bayesians?\n",
    "* Probabilistic Programming is a new paradigm.\n",
    "* Attributions: My friend Thomas Wiecki influenced a lot of my thinking on this.\n",
    "* I'm going to compare Blackbox Machine Learning with scikit-learn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<img src='https://raw.githubusercontent.com/twiecki/pymc3_talk/master/blackbox.png'>\n",
    "\n",
    "* Source: <a href='https://speakerdeck.com/ogrisel/trends-in-machine-learning'>Olivier Grisel's talk on ML</a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Limitations of Machine learning\n",
    "* A big limitation of Machine Learning is that most of the models are black boxes.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "\n",
    "<img src='https://raw.githubusercontent.com/twiecki/pymc3_talk/master/openbox.png'>\n",
    "\n",
    "Source: <a href='https://speakerdeck.com/ogrisel/trends-in-machine-learning'>Olivier Grisel's talk on ML</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Probabilistic Programming - what's the big deal?\n",
    "* We are able to use data and our prior beliefs to generate a model. \n",
    "* Generating a model is extremely powerful\n",
    "* We can tell a story, which appeals to our understanding of stories. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import math\n",
    "import warnings\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import pymc # I know folks are switching to \"as pm\" but I'm just not there yet\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Six Nations Rugby\n",
    "* Rugby is a physical sport popular worldwide.\n",
    "* Six Nations consists of Italy, Ireland, Scotland, England, France and Wales\n",
    "* Game consists of scoring tries (similar to touch downs) or kicking the goal.\n",
    "* Average player is something like 100kg and 1.82m tall.\n",
    "* Paul O'Connell the Irish captain is Height: 6' 6\" (1.98 m) Weight: 243 lbs (110 kg)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#They compete for this!\n",
    "* Significant this year because the World Cup occurs in 2015.\n",
    "<img src=\"http://www.hostelrome.com/wp-content/uploads/2015/01/Rugby_Sei-Nazioni-Uomini.jpg\">\n",
    "* Photo: Hostelrome"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Motivation\n",
    "\n",
    "\n",
    "* Your estimate of the strength of a team depends on your estimates of the other strengths\n",
    "* Ireland are a stronger team than Italy for example - but by how much?\n",
    "* Source for Results 2014 are Wikipedia. \n",
    "* I handcrafted these results\n",
    "* Small data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<img src='smalldata.jpg'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "DATA_DIR = os.path.join(os.getcwd(), 'data/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "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>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Scotland</td>\n",
       "      <td>France</td>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>England</td>\n",
       "      <td>Wales</td>\n",
       "      <td>29</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Italy</td>\n",
       "      <td>England</td>\n",
       "      <td>11</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Wales</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>51</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>France</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>20</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   home_team away_team  home_score  away_score\n",
       "10  Scotland    France          17          19\n",
       "11   England     Wales          29          18\n",
       "12     Italy   England          11          52\n",
       "13     Wales  Scotland          51           3\n",
       "14    France   Ireland          20          22"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The results_2014 is a handcrafted results table from Wikipedia\n",
    "data_file = DATA_DIR + 'results_2014.csv'\n",
    "df = pd.read_csv(data_file, sep=',')\n",
    "df.tail()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "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>team</th>\n",
       "      <th>i</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Wales</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>France</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ireland</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Scotland</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Italy</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       team  i\n",
       "0     Wales  0\n",
       "1    France  1\n",
       "2   Ireland  2\n",
       "3  Scotland  3\n",
       "4     Italy  4"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "teams = df.home_team.unique()\n",
    "teams = pd.DataFrame(teams, columns=['team'])\n",
    "teams['i'] = teams.index\n",
    "teams.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "* Now we need to do some merging and munging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "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>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
       "      <th>i_home</th>\n",
       "      <th>i_away</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Wales</td>\n",
       "      <td>Italy</td>\n",
       "      <td>23</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>France</td>\n",
       "      <td>England</td>\n",
       "      <td>26</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ireland</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>28</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ireland</td>\n",
       "      <td>Wales</td>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  home_team away_team  home_score  away_score  i_home  i_away\n",
       "0     Wales     Italy          23          15       0       4\n",
       "1    France   England          26          24       1       5\n",
       "2   Ireland  Scotland          28           6       2       3\n",
       "3   Ireland     Wales          26           3       2       0\n",
       "4  Scotland   England           0          20       3       5"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.merge(df, teams, left_on='home_team', right_on='team', how='left')\n",
    "df = df.rename(columns = {'i': 'i_home'}).drop('team', 1)\n",
    "df = pd.merge(df, teams, left_on='away_team', right_on='team', how='left')\n",
    "df = df.rename(columns = {'i': 'i_away'}).drop('team', 1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "observed_home_goals = df.home_score.values\n",
    "observed_away_goals = df.away_score.values\n",
    "home_team = df.i_home.values\n",
    "away_team = df.i_away.values\n",
    "num_teams = len(df.i_home.drop_duplicates())\n",
    "num_games = len(home_team)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Now we need to prepare the model for PyMC."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "g = df.groupby('i_away')\n",
    "att_starting_points = np.log(g.away_score.mean())\n",
    "g = df.groupby('i_home')\n",
    "def_starting_points = -np.log(g.away_score.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# What do we want to infer?\n",
    "* We want to infer the latent paremeters (every team's strength) that are generating the data we observe (the scorelines). \n",
    "* Moreover, we know that the scorelines are a noisy measurement of team strength, so ideally, we want a model that makes it easy to quantify our uncertainty about the underlying strengths.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# While my MCMC gently samples\n",
    "* Often we don't know what the Bayesian Model is explicitly, so we have to 'estimate' the Bayesian Model'\n",
    "* If we can't solve something, approximate it.\n",
    "* Markov-Chain Monte Carlo (MCMC) instead draws samples from the posterior.\n",
    "* Fortunately, this algorithm can be applied to almost any model.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# What do we want?\n",
    "* We want to quantify our uncertainty\n",
    "* We want to also use this to generate a model\n",
    "* We want the answers as distributions *not* point estimates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## What assumptions do we know for our 'generative story'?\n",
    "* We know that the Six Nations in Rugby only has 6 teams.\n",
    "* We have data from last year!\n",
    "* We also know that in sports scoring is modelled as a Poisson distribution\n",
    "<img src=\"http://upload.wikimedia.org/wikipedia/commons/1/16/Poisson_pmf.svg\">\n",
    "* Attribution: *Wikipedia*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#The model.\n",
    "<p>The league is made up by a total of T= 6 teams, playing each other once \n",
    "in a season. We indicate the number of points scored by the home and the away team in the g-th game of the season (15 games) as $y_{g1}$ and $y_{g2}$ respectively. </p>\n",
    "<p>The vector of observed counts $\\mathbb{y} = (y_{g1}, y_{g2})$ is modelled as independent Poisson:\n",
    "$y_{gi}| \\theta_{gj} \\tilde\\;\\;  Poisson(\\theta_{gj})$\n",
    "where the theta parameters represent the scoring intensity in the g-th game for the team playing at home (j=1) and away (j=2), respectively.</p>\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<p>We model these parameters according to a formulation that has been used widely in the statistical literature, assuming a log-linear random effect model:\n",
    "$$log \\theta_{g1} = home + att_{h(g)} + def_{a(g)} $$\n",
    "$$log \\theta_{g2} = att_{a(g)} + def_{h(g)}$$\n",
    "the parameter home represents the advantage for the team hosting the game\n",
    "and we assume that this effect is constant for all the teams and\n",
    "throughout the season. \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "* Key assumption home effect is an advantage in Sports\n",
    "* We know these things empirically from our 'domain specific' knowledge\n",
    "* Bayesian Models allow you to incorporate *beliefs* or *knowledge* into your model!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "In addition, the scoring intensity is \n",
    "determined jointly by the attack\n",
    "and defense ability of the two teams involved, represented by the parameters att and def, respectively.\n",
    "In line with the Bayesian approach, we have to specify some suitable prior distributions for all the \n",
    "random parameters in our model. The variable $home$ \n",
    "is modelled as a fixed effect, assuming a standard \n",
    "flat prior distribution. We use the notation of describing the Normal distribution in terms of mean \n",
    "and the precision. \n",
    "$home \\tilde\\; Normal(0,0.0001)$</p>\n",
    "<p>Conversely, for each t = 1, ..., T, the team-specific effects are modelled as exchangeable from a common distribution:\n",
    "$att_t \\tilde\\; Normal(\\mu_{att}, \\tau_{att})$\n",
    "and $def_t \\tilde\\; Normal(\\mu_{def}, \\tau_{def})$</p>\n",
    "<p>Note that they're breaking out team strength into attacking and defending strength. \n",
    "A negative defense parameter will sap the mojo from the opposing team&#39;s attacking parameter.</p>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "<p>I made two tweaks to the model. It didn't make sense to me to have a $\\mu_{att}$ when we&#39;re enforcing the sum-to-zero constraint by subtracting the mean anyway. So I eliminated $\\mu_{att}$ and $\\mu_{def}$</p>\n",
    "<p>Also because of the sum-to-zero constraint, \n",
    "it seemed to me that we needed an intercept term in the log-linear model, capturing the average goals scored per game by the away team.\n",
    "This we model with the following hyperprior.\n",
    "$$intercept \\tilde\\; Normal(0, 0.001)$$</p>\n",
    "\n",
    "* The hyper-priors on the attack and defense parameters are also flat\n",
    "* $\\mu_att \\tilde\\; Normal(0,0.001)$\n",
    "* $\\mu_def \\tilde\\; Normal(0,0.001)$\n",
    "* $\\tau_att \\tilde\\; \\Gamma(0.1,0.1)$\n",
    "* $\\tau_def \\tilde\\; \\Gamma(0.1,0.1)$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "###Digression: Why the flat priors were picked (our hyperpriors are flat)\n",
    "* There are lots of different ways of picking priors - see the literature\n",
    "* I learned from this that it didn't make any difference to the results picking a uniform prior versus a flat prior\n",
    "\n",
    "Often it simply doesn't matter much what prior you use. \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "\n",
    "###Definition: A prior distribution is non-informative if the prior is “flat” relative to the likelihood function.\n",
    "\n",
    "\n",
    "* We are trying to have a reasonable model and let inference happen from the data set.\n",
    "* ##Key takeaway - Often in Bayesian modelling it doesn't matter what your priors are. Let the data generate the story :) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "#hyperpriors - which apply to all teams\n",
    "home = pymc.Normal('home', 0, .0001, value=0)\n",
    "tau_att = pymc.Gamma('tau_att', .1, .1, value=10)\n",
    "tau_def = pymc.Gamma('tau_def', .1, .1, value=10)\n",
    "intercept = pymc.Normal('intercept', 0, .0001, value=0)\n",
    "\n",
    "#team-specific parameters\n",
    "atts_star = pymc.Normal(\"atts_star\", \n",
    "                        mu=0, \n",
    "                        tau=tau_att, \n",
    "                        size=num_teams, \n",
    "                        value=att_starting_points.values)\n",
    "defs_star = pymc.Normal(\"defs_star\", \n",
    "                        mu=0, \n",
    "                        tau=tau_def, \n",
    "                        size=num_teams, \n",
    "                        value=def_starting_points.values) \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# trick to code the sum to zero constraint\n",
    "@pymc.deterministic\n",
    "def atts(atts_star=atts_star):\n",
    "    atts = atts_star.copy()\n",
    "    atts = atts - np.mean(atts_star)\n",
    "    return atts\n",
    "\n",
    "@pymc.deterministic\n",
    "def defs(defs_star=defs_star):\n",
    "    defs = defs_star.copy()\n",
    "    defs = defs - np.mean(defs_star)\n",
    "    return defs\n",
    "\n",
    "@pymc.deterministic\n",
    "def home_theta(home_team=home_team, \n",
    "               away_team=away_team, \n",
    "               home=home, \n",
    "               atts=atts, \n",
    "               defs=defs, \n",
    "               intercept=intercept): \n",
    "    return np.exp(intercept + \n",
    "                  home + \n",
    "                  atts[home_team] + \n",
    "                  defs[away_team])\n",
