 08a. Programming a binary genetic algorithm.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Programming a binary genetic algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn, time\n",
    "seaborn.set_context('talk')\n",
    "seaborn.set_style('ticks')\n",
    "plt.rc('font', family='serif')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [],
   "source": [
    "from ipywidgets import IntProgress\n",
    "from IPython.display import display, clear_output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Our problem:\n",
    "\n",
    "$$\n",
    "\\max_{x} f(x)=x\\sin\\left(10\\pi x\\right) +1,\n",
    "$$\n",
    "with\n",
    "$$\n",
    "x\\in\\mathbb{R}\\ \\text{and}\\ -1\\leq x\\leq 2.\n",
    "$$\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "That is, find $x$, such that,\n",
    "$$\n",
    "x=\\text{arg}\\max\\ x\\sin\\left(10\\pi x\\right) +1\n",
    "$$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Let's start by taking a look at out function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [],
   "source": [
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [],
   "source": [
    "def f(x):\n",
    "    return x*math.sin(x*math.pi*10) +1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "x_sample = np.linspace(-1, 2, 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [],
   "source": [
    "y = [f(x) for x in x_sample]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'$f(x)$')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 334,
       "width": 640
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(x_sample, y)\n",
    "plt.xlabel('$x$')\n",
    "plt.ylabel('$f(x)$')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "We are going to rely on the DEAP Python library for coding our GA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "from deap import base\n",
    "from deap import creator\n",
    "from deap import tools"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "We have to start by defining a new Python class that represents the fitness.\n",
    "\n",
    "* We will use the basic DEAP fitness `deap.base.Fitness`.\n",
    "* It can take multi-objective functions\n",
    "* Weights decide how those objecives are scaled and if the problem is to maximize or minimize them:\n",
    "$$\n",
    "\\min f(x)\\iff\\max -f(x)\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [],
   "source": [
    "creator.create(\"FitnessMax\", base.Fitness, weights=(1.0,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "deap.creator.FitnessMax"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "creator.FitnessMax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "We now define how our individuals will be. In particular, they represent the binary string a Python `list`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [],
   "source": [
    "creator.create(\"Individual\", list, fitness=creator.FitnessMax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "deap.creator.Individual"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "creator.Individual"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "We also need a function that converts a binary-string individual into its real number representation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [],
   "source": [
    "def convert_binary_individual(x_bin, min_lim=-1, max_lim=2):\n",
    "    # converts the list of 0's and 1's to a string\n",
    "    binary_str = ''.join([str(i) for i in x_bin])\n",
    "    \n",
    "    # convert the binary number represented by the string to an integer\n",
    "    x_int = int(binary_str, 2)\n",
    "    \n",
    "    # scale the integer to the valid interval\n",
    "    x_float = x_int/(2**len(x_bin)-1) * (max_lim - min_lim) + min_lim\n",
    "    return x_float"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Consequently, for evaluating an individual we can use the following function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [],
   "source": [
    "def evaluate(ind):\n",
    "    return f(convert_binary_individual(ind)), # <- remember that these functions must return an iterable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Now, we must configure the algorithm: \n",
    "\n",
    "* the length of our individuals,\n",
    "* the cross-over probablity,\n",
    "* the mutation probability, and\n",
    "* the maximum number of iterations. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [],
   "source": [
    "CHROMOSOME_LENGTH = 22\n",
    "CXPB = 0.1\n",
    "MUTPB = 0.01\n",
    "MAX_GEN = 200"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "inputHidden": false,
    "outputHidden": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "DEAP uses the `Toolbox` class as a \"knapsack\" where we store the problem and algorithm configuration. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [],
   "source": [
    "toolbox = base.Toolbox()\n",
    "\n",
    "# Attribute generator, only 0s and 1s\n",
    "toolbox.register(\"attr_bool\", random.randint, 0, 1)\n",
    "\n",
    "# How do we initialize an individual?\n",
    "toolbox.register(\"individual\", tools.initRepeat, creator.Individual, toolbox.attr_bool, CHROMOSOME_LENGTH)\n",
    "\n",
    "# ...and the population?\n",
    "toolbox.register(\"population\", tools.initRepeat, list, toolbox.individual)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Set the individual evaluation to use the function we defined above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [],
   "source": [
    "toolbox.register(\"evaluate\", evaluate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Use two-point crossover for mating,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [],
   "source": [
    "toolbox.register(\"mate\", tools.cxTwoPoint)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "...and bit-flip mutation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [],
   "source": [
    "toolbox.register(\"mutate\", tools.mutFlipBit, indpb=0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Selection will use tournament of size 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [],
   "source": [
    "toolbox.register(\"select\", tools.selTournament, tournsize=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "All set, we can create a new population that we will evolve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [],
   "source": [
    "pop = toolbox.population(n=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But let's store some statistics while we perform the optimization to see how it behaves."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [],
