tensorflow_probability/examples/jupyter_notebooks/Linear_Mixed_Effects_Models.ipynb

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      "source": [
        "##### Copyright 2018 The TensorFlow Probability Authors.\n",
        "\n",
        "Licensed under the Apache License, Version 2.0 (the \"License\");"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "G5eriUZ9g1Ia"
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      "outputs": [],
      "source": [
        "#@title Licensed under the Apache License, Version 2.0 (the \"License\"); { display-mode: \"form\" }\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iCUZvZvBB7VD"
      },
      "source": [
        "# Linear Mixed Effects Models\n",
        "\n",
        "\u003ctable class=\"tfo-notebook-buttons\" align=\"left\"\u003e\n",
        "  \u003ctd\u003e\n",
        "    \u003ca target=\"_blank\" href=\"https://www.tensorflow.org/probability/examples/Linear_Mixed_Effects_Models\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" /\u003eView on TensorFlow.org\u003c/a\u003e\n",
        "  \u003c/td\u003e\n",
        "  \u003ctd\u003e\n",
        "    \u003ca target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/probability/blob/main/tensorflow_probability/examples/jupyter_notebooks/Linear_Mixed_Effects_Models.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" /\u003eRun in Google Colab\u003c/a\u003e\n",
        "  \u003c/td\u003e\n",
        "  \u003ctd\u003e\n",
        "    \u003ca target=\"_blank\" href=\"https://github.com/tensorflow/probability/blob/main/tensorflow_probability/examples/jupyter_notebooks/Linear_Mixed_Effects_Models.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" /\u003eView source on GitHub\u003c/a\u003e\n",
        "  \u003c/td\u003e\n",
        "  \u003ctd\u003e\n",
        "    \u003ca href=\"https://storage.googleapis.com/tensorflow_docs/probability/tensorflow_probability/examples/jupyter_notebooks/Linear_Mixed_Effects_Models.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/download_logo_32px.png\" /\u003eDownload notebook\u003c/a\u003e\n",
        "  \u003c/td\u003e\n",
        "\u003c/table\u003e"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yDCkuCjq2DfW"
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      "source": [
        "A linear mixed effects model is a simple approach for modeling structured linear relationships (Harville, 1997; Laird and Ware, 1982). Each data point consists of inputs of varying type—categorized into groups—and a real-valued output. A linear mixed effects model is a _hierarchical model_: it shares statistical strength across groups in order to improve inferences about any individual data point.\n",
        "\n",
        "In this tutorial, we demonstrate linear mixed effects models with a real-world example in TensorFlow Probability. We'll use the JointDistributionCoroutine and Markov Chain Monte Carlo (`tfp.mcmc`) modules."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uiR4-VOt9NFX"
      },
      "source": [
        "### Dependencies \u0026 Prerequisites\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "coUnDhkpT5_6"
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      "source": [
        "#@title Import and set ups{ display-mode: \"form\" }\n",
        "\n",
        "\n",
        "import csv\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import requests\n",
        "\n",
        "import tensorflow as tf\n",
        "import tf_keras\n",
        "import tensorflow_probability as tfp\n",
        "\n",
        "tfd = tfp.distributions\n",
        "tfb = tfp.bijectors\n",
        "\n",
        "dtype = tf.float64\n",
        "\n",
        "%config InlineBackend.figure_format = 'retina'\n",
        "%matplotlib inline\n",
        "plt.style.use('ggplot')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7nnwjUdVoWN2"
      },
      "source": [
        "### Make things Fast!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2CK9RaDcoYPG"
      },
      "source": [
        "Before we dive in, let's make sure we're using a GPU for this demo.\n",
        "\n",
        "To do this, select \"Runtime\" -\u003e \"Change runtime type\" -\u003e \"Hardware accelerator\" -\u003e \"GPU\".\n",
        "\n",
        "The following snippet will verify that we have access to a GPU."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qP_4Xr8vpA42"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "WARNING: GPU device not found.\n"
          ]
        }
      ],
      "source": [
        "if tf.test.gpu_device_name() != '/device:GPU:0':\n",
        "  print('WARNING: GPU device not found.')\n",
        "else:\n",
        "  print('SUCCESS: Found GPU: {}'.format(tf.test.gpu_device_name()))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FJRBc_S0ppfE"
      },
      "source": [
        "Note: if for some reason you cannot access a GPU, this colab will still work. (Training will just take longer.)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eikJTmPgB7VJ"
      },
      "source": [