    "  \n",
    "@pymc.deterministic\n",
    "def away_theta(home_team=home_team, \n",
    "               away_team=away_team, \n",
    "               home=home, \n",
    "               atts=atts, \n",
    "               defs=defs, \n",
    "               intercept=intercept): \n",
    "    return np.exp(intercept + \n",
    "                  atts[away_team] + \n",
    "                  defs[home_team])   \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Let us run the model!\n",
    "* We specify the priors as Gamma distributions\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [-----------------100%-----------------] 200000 of 200000 complete in 74.3 sec"
     ]
    }
   ],
   "source": [
    "#likelihood of observed data\n",
    "home_points = pymc.Poisson('home_points', \n",
    "                          mu=home_theta, \n",
    "                          value=observed_home_goals, \n",
    "                          observed=True)\n",
    "away_points = pymc.Poisson('away_points', \n",
    "                          mu=away_theta, \n",
    "                          value=observed_away_goals, \n",
    "                          observed=True)\n",
    "\n",
    "#We need to approximate the Posterior by running a sampling algorithm \n",
    "mcmc = pymc.MCMC([home, intercept, tau_att, tau_def, \n",
    "                  home_theta, away_theta, \n",
    "                  atts_star, defs_star, atts, defs, \n",
    "                  home_points, away_points])\n",
    "map_ = pymc.MAP( mcmc )\n",
    "map_.fit()\n",
    "\n",
    "mcmc.sample(200000, 40000, 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#Diagnostics\n",
    "Let's see if/how the model converged. \n",
    "The home parameter looks good, and indicates that home field advantage amounts to goals per game at the intercept.\n",
    "We can see that it converges just like the model for the Premier League in the other tutorial.\n",
    "I wonder and this is left as a question if all field sports have models of this form that converge."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting home\n"
     ]
    },
    {
     "data": {
      "image/png": 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psuqO8PcVQL7N1SOdzR8DfB7YIVK/TStsuj3Acy+9BcH8j2sBv05n85cB2wB/LuQys9pd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\nREREJEMqrkREREQypOJKREREJEP/B4JlkvsV+Ml8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12361d438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pymc.Matplot.plot(home)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "\n",
    "* We can see in this analysis that home advantage gives about 0.55 points advantage.\n",
    "* We also see not too much auto-correlation, so this looks quite good plot wise.\n",
    "* We see here how *probabilistic programming* allows us to quantify our uncertainty about certain parameters.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting atts_0\n",
      "Plotting atts_1\n",
      "Plotting atts_2\n",
      "Plotting atts_3\n",
      "Plotting atts_4\n",
      "Plotting atts_5\n"
     ]
    },
    {
     "data": {
      "image/png": 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K4ZJ8vhTxvYXBuu7ACtz/3JIgk/+hwK1xbbbHGci3RnzvvTj9/g93HXSJ69MpwP3AYcBf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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12361d160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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jrW9ya1R90a1L+75A1HSgakTB6EBV50Dtiet2R1XfF5HhCdvX41bcVPqF2Mk2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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1229a25f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pymc.Matplot.plot(atts)\n",
    "# We can plot all of the parameters, just to see. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "observed_season = DATA_DIR + 'table_2014.csv'\n",
    "df_observed = pd.read_csv(observed_season)\n",
    "df_observed.loc[df_observed.QR.isnull(), 'QR'] = ''\n",
    "df_avg = pd.DataFrame({'avg_att': atts.stats()['mean'],\n",
    "                       'avg_def': defs.stats()['mean']}, \n",
    "                      index=teams.team.values)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_new = df_avg.merge(df_observed, left_index=True, right_on='team', how='left')\n",
    "\n",
    "#Bring in the new csv file\n",
    "df_results = pd.read_csv('df_avg_data_edited.csv', sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>avg_att</th>\n",
       "      <th>avg_def</th>\n",
       "      <th>points</th>\n",
       "      <th>team</th>\n",
       "      <th>position</th>\n",
       "      <th>QR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.096729</td>\n",
       "      <td>0.150764</td>\n",
       "      <td>6</td>\n",
       "      <td>France</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.179363</td>\n",
       "      <td>-0.560665</td>\n",
       "      <td>8</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>1</td>\n",
       "      <td>winners</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.549843</td>\n",
       "      <td>0.289372</td>\n",
       "      <td>2</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.251418</td>\n",
       "      <td>0.515101</td>\n",
       "      <td>0</td>\n",
       "      <td>Italy</td>\n",
       "      <td>6</td>\n",
       "      <td>wooden_spoon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.399824</td>\n",
       "      <td>-0.255811</td>\n",
       "      <td>8</td>\n",
       "      <td>England</td>\n",
       "      <td>2</td>\n",
       "      <td>triple_crown</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    avg_att   avg_def  points      team  position            QR\n",
       "1  0.096729  0.150764       6    France         4           NaN\n",
       "2  0.179363 -0.560665       8   Ireland         1       winners\n",
       "3 -0.549843  0.289372       2  Scotland         5           NaN\n",
       "4 -0.251418  0.515101       0     Italy         6  wooden_spoon\n",
       "5  0.399824 -0.255811       8   England         2  triple_crown"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_results.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "def fig3():\n",
    "    fig, ax = plt.subplots(figsize=(12,12))\n",
    "    for outcome in ['winners', 'wooden_spoon', 'triple_crown', 'NaN']:\n",
    "        ax.plot(df_results.avg_att[df_results.QR == outcome], \n",
    "    df_results.avg_def[df_results.QR == outcome], 'o', label=outcome)\n",
    "    for label, x, y in zip(df_results.team.values, df_results.avg_att.values, df_results.avg_def.values):\n",
    "        ax.annotate(label, xy=(x,y), xytext = (-5,5), textcoords = 'offset points')\n",
    "        ax.set_title('Attack vs Defense avg effect: 13-14 Six Nations')\n",
    "        ax.set_xlabel('Avg attack effect')\n",
    "        ax.set_ylabel('Avg defense effect')\n",
    "        ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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YlwITM/NYYArwgczcDPhVRIwZQvtFkyYlSZIk9fUD4Ajgd8DhwME0HQUzgdW7jrup/X0v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\nAABDEjsAAMCQ/j9TnjL/9fcFyAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1270d85c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig3()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#Simulating a season\n",
    "We would like to now simulate a season. Just to see what happens. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "def simulate_season():\n",
    "    \"\"\"\n",
    "    Simulate a season once, using one random draw from the mcmc chain. \n",
    "    \"\"\"\n",
    "    num_samples = atts.trace().shape[0]\n",
    "    draw = np.random.randint(0, num_samples)\n",
    "    atts_draw = pd.DataFrame({'att': atts.trace()[draw, :],})\n",
    "    defs_draw = pd.DataFrame({'def': defs.trace()[draw, :],})\n",
    "    home_draw = home.trace()[draw]\n",
    "    intercept_draw = intercept.trace()[draw]\n",
    "    season = df.copy()\n",
    "    season = pd.merge(season, atts_draw, left_on='i_home', right_index=True)\n",
    "    season = pd.merge(season, defs_draw, left_on='i_home', right_index=True)\n",
    "    season = season.rename(columns = {'att': 'att_home', 'def': 'def_home'})\n",
    "    season = pd.merge(season, atts_draw, left_on='i_away', right_index=True)\n",
    "    season = pd.merge(season, defs_draw, left_on='i_away', right_index=True)\n",
    "    season = season.rename(columns = {'att': 'att_away', 'def': 'def_away'})\n",
    "    season['home'] = home_draw\n",
    "    season['intercept'] = intercept_draw\n",
    "    season['home_theta'] = season.apply(lambda x: math.exp(x['intercept'] + \n",
    "                                                           x['home'] + \n",
    "                                                           x['att_home'] + \n",
    "                                                           x['def_away']), axis=1)\n",
    "    season['away_theta'] = season.apply(lambda x: math.exp(x['intercept'] + \n",
    "                                                           x['att_away'] + \n",
    "                                                           x['def_home']), axis=1)\n",
    "    season['home_goals'] = season.apply(lambda x: np.random.poisson(x['home_theta']), axis=1)\n",
    "    season['away_goals'] = season.apply(lambda x: np.random.poisson(x['away_theta']), axis=1)\n",
    "    season['home_outcome'] = season.apply(lambda x: 'win' if x['home_goals'] > x['away_goals'] else \n",
    "                                                    'loss' if x['home_goals'] < x['away_goals'] else 'draw', axis=1)\n",
    "    season['away_outcome'] = season.apply(lambda x: 'win' if x['home_goals'] < x['away_goals'] else \n",
    "                                                    'loss' if x['home_goals'] > x['away_goals'] else 'draw', axis=1)\n",
    "    season = season.join(pd.get_dummies(season.home_outcome, prefix='home'))\n",
    "    season = season.join(pd.get_dummies(season.away_outcome, prefix='away'))\n",
    "    return season\n",
    "\n",
    "\n",
    "def create_season_table(season):\n",
    "    \"\"\"\n",
    "    Using a season dataframe output by simulate_season(), create a summary dataframe with wins, losses, goals for, etc.\n",
    "    \n",
    "    \"\"\"\n",
    "    g = season.groupby('i_home')    \n",
    "    home = pd.DataFrame({'home_goals': g.home_goals.sum(),\n",
    "                         'home_goals_against': g.away_goals.sum(),\n",
    "                         'home_wins': g.home_win.sum(),\n",
    "                         'home_losses': g.home_loss.sum()\n",
    "                         })\n",
    "    g = season.groupby('i_away')    \n",
    "    away = pd.DataFrame({'away_goals': g.away_goals.sum(),\n",
    "                         'away_goals_against': g.home_goals.sum(),\n",
    "                         'away_wins': g.away_win.sum(),\n",
    "                         'away_losses': g.away_loss.sum()\n",
    "                         })\n",
    "    df = home.join(away)\n",
    "    df['wins'] = df.home_wins + df.away_wins\n",
    "    df['losses'] = df.home_losses + df.away_losses\n",
    "    df['points'] = df.wins * 2\n",
    "    df['gf'] = df.home_goals + df.away_goals\n",
    "    df['ga'] = df.home_goals_against + df.away_goals_against\n",
    "    df['gd'] = df.gf - df.ga\n",
    "    df = pd.merge(teams, df, left_on='i', right_index=True)\n",
    "    df = df.sort_index(by='points', ascending=False)\n",
    "    df = df.reset_index()\n",
    "    df['position'] = df.index + 1\n",
    "    df['champion'] = (df.position == 1).astype(int)\n",
    "    df['relegated'] = (df.position > 5).astype(int)\n",
    "    return df  \n",
    "    \n",
    "def simulate_seasons(n=100):\n",
    "    dfs = []\n",
    "    for i in range(n):\n",
    "        s = simulate_season()\n",
    "        t = create_season_table(s)\n",
    "        t['iteration'] = i\n",
    "        dfs.append(t)\n",
    "    return pd.concat(dfs, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Simulation\n",
    "* We are going to simulate 1000 seasons\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "simuls = simulate_seasons(1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "def fig1():\n",
    "    figsize(9,9)\n",
    "    ax = simuls.points[simuls.team == 'Ireland'].hist()\n",
    "    median = simuls.points[simuls.team == 'Ireland'].median()\n",
    "    ax.set_title('Ireland: 2015 points, 1000 simulations')\n",
    "    ax.plot([median, median], ax.get_ylim())\n",
    "    plt.annotate('Median: %s' % median, xy=(median + 1, ax.get_ylim()[1]-10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
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OpzMX2Glj7ycziYhFGz+ijXJzt9t9aMhjkKRNwgDRuNnpWYe/K7deuP1G3clx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\nJKmcASJJksr9N5/RB6RaasnkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12753b940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "* So what have we learned so far, we've got 1000 simulations of Ireland and their median points in the table is 8.\n",
    "* In Rugby you get 2 points per win, and there are 5 games per year. So this model predicted that Ireland would win most of the time 4 games.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "def fig2():\n",
    "    ax = simuls.gf[simuls.team == 'Ireland'].hist(figsize=(7,5))\n",
    "    median = simuls.gf[simuls.team == 'Ireland'].median()\n",
    "    ax.set_title('Ireland: 2015 scores for, 1000 simulations')\n",
    "    ax.plot([median, median], ax.get_ylim())\n",
    "    plt.annotate('Median: %s' % median, xy=(median + 1, ax.get_ylim()[1]-10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SVD3DTJJUPcNMklQ9w0ySVD3DTJJUPcNMklQ9w0ySVD3DTJJUPcNMklQ9w0ySVD3DTJJU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\nMJMkVW9C+mZsurm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      "text/plain": [
       "<matplotlib.figure.Figure at 0x127afb278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig2()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#What happened in reality?\n",
    "* Well Ireland actually scored 119 points, so the model over predicted this!\n",
    "* We call this 'shrinkage' in the literature. \n",
    "* All models are wrong, but some are useful"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# What are the predictions of the model?\n",
    "* So let us look at the winning team on average.\n",
    "* We do a simulation and we'll assign probability of 'winning' to the team\n",
    "* We used the MCMC to do this.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "g = simuls.groupby('team')\n",
