deploy_xgboost.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
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   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import xgboost as xgb\n",
    "import re"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  1. Load data and build a model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"data/train.csv\")\n",
    "test  = pd.read_csv(\"data/test.csv\")\n",
    "X_y_train = xgb.DMatrix(data=train[['Pclass', 'Age', 'Fare', 'SibSp', 'Parch']], label= train['Survived'])\n",
    "X_test    = xgb.DMatrix(data=test[['Pclass', 'Age', 'Fare', 'SibSp', 'Parch']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
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       "      <td>1</td>\n",
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       "      <th>3</th>\n",
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       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "   Pclass   Age     Fare  SibSp  Parch  Survived\n",
       "0       3  22.0   7.2500      1      0         0\n",
       "1       1  38.0  71.2833      1      0         1\n",
       "2       3  26.0   7.9250      0      0         1\n",
       "3       1  35.0  53.1000      1      0         1\n",
       "4       3  35.0   8.0500      0      0         0"
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[['Pclass', 'Age', 'Fare', 'SibSp', 'Parch', 'Survived']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "params = {\n",
    "          'base_score': np.mean(train['Survived']),\n",
    "          'eta':  0.1,\n",
    "          'max_depth': 3,\n",
    "          'gamma' :3,\n",
    "          'objective'   :'reg:linear',\n",
    "          'eval_metric' :'mae'\n",
    "         }\n",
    "model = xgb.train(params=params, \n",
    "                  dtrain=X_y_train, \n",
    "                  num_boost_round=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### visualization of model (need to install graphviz in order to use this feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
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   "outputs": [
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   "source": [
    "xgb.to_graphviz(booster = model, num_trees=0)"
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     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb.to_graphviz(booster = model, num_trees=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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    "xgb.to_graphviz(booster = model, num_trees=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
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     "metadata": {},
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   ],
   "source": [
    "model.get_dump()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### convert dump string to .py file "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def string_parser(s):\n",
    "    if len(re.findall(r\":leaf=\", s)) == 0:\n",
    "        out  = re.findall(r\"[\\w.-]+\", s)\n",
    "        tabs = re.findall(r\"[\\t]+\", s)\n",
    "        if (out[4] == out[8]):\n",
    "            missing_value_handling = (\" or np.isnan(x['\" + out[1] + \"']) \")\n",
    "        else:\n",
    "            missing_value_handling = \"\"\n",
    "            \n",
    "        if len(tabs) > 0:\n",
    "            return (re.findall(r\"[\\t]+\", s)[0].replace('\\t', '    ') + \n",
    "                    '        if state == ' + out[0] + ':\\n' +\n",
    "                    re.findall(r\"[\\t]+\", s)[0].replace('\\t', '    ') +\n",
    "                    '            state = (' + out[4] +  \n",
    "                    ' if ' +  \"x['\" + out[1] +\"']<\" + out[2] + missing_value_handling + \n",
    "                    ' else ' + out[6] + ')\\n' )\n",
    "        \n",
    "        else:\n",
    "            return ('        if state == ' + out[0] + ':\\n' +\n",
    "                    '            state = (' + out[4] +  \n",
    "                    ' if ' +  \"x['\" + out[1] +\"']<\" + out[2] + missing_value_handling +\n",
    "                    ' else ' + out[6] + ')\\n' )\n",
    "    else:\n",
    "        out = re.findall(r\"[\\d.-]+\", s)\n",
    "        return (re.findall(r\"[\\t]+\", s)[0].replace('\\t', '    ') + \n",
    "                '        if state == ' + out[0] + ':\\n    ' +\n",
    "                re.findall(r\"[\\t]+\", s)[0].replace('\\t', '    ') + \n",
    "                '        return ' + out[1] + '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def tree_parser(tree, i):\n",
    "    if i == 0:\n",
    "        return ('    if num_booster == 0:\\n        state = 0\\n'\n",
    "             + \"\".join([string_parser(tree.split('\\n')[i]) for i in range(len(tree.split('\\n'))-1)]))\n",
    "    else:\n",
    "        return ('    elif num_booster == '+str(i)+':\\n        state = 0\\n'\n",
    "             + \"\".join([string_parser(tree.split('\\n')[i]) for i in range(len(tree.split('\\n'))-1)])) \n",
    "    \n",
    "def model_to_py(base_score, model, out_file):\n",
    "    trees = model.get_dump()\n",
    "    result = [\"import numpy as np\\n\\n\" \n",
    "             +\"def xgb_tree(x, num_booster):\\n\"]\n",
    "    \n",
    "    for i in range(len(trees)):\n",
    "        result.append(tree_parser(trees[i], i))\n",
    "    \n",
    "    with open(out_file, 'a') as the_file:\n",
    "        the_file.write(\"\".join(result) + \"\\ndef xgb_predict(x):\\n    predict = \" + str(base_score) + \"\\n\"\n",
    "                 + \"# initialize prediction with base score\\n\" \n",
    "                 + \"    for i in range(\" \n",
    "                 + str(len(trees))  \n",
    "                 + \"):\\n        predict = predict + xgb_tree(x, i)\"\n",
    "                 + \"\\n    return predict\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model_to_py(params['base_score'], model, 'xgb_model.py')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### prediction using dump file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.34158018383838384\n",
      "0.4139655918383838\n"
     ]
    }
   ],
   "source": [
    "import xgb_model\n",
    "\n",
    "passenger_data_1 = {'Pclass':3, 'Age':np.nan, 'SibSp':0, 'Parch':0, 'Fare':7.8958}\n",
    "passenger_data_2 = {'Pclass':1, 'Age':46, 'SibSp':0, 'Parch':0, 'Fare':26}\n",
    "\n",
    "print(xgb_model.xgb_predict(passenger_data_1))\n",
    "print(xgb_model.xgb_predict(passenger_data_2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test=  model.predict(X_test)   \n",
    "test['pred'] = y_test\n",
    "test[['Pclass', 'Age', 'Fare', 'SibSp', 'Parch','pred']].iloc[10:].head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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