        "## Data\n",
        "\n",
        "We use the `InstEval` data set from the popular [`lme4` package in R](https://CRAN.R-project.org/package=lme4) (Bates et al., 2015). It is a data set of courses and their evaluation ratings. Each course includes metadata such as `students`, `instructors`, and `departments`, and the response variable of interest is the evaluation rating."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lZ8OfS3cDMeG"
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      "outputs": [],
      "source": [
        "def load_insteval():\n",
        "  \"\"\"Loads the InstEval data set.\n",
        "\n",
        "  It contains 73,421 university lecture evaluations by students at ETH\n",
        "  Zurich with a total of 2,972 students, 2,160 professors and\n",
        "  lecturers, and several student, lecture, and lecturer attributes.\n",
        "  Implementation is built from the `observations` Python package.\n",
        "\n",
        "  Returns:\n",
        "    Tuple of np.ndarray `x_train` with 73,421 rows and 7 columns and\n",
        "    dictionary `metadata` of column headers (feature names).\n",
        "  \"\"\"\n",
        "  url = ('https://raw.github.com/vincentarelbundock/Rdatasets/master/csv/'\n",
        "         'lme4/InstEval.csv')\n",
        "  with requests.Session() as s:\n",
        "    download = s.get(url)\n",
        "    f = download.content.decode().splitlines()\n",
        "\n",
        "  iterator = csv.reader(f)\n",
        "  columns = next(iterator)[1:]\n",
        "  x_train = np.array([row[1:] for row in iterator], dtype=np.int)\n",
        "  metadata = {'columns': columns}\n",
        "  return x_train, metadata"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Um0EhvaDQcVI"
      },
      "source": [
        "We load and preprocess the data set. We hold out 20% of the data so we can evaluate our fitted model on unseen data points. Below we visualize the first few rows."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YY_VbNt6fkcp"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\u003cipython-input-4-5d7a9eabeea1\u003e:21: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  x_train = np.array([row[1:] for row in iterator], dtype=np.int)\n"
          ]
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            "text/plain": [
              "       students  instructors  studage  lectage  service  departments  ratings\n",
              "60982      2471            8        6        1        0            1        2\n",
              "12205       469         1093        6        2        0            1        1\n",
              "34728      1409          612        8        3        0            3        3\n",
              "42492      1726          361        2        1        0           11        3\n",
              "48252      1944          640        2        1        0            8        4"
            ]
          },
          "execution_count": 0,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data, metadata = load_insteval()\n",
        "data = pd.DataFrame(data, columns=metadata['columns'])\n",
        "data = data.rename(columns={'s': 'students',\n",
        "                            'd': 'instructors',\n",
        "                            'dept': 'departments',\n",
        "                            'y': 'ratings'})\n",
        "data['students'] -= 1  # start index by 0\n",
        "# Remap categories to start from 0 and end at max(category).\n",
        "data['instructors'] = data['instructors'].astype('category').cat.codes\n",
        "data['departments'] = data['departments'].astype('category').cat.codes\n",
        "\n",
        "train = data.sample(frac=0.8)\n",
        "test = data.drop(train.index)\n",
        "\n",
        "train.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qWttG6OaVFMO"
      },
      "source": [
        "We set up the data set in terms of a `features` dictionary of inputs and a `labels` output corresponding to the ratings. Each feature is encoded as an integer and each label (evaluation rating) is encoded as a floating point number."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NzfVQJN9B7VQ"
      },
      "outputs": [],
      "source": [
        "get_value = lambda dataframe, key, dtype: dataframe[key].values.astype(dtype)\n",
        "features_train = {\n",
        "    k: get_value(train, key=k, dtype=np.int32)\n",
        "    for k in ['students', 'instructors', 'departments', 'service']}\n",
        "labels_train = get_value(train, key='ratings', dtype=np.float32)\n",
        "\n",
        "features_test = {k: get_value(test, key=k, dtype=np.int32)\n",
        "                 for k in ['students', 'instructors', 'departments', 'service']}\n",
        "labels_test = get_value(test, key='ratings', dtype=np.float32)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "80ylfxWtB7VT"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of students: 2972\n",
            "Number of instructors: 1128\n",
