Chapter_3.ipynb

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        "# Probabilistic Programming and Bayesian Methods for Hackers Chapter 3\n",
        "\n",
        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/blob/master/Chapter3_MCMC/Ch3_IntroMCMC_TFP.ipynb\"><img height=\"32px\" src=\"https://colab.research.google.com/img/colab_favicon.ico\" />Run in Google Colab</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/blob/master/Chapter3_MCMC/Ch3_IntroMCMC_TFP.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
        "  </td>\n",
        "</table>\n",
        "<br>\n",
        "<br>\n",
        "<br>\n",
        "\n",
        "Original content ([this Jupyter notebook](https://nbviewer.jupyter.org/github/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/blob/master/Chapter3_MCMC/Ch3_IntroMCMC_PyMC2.ipynb)) created by Cam Davidson-Pilon ([`@Cmrn_DP`](https://twitter.com/Cmrn_DP))\n",
        "\n",
        "Welcome to Bayesian Methods for Hackers. The full Github repository is available at [github/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers). The other chapters can be found on the project's [homepage](https://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/). We hope you enjoy the book, and we encourage any contributions!\n",
        "\n",
        "---\n",
        "### Table of Contents\n",
        "- Opening the black box of MCMC\n",
        "  - The Bayesian landscape\n",
        "  - Exploring the landscape using the MCMC\n",
        "  - Why Thousands of Samples?\n",
        "  - Algorithms to perform MCMC\n",
        "  - Other aproximation solutions to the posterior\n",
        "- Example: Unsupervised Clustering Using a Mixture Model\n",
        "  - Cluster Investigation\n",
        "  - Important: Don't mix posterior samples\n",
        "  - Returning to Clustering: Prediction\n",
        "- Diagnosing Convergence\n",
        "  - Autocorrelation\n",
        "  - How does this relate to MCMC convergence?\n",
        "  - Thinning\n",
        "  - Additional Plotting Options\n",
        "- Useful tips for MCMC\n",
        "  - Intelligent starting values\n",
        "     - Priors\n",
        "     - Covariance matrices and eliminating parameters\n",
        "     - The Folk Theorem of Statistical Computing\n",
        "- Conclusion\n",
        "  - References\n"
      ]
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      "source": [
        "!pip3 install -Uq pip\n",
        "!pip3 uninstall -yq tensorflow tensorflow-probability\n",
        "!pip3 install -Uq jax oryx tfp-nightly[jax]\n",
        "\n",
        "!pip3 install -q pandas_datareader wget"
      ],
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            "\u001b[?25hFound existing installation: tensorflow 2.8.2+zzzcolab20220719082949\n",
            "Uninstalling tensorflow-2.8.2+zzzcolab20220719082949:\n",
            "  Successfully uninstalled tensorflow-2.8.2+zzzcolab20220719082949\n",
            "Found existing installation: tensorflow-probability 0.16.0\n",
            "Uninstalling tensorflow-probability-0.16.0:\n",
            "  Successfully uninstalled tensorflow-probability-0.16.0\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m12.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "\u001b[?25h  Building wheel for jax (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for wget (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iRkcSCrUJJ7A",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9b4480ee-c0ff-481e-d19a-e3e004abead5"
      },
      "source": [
        "\"\"\"\n",
        "The book uses a custom matplotlibrc file, which provides the unique styles for\n",
        "matplotlib plots. If executing this book, and you wish to use the book's\n",
        "styling, provided are two options:\n",
        "    1. Overwrite your own matplotlibrc file with the rc-file provided in the\n",
        "       book's styles/ dir. See http://matplotlib.org/users/customizing.html\n",
        "    2. Also in the styles is  bmh_matplotlibrc.json file. This can be used to\n",
        "       update the styles in only this notebook. Try running the following code:\n",
        "\n",
        "        import json\n",
        "        s = json.load(open(\"../styles/bmh_matplotlibrc.json\"))\n",
        "        matplotlib.rcParams.update(s)\n",
        "\"\"\"\n",
        "from jax.numpy import *\n",
        "from jax.numpy import int32 as i32, float32 as f32\n",
        "from oryx.distributions import (\n",
        "    Categorical, Exponential, MixtureSameFamily, Normal, Poisson, Uniform)\n",
        "from tensorflow_probability.substrates import jax as tfp\n",
        "\n",
        "from IPython.core.pylabtools import figsize\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "import wget\n",
        "\n",
        "plt.style.use('fivethirtyeight')\n",
        "\n",
        "%config InlineBackend.figure_format = 'retina'\n",
        "\n",
        "%matplotlib inline\n",
        "\n",
        "sns.set_context('notebook')\n",
        "\n",
        "TFColor = [\n",
        "    '#F15854',\n",
        "    '#5DA5DA',\n",
        "    '#FAA43A',\n",
        "    '#60BD68',\n",
        "    '#F17CB0',\n",
        "    '#B2912F',\n",
        "    '#B276B2',\n",
        "    '#DECF3F',\n",
        "    '#4D4D4D']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NSdVS13jJJ68"
      },
      "source": [
        "____\n",
        "\n",
        "## Opening the black box of MCMC\n",
        "\n",
        "The previous two chapters hid the inner-mechanics of TFP, and more generally Markov Chain Monte Carlo (MCMC), from the reader. The reason for including this chapter is three-fold. The first is that any book on Bayesian inference must discuss MCMC. I cannot fight this. Blame the statisticians. Secondly, knowing the process of MCMC gives you insight into whether your algorithm has converged (converged to what? We will get to that).  Thirdly, we'll understand *why* we are returned thousands of samples from the posterior as a solution, which at first thought can be odd. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZDFPrmtUJJ6-"
      },
      "source": [
        "### The Bayesian landscape\n",
        "\n",
        "When we setup a Bayesian inference problem with $N$ unknowns, we are implicitly creating an $N$ dimensional space for the prior distributions to exist in. Associated with the space is an additional dimension, which we can describe as the *surface*, or *curve*, that sits on top of the space, that reflects the *prior probability* of a particular point. The surface on the space is defined by our prior distributions. For example, if we have two unknowns $p_1$ and $p_2$, and priors for both are $\\text{Uniform}(0,5)$, the space created is a square of length 5 and the surface is a flat plane that sits on top of the square (representing that every point is equally likely). "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h8wzyt9sJ9R1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 409
        },
        "outputId": "25ba1432-beb3-4187-c45b-0bdab2967228"
      },
      "source": [
        "x = y = linspace(0., 5., 100)\n",
        "X, Y = meshgrid(x, y)\n",
        "\n",
        "uni_x = Uniform(low = 0., high = 5.).prob(x)\n",
        "m = median(uni_x)\n",
        "\n",
        "uni_y = Uniform(low = 0., high = 5.).prob(y)\n",
        "M = matmul(expand_dims(uni_x, 1), expand_dims(uni_y, 0))\n",
        "\n",
        "plt.figure(figsize(12.5, 6))\n",
        "jet = plt.cm.jet\n",
        "fig = plt.figure()\n",
        "plt.subplot(121)\n",
        " \n",
        "im = plt.imshow(M, interpolation='none', origin='lower',\n",
        "                cmap=jet, vmax=1, vmin=-.15, extent=(0, 5, 0, 5))\n",
        "\n",
        "plt.xlim(0, 5)\n",
        "plt.ylim(0, 5)\n",
        "plt.title(\"Landscape formed by Uniform priors\")\n",
        " \n",
        "ax = fig.add_subplot(122, projection='3d')\n",
        "ax.plot_surface(X, Y, M, cmap=plt.cm.jet, vmax=1, vmin=-.15)\n",
        "ax.view_init(azim=390)\n",
        "plt.title(\"Uniform prior landscape; alternate view\");"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x432 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 900x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 799,
              "height": 375
            },
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qkRmzShuJJ7Y"
      },
      "source": [
        "Alternatively, if the two priors are $\\text{Exponential}(3)$ and $\\text{Exponential}(10)$\n",
        "\n",
        "---\n",
        "\n",
        "$, then the space is all positive numbers on the 2-D plane, and the surface induced by the priors looks like a water fall that starts at the point `(0, 0)` and flows over the positive numbers. \n",
        "\n",
        "The plots below visualise this. The more <font color=\"#8b0000\">dark red</font> the color, the more prior probability is assigned to that location. Conversely, areas with <font color=\"#00008B\">darker blue</font> represent that our priors assign very low probability to that location. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o8VlhghcJJ7a"
