{ "cells": [ { "cell_type": "markdown", "metadata": { "toc": "true" }, "source": [ "# Table of Contents\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "# Easily creating MAB problems\n", "First, be sure to be in the main folder, or to have installed [`SMPyBandits`](https://github.com/SMPyBandits/SMPyBandits), and import `MAB` from `Environment` package:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: SMPyBandits in ./venv3/lib/python3.6/site-packages (0.9.4)\n", "Requirement already satisfied: watermark in ./venv3/lib/python3.6/site-packages (1.7.0)\n", "Requirement already satisfied: scikit-learn in ./venv3/lib/python3.6/site-packages (from SMPyBandits) (0.20.0)\n", "Requirement already satisfied: scikit-optimize in ./venv3/lib/python3.6/site-packages (from SMPyBandits) (0.5.2)\n", "Requirement already satisfied: scipy>0.9 in ./venv3/lib/python3.6/site-packages (from SMPyBandits) (1.1.0)\n", "Requirement already satisfied: numpy in ./venv3/lib/python3.6/site-packages (from SMPyBandits) (1.15.4)\n", "Requirement already satisfied: matplotlib>=2 in ./venv3/lib/python3.6/site-packages (from SMPyBandits) (3.0.2)\n", "Requirement already satisfied: seaborn in ./venv3/lib/python3.6/site-packages (from SMPyBandits) (0.9.0)\n", "Requirement already satisfied: joblib in ./venv3/lib/python3.6/site-packages (from SMPyBandits) (0.13.0)\n", "Requirement already satisfied: ipython in ./venv3/lib/python3.6/site-packages (from watermark) (7.1.1)\n", "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in ./venv3/lib/python3.6/site-packages (from matplotlib>=2->SMPyBandits) (2.3.0)\n", "Requirement already satisfied: kiwisolver>=1.0.1 in ./venv3/lib/python3.6/site-packages (from matplotlib>=2->SMPyBandits) (1.0.1)\n", "Requirement already satisfied: python-dateutil>=2.1 in ./venv3/lib/python3.6/site-packages (from matplotlib>=2->SMPyBandits) (2.7.5)\n", "Requirement already satisfied: cycler>=0.10 in ./venv3/lib/python3.6/site-packages (from matplotlib>=2->SMPyBandits) (0.10.0)\n", "Requirement already satisfied: pandas>=0.15.2 in ./venv3/lib/python3.6/site-packages (from seaborn->SMPyBandits) (0.23.4)\n", "Requirement already satisfied: setuptools>=18.5 in ./venv3/lib/python3.6/site-packages (from ipython->watermark) (40.6.2)\n", "Requirement already satisfied: pexpect; sys_platform != \"win32\" in ./venv3/lib/python3.6/site-packages (from ipython->watermark) (4.6.0)\n", "Requirement already satisfied: jedi>=0.10 in ./venv3/lib/python3.6/site-packages (from ipython->watermark) (0.13.1)\n", "Requirement already satisfied: backcall in ./venv3/lib/python3.6/site-packages (from ipython->watermark) (0.1.0)\n", "Requirement already satisfied: traitlets>=4.2 in ./venv3/lib/python3.6/site-packages (from ipython->watermark) (4.3.2)\n", "Requirement already satisfied: pickleshare in ./venv3/lib/python3.6/site-packages (from ipython->watermark) (0.7.5)\n", "Requirement already satisfied: pygments in ./venv3/lib/python3.6/site-packages (from ipython->watermark) (2.2.0)\n", "Requirement already satisfied: prompt-toolkit<2.1.0,>=2.0.0 in ./venv3/lib/python3.6/site-packages (from ipython->watermark) (2.0.7)\n", "Requirement already satisfied: decorator in ./venv3/lib/python3.6/site-packages (from ipython->watermark) (4.3.0)\n", "Requirement already satisfied: six>=1.5 in ./venv3/lib/python3.6/site-packages (from python-dateutil>=2.1->matplotlib>=2->SMPyBandits) (1.11.0)\n", "Requirement already satisfied: pytz>=2011k in ./venv3/lib/python3.6/site-packages (from pandas>=0.15.2->seaborn->SMPyBandits) (2018.7)\n", "Requirement