   "source": [
    "mins, maxs, avgs, stds = [], [], [], []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "interactive_plot = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m~/anaconda/lib/python3.6/site-packages/IPython/core/display.py\u001b[0m in \u001b[0;36mdisplay\u001b[0;34m(include, exclude, metadata, transient, display_id, *objs, **kwargs)\u001b[0m\n\u001b[1;32m    296\u001b[0m             \u001b[0mpublish_display_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    297\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 298\u001b[0;31m             \u001b[0mformat_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmd_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minclude\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mexclude\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    299\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mformat_dict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    300\u001b[0m                 \u001b[0;31m# nothing to display (e.g. _ipython_display_ took over)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/lib/python3.6/site-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36mformat\u001b[0;34m(self, obj, include, exclude)\u001b[0m\n\u001b[1;32m    178\u001b[0m             \u001b[0mmd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    179\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 180\u001b[0;31m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mformatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    181\u001b[0m             \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m                 \u001b[0;31m# FIXME: log the exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<decorator-gen-9>\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n",
      "\u001b[0;32m~/anaconda/lib/python3.6/site-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36mcatch_format_error\u001b[0;34m(method, self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    222\u001b[0m     \u001b[0;34m\"\"\"show traceback on failed format call\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    223\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 224\u001b[0;31m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    225\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    226\u001b[0m         \u001b[0;31m# don't warn on NotImplementedErrors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/lib/python3.6/site-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    339\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    340\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 341\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    342\u001b[0m             \u001b[0;31m# Finally look for special method names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/lib/python3.6/site-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m    241\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'png'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'retina'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m'png2x'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 243\u001b[0;31m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    244\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'jpg'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m'jpeg'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    245\u001b[0m         \u001b[0mjpg_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'jpg'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/lib/python3.6/site-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36mretina_figure\u001b[0;34m(fig, **kwargs)\u001b[0m\n\u001b[1;32m    131\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    132\u001b[0m     \u001b[0;34m\"\"\"format a figure as a pixel-doubled (retina) PNG\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 133\u001b[0;31m     \u001b[0mpngdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfmt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'retina'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    134\u001b[0m     \u001b[0;31m# Make sure that retina_figure acts just like print_figure and returns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    135\u001b[0m     \u001b[0;31m# None when the figure is empty.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/lib/python3.6/site-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m    123\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    124\u001b[0m     \u001b[0mbytes_io\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 125\u001b[0;31m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    126\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    127\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'svg'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/lib/python3.6/site-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs)\u001b[0m\n\u001b[1;32m   2261\u001b[0m                 \u001b[0morientation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2262\u001b[0m                 \u001b[0mbbox_inches_restore\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_bbox_inches_restore\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2263\u001b[0;31m                 **kwargs)\n\u001b[0m\u001b[1;32m   2264\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2265\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mbbox_inches\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mrestore_bbox\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[0;34m(self, filename_or_obj, *args, **kwargs)\u001b[0m\n\u001b[1;32m    511\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    512\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mprint_png\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename_or_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 513\u001b[0;31m         \u001b[0mFigureCanvasAgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    514\u001b[0m         \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    515\u001b[0m         \u001b[0moriginal_dpi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    431\u001b[0m             \u001b[0;31m# if toolbar:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    432\u001b[0m             \u001b[0;31m#     toolbar.set_cursor(cursors.WAIT)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 433\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    434\u001b[0m             \u001b[0;31m# A GUI class may be need to update a window using this draw, so\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    435\u001b[0m             \u001b[0;31m# don't forget to call the superclass.