{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting started with PyMC3\n",
    "\n",
    "Authors: John Salvatier, Thomas V. Wiecki, Christopher Fonnesbeck\n",
    "\n",
    "Note: This text is taken from the [PeerJ CS publication on PyMC3](https://peerj.com/articles/cs-55/).\n",
    "\n",
    "## Abstract\n",
    "\n",
    "Probabilistic Programming allows for automatic Bayesian inference on user-defined probabilistic models. Recent advances in Markov chain Monte Carlo (MCMC) sampling allow inference on increasingly complex models. This class of MCMC, known as Hamliltonian Monte Carlo, requires gradient information which is often not readily available. PyMC3 is a new open source Probabilistic Programming framework written in Python that uses Theano to compute gradients via automatic differentiation as well as compile probabilistic programs on-the-fly to C for increased speed. Contrary to other Probabilistic Programming languages, PyMC3 allows model specification directly in Python code. The lack of a domain specific language allows for great flexibility and direct interaction with the model. This paper is a tutorial-style introduction to this software package.\n",
    "\n",
    "## Introduction\n",
    "\n",
    "Probabilistic programming (PP) allows flexible specification of Bayesian statistical models in code. PyMC3 is a new, open-source PP framework with an intuitive and readable, yet powerful, syntax that is close to the natural syntax statisticians use to describe models. It features next-generation Markov chain Monte Carlo (MCMC) sampling algorithms such as the No-U-Turn Sampler (NUTS; Hoffman, 2014), a self-tuning variant of Hamiltonian Monte Carlo (HMC; Duane, 1987). This class of samplers works well on high dimensional and complex posterior distributions and allows many complex models to be fit without specialized knowledge about fitting algorithms. HMC and NUTS take advantage of gradient information from the likelihood to achieve much faster convergence than traditional sampling methods, especially for larger models. NUTS also has several self-tuning strategies for adaptively setting the tunable parameters of Hamiltonian Monte Carlo, which means you usually don't need to have specialized knowledge about how the algorithms work. PyMC3, Stan (Stan Development Team, 2014), and the LaplacesDemon package for R are currently the only PP packages to offer HMC.\n",
    "\n",
    "Probabilistic programming in Python confers a number of advantages including multi-platform compatibility, an expressive yet clean and readable syntax, easy integration with other scientific libraries, and extensibility via C, C++, Fortran or Cython. These features make it relatively straightforward to write and use custom statistical distributions, samplers and transformation functions, as required by Bayesian analysis.\n",
    "\n",
    "While most of PyMC3's user-facing features are written in pure Python, it leverages Theano (Bergstra et al., 2010) to transparently transcode models to C and compile them to machine code, thereby boosting performance. Theano is a library that allows expressions to be defined using generalized vector data structures called *tensors*, which are tightly integrated with the popular NumPy `ndarray` data structure, and similarly allow for broadcasting and advanced indexing, just as NumPy arrays do. Theano also automatically optimizes the likelihood's computational graph for speed and provides simple GPU integration.\n",
    "\n",
    "Here, we present a primer on the use of PyMC3 for solving general Bayesian statistical inference and prediction problems. We will first see the basics of how to use PyMC3, motivated by a simple example: installation, data creation, model definition, model fitting and posterior analysis. Then we will cover two case studies and use them to show how to define and fit more sophisticated models. Finally we will show how to extend PyMC3 and discuss other useful features: the Generalized Linear Models subpackage, custom distributions, custom transformations and alternative storage backends."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Installation\n",
    "\n",
    "Running PyMC3 requires a working Python interpreter, either version 2.7 (or more recent) or 3.4 (or more recent); we recommend that new users install version 3.4. A complete Python installation for Mac OSX, Linux and Windows can most easily be obtained by downloading and installing the free [`Anaconda Python Distribution`](https://store.continuum.io/cshop/anaconda/) by ContinuumIO. \n",
    "\n",
    "`PyMC3` can be installed using `pip` (https://pip.pypa.io/en/latest/installing.html):\n",
    "\n",
    "```\n",
    "pip install git+https://github.com/pymc-devs/pymc3\n",
    "```\n",
    "\n",
    "PyMC3 depends on several third-party Python packages which will be automatically installed when installing via pip. The four required dependencies are: `Theano`, `NumPy`, `SciPy`, and `Matplotlib`. \n",
    "\n",
    "To take full advantage of PyMC3, the optional dependencies `Pandas` and `Patsy` should also be installed. These are *not* automatically installed, but can be installed by:\n",
    "\n",
    "```\n",
    "pip install patsy pandas\n",
    "```\n",
    "\n",
    "The source code for PyMC3 is hosted on GitHub at https://github.com/pymc-devs/pymc3 and is distributed under the liberal [Apache License 2.0](https://github.com/pymc-devs/pymc3/blob/master/LICENSE). On the GitHub site, users may also report bugs and other issues, as well as contribute code to the project, which we actively encourage."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A Motivating Example: Linear Regression\n",
    "\n",
    "To introduce model definition, fitting and posterior analysis, we first consider a simple Bayesian linear regression model with normal priors for the parameters. We are interested in predicting outcomes $Y$ as normally-distributed observations with an expected value $\\mu$ that is a linear function of two predictor variables, $X_1$ and $X_2$.\n",
    "\n",
    "$$\\begin{aligned} \n",
    "Y  &\\sim \\mathcal{N}(\\mu, \\sigma^2) \\\\\n",
    "\\mu &= \\alpha + \\beta_1 X_1 + \\beta_2 X_2\n",
    "\\end{aligned}$$\n",
    "\n",
    "where $\\alpha$ is the intercept, and $\\beta_i$ is the coefficient for covariate $X_i$, while $\\sigma$ represents the observation error. Since we are constructing a Bayesian model, the unknown variables in the model must be assigned a prior distribution. We choose zero-mean normal priors with variance of 100 for both regression coefficients, which corresponds to *weak* information regarding the true parameter values. We choose a half-normal distribution (normal distribution bounded at zero) as the prior for $\\sigma$.\n",
    "\n",
    "$$\\begin{aligned} \n",
    "\\alpha &\\sim \\mathcal{N}(0, 100) \\\\\n",
    "\\beta_i &\\sim \\mathcal{N}(0, 100) \\\\\n",
    "\\sigma &\\sim \\lvert\\mathcal{N}(0, 1){\\rvert}\n",
    "\\end{aligned}$$\n",
    "\n",
    "### Generating data\n",
    "\n",
    "We can simulate some artificial data from this model using only NumPy's `random` module, and then use PyMC3 to try to recover the corresponding parameters. We are intentionally generating the data to closely correspond the PyMC3 model structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('seaborn-darkgrid')\n",
    "\n",
    "# Initialize random number generator\n",
    "np.random.seed(123)\n",
    "\n",
    "# True parameter values\n",
    "alpha, sigma = 1, 1\n",
    "beta = [1, 2.5]\n",
    "\n",
    "# Size of dataset\n",
    "size = 100\n",
    "\n",
    "# Predictor variable\n",
    "X1 = np.random.randn(size)\n",
    "X2 = np.random.randn(size) * 0.2\n",
    "\n",
    "# Simulate outcome variable\n",
    "Y = alpha + beta[0]*X1 + beta[1]*X2 + np.random.randn(size)*sigma"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is what the simulated data look like. We use the `pylab` module from the plotting library matplotlib. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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i9ZK+U/PxdyXVtUJfvrxX5fLV/iXd3V0aHFzWmehi0Il4gwZi1OeujfeR57/t\nOy0uSX09XbrvDtPyv2/VQJ/OTEx5fj7s39nu6xtHDFFk7edXymbMHRQph2XttSTe1tx3h9H+x1/S\n1BX//NmoWb7udK7yk5bXOKy0xZvqTfNvvllfTHAkdamgjsRRn3twcJn+6K/+b+DRZWm+J8yerWv1\n1sVpbf2Nv2hp79g9W9Z4XoB9z5Y1i3E325vW7usbJoY4Ze3nV3J2rLqjz5ek2hyWte8/8bZm25pB\n7bt9fdM82uh9n31WF6ZmPHPdPVvW6NMjtm4vbbmkxHKVn7S8xmGlLX+5XFJ8XdI7az5+x8LnEEGc\nHYmfOHGm6TLicH/v4p2M7ewda9bYL8m9aWFjAJogh8HTjo1DenL3zRoOubxYkTQxNROY60qlUuDH\nSD+XM1x/LWm9MWad5pPUT0j69w7jyaQ4L30+cORU8DJiTSEXxwXOQb2sOnVBNP200AZyGALt2bp2\nySx6uSTNVuTZSLqqMdcdPHZaVxoupL0yV4k9HyJZzgoua+2MMebjkp7W/JHqL1lrT7qKJ8viKhrO\neuwRqBpuKOSS3sTZ6U2i9LhBVOQwNPLKI/u2r1/yuQcO26Z/V22uY9N8Pjjdw2WtPSzpsMsYcJXf\nxszaZcSqpG+zT/rvr0WPG7SKHIYqvzyyb/v6JfkzzP6u2lzXyXyI5HB5NRbtvX1D6P1gSd9mn/Tf\nX4seNwDaFSWPeOW3Wo25rpP5EMlJ9SnFokjLctbdm1brrYvToWKJc++Yl6T//lpM1wNoV5Q8smPj\nkE68PqH/8+KYZivzF1f3lUuamql45rpO5kMkh4LLsbQtZ9XuB6sWgg8ctr5JIMkYO7Whnel6AFE1\nvlFe0VfWxNTSO2e98sjI6LieOnlO1SbzFUkVlfTrP7LBN+dxwCf7WFJ0LK3LWZ1oy5AWTNcDiMIr\nP741PaOervpWDX55JK15H8mi4HIsrctZRUoI9OMCEIVXfpypSG/r6QqVR9Ka95EslhQdS+tyVtES\nAtP1AMLyy4OT07N67uNbmn59WvM+ksUMl2NxLmeNjI5r56Hj2nzgqHYeOt7W8p/fwCchACi6dvOj\nX97f8q7rYsvhSB8KLsfiWs6Ke88V+5oAwFu7+bGa91cP9C3m/Q+/5+166uS5QuybLSqWFFMgjuWs\nuK/C4RgyAHiLIz/u2DikXbesW7xceeeh4x25zgzuUHDlRJQ9V2H7frGvCQC8tZMfvXJw0fbNFhFL\nijkRdk8G4zToAAAM6klEQVRBkdo9AEDa+OXg/t5uz8ezbzY/KLhyIuyegiK1ewCAtPHLwaVSiX2z\nOUfBlRNhN98zbQ0A7vjl2gtTM/QDzDn2cOVImD0FSfR/SctdkACQdn45uFTS4jVqv/4jhhyaQxRc\nBbNn69q6uxul9qatk74LkmIOQFZ55S+vHCxJcwv3Krq+TxfJoeAqmKjHmZsVPHG3o2h87jRd7A0A\nYXnlr/sPWw30lfXh97xdz7/2PY1PTqtUulpsVdEOIp8ouAoo7HHmMAVPknvCkizmACBJXvlLkiam\nZvTUyXPat329dt2yThs+9RXPr2dfbf442TRvjPlxY8xJY8ycMeZ9LmJAc2FONCZ5BRAb/JFW5DA0\nE5SnavMo16gVh6tTii9J+lFJRx09P0IIU/AkeQUQiQgpRg5DoGZ5qppHuUatOJwUXNbaUWutdfHc\nCC9MwRPXXZBeSERIK3IYmvHKX7WqeTTJHIp0YQ8XfIU90ZjUFUDc5wggq6p56uHnXtGF6dm6P2vM\no1yjVgylSqXS/FEtMMY8K2nY44/2W2sfX3jM1yTda639ptffcenS5Uq5fPW6g+7uLs3OLt2EmFZ5\niPeJE2d04MgpnZ2Y0qqBPu29fYPu3rTaUYT18vD6pp2LmHt6uksdfUIfceewrH3/iTc+fnk0zTF7\nId7mgvJXYgVXGM2S1RtvTNYFNzi4bPFm9SwgXn9x9Nfi9U2ei5hXruxPRcEVRpQclrXvP/Ema2R0\nXI88/22dnZjKzOx91l7jtOUvlhRTKi0NP8PEETVW+msBKJpqnvTqMj82Oa3PfOWUJHJgnrlqC/FR\nY8x3Jd0i6SljzNMu4kgrv9vkR0bHUxdHK7FygTayjhyGKGrzpJ8rcxUd+OqrHYwKneZkhsta+5ik\nx1w8dxakpeFnmDjCxlo7C+a3iE1/LWQFOay4Wll98GuC2mhiaiauMJFCLCmmUFoafoaJI8xjGpcQ\n/dBfC0CatbodgjeTkCi4UsnvNvlOFyRh4gjzmDDv7nq6Srp4eUabDxzNzAZSAMUSZkbfawbML082\nWtHb3fQxyC5XneYRIC0NP8PEEeYxQe/uSpIG+sqqVCq6MD3rdM8aAARpNqPvt6d1y7uuC2yCKknl\nknTvbTfEHTJShBmuBS5OBfo9Z1oafoaJI8xj/N7dDff36sndN2vnoeNL9i5wSTWAtGk2o+83A/bY\ni2P66E3Dev6172lsclpdJWmuZjNrV0n6yE3D5Luco+CSmzYFzZ4zLZ2Hw8TR7DHNOtanZc8aAARp\nNZfNVaSnTp5bvLKnMf9X/3zT9QOpyPtIBkuKctOmoEitEZrdFcYl1QCyoNVcJtXn9yLlf1zFDJfc\nzLAUbVYnaBYs7J2NAOBa1FxWq5rfi5b/MY8ZLrmZYWn3OUdGx7Xz0HFtPnBUOw8dz/QG82bvGgEg\nC6q5rMvncpdqfvfL8xUp8/kc/nIzw9XOpncXMyztPGcer8ZJy541AGhV9ffQnEd359r8HjQTlod8\nDm+5mOFq9yocFzMs7Twn6/8AkC5B1/c05vdq/l890Of5d5HP8ykXM1xxXIXjYoal1ed0vf6flou1\nASAt/Bo8V5uZPnDY6uCx03Xtf3bdsk4bPvUVz+vO2M+VP7mY4XJdgHSay1N9ablYGwDSxO/3zYXp\n2cB8ySnt4shFwVW0H1iXnehZzgSApcL+vmnMl2m5WQTJy0XBVbQfWJen+oo2mwgAYXj9HvJTmy85\npV0cudjDlZarcDrJ1am+tFysDQBp4vV76NKV2SXXlklL8yWntIshFwWXxA9sp9CkFAC8Nf4eamzh\nI5Eviyw3BRc6o4iziQDQCvIlalFwITJmEwEgHPIlqpwUXMaYhyTtlHRZ0quSftpae95FLAAQFTkM\nQFSuTikekfRea+1Nkk5J+qSjOACgFeQwAJE4meGy1j5T8+E3JP1bF3EUFZ3igfaQw/KPPIm4paEP\n189IGnEdRFHQKR6IHTksZ8iTSEKpUvG6xal9xphnJQ17/NF+a+3jC4/ZL+l9kn7UWrskkEuXLlfK\n5e7Fj7u7uzQ7u/SuqrRKY7w/9PDXdGZiasnnVw/06eu/cmvq4g2Sxtc3SNbildzE3NPTXeroE/qI\nO4dl7ftf5HiD8uRf3vvBWJ5DKvZr3Alpy1+JLSlaaz8U9OfGmI9JukvSbV6JSpLefLO+webg4DKd\nP38xrhATl8Z4z3okkernZ2fnUhdvkDS+vkGyFq/kJuaVK/s7+nx+4s5hWfv+FzneoDwZ52tS5Ne4\nE9KWv5wsKRpj7pT0y5LuttZm57uXA0W7dxJIAjks38iTSIKrPVy/Jalf0hFjzLeMMY84iqMtI6Pj\n2nnouDYfOKqdh45nYn2/aPdOAgnJRQ6Dt07mySz+HkFrXJ1SvMHF88ap8cqG6qZKSak+yULnY6B9\nechh8NepPPnEiTOZ/D2C1tBpvkUHj52uux9LkqZm5nTw2OnUDxQ6HwNAsE7kyQNHTmX29wiiS0Nb\niEwan5yO9HkAAGr5bc7n90g+UXC1iE2VAIB2rBro8/w8v0fyiYKrRUXbfM7GTgCINxfuvX1DoX6P\nFB17uFpUpM3nWT0gAABxijsX3r1ptd66OF2I3yOg4GpLUTafZ/mAAADEJYlcWJTfI2BJESFwQAAA\nyIVoDwUXmuKAAACQC9EeCi40VbQDAgDghVyIdrCHC00V6YAAAPghF6IdFFwIhY2dAEAuROtYUgQA\nAEgYBRcAAEDCKLgAAAASRsEFAACQMAouAACAhJUqlYrrGAAAAHKNGS4AAICEUXABAAAkjIILAAAg\nYZnqNG+M+Yykj0iak3RO0sestWfcRhXMGPOQpJ2SLkt6VdJPW2vPu43KnzHmxyX9mqSNkjZba7/p\nNiJvxpg7Jf2mpG5JX7TWPug4JF/GmC9JukvSOWvte13H04wx5p2S/kDSkKSKpEPW2t90G1U+ZC2H\nkb+SkaX8JZHD4pK1Ga6HrLU3WWt/QNKfS7rfdUAhHJH0XmvtTZJOSfqk43iaeUnSj0o66joQP8aY\nbkm/LWmHpBsl7TLG3Og2qkC/J+lO10FEMCNpr7X2Rknvl/TzKX99syRrOYz8FbMM5i+JHBaLTBVc\n1toLNR9eq/nKNdWstc9Ya2cWPvyGpHe4jKcZa+2otda6jqOJzZJesda+Zq29LOmPNT9rkErW2qOS\n/tl1HGFZa89aa/924b8nJY1Kut5tVPmQtRxG/kpEpvKXRA6LS6aWFCXJGPNZST8laULSDzsOJ6qf\nkfQnroPIgeslfafm4+9KutlRLLlmjFkr6QclHXccSm5kOIeRv+JB/uqgNOWw1BVcxphnJQ17/NF+\na+3j1tr9kvYbYz4p6eOSHuhogB6axbzwmP2an+b8cidj8xImXsAYs1zSo5I+0TAzgwBZy2HkL+RV\n2nJY6goua+2HQj70y5IOKwUFV7OYjTEf0/yGw9ustc6XECK8xmn1uqR31nz8joXPISbGmB7NJ6ov\nW2v/zHU8WZK1HEb+6jjyVwekMYdlag+XMWZ9zYcfkfQPrmIJa+E0yi9Luttae9F1PDnx15LWG2PW\nGWOukfQTkp5wHFNuGGNKkn5X0qi19vOu48mTrOUw8lciyF8JS2sOy9TVPsaYRyUZzR+p/rake6y1\nqX5nYIx5RVKvpH9a+NQ3rLX3OAwpkDHmo5L+u6SVks5L+pa19g63US1ljPkRSV/Q/LHqL1lrP+s4\nJF/GmD+S9EFJ/0LSuKQHrLW/6zSoAMaYD0g6JunvND/WJGmftfawu6jyIWs5jPyVjCzlL4kcFpdM\nFVwAAABZlKklRQAAgCyi4AIAAEgYBRcAAEDCKLgAAAASRsEFAACQsNQ1PkWxLNzqflTSv7bW/rMx\n5jpJf6v5K08e0fzFo1+31t7lMEwAWCIgf31U0u9IWiFpVtJnrbVci1RwzHDBKWvtdzSfmB5c+NSD\nkg5Za09LekjSTzoKDQAC+eUvSRcl/ZS19j2S7pT0BWPMoJsokRYUXEiD/ybp/caYT0j6gKSHJcla\n+5ykSZeBAUATS/KXtfaUtfZlSbLWnpF0TvPNWFFgFFxwzlp7RdJ9mk9cn1j4GABSr1n+MsZslnSN\npFcdhIcUoeBCWuyQdFbSe10HAgAReeYvY8wqSX8o6aettXNeX4jioOCCc8aYH5B0u+Y3yP/SQpIC\ngNTzy1/GmBWSnpK031r7DYchIiUouODUwq3uv6P5qfj/p/mN8g+7jQoAmvPLX8aYayQ9JukPrLV/\n6jJGpAeXV8MpY8xuSbdZa//dwsfdkv5a0i9J+s+S/qWk5ZL+SdLPWmufdhUrANQKyF+PS/pPkk7W\nPPxj1tpvdT5KpAUFFwAAQMJYUgQAAEgYBRcAAEDCKLgAAAASRsEFAACQMAouAACAhFFwAQAAJIyC\nCwAAIGEUXAAAAAn7/+oZOE8yIwUsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6fec055b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline \n",
    "\n",
    "fig, axes = plt.subplots(1, 2, sharex=True, figsize=(10,4))\n",
    "axes[0].scatter(X1, Y)\n",
    "axes[1].scatter(X2, Y)\n",
    "axes[0].set_ylabel('Y'); axes[0].set_xlabel('X1'); axes[1].set_xlabel('X2');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Specification\n",
    "\n",
    "Specifying this model in PyMC3 is straightforward because the syntax is as close to the statistical notation. For the most part, each line of Python code corresponds to a line in the model notation above. \n",
    "\n",
    "First, we import PyMC. We use the convention of importing it as `pm`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on PyMC3 v3.3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/maxwell/anaconda3/envs/bayes/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n"
     ]
    }
   ],
   "source": [
    "import pymc3 as pm\n",
    "print('Running on PyMC3 v{}'.format(pm.__version__))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we build our model, which we will present in full first, then explain each part line-by-line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "basic_model = pm.Model()\n",
    "\n",
    "with basic_model:\n",
    "    \n",
    "    # Priors for unknown model parameters\n",
    "    alpha = pm.Normal('alpha', mu=0, sd=10)\n",
    "    beta = pm.Normal('beta', mu=0, sd=10, shape=2)\n",
    "    sigma = pm.HalfNormal('sigma', sd=1)\n",
    "    \n",
    "    # Expected value of outcome\n",
    "    mu = alpha + beta[0]*X1 + beta[1]*X2\n",
    "    \n",
    "    # Likelihood (sampling distribution) of observations\n",
    "    Y_obs = pm.Normal('Y_obs', mu=mu, sd=sigma, observed=Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first line,\n",
    "\n",
    "```python\n",
    "basic_model = Model()\n",
    "```\n",
    "\n",
    "creates a new `Model` object which is a container for the model random variables.\n",
    "\n",
    "Following instantiation of the model, the subsequent specification of the model components is performed inside a  `with` statement:\n",
    "\n",
    "```python\n",
    "with basic_model:\n",
    "```\n",
    "This creates a *context manager*, with our `basic_model` as the context, that includes all statements until the indented block ends. This means all PyMC3 objects introduced in the indented code block below the `with` statement are added to the model behind the scenes. Absent this context manager idiom, we would be forced to manually associate each of the variables with `basic_model` right after we create them. If you try to create a new random variable without a `with model:` statement, it will raise an error since there is no obvious model for the variable to be added to.\n",
    "\n",
    "The first three statements in the context manager:\n",
    "\n",
    "```python\n",
    "alpha = Normal('alpha', mu=0, sd=10)\n",
    "beta = Normal('beta', mu=0, sd=10, shape=2)\n",
    "sigma = HalfNormal('sigma', sd=1)\n",
    "```\n",
