{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(GLM_linear)=\n",
    "# GLM: Linear regression\n",
    "\n",
    "This tutorial is adapted from a [blog post by Thomas Wiecki called \"The Inference Button: Bayesian GLMs made easy with PyMC\"](http://twiecki.github.io/blog/2013/08/12/bayesian-glms-1/).\n",
    "\n",
    "While the theoretical benefits of Bayesian over frequentist methods have been discussed at length elsewhere (see *Further Reading* below), the major obstacle that hinders wider adoption is *usability*.  This is mildly ironic because the beauty of Bayesian statistics is their generality. Frequentist stats involve coming up with a test statistic that is specific for the application at hand, whereas with Bayes you define your model exactly as you think is appropriate and hit the *Inference Button(TM)* (i.e. running the magical MCMC sampling algorithm).\n",
    "\n",
    "Over the past few decades, some great Bayesian software packages have been created including [JAGS](http://mcmc-jags.sourceforge.net/), [BUGS](http://www.mrc-bsu.cam.ac.uk/bugs/), and [Stan](http://mc-stan.org/), however they are written for statisticians who know very well what model they want to build. \n",
    "\n",
    "Unfortunately, [\"the vast majority of statistical analysis is not performed by statisticians\"](http://simplystatistics.org/2013/06/14/the-vast-majority-of-statistical-analysis-is-not-performed-by-statisticians/) -- so what we really need are tools for *scientists* and not for statisticians. PyMC makes it easy to construct statistical models for the application at hand, independent of how the various fitting algorithms are implemented.\n",
    "\n",
    "## Linear Regression\n",
    "\n",
    "In this example, we will start with the simplest GLM -- linear regression.\n",
    "In general, frequentists think about linear regression as follows:\n",
    "\n",
    "$$ Y = X\\beta + \\epsilon $$\n",
    "\n",
    "where $Y$ is the output we want to predict (or *dependent* variable), $X$ is our predictor (or *independent* variable), and $\\beta$ are the coefficients (or parameters) of the model we want to estimate. $\\epsilon$ is an error term which is assumed to be normally distributed. \n",
    "\n",
    "We can then use ordinary least squares (OLS) or maximum likelihood to find the best fitting $\\beta$.\n",
    "\n",
    "## Probabilistic Reformulation\n",
    "\n",
    "Bayesians take a probabilistic view of the world and express this model in terms of probability distributions. Our above linear regression can be reformulated as:\n",
    "\n",
    "$$ Y \\sim \\mathcal{N}(X \\beta, \\sigma^2) $$\n",
    "\n",
    "In words, we view $Y$ as a random variable (or random vector) of which each element (data point) is distributed according to a Normal distribution. The mean of this normal distribution is provided by our linear predictor with variance $\\sigma^2$.\n",
    "\n",
    "While this is essentially the same model, there are two critical advantages of Bayesian estimation:\n",
    "\n",
    " - Priors: We can quantify any prior knowledge we might have by placing priors on the parameters. For example, if we think that $\\sigma$ is likely to be small we would choose a prior with more probability mass on low values.\n",
    " - Quantifying uncertainty: We do not get a single estimate of $\\beta$ as above but instead a complete posterior distribution about how likely different values of $\\beta$ are. For example, with few data points our uncertainty in $\\beta$ will be very high and we'd be getting very wide posteriors."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bayesian GLMs in PyMC\n",
    "\n",
    "To get started building GLMs in PyMC, let's first import the required modules."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on PyMC v4.4.0+207.g7c3068a1c.dirty\n"
     ]
    }
   ],
   "source": [
    "import arviz as az\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pymc as pm\n",
    "import xarray as xr\n",
    "\n",
    "from pymc import HalfCauchy, Model, Normal, sample\n",
    "\n",
    "print(f\"Running on PyMC v{pm.__version__}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "RANDOM_SEED = 8927\n",
    "rng = np.random.default_rng(RANDOM_SEED)\n",
    "\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "az.style.use(\"arviz-darkgrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generating data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Essentially we are creating a regression line defined by intercept and slope and add data points by sampling from a Normal with the mean set to the regression line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "size = 200\n",
    "true_intercept = 1\n",
    "true_slope = 2\n",
    "\n",
    "x = np.linspace(0, 1, size)\n",
    "# y = a + b*x\n",
    "true_regression_line = true_intercept + true_slope * x\n",
    "# add noise\n",
    "y = true_regression_line + rng.normal(scale=0.5, size=size)\n",
    "\n",
    "data = pd.DataFrame(dict(x=x, y=y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 711,
       "width": 711
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(7, 7))\n",
    "ax = fig.add_subplot(111, xlabel=\"x\", ylabel=\"y\", title=\"Generated data and underlying model\")\n",
    "ax.plot(x, y, \"x\", label=\"sampled data\")\n",
    "ax.plot(x, true_regression_line, label=\"true regression line\", lw=2.0)\n",
    "plt.legend(loc=0);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Estimating the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets fit a Bayesian linear regression model to this data. In PyMC, the model specifications takes place in a `with` expression, called a context manager. By default, models are fit using the [NUTS sampler](http://arxiv.org/abs/1111.4246), resulting in a trace of samples representing the marginal posterior distribution of the latent model parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (3 chains in 3 jobs)\n",
      "NUTS: [sigma, Intercept, slope]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      <progress value='12000' class='' max='12000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [12000/12000 00:06&lt;00:00 Sampling 3 chains, 0 divergences]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 3 chains for 1_000 tune and 3_000 draw iterations (3_000 + 9_000 draws total) took 35 seconds.\n"
     ]
    }
   ],
   "source": [
    "with Model() as model:  # model specifications in PyMC are wrapped in a with-statement\n",
