add_featurenames.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.4'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import xgboost as xgb\n",
    "from sklearn import datasets\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "xgb.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal length (cm)',\n",
       " 'sepal width (cm)',\n",
       " 'petal length (cm)',\n",
       " 'petal width (cm)']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris = datasets.load_iris()\n",
    "iris.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "iris = datasets.load_iris()\n",
    "dm = xgb.DMatrix(iris.data, label=iris.target,\n",
    "                 feature_names=['SepalLength', 'SepalWidth', 'PetalLength', u'PetalWidth'])\n",
    "\n",
    "np.random.seed(1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['SepalLength', 'SepalWidth', 'PetalLength', u'PetalWidth']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params={'objective': 'multi:softprob',\n",
    "        'eval_metric': 'mlogloss',\n",
    "        'eta': 0.3,\n",
    "        'num_class': 3}\n",
    "\n",
    "bst = xgb.train(params, dm, num_boost_round=18)\n",
    "bst.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'PetalLength': 95, 'PetalWidth': 59, 'SepalLength': 17, 'SepalWidth': 16}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bst.get_fscore()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SepalLength\n",
      "PetalWidth\n",
      "PetalLength\n",
      "SepalWidth\n"
     ]
    }
   ],
   "source": [
    "for k in bst.get_fscore():\n",
    "    print(k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x106389fd0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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urbUuJydHa3nfvn0YNmwYQkJCUFJSgvDwcJ2PO27cOPj6+rZab2RkJD6Wy+XQaDQ677OF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\nsrKyMHbsWAB4oOO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      "text/plain": [
       "<matplotlib.figure.Figure at 0x103125d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xgb.plot_importance(bst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
       "<!-- Generated by graphviz version 2.28.0 (20120430.0057)\n",
       " -->\n",
       "<!-- Title: _anonymous_0 Pages: 1 -->\n",
       "<svg width=\"225pt\" height=\"258pt\"\n",
       " viewBox=\"0.00 0.00 225.00 258.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       "<g id=\"graph1\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 254)\">\n",
       "<title>_anonymous_0</title>\n",
       "<polygon fill=\"white\" stroke=\"white\" points=\"-4,5 -4,-254 222,-254 222,5 -4,5\"/>\n",
       "<!-- 0 -->\n",
       "<g id=\"node1\" class=\"node\"><title>0</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"107\" cy=\"-170\" rx=\"80.0675\" ry=\"80.09\"/>\n",
       "<text text-anchor=\"middle\" x=\"107\" y=\"-164.4\" font-family=\"Times,serif\" font-size=\"14.00\">PetalLength&lt;2.45</text>\n",
       "</g>\n",
       "<!-- 1 -->\n",
       "<g id=\"node3\" class=\"node\"><title>1</title>\n",
       "<polygon fill=\"none\" stroke=\"black\" points=\"96.5306,-36 -0.530646,-36 -0.530646,-0 96.5306,-0 96.5306,-36\"/>\n",
       "<text text-anchor=\"middle\" x=\"48\" y=\"-12.4\" font-family=\"Times,serif\" font-size=\"14.00\">leaf=0.430622</text>\n",
       "</g>\n",
       "<!-- 0&#45;&gt;1 -->\n",
       "<g id=\"edge2\" class=\"edge\"><title>0&#45;&gt;1</title>\n",
       "<path fill=\"none\" stroke=\"#0000ff\" d=\"M78.0185,-95.3183C71.0303,-77.5517 64.0033,-59.6864 58.5067,-45.712\"/>\n",
       "<polygon fill=\"#0000ff\" stroke=\"#0000ff\" points=\"61.7148,-44.3061 54.7973,-36.2813 55.2006,-46.8684 61.7148,-44.3061\"/>\n",
       "<text text-anchor=\"middle\" x=\"103.614\" y=\"-57.4\" font-family=\"Times,serif\" font-size=\"14.00\">yes, missing</text>\n",
       "</g>\n",
       "<!-- 2 -->\n",
       "<g id=\"node5\" class=\"node\"><title>2</title>\n",
       "<polygon fill=\"none\" stroke=\"black\" points=\"217.028,-36 114.972,-36 114.972,-0 217.028,-0 217.028,-36\"/>\n",
       "<text text-anchor=\"middle\" x=\"166\" y=\"-12.4\" font-family=\"Times,serif\" font-size=\"14.00\">leaf=&#45;0.220049</text>\n",
       "</g>\n",
       "<!-- 0&#45;&gt;2 -->\n",
       "<g id=\"edge4\" class=\"edge\"><title>0&#45;&gt;2</title>\n",
       "<path fill=\"none\" stroke=\"#ff0000\" d=\"M135.981,-95.3183C142.97,-77.5517 149.997,-59.6864 155.493,-45.712\"/>\n",
       "<polygon fill=\"#ff0000\" stroke=\"#ff0000\" points=\"158.799,-46.8684 159.203,-36.2813 152.285,-44.3061 158.799,-46.8684\"/>\n",
       "<text text-anchor=\"middle\" x=\"159\" y=\"-57.4\" font-family=\"Times,serif\" font-size=\"14.00\">no</text>\n",
       "</g>\n",
       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.dot.Digraph at 0x103125d50>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb.to_graphviz(bst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "2.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}

add_featuretypes.ipynb

pandas+cv.ipynb

visualize_xgboost.ipynb