    "create a **stochastic** random variables with a Normal prior distributions for the regression coefficients with a mean of 0 and standard deviation of 10 for the regression coefficients, and a half-normal distribution for the standard deviation of the observations, $\\sigma$. These are stochastic because their values are partly determined by its parents in the dependency graph of random variables, which for priors are simple constants, and partly random (or stochastic). \n",
    "\n",
    "We call the `Normal` constructor to create a random variable to use as a normal prior. The first argument is always the *name* of the random variable, which should almost always match the name of the Python variable being assigned to, since it sometimes used to retrieve the variable from the model for summarizing output. The remaining required arguments for a stochastic object are the parameters, in this case `mu`, the mean, and `sd`, the standard deviation, which we assign hyperparameter values for the model. In general, a distribution's parameters are values that determine the location, shape or scale of the random variable, depending on the parameterization of the distribution. Most commonly used distributions, such as `Beta`, `Exponential`, `Categorical`, `Gamma`, `Binomial` and many others, are available in PyMC3.\n",
    "\n",
    "The `beta` variable has an additional `shape` argument to denote it as a vector-valued parameter of size 2. The `shape` argument is available for all distributions and specifies the length or shape of the random variable, but is optional for scalar variables, since it defaults to a value of one. It can be an integer, to specify an array, or a tuple, to specify a multidimensional array (*e.g.* `shape=(5,7)` makes random variable that takes on 5 by 7 matrix values). \n",
    "\n",
    "Detailed notes about distributions, sampling methods and other PyMC3 functions are available via the `help` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on class Normal in module pymc3.distributions.continuous:\n",
      "\n",
      "class Normal(pymc3.distributions.distribution.Continuous)\n",
      " |  Univariate normal log-likelihood.\n",
      " |  \n",
      " |  .. math::\n",
      " |  \n",
      " |     f(x \\mid \\mu, \\tau) =\n",
      " |         \\sqrt{\\frac{\\tau}{2\\pi}}\n",
      " |         \\exp\\left\\{ -\\frac{\\tau}{2} (x-\\mu)^2 \\right\\}\n",
      " |  \n",
      " |  ========  ==========================================\n",
      " |  Support   :math:`x \\in \\mathbb{R}`\n",
      " |  Mean      :math:`\\mu`\n",
      " |  Variance  :math:`\\dfrac{1}{\\tau}` or :math:`\\sigma^2`\n",
      " |  ========  ==========================================\n",
      " |  \n",
      " |  Normal distribution can be parameterized either in terms of precision\n",
      " |  or standard deviation. The link between the two parametrizations is\n",
      " |  given by\n",
      " |  \n",
      " |  .. math::\n",
      " |  \n",
      " |     \\tau = \\dfrac{1}{\\sigma^2}\n",
      " |  \n",
      " |  .. plot::\n",
      " |  \n",
      " |      import matplotlib.pyplot as plt\n",
      " |      import numpy as np\n",
      " |      import scipy.stats as st\n",
      " |      x = np.linspace(-5.0, 5.0, 1000)\n",
      " |      fig, ax = plt.subplots()\n",
      " |      f = lambda mu, sd : st.norm.pdf(x, loc=mu, scale=sd)\n",
      " |      plot_pdf = lambda a, b : ax.plot(x, f(a,b), label=r'$\\mu$={0}, $\\sigma$={1}'.format(a,b))\n",
      " |      plot_pdf(0.0, 0.4)\n",
      " |      plot_pdf(0.0, 1.0)\n",
      " |      plot_pdf(0.0, 2.0)\n",
      " |      plot_pdf(-2.0, 0.4)\n",
      " |      plt.legend(loc='upper right', frameon=False)\n",
      " |      ax.set(xlim=[-5,5], ylim=[0,1.2], xlabel='x', ylabel='f(x)')\n",
      " |      plt.show()\n",
      " |  \n",
      " |  Parameters\n",
      " |  ----------\n",
      " |  mu : float\n",
      " |      Mean.\n",
      " |  sd : float\n",
      " |      Standard deviation (sd > 0) (only required if tau is not specified).\n",
      " |  tau : float\n",
      " |      Precision (tau > 0) (only required if sd is not specified).\n",
      " |      \n",
      " |  Examples\n",
      " |  --------\n",
      " |  .. code-block:: python\n",
      " |  \n",
      " |      with pm.Model():\n",
      " |          x = pm.Normal('x', mu=0, sd=10)\n",
      " |          \n",
      " |      with pm.Model():\n",
      " |          x = pm.Normal('x', mu=0, tau=1/23)\n",
      " |  \n",
      " |  Method resolution order:\n",
      " |      Normal\n",
      " |      pymc3.distributions.distribution.Continuous\n",
      " |      pymc3.distributions.distribution.Distribution\n",
      " |      builtins.object\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __init__(self, mu=0, sd=None, tau=None, **kwargs)\n",
      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
      " |  \n",
      " |  logp(self, value)\n",
      " |  \n",
      " |  random(self, point=None, size=None, repeat=None)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from pymc3.distributions.distribution.Distribution:\n",
      " |  \n",
      " |  __getnewargs__(self)\n",
      " |  \n",
      " |  __latex__ = _repr_latex_(self, name=None, dist=None)\n",
      " |      Magic method name for IPython to use for LaTeX formatting.\n",
      " |  \n",
      " |  default(self)\n",
      " |  \n",
      " |  get_test_val(self, val, defaults)\n",
      " |  \n",
      " |  getattr_value(self, val)\n",
      " |  \n",
      " |  logp_nojac(self, *args, **kwargs)\n",
      " |      Return the logp, but do not include a jacobian term for transforms.\n",
      " |      \n",
      " |      If we use different parametrizations for the same distribution, we\n",
      " |      need to add the determinant of the jacobian of the transformation\n",
      " |      to make sure the densities still describe the same distribution.\n",
      " |      However, MAP estimates are not invariant with respect to the\n",
      " |      parametrization, we need to exclude the jacobian terms in this case.\n",
      " |      \n",
      " |      This function should be overwritten in base classes for transformed\n",
      " |      distributions.\n",
      " |  \n",
      " |  logp_sum(self, *args, **kwargs)\n",
      " |      Return the sum of the logp values for the given observations.\n",
      " |      \n",
      " |      Subclasses can use this to improve the speed of logp evaluations\n",
      " |      if only the sum of the logp values is needed.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Class methods inherited from pymc3.distributions.distribution.Distribution:\n",
      " |  \n",
      " |  dist(*args, **kwargs) from builtins.type\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Static methods inherited from pymc3.distributions.distribution.Distribution:\n",
      " |  \n",
      " |  __new__(cls, name, *args, **kwargs)\n",
      " |      Create and return a new object.  See help(type) for accurate signature.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors inherited from pymc3.distributions.distribution.Distribution:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(pm.Normal) #try help(Model), help(Uniform) or help(basic_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Having defined the priors, the next statement creates the expected value `mu` of the outcomes, specifying the linear relationship:\n",
    "\n",
    "```python\n",
    "mu = alpha + beta[0]*X1 + beta[1]*X2\n",
    "```\n",
    "This creates a **deterministic** random variable, which implies that its value is *completely* determined by its parents' values. That is, there is no uncertainty beyond that which is inherent in the parents' values. Here, `mu` is just the sum of the intercept `alpha` and the two products of the coefficients in `beta` and the predictor variables, whatever their values may be. \n",
    "\n",
    "PyMC3 random variables and data can be arbitrarily added, subtracted, divided, multiplied together and indexed-into to create new random variables. This allows for great model expressivity. Many common mathematical functions like `sum`, `sin`, `exp` and linear algebra functions like `dot` (for inner product) and `inv` (for inverse) are also provided. \n",
    "\n",
    "The final line of the model, defines `Y_obs`, the sampling distribution of the outcomes in the dataset.\n",
    "\n",
    "```python\n",
    "Y_obs = Normal('Y_obs', mu=mu, sd=sigma, observed=Y)\n",
    "```\n",
    "\n",