      },
      "source": [
        "exp_x = Exponential(rate = 1. / 3.).prob(x)\n",
        "exp_y = Exponential(rate = 1. / 10.).prob(y)\n",
        "\n",
        "M = matmul(expand_dims(exp_x, 1), expand_dims(exp_y, 0))\n",
        "\n",
        "plt.figure(figsize(12.5, 6))\n",
        "jet = plt.cm.jet\n",
        "fig = plt.figure()\n",
        "plt.subplot(121)\n",
        "CS = plt.contour(X, Y, M)\n",
        "im = plt.imshow(M, interpolation='none', origin='lower',\n",
        "                cmap=jet, extent=(0, 5, 0, 5))\n",
        "plt.xlim(0, 5)\n",
        "plt.ylim(0, 5)\n",
        "plt.title(\"$Exp(3), Exp(10)$ prior landscape\")\n",
        "\n",
        "ax = fig.add_subplot(122, projection='3d')\n",
        "ax.plot_surface(X, Y, M, cmap=plt.cm.jet)\n",
        "ax.view_init(azim=30)\n",
        "plt.title(\"$Exp(3), Exp(10)$ prior landscape; alternate view\");"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HvTFW8YoJJ7h"
      },
      "source": [
        "These are simple examples in 2D space, where our brains can understand surfaces well. In practice, spaces and surfaces generated by our priors can be much higher dimensional. \n",
        "\n",
        "If these surfaces describe our *prior distributions* on the unknowns, what happens to our space after we incorporate our observed data $X$? The data $X$ does not change the space, but it changes the surface of the space by *pulling and stretching the fabric of the prior surface* to reflect where the true parameters likely live. More data means more pulling and stretching, and our original shape becomes mangled or insignificant compared to the newly formed shape. Less data, and our original shape is more present.  Regardless, the resulting surface describes the *posterior distribution*. \n",
        "\n",
        "Again I must stress that it is, unfortunately, impossible to visualise this in large dimensions. For two dimensions, the data essentially *pushes up* the original surface to make *tall mountains*. The tendency of the observed data to *push up* the posterior probability in certain areas is checked by the prior probability distribution, so that less prior probability means more resistance. Thus in the double-exponential prior case above, a mountain (or multiple mountains) that might erupt near the `(0, 0)` corner would be much higher than mountains that erupt closer to `(5, 5)`, since there is more resistance (low prior probability) near `(5, 5)`. The peak reflects the posterior probability of where the true parameters are likely to be found. Importantly, if the prior has assigned a probability of `0`, then no posterior probability will be assigned there. \n",
        "\n",
        "Suppose the priors mentioned above represent different parameters $\\lambda$ of two Poisson distributions. We observe a few data points and visualise the new landscape: "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z6bpeXMD50qV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "outputId": "bbc0c6fc-089d-409a-e0d7-dd4ad82b1b61"
      },
      "source": [
        "# Create the observed data\n",
        "# Sample size of data we observe, trying varying this (keep it less than 100)\n",
        "sample_size = 1 \n",
        "\n",
        "# The true parameters, but of course we do not see these values...\n",
        "λ_1_true = 1.\n",
        "λ_2_true = 3.\n",
        "\n",
        "# ...we see the data generated, dependent on the above two values\n",
        "data = concatenate([\n",
        "    Poisson(rate = λ_1_true).sample(sample_shape = (sample_size, 1), seed = 4),\n",
        "    Poisson(rate = λ_2_true).sample(sample_shape = (sample_size, 1), seed = 8)\n",
        "], axis = 1)\n",
        "print(f\"Observed (2-dimensional,sample size = {sample_size}): \\n\", data)\n",
        "\n",
        "# Plot details\n",
        "x = y = linspace(.01, 5, 100)\n",
        "\n",
        "likelihood_x = prod(Poisson(rate = x).prob(data[:, 0][:, newaxis]), axis = 0)\n",
        "likelihood_y = prod(Poisson(rate = y).prob(data[:, 1][:, newaxis]), axis = 0)\n",
        "\n",
        "L = matmul(likelihood_x[:, newaxis], likelihood_y[newaxis, :])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-11-4dcccea57fdf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;31m# ...we see the data generated, dependent on the above two values.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m data = np.concatenate([\n\u001b[0;32m---> 11\u001b[0;31m     \u001b[0mPoisson\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mλ_1_true\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msample_shape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0msample_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m     \u001b[0mPoisson\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mλ_2_true\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msample_shape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0msample_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m ], axis = 1)\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow_probability/substrates/jax/distributions/distribution.py\u001b[0m in \u001b[0;36msample\u001b[0;34m(self, sample_shape, seed, name, **kwargs)\u001b[0m\n\u001b[1;32m   1210\u001b[0m     \"\"\"\n\u001b[1;32m   1211\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_name_and_control_scope\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1212\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_sample_n\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msample_shape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseed\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1213\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1214\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_call_sample_and_log_prob\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_shape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseed\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow_probability/substrates/jax/distributions/distribution.py\u001b[0m in \u001b[0;36m_call_sample_n\u001b[0;34m(self, sample_shape, seed, **kwargs)\u001b[0m\n\u001b[1;32m   1187\u001b[0m         sample_shape, 'sample_shape')\n\u001b[1;32m   1188\u001b[0m     samples = self._sample_n(\n\u001b[0;32m-> 1189\u001b[0;31m         n, seed=seed() if callable(seed) else seed, **kwargs)\n\u001b[0m\u001b[1;32m   1190\u001b[0m     \u001b[0mbatch_event_shape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mps\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msamples\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1191\u001b[0m     \u001b[0mfinal_shape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mps\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msample_shape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_event_shape\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow_probability/substrates/jax/distributions/poisson.py\u001b[0m in \u001b[0;36m_sample_n\u001b[0;34m(self, n, seed)\u001b[0m\n\u001b[1;32m    361\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    362\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_sample_n\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseed\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 363\u001b[0;31m     \u001b[0mseed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msamplers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msanitize_seed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseed\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    364\u001b[0m     return random_poisson(\n\u001b[1;32m    365\u001b[0m         \u001b[0mshape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mps\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_to_shape_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow_probability/substrates/jax/internal/samplers.py\u001b[0m in \u001b[0;36msanitize_seed\u001b[0;34m(seed, salt, name)\u001b[0m\n\u001b[1;32m    119\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mJAX_MODE\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    120\u001b[0m         raise ValueError(\n\u001b[0;32m--> 121\u001b[0;31m             'TFP-on-JAX requires a `jax.random.PRNGKey` `seed` arg.')\n\u001b[0m\u001b[1;32m    122\u001b[0m       elif (tf.distribute.get_replica_context() is not None and\n\u001b[1;32m    123\u001b[0m             tf.distribute.get_replica_context().num_replicas_in_sync > 1):\n",
            "\u001b[0;31mValueError\u001b[0m: TFP-on-JAX requires a `jax.random.PRNGKey` `seed` arg."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6TVcoduJddFJ"
      },
      "source": [
        "plt.figure(figsize(12.5, 15.0))\n",
        "# matplotlib heavy lifting below, beware!\n",
        "\n",
        "# Subplot for regular Uniform\n",
        "plt.subplot(221)\n",
        "\n",
        "uni_x = Uniform(low = 0., high = 5.).prob(x.astype(f32))\n",
        "m = median(uni_x[uni_x > 0])\n",
        "uni_x = where(uni_x == 0, m, uni_x)\n",