            "Number of departments: 14\n",
            "Number of observations: 58737\n"
          ]
        }
      ],
      "source": [
        "num_students = max(features_train['students']) + 1\n",
        "num_instructors = max(features_train['instructors']) + 1\n",
        "num_departments = max(features_train['departments']) + 1\n",
        "num_observations = train.shape[0]\n",
        "\n",
        "print(\"Number of students:\", num_students)\n",
        "print(\"Number of instructors:\", num_instructors)\n",
        "print(\"Number of departments:\", num_departments)\n",
        "print(\"Number of observations:\", num_observations)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jMRMLuWwB7VX"
      },
      "source": [
        "## Model\n",
        "\n",
        "A typical linear model assumes independence, where any pair of data points has a constant linear relationship. In the `InstEval` data set, observations arise in groups each of which may have varying slopes and intercepts. Linear mixed effects models, also known as hierarchical linear models or multilevel linear models, capture this phenomenon (Gelman \u0026 Hill, 2006).\n",
        "\n",
        "Examples of this phenomenon include:\n",
        "\n",
        "+ __Students__. Observations from a student are not independent: some students may systematically give low (or high) lecture ratings.\n",
        "+ __Instructors__. Observations from an instructor are not independent: we expect good teachers to generally have good ratings and bad teachers to generally have bad ratings.\n",
        "+ __Departments__. Observations from a department are not independent: certain departments may generally have dry material or stricter grading and thus be rated lower than others.\n",
        "\n",
        "To capture this, recall that for a data set of $N\\times D$ features $\\mathbf{X}$ and $N$ labels $\\mathbf{y}$, linear regression posits the model\n",
        "\n",
        "$$\n",
        "\\begin{equation*}\n",
        "\\mathbf{y} = \\mathbf{X}\\beta + \\alpha + \\epsilon,\n",
        "\\end{equation*}\n",
        "$$\n",
        "\n",
        "where there is a slope vector $\\beta\\in\\mathbb{R}^D$, intercept $\\alpha\\in\\mathbb{R}$, and random noise $\\epsilon\\sim\\text{Normal}(\\mathbf{0}, \\mathbf{I})$. We say that $\\beta$ and $\\alpha$ are \"fixed effects\": they are effects held constant across the population of data points $(x, y)$. An equivalent formulation of the equation as a likelihood is $\\mathbf{y} \\sim \\text{Normal}(\\mathbf{X}\\beta + \\alpha, \\mathbf{I})$. This likelihood is maximized during inference in order to find point estimates of $\\beta$ and $\\alpha$ that fit the data.\n",
        "\n",
        "A linear mixed effects model extends linear regression as\n",
        "\n",
        "$$\n",
        "\\begin{align*}\n",
        "\\eta \u0026\\sim \\text{Normal}(\\mathbf{0}, \\sigma^2 \\mathbf{I}), \\\\\n",
        "\\mathbf{y} \u0026= \\mathbf{X}\\beta + \\mathbf{Z}\\eta + \\alpha + \\epsilon.\n",
        "\\end{align*}\n",
        "$$\n",
        "\n",
        "where there is still a slope vector $\\beta\\in\\mathbb{R}^P$, intercept $\\alpha\\in\\mathbb{R}$, and random noise $\\epsilon\\sim\\text{Normal}(\\mathbf{0}, \\mathbf{I})$. In addition, there is a term $\\mathbf{Z}\\eta$, where $\\mathbf{Z}$ is a features matrix and $\\eta\\in\\mathbb{R}^Q$ is a vector of random slopes; $\\eta$ is normally distributed with variance component parameter $\\sigma^2$. $\\mathbf{Z}$ is formed by partitioning the original $N\\times D$ features matrix in terms of a new $N\\times P$ matrix $\\mathbf{X}$ and $N\\times Q$ matrix $\\mathbf{Z}$, where $P + Q=D$: this partition allows us to model the features separately using the fixed effects $\\beta$ and the latent variable $\\eta$ respectively.\n",
        "\n",
        "We say the latent variables $\\eta$ are \"random effects\": they are effects that vary across the population (although they may be constant across subpopulations). In particular, because the random effects $\\eta$ have mean 0, the data label's mean is captured by $\\mathbf{X}\\beta + \\alpha$. The random effects component $\\mathbf{Z}\\eta$ captures variations in the data: for example, \"Instructor \\#54 is rated 1.4 points higher than the mean.\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7B6ROTDQdTjH"
      },
      "source": [
        "In this tutorial, we posit the following effects:\n",
        "\n",
        "+ Fixed effects: `service`. `service` is a binary covariate corresponding to whether the course belongs to the instructor's main department. No matter how much additional data we collect, it can only take on values $0$ and $1$.\n",
        "+ Random effects: `students`, `instructors`, and `departments`. Given more observations from the population of course evaluation ratings, we may be looking at new students, teachers, or departments.\n",
        "\n",
        "In the syntax of R's lme4 package (Bates et al., 2015), the model can be summarized as\n",