    "This is a special case of a stochastic variable that we call an **observed stochastic**, and represents the data likelihood of the model. It is identical to a standard stochastic, except that its `observed` argument, which passes the data to the variable, indicates that the values for this variable were observed, and should not be changed by any fitting algorithm applied to the model. The data can be passed in the form of either a `numpy.ndarray` or `pandas.DataFrame` object.\n",
    "\n",
    "Notice that, unlike for the priors of the model, the parameters for the normal distribution of `Y_obs` are not fixed values, but rather are the deterministic object `mu` and the stochastic `sigma`. This creates parent-child relationships between the likelihood and these two variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model fitting\n",
    "\n",
    "Having completely specified our model, the next step is to obtain posterior estimates for the unknown variables in the model. Ideally, we could calculate the posterior estimates analytically, but for most non-trivial models, this is not feasible. We will consider two approaches, whose appropriateness depends on the structure of the model and the goals of the analysis: finding the *maximum a posteriori* (MAP) point using optimization methods, and computing summaries based on samples drawn from the posterior distribution using Markov Chain Monte Carlo (MCMC) sampling methods.\n",
    "\n",
    "#### Maximum a posteriori methods\n",
    "\n",
    "The **maximum a posteriori (MAP)** estimate for a model, is the mode of the posterior distribution and is generally found using numerical optimization methods. This is often fast and easy to do, but only gives a point estimate for the parameters and can be biased if the mode isn't representative of the distribution. PyMC3 provides this functionality with the `find_MAP` function.\n",
    "\n",
    "Below we find the MAP for our original model. The MAP is returned as a parameter **point**, which is always represented by a Python dictionary of variable names to NumPy arrays of parameter values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "logp = -149.58, ||grad|| = 12.242: 100%|██████████| 19/19 [00:00<00:00, 346.18it/s]  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'alpha': array(0.90660093),\n",
       " 'beta': array([0.94848596, 2.60711845]),\n",
       " 'sigma': array(0.96298858),\n",
       " 'sigma_log__': array(-0.03771373)}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "map_estimate = pm.find_MAP(model=basic_model)\n",
    "    \n",
    "map_estimate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, `find_MAP` uses the Broyden–Fletcher–Goldfarb–Shanno (BFGS) optimization algorithm to find the maximum of the log-posterior but also allows selection of other optimization algorithms from the `scipy.optimize` module. For example, below we use Powell's method to find the MAP."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/maxwell/anaconda3/envs/bayes/lib/python3.6/site-packages/pymc3/tuning/starting.py:92: UserWarning: In future versions, set the optimization algorithm with a string. For example, use `method=\"L-BFGS-B\"` instead of `fmin=sp.optimize.fmin_l_bfgs_b\"`.\n",
      "  warnings.warn('In future versions, set the optimization algorithm with a string. '\n",
      "logp = -149.47:   4%|▎         | 176/5000 [00:00<00:02, 1864.63it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 148.984564\n",
      "         Iterations: 4\n",
      "         Function evaluations: 176\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'alpha': array(0.90907964),\n",
       " 'beta': array([0.9514399 , 2.61452795]),\n",
       " 'sigma': array(0.96568062),\n",
       " 'sigma_log__': array(-0.03492212)}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy import optimize\n",
    "\n",
    "map_estimate = pm.find_MAP(model=basic_model, fmin=optimize.fmin_powell)\n",
    "    \n",
    "map_estimate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is important to note that the MAP estimate is not always reasonable, especially if the mode is at an extreme. This can be a subtle issue; with high dimensional posteriors, one can have areas of extremely high density but low total probability because the volume is very small. This will often occur in hierarchical models with the variance parameter for the random effect. If the individual group means are all the same, the posterior will have near infinite density if the scale parameter for the group means is almost zero, even though the probability of such a small scale parameter will be small since the group means must be extremely close together. \n",
    "\n",
    "Most techniques for finding the MAP estimate also only find a *local* optimum (which is often good enough), but can fail badly for multimodal posteriors if the different modes are meaningfully different."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sampling methods\n",
    "\n",
    "Though finding the MAP is a fast and easy way of obtaining estimates of the unknown model parameters, it is limited because there is no associated estimate of uncertainty produced with the MAP estimates. Instead, a simulation-based approach such as Markov chain Monte Carlo (MCMC) can be used to obtain a Markov chain of values that, given the satisfaction of certain conditions, are indistinguishable from samples from the posterior distribution. \n",
    "\n",
    "To conduct MCMC sampling to generate posterior samples in PyMC3, we specify a **step method** object that corresponds to a particular MCMC algorithm, such as Metropolis, Slice sampling, or the No-U-Turn Sampler (NUTS). PyMC3's `step_methods` submodule contains the following samplers: `NUTS`, `Metropolis`, `Slice`, `HamiltonianMC`, and `BinaryMetropolis`. These step methods can be assigned manually, or assigned automatically by PyMC3. Auto-assignment is based on the attributes of each variable in the model. In general:\n",
    "\n",
    "* Binary variables will be assigned to `BinaryMetropolis`\n",
    "* Discrete variables will be assigned to `Metropolis`\n",
    "* Continuous variables will be assigned to `NUTS`\n",
    "\n",
    "Auto-assignment can be overriden for any subset of variables by specifying them manually prior to sampling."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gradient-based sampling methods\n",
    "\n",
    "PyMC3 has the standard sampling algorithms like adaptive Metropolis-Hastings and adaptive slice sampling, but PyMC3's most capable step method is the No-U-Turn Sampler. NUTS is especially useful on models that have many continuous parameters, a situation where other MCMC algorithms work very slowly. It takes advantage of information about where regions of higher probability are, based on the gradient of the log posterior-density. This helps it achieve dramatically faster convergence on large problems than traditional sampling methods achieve. PyMC3 relies on Theano to analytically compute model gradients via automatic differentiation of the posterior density. NUTS also has several self-tuning strategies for adaptively setting the tunable parameters of Hamiltonian Monte Carlo. For random variables that are undifferentiable (namely, discrete variables) NUTS cannot be used, but it may still be used on the differentiable variables in a model that contains undifferentiable variables. \n",
    "\n",
    "NUTS requires a scaling matrix parameter, which is analogous to the variance parameter for the jump proposal distribution in Metropolis-Hastings, although NUTS uses it somewhat differently. The matrix gives the rough shape of the distribution so that NUTS does not make jumps that are too large in some directions and too small in other directions. It is important to set this scaling parameter to a reasonable value to facilitate efficient sampling. This is especially true for models that have many unobserved stochastic random variables or models with highly non-normal posterior distributions. Poor scaling parameters will slow down NUTS significantly, sometimes almost stopping it completely. A reasonable starting point for sampling can also be important for efficient sampling, but not as often.\n",
    "\n",
    "Fortunately `PyMC3` automatically initializes NUTS using another inference algorithm called ADVI (auto-diff variational inference). Moreover, `PyMC3` will automatically assign an appropriate sampler if we don't supply it via the `step` keyword argument (see below for an example of how to explicitly assign step methods)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "/home/maxwell/anaconda3/envs/bayes/lib/python3.6/site-packages/pymc3/model.py:384: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  if not np.issubdtype(var.dtype, float):\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [sigma_log__, beta, alpha]\n",