        "uni_y = Uniform(low = 0., high = 5.).prob(y.astype(f32))\n",
        "m = median(uni_y[uni_y > 0])\n",
        "uni_y = where(uni_y == 0, m, uni_y)\n",
        "\n",
        "M = matmul(expand_dims(uni_x, 1), expand_dims(uni_y, 0))\n",
        "\n",
        "im = plt.imshow(M, interpolation='none', origin='lower',\n",
        "                cmap=jet, vmax=1, vmin=-.15, extent=(0, 5, 0, 5))\n",
        "plt.scatter(λ_2_true, λ_1_true, c='k', s=50, edgecolor='none')\n",
        "plt.xlim(0, 5)\n",
        "plt.ylim(0, 5)\n",
        "plt.title(\"Landscape formed by Uniform priors on $p_1, p_2$.\")\n",
        "\n",
        "# Subplot for Uniform + Data point\n",
        "plt.subplot(223)\n",
        "plt.contour(x, y, M * L)\n",
        "im = plt.imshow(M * L, interpolation='none', origin='lower',\n",
        "                cmap=jet, extent=(0, 5, 0, 5))\n",
        "plt.title(f\"Landscape warped by {N} data observation;\\n Uniform priors on $p_1, p_2$.\")\n",
        "plt.scatter(λ_2_true, λ_1_true, c='k', s=50, edgecolor='none')\n",
        "plt.xlim(0, 5)\n",
        "plt.ylim(0, 5)\n",
        "\n",
        "# Subplot for regular Exponential\n",
        "plt.subplot(222)\n",
        "exp_x = Exponential(rate = .3).prob(x.astype(f32))\n",
        "exp_x = where(isnan(exp_x), exp_x[1], exp_x)\n",
        "exp_y = Exponential(rate = .10).prob(y.astype(f32))\n",
        "exp_y = where(isnan(exp_y), exp_y[1], exp_y)\n",
        "M = matmul(expand_dims(exp_x, 1), expand_dims(exp_y, 0))\n",
        "plt.contour(x, y, M)\n",
        "im = plt.imshow(M, interpolation='none', origin='lower',\n",
        "                cmap=jet, extent=(0, 5, 0, 5))\n",
        "plt.scatter(λ_2_true, λ_1_true, c='k', s=50, edgecolor='none')\n",
        "plt.xlim(0, 5)\n",
        "plt.ylim(0, 5)\n",
        "plt.title(\"Landscape formed by Exponential priors on $p_1, p_2$.\")\n",
        "\n",
        "# Subplot for Exponential + Data point\n",
        "plt.subplot(224)\n",
        "# This is the likelihood times prior, that results in the posterior.\n",
        "plt.contour(x, y, M * L)\n",
        "im = plt.imshow(M * L, interpolation='none', origin='lower',\n",
        "                cmap=jet, extent=(0, 5, 0, 5))\n",
        "\n",
        "plt.scatter(λ_2_true, λ_1_true, c='k', s=50, edgecolor='none')\n",
        "plt.title(f\"Landscape warped by {N} data observation;\\n Exponential priors on $p_1, p_2$\")\n",
        "plt.xlim(0, 5)\n",
        "plt.ylim(0, 5);"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "22glS6RNJJ7_"
      },
      "source": [
        "The plot on the left is the deformed landscape with the $\\text{Uniform}(0,5)$ priors, and the plot on the right is the deformed landscape with the exponential priors. Notice that the posterior landscapes look different from one another, though the data observed is identical in both cases. The reason is as follows. Notice the exponential-prior landscape, top right figure, puts very little *posterior* weight on values in the upper right corner of the figure: this is because *the prior does not put much weight there*. On the other hand, the uniform-prior landscape is happy to put posterior weight in the upper-right corner, as the prior puts more weight there. \n",
        "\n",
        "Notice also the highest-point, corresponding the darkest red, is biased towards `(0, 0)` in the exponential case, which is the result from the exponential prior putting more prior weight in the `(0, 0)` corner.\n",
        "\n",
        "The black dot represents the true parameters. Even with 1 sample point, the mountains attempts to contain the true parameter. Of course, inference with a sample size of `1` is incredibly naive, and choosing such a small sample size was only illustrative. \n",
        "\n",
        "It's a great exercise to try changing the sample size to other values (try `2`, `5`, `10`, `100`?...) and observing how our \"mountain\" posterior changes. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tMurBAY5JJ7_"
      },
      "source": [
        "### Exploring the landscape using the MCMC\n",
        "\n",
        "We should explore the deformed posterior space generated by our prior surface and observed data to find the posterior mountain. However, we cannot naively search the space: any computer scientist will tell you that traversing $N$-dimensional space is exponentially difficult in $N$: the size of the space quickly blows-up as we increase $N$ (see [the curse of dimensionality](http://en.wikipedia.org/wiki/Curse_of_dimensionality)). What hope do we have to find these hidden mountains? The idea behind MCMC is to perform an intelligent search of the space. To say 'search' implies we are looking for a particular point, which is perhaps not an accurate as we are really looking for a broad mountain. \n",
        "\n",
        "Recall that MCMC returns *samples* from the posterior distribution, not the distribution itself. Stretching our mountainous analogy to its limit, MCMC performs a task similar to repeatedly asking  \"How likely is this pebble I found to be from the mountain I am searching for?\", and completes its task by returning thousands of accepted pebbles in hopes of reconstructing the original mountain.\n",
        "\n",
        "When I say MCMC intelligently searches, I really am saying MCMC will *hopefully* converge towards the areas of high posterior probability. MCMC does this by exploring nearby positions and moving into areas with higher probability. Again, perhaps 'converge' is not an accurate term to describe MCMC's progression. Converging usually implies moving towards a point in space, but MCMC moves towards a *broader area* in the space and randomly walks in that area, picking up samples from that area.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RFcv5Inpx_LI"
      },
      "source": [
        "### Why Thousands of Samples?\n",
        "\n",
        "At first, returning thousands of samples to the user might sound like being an inefficient way to describe the posterior distributions. I would argue that this is extremely efficient. Consider the alternative possibilities:\n",
        "\n",
        "1. Returning a mathematical formula for the 'mountain ranges' would involve describing a N-dimensional surface with arbitrary peaks and valleys.\n",
        "2. Returning the 'peak' of the landscape, while mathematically possible and a sensible thing to do as the highest point corresponds to most probable estimate of the unknowns, ignores the shape of the landscape, which we have previously argued is very important in determining posterior confidence in unknowns. \n",
        "\n",
        "Besides computational reasons, likely the strongest reason for returning samples is that we can easily use *The Law of Large Numbers* to solve otherwise intractable problems. I postpone this discussion for the next chapter. With the thousands of samples, we can reconstruct the posterior surface by organising them in a histogram. \n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YAwoy1cByBcj"
      },
      "source": [
        "### Algorithms to perform MCMC\n",
        "\n",
        "There is a large family of algorithms that perform MCMC. Most of these algorithms can be expressed at a high level as follows: (Mathematical details can be found in the appendix.)\n",
        "\n",
        "1. Start at current position.\n",
        "2. Propose moving to a new position (investigate a pebble near you).\n",
        "3. Accept/Reject the new position based on the position's adherence to the data and prior distributions (ask if the pebble likely came from the mountain).\n",
        "4.  1.  If you accept: Move to the new position. Return to Step 1.\n",
        "    2. Else: Do not move to new position. Return to Step 1. \n",
        "5. After a large number of iterations, return all accepted positions.\n",
        "\n",
        "This way we move in the general direction towards the regions where the posterior distributions exist, and collect samples sparingly on the journey. Once we reach the posterior distribution, we can easily collect samples as they likely all belong to the posterior distribution. \n",
        "\n",
        "If the current position of the MCMC algorithm is in an area of extremely low probability, which is often the case when the algorithm begins (typically at a random location in the space), the algorithm will move in positions *that are likely not from the posterior* but better than everything else nearby. Thus the first moves of the algorithm are not reflective of the posterior.\n",
        "\n",
        "In the above algorithm's pseudocode, notice that only the current position matters (new positions are investigated only near the current position). We can describe this property as *memorylessness*, i.e. the algorithm does not care *how* it arrived at its current position, only that it is there. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0f8WWuHIyEdL"
      },
      "source": [
        "### Other approximation solutions to the posterior\n",