    "    # Define priors\n",
    "    sigma = HalfCauchy(\"sigma\", beta=10)\n",
    "    intercept = Normal(\"Intercept\", 0, sigma=20)\n",
    "    slope = Normal(\"slope\", 0, sigma=20)\n",
    "\n",
    "    # Define likelihood\n",
    "    likelihood = Normal(\"y\", mu=intercept + slope * x, sigma=sigma, observed=y)\n",
    "\n",
    "    # Inference!\n",
    "    # draw 3000 posterior samples using NUTS sampling\n",
    "    idata = sample(3000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This should be fairly readable for people who know probabilistic programming. However, would a non-statistican know what all this does? Moreover, recall that this is an extremely simple model that would be one line in R. Having multiple, potentially transformed regressors, interaction terms or link-functions would also make this much more complex and error prone. \n",
    "\n",
    "To make things even simpler, the [`bambi`](https://bambinos.github.io/bambi/main/index.html) library  takes a [formula](https://bambinos.github.io/formulae/) linear model specifier from which it creates a design matrix. `bambi` then adds random variables for each of the coefficients and an appropriate likelihood to the model. \n",
    "\n",
    "If `bambi` is not installed, you can install it with `pip install bambi`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# NOTE: Until bambi has released an update compatible with PyMC 5.0.0, this and the following cells remain disabled.\n",
    "\n",
    "# import sys\n",
    "\n",
    "# try:\n",
    "#     import bambi as bmb\n",
    "# except ImportError:\n",
    "#     !{sys.executable} -m pip install --upgrade bambi\n",
    "#     import bambi as bmb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model = bmb.Model(\"y ~ x\", data)\n",
    "# idata = model.fit(draws=3000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Much shorter, but this code does the exact same thing as the previous specification (you can change priors and everything else too if we wanted). `bambi` parses the `formulae` model string, adds random variables for each regressor (`Intercept` and slope `x` in this case), adds a likelihood (by default, a Normal is chosen), and all other variables (`sigma`). Finally, `bambi` then initializes the parameters to a good starting point by estimating a frequentist linear model using [statsmodels](http://statsmodels.sourceforge.net/devel/).\n",
    "\n",
    "If you are not familiar with R's syntax, `'y ~ x'` specifies that we have an output variable `y` that we want to estimate as a linear function of `x`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyzing the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bayesian inference does not give us only one line of best fit (as maximum likelihood does) but rather a whole posterior distribution of plausible parameters. Lets plot the posterior distribution of our parameters and the individual samples we drew."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# az.plot_trace(idata, figsize=(10, 7));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The left side shows our marginal posterior -- for each parameter value on the x-axis we get a probability on the y-axis that tells us how likely that parameter value is.\n",
    "\n",
    "There are a couple of things to see here. The first is that our sampling chains for the individual parameters (left side) seem homogeneous and stationary (there are no large drifts or other odd patterns).\n",
    "\n",
    "Secondly, the maximum posterior estimate of each variable (the peak in the left side distributions) is very close to the true parameters used to generate the data (`x` is the regression coefficient and `sigma` is the standard deviation of our normal)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In GLMs, we thus do not only have one best fitting regression line, but many. A posterior predictive plot takes multiple samples from the posterior (intercepts and slopes) and plots a regression line for each of them. We can manually generate these regression lines using the posterior samples directly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# idata.posterior[\"y_model\"] = idata.posterior[\"Intercept\"] + idata.posterior[\"x\"] * xr.DataArray(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# _, ax = plt.subplots(figsize=(7, 7))\n",
    "# az.plot_lm(idata=idata, y=\"y\", num_samples=100, axes=ax, y_model=\"y_model\")\n",
    "# ax.set_title(\"Posterior predictive regression lines\")\n",
    "# ax.set_xlabel(\"x\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, our estimated regression lines are very similar to the true regression line. But since we only have limited data we have *uncertainty* in our estimates, here expressed by the variability of the lines."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " - Usability is currently a huge hurdle for wider adoption of Bayesian statistics.\n",
    " - `Bambi` allows GLM specification with convenient syntax borrowed from R. Inference can then be carried out with `pymc`.\n",
    " - Posterior predictive plots allow us to evaluate fit and our uncertainty in it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Further reading"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For additional background, here are a few good resources on Bayesian statistics:\n",
    "\n",
    "  - The excellent book [Doing Bayesian Data Analysis by John Kruschke](https://sites.google.com/site/doingbayesiandataanalysis).\n",
    "  - [Andrew Gelman's blog](http://andrewgelman.com/)\n",
    "  \n",
    "Author: Thomas Wiecki"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last updated: Tue Dec 06 2022\n",
      "\n",
      "Python implementation: CPython\n",
      "Python version       : 3.11.0\n",
      "IPython version      : 8.7.0\n",
      "\n",
      "pytensor: 2.8.10\n",
      "\n",
      "pandas    : 1.5.2\n",
      "numpy     : 1.23.4\n",
      "matplotlib: 3.6.2\n",
      "pymc      : 4.4.0+207.g7c3068a1c\n",
      "xarray    : 2022.12.0\n",
      "arviz     : 0.14.0\n",
      "\n",
      "Watermark: 2.3.1\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "\n",
    "%watermark -n -u -v -iv -w -p pytensor"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.0"
  },
  "latex_envs": {
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 0
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}