ga.ipynb

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   "source": [
    "# Genetic Risk\n",
    "\n",
    "This notebook is everything you need to run the genetic risk algorithm.\n",
    "\n",
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "from src.riskga import PlayerPool\n",
    "from src.geneticplayer import GeneticPlayer\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Functions\n",
    "\n",
    "These functions allow plotting and running the algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot(ga):\n",
    "    plot_args={'layout':(3, 8), 'figsize':(16,8)}\n",
    "    ga.gene_df.drop('iteration', axis=1).hist(**plot_args)\n",
    "    plt.show()\n",
    "def run(ga, n):\n",
    "    for i in range(n):\n",
    "        ga.iteration()\n",
    "        plot(ga)\n",
    "    ga.save('risk.json')\n",
    "    ga.save_log('risk.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initialize\n",
    "\n",
    "The intermediate state of the model is stored in `risk.json`. If such a file is found, initialize from the file. Else start a clean pool."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
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   "source": [
    "%%time\n",
    "if os.path.exists('risk.json'):\n",
    "    ga = PlayerPool.load(GeneticPlayer, 'risk.json',  ranking_iterations=16, pool_size=1500)\n",
    "else:\n",
    "    ga = PlayerPool(GeneticPlayer, pool_size=1500, ranking_iterations=16)\n",
    "plot(ga)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run\n",
    "\n",
    "This takes a while..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
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   },
   "outputs": [],
   "source": [
    "%%time\n",
    "run(ga, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "run(ga, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "run(ga, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "run(ga, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "run(ga, 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "run(ga, 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "run(ga, 30)"
   ]
  }
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