    "df_champs = pd.DataFrame({'percent_champs': g.champion.mean()})\n",
    "df_champs = df_champs.sort_index(by='percent_champs')\n",
    "df_champs = df_champs[df_champs.percent_champs > .05]\n",
    "df_champs = df_champs.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZIwJwaWbu3P5ERGxD6REBuBtYFng9cE1mtoAnImLmiPU8CKwXEdOAR4CF26a1r2tjYGXg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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x127b94710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, ax = plt.subplots(figsize=(8,6))\n",
    "ax.barh(df_champs.index.values, df_champs.percent_champs.values)\n",
    "\n",
    "for i, row in df_champs.iterrows():\n",
    "    label = \"{0:.1f}%\".format(100 * row['percent_champs'])\n",
    "    ax.annotate(label, xy=(row['percent_champs'], i), xytext = (3, 10), textcoords = 'offset points')\n",
    "ax.set_ylabel('Team')\n",
    "ax.set_title('% of Simulated Seasons In Which Team Finished Winners')\n",
    "_= ax.set_yticks(df_champs.index + .5)\n",
    "_= ax.set_yticklabels(df_champs['team'].values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Unfortunately it seems that in most of the Universes England come top of the Six Nations. And as an Irish man this is firm proof that I put Mathematical rigour before patriotism :)\n",
    "This is a reasonable result, and I hope it proved a nice example of Bayesian models in Rugby Analytics."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# What actually happened\n",
    "We need to investigate like 'scientists' what actually happened.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "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>Rank</th>\n",
       "      <th>Team</th>\n",
       "      <th>Games</th>\n",
       "      <th>Wins</th>\n",
       "      <th>Draws</th>\n",
       "      <th>Losses</th>\n",
       "      <th>Points_For</th>\n",
       "      <th>Points_Against</th>\n",
       "      <th>Points</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>119</td>\n",
       "      <td>56</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>England</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>157</td>\n",
       "      <td>100</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Wales</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>146</td>\n",
       "      <td>93</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>France</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>103</td>\n",
       "      <td>101</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Italy</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>62</td>\n",
       "      <td>182</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>73</td>\n",
       "      <td>128</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rank      Team  Games  Wins  Draws  Losses  Points_For  Points_Against  \\\n",
       "0     1  Ireland       5     4      0       1         119              56   \n",
       "1     2  England       5     4      0       1         157             100   \n",
       "2     3  Wales         5     4      0       1         146              93   \n",
       "3     4  France        5     2      0       3         103             101   \n",
       "4     5  Italy         5     1      0       4          62             182   \n",
       "5     6  Scotland      5     0      0       5          73             128   \n",
       "\n",
       "   Points  \n",
       "0       8  \n",
       "1       8  \n",
       "2       8  \n",
       "3       4  \n",
       "4       2  \n",
       "5       0  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('data/results_2015.csv', sep='\\t', header=0, \n",
    "            names =['Rank','Team','Games','Wins','Draws',\n",
    "                    'Losses','Points_For','Points_Against','Points'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "##Ireland won the Six Nations!\n",
    "* The model incorrectly predicted that England would come out on top.\n",
    "* Ireland actually won by points difference of 6 points. \n",
    "* It really came down to the wire!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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QelplqCwzliDCFuA7S4vrqPlMRJzjK10/lCWkHHL011mnozxAJkbrmez+drfi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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='onedoesnotsimply.jpg') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "* One of the powers of Probabilistic Programming is that we can model 'latent' variables. \n",
    "* In Quantitative Finance we could model the 'fear' of investors. While we can't measure this directly.\n",
    "* In our Rugby Prediction model we are trying to infer the 'strength' of the team. Whereas in reality we can only measure the 'score' of the team. \n",
    "\n",
    "Bayes' formula allows us to then infer backwards: given the observable data (e.g. the stock price), what is the probability of the latent construct (e.g. investor fear)? \n",
    "\n",
    "Attribution: Thomas Wiecki at Quantopian influenced me on this. \n",
    "\n",
    "\n",
    "What are the advantages of PyMC3?\n",
    "What are the computational remarks?\n",
    "Where is my table of contents?\n",
    "Where is Bayes formula? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#Stochastic volatility model\n",
    "One elegant example of how Probabilistic Programming can infer \n",
    "unobservable quantities of the stock market is the stochastic volatility model. Volatility is an important concept of quantitative finance as it relates to risk. When I studied Financial Mathematics I was always confused about 'volatility' - since you can't measure it. \n",
    "\n",
    "If we can't measure something, the next best thing we can do is to try and model \n",
    "it. One way to do this is in a probabilistic framework is the concept of stochastic \n",
    "volatility: If we assume that returns are normally distributed, the volatility would be captured \n",
    "as the standard deviation of the normal distribution. Thus, the standard deviation gives rise to stochastic volatility. Intuitively we would assume that the standard deviation is high during times of market turmoil like the 2008 crash.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "So the trivial thing to do would be to look at the rolling standard deviation of returns. But this is unsatisfying for multiple reasons:\n",
    "\n",
    "* it often lags behind volatility,\n",
    "* is a rather unstable measure,\n",
    "* strongly dependent on the window size with no principled way of choosing it.\n",
    "\n",
    "In fact a lot of 'business processes' we encounter as Data Scientists can't be measured directly. We can only 'model' how likely a person is to churn from our service for example. \n",
    "We can't infer exactly. \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Introducing PyMC3\n",
    "* PyMC3 is a complete rewrite\n",
    "* Uses Theano and all of its computational coolness rather than Fortran.\n",
    "* The API is cleaner, but I found it difficult to use since I know PyMC2 better\n",
    "* The example I'll show first is a FinTech example for Stochastic Volatility \n",
    "* Attribution: The PyMC3 Development team."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# PyMC3 - continued\n",
    "* Probabilistic Programming framework written in Python.\n",
    "* Allows for construction of probabilistic models using intuitive syntax.\n",
    "* Features advanced MCMC samplers.\n",
    "* Fast: Just-in-time compiled by Theano.\n",
    "* Extensible: easily incorporates custom MCMC algorithms and unusual probability distributions.\n",
    "* Authors: John Salvatier, Chris Fonnesbeck, Thomas Wiecki\n",
    "* Upcoming beta release!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Why Theano?\n",
    "\n",
    "* Numpy broadcasting and advanced indexing\n",
    "* Linear algebra operators\n",
    "* Computation optimization and dynamic C compilation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Other reasons\n",
    "* Theano compiles computations down to C, making computations faster, especially larger models\n",
    "\n",
    "* Theano: automatic differentiation, automatic likelihood optimization and simple GPU integration.\n",
    "\n",
    "* Automatic differentiation is a different way to computationally work out derivatives unlike say Symbolic or Numeric Differentiation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Other other reasons\n",
    "(From John Salvatier the main author of PyMC3)\n",
    "* Bayesian inference is a powerful and flexible way to learn from data, that is easy to understand. \n",
    "Unfortunately larger problems are often computationally intractable. Markov Chain Monte Carlo sampling techniques help, but are still computationally limited. New gradient-based methods like the No U-Turn Sampler (NUTS) dramatically increase performance on hard problems.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# PyMC3 Sales Pitch :) \n",
    "* From John Salvatier too\n",
    "* PyMC 3 provides a easy and concise way to specify models and provides powerful yet easy to use samplers like NUTS. This enables users easily fit large and complex models with thousands of parameters. PyMC 3 is a complete rewrite of PyMC 2 based on Theano. PyMC expands its powerful NumPy-like syntax, and is now easier to extend and automatically optimized by Theano."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Alternatives to PyMC3\n",
    "* [Frequentist and Bayesianism](http://arxiv.org/abs/1411.5018) - An article by Jake Vander Plas discusses emcee, PyStan and PyMC2 and compares them\n",
    "* JAGS/BUGS are also alternatives\n",
    "* All are non-Pythonic and are in fact domain specific languages\n",
    "* There are challenges getting your data into and out of these"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pymc3 as pm3\n",
    "from pymc3.distributions.timeseries import GaussianRandomWalk\n",
    "\n",
    "from scipy.sparse import csc_matrix\n",
    "from scipy import optimize"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Asset prices have time-varying volatility (variance of day over day returns). In some periods, returns are highly variable, while in others very stable. Stochastic volatility models model this with a latent volatility variable, modeled as a stochastic process. The following model is similar to the one described in the No-U-Turn Sampler paper, Hoffman (2011) p21.\n",
    "* $\\sigma \\;\\; \\tilde \\;\\;Exponential(50)$\n",
    "\n",
    "* $\\mu \\;\\;\\tilde \\;\\; Exponential(.1)$\n",
    "\n",
    "* $s_i\\;\\; \\tilde \\;\\; Normal(s_{i−1},\\sigma−2)$\n",
    "\n",
    "* $\\log(\\frac{y_i}{yi−1})\\;\\;\\tilde \\;\\;t(\\nu ,0,\\exp(−2s_{i}))$\n",
    "\n",
    "\n",
    "Here, y is the daily return series and s is the latent log volatility process.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Build the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "def fig4():\n",
    "    n = 400\n",
    "    returns = np.genfromtxt(\"data/S&P500.csv\")[-n:]\n",
    "    returns[:5]\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    np.array([-0.00637 , -0.004045, -0.02547 ,  0.005102, -0.047733])\n",
    "    plt.plot(returns)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
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H9UzvQkjz8sOsHBZbQtMK9bkVm5bVwHH6/1nTTodFGgchDSJYBCEd5LCMdLUUEfSyYDE5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\nc4XezNsFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x127ac1198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig4()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "* So so far we plotted our returns from the S and P 500.\n",
    "Specifying the model in pymc3 mirrors its statistical specification.\n",
    "\n",
    "However, it is easier to sample the scale of the log volatility process innovations, σ, on a log scale, so we create it using TransformedVar and use logtransform. \n",
    "\n",
    "TransformedVar creates one variable in the transformed space and one in the normal space. The one in the transformed space (here log(σ)) is the one over which sampling will occur, and the one in the normal space is the one to use throughout the rest of the model.\n",
    "\n",
    "It takes a variable name, a distribution and a transformation to use.\n",
    "\n",
    "One of the strengths of the PyMC3 API is that it allows you to write statistics quite naturally.\n",
    "\n",
    "Personally it appeals to Data Developers like myself who are from a Mathematics Background. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "model = pm3.Model()\n",
    "with model:\n",
    "    n = 400\n",
    "    returns = np.genfromtxt(\"data/S&P500.csv\")[-n:]\n",
    "    sigma, log_sigma = model.TransformedVar('sigma', pm3.Exponential.dist(1./.02, testval=.1),\n",
    "                                            pm3.logtransform)\n",
    "\n",
    "    nu = pm3.Exponential('nu', 1./10)\n",
    "    s = GaussianRandomWalk('s', sigma**-2, shape=n)\n",
    "\n",
    "    r = pm3.T('r', nu, lam=pm3.exp(-2*s), observed=returns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Fit the model\n",
    "* Now that we have built the model and you can see the Syntax already.\n",
    "* We need to fit the model. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "For this model, the full maximum a posteriori (MAP) point is degenerate and has infinite density. However, if we fix log_sigma and $\\nu$ it is no longer degenerate, so we find the MAP with respect to the volatility process, 's', keeping log_sigma and $\\nu$ constant at their default values.\n",
    "\n",
    "We use L-BFGS because it is more efficient for high dimensional functions (s has n elements).\n",
    "\n",
    "### Key Tip: The usage of clever scaling parameters to avoid degeneracy is an art of Bayesian Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "with model:\n",
    "    start = pm3.find_MAP(vars=[s], fmin=optimize.fmin_l_bfgs_b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "We do a short initial run to get near the right area, then start again using a new Hessian at the new starting point to get faster sampling due to better scaling. \n",
    "We do a short run since this is an interactive example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [-----------------100%-----------------] 2000 of 2000 complete in 193.5 sec"
     ]
    },
    {
     "data": {
