ModelEnvironmentalScience.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import findspark\n",
    "findspark.init('/home/sdmohant/spark-3.0.0-bin-hadoop2.7')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyspark\n",
    "from pyspark import SparkContext,SparkConf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.sql import SparkSession"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sdmohant/spark-3.0.0-bin-hadoop2.7/python/pyspark/context.py:220: DeprecationWarning: Support for Python 2 and Python 3 prior to version 3.6 is deprecated as of Spark 3.0. See also the plan for dropping Python 2 support at https://spark.apache.org/news/plan-for-dropping-python-2-support.html.\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "#spark = SparkSession.builder.appName(\"MyApp\").getOrCreate()\n",
    "#spark = SparkSession.builder.appName(\"MyName\").getOrCreate()\n",
    "conf = SparkConf().set(\"spark.cores.max\", \"16\") \\\n",
    "    .set(\"spark.driver.memory\", \"16g\") \\\n",
    "    .set(\"spark.executor.memory\", \"16g\") \\\n",
    "    .set(\"spark.executor.memory_overhead\", \"16g\") \\\n",
    "    .set(\"spark.driver.maxResultsSize\", \"0\") \\\n",
    "    .set(\"spark.sql.shuffle.partitions\",\"300\")\n",
    "\n",
    "sc = SparkContext(appName=\"envsciencemodel\", conf=conf)\n",
    "spark = SparkSession(sc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.sql.functions import isnan, count, col, when"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "envscienceFOSID = 39432304"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "papers = spark.read.options(header = True, inferSchema = True,delimiter=',').csv(\"AllPapersFOS1.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "papers_filtered = papers.filter(col('FOS_ID').isin(['39432304']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pyspark.sql.dataframe.DataFrame"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(papers_filtered)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35780"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "papers_filtered.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "papers_pd = papers_filtered.toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PaperID</th>\n",
       "      <th>Rank</th>\n",
       "      <th>PaperYear</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Issue</th>\n",
       "      <th>ReferenceCount</th>\n",
       "      <th>CitationCount</th>\n",
       "      <th>EstimatedCount</th>\n",
       "      <th>FOS_ID</th>\n",
       "      <th>PaperAge</th>\n",
       "      <th>IsJournal</th>\n",
       "      <th>min_FOSRank</th>\n",
       "      <th>max_FOSRank</th>\n",
       "      <th>max_FOSPaperCount</th>\n",
       "      <th>min_FOSPaperCount</th>\n",
       "      <th>min_FOSCitationCount</th>\n",
       "      <th>max_FOSCitationCount</th>\n",
       "      <th>PaperRefCnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7947795</td>\n",
       "      <td>23352</td>\n",
       "      <td>1964</td>\n",
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       "      <td>22</td>\n",
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       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>8436</td>\n",
       "      <td>12935</td>\n",
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       "      <td>81162</td>\n",
       "      <td>338871</td>\n",
       "      <td>6506374</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>57879122</td>\n",
       "      <td>25330</td>\n",
       "      <td>2014</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>39432304</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>8436</td>\n",
       "      <td>10284</td>\n",
       "      <td>1830253</td>\n",
       "      <td>37168</td>\n",
       "      <td>216494</td>\n",
       "      <td>6506374</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>87903810</td>\n",
       "      <td>23667</td>\n",
       "      <td>1975</td>\n",
       "      <td>254</td>\n",
       "      <td>5497</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>39432304</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>8188</td>\n",
       "      <td>11856</td>\n",
       "      <td>1830253</td>\n",
       "      <td>2825</td>\n",
       "      <td>10036</td>\n",
       "      <td>34103285</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>88768105</td>\n",
       "      <td>25658</td>\n",
