{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# PyMC Intro & Install" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* 싸이그래머 / 베이지안 통계 (Python) : 파트4 [1]\n", "* 김무성" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Contetns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "0. 실습환경 설정\n", "1. PyMC 설치\n", "2. 실습 코드(& jupyter notebook) 설치\n", "3. PyMC 소개" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 1. 실습환경 설정" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* docker toolbox 설치\n", "* docker 기반 jupyter(ipython notebook) 환경 셋팅" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## docker toolbox 설치" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* download - https://www.docker.com/toolbox" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## docker 기반 jupyter(ipython notebook) 환경 셋팅" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* jupyter/scipy-notebook\n", " - https://hub.docker.com/r/jupyter/scipy-notebook/\n", " - jupyter에서 빌드해서 내놓은 이미지가 여러개 있다. \n", " - 그 중에 scipy-notebook을 써보자 " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### docker image pull" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "docker pull jupyter/scipy-notebook" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### docker container 실행" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* 이때 이후에 여러 다른 파이썬 패키지를 설치해야 하므로 -e GRANT_SUDO=yes 옵션도 추가해서 실행" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "docker run -d -e GRANT_SUDO=yes -p 8888:8888 jupyter/scipy-notebook" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### jupyter 웹 UI 접속" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* localhost:8888 혹은 docker toolbox에 매핑된 ip:8888로 브라우저에서 연다." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 2. PyMC 설치" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* jupyter 웹UI의 터미널 실행\n", "* root로 유저 변환\n", " - python-qt4 설치\n", " - pymc 설치\n", " - python 3의 경우\n", " - conda를 이용해 설치 \n", " - python2의 경우\n", " - python2 activate\n", " - conda를 이용해 설치치치" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## jupyter 웹UI의 터미널 실행" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## root로 유저 변환" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sudo su -" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### python-qt4 설치" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* 현재 이미지는 matplotlib의 백엔드로 qt4를 쓰는데, 설치되어 있지 않다.\n", "* 다음처럼, 전체 시스템에 설치하자" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "apt-get install python-qt4" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### pymc 설치" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### python 3의 경우" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### conda를 이용해 설치 " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "conda install pymc" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### python2의 경우" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### python2 activate" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "source activate python2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### conda를 이용해 설치" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### 기본 설정으로 돌아오자(python3)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "source deactivate" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### root에서 일반사용자(jovyan)로 돌아옴" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "exit" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 2. 실습 코드(& jupyter notebook) 설치" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 터미널의 기본 경로에, 싸이그래머 베이지안 트랙 github 저장소를 바로 클로닝한다." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "git clone https://github.com/psygrammer/bayesianPy.git" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### jupyter UI를 통해 바로 접근" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 팁 : 웹 브라우저를 통해 바로 이미지도 노트북에 업로드 가능." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 팁 : 작업 후, github에 반영하기" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* 터미널로 들어가서 git add, git commit, git push의 정통적인 단계를 거친다." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### 그러나 최초 git 설정을 해줘야 한다." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* commit을 하려하면, git 사용자 정보를 셋팅해주라고 한다. 해주자." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# 그냥 이 시스템에서(컨테이너에서) 쓸 것이다.\n", "git config --global user.email \"your@example.com\" # 본인 이메일\n", "git config --global user.name \"Your Nmae\" # 본인 아이디" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* 다시 커밋 시도. 성공한다." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "git push # 자신의 깃헙 아이디와 비밀번호를 치면 (해당 저장소에 권한이 있다면) 성공한다." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 3. PyMC 소개 [1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Purpose\n", "* Features\n", "* What’s new in version 2\n", "* Usage\n", "* History\n", "* Relationship to other packages" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Purpose" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* PyMC is a python module that implements Bayesian statistical models and fitting algorithms, including Markov chain Monte Carlo. Its flexibility and extensibility make it applicable to a large suite of problems. Along with core sampling functionality, PyMC includes methods for summarizing output, plotting, goodness-of-fit and convergence diagnostics." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Features" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* PyMC provides functionalities to make Bayesian analysis as painless as possible. Here is a short list of some of its features:\n", " - Fits Bayesian statistical models with Markov chain Monte Carlo and other algorithms.\n", " - Includes a large suite of well-documented statistical distributions.\n", " - Uses NumPy for numerics wherever possible.