{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Multilevel Gravity Survey with MLDA" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The MLDA sampler\n", "This notebook is designed to demonstrate the Multi-Level Delayed Acceptance MCMC algorithm (MLDA) proposed in Dodwell (2019), as implemented within PyMC3. If you are using MLDA for the first time, we recommend first running the `MLDA_simple_linear_regression.ipynb` notebook in the same folder.\n", "\n", "The MLDA sampler can be more efficient than other MCMC samplers when dealing with computationally intensive problems where we have access not only to the desired (fine) posterior distribution but also to a set of approximate (coarse) posteriors of decreasing accuracy and decreasing computational cost. In simple terms, we can use multiple chains on different coarseness levels and coarser chains' samples are used as proposals for the finer chains. This has been shown to improve the effective sample size of the finest chain and this allows us to reduce the number of expensive fine-chain likelihood evaluations. \n", "\n", "The notebook initially defines the necessary classes that describe the model. These classes use scipy to do the numerical solve in the forward model. It then instantiates models in two levels (with different granularities) and generates data for inference. Finally, the model classes are passed to two pymc3 models using Theano Ops and inference is done using three different MCMC methods (including MLDA). Some summary results and comparison plots are shown at the end to demonstrate the results. The use of Theano Ops is common when users want to use external code to calculate their likelihood (e.g. some fast PDE solver) and this example is designed to serve as a starting point for users to employ MLDA in their own problems.\n", "\n", "Please note that the MLDA sampler is new in PyMC3. The user should be extra critical about the results and report any problems as issues in the pymc3's github repository.\n", "\n", "The notebook results shown below were generated on a MacBook Pro with a 2.6 GHz 6-core Intel Core i7, 32 GB DDR4 and macOS 10.15.4.\n", "\n", "### Gravity Surveying\n", "In this notebook, we solve a 2-dimensional gravity surveying problem, adapted from the 1D problem presented in Hansen (2010). \n", "\n", "Our aim is to recover a two-dimensional mass distribution $f(\\vec{t})$ at a known depth $d$ below the surface from measurements $g(\\vec{s})$ of the vertical component of the gravitational field at the surface. The contribution to $g(\\vec{s})$ from infinitesimally small areas of the subsurface mass distribution are given by:\n", "\n", "\\begin{equation}\n", " dg = \\frac{\\sin \\theta}{r^2} f(\\vec{t}) \\: d\\vec{t}\n", "\\end{equation}\n", "where $\\theta$ is the angle between the vertical plane and a straight line between two points $f(\\vec{t})$ and $g(\\vec{s})$, and $r = | \\vec{s} - \\vec{t} |$ is the Eucledian distance between the points. We exploit that $\\sin \\theta = \\frac{d}{r}$, so that\n", "\n", "\\begin{equation}\n", " \\frac{\\sin \\theta}{r^2} f(\\vec{t}) \\: d\\vec{t} = \\frac{d}{r^3} f(\\vec{t}) \\: d\\vec{t} = \\frac{d}{ | \\vec{s} - \\vec{t} |^3} f(\\vec{t}) \\: d\\vec{t}\n", "\\end{equation}\n", "\n", "This yields the integral equation,\n", "\n", "\\begin{equation}\n", " g(\\vec{s}) = \\iint_T \\frac{d}{ | \\vec{s} - \\vec{t} |^3} f(\\vec{t}) \\: d\\vec{t}\n", "\\end{equation}\n", "\n", "where $T = [0,1]^2$ is the domain of the function $f(\\vec{t})$. This constitutes our forward model.\n", "\n", "We solve this integral numerically using midpoint quadrature. For simplicity, we use the same number of quadrature points along each axis, so that in discrete form our forward model becomes\n", "\n", "\\begin{equation}\n", " g(\\vec{s}_i) = \\sum_{j=1}^{m} \\omega_j \\frac{d}{ | \\vec{s}_i - \\vec{t}_j |^3} \\hat{f}(\\vec{t}_j), \\quad i = 1, \\dots, n, \\quad j = 1, \\dots, m\n", "\\end{equation}\n", "\n", "where $\\omega_j = \\frac{1}{m}$ are quadrature weights, $\\hat{f}(\\vec{t}_j)$ is the approximate subsurface mass at quadrature points $j = 1, \\dots, m$, and $g(\\vec{s}_i)$ is surface measurements at collocation points $i = 1, \\dots, n$. Hence when $n > m$, we are dealing with an overdetermined problem and vice versa. \n", "\n", "This results in a linear system $\\mathbf{Ax = b}$, where\n", "\\begin{equation}\n", " a_{ij} = \\omega_j \\frac{d}{ | \\vec{s}_i - \\vec{t}_j |^3}, \\quad x_j = \\hat{f}(\\vec{t}_j), \\quad b_i = g(\\vec{s}_i).\n", "\\end{equation}\n", "In this particular problem, the matrix $\\mathbf{A}$ has a very high condition number, leading to an ill-posed inverse problem, which entails numerical instability and spurious, often oscillatory, solutions for noisy right hand sides. These types of problems are traditionally solved by way of some manner of *regularisation*, but they can be handled in a natural and elegant fashion in the context of a Bayesian inverse problem.