        "Besides MCMC, there are other procedures available for determining the posterior distributions. A Laplace approximation is an approximation of the posterior using simple functions. A more advanced method is [Variational Bayes](http://en.wikipedia.org/wiki/Variational_Bayesian_methods). All three methods, Laplace Approximations, Variational Bayes, and classical MCMC have their pros and cons. We will only focus on MCMC in this book. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jhSDgJ0oJJ8D"
      },
      "source": [
        "## Example: Unsupervised Clustering using a Mixture Model\n",
        "\n",
        "\n",
        "Suppose we are given the following dataset:\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qkEz8lVojOdJ"
      },
      "source": [
        "url = 'https://raw.githubusercontent.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter3_MCMC/data/mixture_data.csv'\n",
        "filename = wget.download(url)\n",
        "filename"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eykbFzU3JJ8E"
      },
      "source": [
        "plt.figure(figsize(12.5, 6))\n",
        "data = loadtxt('mixture_data.csv', delimiter=',')\n",
        "\n",
        "plt.hist(data, bins=20, histtype='stepfilled', alpha=.8)\n",
        "plt.title(\"Histogram of the dataset\")\n",
        "plt.ylim([0, None])\n",
        "print(data[:10], \"...\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "osH9H2ibJJ8K"
      },
      "source": [
        "What does the data suggest? It appears the data has a bimodal form, that is, it appears to have two peaks, one near `120` and the other near `200`. Perhaps there are *two clusters* within this dataset. \n",
        "\n",
        "This dataset is a good example of the data-generation modeling technique from last chapter. We can propose *how* the data might have been created. I suggest the following data generation algorithm: \n",
        "\n",
        "1. For each data point, choose cluster 1 with probability $p$, else choose cluster 2. \n",
        "2. Draw a random variate from a Normal distribution with parameters $\\mu_i$ and $\\sigma_i$ where $i$ was chosen in step 1.\n",
        "3. Repeat.\n",
        "\n",
        "This algorithm would create a similar effect as the observed dataset, so we choose this as our model. Of course, we do not know $p$ or the parameters of the Normal distributions. Hence we must infer, or *learn*, these unknowns.\n",
        "\n",
        "Denote the Normal distributions $\\text{N}_0$ and $\\text{N}_1$ (having variables' index start at `0` is just Pythonic). Both currently have unknown mean and standard deviation, denoted $\\mu_i$ and $\\sigma_i, \\; i =0,1$ respectively. A specific data point can be from either $\\text{N}_0$ or $\\text{N}_1$, and we assume that the data point is assigned to $\\text{N}_0$ with probability $p$.\n",
        "\n",
        "\n",
        "An appropriate way to assign data points to clusters is to use a [TF `Categorical` variable](https://www.tensorflow.org/probability/api_docs/python/tfp/distributions/Categorical). Its parameter is a $k$-length array of probabilities that must sum to one and its `value` attribute is a integer between `0` and $k-1$ randomly chosen according to the crafted array of probabilities (In our case $k=2$). *A priori*, we do not know what the probability of assignment to cluster 1 is, so we form a uniform variable on $(0, 1)$. We call call this $p_1$, so the probability of belonging to cluster 2 is therefore $p_2 = 1 - p_1$.\n",
        "\n",
        "Fortunately, we can we just give `[p1, p2]` to our `Categorical` variable. If needed, we can also to use `tf.stack()` to combine $p_1$ and $p_2$ into a vector that it can understand. We pass this vector into the `Categorical` variableto give an idea of the odds of choosing from our multiple distributions."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rhgmIUpVJJ8M"
      },
      "source": [
        "p1 = Uniform(low = 0., high = 1.).sample()\n",
        "p2 = 1 - p1\n",
        "p = tf.stack([p1, p2])\n",
        "\n",
        "rv_assignment = Categorical(probs = p) \n",
        "assignment = rv_assignment.sample(sample_shape = data.shape[0])\n",
        "\n",
        "print(f\"Prior assignment with p = {p[0]:.2f}:\", assignment[:10])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8dlV_Nu3JJ8R"
      },
      "source": [
        "Looking at the above dataset, I would guess that the standard deviations of the two Normals are different. To maintain ignorance of what the standard deviations might be, we will initially model them as uniform on `0` to `100`. We will include both standard deviations in our model using a single line of TFP code:\n",
        "\n",
        "```python\n",
        "rv_scales = Uniform(low = (0., 0.), high = (100., 100.))\n",
        "```\n",
        "\n",
        "Here, we are using a batch shape of 2, creating two independent distributions, that happen to have the same parameters. See the [colab on TFP shapes](https://https://github.com/tensorflow/probability/blob/master/tensorflow_probability/examples/jupyter_notebooks/Understanding_TensorFlow_Distributions_Shapes.ipynb) for more info.\n",
        "\n",
        "We also need to specify priors on the centers of the clusters. The centers are really the $\\mu$ parameters in these Normal distributions. Their priors can be modeled by a Normal distribution. Looking at the data, I have an idea where the two centers might be &mdash; I would guess somewhere around `120` and `190` respectively, though I am not very confident in these eyeballed estimates. Hence I will set $\\mu_0 = 120, \\mu_1 = 190$ and $\\sigma_0 = \\sigma_1 = 10$.\n",
        "\n",
        "Finally, we use the [MixtureSameFamily](https://https://www.tensorflow.org/probability/api_docs/python/tfp/distributions/MixtureSameFamily) distribution to implement a mixture of our two Normal distributions, employing our [Categorical](https://www.tensorflow.org/api_docs/python/tf/distributions/Categorical) distribution as our selecting function. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7np_ug0CkqKI"
      },
      "source": [
        "rv_scales = Uniform(low = (0., 0.), high = (100., 100.))\n",
        "rv_locs = Normal(loc = (120., 190.), scale = (10., 10.))\n",
        "    \n",
        "scales = rv_scales.sample()\n",
        "print(\"Shape of scales sample:\", scales.shape)\n",
        "locs = rv_locs.sample()\n",
        "\n",
        "rv_assignments = Categorical(probs = tf.stack([.4, .6]))\n",
        "assignments = rv_assignments.sample(sample_shape = (10,))\n",
        "\n",
        "# and to combine it with the observations:\n",
        "rv_observations = MixtureSameFamily(\n",
        "    mixture_distribution = rv_assignments,\n",
        "    components_distribution = Normal(\n",
        "        loc = locs,\n",
        "        scale = scales))\n",
        "\n",
        "observations = rv_observations.sample(sample_shape = (10,))\n",
        "\n",
        "print(\"Simulated data: \", observations[:4], \"...\")\n",
        "print(\"Random assignments: \", assignments[:4], \"...\")\n",
        "print(\"Assigned center: \", locs[:4], \"...\")\n",
        "print(\"Assigned standard deviation: \", sds[:4],\"...\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OUWoWyXv2Jyg"
      },
      "source": [
        "Similarly, in the joint `log_prob` function below, we create two clusters each with our priors on the centers and standard deviations. Then, we mix them in proportion to their weights as determined by our [`Categorical`](https://www.tensorflow.org/api_docs/python/tf/distributions/Categorical) variable, creating a mixture of gaussians distribution. Finally, for each data point, we generate a sample from that mixture.\n",
        "\n",
        "Note that this model is marginalising out the cluster assignment variable,  so that all the remaining random variables are continuous, making it all amenable to simple MCMC--[`HamiltonianMonteCarlo`](https://www.tensorflow.org/probability/api_docs/python/tfp/mcmc/HamiltonianMonteCarlo) in particular."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6LJmUYOGsms2"
      },
      "source": [
        "def joint_log_prob(data, sample_prob_0, sample_locs, sample_scales):\n",
        "    \"\"\"Joint log probability optimisation function.\n",
        "        \n",
        "    Args:\n",
        "      data: tensor array representation of original data\n",
        "      sample_prob_0: Scalar representing probability (out of 1.0) of assignment \n",
        "        being 0\n",
        "        sample_locs: 2d vector containing centers for both normal dists in model\n",
        "      sample_scales: 2d vector containing standard deviations for both normal dists\n",
        "        in model\n",
        "    Returns: \n",
        "      Joint log probability optimisation function\n",
        "    \"\"\"  \n",
        "    # Create a mixture of two scalar Gaussians\n",
        "    rv_prob = Uniform(low = 0., high = 1.)\n",
        "    sample_prob_1 = 1. - sample_prob_0\n",