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/lib/python3.6/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/lib/python3.6/site-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   1473\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1474\u001b[0m             mimage._draw_list_compositing_images(\n\u001b[0;32m-> 1475\u001b[0;31m                 renderer, self, artists, self.suppressComposite)\n\u001b[0m\u001b[1;32m   1476\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1477\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'figure'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/lib/python3.6/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    139\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 141\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    142\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    143\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/lib/python3.6/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/lib/python3.6/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, inframe)\u001b[0m\n\u001b[1;32m   2605\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2606\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2607\u001b[0;31m         \u001b[0mmimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_draw_list_compositing_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2608\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2609\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'axes'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/lib/python3.6/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    139\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 141\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    142\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    143\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/lib/python3.6/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/lib/python3.6/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1202\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_label_position\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1203\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1204\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1205\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1206\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_offset_text_position\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mticklabelBoxes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mticklabelBoxes2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/lib/python3.6/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/lib/python3.6/site-packages/matplotlib/text.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    704\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    705\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0m_wrap_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtextobj\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 706\u001b[0;31m             \u001b[0mbbox\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdescent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtextobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_layout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    707\u001b[0m             \u001b[0mtrans\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtextobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    708\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/lib/python3.6/site-packages/matplotlib/text.py\u001b[0m in \u001b[0;36m_get_layout\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    298\u001b[0m         tmp, lp_h, lp_bl = renderer.get_text_width_height_descent('lp',\n\u001b[1;32m    299\u001b[0m                                                          \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fontproperties\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 300\u001b[0;31m                                                          ismath=False)\n\u001b[0m\u001b[1;32m    301\u001b[0m         \u001b[0moffsety\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlp_h\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mlp_bl\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_linespacing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    302\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mget_text_width_height_descent\u001b[0;34m(self, s, prop, ismath)\u001b[0m\n\u001b[1;32m    240\u001b[0m         \u001b[0mflags\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_hinting_flag\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    241\u001b[0m         \u001b[0mfont\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_agg_font\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 242\u001b[0;31m         \u001b[0mfont\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mflags\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    243\u001b[0m         \u001b[0mw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfont\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_width_height\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# width and height of unrotated string\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    244\u001b[0m         \u001b[0md\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfont\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_descent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 349,
       "width": 640
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "# prepare a figure\n",
    "if interactive_plot:\n",
    "    plt.figure(figsize=(10, 5))\n",
    "\n",
    "# create a population\n",
    "pop = toolbox.population(n=50)\n",
    "\n",
    "# Evaluate the entire population\n",
    "fitnesses = list(map(toolbox.evaluate, pop))\n",
    "for ind, fit in zip(pop, fitnesses):\n",
    "    ind.fitness.values = fit\n",
    "# Variable keeping track of the number of generations\n",
    "g = 0\n",
    "\n",
    "# Begin the evolution\n",
    "while g < MAX_GEN:\n",
    "    # A new generation\n",
    "    g = g + 1\n",
    "\n",
    "    # Select the next generation individuals\n",
    "    offspring = toolbox.select(pop, len(pop))\n",
    "    # Clone the selected individuals\n",
    "    offspring = list(map(toolbox.clone, offspring))\n",
    "    \n",
    "    # Apply crossover and mutation on the offspring\n",
    "    for child1, child2 in zip(offspring[::2], offspring[1::2]):\n",
    "        if random.random() < CXPB:\n",
    "            toolbox.mate(child1, child2)\n",
    "            del child1.fitness.values\n",
    "            del child2.fitness.values\n",
    "\n",
    "    for mutant in offspring:\n",
    "        if random.random() < MUTPB:\n",
    "            toolbox.mutate(mutant)\n",
    "            del mutant.fitness.values\n",
    "\n",
    "    # Evaluate the individuals with an invalid fitness\n",
    "    invalid_ind = [ind for ind in offspring if not ind.fitness.valid]\n",
    "    fitnesses = map(toolbox.evaluate, invalid_ind)\n",
    "    for ind, fit in zip(invalid_ind, fitnesses):\n",
    "        ind.fitness.values = fit\n",
    "\n",
    "    pop[:] = offspring\n",
    "\n",
    "    # Gather all the fitnesses in one list and print the stats\n",
    "    fits = [ind.fitness.values[0] for ind in pop]\n",
    "\n",
    "    length = len(pop)\n",
    "    mean = sum(fits) / length\n",
    "    sum2 = sum(x * x for x in fits)\n",
    "    std = abs(sum2 / length - mean**2)**0.5\n",
    "    \n",
    "    if interactive_plot:\n",
    "        plt.clf()\n",
    "\n",
    "        for ind in pop:\n",
    "            plt.scatter(convert_binary_individual(ind), ind.fitness.values[0])\n",
    "\n",
    "        plt.plot(x_sample, y, linewidth=1)\n",
    "        plt.xlabel('$x$')\n",
    "        plt.ylabel('$f(x)$')\n",
    "        plt.title('Generation {}/{}'.format(g, MAX_GEN))\n",
    "        display(plt.gcf())\n",
    "        clear_output(wait=True)\n",
    "        # time.sleep(0.05)\n",
    "\n",
    "    mins += [min(fits)]\n",
    "    maxs += [max(fits)]\n",
    "    avgs += [mean]\n",
    "    stds += [std]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Value')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 279,
       "width": 459
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,4))\n",
    "xx= np.arange(1,len(avgs)+1)\n",
    "plt.plot(xx, maxs, label='Max fitness')\n",
    "plt.plot(xx, avgs, label='Mean fitness')\n",
    "plt.fill_between(xx, mins, maxs, alpha=0.25)\n",
    "plt.plot(xx, stds, label='Std. deviation')\n",
    "plt.legend(frameon=True)\n",
    "plt.xlabel('Iterations')\n",
    "plt.ylabel('Value')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Introducing elitism"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [],
   "source": [
    "ELITE_SIZE = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [],
   "source": [
    "mins_elite, maxs_elite, avgs_elite, stds_elite = [], [], [], []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "interactive_plot = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 392 ms, sys: 4.49 ms, total: 397 ms\n",
      "Wall time: 396 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# prepare a figure\n",
    "if interactive_plot:\n",
    "    plt.figure(figsize=(10, 5))\n",
    "    \n",
    "pop = toolbox.population(n=50)\n",
    "\n",
    "# Evaluate the entire population\n",
    "fitnesses = list(map(toolbox.evaluate, pop))\n",
    "for ind, fit in zip(pop, fitnesses):\n",
    "    ind.fitness.values = fit\n",
    "    \n",
    "# Variable keeping track of the number of generations\n",
    "g = 0\n",
    "\n",
    "# Begin the evolution\n",
    "while g < MAX_GEN:\n",
    "    # A new generation\n",
    "    g = g + 1\n",
    "    \n",
    "    # Select the next generation individuals\n",
    "    elite = tools.selBest(pop, ELITE_SIZE)\n",
    "    \n",
    "    offspring = toolbox.select(pop, len(pop))\n",
    "    # Clone the selected individuals\n",
    "    offspring = list(map(toolbox.clone, offspring))\n",
    "            # Apply crossover and mutation on the offspring\n",
    "\n",
    "    for child1, child2 in zip(offspring[::2], offspring[1::2]):\n",
    "        if random.random() < CXPB:\n",
    "            toolbox.mate(child1, child2)\n",
    "            del child1.fitness.values\n",
    "            del child2.fitness.values\n",
    "\n",
    "    for mutant in offspring:\n",
    "        if random.random() < MUTPB:\n",
    "            toolbox.mutate(mutant)\n",
    "            del mutant.fitness.values\n",
    "            \n",
    "    # Evaluate the individuals with an invalid fitness\n",
    "    invalid_ind = [ind for ind in offspring if not ind.fitness.valid]\n",
    "    fitnesses = map(toolbox.evaluate, invalid_ind)\n",
    "    for ind, fit in zip(invalid_ind, fitnesses):\n",
    "        ind.fitness.values = fit\n",
    "    \n",
    "    pop[:] = elite = tools.selBest(offspring + elite, len(pop))\n",
    "    \n",
    "    # Gather all the fitnesses in one list and print the stats\n",
    "    fits = [ind.fitness.values[0] for ind in pop]\n",
    "\n",
    "    length = len(pop)\n",
    "    mean = sum(fits) / length\n",
    "    sum2 = sum(x*x for x in fits)\n",
    "    std = abs(sum2 / length - mean**2)**0.5\n",
    "\n",
    "    mins_elite += [min(fits)]\n",
    "    maxs_elite += [max(fits)]\n",
    "    avgs_elite += [mean]\n",
    "    stds_elite += [std]\n",
    "    \n",
    "    if interactive_plot:\n",
    "        plt.clf()\n",
    "\n",
    "        for ind in pop:\n",
    "            plt.scatter(convert_binary_individual(ind), ind.fitness.values[0])\n",
    "\n",
    "        plt.plot(x_sample, y, linewidth=1)\n",
    "        plt.xlabel('$x$')\n",
    "        plt.ylabel('$f(x)$')\n",
    "        plt.title('Generation {}/{}'.format(g, MAX_GEN))\n",
    "        display(plt.gcf())\n",
    "        clear_output(wait=True)\n",
    "        # time.sleep(0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Value')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 279,
       "width": 461
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,4))\n",
    "xx= np.arange(1,len(avgs_elite)+1)\n",
    "plt.plot(xx, maxs_elite, label='Max fitness')\n",
    "plt.plot(xx, avgs_elite, label='Mean fitness')\n",
    "plt.fill_between(xx, mins_elite, maxs_elite, alpha=0.25)\n",
    "plt.plot(xx, stds_elite, label='Std. deviation')\n",
    "plt.legend(frameon=True)\n",
    "plt.xlabel('Iterations')\n",
    "plt.ylabel('Value')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernel_info": {
   "name": "python3"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  },
  "nteract": {
   "version": "0.2.0"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}