\n", "\n", "### Mass Distribution as a Gaussian Random Process\n", "We model the unknown mass distribution as a Gaussian Random Process with a Matern 5/2 covariance kernel (Rasmussen and Williams, 2006):\n", "\\begin{equation}\n", " C_{5/2}(\\vec{t}, \\vec{t}') = \\sigma^2 \\left( 1 + \\frac{\\sqrt{5} | \\vec{t}-\\vec{t}' | }{l} + \\frac{5 | \\vec{t}-\\vec{t}' |^2}{3l^2} \\right) \\exp \\left( - \\frac{\\sqrt{5} | \\vec{t}-\\vec{t}' | }{l} \\right)\n", "\\end{equation}\n", "where $l$ is the covariance length scale and $\\sigma^2$ is the variance.\n", "\n", "### Comparison\n", "Within this notebook, a simple MLDA sampler is compared to a Metropolis and a DEMetropolisZ sampler. The example demonstrates that MLDA is more efficient than the other samplers when measured by the Effective Samples per Second they can generate from the posterior. \n", "\n", "### References\n", "\n", "Dodwell, Tim & Ketelsen, Chris & Scheichl, Robert & Teckentrup, Aretha. (2019). Multilevel Markov Chain Monte Carlo. SIAM Review. 61. 509-545. https://doi.org/10.1137/19M126966X\n", "\n", "Per Christian Hansen. *Discrete Inverse Problems: Insight and Algorithms*. Society for Industrial and Applied Mathematics, January 2010.\n", "\n", "Carl Edward Rasmussen and Christopher K. I. Williams. *Gaussian processes for machine learning*. Adaptive computation and machine learning. 2006." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Import modules" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os as os\n", "import warnings\n", "\n", "os.environ[\"OPENBLAS_NUM_THREADS\"] = \"1\" # Set environment variable\n", "\n", "import sys as sys\n", "import time as time\n", "\n", "import arviz as az\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "import pymc3 as pm\n", "import theano\n", "import theano.tensor as tt\n", "\n", "from numpy.linalg import inv\n", "from scipy.interpolate import RectBivariateSpline\n", "from scipy.linalg import eigh\n", "from scipy.spatial import distance_matrix" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "warnings.simplefilter(action=\"ignore\", category=FutureWarning)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "RANDOM_SEED = 123446\n", "np.random.seed(RANDOM_SEED)\n", "az.style.use(\"arviz-darkgrid\")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Theano version: 1.1.2\n", "PyMC3 version: 3.11.0\n" ] } ], "source": [ "# Checking versions\n", "print(f\"Theano version: {theano.__version__}\")\n", "print(f\"PyMC3 version: {pm.__version__}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Define Matern52 kernel for modelling Gaussian Random Field\n", "This is utility code which is necessary for defining the model later - you are free to ignore it or place it in an external file" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "class SquaredExponential:\n", " def __init__(self, coords, mkl, lamb):\n", " \"\"\"\n", " This class sets up a random process\n", " on a grid and generates\n", " a realisation of the process, given\n", " parameters or a random vector.\n", " \"\"\"\n", "\n", " # Internalise the grid and set number of vertices.\n", " self.coords = coords\n", " self.n_points = self.coords.shape[0]\n", " self.eigenvalues = None\n", " self.eigenvectors = None\n", " self.parameters = None\n", " self.random_field = None\n", "\n", " # Set some random field parameters.\n", " self.mkl = mkl\n", " self.lamb = lamb\n", "\n", " self.assemble_covariance_matrix()\n", "\n", " def assemble_covariance_matrix(self):\n", " \"\"\"\n", " Create a snazzy distance-matrix for rapid\n", " computation of the covariance matrix.\n", " \"\"\"\n", " dist = distance_matrix(self.coords, self.coords)\n", "\n", " # Compute the covariance between all\n", " # points in the space.\n", " self.cov = np.exp(-0.5 * dist**2 / self.lamb**2)\n", "\n", " def plot_covariance_matrix(self):\n", " \"\"\"\n", " Plot the covariance matrix.\n", " \"\"\"\n", " plt.figure(figsize=(10, 8))\n", " plt.imshow(self.cov, cmap=\"binary\")\n", " plt.colorbar()\n", " plt.show()\n", "\n", " def compute_eigenpairs(self):\n", " \"\"\"\n", " Find eigenvalues and eigenvectors using Arnoldi iteration.\n", " \"\"\"\n", " eigvals, eigvecs = eigh(self.cov, eigvals=(self.n_points - self.mkl, self.n_points - 1))\n", "\n", " order = np.flip(np.argsort(eigvals))\n", " self.eigenvalues = eigvals[order]\n", " self.eigenvectors = eigvecs[:, order]\n", "\n", " def generate(self, parameters=None):\n", " \"\"\"\n", " Generate a random field, see\n", " Scarth, C., Adhikari, S., Cabral, P. H.,\n", " Silva, G. H. C., & Prado, A. P. do. (2019).\n", " Random field simulation over curved surfaces:\n", " Applications to computational structural mechanics.\n", " Computer Methods in Applied Mechanics and Engineering,\n", " 345, 283–301. https://doi.org/10.1016/j.cma.2018.10.026\n", " \"\"\"\n", "\n", " if parameters is None:\n", " self.parameters = np.random.normal(size=self.mkl)\n", " else:\n", " self.parameters = np.array(parameters).flatten()\n", "\n", " self.random_field = np.linalg.multi_dot(\n", " (self.eigenvectors, np.sqrt(np.diag(self.eigenvalues)), self.parameters)\n", " )\n", "\n", " def plot(self, lognormal=True):\n", " \"\"\"\n", " Plot the random field.\n", " \"\"\"\n", "\n", " if lognormal:\n", " random_field = self.random_field\n", " contour_levels = np.linspace(min(random_field), max(random_field), 20)\n", " else:\n", " random_field = np.exp(self.random_field)\n", " contour_levels = np.linspace(min(random_field), max(random_field), 20)\n", "\n", " plt.figure(figsize=(12, 10))\n", " plt.tricontourf(\n", " self.coords[:, 0],\n", " self.coords[:, 1],\n", " random_field,\n", " levels=contour_levels,\n", " cmap=\"plasma\",\n", " )\n", " plt.colorbar()\n", " plt.show()\n", "\n", "\n", "class Matern52(SquaredExponential):\n", " def assemble_covariance_matrix(self):\n", " \"\"\"\n", " This class inherits from RandomProcess and creates a Matern 5/2 covariance matrix.\n", " \"\"\"\n", "\n", " # Compute scaled distances.\n", " dist = np.sqrt(5) * distance_matrix(self.coords, self.coords) / self.lamb\n", "\n", " # Set up Matern 5/2 covariance matrix.\n", " self.cov = (1 + dist + dist**2 / 3) * np.exp(-dist)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Define the Gravity model and generate data\n", "This is a bit lengthy due to the model used in this case, it contains class definitions and also instantiation of class objects and data generation." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# Set the model parameters.\n", "depth = 0.1\n", "n_quad = 64\n", "n_data = 64\n", "\n", "# noise level\n", "noise_level = 0.02\n", "\n", "# Set random process parameters.\n", "lamb = 0.1\n", "mkl = 14\n", "\n", "# Set the quadrature degree for each model level (coarsest first)\n", "n_quadrature = [16, 64]" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "class Gravity:\n", " \"\"\"\n", " Gravity is a class that implements a simple gravity surveying problem,\n", " as described in Hansen, P. C. (2010). Discrete Inverse Problems: Insight and Algorithms.\n", " Society for Industrial and Applied Mathematics.\n", " It uses midpoint quadrature to evaluate a Fredholm integral of the first kind.\n", " \"\"\"\n", "\n", " def __init__(self, f_function, depth, n_quad, n_data):\n", "\n", " # Set the function describing the distribution of subsurface density.\n", " self.f_function = f_function\n", "\n", " # Set the depth of the density (distance to the surface measurements).\n", " self.depth = depth\n", "\n", " # Set the quadrature degree along one dimension.\n", " self.n_quad = n_quad\n", "\n", " # Set the number of data points along one dimension\n", " self.n_data = n_data\n", "\n", " # Set the quadrature points.\n", " x = np.linspace(0, 1, self.n_quad + 1)\n", " self.tx = (x[1:] + x[:-1]) / 2\n", " y = np.linspace(0, 1, self.n_quad + 1)\n", " self.ty = (y[1:] + y[:-1]) / 2\n", " TX, TY = np.meshgrid(self.tx, self.ty)\n", "\n", " # Set the measurement points.\n", " self.sx = np.linspace(0, 1, self.n_data)\n", " self.sy = np.linspace(0, 1, self.n_data)\n", " SX, SY = np.meshgrid(self.sx, self.sy)\n", "\n", " # Create coordinate vectors.\n", " self.T_coords = np.c_[TX.ravel(), TY.ravel(), np.zeros(self.n_quad**2)]\n", " self.S_coords = np.c_[SX.ravel(), SY.ravel(), self.depth * np.ones(self.n_data**2)]\n", "\n", " # Set the quadrature weights.\n", " self.w = 1 / self.n_quad**2\n", "\n", " # Compute a distance matrix\n", " dist = distance_matrix(self.S_coords, self.T_coords)\n", "\n", " # Create the Fremholm kernel.\n", " self.K = self.w * self.depth / dist**3\n", "\n", " # Evaluate the density function on the quadrature points.\n", " self.f = self.f_function(TX, TY).flatten()\n", "\n", " # Compute the surface density (noiseless measurements)\n", " self.g = np.dot(self.K, self.f)\n", "\n", " def plot_model(self):\n", "\n", " # Plot the density and the signal.\n", " fig, axes = plt.subplots(1, 2, figsize=(16, 6))\n", " axes[0].set_title(\"Density\")\n", " f = axes[0].imshow(\n", " self.f.reshape(self.n_quad, self.n_quad),\n", " extent=(0, 1, 0, 1),\n", " origin=\"lower\",\n", " cmap=\"plasma\",\n", " )\n", " fig.colorbar(f, ax=axes[0])\n", " axes[1].set_title(\"Signal\")\n", " g = axes[1].imshow(\n", " self.g.reshape(self.n_data, self.n_data),\n", " extent=(0, 1, 0, 1),\n", " origin=\"lower\",\n", " cmap=\"plasma\",\n", " )\n", " fig.colorbar(g, ax=axes[1])\n", " plt.show()\n", "\n", " def plot_kernel(self):\n", "\n", " # Plot the kernel.\n", " plt.figure(figsize=(8, 6))\n", " plt.imshow(self.K, cmap=\"plasma\")\n", " plt.colorbar()\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# This is a function describing the subsurface density.\n", "def f(TX, TY):\n", " f = np.sin(np.pi * TX) + np.sin(3 * np.pi * TY) + TY + 1\n", " f = f / f.max()\n", " return f" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# Initialise a model\n", "model_true = Gravity(f, depth, n_quad, n_data)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Add noise to the data.\n", "np.random.seed(123)\n", "noise = np.random.normal(0, noise_level, n_data**2)\n", "data = model_true.g + noise\n", "\n", "# Plot the density and the signal.\n", "fig, axes = plt.subplots(1, 2, figsize=(16, 6))\n", "axes[0].set_title(\"Noiseless Signal\")\n", "g = axes[0].imshow(\n", " model_true.g.reshape(n_data, n_data),\n", " extent=(0, 1, 0, 1),\n", " origin=\"lower\",\n", " cmap=\"plasma\",\n", ")\n", "fig.colorbar(g, ax=axes[0])\n", "axes[1].set_title(\"Noisy Signal\")\n", "d = axes[1].imshow(data.reshape(n_data, n_data), extent=(0, 1, 0, 1), origin=\"lower\", cmap=\"plasma\")\n", "fig.colorbar(d, ax=axes[1])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "class Gravity_Forward(Gravity):\n", " \"\"\"\n", " Gravity forward is a class that implements the gravity problem,\n", " but computation of signal and density is delayed to the \"solve\"\n", " method, since it relied on a Gaussian Random Field to model\n", " the (unknown) density.