        "\n",
        "```\n",
        "ratings ~ service + (1|students) + (1|instructors) + (1|departments) + 1\n",
        "```\n",
        "where `x` denotes a fixed effect,`(1|x)` denotes a random effect for `x`, and `1` denotes an intercept term.\n",
        "\n",
        "We implement this model below as a JointDistribution. To have better support for parameter tracking (e.g., we want to track all the `tf.Variable` in `model.trainable_variables`), we implement the model template as `tf.Module`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GS7SjqREp9wC"
      },
      "outputs": [],
      "source": [
        "class LinearMixedEffectModel(tf.Module):\n",
        "  def __init__(self):\n",
        "    # Set up fixed effects and other parameters.\n",
        "    # These are free parameters to be optimized in E-steps\n",
        "    self._intercept = tf.Variable(0., name=\"intercept\")            # alpha in eq\n",
        "    self._effect_service = tf.Variable(0., name=\"effect_service\")  #  beta in eq\n",
        "    self._stddev_students = tfp.util.TransformedVariable(\n",
        "        1., bijector=tfb.Exp(), name=\"stddev_students\")            # sigma in eq\n",
        "    self._stddev_instructors = tfp.util.TransformedVariable(\n",
        "        1., bijector=tfb.Exp(), name=\"stddev_instructors\")         # sigma in eq\n",
        "    self._stddev_departments = tfp.util.TransformedVariable(\n",
        "        1., bijector=tfb.Exp(), name=\"stddev_departments\")         # sigma in eq\n",
        "\n",
        "  def __call__(self, features):\n",
        "    model = tfd.JointDistributionSequential([\n",
        "      # Set up random effects.\n",
        "      tfd.MultivariateNormalDiag(\n",
        "          loc=tf.zeros(num_students),\n",
        "          scale_diag=self._stddev_students * tf.ones(num_students)),\n",
        "      tfd.MultivariateNormalDiag(\n",
        "          loc=tf.zeros(num_instructors),\n",
        "          scale_diag=self._stddev_instructors * tf.ones(num_instructors)),\n",
        "      tfd.MultivariateNormalDiag(\n",
        "          loc=tf.zeros(num_departments),\n",
        "          scale_diag=self._stddev_departments * tf.ones(num_departments)),\n",
        "      # This is the likelihood for the observed.\n",
        "      lambda effect_departments, effect_instructors, effect_students: tfd.Independent(\n",
        "          tfd.Normal(\n",
        "              loc=(self._effect_service * features[\"service\"] +\n",
        "                  tf.gather(effect_students, features[\"students\"], axis=-1) +\n",
        "                  tf.gather(effect_instructors, features[\"instructors\"], axis=-1) +\n",
        "                  tf.gather(effect_departments, features[\"departments\"], axis=-1) +\n",
        "                  self._intercept),\n",
        "              scale=1.),\n",
        "              reinterpreted_batch_ndims=1)\n",
        "    ])\n",
        "\n",
        "    # To enable tracking of the trainable variables via the created distribution,\n",
        "    # we attach a reference to `self`. Since all TFP objects sub-class\n",
        "    # `tf.Module`, this means that the following is possible:\n",
        "    # LinearMixedEffectModel()(features_train).trainable_variables\n",
        "    # ==\u003e tuple of all tf.Variables created by LinearMixedEffectModel.\n",
        "    model._to_track = self\n",
        "    return model\n",
        "\n",
        "lmm_jointdist = LinearMixedEffectModel()\n",
        "# Conditioned on feature/predictors from the training data\n",
        "lmm_train = lmm_jointdist(features_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WmaYmavUtpGh"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(\u003ctf.Variable 'effect_service:0' shape=() dtype=float32, numpy=0.0\u003e,\n",
              " \u003ctf.Variable 'intercept:0' shape=() dtype=float32, numpy=0.0\u003e,\n",
              " \u003ctf.Variable 'stddev_departments:0' shape=() dtype=float32, numpy=0.0\u003e,\n",
              " \u003ctf.Variable 'stddev_instructors:0' shape=() dtype=float32, numpy=0.0\u003e,\n",
              " \u003ctf.Variable 'stddev_students:0' shape=() dtype=float32, numpy=0.0\u003e)"
            ]
          },
          "execution_count": 0,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "lmm_train.trainable_variables"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3G_0t3jiZps2"
      },
      "source": [
        "As a Probabilistic graphical program, we can also visualize the model's structure in terms of its computational graph. This graph encodes dataflow across the random variables in the program, making explicit their relationships in terms of a graphical model (Jordan, 2003).\n",
        "\n",
