{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# XGBoost Model Analysis\n",
    "\n",
    "This notebook can be used to load and analysis model learnt from all xgboost bindings, including distributed training. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Please change the ```pkg_path``` and ```model_file``` to be correct path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pkg_path = '../../python-package/'\n",
    "model_file = 's3://my-bucket/xgb-demo/model/0002.model'\n",
    "sys.path.insert(0, pkg_path)\n",
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot the Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# plot the first two trees.\n",
    "bst = xgb.Booster(model_file=model_file)\n",
    "xgb.plot_importance(bst)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot the First Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tree_id = 0\n",
    "xgb.to_graphviz(bst, tree_id)"
   ]
  }
 ],
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