       "      <td>2011</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>39432304</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>8436</td>\n",
       "      <td>12623</td>\n",
       "      <td>1830253</td>\n",
       "      <td>88590</td>\n",
       "      <td>159405</td>\n",
       "      <td>6506374</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>92491293</td>\n",
       "      <td>30666</td>\n",
       "      <td>1963</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>39432304</td>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>8436</td>\n",
       "      <td>10775</td>\n",
       "      <td>1830253</td>\n",
       "      <td>621643</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    PaperID   Rank  PaperYear  Volume  Issue  ReferenceCount  CitationCount  \\\n",
       "0   7947795  23352       1964       0      0               0             22   \n",
       "1  57879122  25330       2014       0      0               0              0   \n",
       "2  87903810  23667       1975     254   5497               0              0   \n",
       "3  88768105  25658       2011       0      0               0              0   \n",
       "4  92491293  30666       1963       0      0               0              0   \n",
       "\n",
       "   EstimatedCount    FOS_ID  PaperAge  IsJournal  min_FOSRank  max_FOSRank  \\\n",
       "0              22  39432304        56          1         8436        12935   \n",
       "1               0  39432304         6          1         8436        10284   \n",
       "2               0  39432304        45          0         8188        11856   \n",
       "3               0  39432304         9          1         8436        12623   \n",
       "4               0  39432304        57          1         8436        10775   \n",
       "\n",
       "   max_FOSPaperCount  min_FOSPaperCount  min_FOSCitationCount  \\\n",
       "0            1830253              81162                338871   \n",
       "1            1830253              37168                216494   \n",
       "2            1830253               2825                 10036   \n",
       "3            1830253              88590                159405   \n",
       "4            1830253             621643               4556314   \n",
       "\n",
       "   max_FOSCitationCount  PaperRefCnt  \n",
       "0               6506374            0  \n",
       "1               6506374            0  \n",
       "2              34103285            0  \n",
       "3               6506374            0  \n",
       "4               6506374            0  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "papers_pd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "papers_pd.rename(columns = {'CitationCount':'label'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'PaperID', u'Rank', u'PaperYear', u'Volume', u'Issue',\n",
       "       u'ReferenceCount', u'label', u'EstimatedCount', u'FOS_ID', u'PaperAge',\n",
       "       u'IsJournal', u'min_FOSRank', u'max_FOSRank', u'max_FOSPaperCount',\n",
       "       u'min_FOSPaperCount', u'min_FOSCitationCount', u'max_FOSCitationCount',\n",
       "       u'PaperRefCnt'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "papers_pd.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "papers_pd_new = papers_pd.drop([\"PaperID\",\"EstimatedCount\",\"PaperYear\",\"Issue\",\"Volume\",\"FOS_ID\"],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'Rank', u'ReferenceCount', u'label', u'PaperAge', u'IsJournal',\n",
       "       u'min_FOSRank', u'max_FOSRank', u'max_FOSPaperCount',\n",
       "       u'min_FOSPaperCount', u'min_FOSCitationCount', u'max_FOSCitationCount',\n",
       "       u'PaperRefCnt'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "papers_pd_new.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "papers_pd_new = papers_pd_new.reindex(columns=['Rank','ReferenceCount','PaperAge','min_FOSRank','max_FOSRank','IsJournal','max_FOSPaperCount',\\\n",
    "                               'min_FOSPaperCount','min_FOSCitationCount','max_FOSCitationCount','PaperRefCnt','label'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>ReferenceCount</th>\n",
       "      <th>PaperAge</th>\n",
       "      <th>min_FOSRank</th>\n",
       "      <th>max_FOSRank</th>\n",
       "      <th>IsJournal</th>\n",
       "      <th>max_FOSPaperCount</th>\n",