      "100%|██████████| 1000/1000 [00:01<00:00, 768.80it/s]\n"
     ]
    }
   ],
   "source": [
    "from scipy import optimize\n",
    "\n",
    "with basic_model:\n",
    "    # draw 500 posterior samples\n",
    "    trace = pm.sample(500) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `sample` function runs the step method(s) assigned (or passed) to it for the given number of iterations and returns a `Trace` object containing the samples collected, in the order they were collected. The `trace` object can be queried in a similar way to a `dict` containing a map from variable names to `numpy.array`s. The first dimension of the array is the sampling index and the later dimensions match the shape of the variable. We can see the last 5 values for the `alpha` variable as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.88393746, 0.88393746, 0.911917  , 0.92470843, 0.90057717])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trace['alpha'][-5:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we wanted to use the slice sampling algorithm to `sigma` instead of NUTS (which was assigned automatically), we could have specified this as the `step` argument for `sample`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/maxwell/anaconda3/envs/bayes/lib/python3.6/site-packages/pymc3/tuning/starting.py:92: UserWarning: In future versions, set the optimization algorithm with a string. For example, use `method=\"L-BFGS-B\"` instead of `fmin=sp.optimize.fmin_l_bfgs_b\"`.\n",
      "  warnings.warn('In future versions, set the optimization algorithm with a string. '\n",
      "logp = -149.47:   4%|▎         | 176/5000 [00:00<00:02, 1853.26it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 148.984564\n",
      "         Iterations: 4\n",
      "         Function evaluations: 176\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Slice: [sigma_log__]\n",
      ">Slice: [beta]\n",
      ">Slice: [alpha]\n",
      "100%|██████████| 5500/5500 [00:10<00:00, 542.42it/s]\n"
     ]
    }
   ],
   "source": [
    "with basic_model:\n",
    "    \n",
    "    # obtain starting values via MAP\n",
    "    start = pm.find_MAP(fmin=optimize.fmin_powell)\n",
    "    \n",
    "    # instantiate sampler\n",
    "    step = pm.Slice() \n",
    "    \n",
    "    # draw 5000 posterior samples\n",
    "    trace = pm.sample(5000, step=step, start=start)   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Posterior analysis\n",
    "`PyMC3` provides plotting and summarization functions for inspecting the sampling output. A simple posterior plot can be created using `traceplot`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8RnwhEFLiC5+C02aRwwo/Q3Zg1xcC/DOnce//flNYcI2xmVmuOPEw3SfOEK1U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6kxHdJRYMw0zXdLnoBhGEPEv68hEJimefdm3UPrOQZLnEfjxvgL4B3ALcAHDMO4Zck6R4BfB+4z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f6fjlTEqFgcufT5Gc56hxo2kjfiyNt/N307LUzv8PC0bY4nm2rPJF80sMbye6ZDCgbRzz53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\nG1jOOeecc845VyL/A5+UPp1UTSAGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6f7f6768d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = pm.traceplot(trace)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The left column consists of a smoothed histogram (using kernel density estimation) of the marginal posteriors of each stochastic random variable while the right column contains the samples of the Markov chain plotted in sequential order. The `beta` variable, being vector-valued, produces two histograms and two sample traces, corresponding to both predictor coefficients.\n",
    "\n",
    "In addition, the `summary` function provides a text-based output of common posterior statistics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>mc_error</th>\n",
       "      <th>hpd_2.5</th>\n",
       "      <th>hpd_97.5</th>\n",
       "      <th>n_eff</th>\n",
       "      <th>Rhat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alpha</th>\n",
       "      <td>0.907832</td>\n",
       "      <td>0.099196</td>\n",
       "      <td>0.000669</td>\n",
       "      <td>0.713416</td>\n",
       "      <td>1.099686</td>\n",
       "      <td>19664.0</td>\n",
       "      <td>0.999914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta__0</th>\n",
       "      <td>0.948995</td>\n",
       "      <td>0.089483</td>\n",
       "      <td>0.000570</td>\n",
       "      <td>0.772646</td>\n",
       "      <td>1.123687</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>0.999925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta__1</th>\n",
       "      <td>2.604886</td>\n",
       "      <td>0.512019</td>\n",
       "      <td>0.003777</td>\n",
       "      <td>1.584799</td>\n",
       "      <td>3.597961</td>\n",
       "      <td>19790.0</td>\n",
       "      <td>1.000037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sigma</th>\n",
       "      <td>0.990786</td>\n",
       "      <td>0.071210</td>\n",
       "      <td>0.000465</td>\n",
       "      <td>0.855026</td>\n",
       "      <td>1.132712</td>\n",
       "      <td>17930.0</td>\n",
       "      <td>0.999966</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             mean        sd  mc_error   hpd_2.5  hpd_97.5    n_eff      Rhat\n",
       "alpha    0.907832  0.099196  0.000669  0.713416  1.099686  19664.0  0.999914\n",
       "beta__0  0.948995  0.089483  0.000570  0.772646  1.123687  20000.0  0.999925\n",
       "beta__1  2.604886  0.512019  0.003777  1.584799  3.597961  19790.0  1.000037\n",
       "sigma    0.990786  0.071210  0.000465  0.855026  1.132712  17930.0  0.999966"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pm.summary(trace)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Case study 1: Stochastic volatility\n",
    "\n",
    "We present a case study of stochastic volatility, time varying stock market volatility, to illustrate PyMC3's use in addressing a more realistic problem. The distribution of market returns is highly non-normal, which makes sampling the volatilities significantly more difficult. This example has 400+ parameters so using common sampling algorithms like Metropolis-Hastings would get bogged down, generating highly autocorrelated samples. Instead, we use NUTS, which is dramatically more efficient.\n",
    "\n",
    "### The Model\n",
    "\n",
    "Asset prices have time-varying volatility (variance of day over day `returns`). In some periods, returns are highly variable, while in others they are very stable. Stochastic volatility models address this with a latent volatility variable, which changes over time. The following model is similar to the one described in the NUTS paper (Hoffman 2014, p. 21).\n",
    "\n",
    "$$\n",
    "\\begin{aligned} \n",
    "  \\sigma &\\sim exp(50) \\\\\n",
    "  \\nu &\\sim exp(.1) \\\\\n",
    "  s_i &\\sim \\mathcal{N}(s_{i-1}, \\sigma^{-2}) \\\\\n",
    "  log(y_i) &\\sim t(\\nu, 0, exp(-2 s_i))\n",
    "\\end{aligned}\n",
    "$$\n",
    "\n",
    "Here, $y$ is the daily return series which is modeled with a Student-t distribution with an unknown degrees of freedom parameter, and a scale parameter determined by a latent process $s$. The individual $s_i$ are the individual daily log volatilities in the latent log volatility process. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Data\n",
    "\n",
    "Our data consist of daily returns of the S&P 500 during the 2008 financial crisis. Here, we use `pandas-datareader` to obtain the price data from Google Finance; it can be installed with `pip install pandas-datareader`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pandas_datareader import data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/maxwell/anaconda3/envs/bayes/lib/python3.6/site-packages/pandas_datareader/google/daily.py:40: UnstableAPIWarning: \n",
      "The Google Finance API has not been stable since late 2017. Requests seem\n",
      "to fail at random. Failure is especially common when bulk downloading.\n",
      "\n",
      "  warnings.warn(UNSTABLE_WARNING, UnstableAPIWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "400"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "returns = data.get_data_google('SPY', start='2008-5-1', end='2009-12-1')['Close'].pct_change()\n",
    "\n",