\n", " - Includes a module for modeling Gaussian processes.\n", " - Sampling loops can be paused and tuned manually, or saved and restarted later.\n", " - Creates summaries including tables and plots.\n", " - Traces can be saved to the disk as plain text, Python pickles, SQLite or MySQL database, or hdf5 archives.\n", " - Several convergence diagnostics are available.\n", " - Extensible: easily incorporates custom step methods and unusual probability distributions.\n", " - MCMC loops can be embedded in larger programs, and results can be analyzed with the full power of Python." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## What’s new in version 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Usage" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "figures pymc_intro_install.ipynb\r\n" ] } ], "source": [ "!ls" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### First, define your model in a file, say mymodel.py (with comments, of course!):" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Writing mymodel.py\n" ] } ], "source": [ "%%writefile mymodel.py\n", "\n", "# Import relevant modules\n", "import pymc\n", "import numpy as np\n", "\n", "# Some data\n", "n = 5*np.ones(4,dtype=int)\n", "x = np.array([-.86,-.3,-.05,.73])\n", "\n", "# Priors on unknown parameters\n", "alpha = pymc.Normal('alpha',mu=0,tau=.01)\n", "beta = pymc.Normal('beta',mu=0,tau=.01)\n", "\n", "# Arbitrary deterministic function of parameters\n", "@pymc.deterministic\n", "def theta(a=alpha, b=beta):\n", " \"\"\"theta = logit^{-1}(a+b)\"\"\"\n", " return pymc.invlogit(a+b*x)\n", "\n", "# Binomial likelihood for data\n", "d = pymc.Binomial('d', n=n, p=theta, value=np.array([0.,1.,3.,5.]),\\\n", " observed=True)\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "figures mymodel.py pymc_intro_install.ipynb\r\n" ] } ], "source": [ "!ls" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 팁 : 모델 시각화" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* pymc로 만든 모델을 그래프로 만들 수 있다.\n", "* 그러나 graphviz 기반으로 작동하는 pydot을 설치해야 한다.\n", "* 현재 시스템에는 둘 다 없으므로 다음의 명령을 차례로." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 시스템 전체에 graphviz를 설치하자" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "apt-get install graphviz" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 그리고 conda를 이용해서 pydot 설치 (파이썬3는 아직 지원안함)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "conda install pydot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 그려보자" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Build a model out of these three variables\n", "import pymc\n", "import mymodel\n", "\n", "model = pymc.Model(mymodel)\n", "\n", "#Make a slightly pretty graph out of it\n", "graph = pymc.graph.graph(model)\n", "graph.write_png(\"graph.png\")" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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jx8LU1BQ7duxgLQWhoaHw8fFB165dWUtplHBmfwcikQgTJ07Exo0bmU7sSE9P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\naxiJRAIAKC0tZaxE/+DMrkH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"text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import Image\n", "Image(filename='graph.png') " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 만든 모델을 로딩하고, MCMC를 돌린 후에, 결과를 그래프로 확인해보자" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### notebook 안에서 그림이 나오도록 하려면 아래의 코드를 실행해야 한다. (pymc.Matplot 모듈을 쓰려해도) " ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### MCMC 실행 & 결과 그래프" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " [-----------------100%-----------------] 10000 of 10000 complete in 0.9 secPlotting beta\n", "Plotting alpha\n", "Plotting theta_0\n", "Plotting theta_1\n", "Plotting theta_2\n", "Plotting theta_3\n" ] }, { "data": { "image/png": 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Kz3ZwNcnG5QdkWl2CBqVng59VJQizZrkPiA0NQJs2wdqg75dpoXMO2mPHAjNm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\nTAOn2xf6fOvp1g1V1S+0MsRdhhjMzCZrCbfddiO3337hyGvaseMJ3vK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dgalgcCd08bTXXjKrt16e0/k6O3Zk1mNed8stMp5GP/bDD0Dr1nKI6Kyz5Awx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\nEPsLIRoKIVoJIZ4wzyGSv17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qduqUQN++iZr3EcVq/B3AnwHoy4C3EEJUprYrASif4v4A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\nAPAOgLb6FxZ/f0k42Wbh4WSb8cHP9xfnc2IYpq7RHcCFAD4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UVXXVqlV60kkn6d69e/W7777Tiy++WFVVO3TooFu2bFFV1S5duuj69es1MzNT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\nD9x4Y3Bb1V2bDz6Axo2L104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0qn4XWv6YY4Kz1eKlOBGss7MLpuPwx85p27Zw+VgtQ/5E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4/1UbPm//d/kJcXVmY/qlbVH11LdoBMj69EuDoiYelSu02HHgrvvac/WA8+qJWQlAjUXqutlULc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\nJ3Pef6nqt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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import pymc\n", "import mymodel\n", "\n", "S = pymc.MCMC(mymodel, db='pickle')\n", "S.sample(iter=10000, burn=5000, thin=2)\n", "pymc.Matplot.plot(S)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## History" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Relationship to other packages" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 참고자료" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* [1] PyMC User’s Guide - https://pymc-devs.github.io/pymc/\n", "* [2] 도커(Docker) 튜토리얼 : 깐 김에 배포까지 - http://blog.nacyot.com/articles/2014-01-27-easy-deploy-with-docker/\n", "* [3] Docker란 무엇인가? : Docker 기본 사용법 - http://www.slideshare.net/pyrasis/docker-docker-38286477\n", "* [4] ANNOUNCING DOCKER TOOLBOX - https://blog.docker.com/2015/08/docker-toolbox/" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }