07-05-XGBoost Installation Instruction.ipynb

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    "# XGBoost\n",
    "\n",
    "Installation Guide\n",
    "\n",
    "Following [part 1](https://www.ibm.com/developerworks/community/blogs/jfp/entry/Installing_XGBoost_For_Anaconda_on_Windows?lang=en) works for me.\n",
    "\n",
    "However, I do need to download [windows x64 binaries](http://www.picnet.com.au/blogs/guido/post/2016/09/22/xgboost-windows-x64-binaries-for-download/)\n",
    "\n",
    "That's all that I needed\n",
    "\n",
    "More info:\n",
    "\n",
    "http://www.picnet.com.au/blogs/guido/post/2016/09/22/how-to-build-xgboost-on-windows/\n",
    "\n",
    "http://xgboost.readthedocs.io/en/latest/build.html\n",
    "\n",
    "https://github.com/dmlc/xgboost/blob/master/doc/build.md#python-package-installation\n",
    "\n",
    "\n",
    "# MacOS\n",
    "Just follow the following link\n",
    "\n",
    "https://anaconda.org/conda-forge/xgboost\n",
    "\n"
   ]
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
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   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "import numpy as np"
   ]
  },
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   "cell_type": "code",
   "execution_count": 2,
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   "source": [
    "data = np.random.rand(5,10) # 5 entities, each contains 10 features\n",
    "label = np.random.randint(2, size=5) # binary target\n",
    "dtrain = xgb.DMatrix( data, label=label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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   "source": [
    "dtest = dtrain"
   ]
  },
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    "param = {'bst:max_depth':2, 'bst:eta':1, 'silent':1, 'objective':'binary:logistic' }\n",
    "param['nthread'] = 4\n",
    "param['eval_metric'] = 'auc'"
   ]
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   "execution_count": 5,
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   "source": [
    "evallist  = [(dtest,'eval'), (dtrain,'train')]"
   ]
  },
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   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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     "output_type": "stream",
     "text": [
      "[0]\teval-auc:0.5\ttrain-auc:0.5\n",
      "[1]\teval-auc:0.5\ttrain-auc:0.5\n",
      "[2]\teval-auc:0.5\ttrain-auc:0.5\n",
      "[3]\teval-auc:0.5\ttrain-auc:0.5\n",
      "[4]\teval-auc:0.5\ttrain-auc:0.5\n",
      "[5]\teval-auc:0.5\ttrain-auc:0.5\n",
      "[6]\teval-auc:0.5\ttrain-auc:0.5\n",
      "[7]\teval-auc:0.5\ttrain-auc:0.5\n",
      "[8]\teval-auc:0.5\ttrain-auc:0.5\n",
      "[9]\teval-auc:0.5\ttrain-auc:0.5\n"
     ]
    }
   ],
   "source": [
    "num_round = 10\n",
    "bst = xgb.train( param, dtrain, num_round, evallist )"
   ]
  }
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