       "      <th>min_FOSPaperCount</th>\n",
       "      <th>min_FOSCitationCount</th>\n",
       "      <th>max_FOSCitationCount</th>\n",
       "      <th>PaperRefCnt</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23352</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>22</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25330</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>8436</td>\n",
       "      <td>10284</td>\n",
       "      <td>1</td>\n",
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       "      <td>37168</td>\n",
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       "      <td>6506374</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23667</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>8188</td>\n",
       "      <td>11856</td>\n",
       "      <td>0</td>\n",
       "      <td>1830253</td>\n",
       "      <td>2825</td>\n",
       "      <td>10036</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25658</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>8436</td>\n",
       "      <td>12623</td>\n",
       "      <td>1</td>\n",
       "      <td>1830253</td>\n",
       "      <td>88590</td>\n",
       "      <td>159405</td>\n",
       "      <td>6506374</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30666</td>\n",
       "      <td>0</td>\n",
       "      <td>57</td>\n",
       "      <td>8436</td>\n",
       "      <td>10775</td>\n",
       "      <td>1</td>\n",
       "      <td>1830253</td>\n",
       "      <td>621643</td>\n",
       "      <td>4556314</td>\n",
       "      <td>6506374</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Rank  ReferenceCount  PaperAge  min_FOSRank  max_FOSRank  IsJournal  \\\n",
       "0  23352               0        56         8436        12935          1   \n",
       "1  25330               0         6         8436        10284          1   \n",
       "2  23667               0        45         8188        11856          0   \n",
       "3  25658               0         9         8436        12623          1   \n",
       "4  30666               0        57         8436        10775          1   \n",
       "\n",
       "   max_FOSPaperCount  min_FOSPaperCount  min_FOSCitationCount  \\\n",
       "0            1830253              81162                338871   \n",
       "1            1830253              37168                216494   \n",
       "2            1830253               2825                 10036   \n",
       "3            1830253              88590                159405   \n",
       "4            1830253             621643               4556314   \n",
       "\n",
       "   max_FOSCitationCount  PaperRefCnt  label  \n",
       "0               6506374            0     22  \n",
       "1               6506374            0      0  \n",
       "2              34103285            0      0  \n",
       "3               6506374            0      0  \n",
       "4               6506374            0      0  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "papers_pd_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(papers_pd_new.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = papers_pd_new.iloc[:, 0:11].values\n",
    "y = papers_pd_new.iloc[:, 11].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   23352,        0,       56,     8436,    12935,        1,\n",
       "         1830253,    81162,   338871,  6506374,        0],\n",
       "       [   25330,        0,        6,     8436,    10284,        1,\n",
       "         1830253,    37168,   216494,  6506374,        0],\n",
       "       [   23667,        0,       45,     8188,    11856,        0,\n",
       "         1830253,     2825,    10036, 34103285,        0],\n",
       "       [   25658,        0,        9,     8436,    12623,        1,\n",
       "         1830253,    88590,   159405,  6506374,        0],\n",
       "       [   30666,        0,       57,     8436,    10775,        1,\n",
       "         1830253,   621643,  4556314,  6506374,        0]], dtype=int32)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kf = KFold(n_splits=5)\n",
    "kf.get_n_splits(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Mean Absolute Error:', 1.9500956719040452)\n",
      "('Mean Squared Error:', 923.9964595634815)\n",
      "('Root Mean Squared Error:', 30.397310071180335)\n",
      "('Mean Absolute Error:', 1.6542599062085788)\n",