\n", " \"\"\"\n", "\n", " def __init__(self, depth, n_quad, n_data):\n", "\n", " # Set the depth of the density (distance to the surface measurements).\n", " self.depth = depth\n", "\n", " # Set the quadrature degree along one axis.\n", " self.n_quad = n_quad\n", "\n", " # Set the number of data points along one axis.\n", " self.n_data = n_data\n", "\n", " # Set the quadrature points.\n", " x = np.linspace(0, 1, self.n_quad + 1)\n", " self.tx = (x[1:] + x[:-1]) / 2\n", " y = np.linspace(0, 1, self.n_quad + 1)\n", " self.ty = (y[1:] + y[:-1]) / 2\n", " TX, TY = np.meshgrid(self.tx, self.ty)\n", "\n", " # Set the measurement points.\n", " self.sx = np.linspace(0, 1, self.n_data)\n", " self.sy = np.linspace(0, 1, self.n_data)\n", " SX, SY = np.meshgrid(self.sx, self.sy)\n", "\n", " # Create coordinate vectors.\n", " self.T_coords = np.c_[TX.ravel(), TY.ravel(), np.zeros(self.n_quad**2)]\n", " self.S_coords = np.c_[SX.ravel(), SY.ravel(), self.depth * np.ones(self.n_data**2)]\n", "\n", " # Set the quadrature weights.\n", " self.w = 1 / self.n_quad**2\n", "\n", " # Compute a distance matrix\n", " dist = distance_matrix(self.S_coords, self.T_coords)\n", "\n", " # Create the Fremholm kernel.\n", " self.K = self.w * self.depth / dist**3\n", "\n", " def set_random_process(self, random_process, lamb, mkl):\n", "\n", " # Set the number of KL modes.\n", " self.mkl = mkl\n", "\n", " # Initialise a random process on the quadrature points.\n", " # and compute the eigenpairs of the covariance matrix,\n", " self.random_process = random_process(self.T_coords, self.mkl, lamb)\n", " self.random_process.compute_eigenpairs()\n", "\n", " def solve(self, parameters):\n", "\n", " # Internalise the Random Field parameters\n", " self.parameters = parameters\n", "\n", " # Create a realisation of the random process, given the parameters.\n", " self.random_process.generate(self.parameters)\n", " mean = 0.0\n", " stdev = 1.0\n", "\n", " # Set the density.\n", " self.f = mean + stdev * self.random_process.random_field\n", "\n", " # Compute the signal.\n", " self.g = np.dot(self.K, self.f)\n", "\n", " def get_data(self):\n", "\n", " # Get the data vector.\n", " return self.g" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "# We project the eigenmodes of the fine model to the quadrature points\n", "# of the coarse model using linear interpolation.\n", "def project_eigenmodes(model_coarse, model_fine):\n", " model_coarse.random_process.eigenvalues = model_fine.random_process.eigenvalues\n", " for i in range(model_coarse.mkl):\n", " interpolator = RectBivariateSpline(\n", " model_fine.tx,\n", " model_fine.ty,\n", " model_fine.random_process.eigenvectors[:, i].reshape(\n", " model_fine.n_quad, model_fine.n_quad\n", " ),\n", " )\n", " model_coarse.random_process.eigenvectors[:, i] = interpolator(\n", " model_coarse.tx, model_coarse.ty\n", " ).ravel()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "# Initialise the models, according the quadrature degree.\n", "my_models = []\n", "for i, n_quad in enumerate(n_quadrature):\n", " my_models.append(Gravity_Forward(depth, n_quad, n_data))\n", " my_models[i].set_random_process(Matern52, lamb, mkl)\n", "\n", "# Project the eigenmodes of the fine model to the coarse model.\n", "for m in my_models[:-1]:\n", " project_eigenmodes(m, my_models[-1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Solve and plot models to demonstrate coarse/fine difference" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Level 0:\n" ] }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot the same random realisation for each level, and the corresponding signal,\n", "# to validate that the levels are equivalents.\n", "for i, m in enumerate(my_models):\n", " print(f\"Level {i}:\")\n", " np.random.seed(2)\n", " m.solve(np.random.normal(size=mkl))\n", " m.plot_model()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.title(f\"Largest {mkl} KL eigenvalues of GP prior\")\n", "plt.plot(my_models[-1].random_process.eigenvalues)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Compare computation cost of coarse and fine model solve\n", "The bigger the difference in time, the more MLDA has potential to increase efficiency" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "259 µs ± 3.7 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n" ] } ], "source": [ "%%timeit\n", "my_models[0].solve(np.random.normal(size=mkl))" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5.4 ms ± 122 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" ] } ], "source": [ "%%timeit\n", "my_models[-1].solve(np.random.normal(size=mkl))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Set MCMC parameters for inference" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "# Number of draws from the distribution\n", "ndraws = 15000\n", "\n", "# Number of burn-in samples\n", "nburn = 10000\n", "\n", "# MLDA and Metropolis tuning parameters\n", "tune = True\n", "tune_interval = 100 # Set high to prevent tuning.