    "len(returns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VbGmRuUou0eWDi+Mox5zHsNHZIYi1R0MLm2EY4wC+tIxjIYg1g6jDhvrJt9wkho0z37Ie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cCKsW5Fkw5oMbRbdIkemV7AibkytjWwsIVbUwXvRy1s/XkXlmXBtmrG/o1dogBU4kH69bVwa6oV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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6f8f73f518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "returns.plot(figsize=(10, 6))\n",
    "plt.ylabel('daily returns in %');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Specification\n",
    "\n",
    "As with the linear regression example, specifying the model in PyMC3 mirrors its statistical specification. This model employs several new distributions: the `Exponential` distribution for the $\\nu$ and $\\sigma$ priors, the Student-T (`StudentT`) distribution for distribution of returns, and the `GaussianRandomWalk` for the prior for the latent volatilities.   \n",
    "\n",
    "In PyMC3, variables with purely positive priors like `Exponential` are transformed with a log transform. This makes sampling more robust. Behind the scenes, a variable in the unconstrained space (named \"variableName_log\") is added to the model for sampling. In this model this happens behind the scenes for both the degrees of freedom, `nu`, and the scale parameter for the volatility process, `sigma`, since they both have exponential priors. Variables with priors that constrain them on two sides, like `Beta` or `Uniform`, are also transformed to be unconstrained but with a log odds transform. \n",
    "\n",
    "Although, unlike model specification in PyMC2, we do not typically provide starting points for variables at the model specification stage, we can also provide an initial value for any distribution (called a \"test value\") using the `testval` argument. This overrides the default test value for the distribution (usually the mean, median or mode of the distribution), and is most often useful if some values are illegal and we want to ensure we select a legal one. The test values for the distributions are also used as a starting point for sampling and optimization by default, though this is easily overriden. \n",
    "\n",
    "The vector of latent volatilities `s` is given a prior distribution by `GaussianRandomWalk`. As its name suggests GaussianRandomWalk is a vector valued distribution where the values of the vector form a random normal walk of length n, as specified by the `shape` argument. The scale of the innovations of the random walk, `sigma`, is specified in terms of the precision of the normally distributed innovations and can be a scalar or vector. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with pm.Model() as sp500_model:\n",
    "    nu = pm.Exponential('nu', 1./10, testval=5.)\n",
    "    sigma = pm.Exponential('sigma', 1./.02, testval=.1)\n",
    "\n",
    "    s = pm.GaussianRandomWalk('s', sigma**-2, shape=len(returns))\n",
    "    volatility_process = pm.Deterministic('volatility_process', pm.math.exp(-2*s))\n",
    "\n",
    "    r = pm.StudentT('r', nu, lam=1/volatility_process, observed=returns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that we transform the log volatility process `s` into the volatility process by `exp(-2*s)`. Here, `exp` is a Theano function, rather than the corresponding function in NumPy; Theano provides a large subset of the mathematical functions that NumPy does.\n",
    "\n",
    "Also note that we have declared the `Model` name `sp500_model` in the first occurrence of the context manager, rather than splitting it into two lines, as we did for the first example."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "/home/maxwell/anaconda3/envs/bayes/lib/python3.6/site-packages/pymc3/model.py:384: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  if not np.issubdtype(var.dtype, float):\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [r_missing, s, sigma_log__, nu_log__]\n",
      "100%|██████████| 2500/2500 [02:26<00:00, 17.08it/s]\n",
      "The estimated number of effective samples is smaller than 200 for some parameters.\n"
     ]
    }
   ],
   "source": [
    "with sp500_model:\n",
    "    trace = pm.sample(2000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can check our samples by looking at the traceplot for `nu` and `sigma`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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WOIVZ8cmUVKY3Kuu9uJh254jb4/SmehuWWSyyo6QWJTtNcGzv0i9oK8W9/PeAl65OWbi5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\nGyKRlGFOn9MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6f695dd860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pm.traceplot(trace, [nu, sigma]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally we plot the distribution of volatility paths by plotting many of our sampled volatility paths on the same graph. Each is rendered partially transparent (via the `alpha` argument in Matplotlib's `plot` function) so the regions where many paths overlap are shaded more darkly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+jqJ9fqqoiIiILC+9ExGRrhHFCTnbhK3ObDpPx841c0LLMiFQlUERERFZZgqD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Fe5FBNmErUnF8SUIGU30kpa/ZWUA24aNeHdvFBU4xkYPfhK5wZNDdHBkMtWq4eU8JQJCuRsFq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\nIf8Xo1/5kZ45NkgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6f694954e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 8))\n",
    "returns.plot(ax=ax)\n",
    "ax.plot(returns.index, 1/np.exp(trace['s',::5].T), 'r', alpha=.03);\n",
    "ax.set(title='volatility_process', xlabel='time', ylabel='volatility');\n",
    "ax.legend(['S&P500', 'stochastic volatility process']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, the model correctly infers the increase in volatility during the 2008 financial crash. Moreover, note that this model is quite complex because of its high dimensionality and dependency-structure in the random walk distribution. NUTS as implemented in PyMC3, however, correctly infers the posterior distribution with ease."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Case study 2: Coal mining disasters\n",
    "\n",
    "Consider the following time series of recorded coal mining disasters in the UK from 1851 to 1962 (Jarrett, 1979). The number of disasters is thought to have been affected by changes in safety regulations during this period. Unfortunately, we also have pair of years with missing data, identified as missing by a NumPy MaskedArray using -999 as the marker value. \n",
    "\n",
    "Next we will build a model for this series and attempt to estimate when the change occurred. At the same time, we will see how to handle missing data, use multiple samplers and sample from discrete random variables. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BHDg833Yyy9UaR0vLHDg8H+mcR59YbHtO3L4kvuZ4tyvoAKs1WK3WN3xzNufj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\nRFLk/wMD9eShz5hfMgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6f5bd56898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "disaster_data = np.ma.masked_values([4, 5, 4, 0, 1, 4, 3, 4, 0, 6, 3, 3, 4, 0, 2, 6,\n",
    "                            3, 3, 5, 4, 5, 3, 1, 4, 4, 1, 5, 5, 3, 4, 2, 5,\n",
    "                            2, 2, 3, 4, 2, 1, 3, -999, 2, 1, 1, 1, 1, 3, 0, 0,\n",
    "                            1, 0, 1, 1, 0, 0, 3, 1, 0, 3, 2, 2, 0, 1, 1, 1,\n",
    "                            0, 1, 0, 1, 0, 0, 0, 2, 1, 0, 0, 0, 1, 1, 0, 2,\n",
    "                            3, 3, 1, -999, 2, 1, 1, 1, 1, 2, 4, 2, 0, 0, 1, 4,\n",
    "                            0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1], value=-999)\n",
    "year = np.arange(1851, 1962)\n",
    "\n",
    "plt.plot(year, disaster_data, 'o', markersize=8);\n",
    "plt.ylabel(\"Disaster count\")\n",
    "plt.xlabel(\"Year\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Occurrences of disasters in the time series is thought to follow a Poisson process with a large rate parameter in the early part of the time series, and from one with a smaller rate in the later part. We are interested in locating the change point in the series, which perhaps is related to changes in mining safety regulations.\n",
    "\n",
    "In our model, \n",
    "\n",
    "$$ \n",
    "\\begin{aligned}  \n",
    "  D_t &\\sim \\text{Pois}(r_t), r_t= \\begin{cases} \n",
    "   l, & \\text{if } t \\lt s \\\\\n",
    "   e, & \\text{if } t \\ge s \n",
    "   \\end{cases} \\\\\n",
    "  s &\\sim \\text{Unif}(t_l, t_h)\\\\         \n",
    "  e &\\sim \\text{exp}(1)\\\\\n",