        "    rv_assignments = Categorical(probs = tf.stack([sample_prob_0, sample_prob_1]))\n",
        "    \n",
        "    rv_locs = Normal(loc = (120., 190.), scale = (10., 10.))\n",
        "    rv_scales = Uniform(low = (0., 0.), high = (100., 100.))\n",
        "    \n",
        "    rv_observations = MixtureSameFamily(\n",
        "        mixture_distribution = rv_assignments,\n",
        "        components_distribution = Normal(\n",
        "            loc = sample_locs,  # One for each component\n",
        "            scale = sample_scales))  # And same here\n",
        "\n",
        "    return (\n",
        "          rv_prob.log_prob(sample_prob_0)\n",
        "        + rv_prob.log_prob(sample_prob_1)\n",
        "        + sum(rv_observations.log_prob(data))  # Sum over samples\n",
        "        + sum(rv_locs.log_prob(sample_locs))  # Sum over components\n",
        "        + sum(rv_scales.log_prob(sample_scales)))  # Sum over components"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pr5WXm9CJJ8d"
      },
      "source": [
        "\n",
        "\n",
        "We will use our HMC sampling methods to explore the space by using 25000 sample iterations below.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bnH5NLrkJJ8e",
        "cellView": "code"
      },
      "source": [
        "num_results = 5000\n",
        "num_burnin_steps = 1000\n",
        "num_leapfrog_steps = 3\n",
        "\n",
        "# Set the chain's start state\n",
        "initial_chain_state = [\n",
        "    tf.constant(.5),\n",
        "    tf.constant((120., 190.)),\n",
        "    tf.constant((10., 10.))]\n",
        "\n",
        "# Since MCMC operates over unconstrained space, we need to transform the\n",
        "# samples so they live in real-space\n",
        "unconstraining_bijectors = [\n",
        "    tfb.Identity(),  # Maps R -> R\n",
        "    tfb.Identity(),  # Maps R -> R\n",
        "    tfb.Identity()]  # Maps R -> R\n",
        "\n",
        "# Define a closure over our joint_log_prob\n",
        "unnormalised_posterior_log_prob = lambda *args: joint_log_prob(data, *args)\n",
        "\n",
        "\n",
        "# Initialise the step_size (it will be automatically adapted)\n",
        "step_size = tf.Variable(\n",
        "    initial_value = tf.constant(.5, dtype = f32),\n",
        "    # trainable = False,\n",
        "    name = 'step_size')\n",
        "\n",
        "# Define the HMC\n",
        "kernel = tfp.mcmc.TransformedTransitionKernel(\n",
        "    inner_kernel = tfp.mcmc.HamiltonianMonteCarlo(\n",
        "        target_log_prob_fn = unnormalised_posterior_log_prob,\n",
        "        num_leapfrog_steps = num_leapfrog_steps,\n",
        "        step_size = step_size,\n",
        "        state_gradients_are_stopped = True),\n",
        "    bijector = unconstraining_bijectors)\n",
        "\n",
        "kernel = tfp.mcmc.SimpleStepSizeAdaptation(\n",
        "    inner_kernel = kernel,\n",
        "    num_adaptation_steps = int(num_burnin_steps * .8))\n",
        "\n",
        "# Sample from the chain\n",
        "(posterior_prob, posterior_locs, posterior_scales), kernel_results = tfp.mcmc.sample_chain(\n",
        "    num_results = num_results,\n",
        "    num_burnin_steps = num_burnin_steps,\n",
        "    current_state = initial_chain_state,\n",
        "    kernel = kernel)\n",
        "\n",
        "# print(\"Acceptance rate:\", mean(kernel_results.inner_results.inner_results.is_accepted))\n",
        "# print(\"Final step size:\", mean(kernel_results.new_step_size[-100:]))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uGLNaT4apO-K"
      },
      "source": [
        "*Returned type\n",
        "\n",
        "```python\n",
        "TransformedTransitionKernelResults(\n",
        "    transformed_state=[tf.Tensor(shape=(num_results,), dtype = f32), ...],\n",
        "    inner_results=MetropolisHastingsKernelResults(\n",
        "        accepted_results=UncalibratedHamiltonianMonteCarloKernelResults(\n",
        "            log_acceptance_correction=tf.Tensor(shape=(num_results,), dtype = f32),\n",
        "            target_log_prob=tf.Tensor(shape=(num_results,), dtype = f32),\n",
        "            grads_target_log_prob=[tf.Tensor(shape=(num_results,), dtype = f32), ...],\n",
        "            step_size=tf.Tensor(shape=(num_results,), dtype = f32),\n",
        "            num_leapfrog_steps=tf.Tensor(shape=(num_results,), dtype = f32)),\n",
        "        is_accepted=tf.Tensor(shape=(num_results,), dtype = bool),\n",
        "        log_accept_ratio=tf.Tensor(shape=(num_results,), dtype = f32),\n",
        "        proposed_state=[tf.Tensor(shape=(num_results,), dtype = f32), ...],\n",
        "        proposed_results=UncalibratedHamiltonianMonteCarloKernelResults(\n",
        "            log_acceptance_correction=tf.Tensor(shape=(num_results,), dtype = f32),\n",
        "            target_log_prob=tf.Tensor(shape=(num_results,), dtype = f32),\n",
        "            grads_target_log_prob=[tf.Tensor(shape=(num_results,), dtype = f32)), ...],\n",
        "            step_size=tf.Tensor(shape=(num_results,), dtype = f32),\n",
        "            num_leapfrog_steps=tf.Tensor(shape=(num_results,), dtype = f32),\n",
        "        extra=[]))\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oMjUhg4GDP9f"
      },
      "source": [
        "Let's examine the traces for our unknown parameters. In other words, the routes the unknown parameters (centers, precisions, and  p ) have taken thus far."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mYnyXN4h5KIW"
      },
      "source": [
        "plt.figure(figsize(12.5, 12))\n",
        "plt.subplot(311)\n",
        "lw = 1\n",
        "loc_trace = posterior_locs\n",
        "\n",
        "# for pretty colors later in the book\n",
        "colors = [TFColor[3], TFColor[0]] if loc_trace[-1, 0] > loc_trace[-1, 1] \\\n",
        "          else [TFColor[0], TFColor[3]]\n",
        "\n",
        "plt.plot(loc_trace[:, 0], label='Trace of center 0', c=colors[0], lw=lw)\n",
        "plt.plot(loc_trace[:, 1], label='Trace of center 1', c=colors[1], lw=lw)\n",
        "plt.title(\"Traces of unknown parameters\")\n",
        "leg = plt.legend(loc='upper right')\n",
        "leg.get_frame().set_alpha(.7)\n",
        "\n",
        "plt.subplot(312)\n",
        "scale_trace = posterior_scales\n",
        "plt.plot(scale_trace[:, 0], label='Trace of standard deviation of cluster 0',\n",
        "     c=colors[0], lw=lw)\n",
        "plt.plot(scale_trace[:, 1], label='Trace of standard deviation of cluster 1',\n",
        "     c=colors[1], lw=lw)\n",
        "plt.legend(loc='upper left')\n",
        "\n",
        "plt.subplot(313)\n",
        "p_trace = posterior_prob\n",
        "plt.plot(p_trace, label='$p$: frequency of assignment to cluster 0',\n",
        "     color=TFColor[2], lw=lw)\n",
        "plt.xlabel('Steps')\n",
        "plt.ylim(0, 1)\n",
        "plt.legend();"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oG-Z7bx4JJ8n"
      },
      "source": [
        "Notice the following characteristics:\n",
        "\n",
        "1. The traces converge, not to a single point, but to a *distribution* of possible points. This is *convergence* in an MCMC algorithm.\n",
        "2. Inference using the first few thousand points is a bad idea, as they are unrelated to the final distribution we are interested in. Thus is it a good idea to discard those samples before using the samples for inference. We call this period before converge the *burn-in period*.\n",
        "3. The traces appear as a random 'walk' around the space, that is, the paths exhibit correlation with previous positions. This is both good and bad. We will always have correlation between current positions and the previous positions, but too much of it means we are not exploring the space well. This will be detailed in the Diagnostics section  later in this chapter.\n",
        "\n",
        "\n",
        "To achieve further convergence, we will perform more MCMC steps. In the pseudo-code algorithm of MCMC above, the only position that matters is the current position (new positions are investigated near the current position). To continue where we left off, we pass the current values of the unknown parameters into the `initial_chain_state` variable. The values that we have already calculated will not be overwritten. This ensures that our sampling continues where it left off in the same way that it left off. \n",
        "\n",
        "We will sample the MCMC 50,000 more times and visualise the progress below:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zYEHgLTTubLZ",
        "cellView": "code"
      },
      "source": [
        "num_steps = 5000\n",
        "num_burnin_steps = 1000\n",
        "\n",
        "# Set the chain's start state\n",
        "initial_chain_state = [\n",
        "    tf.constant(posterior_prob[-1]),\n",
        "    tf.constant(posterior_locs[-1]),\n",
        "    tf.constant(posterior_scales[-1])]\n",