      "image/png": 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O9A9PrnC8oMPxgq0itbVmMQkz2DggZUszIgHJHk+713GcSm0dknhdxcZG/8ao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N39K2wWd3FK1tGYxPftwveNwzlMcSKXyLR6Xj8PJDL4PD/YjfQIY9GyqJFezsgalRYiPky0epzn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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12846d390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with model:\n",
    "    step = pm3.NUTS(scaling=start)\n",
    "    start2 = pm3.sample(500, step, progressbar=True)[-1]\n",
    "\n",
    "    # Start next run at the last sampled position.\n",
    "    step = pm3.NUTS(scaling=start2)\n",
    "    trace = pm3.sample(2000, step, start=start2, progressbar=True)\n",
    "\n",
    "\n",
    "figsize(12,6)\n",
    "pm3.traceplot(trace, model.vars[:-1]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "* Looking at the returns over time and overlaying the estimated standard deviation we can see how the model tracks the volatility over time.\n",
    "\n",
    "* This is exactly the sort of insight we expected.\n",
    "\n",
    "* [PyMC3 Docs](http://pymc-devs.github.io/pymc3/stochastic_volatility/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1290d4400>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iKEfD+wmGxSuOeyCKohwqDxC+p0+0I766QjxNoe0yuU3rGH+WIFw7QIZNudpN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EN0uq0LW4siqgsGs9MOUCtF4iq6jh9mN4wXFgh8buTETzrDi1kzhWa7BVwqNgYummrjqGATcauz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\noB6mydJtSnTTjCvP8Wr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Foa5rsTfL6BwA/DhNZpWB4fJ4QSfReJSu0t5xapPgO5SG85uL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      "text/plain": [
       "<matplotlib.figure.Figure at 0x127b5eb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(returns)\n",
    "plot(np.exp(trace[s][::10].T), 'r', alpha=.03);\n",
    "sd = np.exp(trace[s].T)\n",
    "plot(-np.exp(trace[s][::10].T), 'r', alpha=.03);\n",
    "xlabel('time')\n",
    "ylabel('returns')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Rugby Analytics in PyMC3\n",
    "* This is a ported version of PyMC2 code that I already showed.\n",
    "* Thanks to John Salvatier and Abraham Flaxman on Stackoverflow.\n",
    "* Grateful to live in an age of open source and Stackoverflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wed 17 Jun 2015 18:24:11 CEST\r\n"
     ]
    }
   ],
   "source": [
    "!date\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "try:\n",
    "    from StringIO import StringIO\n",
    "except ImportError:\n",
    "    from io import StringIO\n",
    "%matplotlib inline\n",
    "import pymc3 as pm3, theano.tensor as tt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "data_csv = StringIO(\"\"\"home_team,away_team,home_score,away_score\n",
    "Wales,Italy,23,15\n",
    "France,England,26,24\n",
    "Ireland,Scotland,28,6\n",
    "Ireland,Wales,26,3\n",
    "Scotland,England,0,20\n",
    "France,Italy,30,10\n",
    "Wales,France,27,6\n",
    "Italy,Scotland,20,21\n",
    "England,Ireland,13,10\n",
    "Ireland,Italy,46,7\n",
    "Scotland,France,17,19\n",
    "England,Wales,29,18\n",
    "Italy,England,11,52\n",
    "Wales,Scotland,51,3\n",
    "France,Ireland,20,22\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "* We do the same munging and cleaning as in PyMC2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "df = pd.read_csv(data_csv)\n",
    "\n",
    "teams = df.home_team.unique()\n",
    "teams = pd.DataFrame(teams, columns=['team'])\n",
    "teams['i'] = teams.index\n",
    "\n",
    "df = pd.merge(df, teams, left_on='home_team', right_on='team', how='left')\n",
    "df = df.rename(columns = {'i': 'i_home'}).drop('team', 1)\n",
    "df = pd.merge(df, teams, left_on='away_team', right_on='team', how='left')\n",
    "df = df.rename(columns = {'i': 'i_away'}).drop('team', 1)\n",
    "\n",
    "observed_home_goals = df.home_score.values\n",
    "observed_away_goals = df.away_score.values\n",
    "\n",
    "home_team = df.i_home.values\n",
    "away_team = df.i_away.values\n",
    "\n",
    "num_teams = len(df.i_home.drop_duplicates())\n",
    "num_games = len(home_team)\n",
    "\n",
    "g = df.groupby('i_away')\n",
    "att_starting_points = np.log(g.away_score.mean())\n",
    "g = df.groupby('i_home')\n",
    "def_starting_points = -np.log(g.away_score.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "* We will see now one of the major differences between PyMC2 and PyMC3\n",
    "* PyMC3 has more samplers. \n",
    "* Also PyMC2 has the initial values specified in the model\n",
    "* PyMC3 has the intitial values included when you fit the model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "model = pm3.Model()\n",
    "with pm3.Model() as model:\n",
    "    # global model parameters\n",
    "    home        = pm3.Normal('home',      0, .0001)\n",
    "    tau_att     = pm3.Gamma('tau_att',   .1, .1)\n",
    "    tau_def     = pm3.Gamma('tau_def',   .1, .1)\n",
    "    intercept   = pm3.Normal('intercept', 0, .0001)\n",
    "    \n",
    "    # team-specific model parameters\n",
    "    atts_star   = pm3.Normal(\"atts_star\", \n",
    "                           mu   =0,\n",
    "                           tau  =tau_att, \n",
    "                           shape=num_teams)\n",
    "    defs_star   = pm3.Normal(\"defs_star\", \n",
    "                           mu   =0,\n",
    "                           tau  =tau_def,  \n",
    "                           shape=num_teams) \n",
    " \n",
    "    atts        = pm3.Deterministic('atts', atts_star - tt.mean(atts_star))\n",
    "    defs        = pm3.Deterministic('defs', defs_star - tt.mean(defs_star))\n",
    "    home_theta  = tt.exp(intercept + home + atts[away_team] + defs[home_team])\n",
    "    away_theta  = tt.exp(intercept + atts[away_team] + defs[home_team])\n",
    "    \n",
    "    # likelihood of observed data\n",
    "    home_points = pm3.Poisson('home_points', mu=home_theta, observed=observed_home_goals)\n",
    "    away_points = pm3.Poisson('away_points', mu=away_theta, observed=observed_away_goals)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "* We specified the model and the likelihood function\n",
    "* Now we need to fit our model using the Maximum A Posteriori algorithm to decide where to start out No U Turn Sampler\n",
    "* All this runs on a Theano graph under the hood\n",
    "* Very clever from a Computational Engineering point of view\n",
    "* **Remark** The NUTS sampler is new from Gelman and triggered the rewrite"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [-----------------100%-----------------] 2000 of 2000 complete in 49.3 sec"
     ]
    },
    {
     "data": {
      "image/png": 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SbIM686mmhgdZ7v5Eo7ZlZnsCP3T3TqxyMCrxrsD/A56gDSf7epWKhf4kzS4G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PHZ9hJ7cmWZN4cF0DlaUrfJo9LSUsdvNtpMsRjEtTBuoxy1ZCEJtBurBaV6/NtZKCLa82xl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qI4fvuBoAns7N1oyyiktul8DcM5jsed3tWb/6ONbk4wye6d0AnkWRJ9a86oa7HgRWJ6td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Su153J8L6JAdMzmrZvungfBLM7Exib7oWRG50nZ3vBoAWt7vVKK9rCK+8riFz05otXIua+6uW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\ntva015HN6egEsNvmdAyp31PtO0XJmoE8m0pR+xt015FJoUyD5jS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QPKcU89LadNicDrfL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\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HfS2UR/gTuVzu/Zc/Csjlch/I5XIv9r6giGfi0Lo6FlDB5/P5MJ/Pl3K5HOVyud8FcCSf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\nxMQkiZhClomJiYmJiYmJiYmJSRL5/2jQ8OGqv4uaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x127580208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with model:\n",
    "\n",
    "    start = pm3.find_MAP()\n",
    "    step = pm3.NUTS(state=start)\n",
    "    trace = pm3.sample(2000, step, start=start, progressbar=True)\n",
    "\n",
    "    pm3.traceplot(trace)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mBRGxFHhb+/y9wGYRcQuwDXDjNOu6AXg+cD7w3Pax7wM/BHbNzJUR8Q7gSmCTOezDCmDJ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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1005d4278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pm3.forestplot(trace, vars=['tau_att', 'tau_def']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "home:\n",
      " \n",
      "  Mean             SD               MC Error         95% HPD interval\n",
      "  -------------------------------------------------------------------\n",
      "  \n",
      "  0.447            0.117            0.005            [0.282, 0.619]\n",
      "\n",
      "  Posterior quantiles:\n",
      "  2.5            25             50             75             97.5\n",
      "  |--------------|==============|==============|--------------|\n",
      "  \n",
      "  0.268          0.386          0.445          0.498          0.610\n",
      "\n",
      "\n",
      "tau_att:\n",
      " \n",
      "  Mean             SD               MC Error         95% HPD interval\n",
      "  -------------------------------------------------------------------\n",
      "  \n",
      "  15.896           10.767           0.819            [0.694, 35.935]\n",
      "\n",
      "  Posterior quantiles:\n",
      "  2.5            25             50             75             97.5\n",
      "  |--------------|==============|==============|--------------|\n",
      "  \n",
      "  2.517          8.354          13.544         20.961         44.530\n",
      "\n",
      "\n",
      "tau_def:\n",
      " \n",
      "  Mean             SD               MC Error         95% HPD interval\n",
      "  -------------------------------------------------------------------\n",
      "  \n",
      "  12.108           8.269            0.633            [0.483, 28.138]\n",
      "\n",
      "  Posterior quantiles:\n",
      "  2.5            25             50             75             97.5\n",
      "  |--------------|==============|==============|--------------|\n",
      "  \n",
      "  1.548          5.843          10.227         16.477         33.869\n",
      "\n",
      "\n",
      "intercept:\n",
      " \n",
      "  Mean             SD               MC Error         95% HPD interval\n",
      "  -------------------------------------------------------------------\n",
      "  \n",
      "  2.699            0.171            0.012            [2.587, 2.854]\n",
      "\n",
      "  Posterior quantiles:\n",
      "  2.5            25             50             75             97.5\n",
      "  |--------------|==============|==============|--------------|\n",
      "  \n",
      "  2.577          2.663          2.710          2.755          2.847\n",
      "\n",
      "\n",
      "atts_star:\n",
      " \n",
      "  Mean             SD               MC Error         95% HPD interval\n",
      "  -------------------------------------------------------------------\n",
      "  \n",
      "  -0.002           0.171            0.005            [-0.340, 0.341]\n",
      "  0.003            0.177            0.005            [-0.371, 0.331]\n",
      "  -0.198           0.176            0.005            [-0.547, 0.149]\n",
      "  0.026            0.158            0.004            [-0.291, 0.337]\n",
      "  0.069            0.152            0.004            [-0.227, 0.382]\n",
      "  0.095            0.163            0.004            [-0.246, 0.405]\n",
      "\n",
      "  Posterior quantiles:\n",
      "  2.5            25             50             75             97.5\n",
      "  |--------------|==============|==============|--------------|\n",
      "  \n",
      "  -0.340         -0.107         0.001          0.107          0.341\n",
      "  -0.355         -0.106         -0.001         0.113          0.358\n",
      "  -0.567         -0.307         -0.190         -0.084         0.135\n",
      "  -0.300         -0.066         0.027          0.125          0.331\n",
      "  -0.242         -0.022         0.067          0.163          0.372\n",
      "  -0.237         -0.008         0.092          0.198          0.421\n",
      "\n",
      "\n",
      "defs_star:\n",
      " \n",
      "  Mean             SD               MC Error         95% HPD interval\n",
      "  -------------------------------------------------------------------\n",
      "  \n",
      "  0.031            0.191            0.005            [-0.384, 0.366]\n",
      "  0.115            0.185            0.005            [-0.228, 0.531]\n",
      "  -0.036           0.189            0.005            [-0.386, 0.376]\n",
      "  -0.320           0.206            0.006            [-0.771, 0.050]\n",
      "  0.207            0.189            0.005            [-0.145, 0.636]\n",
      "  -0.016           0.208            0.006            [-0.450, 0.379]\n",
      "\n",
      "  Posterior quantiles:\n",
      "  2.5            25             50             75             97.5\n",
      "  |--------------|==============|==============|--------------|\n",
      "  \n",
      "  -0.359         -0.080         0.025          0.147          0.410\n",
      "  -0.265         0.012          0.112          0.217          0.504\n",
      "  -0.428         -0.143         -0.039         0.075          0.349\n",
      "  -0.765         -0.438         -0.313         -0.192         0.064\n",
      "  -0.156         0.097          0.200          0.312          0.627\n",
      "  -0.439         -0.137         -0.013         0.112          0.401\n",
      "\n",
      "\n",
      "atts:\n",
      " \n",
      "  Mean             SD               MC Error         95% HPD interval\n",
      "  -------------------------------------------------------------------\n",
      "  \n",
      "  -0.001           0.112            0.003            [-0.228, 0.208]\n",
      "  0.004            0.117            0.003            [-0.215, 0.233]\n",
      "  -0.197           0.118            0.003            [-0.429, 0.044]\n",
      "  0.028            0.088            0.002            [-0.151, 0.184]\n",
      "  0.070            0.083            0.002            [-0.101, 0.227]\n",
      "  0.096            0.095            0.002            [-0.094, 0.273]\n",
      "\n",
      "  Posterior quantiles:\n",
      "  2.5            25             50             75             97.5\n",
      "  |--------------|==============|==============|--------------|\n",
      "  \n",
      "  -0.221         -0.073         -0.002         0.074          0.219\n",
      "  -0.218         -0.076         0.002          0.083          0.232\n",
      "  -0.443         -0.271         -0.192         -0.120         0.032\n",
      "  -0.147         -0.029         0.029          0.088          0.192\n",
      "  -0.096         0.015          0.071          0.126          0.233\n",