        "As a statistical tool, we might look at the graph in order to better see, for example, that `intercept` and `effect_service` are conditionally dependent given `ratings`; this may be harder to see from the source code if the program is written with classes, cross references across modules, and/or subroutines. As a computational tool, we might also notice latent variables flow into the `ratings` variable via `tf.gather` ops. This may be a bottleneck on certain hardware accelerators if indexing `Tensor`s is expensive; visualizing the graph makes this readily apparent.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ox_kZeuOygqn"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(('effect_students', ()),\n",
              " ('effect_instructors', ()),\n",
              " ('effect_departments', ()),\n",
              " ('x', ('effect_departments', 'effect_instructors', 'effect_students')))"
            ]
          },
          "execution_count": 0,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "lmm_train.resolve_graph()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZPZTWsCeB7Va"
      },
      "source": [
        "## Parameter Estimation\n",
        "\n",
        "Given data, the goal of inference is to fit the model's fixed effects slope $\\beta$, intercept $\\alpha$, and variance component parameter $\\sigma^2$. The maximum likelihood principle formalizes this task as\n",
        "\n",
        "$$\n",
        "\\max_{\\beta, \\alpha, \\sigma}~\\log p(\\mathbf{y}\\mid \\mathbf{X}, \\mathbf{Z}; \\beta, \\alpha, \\sigma) = \\max_{\\beta, \\alpha, \\sigma}~\\log \\int p(\\eta; \\sigma) ~p(\\mathbf{y}\\mid \\mathbf{X}, \\mathbf{Z}, \\eta; \\beta, \\alpha)~d\\eta.\n",
        "$$\n",
        "\n",
        "In this tutorial, we use the Monte Carlo EM algorithm to maximize this marginal density (Dempster et al., 1977; Wei and Tanner, 1990).¹ We perform Markov chain Monte Carlo to compute the expectation of the conditional likelihood with respect to the random effects (\"E-step\"), and we perform gradient descent to maximize the expectation with respect to the parameters (\"M-step\"):\n",
        "\n",
        "+ For the E-step, we set up Hamiltonian Monte Carlo (HMC). It takes a current state—the student, instructor, and department effects—and returns a new state. We assign the new state to TensorFlow variables, which will denote the state of the HMC chain.\n",
        "\n",
        "+ For the M-step, we use the posterior sample from HMC to calculate an unbiased estimate of the marginal likelihood up to a constant. We then apply its gradient with respect to the parameters of interest. This produces an unbiased stochastic descent step on the marginal likelihood. We implement it with the Adam TensorFlow optimizer and minimize the negative of the marginal."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "U1Ro35iA7UPG"
      },
      "outputs": [],
      "source": [
        "target_log_prob_fn = lambda *x: lmm_train.log_prob(x + (labels_train,))\n",
        "trainable_variables = lmm_train.trainable_variables\n",
        "current_state = lmm_train.sample()[:-1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QQxq9acQ9MpO"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "\u003ctf.Tensor: shape=(), dtype=float32, numpy=-485996.53\u003e"
            ]
          },
          "execution_count": 0,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# For debugging\n",
        "target_log_prob_fn(*current_state)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "F7uOcwQFB7Vb"
      },
      "outputs": [],
      "source": [
        "# Set up E-step (MCMC).\n",
        "hmc = tfp.mcmc.HamiltonianMonteCarlo(\n",
        "    target_log_prob_fn=target_log_prob_fn,\n",
        "    step_size=0.015,\n",
        "    num_leapfrog_steps=3)\n",
        "kernel_results = hmc.bootstrap_results(current_state)\n",
        "\n",
        "@tf.function(autograph=False, jit_compile=True)\n",
        "def one_e_step(current_state, kernel_results):\n",
        "  next_state, next_kernel_results = hmc.one_step(\n",
        "      current_state=current_state,\n",
        "      previous_kernel_results=kernel_results)\n",
        "  return next_state, next_kernel_results\n",
        "\n",
        "optimizer = tf_keras.optimizers.Adam(learning_rate=.01)\n",
        "\n",
        "# Set up M-step (gradient descent).\n",
        "@tf.function(autograph=False, jit_compile=True)\n",
        "def one_m_step(current_state):\n",
        "  with tf.GradientTape() as tape:\n",
        "    loss = -target_log_prob_fn(*current_state)\n",
        "  grads = tape.gradient(loss, trainable_variables)\n",
        "  optimizer.apply_gradients(zip(grads, trainable_variables))\n",
        "  return loss"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6BaHczzpkt0k"
      },
      "source": [
        "We perform a warm-up stage, which runs one MCMC chain for a number of iterations so that training may be initialized within the posterior's probability mass. We then run a training loop. It jointly runs the E and M-steps and records values during training."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XwZMt2uqVDzh"
      },
      "outputs": [],
      "source": [