\n", "discard_tuning = True\n", "\n", "# Number of independent chains.\n", "nchains = 3\n", "\n", "# Subsampling rate for MLDA\n", "nsub = 5\n", "\n", "# Set prior parameters for multivariate Gaussian prior distribution.\n", "mu_prior = np.zeros(mkl)\n", "cov_prior = np.eye(mkl)\n", "\n", "# Set the sigma for inference.\n", "sigma = 1.0\n", "\n", "# Sampling seed\n", "sampling_seed = RANDOM_SEED" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Define a Theano Op for the likelihood\n", "This creates the theano op needed to pass the above model to the PyMC3 sampler" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "def my_loglik(my_model, theta, data, sigma):\n", " \"\"\"\n", " This returns the log-likelihood of my_model given theta,\n", " datapoints, the observed data and sigma. It uses the\n", " model_wrapper function to do a model solve.\n", " \"\"\"\n", " my_model.solve(theta)\n", " output = my_model.get_data()\n", " return -(0.5 / sigma**2) * np.sum((output - data) ** 2)\n", "\n", "\n", "class LogLike(tt.Op):\n", " \"\"\"\n", " Theano Op that wraps the log-likelihood computation, necessary to\n", " pass \"black-box\" code into pymc3.\n", " Based on the work in:\n", " https://docs.pymc.io/notebooks/blackbox_external_likelihood.html\n", " https://docs.pymc.io/Advanced_usage_of_Theano_in_PyMC3.html\n", " \"\"\"\n", "\n", " # Specify what type of object will be passed and returned to the Op when it is\n", " # called. In our case we will be passing it a vector of values (the parameters\n", " # that define our model and a model object) and returning a single \"scalar\"\n", " # value (the log-likelihood)\n", " itypes = [tt.dvector] # expects a vector of parameter values when called\n", " otypes = [tt.dscalar] # outputs a single scalar value (the log likelihood)\n", "\n", " def __init__(self, my_model, loglike, data, sigma):\n", " \"\"\"\n", " Initialise the Op with various things that our log-likelihood function\n", " requires. Below are the things that are needed in this particular\n", " example.\n", "\n", " Parameters\n", " ----------\n", " my_model:\n", " A Model object (defined in model.py) that contains the parameters\n", " and functions of out model.\n", " loglike:\n", " The log-likelihood function we've defined, in this example it is\n", " my_loglik.\n", " data:\n", " The \"observed\" data that our log-likelihood function takes in. These\n", " are the true data generated by the finest model in this example.\n", " x:\n", " The dependent variable (aka 'x') that our model requires. This is\n", " the datapoints in this example.\n", " sigma:\n", " The noise standard deviation that our function requires.\n", " \"\"\"\n", " # add inputs as class attributes\n", " self.my_model = my_model\n", " self.likelihood = loglike\n", " self.data = data\n", " self.sigma = sigma\n", "\n", " def perform(self, node, inputs, outputs):\n", " # the method that is used when calling the Op\n", " theta = inputs # this will contain my variables\n", "\n", " # call the log-likelihood function\n", " logl = self.likelihood(self.my_model, theta, self.data, self.sigma)\n", "\n", " outputs[0][0] = np.array(logl) # output the log-likelihood" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "# create Theano Ops to wrap likelihoods of all model levels and store them in list\n", "logl = []\n", "for i, m_i in enumerate(my_models):\n", " logl.append(LogLike(m_i, my_loglik, data, sigma))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create coarse model in PyMC3" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "# Set up models in pymc3 for each level - excluding finest model level\n", "coarse_models = []\n", "for j in range(len(my_models) - 1):\n", " with pm.Model() as model:\n", "\n", " # Multivariate normal prior.\n", " theta = pm.MvNormal(\"theta\", mu=mu_prior, cov=cov_prior, shape=mkl)\n", "\n", " # Use the Potential class to evaluate likelihood\n", " pm.Potential(\"likelihood\", logl[j](theta))\n", "\n", " coarse_models.append(model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create fine model and perform inference\n", "Note that we sample using all three methods and that we use the MAP as the starting point for sampling" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Warning: gradient not available.(E.g. vars contains discrete variables). MAP estimates may not be accurate for the default parameters. Defaulting to non-gradient minimization 'Powell'.\n" ] }, { "data": { "text/html": [ "\n", "