      "  -0.082         0.032          0.091          0.159          0.291\n",
      "\n",
      "\n",
      "defs:\n",
      " \n",
      "  Mean             SD               MC Error         95% HPD interval\n",
      "  -------------------------------------------------------------------\n",
      "  \n",
      "  0.034            0.092            0.002            [-0.132, 0.221]\n",
      "  0.118            0.087            0.002            [-0.057, 0.288]\n",
      "  -0.033           0.092            0.002            [-0.202, 0.160]\n",
      "  -0.317           0.125            0.004            [-0.546, -0.063]\n",
      "  0.210            0.100            0.003            [0.017, 0.399]\n",
      "  -0.013           0.122            0.003            [-0.250, 0.218]\n",
      "\n",
      "  Posterior quantiles:\n",
      "  2.5            25             50             75             97.5\n",
      "  |--------------|==============|==============|--------------|\n",
      "  \n",
      "  -0.139         -0.030         0.032          0.092          0.218\n",
      "  -0.054         0.060          0.118          0.175          0.297\n",
      "  -0.214         -0.092         -0.036         0.024          0.155\n",
      "  -0.571         -0.397         -0.314         -0.232         -0.083\n",
      "  0.018          0.141          0.212          0.280          0.400\n",
      "  -0.249         -0.093         -0.012         0.069          0.222\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pm3.plots.summary(trace)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#Conclusion - Six Nations model\n",
    "* 'All models are wrong, some are useful'\n",
    "* Model correctly predicts that Ireland would win 4 games\n",
    "* Model incorrectly predicted that England would come out on top\n",
    "* In reality it was very close\n",
    "* Recommendation: Don't use this model to bet on the Six Nations next years"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "The big difference, as I see it, between PyMC2 and 3 model building is that the whole business of initial values in PyMC2 is not included in model building in PyMC3.\n",
    "It is pushed off into the model fitting portion of the code."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# A financial example\n",
    "* We are going to use a Financial Example from Quantopian. - Thomas Wiecki produced this\n",
    "* [Value at Risk](https://en.wikipedia.org/wiki/Value_at_risk) is a standard financial measure in risk.\n",
    "* The underlying message will be to look at the distributional assumptions in our models.\n",
    "* Bayesian Statistics makes these very explicit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Why does this matter?\n",
    "* A lot of models have naive assumptions like 'the underlying phenomenoa is a Gaussian distribution'\n",
    "* Those of us who deal with ecommerce data or commodity data or whatever know this just isn't true\n",
    "* I want to show the power of this framework in Fin Tech :) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# As we know: We want to approximate our Posterior with MCMC Sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "def plot_want_get():\n",
    "    from scipy import stats\n",
    "    fig = plt.figure(figsize=(14, 6))\n",
    "    ax1 = fig.add_subplot(121, title='What we want', ylim=(0, .5), xlabel='', ylabel='')\n",
    "    ax1.plot(np.linspace(-4, 4, 100), stats.norm.pdf(np.linspace(-3, 3, 100)), lw=4.)\n",
    "    ax2 = fig.add_subplot(122, title='What we get')#, xlim=(-4, 4), ylim=(0, 1800), xlabel='', ylabel='\\# of samples')\n",
    "    sns.distplot(np.random.randn(50000), ax=ax2, kde=False, norm_hist=True);\n",
    "    ax2.set_xlim((-4, 4));\n",
    "    ax2.set_ylim((0, .5));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
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HA9XhStcBnw+URZOdV2svj9P83kGSSJJ63tjY9hYw1Gq1hjtffgbVPvNNpNlS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\nacTM5gLrgUF3/33W8TQrShAr3P0zWcdSi5kVgO3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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12dc426d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_want_get()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# MCMC allows us to approximate an integral\n",
    "* This is the basic idea behind simulation methods like Monte Carlo\n",
    "* There are other samplers which work better for integrals with discontinuities etc\n",
    "* We could teach an entire 3 day workshop on all this cool stuff :) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "# We want to model our returns - specify our Priors :) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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EZFVgGq73MK6G51w9jUBbKZkZcQ3wI+DR5yd/sOVcQwd9TPnnWVt+\nxVbq/BZZcO7ON9+fujQw8Ya7X1rxhrtfOhM4Nlld3ZZSWwQnIh18NVsJ3PL1dYH5VPUyEdkNGI2b\nyXSXqp6cSiA54gXRvMDNuBrz9wHbx6Hfdeqt1H98WH5F1xb5eUG0FDARN3PwUuCQOPRnZRpZRmyF\ndIt4QbQQMB74fvJzl2RArEtb/PFlHUSKLL9iqyxfswRuo6C1gFuA3bv9rbYFWwTXAl4QrQg8gmsY\nrgV2asc3mzFFkOw5vQmu6OUOwJ1eEC2cbVStZ41Dyrwg2gTXMKwEnAnsGYf+jGyjMsZUk5zu3Qa3\nb/uGwKNeEK2SbVStZaeVUuQF0X64cZcO4MA49K+ocve26baXlOVXbL3txDgAtwfEscAnwE/i0I9b\nHFsmrHFIgRdE8wAX4QbhP8SdRrq3j4e15R9fiVh+xVY1v2QPiHHAXMAJwOllX7BqjUOTJeMLN+Jm\naT0B7ByH/uQaHtrWf3wlYPkVW5/5JeX0b8HVZLoLd4r4rRbElglrHJokWVW5D3AeMB9wCTA6Dv3P\na3yKtv/jKzjLr9hqys8LokWA3wPb4YqG/jQO/Qkpx5YJaxyaIJn6djmwLW5l+EFx6F9b59PYH1+x\nWX7FVnN+yRfBw3B7Uw/BfRH8RRz6n6QXXutZ49APyZtkD+Bc4BvA3cDecei/0cDT2R9fsVl+xVZ3\nfl4QrQ1cjSvp8zpwQLKZUClY49CgZD/ai3HzoacDxwAX92OQyv74is3yK7aG8kvK4RyXXAbhSuP8\nIg79/zQ3vNazxqFOXhAtiHsjjMa9Gf4CHF6xF22j7I+v2Cy/YutXfl4QfQc3m2ldYCpu+us5dYw5\n5o41DjVKviEcAowBFsZ1Iw+LQ/8vTXoJ++MrNsuv2PqdX1KGf2/gdGBRYDJwMnBNsrlQoVjj0Acv\niOYC9sItglkWmII7+Bc2uQSG/fEVm+VXbE3LLzm7cALuy+QQQIGTgD8VaW2ENQ698IJoGG4zogBY\nAvgMNyvhtDj030/hJe2Pr9gsv2Jren5eEC0D/ArXmxgEPI/bLvnaOPQ/a+ZrpcEah268IBJgFK63\nsCBuyfzFwLlx6L+d4kvbH1+xWX7Fllp+XhCtgOtJ/AQYCLyD+6J5ScqfKf1ijQNfnjraDjgIt/EQ\nwNvAWOCiOPQ/bEEY9sdXbJZfsaWenxdEw4FDgQNwXzxnADFuUd2EvI1LtG3jkAwejQR2B3YCFkhu\nuhfXqt/S4j2d7Y+v2Cy/YmtZfl4QzQf8DHfaeo3k6reAP+JK7zyWhx3o2qpxSMYRtsCtZN4WWDy5\n6Q3geuAPceg/n1F49sdXbJZfsbU8v2QR7Tq4MYndgYWSm/6N2zHyFuDBFn9J/VKpGwcviAYBa+MW\nqm2V/Byc3PwucBNu850HczCLwP74is3yK7ZM80tObW+J21t+e9xpJ3BrJu4F7gT+CrzQql5FqRqH\npCjW2sAIYGPczmvzVtzlH7gtOm8FHs9Bg1DJ/viKzfIrttzk5wXRENwp721xZzpWrrj5HdzmYQ8n\nP/8eh/7UNOIoZOOQLEhbARBcaex1cI3C8G53/Rfwt+Rybxz6/21lnHXKzZszJZZfsVl+GUkGsrdI\nLhvy9c+52cCLwD+BZ5Kf/wRe7e+X39QaBxEZgJsCuibwObCfqr5ccbsHHA/MBK5Q1cv7ek4viG7F\nNQjLMecWp2/hegZP4vZReCAO/XebkEqr5PbN2SSWX7FZfjnhBdE3gfWBDXBnSdYE5u92t+nAy8BL\ncejv1MjrDOpPkH3YARiiqt8XkRG4xR87AIjIYOAcYD1gGvCgiPxFVd/p4zm3wY0VPIRbdfgi8Bzw\njzj030wnDWOMyY+kqN9NyaVrYHs4bubTmsnPlYEVgdUbfZ00G4cNgQkAqvqoiKxXcdsqwCRVnQIg\nIg/gxghu7OM5F27RmgNjjCmEZID69eRya9f1SaOxeG+P60uajcP8uI1vuswSkQGqOju5bUrFbZ/w\n1TqDXlnDYIwxtUkajYa3Me1+3r6ZPgaGVb5W0jCAaxgqbxsGtPsHfyHOd/aD5Vdsll+bSbNxeBA3\nRoCIrI8bSe/yArCiiCwkIkNwp5QeTjEWY4wxdUhztlIHX81WArcKcF1gPlW9TES2wxWjGgCMU9VL\nUgnEGGNM3Qq5zsEYY0y60jytZIwxpqCscTDGGDMHaxyMMcbMwRoHY4wxc0hzEVy/iMjcuM0vFsUt\nkvuZqr7X7T6H4DbN6ATOVtUbWh5og2rMbzTw/5Jfb1PVX7c2ysbVkl9yv0Vx055XV9VM6tbXKo16\nYXnTV47JfebBlZDeR1W19VE2pobjtxtwBO74/RM4WFULM2Onhvx+BPwC93l5japeUO358txzOAh4\nWlU3Bq7CbdT9JRFZBLfX8wbAD3C1m4qkr/yWx20AsoGqrg9sKSJrzPk0uVU1PwAR2QpXo36xFsfW\nqC/rhQHHUvGeq6gXtgVu35ADRKQoeVXqNUeApAzO33DFLwvzwZmodvzmBk4BNlXVjXAVG7bLJMrG\nVctvIPAb3GflBsDBIrJwtSfLc+PwZW2m5OfmlTcm30K/o6qzgCWBz1obXr9VzQ9XJ2Wrim8ug3GV\nFouir/wAZuHerEVZHf+1emG4wpFdvqwXpqozgK56YUVTLUeAIbgPocL0GCpUy+0z3Bexrs+RQRTr\n7w2q5Jd8Tq6sqp/gevMDgao99VycVhKRfYEju139Nl/VZuqx9pKqzk5OLZ0MnJ9qkP3QSH6qOhP4\nIFlMeBbwD1WdlHasjejH8bsreXyq8TVR0+uF5VC1HFHVh6BQx6xSr7klX8LeBRCRw4B5u96fBdLX\nsZstIjsBF+E2PZtW7cly0Tio6jhgXOV1InITX9VfGgZ81Mtjx4rI/wK3i8j9qnpvmrE2otH8RGQu\n4Arch87BKYfZsP4cv4Jph3ph1XIsuqq5JefszwS+jduus2j6PHaqerOI/Bn4A/DT5GeP8nxa6cva\nTMAPcec5vyTOzcmvM3EDMLNaF16/9ZVfBxABT6nqQUUaGEtUza+g2qFeWLUci66v3C4FhgI7Vpxe\nKpJe8xOR+UXkPhEZknyWTKWPz8vcls9IBoiuxI0nfA7srqrvJDN4JqlqLCIn4D54OnGzeU7NLuL6\n9JUf7pzgdbgPmK6Kkb9U1UeyiLdetRy/ivu+gjsfmvfZSqWvF9ZXjhX3mwgcqKovtj7KxlTLDXg8\nuVR+iTlfVW9paZD9UMP7c39gX2AG8DRwWLUvnbltHIwxxmQnz6eVjDHGZMQaB2OMMXOwxsEYY8wc\nrHEwxhgzB2scjDHGzMEaB2OMMXOwxsEYY8wcrHEwxhgzh1zUVjKmCERkU2BM8usKwI24mko74Fax\nbwu8qaoDkvvvBWyiqnu3PFhj+sl6DsbU53vAXsBquD0r3lHV7+Lq2Oza7b5WfsAUlvUcjKnPs6r6\nHwAReQ+4O7n+NWDBzKIypsms52BMfboXB5xZ5b5D0gzEmDRZz8GY5npPRFYDnge2B+bYN9uYIrCe\ngzG166T6OEInbu/e8cBDuD0ebNzBFJKV7DbGGDMH6zkYY4yZgzUOxhhj5mCNgzHGmDlY42CMMWYO\n1jgYY4yZgzUOxhhj5mCNgzHGmDn8f68UuDkoscs2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1306f51d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import theano.tensor as T\n",
    "x = np.linspace(-.3, .3, 500)\n",
    "plt.plot(x, T.exp(pm3.Normal.dist(mu=0, sd=.1).logp(x)).eval())\n",
    "plt.title(u'Prior: mu ~ Normal(0, $.1^2$)'); plt.xlabel('mu'); plt.ylabel('Probability Density'); plt.xlim((-.3, .3));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JPyejlPozk8TRG8CRhG+w3ys4HJGOp8SfD+X97P2cMG3zDpVqbYuigxHpZJkl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\n6TFK/CIiPeb/ASJokgvTE+vTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1306f24e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-.1, .5, 500)\n",
    "plt.plot(x, T.exp(pm3.HalfNormal.dist(sd=.1).logp(x)).eval())\n",
    "plt.title(u'Prior: sigma ~ HalfNormal($.1^2$)'); plt.xlabel('sigma'); plt.ylabel('Probability Density');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Let us recap. \n",
    "* We have specified our priors, in terms of $\\mu$ and $\\sigma$. \n",
    "* We now need to specify our Bayesian Sharpe Ratio\n",
    "* The Sharpe ratio is a common metric used to measure financial performance. \n",
    "* This could be any classical metric in your field. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Bayesian Sharpe Ratio\n",
    "* $\\mu \\;\\; \\tilde \\;\\; Normal(0, .1^{2})$ <- Prior\n",
    "* $\\sigma \\;\\; \\tilde \\;\\; HalfNormal(.1^{2})$ <- Prior\n",
    "* Returns $\\; \\tilde \\;\\; Normal(\\mu,  \\sigma^{2})$ <- Observed\n",
    "* Sharpe ratio $= \\frac{\\mu}{\\sigma}$\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<img src=\"http://blog.quantopian.com/wp-content/uploads/2014/12/GraphicalModel.jpg\">\n",
    "Attribution: Quantopian blog"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Not a bad idea to show the data :) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "2011-10-18 20:00:00   -0.011876\n",
      "2011-10-19 20:00:00   -0.000422\n",
      "2011-10-20 20:00:00   -0.017066\n",
      "2011-10-21 20:00:00   -0.012890\n",
      "2011-10-24 20:00:00   -0.005419\n",
      "Name: 1, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(data_0.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "with pm3.Model() as model:\n",
    "    # Priors on Random Variables\n",
    "    mean_return = pm3.Normal('mean return', mu=0, sd=.1)\n",
    "    volatility = pm3.HalfNormal('volatility', sd=.1)\n",
    "\n",
    "    # Model returns as Normal\n",
    "    obs = pm3.Normal('returns', \n",
    "                 mu=mean_return, \n",
    "                 sd=volatility,\n",
    "                 observed=data_0)\n",
    "    \n",
    "    #There are 252 trading days in a year hence the 252 below\n",
    "    sharpe = pm3.Deterministic('sharpe ratio', \n",
    "                           mean_return / volatility * np.sqrt(252))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [-----------------100%-----------------] 1000 of 1000 complete in 1.0 sec"
     ]
    }
   ],
   "source": [
    "with model:\n",
    "    # Instantiate MCMC sampler - in fact a No-U turn sampler\n",
    "    step = pm3.NUTS()\n",
    "    # Draw 1000 samples from the posterior\n",
    "    trace = pm3.sample(1000, step)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Now that we need to analyse our Posterior.\n",
    "* Let us do some exploratory data analysis on our Posterior to see what it looks like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1317164a8>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JwKvAucBgIH+t3kXAUGAIsLDdc8Y0gmoniFf69u4LliBMHSmqDaKdO51zuWRwJ/A7dGWs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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13170cf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(results_normal[0]['mean returns'], hist=False, label='etrade')\n",
    "sns.distplot(results_normal[1]['mean returns'], hist=False, label='IB')\n",
    "plt.title('Posterior of the mean'); plt.xlabel('mean returns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x13178e198>"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pumoSD8bt7iV+n6qRtZBPJhYrenHgOc3tqwlBr5AXSU+p++MTD8TtHiV+n6ox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\ndUwhLyJSxxTyIiJ17P8DtxUvfv0EKNIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13170c668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(results_normal[0]['volatility'], hist=False, label='etrade')\n",
    "sns.distplot(results_normal[1]['volatility'], hist=False, label='IB')\n",
    "plt.title('Posterior of the volatility')\n",
    "plt.xlabel('volatility')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ejRyUKmTs77ofsh+6CdoqwALMbrm2TKqQGQSkgA9wXQ9NU1VBcblEtrR4M8/V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\ngCVzY4xpA5bMjTGmDVgyN8aYNvD/QmUms2XyNMoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13186aa58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(results_normal[0]['sharpe'], hist=False, label='etrade')\n",
    "sns.distplot(results_normal[1]['sharpe'], hist=False, label='IB')\n",
    "plt.title('Bayesian Sharpe ratio'); plt.xlabel('Sharpe ratio');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P(Sharpe ratio IB > 0) = 96.22%\n"
     ]
    }
   ],
   "source": [
    "print('P(Sharpe ratio IB > 0) = %.2f%%' % \\\n",
    "    (np.mean(results_normal[1]['sharpe'] > 0) * 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P(Sharpe ratio IB > Sharpe ratio etrade) = 79.58%\n"
     ]
    }
   ],
   "source": [
    "print('P(Sharpe ratio IB > Sharpe ratio etrade) = %.2f%%' % \\\n",
    "    (np.mean(results_normal[1]['sharpe'] > results_normal[0][0]['sharpe']) * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Value at Risk with uncertainty.\n",
    "* We want to now compute our Value at Risk with Uncertainty \n",
    "* This shows the power of the Probabilistic Programming approach\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<MultiTrace: 1 chains, 5000 iterations, 5 variables>"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_normal[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LbLdzgOtEZDVs4MGJwDUZjr0P0F1VBwGnAxenjtkNuAQYCgwBjhKRfqFMj8IyIrKCiIwC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\nw6IEN2HJt4ofyPKs/AlLXvUu5uIjltj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      "text/plain": [