        "\n",
        "\n",
        "# Initialise the step_size (it will be automatically adapted)\n",
        "step_size = tf.Variable(\n",
        "    initial_value = tf.constant(.5, dtype = f32),\n",
        "    trainable = False,\n",
        "    name = 'step_size_2')\n",
        "\n",
        "# Define the HMC\n",
        "kernel = tfp.mcmc.TransformedTransitionKernel(\n",
        "    inner_kernel = tfp.mcmc.HamiltonianMonteCarlo(\n",
        "        target_log_prob_fn = unnormalised_posterior_log_prob,\n",
        "        num_leapfrog_steps = num_leapfrog_steps,\n",
        "        step_size = step_size,\n",
        "        state_gradients_are_stopped = True),\n",
        "    bijector = unconstraining_bijectors)\n",
        "\n",
        "kernel = tfp.mcmc.SimpleStepSizeAdaptation(\n",
        "    inner_kernel = kernel,\n",
        "    num_adaptation_steps = int(num_burnin_steps * .8))\n",
        "\n",
        "# Sample from the chain\n",
        "(posterior_prob_2, posterior_locs_2, posterior_scales_2), kernel_results = tfp.mcmc.sample_chain(\n",
        "    num_results = num_results,\n",
        "    num_burnin_steps = num_burnin_steps,\n",
        "    current_state = initial_chain_state,\n",
        "    kernel = kernel)\n",
        "\n",
        "# print(\"Acceptance rate:\", mean(kernel_results.inner_results.inner_results.is_accepted))\n",
        "# print(\"Final step size:\", kernel_results.new_step_size[-100:].mean())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mD5kUVVNJJ8r"
      },
      "source": [
        "plt.figure(figsize(12.5, 4))\n",
        "loc_trace = posterior_locs_2\n",
        "prev_loc_trace = posterior_locs\n",
        "\n",
        "x = arange(5000)\n",
        "plt.plot(x, prev_loc_trace[:, 0], label='Previous trace of loc 0',\n",
        "         lw=lw, alpha=.4, c=colors[1])\n",
        "plt.plot(x, prev_loc_trace[:, 1], label='Previous trace of loc 1',\n",
        "         lw=lw, alpha=.4, c=colors[0])\n",
        "\n",
        "x = arange(5000, 10000)\n",
        "plt.plot(x, loc_trace[:, 0], label='New trace of loc 0', lw=lw, c='#5DA5DA')\n",
        "plt.plot(x, loc_trace[:, 1], label='New trace of loc 1', lw=lw, c='#F15854')\n",
        "\n",
        "plt.title('Traces of unknown loc parameters')\n",
        "leg = plt.legend(loc='upper right')\n",
        "leg.get_frame().set_alpha(.8)\n",
        "plt.xlabel('Steps');"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PZdiLZKwJJ8u"
      },
      "source": [
        "\n",
        "\n",
        "### Cluster Investigation\n",
        "\n",
        "We have not forgotten our main challenge: identify the clusters. We have determined posterior distributions for our unknowns. We plot the posterior distributions of the center and standard deviation variables below:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mrG6NEh30kAf"
      },
      "source": [
        "plt.figure(figsize(12.5, 8))\n",
        "scale_trace = posterior_scales_2\n",
        "prev_scale_trace = posterior_scales\n",
        "\n",
        "_i = [1, 2, 3, 4]\n",
        "for i in range(2):\n",
        "    plt.subplot(2, 2, _i[2 * i])\n",
        "    plt.title(f\"Posterior of center of cluster {i}\")\n",
        "    plt.hist(loc_trace[:, i], color=colors[i], bins=30,\n",
        "             histtype='stepfilled')\n",
        "\n",
        "    plt.subplot(2, 2, _i[2 * i + 1])\n",
        "    plt.title(f\"Posterior of standard deviation of cluster {i}\")\n",
        "    plt.hist(scale_trace[:, i], color=colors[i], bins=30,\n",
        "             histtype='stepfilled')\n",
        "    plt.autoscale(tight=True)\n",
        "\n",
        "plt.tight_layout()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6ilzTlnAJJ8x"
      },
      "source": [
        "The MCMC algorithm has proposed that the most likely centers of the two clusters are near 120 and 200 respectively. Similar inference can be applied to the standard deviation. \n",
        "\n",
        "In the PyMC3 version of this chapter, we depict the posterior distributions for the labels each data point. However, in our TFP version, since our model marginalised out the assignment variable, we don't have traces for that variable from the MCMC. \n",
        "\n",
        "As a substitute, below we can generate a posterior predictive distribution over the assignments, and then generate some samples from it. \n",
        "\n",
        "Below is a visualisation of this. The y-axis represents our samples from the posterior predictive distribution. The x-axis are the sorted values of the original data points. A red square is an assignment to cluster 0, and a blue square is an assignment to cluster 1. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D_n2opAcvHQL"
      },
      "source": [
        "# Put the data into a tensor\n",
        "data = tf.constant(data, dtype = f32)\n",
        "data = data[:, newaxis]\n",
        "\n",
        "# This produces a cluster per MCMC chain\n",
        "rv_clusters_0 = Normal(posterior_locs_2[:, 0], posterior_scales_2[:, 0])\n",
        "rv_clusters_1 = Normal(posterior_locs_2[:, 1], posterior_scales_2[:, 1])\n",
        "\n",
        "# Compute the un-normalised log probabilities for each cluster\n",
        "cluster_0_log_prob = rv_clusters_0.log_prob(data) + tf.math.log(posterior_prob_2)\n",
        "cluster_1_log_prob = rv_clusters_1.log_prob(data) + tf.math.log(1. - posterior_prob_2)\n",
        "\n",
        "x = tf.stack([cluster_0_log_prob, cluster_1_log_prob], axis = -1)\n",
        "y = tf.reduce_logsumexp(x, -1)\n",
        "\n",
        "# Bayes rule to compute the assignment probability: P(cluster = 1 | data) ∝ P(data | cluster = 1) P(cluster = 1)\n",
        "log_p_assign_1 = cluster_0_log_prob \\\n",
        "    - tf.reduce_logsumexp(tf.stack([cluster_0_log_prob, cluster_1_log_prob], axis = -1), -1)\n",
        "\n",
        "# Average across the MCMC chain\n",
        "log_p_assign_1 = tf.reduce_logsumexp(log_p_assign_1, -1) - tf.math.log(tf.cast(log_p_assign_1.shape[-1], f32))\n",
        " \n",
        "p_assign_1 = exp(log_p_assign_1)\n",
        "p_assign = tf.stack([p_assign_1, 1 - p_assign_1], axis = -1)\n",
        "\n",
        "# for charting \n",
        "probs_assignments = p_assign_1 "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OusqBiRafHdm"
      },
      "source": [
        "burned_assignment_trace = Categorical(probs = p_assign).sample(sample_shape = (200,))\n",
        "plt.figure(figsize(12.5, 5))\n",
        "plt.cmap = mpl.colors.ListedColormap(colors)\n",
        "plt.imshow(burned_assignment_trace[:, argsort(data)],\n",
        "           cmap=plt.cmap, aspect=.4, alpha=.9)\n",
        "plt.xticks(arange(0, data.shape[0], 40),\n",
        "           [f'{s:.2f}' for s in sort(data)[::40]])\n",
        "plt.ylabel(\"Posterior sample\")\n",
        "plt.xlabel(\"Value of $i$th data point\")\n",
        "plt.title(\"Posterior labels of data points\");"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N8DzxclkJJ82"
      },
      "source": [
        "Looking at the above plot, it appears that the most uncertainty is between 150 and 170. The above plot slightly misrepresents things, as the x-axis is not a true scale (it displays the value of the $i$th sorted data point.) A more clear diagram is below, where we have estimated the *frequency* of each data point belonging to the labels 0 and 1. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "568aaUzJh9TD"
      },
      "source": [
        "plt.figure(figsize(12.5, 5))\n",
        "\n",
        "cmap = mpl.colors.LinearSegmentedColormap.from_list('BMH', colors)\n",
        "assign_trace = probs_assignments[argsort(data)]\n",
        "plt.scatter(data[argsort(data)], assign_trace, cmap=cmap,\n",
        "            c=1 - assign_trace, s=50)\n",
        "plt.ylim(-.05, 1.05)\n",
        "plt.xlim(35, 300)\n",
        "plt.title(\"Probability of data point belonging to cluster 0\")\n",
        "plt.ylabel('Probability')\n",
        "plt.xlabel('Value of data point');"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lmqny45RJJ86"
      },
      "source": [
        "Even though we modeled the clusters using Normal distributions, we didn't get just a single Normal distribution that *best* fits the data (whatever our definition of best is), but a distribution of values for the Normal's parameters. How can we choose just a single pair of values for the mean and variance and determine a *sorta-best-fit* gaussian? \n",
        "\n",
        "One quick and dirty way (which has nice theoretical properties we will see in Chapter 5), is to use the *mean* of the posterior distributions. Below we overlay the Normal density functions, using the mean of the posterior distributions as the chosen parameters, with our observed data:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5U94W5ILJJ87"
      },
      "source": [
        "x = linspace(20., 300., 500)\n",
        "posterior_loc_means = mean(posterior_locs_2, axis = 0)\n",