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\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "The number of effective samples is smaller than 10% for some parameters.\n" ] } ], "source": [ "# Set up finest model and perform inference with PyMC3, using the MLDA algorithm\n", "# and passing the coarse_models list created above.\n", "method_names = []\n", "traces = []\n", "runtimes = []\n", "\n", "with pm.Model() as model:\n", "\n", " # Multivariate normal prior.\n", " theta = pm.MvNormal(\"theta\", mu=mu_prior, cov=cov_prior, shape=mkl)\n", "\n", " # Use the Potential class to evaluate likelihood\n", " pm.Potential(\"likelihood\", logl[-1](theta))\n", "\n", " # Find the MAP estimate which is used as the starting point for sampling\n", " MAP = pm.find_MAP()\n", "\n", " # Initialise a Metropolis, DEMetropolisZ and MLDA step method objects (passing the subsampling rate and\n", " # coarse models list for the latter)\n", " step_metropolis = pm.Metropolis(tune=tune, tune_interval=tune_interval)\n", " step_demetropolisz = pm.DEMetropolisZ(tune_interval=tune_interval)\n", " step_mlda = pm.MLDA(\n", " coarse_models=coarse_models, subsampling_rates=nsub, base_tune_interval=tune_interval\n", " )\n", "\n", " # Inference!\n", " # Metropolis\n", " t_start = time.time()\n", " method_names.append(\"Metropolis\")\n", " traces.append(\n", " pm.sample(\n", " draws=ndraws,\n", " step=step_metropolis,\n", " chains=nchains,\n", " tune=nburn,\n", " discard_tuned_samples=discard_tuning,\n", " random_seed=sampling_seed,\n", " start=MAP,\n", " cores=1,\n", " mp_ctx=\"forkserver\",\n", " )\n", " )\n", " runtimes.append(time.time() - t_start)\n", "\n", " # DEMetropolisZ\n", " t_start = time.time()\n", " method_names.append(\"DEMetropolisZ\")\n", " traces.append(\n", " pm.sample(\n", " draws=ndraws,\n", " step=step_demetropolisz,\n", " chains=nchains,\n", " tune=nburn,\n", " discard_tuned_samples=discard_tuning,\n", " random_seed=sampling_seed,\n", " start=MAP,\n", " cores=1,\n", " mp_ctx=\"forkserver\",\n", " )\n", " )\n", " runtimes.append(time.time() - t_start)\n", "\n", " # MLDA\n", " t_start = time.time()\n", " method_names.append(\"MLDA\")\n", " traces.append(\n", " pm.sample(\n", " draws=ndraws,\n", " step=step_mlda,\n", " chains=nchains,\n", " tune=nburn,\n", " discard_tuned_samples=discard_tuning,\n", " random_seed=sampling_seed,\n", " start=MAP,\n", " cores=1,\n", " mp_ctx=\"forkserver\",\n", " )\n", " )\n", " runtimes.append(time.time() - t_start)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Get post-sampling stats and diagnostics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Print MAP estimate and pymc3 sampling summary" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Detailed summaries and plots:\n", "MAP estimate: [ 2.27538512e+00 1.88388201e-01 -2.23633947e-01 3.35677618e-04\n", " 1.47801781e+00 -8.33319176e-01 7.28992752e-02 -7.31399445e-02\n", " -1.01774451e-01 -1.24098721e-01 4.56230029e-01 -1.30949052e-04\n", " 1.37797972e-03 4.16419123e-02]. Not used as starting point.\n", "\n", "Metropolis Sampler:\n", "\n" ] }, { "data": { "text/html": [ "
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meansdhdi_3%hdi_97%mcse_meanmcse_sdess_meaness_sdess_bulkess_tailr_hat
theta[0]2.2760.0152.2482.3030.0000.0002925.02925.02925.04999.01.00
theta[1]0.1890.0200.1500.2260.0000.0001729.01729.01730.03492.01.00
theta[2]-0.2240.020-0.264-0.1870.0000.0001952.01952.01953.03505.01.00
theta[3]0.0000.026-0.0460.0520.0010.0011177.01177.01179.01925.01.00
theta[4]1.4780.0291.4211.5310.0010.0011013.01012.01013.02008.01.00
theta[5]-0.8330.028-0.885-0.7800.0010.001889.0889.0892.01601.01.01
theta[6]0.0700.037-0.0040.1370.0020.001586.0586.0587.01191.01.00
theta[7]-0.0730.036-0.142-0.0070.0010.001695.0695.0695.01222.01.00
theta[8]-0.1000.044-0.180-0.0160.0020.002429.0429.0431.01038.01.01
theta[9]-0.1180.046-0.206-0.0300.0020.002421.0421.0421.0832.01.01
theta[10]0.4560.0480.3660.5460.0020.002379.0377.0379.0838.01.01
theta[11]-0.0010.053-0.0960.1040.0030.002363.0363.0363.0671.01.01
theta[12]-0.0010.058-0.1090.1060.0040.003224.0224.0225.0552.01.00
theta[13]0.0400.071-0.0880.1730.0070.005111.0111.0106.0348.01.06
\n", "
" ], "text/plain": [ " mean sd hdi_3% hdi_97% mcse_mean mcse_sd ess_mean \\\n", "theta[0] 2.276 0.015 2.248 2.303 0.000 0.000 2925.0 \n", "theta[1] 0.189 0.020 0.150 0.226 0.000 0.000 1729.0 \n", "theta[2] -0.224 0.020 -0.264 -0.187 0.000 0.000 1952.0 \n", "theta[3] 0.000 0.026 -0.046 0.052 0.001 0.001 1177.0 \n", "theta[4] 1.478 0.029 1.421 1.531 0.001 0.001 1013.0 \n", "theta[5] -0.833 0.028 -0.885 -0.780 0.001 0.001 889.0 \n", "theta[6] 0.070 0.037 -0.004 0.137 0.002 0.001 586.0 \n", "theta[7] -0.073 0.036 -0.142 -0.007 0.001 0.001 695.0 \n", "theta[8] -0.100 0.044 -0.180 -0.016 0.002 0.002 429.0 \n", "theta[9] -0.118 0.046 -0.206 -0.030 0.002 0.002 421.0 \n", "theta[10] 0.456 0.048 0.366 0.546 0.002 0.002 379.0 \n", "theta[11] -0.001 0.053 -0.096 0.104 0.003 0.002 363.0 \n", "theta[12] -0.001 0.058 -0.109 0.106 0.004 0.003 224.0 \n", "theta[13] 0.040 0.071 -0.088 0.173 0.007 0.005 111.0 \n", "\n", " ess_sd ess_bulk ess_tail r_hat \n", "theta[0] 2925.0 2925.0 4999.0 1.00 \n", "theta[1] 1729.0 1730.0 3492.0 1.00 \n", "theta[2] 1952.0 1953.0 3505.0 1.00 \n", "theta[3] 1177.0 1179.0 1925.0 1.00 \n", "theta[4] 1012.0 1013.0 2008.0 1.00 \n", "theta[5] 889.0 892.0 1601.0 1.01 \n", "theta[6] 586.0 587.0 1191.0 1.00 \n", "theta[7] 695.0 695.0 1222.0 1.00 \n", "theta[8] 429.0 431.0 1038.0 1.01 \n", "theta[9] 421.0 421.0 832.0 1.01 \n", "theta[10] 377.0 379.0 838.0 1.01 \n", "theta[11] 363.0 363.0 671.0 1.01 \n", "theta[12] 224.0 225.0 552.0 1.00 \n", "theta[13] 111.0 106.0 348.0 1.06 " ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "DEMetropolisZ Sampler:\n", "\n" ] }, { "data": { "text/html": [ "