       "<matplotlib.figure.Figure at 0x131946358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy.stats as stats\n",
    "ppc_etrade = post_pred(var_cov_var_normal, results_normal[0], 1e6, .05, samples=800)\n",
    "ppc_ib = post_pred(var_cov_var_normal, results_normal[1], 1e6, .05, samples=800)\n",
    "sns.distplot(ppc_etrade, label='etrade', norm_hist=True, hist=False, color='b')\n",
    "sns.distplot(ppc_ib, label='IB', norm_hist=True, hist=False, color='g')\n",
    "plt.title('VaR'); plt.legend(loc=0); plt.xlabel('5% daily Value at Risk (VaR) with \\$1MM capital (in \\$)'); \n",
    "plt.ylabel('Probability density'); plt.xticks(rotation=15);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Interim summary\n",
    "* Bayesian stats allows us to reformulate common risk metrics, use priors and quantify uncertainty.\n",
    "* IB strategy seems better in almost every regard. Is it though?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# So far, let's add confidence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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gbJjiEGfJ3AQumsqvhJuk84CvNlhXfs3MrwIP5RKZp4M+fiEdm4erALlRNJUP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\nMS3AkrkxxrSA/wd+EaEkahg81wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x131b620b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(results_normal[0]['sharpe'], hist=False, label='etrade')\n",
    "sns.distplot(results_normal[1]['sharpe'], hist=False, label='IB')\n",
    "plt.title('Bayesian Sharpe ratio'); plt.xlabel('Sharpe ratio');\n",
    "plt.axvline(data_0.mean() / data_0.std() * np.sqrt(252), color='b');\n",
    "plt.axvline(data_1.mean() / data_1.std() * np.sqrt(252), color='g');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "x = np.linspace(-.03, .03, 500)\n",
    "ppc_dist_normal = post_pred(eval_normal, results_normal[1], x=x)\n",
    "ppc_dist_t = post_pred(eval_t, results_t[1], x=x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# How good is our model?\n",
    "* Remember Box said \"All models are wrong some are useful!\"\n",
    "* How useful is our model?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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egBuEEKcDYAAurFyYZCZiSvZQWTUX6S5r0/IZRbUMPRsKPLlPnv96tWMbkvS3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\nAH4E4BgAOoDPCiG2VPIcZHL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      "text/plain": [
       "<matplotlib.figure.Figure at 0x131c550b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data_1, label='data IB', kde=False, norm_hist=True, color='.5')\n",
    "for p in ppc_dist_normal:\n",
    "    plt.plot(x, p, c='r', alpha=.1)\n",
    "plt.plot(x, p, c='r', alpha=.5, label='Normal model')\n",
    "plt.xlabel('Daily returns')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#Can it be improved? Yes!\n",
    "* Identical model as before, but instead, use a heavy-tailed T distribution:\n",
    "* $returns\\;\\tilde \\;\\; T(\\nu,\\mu,\\sigma^{2})$\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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DWnK0ZIQZALAdgRnA3kvpaDnPC8vyYim1g2E1xEqWW7FKBgBgOwIzgL1n1XLR+2F5E5ir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\n2ep0aGrSxtYU71y2fOCyyy578+zyu3btqnqeh30H79h3XhzHMYDO/vO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CzKjWD9QTc0eF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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x132131320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize(12, 12)\n",
    "sns.distplot(data_1, label='data IB', kde=False, norm_hist=True, color='.5')\n",
    "for p in ppc_dist_t:\n",
    "    plt.plot(x, p, c='y', alpha=.1)\n",
    "plt.plot(x, p, c='y', alpha=.5, label='T model')    \n",
    "plt.xlabel('Daily returns')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Volatility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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teya5xzbr79DyWIB2tRG2T5+1LwBgH/YM27XW289hHED7Dh22N9bXNldPnjod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\niLANAACDnAHhs2Sts0uAMwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x132131128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(results_normal[1]['annual volatility'], hist=False, label='normal')\n",
    "sns.distplot(results_t[1]['annual volatility'], hist=False, label='T')\n",
    "plt.xlim((0, 0.2))\n",
    "plt.xlabel('Posterior of annual volatility')\n",
    "plt.ylabel('Probability Density');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# We need to compare posteriors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NNoO5pF4Zta0SO1wAKknqCYO5pF4ZtR1ZOgzmkqSeMJhL6pVR28O8w2AuSeoJ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\nQIm5iIiIiEgOKDEXEREREcmB/wejH4T+ldEDSwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1321310f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(results_normal[1]['mean returns'], hist=False, color='r', label='normal model')\n",
    "sns.distplot(results_t[1]['mean returns'], hist=False, color='y', label='T model')\n",
    "plt.xlabel('Posterior of the mean returns'); plt.ylabel('Probability Density');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Bayesian T-sharpe ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SpNwZoiVlx3EOSVLuDNGSsjPisd/gOIckqWWGaEnZcZxDkpQ7Q7Sk7DjOIUnKnSFaUna2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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1324cb630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(results_normal[1]['sharpe'], hist=False, color='r', label='normal model')\n",
    "sns.distplot(results_t[1]['sharpe'], hist=False, color='y', label='T model')\n",
    "plt.xlabel('Bayesian Sharpe ratio'); plt.ylabel('Probability Density');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# The robustness of the T-distribution is the strength of this model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZAmi1htcY9xzEUQqNOYOxJEnlMgWwtDTcYNx7oIiNscaawViSpHKZBjh4cHij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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13293e8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_data = list(np.random.randn(75)*.01)\n",
    "sim_data.append(-.2)\n",
    "sns.distplot(sim_data, label='data', kde=False, norm_hist=True, color='.5'); sns.distplot(sim_data, label='Normal', fit=stats.norm, kde=False, hist=False, fit_kws={'color': 'r', 'label': 'Normal'}); sns.distplot(sim_data, fit=stats.t, kde=False, hist=False, fit_kws={'color': 'y', 'label': 'T'})\n",
    "plt.xlabel('Daily returns'); plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Estimating tail risk using VaR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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bHXepo8YJ7gfEGLcCzwQ+F0I4ADio2rIkSWq8o/KjwV3SRIwT3P85hHAtsIRs\nVGYIfLrKoiRJaoGmdtwfJLvniqMyUsckaTr7zU9DCMcAt8QYd4YQTo4xXlN9aZVIcUecMrme5XI9\ny+Nalsv13Jck+RbwGGAp4/xlOqX69UySu4A7SNNHVvo+9fN7s1yuZ8PNGtxDCMcCLwVWMfUfM40x\nvqja0irhN2S5XM9yuZ7lcS3L5XruS5LcAmwlTY+f45mTCO7XAweSpkfN+tx283uzXK5nw820j3vh\nH4DL81+FuXQWJEnqliQ5gGw7yK/XXco0NgOH1V2EpHKNE9wXxhh/p/JKJElqj8PJrhNr2nx7YROw\nnCQ5gDTdWXcxksoxzsWpXwsh/HIIYXHl1UiS1A5NvTC1UOws4wWqUoeM03F/LtmMOyGE4nNpjPGA\nqoqSJKnhmh7cR2/CdE+dhUgqz6zBPcb40EkUIklSizQ9uLuXu9RBswb3EMIS4HeAAPx2/ustMcZt\nFdcmSVJTGdwlTdw4M+7vApYDjye7ocMJwCVVFiVJUsMV2yzeUmsV0xsdlZHUEeME98fHGF8DbIsx\n3gv8JnBKtWVJktRoRcd9Q61VTM+Ou9RB4wT3XXvsKHMosKuieiRJaoMjgS2k6Za6C5lG0XE3uEsd\nMk5w/zPgn4EjQgh/BlwFvLPSqiRJarajaO58O8DG/GhwlzpknF1lPhRCuAp4MlnQf2aM8ZrKK5Mk\nqYmSZCmwGvhO3aXMoAjuD6m1Ckmlmja4hxBekP82zY/FjwMfG0J4bIzxQ5VWJklSMx2dH2+utYqZ\nGdylDpqp4/4EstB+EvBw4JNku8o8E1gPGNwlSX20Nj82ObgXN10yuEsdMm1wjzEWd0u9HHhcjPGe\n/OM3AZdNpjxJkhqnCO4/qbWKmRUd95W1ViGpVONcnHo4U1enAzwIrKmmHEmSGq8NozIPAtuw4y51\nyqwXpwKfBr4UQvhHsqD/POBvK61KkqTmav6oTJqmJMlGDO5SpyRpms76pBDCrwEDspn3f4oxfrri\nuqqSAkndRXSI61ku17M8rmW5XM9RSfJZ4OnAQ0jTTbM9fR8ms55Jsh5YSZoeXvl71cfvzXK5ng03\nVnDvEL8hy+V6lsv1LI9rWS7Xc1SSXAMcS5oevJ+vMKng/g2yO50vobt/2fu9WS7Xs+HGmXGXJElT\njqbZF6YWNgKLgKV1FyKpHAZ3SZLGlSTLyHZqae58+xT3cpc6ZtbgHkL4XAjhuSGERZMoSJKkBmv+\nhalT3Mv2Ca4lAAAgAElEQVRd6phxOu5vBZ4G/DCE8K4QwhMqrkmSpKZqU3B3L3epY2bdDjLG+BXg\nKyGEpcBzgI+HEDYD7wP+Ksa4dV/nhRAWABcDJwNbgfNijNePPL4OeB3Z3VgvjTG+f7pzQggPB/4a\n2AVcC7wkxpiGEP5f4EVkF1P8SYzxk/uzCJIkjamNwd2Ou9QRY824hxCeDLwL+BOyu6b+NnAE8KkZ\nTnsWsDjGeAbwauDCkddbBFwEnA2cBVwQQjgsP2fJPs65CHhtjPFMsqudfyWEsBz4XeB04CnAO8f5\nWiRJmofi5kttuDjVURmpY8aZcf8x8AZgCIQY4wUxxi8Bvw8cNsOpTwI+DxBjvBI4deSxk4DrYoyb\nYozbga8BZ+bnXLaPc06JMV6e//4y4JfIuu8Ay4EVwM7ZvhZJkubJjruk2oxz59RnxBivHf1ECOG0\nGOM3gMfNcN7BwOaRj3eGEBbEGHflj43etGILcMg05xzA7nuK3gscEmO8P4Twd8D3gQPIfhogSVKV\n2hjcnXGXOmLa4B5C+M9kgfh9IYTzRh5aBLwbOGGW195M1gkvFKEdstA++tgKsj9g9nXOzhDCrj2f\nG0I4HTgNOJYs2H8hhPD1GOO3ZqmrqzehqIvrWS7XszyuZblcT4BHPhJuvRXuuWfLPF+p+vW88kp4\n4hPhla/8I+CPKn+/+vi9WS7Xszyl38xqpo772WTjKw8F/nDk8zvIgvtsrgDWAR8NIZwGXDPy2Hrg\nhBDCSuC+/H3eTvbNsq9zrg4hnJVfKPs04EtkIzIPxBi3AYQQNpJ17WfjHcHK4x3WyuV6lse1LJfr\nWfj+9zcDN5JtorC/JrOeT3ziCcAPuPDCS/jTPz1v1ue3k9+b5XI9Gy6Z7S7IIYTfiDF+eK4vHEJI\nmNohBuBc4PHA8hjj+0IIzwReTzZnf0mM8a/2dU6M8QchhBPIdrFZTDYac36+q8zbyC5u3Ql8Ncb4\nqlnK8huyXK5nuVzP8riW5XI9AZKkGPO8jDR9+jxeaTLrmSRrgDuAj5Gmz6n8/erh92a5XM+Gmza4\nhxD+MMb4hhDCB9j7P2QaY3zRJAosmd+Q5XI9y+V6lse1LJfrCZAkjyLbkvi9pOn/nMcrTSq4LwK2\nAV8mTX+x8verh9+b5XI9G26mUZl/y49fYR/BvbKKJElqpjZdmAppup0kuQ93lZE6Y6bgfk0I4Rjg\nX/bxmMFdktQ37QrumY0Y3KXOmCm4F5326RxXci2SJDVZcfOltgX3h9ZdhKRyTBvcY4zHTrAOSZKa\nrui4t+GuqYWNwEkkyQLSdNesz5bUaDPt4/7GGOMbO3ZxqiRJ+6uNwf0est3bVrD7jQ8ltdBMozJX\n5cev5MdibCbBGXdJUv+sBX5Kmt5fdyFzcE9+XInBXWq9WfdxBwghHAqcTnbzpStjjHdXXVhF3Oao\nXK5nuVzP8riW5XI9kyQB7gV+QJo+bp6vNrn1TJJ3AC8DTiVNr5rt6S3k92a5XM+GWzDbE0IIzwa+\nA7wQOB/4bgjhaRXXJUlSkzwEOIh2XZgK8NP8uLrWKiSVYqZRmcIbgZ+PMd4KEEL4OeBTwGUV1iVJ\nUpO0cb4dDO5Sp8zacQe2AxuKD2KMPwZ2VlaRJEnN08Y93MHgLnXKTLvK/Fr+2x8AH8t3l9kJPB+4\negK1SZLUFAZ3SbWbaVRmHdlFCg8CW4Fn55/fwXidekmSuqKNN18Cg7vUKTPdgOmF0z0WQjiokmok\nSWqmtnfcV9VahaRSzHpxagjhOcDrgWVknfYDgCXA4dWWJklSYxTB/ZZaq5i7YvtmO+5SB4yzq8zb\ngPOAVwB/DDyVbC9bSZL6Yi1wB2m6te5C5uh+snFXg7vUAePMqt8TY/wy8A3gkBjjG4FfrbQqSZKa\nIrv50tG0b0wGsrss/hSDu9QJ4wT3+0MIjwDWA4MQgmMykqQ+WQ0cSBuDe8bgLnXEOMH9D8hGZD4N\n/CJwO/DJKouSJKlB2nphauGnwCEkyTjjsZIabNb/iWOMXwG+kn/4hBDCyhjjPdWWJUlSY7T1rqmF\n0Z1l7qizEEnzM86uMkcB7yDrtu8A/imE8PIY451VFydJUgN0oeMO2biMwV1qsXFGZS4FhsBxwCOA\nq4APVFiTJElN0pXg7l7uUsuNM+92WIzx4pGP3xFCeEFVBUmS1DBtvWtqwbunSh0xTsf96hDCz7Z/\nDCGcDVxTXUmSJDXKWiAFbq27kP3kTZikjpi24x5CuJfsD6oFwAtDCBvJZtwPxRk5SVJ/HA3cRppu\nr7uQ/WTHXeqIaYN7jHH5JAuRJKlxkmQBWXD/Tt2lzIPBXeqIcXaVWQa8gWxXmYXAl4E/iDHeV3Ft\nkiTV7VBgMe3dChIM7lJnjDPj/pfAQcC5wAvI/gB7d5VFSZLUEMWFqQZ3SbUbZ1eZx8cYTx75+CUh\nhP+oqiBJkhqkC8H9bmAXsKbuQiTNzzgd9ySEsLL4IP99Wy/QkSRpLtq+FSSk6U7gLuDwukuRND/j\ndNwvAr4ZQvgUkAC/DPyvSquSJKkZipsvtbnjDnA7cEzdRUian3GC+2eAfwPOIgvuvxpj/F6lVUmS\n1AxdGJWBbBvnx5AkS0jTrXUXI2n/jBPcvxpjPBEwrEuS+uZo2n3zpcLt+fEw2jz2I/XcOMH9OyGE\n3wSuBB4oPhljvKmyqiRJaoajgdtJ0211FzJPRXA/HIO71FrjBPfTgCfu4/PHlVyLJEnNkSQJWXC/\ntu5SSlDc8dwLVKUWmzW4xxiPnUAdkiQ1zWrgQNo/3w67d9wltdS0wT2EcBTwF8AjgK8Br44xbpxU\nYZIk1awrF6bC7jPuklpqpn3cPwCsB36XrOPwjolUJElSM7R/D/cpdtylDphpVObIGONrAUII/wx8\ndzIlSZLUCF3Zwx2ccZc6YaaO+8+uoI8xbgfc91WS1CddGpUxuEsdMFNwTyZWhSRJzdOd4J7ddGkj\nzrhLrZakabrPB0II24BbRj51JFM3oEhjjMdXXFsVUvwHSZlcz3K5nuVxLcvVz/VMki8B/wU4sOS7\njdaznkmyHlhFmnYpvPfze7M6rmfDzTTj/oiJVSFJUvOsBe4oObTX6Q7gESTJQtJ0R93FSJq7aYN7\njPHGCdYhSVJzTN186T/qLqVEt5N1Uw8Fbqu5Fkn7YaYZd0mS+molsJQuzLdPcS93qeUM7pIk7a1L\ne7gX3FlGajmDuyRJe+vSHu6FDfnxyFqrkLTfDO6SJO2tO1tBTim+lqNnfJakxjK4S5K0ty4G92Ls\nx+AutZTBXZKkvXVxxt2Ou9RyBndJkvZWzLjfMuOz2mUTcC9TX5ukljG4S5K0t6OBu0jTB+supDTZ\nrdJ/gh13qbUM7pIkjZq6+VKX5tsLNwOrSZKD6i5E0twZ3CVJ2t0hwDK6Nd9eKP4xclStVUjaLwZ3\nSZJ218U93AteoCq1mMFdkqTddXEryELxUwQvUJVayOAuSdLuuhzc7bhLLWZwlyRpd13cw71gx11q\nMYO7JEm7c8ZdUiMZ3CVJ2l0Rart086VCcRMmg7vUQgZ3SZJ2dzRwN2l6f92FlG7qJkzH1F2KpLkz\nuEuStLuj6eZ8e+EGYBVJckjdhUiaG4O7JEmFLMyuoJvz7YXr8uPDaq1C0pwZ3CVJmtLlrSALRXB/\neK1VSJozg7skSVMM7pIay+AuSdKULu/hXjC4Sy1lcJckaUqX93Av3AjswuAutY7BXZKkKd0flUnT\nbcCPMbhLrWNwlyRpSh867gDXAw8lSZbVXYik8RncJUmaspbs5kv31V1IxdwSUmohg7skSQBJkpDd\nUfSmukuZAC9QlVrI4C5JUmYlsIxu7yhTMLhLLWRwlyQpU8y396Hj/sP8GGqtQtKcGNwlScockx/7\nENyvA7YDj6q7EEnjM7hLkpQpgnv3R2WyLSEj8Kh8tl9SCxjcJUnK9GlUBuDfgeVM/YNFUsMZ3CVJ\nyvRpVAbg2vz46FqrkDS2hVW9cAhhAXAxcDKwFTgvxnj9yOPrgNcBO4BLY4zvn+6cEMLDgb8mu0Xz\ntcBLYoxpCOFpwOvzl/xWjPG3q/p6JEmddwzZ3zMb6i5kQkaD+2frLETSeKrsuD8LWBxjPAN4NXBh\n8UAIYRFwEXA2cBZwQQjhsPycJfs45yLgtTHGM4EE+JUQwgrgbcAzYoynA7eEENZU+PVIkrptLXAL\nabqj7kImxI671DJVBvcnAZ8HiDFeCZw68thJwHUxxk0xxu3A14Az83Mu28c5p8QYL89/fxnwS8Dp\nwPeAi0IIlwMbYox3Vvj1SJK6KkkOAI6iDxemTrkBeAB3lpFao7JRGeBgYPPIxztDCAtijLvyxzaN\nPLYFOGSacw4g67Lv+dxDgScD/wm4D/hqCOFfY4w/ZGbp/nwxmpbrWS7XszyuZbm6vZ433wxr18Lz\nnncGk/la61/PNIVTT4Vrr30cO3emHHBA3RXtr/rXsltcz/KUvmNTlcF9M7Bi5OMitEMW2kcfWwFs\nnOacnSGEXSOfOzh/7k/J5trvAMi77o9l6qYS03Hbq/KkuJ5lcj3L41qWq/vruXbtGcAV/N3fvY2/\n/dtXVfxuzVnPq676a+AFLFx4Imka6y5nPzRnLbvB9Wy4KkdlrgCeDhBCOA24ZuSx9cAJIYSVIYTF\nZGMyX5/hnKtDCGflv38acDnwbeDRIYTVIYSFwGlkW1tJkjRX/dnDfXffy48n11qFpLFUGdw/ATwY\nQriC7CLTl4cQzgkhnJ/Ptb8C+AJZYL8kxrhhX+fkr/VK4A9DCF8n+ynBP+bz7K/JX+MbwMdijN+v\n8OuRJHVX3/ZwL3w3P/6nWquQNJYkTXs1yuSPgMrlepbL9SyPa1mu7q9nkvwF8FLgFNL06orfrTnr\nmSRrgDuAz5Cm6+ouZz80Zy27wfVsOG/AJElS/26+lEnTO8n2rbfjLrWAwV2SpGxU5n7g7roLqcF3\ngbUkyaq6C5E0M4O7JElZx/1mejY/mivm3L1AVWo4g7skqd+S5CBgNX0bk5niBapSSxjcJUl919cd\nZQoGd6klDO6SpL7r6x7uhR8AWzG4S41ncJck9V2/O+5pugO4FngUSVLlHdUlzZPBXZLUd33vuEM2\nLrMECHUXIml6BndJUt/1cw/33TnnLrWAwV2S1HfFqEzfO+5gcJcazeAuSeq7Y4C7SNMH6i6kRtfk\nR4O71GAGd0lSfyXJArLg3ucxGUjTe8jWwOAuNZjBXZLUZ0cABwI/qruQBvgucARJcljdhUjaN4O7\nJKnPjsuPBnfn3KXGM7hLkvrs+PxocJ8K7ifXWoWkaRncJUl9VgT3G2qtohm+nx9PrLUKSdMyuEuS\n+sxRmSnXAzsxuEuNZXCXJPXZ8cAu+r6rDECabiX7B4zBXWoog7skqc+OB35Cmm6ru5CGWA8cSpIc\nWnchkvZmcJck9VOSHAgciWMyo9bnx1BrFZL2yeAuSeqrnwMSDO6jiuDuuIzUQAZ3SVJfFRemuqPM\nFIO71GAGd0lSX7mH+95ifjS4Sw1kcJck9dXD8qMd90Ka/hS4E4O71EgGd0lSXxXhdP2Mz+qf9cDx\nJMmSuguRtDuDuySpr04CbidN76m7kIb5IVk+OLbmOiTtweAuSeqfJFlKFkzttu+tmPk/fsZnSZo4\ng7skqY8eQbYV5H/UXUgDGdylhjK4S5L66KT8aMd9bwZ3qaEM7pKkPiouTLXjvjeDu9RQBndJUh8V\nHXeD+97uAu7F4C41jsFdktRHJwL3AT+pu5DGSdOUrOt+PEmS1F2OpCkGd0lSvyTJAUAA1uchVXu7\nAVgOHFp3IZKmGNwlSX1zLLAEx2Rm4py71EAGd0lS33jH1NkVwf24WquQtBuDuySpb7wwdXZ23KUG\nMrhLkvrG4D47g7vUQAZ3SVLfnAjsBK6vu5AGuzE/OiojNYjBXZLUH9n2hicB15Gm2+oup7HS9EHg\nTmBt3aVImmJwlyT1yWHAShyTGcdNwFr3cpeaw+AuSeoTd5QZ383AgbiXu9QYBndJUp94Yer4bs6P\njstIDWFwlyT1icF9fDflR4O71BAGd0lSnxSjMrHWKtqh6LgfU2sVkn7G4C5J6pOTgFtI0811F9IC\ndtylhjG4S5L6IUmWk4VQx2TGY8ddahiDuySpL0J+dEeZ8Wwgu1GVHXepIQzukqS+8MLUuUjTncAt\nGNylxjC4S5L6wuA+dzcDR5EkC+suRJLBXZLUH958ae5uIssKD627EEkGd0lSf5wEbAJuq7uQFvEm\nTFKDGNwlSd2XJIuAE4D1pGladzktYnCXGsTgLknqg+OBhTjfPle35kdHZaQGMLhLkvrAC1P3TxHc\nj6y1CkmAwV2S1A9FcPfC1LnZkB8N7lIDGNwlSX1Q7Chjx31uigt5HZWRGsDgLknqg5OAbcANdRfS\nKmm6FbgLO+5SIxjcJUndliQJWcf9h6TpjrrLaaFbMbhLjWBwlyR13ZHAChyT2V8bgINJkmV1FyL1\nncFdktR17igzP24JKTWEwV2S1HXuKDM/bgkpNYTBXZLUde4oMz9uCSk1hMFdktR1Rcc91lpFezkq\nIzWEwV2S1HUnAT8mTe+vu5CWclRGagiDuySpu5LkEOAInG+fD0dlpIYwuEuSuuyE/PiDWqtoN++e\nKjWEwV2S1GVFcP9hrVW0WZpuA+7EjrtUO4O7JKnLDO7l2IDBXaqdwV2S1GUG93LcCqwgSZbXXYjU\nZwZ3SVKXnQDsAH5cdyEt55aQUgMY3CVJXXYC8CPSdEfdhbScO8tIDbCw7gIkqe+Gw2QRsAw4EFia\nHw8ANue/tgwG6c76KmypJFkFrAL+te5SOsC93KUGMLhL0gQMh0kCrAVOA04Gjh35dSSQzHL+vcBP\nyGa1r8t/rQe+NRikWyoqu+2cby+PozJSAxjcJakiw2HycOBXgCeRBfY9Q88u4Gbgq8BG4EHggfy4\nC1gBHJz/eghZyD9xz9cYDpNrgK/nv/55MEhvr+DLaSP3cC+PozJSAxjcJakkeVf90cCvAc8GHjPy\n8K3Ax4BvAFcBPwJuGQzmNns9HCaryALpw8k696cDTwAeC7wYSIfD5F+BTwCfHAzS6+bzNbWcHffy\nOCojNUCSpmklLxxCWABcTPYXy1bgvBjj9SOPrwNeR3a1/6UxxvdPd04I4eHAX5N1oK4FXhJjTEfe\n57PAJ2OM75mlrJRZfhytOXE9y+V6lmeiazkcJkuA5wEvIwvQANuALwIfB/5pMEh/UuH7LwYeB/wC\nsA74z0xtPvA94BLgw4NBevd+vkU7vzeT5CPA84HjSNMba65mVPvWM0kWkX1PD0nTJ9ddzoj2rWWz\nuZ4NV2XH/VnA4hjjGSGEJwIX5p8jhLAIuAg4FbgfuCKE8Cmyv2yW7OOci4DXxhgvDyH8FdmPnj+Z\nv8+byX6EXM2/QCRpGsNhcjjwW2Sd7sPImgufAP4e+NykZs8Hg3QbcGX+60+Hw2QN8EzgV4GnAu8E\n3jocJh8F3gt8bTCoqGvTLMeTNYdurruQ1kvT7SSJd0+ValZlcH8S8HmAGOOVIYRTRx47CbguxrgJ\nIITwNeBMsh/5XraPc06JMV6e//4y4CnAJ0MIzwF25u/jvxAlTcRwmCwHXg28kmwHmE3A24F3DQZp\n7fuFDwbpncAHgA/kIf43gQuA/5H/uno4TN5MNkqzq75KK3cccBOpO/KU5FbgYXUXIfVZlfu4H0y2\njVlhZz7WUjy2aeSxLcAh05xzALuH8i3AISGERwPnAK/H0C5pAobDZMFwmJxLNjP9+8DdwEuBoweD\n9PeaENr3NBikdw4G6YVkF7UOgI+SjfN8DLhmOEzOGQ6TA2ossRpJshQ4HLih7lI65FZgOUmyou5C\npL6qsuO+mWxHhMKCGGPR2dm0x2MryHZU2Nc5O0MIox2hg/Pn/gZwFPBlsp0WtoUQbogxfnGWuvrw\n4+FJcj3L5XqWp9S13Lz5SpYvfxz33ns1CxYsZe3a3+OYY373yAMOWPaXwF+W+V5VGAymluP++yM/\n/vGfcPvtH3kU7PybpUvD39x112dYvfoZJMm0fZB2fW9+//vwyEfCeef9Is2svYk1zexFL4JLL4UY\nN8/+5Ilq31o2m+tZntIby1VenPpsYF2M8dwQwmnA62KMz8gfWwT8O/BE4D6yLczWkY3K7HVOPv9+\nYYzxKyGEdwNfijF+dOS93gBsiDG+d5ayvOiiXK5nuVzP8pS2lnk3+jXAG8luivS/gdcOBmnr56aH\nw+R4spGfF5F9bV8AXj4YpP+xx1Pb972ZJE8DPgf8AWn6x3WXs4f2rSdAkvwR8AfAk0nTYc3VFNq5\nls3lejZclR33TwBnhxCuyD8+N4RwDrA8xvi+EMIryP6SWABcEmPcEELY65z8+ErgfSGExcD3gX+s\nsG5JAmA4TI4hC+q/QHbzo98YDBoTWOZtMEh/BFwwHCbvJLuA9alk4zN/CfzhYJBurLXA+TkuPzoq\nUx63hJRqVlnHvaH8l2S5XM9yuZ7lmfdaDofJrwHvJ9u16uPA+fPYTrHx8j3o15Ht4vUwspB2/mCQ\nfo42fm8myduB3wGeRJp+ve5y9tC+9QRIkmJHt98hTS+su5xcO9eyuVzPhqvy4lRJap3hMEmGw+T1\nZD/ZWwycDzyny6EdYDBI08Eg/RTwKLJ7bKwBPjscJpfs2LFp5pOb6dj8aMe9PN49VaqZHXfNh+tZ\nLtezPPu1lsNhspDsJnDnAzcCzxgM0u+XW1o7DIfJyWQ3vnvckiVHs3XrT546GKSzXfzfHEnyb2R3\nsT2ItHFbXrbz//UkWQvcBPwtafr8usvJtXMtm8v1bDg77pIEDIfJMrJrc84HrgZO72toBxgM0mvI\nNhB4w7ZttwF8YThM3pL/46YNjgVubGBob7Pb8+NDa61C6jE77poP17Ncrmd55rSW+U2KPk0WVL9I\nNhozkbuetsGWLVenV111ynXAw4HLgecNBumGWU6rT7bP+Gbg86Tp0+ouZx/a+/96dvfUn5KmJ9Zd\nSq69a9lMrmfD2XGX1GvDYbIS+BJZaP8QsM7QvrsVKx4HcCrZRbpnkt15dVBnTbModpS5sc4iOmoD\ndtyl2hjcJfVWPh7zWeAxwLuAFw4G6bZ6q2qmwSDdBDwHeAWwGvjScJi8Mt+NpmmOzY831lhDV20A\nDiZJDqq7EKmPDO6Semk4TJaQdZBPBz4C/PZg0K/ZwbnKd555BzAgm3f+U+Ddw2GyqNbC9rY2P95U\naxXddFt+tOsu1cDgLql38ruhfhh4CvAZ4NzBwIsYxzUYpFcAPw98B7gA+NxwmDyk3qp2UwT31t/d\ntoGKaxuOqLUKqacM7pJ6JR/t+CvguWQXWv63wSDdXm9V7TMYpD8hu6Psp4FfAq4YDpPjZj5rYo7O\njz+ptYpuKoK7HXepBgZ3SX3zCqa2fPzlwSB9oOZ6WmswSO8FfhV4J/BI4BvDYfL4eqsCso57CtxS\ndyEdZHCXamRwl9Qbw2HyX4C3kc3pPiO/4FLzMBikOweD9OXAS8nutvovw2FyVs1lrQVuI/UnKRUw\nuEs1MrhL6oXhMFkL/D2wi2yf9ubuQ95Cg0H6LuAc4ECymzX9ci2FJMkCslEZ59urYXCXamRwl9R5\nw2FyIPAx4FDgZfnFlSrZYJD+PbAO2Al8fDhMfqOGMg4DFmFwr4rBXaqRwV1SH/w58ASyGyxdXHMt\nnTYYpF8Azga2AB8aDpOXTrgEd5SpUpreR/bf1uAu1cDgLqnThsPk/yG7GPU7wG+5V3v1BoP068BZ\nZHu9/8VwmLxsgm9vcK/ebRjcpVoY3CV11nCYPJys274ReLY7yEzOYJBeQxbeNwDvGA6TV0zorYvg\n7laQ1dkArCFp3I23pM4zuEvqpPwmSx+C/9vefYdJVlT/H3/XBpawLDktaQlyBEFYQMlQBAkSVVAE\nFRNIRlDkK0FEwEAwIYggQf2hiKCgSA4lApKXvBRxl5zDEpcN9/dHVbO9zczs7Ex33w6f1/P00zPT\nt2+fPlMzfW7dulXMDezjffFEySF1He+LSFpl9Vng5BDcoU142coc7upxb5zKOPdFS41CpAupcBeR\nTnUosB7wV++L88sOplt5XzxM6nl/GjghBHdYg19SQ2UaTxeoipREhbuIdJy33roH4EekAmO/cqMR\n74tHST3vTwE/DcEd3sCXW5o0q42m+2wcFe4iJVHhLiIdJQQ3Yvz4r0CaEvCb3hevlBySAN4Xj5GK\n9yeB40NwRzbopZYGnqUopjVo/6LCXaQ0KtxFpNP88O237wU4w/visrKDkRm8Lx4nDZuZCBwbgju6\nri+QFl9aAl2Y2mgq3EVKosJdRDpGCO4TwPfmnHN5gO+UHI70wPtiAql4fwL4YQjumBCcq9PuFwGG\nkS6GlcZR4S5SEhXuItIR8iwypwNDzM7C++KtsmOSnnlfTCQNm3kc+AGp970exfvofK/CvbGez/cq\n3EWaTIW7iHSKfYE1gT8usIAvORSZFe+LJ0k9748CR5AuWh1s8a7CvTleBd5HhbtI06lwF5G2F4Ib\nDRwPvEaaBlLagPfF06Se9wh8jzTX+2CKdxXuzVAUBanXffGyQxHpNircRaQT/ByYF/i+98WLZQcj\n/ed98QypeH8QOBj49SCKdxXuzfMcsDiubtcniEg/qHAXkbYWgtsS+AJwC3BmyeHIAHhfPA9sCtwH\n7A/8NgQ3kM8nFe7N8xxpytWFyg5EpJuocBeRthWCmxM4FZgO7ON9Mb3kkGSA8pmSzYC7gW8BZwyg\neFfh3jyaWUakBCrcRaSdHQasCPza++LusoORwfG+eBnYHLgT+AZwTp4tqL9GA+8CbzQgPJmZCneR\nEqhwF5G2FIJbmlS4P0eaUlA6gPfFq8AWwK3AV4A/heCG9fPpo0mrphaNik8+oMJdpAQq3EWkXf0E\nmAs43PvizbKDkfrxvngd2BK4Gfgi8OcQ3PA+n+TcMGAxNEymWTSXu0gJVLiLSNsJwX0S2B24C/hj\nyWOkHH4AACAASURBVOFIA3hfTAK2Bm4AdgEuDMHN1cdTFgMcKtybRT3uIiVQ4S4ibSVPFfiL/O3B\nuiC1c+UzKZ8GrgF2AK4Iwc3Xy+ZL5vtnmhGbfFC4ay53kSZS4S4i7WYXYH3g794XN5QdjDSW98Xb\nwHbAhcDGQAjBLdbDpppRprleBArU4y7SVCrcRaRt5OkffwZMIa20KV3A+2IysCvwO2AN4MYQ3HI1\nm6lwb6aimEoq3lW4izSRCncRaSffBsaQpn98rORYpIm8L6YB+wDHkaYAvSkEN7ZqExXuzfccKtxF\nmkqFu4i0hRDcosDhwMuk4k26jPdF4X1xFHAQaWz1DSG4bfLDKtyb7zlgHpybt+xARLqFCncRaReH\nA/MCx+TpAqVLeV/8GtgZGAb8KwT3LWYU7s/1+kSpN80sI9JkKtxFpOWF4JYhDZN4Ajij5HCkBXhf\n/B3YFHgNOP3hg1izcEyiKN4qObRuorncRZpMhbuItIOjgTmAo70v3i87GGkN3he3AOsCjzy7E4vc\ndzwuBDdP2XF1EfW4izSZCncRaWkhOAO+CjwI/LncaKTVeF88Nv84NpnvHnh1PeYlzTizbNlxdQnN\n5S7SZCrcRaTV/Yj0v+rIPLOIyEzWOIQRq38XFrqZR0jTRd4egtuw7Li6gHrcRZpMhbuItKwQ3JrA\n54HbgYtLDkda1+ghU2HVI/kHsB+wIHBdCG7PkuPqdCrcRZpMhbuItLLKtI+He18UpUYirWw0gCt4\n1vviNOBTwJvAGSG4U0Jww0uNrnOpcBdpMhXuItKSQnAbAdsA1wPXlhyOtLbKVJDPAHhfXA98Argf\n2B+4IgS3UEmxda6ieA94HRXuIk2jwl1EWk4IzgE/zt+qt11mZcl8/8HiS94XjwPrk4ZYbQbcFoJb\ntYTYOp1WTxVpIhXuItKKtgY2BP6Zp/wT6UuPq6Z6X7wJfA44Flge+F8Ibocmx9bpngMWxLkRZQci\n0g1UuItISwnBDQGOBwrgqJLDkfbQ66qp3hfTvS9+QLrIeQhwcQju8HxWRwavsgiTpoQUaQIV7iLS\naj4HjAX+4n1xb9nBSFsYDbxCUUzubQPvi7+RzuI8TTowPC8EN1eT4utkukBVpIlUuItIywjBDSMN\na5hKWi1VpD9GUzNMpifeF+NIF63eDHwRuCEEt2Tfz5JZ0CJMIk2kwl1EWslXAAPO8r54tOxgpA04\nNxIYRT8KdwDvixdIF6ueA6xNWqzpk40LsOOpx12kiVS4i0hLCMGNAH4ITCb1uov0R6Vg7FfhDuB9\nMRn4BnAIsBip5/1LDYitG1TyrjMXIk2gwl1EWsW3gKWB33hfPFN2MNI2epxRZla8Lwrvi18A2wLv\nAX8Kwf0sBDe03gF2uMrfqgp3kSZQ4S4ipQvBjQSOJK12+dOSw5H2MqDCvcL74gpgHeAR4HvAJSG4\nUXWKrRuocBdpIhXuItIKDgIWAU72vni57GCkrQyqcAfwvoik4v1qUg/8TSG4pesQW+crindIq6eO\nntWmIjJ4KtxFpFQhuAWBQ4FXgF+UHI60n0EX7gDeF68BnwZ+A6xKWqxptUHG1i2eQT3uIk2hwl1E\nynYoMB/wE++LSWUHI22nUrgP+roI74upwIGkNrkkcGMIbrPB7rcLPAPMj3Nzlx2ISKdT4S4ipQnB\nLUEaJvMMcFrJ4Uh7Gk1aZfeFeuwsX7R6Emme9zmBK0Jwu9Vj3x1M49xFmkSFu4iU6QhgLuBH3hfv\nlh2MtKUlgRcoiqn13Kn3xfnAVsA7pFVWv13P/XcYFe4iTaLCXURKEYJbDtgLeIy0GI7I7HHO0c9V\nUwfC+yIAG5IWGfpFCO4HITjXiNdqcyrcRZpEhbuIlOVoYDjwA++LKWUHI21pPtIZm4YU7gDeF/eT\nivcJwDHASSreP0SFu0iTqHAXkaYLwa0CfBm4Dzi/5HCkfdVlRplZ8b54nFS8jyettnqGFmqaiQp3\nkSZR4S4iZTiW9P/nCO+L6WUHI22rKYU7QF7NdxNgHPBN0kqrwxr9um1ChbtIk6hwF5GmCsF9Avgs\n8D/g0pLDkfbWtMIdwPviJWBT4GbSrDN/UM87AC8BU1DhLtJwKtxFpNmOz/eHe18UpUYi7a6phTuA\n98UbwNak4n034NyuL96LYjrpAl4V7iINpsJdRJomBLcp8Cng6jxjh8hgNL1wB/C+eBPYBrgF+BJw\nTtcX72m4zBI4p7pCpIH0ByYiTZFn4qj0th9RZizSMUop3AHyKr9bA7eSLrQ+q8uL92eAYcBiZQci\n0slUuItIs2wLrAf8w/vi9rKDkY6wJDCNNMa66fKwma2A24A9gN908VSRT+X7pUqNQqTDqXAXkYYL\nwQ0h9bYXwJElhyOdY0ngmTzGuhRVxfs9wN7MOKvUbSqF+9KlRiHS4VS4i0gzfAH4OPAn74sHyw5G\nOoBzQ0lDZZ4uOxTvi9dJxfsjwPdDcIeWHFIZVLiLNIEKdxFpqBDccOBHpOniflhuNNJBFgOGMmMO\n8VJ5X7xAuvD6aeCEENyeJYfUbCrcRZpAhbuINNo3gBWBM70vnig7GOkYlbHUpfe4V3hfTCQV7y8D\nvwvBfaHkkJpJhbtIE6hwF5GGCcGNJPWyv01aLVWkXipzhrdM4Q7gffEQadjMm6TVVTctOaRmeQGY\nigp3kYZS4S4ijXQwaUjDyd4Xz5cdjHSUlutxr/C+uAvYKX97cQhutTLjaYqimEYatqTCXaSBVLiL\nSEOE4BYBvkeaqu+kksORztOyhTuA98X1pCkiRwGXh+C6oaB9ChiNc8PKDkSkU6lwF5FGOQoYCfwo\nrzQpUk8tXbgDeF/8BTiUNKzn8hDc/CWH1GhPkeqKJcoORKRTNeyo2MyGAKeRpoCbDHwzxvhY1ePb\nkz7YpwJnxxh/39tzzGxF4FxgOnA/sF+MsTCzg0nTzAFcFmP8UaPej4j0XwhuBdKc1o8BZ5QcjnSm\npUjrAjxXdiCzcDIp1oNIw2a28r4oOaSGqb5A9am+NhSRgWlkj/tOwBwxxvWB/yP98wLAzIYDPydd\nfb8JsJeZLZqfM6KH5/wcODzGuDHggB3NbDlgN2C9GOO6wJZm1vnjCEXaw3HAcOAI74v3yw5GOtJS\nwPMUxZSyA+mL90UBHAL8jfR59/ui6IrCXUQaoJGF+wbAFQAxxluBtaseWxl4NMb4RoxxCnAjsHF+\nzuU9PGfNGOMN+evLgS1I/yC2jjFW/gMOB95t3NsRkf4Iwa0F7ArcSSpWROrLOUcq3Ft2mEw174vp\nwFeAW4AvTZzYsYurVn4fKtxFGqSRhfsoYFLV99PyUJjKY29UPfYmMF8vzxlK6mWveAuYL8Y4Ncb4\nipk5MzsJuCvG+Gjd34WI9FsIzgEn5G+/lwsWkXpbGJiDNincAbwv3iOdVZ44YcJRhOA+X3ZMDaAe\nd5EGa+SV35OAeau+HxJjrHyIv1Hz2LzA6708Z5qZTe9hW8xsTuDsvL99+xlXx56jLInyWV9tnc9V\nV72E++/fkQUX3IaPf/yya0sOp61z2YJaJ5/jxsHYsXDAAZ+hleKaBe8L3nrrfsaNW5+imPLXSZNu\n/euoUeuUHVb9vPgiLLoo7LTTgcCBTXzltmkDbUL5rB83601mTyN73G8CPg1gZusC91Y99hDwETNb\nwMzmIA2TubmP54wzs03y19sAN5iZAy4B7o4x7lM1ZGZWnG51uymfyucHtxDciPvv3/ERYNqrr16+\ninLZUbfWyufYsdsDcMoph5Uey2zeRo5c1a2yygVMn/7e9LvuWvfFENyYsmOq223RRYcA73LxxeOa\n+LqU/r4766Z81j+fddXIwv0fwHtmdhPpItODzeyLZrZnHtd+CHAlqWA/K8b4XE/Pyfv6DnCMmd1M\nOktwEemU48bA1mZ2fb6t28D3IyJ92wf4CHC698X4soORjlYZivFMqVEM0EILbQ2pR3pR4NIQ3Khy\nI6qTdNXtBGDZkiMR6Viug69u70lBg46AupTyWV9tm88Q3ILAo6TOgBW9L14uOaS2zWWLaq18OvdT\n4DBgI4rixrLDGYACcCG4XwMHkCZd2MH7Ymq5YdWBc5eRzozPR1FMmtXmddBabbP9KZ8tTgswiUg9\nHA0sABzbAkW7dL5Kj+6EMoOog0NIRfs2pGmPO8HEfK9ed5EGUOEuIoMSgjPSxeGPAb8pORzpDmNI\ni/e1+uJLfco97LuSFhY8IAS3X8kh1cOEfD+mxBhEOpYKdxEZrBNJ1558z/tictnBSFcYAzxJUUwr\nO5DB8r6YBGwHvAD8OgS3dckhDdaEfD+mxBhEOpYKdxEZsBDcp4Dtgf+QLi4XaSzn5gQWp/2HyXzA\n+2IisCPwPnBBCG7VkkMajAn5fkyJMYh0LBXuIjIgIbg5gF8D04GD89LuIo22TL6f2OdWbcb74lZg\nD9JaJZeG4BYtOaSBmpDvNcZdpAFUuIvIQB0IfJQ0/eO4soORrjEm308oMYaG8L64gHSh97LAP0Jw\nc5Yc0kC8CLyHetxFGkKFu4jMthDcaFKB8QpwVMnhSHep9OR2VI97lWOBvwDrA2eG4Nprar40x/RE\nVLiLNIQKdxEZiBOAkcD3vS9eLTsY6Spj8v2EEmNomDzk7OvALcCXgMPLjWhAJgAL4dy8ZQci0mlU\nuIvIbAnBbQzsDtwBnF1yONJ9OmUO9155X7xHWh38SeC4ENzOJYc0uybke41zF6kzFe4i0m8huGHA\nKfnb/b1v/+n4pO2MAaYBz5QcR0N5X7xAmrHpLeCPIbi1Sw5pdkzI92NKjEGkI6lwF5HZsQ/wceCs\nPAuGSLONAZ6mKKaWHUijeV/cC3wRmBP4ZwhuqZJD6q/H8v0KpUYh0oFUuItIv+QLUo8DXqc9x91K\nu3NuDmA0nXth6od4X1wKfAdYglS8z1NySP3xaL5fsdQoRDqQCncR6a9fAaOAw7wvXiw7GOlKywOO\nGYVht/glcCYwFvh/IbhW/+yu9LircBeps1b/4xeRFhCC2w7YGbgJ+H3J4Uj3+ki+f6TUKJoszzSz\nH3A96aLV48uNaBaKYhJpPncV7iJ1psJdRPoUghsJnApMAfbyvpheckjSvVbK9w+XGkUJvC+mkA6e\nHwH+LwT39ZJDmpVHgTE4N7zsQEQ6iQp3EZmVH5GWmf+Z98WDZQcjXa1rC3eAvGbCtsCrwO9CcFuW\nHFJfHgWGkf53iEidqHAXkV6F4NYCDiJ9CP+45HBEKkNlHutzqw7mffEIsANpSswLQ3CrlxxSb3SB\nqkgDqHAXkR7lOdvPIP2f2Nv74t2SQxJZCXiSorvbovfFTcCXgXmBf7foNJG6QFWkAVS4i0hvDgbW\nBP7ofXFt2cFIl3NuHmBJunSYTC3vi78Bh5Jy8u8Q3KiSQ6qlHneRBlDhLiIfEoJbGTgWeAE4pORw\nRGBGAdhVM8rMwsnAaaRF0S4Kwc1RcjzVVLiLNIAKdxGZSQhuKHAOMII0ROaVkkMSgS6/MLUneZrI\ng4B/AlsAf2yZOd6L4lXgNVS4i9RVa/yBi0grORhYB/iL98XFZQcjkqlw74H3xVRgV+BG4AvAr0Jw\nrtyoPvAIsALODSs7EJFOocJdRD4QgvsocBxp8ZQDSg5HpFqlcNdQmRr5wvEdgPuA/YEjyo3oA+OB\n4cAKZQci0ilUuIsIoCEy0vJWAyYDT5QdSCvyvngN2BqYABwbgtur3IgAqKz7sHKpUYh0EBXuIlJx\nMLAuaYjMP8oORuQDafXNjwH3URRTyw6nVXlfPAtsBbwM/DYE9/mSQxqf71cpNQqRDqLCXUQIwX0c\nOJ40RObAksMRqWXAHMA9ZQfS6rwvHib1vL8FnBeC27HEcCo97ircRepEhbtIlwvBzQWcRyqMvuZ9\n8XLJIYnUqqwOqsK9H7wv7gQ+DbwPXBCC26qkUCYA76GhMiJ1o8JdRH4KrAqc5n1xWdnBiPRAhfts\nyqurbg8UwMUhON/0IIpiGhCBlXEtMk2lSJvTH5JIF8s9cQcCD5FWYRRpRZXC/d5So2gz3hfXAZ8B\nhgKXhuDWKyGMB4G5gGVLeG2RjqPCXaRLheAWAc4FpgC7eV+8U25EIr1aA5hAUbxediDtxvvictL8\n7nMCV4bg1m9yCJULVDVcRqQOVLiLdKG8QMuZwOLAkd4X40oOSaRnzi0OLIqGyQxYniVqN2BuUvG+\nURNfXheoitSRCneR7rQPsCMQgJPLDUWkT2vkexXug+B9cQEzet6vaOKY9wfy/WpNej2RjqbCXaTL\nhODWAn4BvAJ82ftiWskhifRlw3z/v1Kj6ADeFxcBO5NWM70sBLd5E172EeAdYGwTXkuk46lwF+ki\nIbj5gb+Rpn78kvfF0yWHJDIrGwPTgZvLDqQTeF9cAnyWGResfrqhL5hmlrkbWAXn5mroa4l0ARXu\nIl0ij2s/G1gO+LH3xRUlhyTSN+fmBNYB7qYoJpUdTqfwvriUNFQO4JIQ3Bcb/JJ3kQ4UNFxGZJBU\nuIt0jwNJU8P9Bzi65FhE+uOTpLND/y07kE6TD9y3JA1jOS8Et08DX65y8buGy4gMkgp3kS4Qgvsk\ncCLwIvBF74upJYck0h+V2U9uKDWKDuV98V/AAy8Bp4Xgjshn5urtrny/ZgP2LdJVVLiLdLgQ3GLA\nhcAw0nztz5Uckkh/bZzv1ePeIHkq2A2BicBxwM9DqPsqpw8C76PCXWTQVLiLdLAQ3Bykon1p0nzt\n15Yckkj/ODc3qaAcT1G8VHY4ncz74hFSrh8Evg2cH4Kbs24vUBTvA/cBq+Hc8LrtV6QLqXAX6Wy/\nIn0g/w34ScmxiMyOrUgLBv2z7EC6QZ5hakPSsKRdgKtDcAvW8SXuAkYAH6vjPkW6jgp3kQ4Vgtsb\n2Ju0cM3XvC+KkkMSmR2fzfcXlRpFF/G+eI10wepfSUX8zSG45eq0+1vy/QZ12p9IV1LhLtKB8pLm\npwAvAzt5X7xdckgi/efcHMD2wJPAHSVH01W8LyYDuwEnAAbcki9uH6zKdQob9bmViPRJhbtIhwnB\nLUsa1+6AXbwvJpQbkchs2xyYD/g7hc4UNZv3xXTvi8OA/YGFgRvqMNf7o8ALwEa4hsxcI9IVVLiL\ndJA8JvVyYFHgIO+LUG5EIgPylXx/YalRdDnvi1OBbYHJwJ9DcMcOeMaZdAB2IzCatAiciAyACneR\nDpFngbgYWBk4OX/oirQX55YCdgbuB24uOZqulxdqWhd4HDgS+FsIbp4B7q4yXGbDesQm0o1UuIt0\ngNwL9kfS+NELgO+VG5HIgO1LWnPgVxom0xq8L8aTVrENpIuGbwrBLT+AXWmcu8ggqXAX6QwnkqZw\nuwHYw/tiesnxiMy+NHf7XsArwHklRyNVvC9eIU3R+TtgdeCOENzWs7mbe4E3gU3rHJ5I11DhLtLm\nQnAHA4cA40kzyLxXckgiA3UwsBBwGkXxbtnByMy8L973vtgb+CZpjv3LQnBH9nvce1FMBa4BVsC5\nlRoXqUjnUuEu0sbyXO0/B54DtsnzMIu0H+cWB/4PeAk4qeRopA/eF2eRxqk/DRwL/CMEN38/n/7v\nfL9tI2IT6XQq3EXaVAjum8BvgReBzb0vJpYckshgHAuMBI6mKCaVHYz0zfviDmAt4FpgB2BcCG6d\nfjz1snyvwl1kAFyXXftTkOa2lvpQPuur3/kMwe0BnAO8Cnjvi/sbGVgbUtusr8bm07nNSAXgA8Aa\neUhFJ+uY9hmCGwb8gDTjzDTgcNKsVr1fZ+PcHaRx8gvV4SCtY3LZIpTPFqced5E2E4LbjVS0vw5s\noaJd2ppzI4GzSEXf17qgaO8o3hdTvS9+AGxBWqn5BODSENwifTzt36SZg7ZsQogiHUWFu0gbyT3t\nfwImAZ/yvri75JBEBuunwBjgBIri9pJjkQHyvriO1It+JbANcF8IbrteNr8k33+hGbGJdBINlZHB\nUD7rq9d8huAccBjwE+A1YGvvi9uaGFu7Udusr8bk0zkPXA88CKxJUUyu+2u0po5tn3mGmUOA44E5\ngLOBg72vGhLjnCMNi1oeWJyieH0QL9mxuSyJ8tni1OMu0uLyB+EvSUX7U8AGKtql7Tk3D6mom04a\nItMtRXtH876Y7n1xErA2cA/wdeDeEJz/YKPUY/gnYARp/QkR6ScV7iItLAQ3grQQzYGkHqr18yqG\nIu3ueGA54EQKHYh2Gu+L+0irrR4PLA1cH4I7PQS3QN7kPFLv7pdLClGkLWmojAyG8llfM+UzBLcY\n8FdgE+AmYHvN095vapv1Vd98Orc+cCPwMGkWmW5bNKyr2meeJvJsYBXS9LXfBs73m3ItaRXVlSmK\nhwa4+67KZRMony1OPe4iLSgEtx5wF6lo/zvpQlQV7dL+nJuTNIsMwDe6sGjvOt4XtwJjSVNFjgL+\nDFz5oueivMlBZcUm0m7U4y6DoXzWV5HHs+9HWg11KPB94ETvu+sPtQ7UNuurfvl07nhSAXcKRXFg\nXfbZfrq2fYbglgdOBbamYMrof/LucmcxfPibLE1RvDKAXXZtLhtE+WxxKtxlMJTPOpo69c3ixhtH\n/T/gS6Rl33fNU6zJ7FPbrK/65NO5scDtpIusV6Mo3hr0PttTV7fPPEvW50hzvi837E1Y6GYuf2Er\nPuP9bF+k3NW5bADls8WpcJfBUD7rJAS39YgRS18+efJTALcCu3hfPFVyWO1MbbO+Bp9P54YDtwFr\nAFtSFFfXIa52pfZJuvh+2JscUjiOnzYSx3QmMIRjgT95X0zp526Uy/pSPlucCncZDOVzkEJwC5KG\nxezh3DCKYupxwHED6HWSmalt1lc9CvcjgOOAsymKb9QjqDam9lnl3SXdMc/syA+e/hzTGMpQ4EnS\nwlzneD/LayCUy/pSPlucCncZDOVzgPKp4s+T5mdfHBi39tp3jx05cnXlsz7UNutrcPl0bnXSEJmX\ngVUGueBOJ1D7rObc3MBj7y3CfHecxXlT5+VLwJzAc8AvgLO8L17t5dnKZX0pny1Os8qINFkIbgtS\nEXM+sADpQr11Ro5cvdS4RBrCuRGkxXaGk2aR6faiXWoVxTvAMXO+xFwb7sAIYAxp/PuofP9MCO7s\nENxaJUYp0hLU4y6DoXzOhvyh81Ngi/yjvwJHeV88kr9XPutHuayvgefTuROAQ4HTKYp96hlUG1P7\nrOXcUNL1PWsBm1EU1+ehhF8D9gFWyFveCpwL/M374hWUy3pTPlucCncZDOVzFkJwQ4HtgAOAzfOP\nrwa+731xZ83mymf9KJf1NbB8OrcxEIDHSAstvV3fsNqW2mdPnFubVJg/BoyttJc8Te6WpKlytyXl\nbipw+SqrnL/9gw/uOo/3xTslRd1p1DZbnAp3GQzlsxchuIVJPUX7kk77AvwHONb74tpenqZ81o9y\nWV+zn0/n5gfGAcsAG1AUtzQgrnal9tkb504GDgHOoCi+VftwCG4pYFdgd9IMRQDvAtcA/wQu9b54\nvknRdiK1zRanwl0GQ/msEoJbBNgJ2AXYjLSA0juk8b2nel/cN4tdKJ/1o1zW1+zlMw17uBTYGjiW\novhBg+JqV2qfvUnXRNwKrA58nqL4W2+bhuBWWWaZwx948skfjwdWrnrodtKZzeuAm70v3m1kyB1G\nbbPFqXCXwejqfIbghgFrApuSTuNuQirWIX1w/AU41/vitX7usqvzWWfKZX3NbuF+EvAd4HJge4pi\nWoPialdqn31xbmXS/9AhwEYUHxpWWK0AXAhuBWD7fNsYGJYffx/4H3BDvr+1jxlqRG2z5alwl8Ho\nqnyG4BYiFeprARuSPhzmrdrkVuBvwIXeFxMH8BJdlc8GUy7rq//5dO7/gJ8AEVhXs8j0SO1zVpzb\nHrgEeB7YhOKDi/hrfSiXIbh5gY1InSqbAWNrtnmY9P/6TuBu4B7v1U4ztc0Wp8JdBqMj8xmCmwdY\niXTqdWVgFVLBPqZm00dJp2KvB0IdxlV2ZD5LolzWV//y6dx3gJOAp4ANKYonGxxXu1L77A/nDgR+\nBbwAbEFR3N/DVrPMZZ6dZh1gPWDd/PWoms0mAPeRDjgrt4eBF73vqkJJbbPFqXCXwWjLfOYZChYH\nlgOW7+F+qR6e9jKpd6Zyu8374uk6h9aW+WxRymV99Z1P54YAPwYOA54lTecXmxNaW1L77C/nDgB+\nDbwN7A/8gZkLl9nOZf4MMNLFrZXbWGCRHjZ/g5kL+UeBifn2vPfF9Nl57TagttniVLjLYLRsPkNw\nc5EK8RVIxfgKVV+PIa3KV2s68DRpKrLxVbeHgGeb0OvSsvlsQ8plffWeT+fmI12AvT3wCLAlRTGh\naZG1J7XP2eHc54EzSb3kN5GGYl1NUbxPHXOZZwNbiVTUV99WJC0gVmsK6ezSk6RCvvr+aeAp74u3\n6hFbE6lttjgV7jIYpeUzBOdIvSOVory2OB/dy1NfAx7Ptydq7p/0vni/sZH3Se2zfpTL+uo5n85t\nBPyRdDB8DfBFiuLlpkbWntQ+Z5dzY0g979vnn0wCbmO//bbg1FOPJA2neTHfngOeoqhPb3ieiGBZ\nUhG/fP56mar7Jfp4+uvkIr63e+9ban0Dtc0Wp8JdBqOh+QzBDSf9Y6wtziv3I3t42nRSb8fjpJ7z\nyv1jwOMtfgGS2mf9KJf1NXM+nVsYOA7YKz/2Y+AYimJqKdG1H7XPgXJuLLAHaWG7FfrY8i3gNuAK\n0uxGD9CggicENwJYmhnF/NL5tlTV/Xx97OJ1ei7sK735T3lfvNeI2HugttniVLjLYAw6nyG4+Zkx\npKW2OF+GGdMrVnubmYvy6vuJJfeaD4baZ/0ol/WV8unccNLy88cA85OGku1JUdxUZnBtSO2zHpyb\nl7vumsSaa24NLAYsmm9LA6sBH6vaeiJwPvBn4L5GFfG9yTPdVBfyPd3XXjBb7Xk+PCSn+uvX6zSc\nU22zxTWscDezIcBpwMeBycA3Y4yPVT2+PXAUadnis2OMv+/tOWa2InAuqTf1fmC/GGNhZnuSkfj4\nUAAAGs9JREFUenymAsfFGP89i7DUIOurz3zm4SwLk3ogxtTcV77u7R/Vc/RenHfqVf5qn/WjXNbT\na68VLLjgt0krWi5DumDvaOA0imJKqbG1J7XP+unr+ovFSGtsbANsy4zPmwdIBfyfW+l6jBDcKFIR\nXynkl2HmITnL0PNYe0hnGGqL+cr906QLaSf3Iwy1zRbXyML9s8B2Mcavm9k6wPdjjDvlx4YDDwJr\nk1aWvIl02mtDYPsY49eqn2Nm/wROijHeYGa/Ba4EbgGuIs2pPRdwI7B2jLGv3lY1yEEIwQ0lzVs+\nChi1+urX33fPPZvuSurpqNwWr/l+jl529xZp+q2JpDHm1cX5E94X7zTunbQstc/6US4HI80SsyJp\n+rztGTHic0yeDGlp+d+TVkN9qcQI253aZ/30d6rSuYBPA7uR6o3KZ9PNpOE0NwK3UbTUePOZ5Nlw\nFmNG59cyPdzP38cuXif13L+Q76u/fgl4fe217/3vHXd8vHJw/lYHzprT9obNepMB24D0x0CM8VYz\nW7vqsZWBR2OMbwCY2Y2kxWzWI41Fq33OmjHGG/LXl5OOoKcBN8UYpwBTzOxRUk/9HQ18T6UJwS1P\nOvU3pA634cCIfJuz6uvq7+chF+hVt5nGlN9zz6aQTj3Wmkz6Z3APaWq4Ccwo0iv3r3Vor7lI63Nu\nXdIQtcrf/XzMGGawHGl6vBl/72PGQIyHAeeoYJe2VBTvAhcBF+Hc/MBngd1JizStn7eahnMPM6O3\n+lngzXx7hzSLzLg+FoNqqFxEP5dvt/S0Te61ry3ol2LmjjWjl4OdO+74OKT3DlCE4N4gFfGvk/Lw\nHukA/r1evn6fVJ/N7q1ygFDk2yPeFw/2OzldpJGF+yjSVd8V08xsSIxxen7sjarH3iR9cPT0nKHM\n3MCqt+1pH53qCuAjTX7NyaTfxyRSIf5G1feTll32yH0nTjzugPxY5fY8MElFuUiLcm4B0tLvvZlO\nmgJ1HGnNgisYP/5BnDuhGeGJNFxazfds4GycW5TU0bgBqYD/KKlzsTfjSYvytSTvi0mkIcU9LVYF\nfDDxwyLMKOYXJw1rnW/06P2OePbZU88j1VPzV90vRzrj3qwzRa+H4BZULfFhjSzcJzHzcvCVoh1S\nAVj92Lyko7menjPNzKpP1YzqZdt5SVP99aVtT016X6xUdgw92G+55Y4tO4ZO0rbtswUpl70piteY\n/fwon/WlfNbP4HJZFC8C/8i3ruB9MYV0NuHZHh4+cqWVftPkiGR2DGngvm8ijSfDzNYF7q167CHg\nI2a2gJnNQRomc3MfzxlnZpvkr7cBbiBN87SRmY0ws/lIR8i9HmGKiIiIiLSzRl6c6pgxQwzA10gX\nko6MMZ5pZtsBPyAdPJwVY/xtT8+JMT5sZh8hrZo2B+mi1j3zrDLfJM0qMwQ4PsbYNUfMIiIiItJd\num0edxERERGRttTIoTIiIiIiIlInKtxFRERERNqACncR6Wr52hqpE+WzfpTL+lI+60v5rJ/ZyWUj\np4MUAcDMFgc2AuaIMZ5XdjwiAGY2F7BKjPHOsmPpBMpn/SiX9aV81pfyWT8DyaUuTq1iZi7PVjMH\n8EVgbeD6GOPfSw6trZjZnMDknMuRpNXqniDNtX8tcFFl1VzpH7XN+jKzHwJbkdaUuBD4V4zxhVKD\namPKZ/0ol/WlfNaX8lk/A82letzp8YhnK+BTpETuY2aFppqcNTPbEDgIWBE408z+AuwE3BxjPMbM\n1iAtMb0FcFGlGC0v4tantjk4OX+fJR3sXAJcnB9aCNiFtET3d0nt8udVqztLD5TP+lEu60v5rC/l\ns37qncuuH+Oej3iuA443s2/lH38OuDHGeDFwDvBJM/t4L7voSma2tJkdYmZfNbPR+cefAm6JMY4l\nLY+8N/Ai8In8+ATgUWBdABXtfVPbnH1mtqiZbWBmS+QfbUT6h3kiaYnv44AFgBVijE+TVmG+mtR2\n0QfPzJTP+lEu60v5rC/ls34ancuuKdzNbC4z293MLjWzPc1sETNbhBlHPLsDy5nZQcBVwJj81NtI\nZybG9LDbjle5YMLM5jazhfLXCwNHAUsDqwBH5XHscwHj81PPJzXUm5ixoNbbpCEzc5nZ8Ka9iRan\ntjlw1Rf0mNlXgOuBfYEjzGweYDHguRjjf0gHOh8D3gdWMLO5Y4zTSIu6Dc1tuKvV5PPLKJ8DZmZz\nmNnK+euvo1wOipkNNbOh+Wv9rQ+SmQ03s8Xy119F+RwwMxtS9XXD/292bOGei5/tqnoj12fWRzxX\nAtsCTwNL5edNBIYCXdU7XPkAz+Oq5wd+TurtBTBgbIzxYODY/LPNSWPYp+bn3UnK82TgNTMbG2Oc\nAiwJPEMXD9Pq4Wh8Q9Q2+83M5jSz/czsYuB3lswNfAlYO8a4Oykne5AOfh42s3lijM8Bk4D5SWd/\nNs+7dMDjpH+uXSfnc18z+zspn0tZujZF+ZxNZraSmX3XzP4EPAb81MyGAV9AuZxtZjbMzHYzs6tJ\nwws2zcMOdkf5nG1mNsrMfmhm5wOXkfI5EtgV5XNAzGwL4N789SI0oW12TOFe01P0DeBy0gfPvjmx\nCwHPz+KIZzwwHXgPWNDMVsk/X4aU1K5gZp8Bfpa/ng/4BbAxqagEeBl4wcxGxhjfJDW+eYBpwFK5\nRx4gki6iPAP4mpkdTOpBfjzG+K51yVRS/ejFXJxZH42rbc5gwDbAScAFwP8j/YNcDKgM27oqfz8K\nmJN0dghSMbUucCqwg5mtRyqq3o4x3lPdc9JFliJdO/Fr0lmc40ljLpcEKgeXyucsWLoo/zOk/PwG\nuBs4MsY4FViW9HcOyuXsWBjYDtgP2AtYMsb4Lun/ntrm7BsOfB04PMb4qRjj+THGt0hnbZXPgfkK\nsISZLRhjfImUy4b+rXdEomsKzSWBA2KMa8cYdwWeAlYjNcqejnieYOYjnifz7UbgsNyrNx24r4lv\nqTRmtg3wfWBMHs7yDnADcAywQD619gbwKrB6ftqzwEjgP8AapHHX8wOP5O1+S+otWRb4JfB36I4x\n7jVtc2H6PhofqbbZMzMbknsuAXYGrowx3hhjvAaYA9iedFZi07zNc6SDnFdI/zB9/vl4YIkY4z9J\nF/geTzpQ+id0zzjNmnx+AngmxhiAh0kH5kOAO0kH7KB89qoql5NjjD+LMR4H3E76P/l+3ux2YJP8\ntXLZh5q2uSnpOqldSMMvyZ8t16G/9X6p5DN3IA0n9Q4fbmbXmtlB+czvNSifs5RzOdRmDCH2pM/j\nB0nj2AHuosF/620/XKGq0JxgZnPEGJ8xs8PNbFju6ViH1DO3ALAWqRfpYdIpiXWB04DtzezlvO3b\nMcbnzewXpCJ0CeCKGOM7TX9zTZCP7CrDYqaRGs8FpLHrY2OMt5nZH0nj1NcFLMZ4g5k9QeoJuQl4\nHtg+xvhzM3sD+DZwNHBNjPGB/FLX5VvX6KFtvmxmY0hH4BNIR+Nrk85UvEfq+XwItc0Pyf/IKv/M\nHPARM1soxvgK8BrpAOdiYE/g96T8rg4cQcrT983s08CCpNPCxBivJBX7Xacmnw8D65jZzcBHgH8A\nK5A+RHYDzkX57FV1LnOv26uknLwdY4x5s/+QzhKdi3LZp5q2uSSp9/IR0tmMI4ANSMXO3sBZKJ99\nqmmfSwL3kwrK/8u3H5Hy+S2Uzz5VF9S5dlof+B2p82gPM3uA9P9zVxr4t952hfusCk3g1hjjZXnb\nLUljrG8lnV5fn3TE8zDpCGmJGOPpZjaZdMTzGOm0O/l03P+a9sZKUtMQR5By+S5pWse1gdtijNPM\n7FlST9zHSD3wFwOnmNkRpLxelPd3lZndG2N8vrnvpHyzaJtrkdrTHaQ2eC4zjsYnkXrdPalwV9us\nkocgbEUqzj9BOtC5FjjLzKaQiqJ1gf+SptLaEFiZdEbj6RjjY2Z2FOmf663dcKanL1X53JQ0Nett\n+VYAO5AO0q8DlifNaKR89qIql57UPsfn4XAvMWNoIcC/gEOUy77V/K1/knTm5zXgiRjjq2Z2BumA\n8iDg18pn32r+1jcjDd/6LvBWjPGdnM9/kPL5K+WzdzW53ICUkxGkjri1SJ9NNwJ/Ao5pZC7begGm\nXGguxoxC89kY42n5sYWBk4Fz8ilgzGwD0pHPVPIRT774ryv18KHzILBnjPFNM9sDWCfGuG/edijw\nDdKwo2/nYv6jpCEKd8YYr8vbaW52emybz8UYT7U0reNmMcYv5Db6G6qOxklFade3zYp8MPR90mnI\nK0lDZB7Mp8s3JU2htQVprvsfm9lawIGkPP42xnh7SaG3pB7yeXmM8SEzuwQ4LcZ4pZktRepF+gzp\n4PPbKJ8f0lvbzI9tB6wJ/DTG+H7+mdpmH2ryeRXw7xhjNLMzgUdijCfkHB5AGqe9Bul/q/LZgz7+\nd44F5oox3pzzuTfp+oGxKJ896qFtXhpjfNjMziJ9Bl0P3AysGWN8w8w+AexPg3LZVoV7fwvNPP7o\nkLzN6cCOwH0xxl9aWgRoLtJ84+3z5uusrw+d/Pi6pCPzvfKp38p4rtHAJTHGt5sedAvrR9tcN8a4\nTx5PeC3pH+XKpF6Qr8YYJ5vZmqhno1/yQfinSR/e8wP7xxjHlRtV+zKzfUgf3MuTDhyPjVrYa7ZZ\nXjjFzL5H+pz5VYzxdXVoDFz+v7gv6Rqp0cDBMcaryo2qfZnZbqQzawuSLpr8bozxxnKjan+WFpw8\nL8Z4aaNfq20K99koNPfOY4knkqYdDKQLg65SsTlrVR88y5HyeUaM8Z6y42plA2ibDT0a70RVw5CK\nyvCunNcpccaqstJPPQzrwsxWA4bo7332VOVyOuDy/88VgXdjjM+UG137qf5bJ/29F5YW+RsVY3yo\n3OjaTy/5XB6YO8Z4f7nRtZfqv/WcRwcMi2mq66Zpm8K9NzWF5qGkQvNuM1ssxvhC2fG1utqGWP3z\n2AVXiTeSDoJERESkntqucFehKa1KbVNEREQaqe0KdxERERGRbtQRCzCJiIiIiHQ6Fe4iIiIiIm1A\nhbuIiIiISBtQ4S4iIiIi0gZUuIuIiIiItAEV7iIiIiIibUCFu4iIiIhIGxhWdgAiIiKzy8wWBvYH\nNgC+H2O8o+SQ2pqZLQt8D1gIODrGGEsOSUR6oAWYRGSWzOw6YFFgSv7RXjHG283sL8CqwL9ijIfn\nbb8H3BdjvLwf+70UODHG+J9eHh8NnBlj3NbMzgWujzH+YRb7XBG4HVgsxvh+1c/3AHaIMX6ul+dN\njzE27CykmV0fY9y0h59PAN4GKrHOD9wB7BFjfMfMxsUYx/ayzzGknCw3i9deE/h8fo1FY4x718YG\nnBxjvLSH53rgUuCR/KOhwEjgZzHG3+V9fyHGeFhfMfQS15nAb4FHgT/EGD/Tn/dkZnMA44CrgZWB\njwKfjTHeWbXNH4DrKu3FzAIwFlikpl3cDbwWY9zUzH4I/ABYP8Z4S9U2vwQObFT7MLNjgNtjjJf2\n1k5qtp8AbBxjfLLm5/MC58QYd67kNsZ4Vz9efyHgTuAKYBFgnXx7A/gDsItWfxZpDepxF5E+mZkD\nDFi6+sPbzFYHRsUYVzOze83sJ8BwYN0Y4wn93H2Rbz2KMT4LbNufbaue86iZ3QtsB/y96qGvACf3\nM65G2KSXnxfANpUizMyGAzeS4j29t6J9Nv0c2AlYELjVzPaLMU7Lr7cM8BHgsj6ef3t1MZl/97eb\n2XkxxrvM7LtmtmqM8f7ZCSrGuGfe3xhgjdl46seAd/L7+hpwCjBv3tdo4HfAZsC1Nc97HdiSdCCC\nmRmwBPBq1TZPAzsDt+RthpB+dw3r5YoxHl31bW/tpFpvsRxNeu8f5LafNiYd7P4ZWA64BFg4xviM\nmV0DfIt0gCUiJVPhLiKzYqRC4QozW5TUA34qqfd2hJkNIxXs00iFw/G97shsBHAG8EngSdJpecxs\nKHA6qSBbDIjAZ4HFmbn31ZnZj4ChMcYj8nPPAS6PMV5Q9VJnA7uRC3czW4pUnF5hZseTiroFgZdJ\nPbUvVMX4Q6CIMR6Tv59AKmyeAU4kFVZDgXNjjL+seX/DSAVO7fs4IT/+vxjjej2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      "text/plain": [
       "<matplotlib.figure.Figure at 0x132c55828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ppc_normal = post_pred(var_cov_var_normal, results_normal[1], 1e6, .05, samples=800)\n",
    "ppc_t = post_pred(var_cov_var_t, results_t[1], 1e6, .05, samples=800)\n",
    "sns.distplot(ppc_normal, label='Normal', norm_hist=True, hist=False, color='r')\n",
    "sns.distplot(ppc_t, label='T', norm_hist=True, hist=False, color='y')\n",
    "plt.legend(loc=0); plt.xlabel('5% daily Value at Risk (VaR) with \\$1MM capital (in \\$)'); \n",
    "plt.ylabel('Probability density'); plt.xticks(rotation=15);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Comparing the Bayesian T-Sharpe Ratios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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n5zYUXYwkSVqaM9WqgzKG6ktJ/tHrqJUkSSVgqFYdDEN1WfapBm9WlCSpVLIc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\n9FqLyxOoPTW6BDfFLLj35QUNj6lNvdaScgVqVwBYazPgs/7b9YBnGx5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      "text/plain": [
       "<matplotlib.figure.Figure at 0x132957390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(results_t[0]['sharpe'], hist=False, label='etrade')\n",
    "sns.distplot(results_t[1]['sharpe'], hist=False, label='IB')\n",
    "plt.xlabel('Bayesian Sharpe ratio'); plt.ylabel('Probability Density');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P(Sharpe ratio IB > Sharpe ratio etrade) = 5.84%\n"
     ]
    }
   ],
   "source": [
    "print('P(Sharpe ratio IB > Sharpe ratio etrade) = %.2f%%' % \\\n",
    "    (np.mean(results_t[1]['sharpe'] > results_t[0]['sharpe']) * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Conclusion: Probabilistic Programming\n",
    "* **Probabilistic Programming** allows us to construct **complex models** in code and **automatically** estimate them.\n",
    "* **Bayesian statistics** provides us with **uncertainty quantification** -- measure orthogonal sources of **risk**.\n",
    "* **PyMC3** puts **advanced samplers** at your fingertips.\n",
    "* **PyMC2** is less powerful than **PyMC3**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Take home point\n",
    "* Bayesian Models by forcing you to specify your priors. Allow you to make better decisions under situations of uncertainty.\n",
    "* As Data Scientists we are always dealing with this."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "##Want to learn more?\n",
    "* [Thomas Wiecki Blog on all things Bayesian](https://twiecki.github.io)\n",
    "* Twitter: [@springcoil](https://twitter.com/springcoil)\n",
    "* [Probabilistic Programming for Hackers](http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/) -- IPython Notebook book on Bayesian stats using PyMC2\n",
    "\n",
    "* [Doing Bayesian Data Analysis](http://www.indiana.edu/~kruschke/DoingBayesianDataAnalysis/) -- Great book by Kruschke.\n",
    "* [Get PyMC3 alpha](https://github.com/pymc-devs/pymc/tree/pymc3)\n",
    "* [Zalando Example](http://dataconomy.com/how-zalando-used-bayesian-statistics-to-save-loads-of-money/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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YE96VdknkU8oVm4rHL5qQ+hxIL+IyOUv/APygx8Gf9mLV/wDylq44Gde/TMKrx8PcR4sq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\nHa3IJ9sG6WojKhWXZ1lLZZCFdct9x0urcW6tQAKlKUTrpCU//9k=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='the-most-interesting-man-in-the-world-meme-generator-i-don-t-give-many-talks-but-when-i-do-i-do-q-a-e38222 copy.jpg') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "<style>\n",
       "\n",
       ".rendered_html {\n",
       "    font-family: \"proxima-nova\", helvetica;\n",
       "    font-size: 100%;\n",
       "    line-height: 1.3;\n",
       "}\n",
       "\n",
       ".rendered_html h1 {\n",
       "    margin: 0.25em 0em 0.5em;\n",
       "    color: #015C9C;\n",
       "    text-align: center;\n",
       "    line-height: 1.2; \n",
       "    page-break-before: always;\n",
       "}\n",
       "\n",
       ".rendered_html h2 {\n",
       "    margin: 1.1em 0em 0.5em;\n",
       "    color: #26465D;\n",
       "    line-height: 1.2;\n",
       "}\n",
       "\n",
       ".rendered_html h3 {\n",
       "    margin: 1.1em 0em 0.5em;\n",
       "    color: #002845;\n",
       "    line-height: 1.2;\n",
       "}\n",
       "\n",
       ".rendered_html li {\n",
       "    line-height: 1.5; \n",
       "}\n",
       "\n",
       ".prompt {\n",
       "    font-size: 120%; \n",
       "}\n",
       "\n",
       ".CodeMirror-lines {\n",
       "    font-size: 120%; \n",
       "}\n",
       "\n",
       ".output_area {\n",
       "    font-size: 120%; \n",
       "}\n",
       "\n",
       "#notebook {\n",
       "    background-image: url('files/images/witewall_3.png');\n",
       "}\n",
       "\n",
       "h1.bigtitle {\n",
       "    margin: 4cm 1cm 4cm 1cm;\n",
       "    font-size: 300%;\n",
       "}\n",
       "\n",
       "h3.point {\n",
       "    font-size: 200%;\n",
       "    text-align: center;\n",
       "    margin: 2em 0em 2em 0em;\n",
       "    #26465D\n",
       "}\n",
       "\n",
       ".logo {\n",
       "    margin: 20px 0 20px 0;\n",
       "}\n",
       "\n",
       "a.anchor-link {\n",
       "    display: none;\n",
       "}\n",
       "\n",
       "h1.title { \n",
       "    font-size: 250%;\n",
       "}\n",
       "\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Format from https://github.com/ellisonbg/talk-2013-scipy\n",
    "from IPython.display import display, HTML\n",
    "s = \"\"\"\n",
    "\n",
    "<style>\n",
    "\n",
    ".rendered_html {\n",
    "    font-family: \"proxima-nova\", helvetica;\n",
    "    font-size: 100%;\n",
    "    line-height: 1.3;\n",
    "}\n",
    "\n",
    ".rendered_html h1 {\n",
    "    margin: 0.25em 0em 0.5em;\n",
    "    color: #015C9C;\n",
    "    text-align: center;\n",
    "    line-height: 1.2; \n",
    "    page-break-before: always;\n",
    "}\n",
    "\n",
    ".rendered_html h2 {\n",
    "    margin: 1.1em 0em 0.5em;\n",
    "    color: #26465D;\n",
    "    line-height: 1.2;\n",
    "}\n",
    "\n",
    ".rendered_html h3 {\n",
    "    margin: 1.1em 0em 0.5em;\n",
    "    color: #002845;\n",
    "    line-height: 1.2;\n",
    "}\n",
    "\n",
    ".rendered_html li {\n",
    "    line-height: 1.5; \n",
    "}\n",
    "\n",
    ".prompt {\n",
    "    font-size: 120%; \n",
    "}\n",
    "\n",
    ".CodeMirror-lines {\n",
    "    font-size: 120%; \n",
    "}\n",
    "\n",
    ".output_area {\n",
    "    font-size: 120%; \n",
    "}\n",
    "\n",
    "#notebook {\n",
    "    background-image: url('files/images/witewall_3.png');\n",
    "}\n",
    "\n",
    "h1.bigtitle {\n",
    "    margin: 4cm 1cm 4cm 1cm;\n",
    "    font-size: 300%;\n",
    "}\n",
    "\n",
    "h3.point {\n",
    "    font-size: 200%;\n",
    "    text-align: center;\n",
    "    margin: 2em 0em 2em 0em;\n",
    "    #26465D\n",
    "}\n",
    "\n",
    ".logo {\n",
    "    margin: 20px 0 20px 0;\n",
    "}\n",
    "\n",
    "a.anchor-link {\n",
    "    display: none;\n",
    "}\n",
    "\n",
    "h1.title { \n",
    "    font-size: 250%;\n",
    "}\n",
    "\n",
    "</style>\n",
    "\"\"\"\n",
    "display(HTML(s))"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "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
}