        "num_warmup_iters = 1000\n",
        "num_iters = 1500\n",
        "num_accepted = 0\n",
        "effect_students_samples = np.zeros([num_iters, num_students])\n",
        "effect_instructors_samples = np.zeros([num_iters, num_instructors])\n",
        "effect_departments_samples = np.zeros([num_iters, num_departments])\n",
        "loss_history = np.zeros([num_iters])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zxbcYtrUt3OG"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Warm-Up Iteration:   0 Acceptance Rate: 1.000\n",
            "Warm-Up Iteration: 500 Acceptance Rate: 0.758\n",
            "Warm-Up Iteration: 999 Acceptance Rate: 0.729\n",
            "Iteration:    0 Acceptance Rate: 1.000 Loss: 98200.422\n",
            "Iteration:  500 Acceptance Rate: 0.649 Loss: 98190.469\n",
            "Iteration: 1000 Acceptance Rate: 0.656 Loss: 98068.664\n",
            "Iteration: 1499 Acceptance Rate: 0.663 Loss: 98155.070\n"
          ]
        }
      ],
      "source": [
        "# Run warm-up stage.\n",
        "for t in range(num_warmup_iters):\n",
        "  current_state, kernel_results = one_e_step(current_state, kernel_results)\n",
        "  num_accepted += kernel_results.is_accepted.numpy()\n",
        "  if t % 500 == 0 or t == num_warmup_iters - 1:\n",
        "    print(\"Warm-Up Iteration: {:\u003e3} Acceptance Rate: {:.3f}\".format(\n",
        "        t, num_accepted / (t + 1)))\n",
        "\n",
        "num_accepted = 0  # reset acceptance rate counter\n",
        "\n",
        "# Run training.\n",
        "for t in range(num_iters):\n",
        "  # run 5 MCMC iterations before every joint EM update\n",
        "  for _ in range(5):\n",
        "    current_state, kernel_results = one_e_step(current_state, kernel_results)\n",
        "  loss = one_m_step(current_state)\n",
        "  effect_students_samples[t, :] = current_state[0].numpy()\n",
        "  effect_instructors_samples[t, :] = current_state[1].numpy()\n",
        "  effect_departments_samples[t, :] = current_state[2].numpy()\n",
        "  num_accepted += kernel_results.is_accepted.numpy()\n",
        "  loss_history[t] = loss.numpy()\n",
        "  if t % 500 == 0 or t == num_iters - 1:\n",
        "    print(\"Iteration: {:\u003e4} Acceptance Rate: {:.3f} Loss: {:.3f}\".format(\n",
        "        t, num_accepted / (t + 1), loss_history[t]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gyo91h1oVx_L"
      },
      "source": [
        "You can also write the warmup for-loop into a `tf.while_loop`, and the training step into a `tf.scan` or `tf.while_loop` for even faster inference. For example:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9WmwCZNQWqh7"
      },
      "outputs": [],
      "source": [
        "@tf.function(autograph=False, jit_compile=True)\n",
        "def run_k_e_steps(k, current_state, kernel_results):\n",
        "  _, next_state, next_kernel_results = tf.while_loop(\n",
        "      cond=lambda i, state, pkr: i \u003c k,\n",
        "      body=lambda i, state, pkr: (i+1, *one_e_step(state, pkr)),\n",
        "      loop_vars=(tf.constant(0), current_state, kernel_results)\n",
        "  )\n",
        "  return next_state, next_kernel_results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r6U2zkdbHj5z"
      },
      "source": [
        "Above, we did not run the algorithm until a convergence threshold was detected. To check whether training was sensible, we verify that the loss function indeed tends to converge over training iterations."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HR4A6FLCwD7b"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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FEAKLwL+Ib7FjiBJKdBUEfsJnwj95/7/hP/Z9Bwhh2jlC2BMIBF0EIbAI/IvPJlnJBPms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zDs3unufMC9lkdNpI/p+hpppgQISQT6b5LXPiz6vszTtimsYfG85s3NzVAUBW1tbZahiltbW9Hc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\nugghpY2dNYmIiALMod26EPIZlqY206lcMBoR9uzvIZpb2D+hDRLueQjiP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Z5QXl6O1atX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\n0i0GLEREREREpFsMWIiIiIiISLf+P0eL0kmdlHXcAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "\u003cFigure size 600x400 with 1 Axes\u003e"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 266,
              "width": 406
            }
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.plot(loss_history)\n",
        "plt.ylabel(r'Loss $-\\log$ $p(y\\mid\\mathbf{x})$')\n",
        "plt.xlabel('Iteration')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fz7FphO9LwVE"
      },
      "source": [