already satisfied: ptyprocess>=0.5 in ./venv3/lib/python3.6/site-packages (from pexpect; sys_platform != \"win32\"->ipython->watermark) (0.6.0)\n", "Requirement already satisfied: parso>=0.3.0 in ./venv3/lib/python3.6/site-packages (from jedi>=0.10->ipython->watermark) (0.3.1)\n", "Requirement already satisfied: ipython-genutils in ./venv3/lib/python3.6/site-packages (from traitlets>=4.2->ipython->watermark) (0.2.0)\n", "Requirement already satisfied: wcwidth in ./venv3/lib/python3.6/site-packages (from prompt-toolkit<2.1.0,>=2.0.0->ipython->watermark) (0.1.7)\n", "Info: Using the Jupyter notebook version of the tqdm() decorator, tqdm_notebook() ...\n", "Lilian Besson \n", "\n", "CPython 3.6.6\n", "IPython 7.1.1\n", "\n", "SMPyBandits 0.9.4\n", "\n", "compiler : GCC 8.0.1 20180414 (experimental) [trunk revision 259383\n", "system : Linux\n", "release : 4.15.0-38-generic\n", "machine : x86_64\n", "processor : x86_64\n", "CPU cores : 4\n", "interpreter: 64bit\n" ] } ], "source": [ "!pip install SMPyBandits watermark\n", "%load_ext watermark\n", "%watermark -v -m -p SMPyBandits -a \"Lilian Besson\"" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from SMPyBandits.Environment import MAB" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And also, import all the types of arms." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "(SMPyBandits.Arms.Constant.Constant,\n", " SMPyBandits.Arms.Bernoulli.Bernoulli,\n", " SMPyBandits.Arms.Gaussian.Gaussian,\n", " SMPyBandits.Arms.Exponential.Exponential,\n", " SMPyBandits.Arms.Exponential.ExponentialFromMean,\n", " SMPyBandits.Arms.Poisson.Poisson,\n", " SMPyBandits.Arms.UniformArm.UniformArm,\n", " SMPyBandits.Arms.Gamma.Gamma,\n", " SMPyBandits.Arms.Gamma.GammaFromMean)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from SMPyBandits.Arms import *\n", "# Check it exists:\n", "Constant, Bernoulli, Gaussian, Exponential, ExponentialFromMean, Poisson, UniformArm, Gamma, GammaFromMean" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "import matplotlib as mpl\n", "mpl.rcParams['figure.figsize'] = (12.4, 7)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Constant arms\n", "\n", "This is the simpler example of arms : rewards are constant, and not randomly drawn from a distribution.\n", "Let consider an example with $K = 3$ arms." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "Creating a new MAB problem ...\n", " Taking arms of this MAB problem from a list of arms 'configuration' = [Constant(0.1), Constant(0.5), Constant(0.9)] ...\n", " - with 'arms' = [Constant(0.1), Constant(0.5), Constant(0.9)]\n", " - with 'means' = [0.1 0.5 0.9]\n", " - with 'nbArms' = 3\n", " - with 'maxArm' = 0.9\n", " - with 'minArm' = 0.1\n", "\n", "This MAB problem has: \n", " - a [Lai & Robbins] complexity constant C(mu) = 2 ... \n", " - a Optimal Arm Identification factor H_OI(mu) = 26.67% ...\n", " - with 'arms' represented as: $[Constant(0.1), Constant(0.5), Constant(0.9)^*]$\n" ] } ], "source": [ "M_C = MAB([Constant(mu) for mu in [0.1, 0.5, 0.9]])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `plotHistogram()` method draws samples from each arm, and plot a histogram of their repartition.\n", "For constant arms, no need to take a lot of samples as they are constant." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Warning: forcing to use putatright = False because there is 3 items in the legend.\n" ] }, { "data": { "image/png": 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\n", 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