    "  l &\\sim \\text{exp}(1)    \n",
    "\\end{aligned}\n",
    "$$\n",
    "\n",
    "the parameters are defined as follows: \n",
    "   * $D_t$: The number of disasters in year $t$\n",
    "   * $r_t$: The rate parameter of the Poisson distribution of disasters in year $t$.\n",
    "   * $s$: The year in which the rate parameter changes (the switchpoint).\n",
    "   * $e$: The rate parameter before the switchpoint $s$.\n",
    "   * $l$: The rate parameter after the switchpoint $s$.\n",
    "   * $t_l$, $t_h$: The lower and upper boundaries of year $t$.\n",
    "   \n",
    "This model is built much like our previous models. The major differences are the introduction of discrete variables with the Poisson and discrete-uniform priors and the novel form of the deterministic random variable `rate`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with pm.Model() as disaster_model:\n",
    "\n",
    "    switchpoint = pm.DiscreteUniform('switchpoint', lower=year.min(), upper=year.max(), testval=1900)\n",
    "\n",
    "    # Priors for pre- and post-switch rates number of disasters\n",
    "    early_rate = pm.Exponential('early_rate', 1)\n",
    "    late_rate = pm.Exponential('late_rate', 1)\n",
    "\n",
    "    # Allocate appropriate Poisson rates to years before and after current\n",
    "    rate = pm.math.switch(switchpoint >= year, early_rate, late_rate)\n",
    "\n",
    "    disasters = pm.Poisson('disasters', rate, observed=disaster_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The logic for the rate random variable,\n",
    "```python\n",
    "rate = switch(switchpoint >= year, early_rate, late_rate)\n",
    "```\n",
    "is implemented using `switch`, a Theano function that works like an if statement. It uses the first argument to switch between the next two arguments.\n",
    "\n",
    "Missing values are handled transparently by passing a `MaskedArray` or a `pandas.DataFrame` with NaN values to the `observed` argument when creating an observed stochastic random variable. Behind the scenes, another random variable, `disasters.missing_values` is created to model the missing values. All we need to do to handle the missing values is ensure we sample this random variable as well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unfortunately because they are discrete variables and thus have no meaningful gradient, we cannot use NUTS for sampling `switchpoint` or the missing disaster observations. Instead, we will sample using a `Metroplis` step method, which implements adaptive Metropolis-Hastings, because it is designed to handle discrete values. `PyMC3` automatically assigns the correct sampling algorithms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/maxwell/anaconda3/envs/bayes/lib/python3.6/site-packages/pymc3/model.py:384: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  if not np.issubdtype(var.dtype, float):\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">CompoundStep\n",
      ">>Metropolis: [disasters_missing]\n",
      ">>Metropolis: [switchpoint]\n",
      ">NUTS: [late_rate_log__, early_rate_log__]\n",
      " 99%|█████████▉| 10434/10500 [00:12<00:00, 849.77it/s]/home/maxwell/anaconda3/envs/bayes/lib/python3.6/site-packages/numpy/core/fromnumeric.py:2957: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "100%|██████████| 10500/10500 [00:12<00:00, 849.78it/s]\n",
      "/home/maxwell/anaconda3/envs/bayes/lib/python3.6/site-packages/numpy/core/fromnumeric.py:2957: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/home/maxwell/anaconda3/envs/bayes/lib/python3.6/site-packages/numpy/core/fromnumeric.py:2957: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/home/maxwell/anaconda3/envs/bayes/lib/python3.6/site-packages/numpy/core/fromnumeric.py:2957: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "Tuning was enabled throughout the whole trace.\n",
      "Tuning was enabled throughout the whole trace.\n",
      "Tuning was enabled throughout the whole trace.\n",
      "Tuning was enabled throughout the whole trace.\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    }
   ],
   "source": [
    "with disaster_model:\n",
    "    trace = pm.sample(10000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the trace plot below we can see that there's about a 10 year span that's plausible for a significant change in safety, but a 5 year span that contains most of the probability mass. The distribution is jagged because of the jumpy relationship between the year switchpoint and the likelihood  and not due to sampling error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Q4g4hxH8Bvr7fQu0V3XZG2omXFje8Hs3N4kYxRhRRcXf+NdcIG0TldOfD93uk6f7l2k13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u15mmmDVtrccRvDsjzVImqlmC3Cws81Ym6sDKfeLV1vErX2lsfpZmeGtmox055ad7xdrXFj5Sp2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\nAV0MoWqyJ4r0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6f58a5fcc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pm.traceplot(trace);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Arbitrary deterministics\n",
    "\n",
    "Due to its reliance on Theano, PyMC3 provides many mathematical functions and operators for transforming random variables into new random variables. However, the library of functions in Theano is not exhaustive, therefore Theano and PyMC3 provide functionality for creating arbitrary Theano functions in pure Python, and including these functions in PyMC models. This is supported with the `as_op` function decorator.\n",
    "\n",
    "Theano needs to know the types of the inputs and outputs of a function, which are specified for `as_op` by `itypes` for inputs and `otypes` for outputs. The Theano documentation includes [an overview of the available types](http://deeplearning.net/software/theano/library/tensor/basic.html#all-fully-typed-constructors)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import theano.tensor as tt\n",
    "from theano.compile.ops import as_op\n",
    "\n",
    "@as_op(itypes=[tt.lscalar], otypes=[tt.lscalar])\n",
    "def crazy_modulo3(value):\n",
    "    if value > 0: \n",
    "        return value % 3\n",
    "    else :\n",
    "        return (-value + 1) % 3\n",
    "    \n",
    "with pm.Model() as model_deterministic:\n",
    "    a = pm.Poisson('a', 1)\n",
    "    b = crazy_modulo3(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An important drawback of this approach is that it is not possible for `theano` to inspect these functions in order to compute the gradient required for the Hamiltonian-based samplers. Therefore, it is not possible to use the HMC or NUTS samplers for a model that uses such an operator. However, it is possible to add a gradient if we inherit from `theano.Op` instead of using `as_op`. The PyMC example set includes [a more elaborate example of the usage of as_op](https://github.com/pymc-devs/pymc3/blob/master/pymc3/examples/disaster_model_theano_op.py)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Arbitrary distributions\n",
    "\n",
    "Similarly, the library of statistical distributions in PyMC3 is not exhaustive, but PyMC3 allows for the creation of user-defined functions for an arbitrary probability distribution. For simple statistical distributions, the `DensityDist` function takes as an argument any function that calculates a log-probability $log(p(x))$. This function may employ other random variables in its calculation. Here is an example inspired by a blog post by Jake Vanderplas on which priors to use for a linear regression (Vanderplas, 2014). \n",
    "\n",
    "```python\n",
    "import theano.tensor as tt\n",
    "\n",
    "with pm.Model() as model:\n",
    "    alpha = pm.Uniform('intercept', -100, 100)\n",
    "    \n",
    "    # Create custom densities\n",
    "    beta = pm.DensityDist('beta', lambda value: -1.5 * tt.log(1 + value**2), testval=0)\n",
    "    eps = pm.DensityDist('eps', lambda value: -tt.log(tt.abs_(value)), testval=1)\n",
    "    \n",
    "    # Create likelihood\n",
    "    like = pm.Normal('y_est', mu=alpha + beta * X, sd=eps, observed=Y)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For more complex distributions, one can create a subclass of `Continuous` or `Discrete` and provide the custom `logp` function, as required. This is how the built-in distributions in PyMC are specified. As an example, fields like psychology and astrophysics have complex likelihood functions for a particular process that may require numerical approximation. In these cases, it is impossible to write the function in terms of predefined theano operators and we must use a custom theano operator using `as_op` or inheriting from `theano.Op`. \n",
    "\n",
    "Implementing the `beta` variable above as a `Continuous` subclass is shown below, along with a sub-function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Beta(pm.Continuous):\n",
    "    def __init__(self, mu, *args, **kwargs):\n",
    "        super(Beta, self).__init__(*args, **kwargs)\n",
    "        self.mu = mu\n",
    "        self.mode = mu\n",
    "\n",
    "    def logp(self, value):\n",
    "        mu = self.mu\n",
    "        return beta_logp(value - mu)\n",
    "    \n",
    "\n",
    "def beta_logp(value):\n",
    "    return -1.5 * np.log(1 + (value)**2)\n",
    "\n",
    "\n",
    "with pm.Model() as model:\n",
    "    beta = Beta('slope', mu=0, testval=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If your logp can not be expressed in Theano, you can decorate the function with `as_op` as follows: `@as_op(itypes=[tt.dscalar], otypes=[tt.dscalar])`. Note, that this will create a blackbox Python function that will be much slower and  not provide the gradients necessary for e.g. NUTS."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generalized Linear Models\n",
    "\n",
    "Generalized Linear Models (GLMs) are a class of flexible models that are widely used to estimate regression relationships between a single outcome variable and one or multiple predictors. Because these models are so common, `PyMC3` offers a `glm` submodule that allows flexible creation of various GLMs with an intuitive `R`-like syntax that is implemented via the `patsy` module.\n",
    "\n",
    "The `glm` submodule requires data to be included as a `pandas` `DataFrame`. Hence, for our linear regression example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Convert X and Y to a pandas DataFrame\n",
    "import pandas \n",
    "\n",
    "df = pandas.DataFrame({'x1': X1, 'x2': X2, 'y': Y})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model can then be very concisely specified in one line of code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "/home/maxwell/anaconda3/envs/bayes/lib/python3.6/site-packages/pymc3/model.py:384: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  if not np.issubdtype(var.dtype, float):\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [sd_log__, x2, x1, Intercept]\n",
      "100%|██████████| 1000/1000 [00:01<00:00, 777.09it/s]\n"
     ]
    }
   ],
   "source": [
    "from pymc3.glm import GLM\n",
    "\n",
    "with pm.Model() as model_glm:\n",
    "    GLM.from_formula('y ~ x1 + x2', df)\n",
    "    trace = pm.sample()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The error distribution, if not specified via the `family` argument, is assumed to be normal. In the case of logistic regression, this can be modified by passing in a `Binomial` family object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pymc3.glm.families import Binomial\n",
    "\n",
    "df_logistic = pandas.DataFrame({'x1': X1, 'y': Y > np.median(Y)})\n",
    "\n",
    "with pm.Model() as model_glm_logistic:\n",
    "    GLM.from_formula('y ~ x1', df_logistic, family=Binomial())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a more complete and flexible formula interface, including hierarchical GLMs, see [Bambi](https://github.com/bambinos/bambi)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discussion\n",
    "\n",
    "Probabilistic programming is an emerging paradigm in statistical learning, of which Bayesian modeling is an important sub-discipline. The signature characteristics of probabilistic programming--specifying variables as probability distributions and conditioning variables on other variables and on observations--makes it a powerful tool for building models in a variety of settings, and over a range of model complexity. Accompanying the rise of probabilistic programming has been a burst of innovation in fitting methods for Bayesian models that represent notable improvement over existing MCMC methods. Yet, despite this expansion, there are few software packages available that have kept pace with the methodological innovation, and still fewer that allow non-expert users to implement models.\n",
    "\n",
    "PyMC3 provides a probabilistic programming platform for quantitative researchers to implement statistical models flexibly and succinctly. A large library of statistical distributions and several pre-defined fitting algorithms allows users to focus on the scientific problem at hand, rather than the implementation details of Bayesian modeling. The choice of Python as a development language, rather than a domain-specific language, means that PyMC3 users are able to work interactively to build models, introspect model objects, and debug or profile their work, using a dynamic, high-level programming language that is easy to learn. The modular, object-oriented design of PyMC3 means that adding new fitting algorithms or other features is straightforward. In addition, PyMC3 comes with several features not found in most other packages, most notably Hamiltonian-based samplers as well as automatical transforms of constrained random variables which is only offered by STAN. Unlike STAN, however, PyMC3 supports discrete variables as well as non-gradient based sampling algorithms like Metropolis-Hastings and Slice sampling.\n",
    "\n",
    "Development of PyMC3 is an ongoing effort and several features are planned for future versions. Most notably, variational inference techniques are often more efficient than MCMC sampling, at the cost of generalizability. More recently, however, black-box variational inference algorithms have been developed, such as automatic differentiation variational inference (ADVI; Kucukelbir et al., in prep). This algorithm is slated for addition to PyMC3. As an open-source scientific computing toolkit, we encourage researchers developing new fitting algorithms for Bayesian models to provide reference implementations in PyMC3. Since samplers can be written in pure Python code, they can be implemented generally to make them work on arbitrary PyMC3 models, giving authors a larger audience to put their methods into use."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "Patil, A., D. Huard and C.J. Fonnesbeck. (2010) PyMC: Bayesian Stochastic Modelling in Python. Journal of Statistical Software, 35(4), pp. 1-81\n",
    "\n",
    "Bastien, F., Lamblin, P., Pascanu, R., Bergstra, J., Goodfellow, I., Bergeron, A., Bouchard, N., Warde-Farley, D., and Bengio, Y. (2012) “Theano: new features and speed improvements”. NIPS 2012 deep learning workshop.\n",
    "\n",
    "Bergstra, J., Breuleux, O., Bastien, F., Lamblin, P., Pascanu, R., Desjardins, G., Turian, J., Warde-Farley, D., and Bengio, Y. (2010) “Theano: A CPU and GPU Math Expression Compiler”. Proceedings of the Python for Scientific Computing Conference (SciPy) 2010. June 30 - July 3, Austin, TX\n",
    "\n",
    "Lunn, D.J., Thomas, A., Best, N., and Spiegelhalter, D. (2000) WinBUGS -- a Bayesian modelling framework: concepts, structure, and extensibility. Statistics and Computing, 10:325--337.\n",
    "\n",
    "Neal, R.M. Slice sampling. Annals of Statistics. (2003). doi:10.2307/3448413.\n",
    "\n",
    "van Rossum, G. The Python Library Reference Release 2.6.5., (2010). URL http://docs.python.org/library/.\n",
    "\n",
    "Duane, S., Kennedy, A. D., Pendleton, B. J., and Roweth, D. (1987) “Hybrid Monte Carlo”, Physics Letters, vol. 195, pp. 216-222.\n",
    "\n",
    "Stan Development Team. (2014). Stan: A C++ Library for Probability and Sampling, Version 2.5.0.   http://mc-stan.org. \n",
    "\n",
    "Gamerman, D. Markov Chain Monte Carlo: statistical simulation for Bayesian inference. Chapman and Hall, 1997.\n",
    "\n",
    "Hoffman, M. D., & Gelman, A. (2014). The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo. The Journal of Machine Learning Research, 30.\n",
    "\n",
    "Kucukelbir A, Ranganath R, Gelman A, and Blei DM. Automatic Variational Inference in Stan http://arxiv.org/abs/1506.03431, in prep.\n",
    "\n",
    "Vanderplas, Jake. \"Frequentism and Bayesianism IV: How to be a Bayesian in Python.\" Pythonic Perambulations. N.p., 14 Jun 2014. Web. 27 May. 2015. <https://jakevdp.github.io/blog/2014/06/14/frequentism-and-bayesianism-4-bayesian-in-python/>.\n",
    "\n",
    "R.G. Jarrett. A note on the intervals between coal mining disasters. Biometrika, 66:191–193, 1979.\n"
   ]
  }
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