        "We also use a trace plot, which shows the Markov chain Monte Carlo algorithm's trajectory across specific latent dimensions. Below we see that specific instructor effects indeed meaningfully transition away from their initial state and explore the state space. The trace plot also indicates that the effects differ across instructors but with similar mixing behavior."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_NvaIhgrvY9o"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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EC1wjk5f5ULtozD/Bv/Czcd97U41KAn893Xpdtm1KAdQpa8mGF16p0G0veN8I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\nphoFw6AG9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3NNWQXn2N//I+94aCGUkqFZ44hwTWH5XEe9+CgR6DAYo1bJk2Canl4R0a44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CYZ4L5AiG63hIijN2SPImQjSPR4UWR/cGWB7onUMqM9CYm0KfxzQKdyy+uwAcFGUlipMUnNT1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lO90HUdsVgM3d3daG9vz/2u/riKJlamlvpcaAYDEPD+O9+HsbExVFZWOjoYItFydYAV3fux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\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3uEY+LBYL7rnnHlx33XUAgHfeeQcOh0P2OElJSXjsscfQv39/AIBWq0VxcTGmTp0KANiy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\n",
            "text/plain": [
              "\u003cFigure size 600x400 with 1 Axes\u003e"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 264,
              "width": 396
            }
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "for i in range(7):\n",
        "  plt.plot(effect_instructors_samples[:, i])\n",
        "\n",
        "plt.legend([i for i in range(7)], loc='lower right')\n",
        "plt.ylabel('Instructor Effects')\n",
        "plt.xlabel('Iteration')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-xVCGWZoB7Vd"
      },
      "source": [
        "## Criticism\n",
        "\n",
        "Above, we fitted the model. We now look into criticizing its fit using data, which lets us explore and better understand the model. One such technique is a residual plot, which plots the difference between the model's predictions and ground truth for each data point. If the model were correct, then their difference should be standard normally distributed; any deviations from this pattern in the plot indicate model misfit.\n",
        "\n",
        "We build the residual plot by first forming the posterior predictive distribution over ratings, which replaces the prior distribution on the random effects with its posterior given training data. In particular, we run the model forward and intercept its dependence on prior random effects with their inferred posterior means.²"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "p4vreJekB7Vf"
      },
      "outputs": [],
      "source": [
        "lmm_test = lmm_jointdist(features_test)\n",
        "\n",
        "[\n",
        "    effect_students_mean,\n",
        "    effect_instructors_mean,\n",
        "    effect_departments_mean,\n",
        "] = [\n",
        "     np.mean(x, axis=0).astype(np.float32) for x in [\n",
        "       effect_students_samples,\n",
        "       effect_instructors_samples,\n",
        "       effect_departments_samples\n",
        "       ]\n",
        "]\n",
        "\n",
        "# Get the posterior predictive distribution\n",
        "(*posterior_conditionals, ratings_posterior), _ = lmm_test.sample_distributions(\n",
        "    value=(\n",
        "        effect_students_mean,\n",
        "        effect_instructors_mean,\n",
        "        effect_departments_mean,\n",
        "))\n",
        "\n",
        "ratings_prediction = ratings_posterior.mean()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zTQJ3d-Hv93z"
      },
      "source": [
        "Upon visual inspection, the residuals look somewhat standard-normally distributed. However, the fit is not perfect: there is larger probability mass in the tails than a normal distribution, which indicates the model might improve its fit by relaxing its normality assumptions.\n",
        "\n",
        "In particular, although it is most common to use a normal distribution to model ratings in the `InstEval` data set, a closer look at the data reveals that course evaluation ratings are in fact ordinal values from 1 to 5. This suggests that we should be using an ordinal distribution, or even Categorical if we have enough data to throw away the relative ordering. This is a one-line change to the model above; the same inference code is applicable."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0jIxfwuvEWLG"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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uqKjIGDRokG1/zZo1MyZPnmx8/fXXxrZt24wNGzYYS5YsMf7617/aHjL1/vvv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\nhkMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "\u003cFigure size 600x400 with 1 Axes\u003e"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 265,
              "width": 378
            }
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.title(\"Residuals for Predicted Ratings on Test Set\")\n",
        "plt.xlim(-4, 4)\n",
        "plt.ylim(0, 800)\n",
        "plt.hist(ratings_prediction - labels_test, 75)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wi4hnI8UxFD2"
      },
      "source": [