        "posterior_scale_means = mean(posterior_scales_2, axis = 0)\n",
        "posterior_prob_mean = mean(posterior_prob_2, axis = 0)\n",
        "\n",
        "plt.hist(data, bins=20, histtype='step', density=True,  # color='k'\n",
        "         lw=2, label='Histogram of data')\n",
        "y = posterior_prob_mean \\\n",
        "    * Normal(loc=posterior_loc_means[0], scale=posterior_scale_means[0]).prob(x)\n",
        "plt.plot(x, y, label='Cluster 0 (using posterior-mean parameters)', lw=3)\n",
        "plt.fill_between(x, y, alpha=.3)  # color=colors[1]\n",
        "\n",
        "y = (1 - posterior_prob_mean) \\\n",
        "    * Normal(loc=posterior_loc_means[1], scale=posterior_scale_means[1]).prob(x)\n",
        "plt.plot(x, y, label='Cluster 1 (using posterior-mean parameters)', lw=3)\n",
        "plt.fill_between(x, y, alpha=.3)  # color=colors[0]\n",
        "\n",
        "plt.legend(loc='upper left')\n",
        "plt.title(\"Visualising Clusters using posterior-mean parameters\");\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "btb0fNbsJJ8-"
      },
      "source": [
        "### Important: Don't mix posterior samples\n",
        "\n",
        "In the above example, a possible (though less likely) scenario is that cluster 0 has a very large standard deviation, and cluster 1 has a small standard deviation. This would still satisfy the evidence, albeit less so than our original inference. Alternatively, it would be incredibly unlikely for *both* distributions to have a small standard deviation, as the data does not support this hypothesis at all. Thus the two standard deviations are *dependent* on each other: if one is small, the other must be large. In fact, *all* the unknowns are related in a similar manner. For example, if a standard deviation is large, the mean has a wider possible space of realisations. Conversely, a small standard deviation restricts the mean to a small area. \n",
        "\n",
        "During MCMC, we are returned vectors representing samples from the unknown posteriors. Elements of different vectors cannot be used together, as this would break the above logic: perhaps a sample has returned that cluster 1 has a small standard deviation, hence all the other variables in that sample would incorporate that and be adjusted accordingly. It is easy to avoid this problem though, just make sure you are indexing traces correctly. \n",
        "\n",
        "Another small example to illustrate the point. Suppose two variables, $x$ and $y$, are related by $x+y=10$. We model $x$ as a Normal random variable with mean 4 and explore 500 samples.  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BQ5Z84afueVa",
        "cellView": "code"
      },
      "source": [
        "num_results = 5000\n",
        "num_burnin_steps = 1000\n",
        "\n",
        "# Set the chain's start state.\n",
        "initial_chain_state = [\n",
        "    tf.cast(1., f32) * ones((), dtype = f32)]\n",
        "\n",
        "# Initialize the step_size. (It will be automatically adapted.)\n",
        "step_size = tf.Variable(\n",
        "    initial_value=tf.constant(.5, dtype = f32),\n",
        "    trainable=False,\n",
        "    name='step_size')\n",
        "\n",
        "# Defining the HMC\n",
        "# Since we're only using one distribution for our simplistic example, \n",
        "# the use of the bijectors and unnormalised log_prob function is \n",
        "# unneccesary\n",
        "#\n",
        "# While not a good example of what to do if you have dependent \n",
        "# priors, this IS a good example of how to set up just one variable \n",
        "# with a simple distribution\n",
        "kernel = tfp.mcmc.HamiltonianMonteCarlo(\n",
        "    target_log_prob_fn=Normal(loc=tf.cast(4., f32), \n",
        "                                  scale=tf.cast(1. / sqrt(10.), f32)).log_prob,\n",
        "    num_leapfrog_steps=2,\n",
        "    step_size=step_size,\n",
        "    state_gradients_are_stopped=True)\n",
        "\n",
        "kernel = tfp.mcmc.SimpleStepSizeAdaptation(\n",
        "    inner_kernel=kernel, num_adaptation_steps=int(num_burnin_steps * .8))\n",
        "\n",
        "# Sampling from the chain.\n",
        "(x_samples,), kernel_results = tfp.mcmc.sample_chain(\n",
        "    num_results=num_results,\n",
        "    num_burnin_steps=num_burnin_steps,\n",
        "    current_state=initial_chain_state,\n",
        "    kernel=kernel)\n",
        "\n",
        "y_samples = 10 - x_samples\n",
        "\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.plot(arange(num_results), x_samples, color=TFColor[3], alpha=.8)\n",
        "plt.plot(arange(num_results), y_samples, color=TFColor[0], alpha=.8)\n",
        "plt.title(\"Displaying (extreme) case of dependence between unknowns\", fontsize=14)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HLXOn3ecJJ9F"
      },
      "source": [
        "As you can see, the two variables are not unrelated, and it would be wrong to add the $i$th sample of $x$ to the $j$th sample of $y$, unless $i = j$."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xipPeX_2JJ9F"
      },
      "source": [
        "### Returning to Clustering: Prediction\n",
        "The above clustering can be generalised to $k$ clusters. Choosing $k=2$ allowed us to visualise the MCMC better, and examine some very interesting plots. \n",
        "\n",
        "What about prediction? Suppose we observe a new data point, say $x = 175$, and we wish to label it to a cluster. It is foolish to simply assign it to the *closer* cluster center, as this ignores the standard deviation of the clusters, and we have seen from the plots above that this consideration is very important. More formally: we are interested in the *probability* (as we cannot be certain about labels) of assigning $x=175$ to cluster 1. Denote the assignment of $x$ as $L_x$, which is equal to 0 or 1, and we are interested in $P(L_x = 1 \\;|\\; x = 175 )$.  \n",
        "\n",
        "A naive method to compute this is to re-run the above MCMC with the additional data point appended. The disadvantage with this method is that it will be slow to infer for each novel data point. Alternatively, we can try a *less precise*, but much quicker method. \n",
        "\n",
        "We will use Bayes Theorem for this. If you recall, Bayes Theorem looks like:\n",
        "\n",
        "$$ P( A | X ) = \\frac{ P( X  | A )P(A) }{P(X) }$$\n",
        "\n",
        "In our case, $A$ represents $L_x = 1$ and $X$ is the evidence we have: we observe that $x = 175$. For a particular sample set of parameters for our posterior distribution, $( \\mu_0, \\sigma_0, \\mu_1, \\sigma_1, p)$, we are interested in asking \"Is the probability that $x$ is in cluster 1 *greater* than the probability that it is in cluster 0?\", where the probability is dependent on the chosen parameters.\n",
        "$$\n",
        "\\begin{align}\n",
        "& P(L_x = 1| x = 175 ) \\gt P(L_x = 0| x = 175 ) \\\\[5pt]\n",
        "& \\frac{ P( x=175  | L_x = 1  )P( L_x = 1 ) }{P(x = 175) } \\gt \\frac{ P( x=175  | L_x = 0  )P( L_x = 0 )}{P(x = 175) }\n",
        "\\end{align}\n",
        "$$\n",
        "As the denominators are equal, they can be ignored (and good riddance, because computing the quantity $P(x = 175)$ can be difficult). \n",
        "\n",
        "$$  P( x=175  | L_x = 1  )P( L_x = 1 ) \\gt  P( x=175  | L_x = 0  )P( L_x = 0 ) $$\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SNr3_p57JJ9J"
      },
      "source": [
        "p_trace = posterior_prob_2[25000:]\n",
        "x = 175\n",
        "v = (1 - p_trace) * Normal(loc=loc_trace[25000:, 1], scale=scale_trace[25000:, 1]).log_prob(x) \\\n",
        "    > p_trace * Normal(loc=loc_trace[25000:, 0], scale=scale_trace[25000:, 0]).log_prob(x)\n",
        "\n",
        "print(\"Probability of belonging to cluster 1:\", mean(tf.cast(v, f32)))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-y6ThW6NJJ9T"
      },
      "source": [
        "Giving us a probability instead of a label is a very useful thing. Instead of the naive \n",
        "\n",
        "    L = 1 if prob > 0.5 else 0\n",
        "\n",
        "we can optimise our guesses using a *loss function*, which the entire fifth chapter is devoted to.  \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OExk531OzCvc"
      },
      "source": [
        "## Diagnosing Convergence"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_5NQ-0bUJJ9T"
      },
      "source": [
        "### Autocorrelation\n",
        "\n",
        "Autocorrelation is a measure of how related a series of numbers is with itself. A measurement of 1.0 is perfect positive autocorrelation, 0 no autocorrelation, and -1 is perfect negative correlation.  If you are familiar with standard *correlation*, then autocorrelation is just how correlated a series, $x_\\tau$, at time $t$ is with the series at time $t-k$:\n",
        "\n",
        "$$R(k) = \\text{Corr}( x_t, x_{t-k} ) $$\n",
        "\n",
        "For example, consider the two series:\n",
        "\n",
        "$$x_t \\sim \\text{Normal}(0,1), \\;\\; x_0 = 0$$\n",