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meansdhdi_3%hdi_97%mcse_meanmcse_sdess_meaness_sdess_bulkess_tailr_hat
theta[0]2.2750.0152.2472.3020.0000.0001114.01114.01115.02017.01.0
theta[1]0.1880.0200.1500.2260.0010.0001053.01046.01056.01822.01.0
theta[2]-0.2230.020-0.259-0.1830.0010.000986.0977.0991.01524.01.0
theta[3]0.0000.025-0.0490.0470.0010.001990.0990.0991.01826.01.0
theta[4]1.4780.0301.4181.5330.0010.001969.0969.0972.01961.01.0
theta[5]-0.8330.030-0.889-0.7770.0010.001994.0994.0995.02251.01.0
theta[6]0.0720.0360.0080.1400.0010.001928.0928.0930.02121.01.0
theta[7]-0.0730.036-0.1360.0000.0010.0011001.01001.01005.01682.01.0
theta[8]-0.1030.046-0.189-0.0130.0020.001945.0945.0947.01927.01.0
theta[9]-0.1240.045-0.207-0.0390.0010.0011002.01002.01002.02001.01.0
theta[10]0.4580.0500.3680.5530.0010.0011109.01109.01110.01778.01.0
theta[11]0.0030.053-0.0990.0980.0020.0011122.01122.01123.01952.01.0
theta[12]0.0010.054-0.0990.1040.0020.0011014.01014.01016.01691.01.0
theta[13]0.0400.071-0.0930.1710.0020.0021100.01100.01100.02144.01.0
\n", "
" ], "text/plain": [ " mean sd hdi_3% hdi_97% mcse_mean mcse_sd ess_mean \\\n", "theta[0] 2.275 0.015 2.247 2.302 0.000 0.000 1114.0 \n", "theta[1] 0.188 0.020 0.150 0.226 0.001 0.000 1053.0 \n", "theta[2] -0.223 0.020 -0.259 -0.183 0.001 0.000 986.0 \n", "theta[3] 0.000 0.025 -0.049 0.047 0.001 0.001 990.0 \n", "theta[4] 1.478 0.030 1.418 1.533 0.001 0.001 969.0 \n", "theta[5] -0.833 0.030 -0.889 -0.777 0.001 0.001 994.0 \n", "theta[6] 0.072 0.036 0.008 0.140 0.001 0.001 928.0 \n", "theta[7] -0.073 0.036 -0.136 0.000 0.001 0.001 1001.0 \n", "theta[8] -0.103 0.046 -0.189 -0.013 0.002 0.001 945.0 \n", "theta[9] -0.124 0.045 -0.207 -0.039 0.001 0.001 1002.0 \n", "theta[10] 0.458 0.050 0.368 0.553 0.001 0.001 1109.0 \n", "theta[11] 0.003 0.053 -0.099 0.098 0.002 0.001 1122.0 \n", "theta[12] 0.001 0.054 -0.099 0.104 0.002 0.001 1014.0 \n", "theta[13] 0.040 0.071 -0.093 0.171 0.002 0.002 1100.0 \n", "\n", " ess_sd ess_bulk ess_tail r_hat \n", "theta[0] 1114.0 1115.0 2017.0 1.0 \n", "theta[1] 1046.0 1056.0 1822.0 1.0 \n", "theta[2] 977.0 991.0 1524.0 1.0 \n", "theta[3] 990.0 991.0 1826.0 1.0 \n", "theta[4] 969.0 972.0 1961.0 1.0 \n", "theta[5] 994.0 995.0 2251.0 1.0 \n", "theta[6] 928.0 930.0 2121.0 1.0 \n", "theta[7] 1001.0 1005.0 1682.0 1.0 \n", "theta[8] 945.0 947.0 1927.0 1.0 \n", "theta[9] 1002.0 1002.0 2001.0 1.0 \n", "theta[10] 1109.0 1110.0 1778.0 1.0 \n", "theta[11] 1122.0 1123.0 1952.0 1.0 \n", "theta[12] 1014.0 1016.0 1691.0 1.0 \n", "theta[13] 1100.0 1100.0 2144.0 1.0 " ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "MLDA Sampler:\n", "\n" ] }, { "data": { "text/html": [ "
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meansdhdi_3%hdi_97%mcse_meanmcse_sdess_meaness_sdess_bulkess_tailr_hat
theta[0]2.2750.0152.2472.3030.0000.0003883.03882.03881.07213.01.0
theta[1]0.1890.0200.1510.2270.0000.0003870.03870.03871.07048.01.0
theta[2]-0.2240.020-0.262-0.1850.0000.0003535.03530.03537.07626.01.0
theta[3]0.0010.026-0.0490.0490.0000.0004165.04165.04164.08082.01.0
theta[4]1.4780.0291.4231.5340.0000.0003626.03620.03621.06677.01.0
theta[5]-0.8340.030-0.890-0.7780.0000.0003770.03767.03769.08090.01.0
theta[6]0.0730.0370.0020.1410.0010.0003941.03941.03942.07531.01.0
theta[7]-0.0730.037-0.142-0.0030.0010.0003786.03786.03788.07152.01.0
theta[8]-0.1030.046-0.190-0.0170.0010.0013837.03837.03838.07555.01.0
theta[9]-0.1240.046-0.208-0.0350.0010.0013996.03996.04000.07458.01.0
theta[10]0.4570.0490.3640.5500.0010.0013854.03823.03859.07195.01.0
theta[11]0.0000.055-0.1010.1040.0010.0014079.04079.04076.07430.01.0
theta[12]0.0000.056-0.1030.1050.0010.0014068.04068.04069.07309.01.0
theta[13]0.0430.071-0.0960.1730.0010.0014223.04223.04224.06875.01.0
\n", "
" ], "text/plain": [ " mean sd hdi_3% hdi_97% mcse_mean mcse_sd ess_mean \\\n", "theta[0] 2.275 0.015 2.247 2.303 0.000 0.000 3883.0 \n", "theta[1] 0.189 0.020 0.151 0.227 0.000 0.000 3870.0 \n", "theta[2] -0.224 0.020 -0.262 -0.185 0.000 0.000 3535.0 \n", "theta[3] 0.001 0.026 -0.049 0.049 0.000 0.000 4165.0 \n", "theta[4] 1.478 0.029 1.423 1.534 0.000 0.000 3626.0 \n", "theta[5] -0.834 0.030 -0.890 -0.778 0.000 0.000 3770.0 \n", "theta[6] 0.073 0.037 0.002 0.141 0.001 0.000 3941.0 \n", "theta[7] -0.073 0.037 -0.142 -0.003 0.001 0.000 3786.0 \n", "theta[8] -0.103 0.046 -0.190 -0.017 0.001 0.001 3837.0 \n", "theta[9] -0.124 0.046 -0.208 -0.035 0.001 0.001 3996.0 \n", "theta[10] 0.457 0.049 0.364 0.550 0.001 0.001 3854.0 \n", "theta[11] 0.000 0.055 -0.101 0.104 0.001 0.001 4079.0 \n", "theta[12] 0.000 0.056 -0.103 0.105 0.001 0.001 4068.0 \n", "theta[13] 0.043 0.071 -0.096 0.173 0.001 0.001 4223.0 \n", "\n", " ess_sd ess_bulk ess_tail r_hat \n", "theta[0] 3882.0 3881.0 7213.0 1.0 \n", "theta[1] 3870.0 3871.0 7048.0 1.0 \n", "theta[2] 3530.0 3537.0 7626.0 1.0 \n", "theta[3] 4165.0 4164.0 8082.0 1.0 \n", "theta[4] 3620.0 3621.0 6677.0 1.0 \n", "theta[5] 3767.0 3769.0 8090.0 1.0 \n", "theta[6] 3941.0 3942.0 7531.0 1.0 \n", "theta[7] 3786.0 3788.0 7152.0 1.0 \n", "theta[8] 3837.0 3838.0 7555.0 1.0 \n", "theta[9] 3996.0 4000.0 7458.0 1.0 \n", "theta[10] 3823.0 3859.0 7195.0 1.0 \n", "theta[11] 4079.0 4076.0 7430.0 1.0 \n", "theta[12] 4068.0 4069.0 7309.0 1.0 \n", "theta[13] 4223.0 4224.0 6875.0 1.0 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "with model:\n", " print(\n", " f\"\\nDetailed summaries and plots:\\nMAP estimate: {MAP['theta']}. Not used as starting point.