        "To explore how the model makes individual predictions, we look at the histogram of effects for students, instructors, and departments. This lets us understand how individual elements in a data point's feature vector tends to influence the outcome.\n",
        "\n",
        "Not surprisingly, we see below that each student typically has little effect on an instructor's evaluation rating. Interestingly, we see that the department an instructor belongs to has a large effect."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MU-L604RFkxg"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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apfDwcL3//vtKSkrSmTNnyt3k365dOyUlJemrr77Spk2bdODAAWVmZurs2bNq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\nAElFTkSuQmCC\n",
            "text/plain": [
              "\u003cFigure size 600x400 with 1 Axes\u003e"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 265,
              "width": 375
            }
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.title(\"Histogram of Student Effects\")\n",
        "plt.hist(effect_students_mean, 75)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "22qgTW7SGulD"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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1pMOHD9fqe2z5e8wZtmzZYv36/vvvr/KtJOX/vfLKK5KufF/96aefrNuWfc7S/4K6s7jq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\nAACG/X94yYtSiyUChAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "\u003cFigure size 600x400 with 1 Axes\u003e"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 265,
              "width": 368
            }
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.title(\"Histogram of Instructor Effects\")\n",
        "plt.hist(effect_instructors_mean, 75)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lTd2_uodGu2F"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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9evXSiBEjmG5jci6GYbIHQQNwiI0bN1pvQJw9e7aGDRtWzT1CTTJlyhRNnTpV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\nTIxADwAAAJjY/wP/4Y6PDE7c+AAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "\u003cFigure size 600x400 with 1 Axes\u003e"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 265,
              "width": 378
            }
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.title(\"Histogram of Department Effects\")\n",
        "plt.hist(effect_departments_mean, 75)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ck3cPwIjvyqO"
      },
      "source": [
        "## Footnotes\n",
        "\n",
        "¹ Linear mixed effect models are a special case where we can analytically compute its marginal density. For the purposes of this tutorial, we demonstrate Monte Carlo EM, which more readily applies to non-analytic marginal densities such as if the likelihood were extended to be Categorical instead of Normal.\n",
        "\n",
        "² For simplicity, we form the predictive distribution's mean using only one forward pass of the model. This is done by conditioning on the posterior mean and is valid for linear mixed effects models. However, this is not valid in general: the posterior predictive distribution's mean is typically intractable and requires taking the empirical mean across multiple forward passes of the model given posterior samples."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8pm6qMKvB7WB"
      },
      "source": [
        "## Acknowledgments\n",
        "\n",
        "This tutorial was originally written in Edward 1.0 ([source](https://github.com/blei-lab/edward/blob/master/notebooks/linear_mixed_effects_models.ipynb)). We thank all contributors to writing and revising that version."
      ]
    },
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      "source": [
        "## References\n",
        "\n",
        "1. Douglas Bates and Martin Machler and Ben Bolker and Steve Walker. Fitting Linear Mixed-Effects Models Using lme4. _Journal of Statistical Software_, 67(1):1-48, 2015.\n",
        "\n",
        "2. Arthur P. Dempster, Nan M. Laird, and Donald B. Rubin. Maximum likelihood from incomplete data via the EM algorithm. _Journal of the Royal Statistical Society, Series B (Methodological)_, 1-38, 1977.\n",
        "\n",
        "3. Andrew Gelman and Jennifer Hill. _Data analysis using regression and multilevel/hierarchical models._ Cambridge University Press, 2006.\n",
        "\n",
        "4. David A. Harville. Maximum likelihood approaches to variance component estimation and to related problems. _Journal of the American Statistical Association_, 72(358):320-338, 1977.\n",
        "\n",
        "5. Michael I. Jordan. An Introduction to Graphical Models. Technical Report, 2003.\n",
        "\n",
        "6. Nan M. Laird and James Ware. Random-effects models for longitudinal data. _Biometrics_, 963-974, 1982.\n",
        "\n",
        "7. Greg Wei and Martin A. Tanner. A Monte Carlo implementation of the EM algorithm and the poor man's data augmentation algorithms. _Journal of the American Statistical Association_, 699-704, 1990."
      ]
    }
  ],
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