        "$$y_t \\sim \\text{Normal}(y_{t-1}, 1 ), \\;\\; y_0 = 0$$\n",
        "\n",
        "which have example paths like:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ED_OoWXEwRJ6"
      },
      "source": [
        "x_t = Normal(loc = 0., scale = 1.).sample(sample_shape = (200,))\n",
        "x_t[0] = 0\n",
        "y_t = zeros(200)\n",
        "for i in range(1, 200):\n",
        "    y_t[i] = Normal(loc = y_t[i - 1], scale = 1.).sample()\n",
        "\n",
        "plt.figure(figsize(12.5, 4))\n",
        "plt.plot(y_t, label='$y_t$', lw=3)\n",
        "plt.plot(x_t, label='$x_t$', lw=3)\n",
        "plt.xlabel('time, $t$')\n",
        "plt.legend();"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BaIWQyM1JJ9X"
      },
      "source": [
        "One way to think of autocorrelation is \"If I know the position of the series at time $s$, can it help me know where I am at time $t$?\" In the series $x_t$, the answer is No. By construction, $x_t$ are random variables. If I told you that $x_2 = 0.5$, could you give me a better guess about $x_3$? No.\n",
        "\n",
        "On the other hand, $y_t$ is autocorrelated. By construction, if I knew that $y_2 = 10$, I can be very confident that $y_3$ will not be very far from 10. Similarly, I can even make a (less confident guess) about $y_4$: it will probably not be near 0 or 20, but a value of 5 is not too unlikely. I can make a similar argument about $y_5$, but again, I am less confident. Taking this to it's logical conclusion, we must concede that as $k$, the lag between time points, increases the autocorrelation decreases. We can visualise this:\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vhLvAgtyJJ9a"
      },
      "source": [
        "def autocorr(x):\n",
        "    # from http://tinyurl.com/afz57c4\n",
        "    result = correlate(x, x, mode='full')\n",
        "    result = result / max(result)\n",
        "    return result[result.size//2:]\n",
        "\n",
        "colors = [TFColor[3], TFColor[0], TFColor[6]]\n",
        "\n",
        "x = arange(1, 200)\n",
        "plt.bar(x, autocorr(y_t)[1:], width=1, label='$y_t$',\n",
        "        edgecolor=colors[0], color=colors[0])\n",
        "plt.bar(x, autocorr(x_t)[1:], width=1, label='$x_t$',\n",
        "        color=colors[1], edgecolor=colors[1])\n",
        "\n",
        "plt.legend(title=\"Autocorrelation')\n",
        "plt.ylabel('Measured correlation \\nbetween $y_t$ and $y_{t-k}$.')\n",
        "plt.xlabel('k (lag)')\n",
        "plt.title(\"Autocorrelation plot of $y_t$ and $x_t$ for differing $k$ lags.\");\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5h6HaBT9JJ9d"
      },
      "source": [
        "Notice that as $k$ increases, the autocorrelation of $y_t$ decreases from a very high point. Compare with the autocorrelation of $x_t$ which looks like noise (which it really is), hence we can conclude no autocorrelation exists in this series. \n",
        "\n",
        "\n",
        "### How does this relate to MCMC convergence?\n",
        "\n",
        "By the nature of the MCMC algorithm, we will always be returned samples that exhibit autocorrelation (this is because of the step `from your current position, move to a position near you`).\n",
        "\n",
        "A chain that is not exploring the space well will exhibit very high autocorrelation. Visually, if the trace seems to meander like a river, and not settle down, the chain will have high autocorrelation.\n",
        "\n",
        "This does not imply that a converged MCMC has low autocorrelation. Hence low autocorrelation is not necessary for convergence, but it is sufficient. TFP has a built-in autocorrelation tools as well. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yJstgksqJJ9d"
      },
      "source": [
        "### Thinning\n",
        "\n",
        "Another issue can arise if there is high-autocorrelation between posterior samples. Many post-processing algorithms require samples to be *independent* of each other. This can be solved, or at least reduced, by only returning to the user every $n$th sample, thus removing some autocorrelation. Below we perform an autocorrelation plot for $y_t$ with differing levels of thinning:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SIeWr23CJJ9e"
      },
      "source": [
        "max_x = 200 // 3 + 1\n",
        "x = arange(1, max_x)\n",
        "\n",
        "plt.bar(x, autocorr(y_t)[1:max_x], edgecolor=colors[0],\n",
        "        label='No thinning', color=colors[0], width=1)\n",
        "plt.bar(x, autocorr(y_t[::2])[1:max_x], edgecolor=colors[1],\n",
        "        label='Keeping every 2nd sample', color=colors[1], width=1)\n",
        "plt.bar(x, autocorr(y_t[::3])[1:max_x], width=1, edgecolor=colors[2],\n",
        "        label='Keeping every 3rd sample', color=colors[2])\n",
        "\n",
        "plt.autoscale(tight=True)\n",
        "plt.legend(title=\"Autocorrelation plot for $y_t$\", loc='upper right')\n",
        "plt.ylabel('Measured correlation \\nbetween $y_t$ and $y_{t-k}$.')\n",
        "plt.xlabel('k (lag)')\n",
        "plt.title(\"Autocorrelation of $y_t$ (no thinning vs. thinning) \\\n",
        "at differing $k$ lags.\");"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QyjO-xpVJJ9h"
      },
      "source": [
        "With more thinning, the autocorrelation drops quicker. There is a tradeoff though: higher thinning requires more MCMC iterations to achieve the same number of returned samples. For example, 10,000 samples unthinned is 100,000 with a thinning of 10 (though the latter has less autocorrelation). \n",
        "\n",
        "What is a good amount of thinning? The returned samples will always exhibit some autocorrelation, regardless of how much thinning is done. So long as the autocorrelation tends to zero, you are probably ok. Typically thinning of more than 10 is not necessary."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_M1NvityzPyu"
      },
      "source": [
        "## Useful tips for MCMC\n",
        "\n",
        "Bayesian inference would be the *de facto* method if it weren't for MCMC's computational difficulties. In fact, MCMC is what turns most users off practical Bayesian inference. Below I present some good heuristics to help convergence and speed up the MCMC engine:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "12fpbZ02JJ9n"
      },
      "source": [
        "### Intelligent starting values\n",
        "\n",
        "It would be great to start the MCMC algorithm off near the posterior distribution, so that it will take little time to start sampling correctly. We can aid the algorithm by telling where we *think* the posterior distribution will be by specifying the `testval` parameter in the `Stochastic` variable creation. In many cases we can produce a reasonable guess for the parameter. For example, if we have data from a Normal distribution, and we wish to estimate the $\\mu$ parameter, then a good starting value would be the *mean* of the data. \n",
        "```python\n",
        "mu = Uniform(low = 0., high = 100.).sample(seed = data.mean())\n",
        "```\n",
        "For most parameters in models, there is a frequentist estimate of it. These estimates are a good starting value for our MCMC algorithms. Of course, this is not always possible for some variables, but including as many appropriate initial values is always a good idea. Even if your guesses are wrong, the MCMC will still converge to the proper distribution, so there is little to lose."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QYPtfunzzTcB"
      },
      "source": [
        "#### Priors\n",
        "\n",
        "If the priors are poorly chosen, the MCMC algorithm may not converge, or at least have difficulty converging. Consider what may happen if the prior chosen does not even contain the true parameter: the prior assigns 0 probability to the unknown, hence the posterior will assign 0 probability as well. This can cause pathological results.\n",
        "\n",
        "For this reason, it is best to carefully choose the priors. Often, lack of covergence or evidence of samples crowding to boundaries implies something is wrong with the chosen priors (see *Folk Theorem of Statistical Computing* below). "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Nxc-2H7zZFR"
      },
      "source": [
        "#### The Folk Theorem of Statistical Computing\n",
        "\n",
        ">   *If you are having computational problems, probably your model is wrong.*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b0DSjmO5JJ9o"
      },
      "source": [
        "## Conclusion\n",
        "\n",
        "TFP provides a very strong backend to performing Bayesian inference, mostly because it allows the user to fine-tune the inner workings of MCMC. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eESYGOBtJJ9o"
      },
      "source": [
        "### References\n",
        "\n",
        "[1] Tensorflow Probability API docs. https://www.tensorflow.org/probability/api_docs/python/tfp"
      ]
    }
  ]
}