\"\n", " )\n", " for i, trace in enumerate(traces):\n", " print(f\"\\n{method_names[i]} Sampler:\\n\")\n", " display(az.summary(trace))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Show ESS and ESS/sec for all samplers" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Metropolis Sampler: 15000 drawn samples in each of 3 chains.\n", "Runtime: 867.003182888031 seconds\n", "Acceptance rate: 0.3532222222222222\n", "ESS list: [2925.385 1729.714 1952.545 1178.667 1013.358 892.182 587.109 694.838\n", " 431.325 421.477 379.216 363.206 224.562 106.035]\n", "Normalised ESS list: [0.065 0.038 0.043 0.026 0.023 0.02 0.013 0.015 0.01 0.009 0.008 0.008\n", " 0.005 0.002]\n", "ESS/sec: [3.374 1.995 2.252 1.359 1.169 1.029 0.677 0.801 0.497 0.486 0.437 0.419\n", " 0.259 0.122]\n", "\n", "DEMetropolisZ Sampler: 15000 drawn samples in each of 3 chains.\n", "Runtime: 1030.1275491714478 seconds\n", "Acceptance rate: 0.27555555555555555\n", "ESS list: [1114.831 1056.146 991.239 991.406 972.288 994.705 929.785 1004.907\n", " 947.364 1002.483 1110.082 1122.778 1016.407 1099.898]\n", "Normalised ESS list: [0.025 0.023 0.022 0.022 0.022 0.022 0.021 0.022 0.021 0.022 0.025 0.025\n", " 0.023 0.024]\n", "ESS/sec: [1.082 1.025 0.962 0.962 0.944 0.966 0.903 0.976 0.92 0.973 1.078 1.09\n", " 0.987 1.068]\n", "\n", "MLDA Sampler: 15000 drawn samples in each of 3 chains.\n", "Runtime: 1926.6128001213074 seconds\n", "Acceptance rate: 0.7311777777777778\n", "ESS list: [3880.842 3871.24 3536.667 4163.642 3620.606 3768.901 3941.67 3788.182\n", " 3837.754 3999.578 3859.423 4075.985 4069.313 4224.038]\n", "Normalised ESS list: [0.086 0.086 0.079 0.093 0.08 0.084 0.088 0.084 0.085 0.089 0.086 0.091\n", " 0.09 0.094]\n", "ESS/sec: [2.014 2.009 1.836 2.161 1.879 1.956 2.046 1.966 1.992 2.076 2.003 2.116\n", " 2.112 2.192]\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "acc = []\n", "ess = []\n", "ess_n = []\n", "performances = []\n", "\n", "# Get some more statistics.\n", "with model:\n", " for i, trace in enumerate(traces):\n", " acc.append(trace.get_sampler_stats(\"accepted\").mean())\n", " ess.append(np.array(az.ess(trace).to_array()))\n", " ess_n.append(ess[i] / len(trace) / trace.nchains)\n", " performances.append(ess[i] / runtimes[i])\n", " print(\n", " f\"\\n{method_names[i]} Sampler: {len(trace)} drawn samples in each of \"\n", " f\"{trace.nchains} chains.\"\n", " f\"\\nRuntime: {runtimes[i]} seconds\"\n", " f\"\\nAcceptance rate: {acc[i]}\"\n", " f\"\\nESS list: {np.round(ess[i][0], 3)}\"\n", " f\"\\nNormalised ESS list: {np.round(ess_n[i][0], 3)}\"\n", " f\"\\nESS/sec: {np.round(performances[i][0], 3)}\"\n", " )\n", "\n", " # Plot the effective sample size (ESS) and relative ESS (ES/sec) of each of the sampling strategies.\n", " colors = [\"firebrick\", \"darkgoldenrod\", \"darkcyan\", \"olivedrab\"]\n", "\n", " fig, axes = plt.subplots(1, 2, figsize=(16, 5))\n", "\n", " axes[0].set_title(\"ESS\")\n", " for i, e in enumerate(ess):\n", " axes[0].bar(\n", " [j + i * 0.2 for j in range(mkl)],\n", " e.ravel(),\n", " width=0.2,\n", " color=colors[i],\n", " label=method_names[i],\n", " )\n", " axes[0].set_xticks([i + 0.3 for i in range(mkl)])\n", " axes[0].set_xticklabels([f\"theta_{i}\" for i in range(mkl)])\n", " axes[0].legend()\n", "\n", " axes[1].set_title(\"ES/sec\")\n", " for i, p in enumerate(performances):\n", " axes[1].bar(\n", " [j + i * 0.2 for j in range(mkl)],\n", " p.ravel(),\n", " width=0.2,\n", " color=colors[i],\n", " label=method_names[i],\n", " )\n", " axes[1].set_xticks([i + 0.3 for i in range(mkl)])\n", " axes[1].set_xticklabels([f\"theta_{i}\" for i in range(mkl)])\n", " axes[1].legend()\n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Plot distributions and trace.\n", "Vertical grey lines represent the MAP estimate of each parameter." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "with model:\n", " lines = ((\"theta\", {}, MAP[\"theta\"].tolist()),)\n", " for i, trace in enumerate(traces):\n", " az.plot_trace(trace, lines=lines)\n", "\n", " # Ugly hack to get some titles in.\n", " x_offset = -0.1 * ndraws\n", " y_offset = trace.get_values(\"theta\").max() + 0.25 * (\n", " trace.get_values(\"theta\").max() - trace.get_values(\"theta\").min()\n", " )\n", " plt.text(x_offset, y_offset, \"{} Sampler\".format(method_names[i]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Plot true and recovered densities\n", "This is useful for verification, i.e. to compare the true model density and signal to the estimated ones from the samplers." ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "True Model\n" ] }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "MAP estimate:\n" ] }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Recovered by: Metropolis\n" ] }, { "data": { "image/png": "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