      "('Mean Squared Error:', 206.88588466663666)\n",
      "('Root Mean Squared Error:', 14.38352824124306)\n",
      "('Mean Absolute Error:', 2.1681637969884235)\n",
      "('Mean Squared Error:', 1070.4210758909849)\n",
      "('Root Mean Squared Error:', 32.717290167295104)\n",
      "('Mean Absolute Error:', 2.2055272184693617)\n",
      "('Mean Squared Error:', 2180.710986753475)\n",
      "('Root Mean Squared Error:', 46.69808333061942)\n",
      "('Mean Absolute Error:', 1.3230572427010467)\n",
      "('Mean Squared Error:', 51.94662605186229)\n",
      "('Root Mean Squared Error:', 7.207400783351949)\n"
     ]
    }
   ],
   "source": [
    "rmse_list = []\n",
    "for train_index, test_index in kf.split(X,y):\n",
    "    X_train, X_test = X[train_index], X[test_index]\n",
    "    y_train, y_test = y[train_index], y[test_index]\n",
    "    regressor = RandomForestRegressor(n_estimators=100, max_depth=30, random_state=0)\n",
    "    regressor.fit(X_train, y_train)\n",
    "    y_pred = regressor.predict(X_test)\n",
    "    rmse_list.append(np.sqrt(metrics.mean_squared_error(y_test, y_pred)))\n",
    "    print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))\n",
    "    print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))\n",
    "    print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Mean RMSE is :', 26.280722518737974)\n"
     ]
    }
   ],
   "source": [
    "rmse_sum = 0\n",
    "for index in range(0,5):\n",
    "    rmse_sum = rmse_sum + rmse_list[index]\n",
    "mean_rmse = rmse_sum/5\n",
    "print(\"Mean RMSE is :\",mean_rmse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparison with Microsoft Estimations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_est = np.array(papers_pd[\"EstimatedCount\"])\n",
    "y_act = np.array(papers_pd[\"label\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Mean Absolute Error of EstimatedCount:', 1.9495807713806597)\n",
      "('Mean Squared Error of EstimatedCount:', 3931.573784237004)\n",
      "('Root Mean Squared of with EstimatedCount:', 62.70226299135466)\n"
     ]
    }
   ],
   "source": [
    "print('Mean Absolute Error of EstimatedCount:', metrics.mean_absolute_error(y_act, y_est))\n",
    "print('Mean Squared Error of EstimatedCount:', metrics.mean_squared_error(y_act, y_est))\n",
    "print('Root Mean Squared of with EstimatedCount:', np.sqrt(metrics.mean_squared_error(y_act, y_est)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=0.33)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "regressor = xgb.XGBRegressor(\n",
    "    n_estimators=150,\n",
    "    reg_lambda=1,\n",
    "    gamma=0,\n",
    "    max_depth=30\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35.75438035166671"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.fit(X_train, y_train)\n",
    "y_pred = regressor.predict(X_test)\n",
    "np.sqrt(metrics.mean_squared_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kf = KFold(n_splits=5)\n",
    "kf.get_n_splits(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Mean Absolute Error of XGBoost Kfold:', 2.1029547167688927)\n",
      "('Mean Squared Error of XGBoost Kfold:', 1144.3993246912696)\n",
      "('Root Mean Squared Error of XGBoost Kfold:', 33.828971676527054)\n",
      "('Mean Absolute Error of XGBoost Kfold:', 1.995200872183879)\n",
      "('Mean Squared Error of XGBoost Kfold:', 523.2402574867415)\n",
      "('Root Mean Squared Error of XGBoost Kfold:', 22.87444551211551)\n",
      "('Mean Absolute Error of XGBoost Kfold:', 2.302791425395005)\n",
      "('Mean Squared Error of XGBoost Kfold:', 951.8769340408951)\n",
      "('Root Mean Squared Error of XGBoost Kfold:', 30.852502881304382)\n",
      "('Mean Absolute Error of XGBoost Kfold:', 2.3225619246951026)\n",
      "('Mean Squared Error of XGBoost Kfold:', 2212.337741947967)\n",
      "('Root Mean Squared Error of XGBoost Kfold:', 47.03549449030984)\n",
      "('Mean Absolute Error of XGBoost Kfold:', 1.4981082770546565)\n",
      "('Mean Squared Error of XGBoost Kfold:', 177.64003683753728)\n",
      "('Root Mean Squared Error of XGBoost Kfold:', 13.328167047180091)\n"
     ]
    }
   ],
   "source": [
    "rmse_list = []\n",
    "for train_index, test_index in kf.split(X,y):\n",
    "    #print(\"TRAIN:\", train_index, \"TEST:\", test_index)\n",
    "    X_train, X_test = X[train_index], X[test_index]\n",
    "    y_train, y_test = y[train_index], y[test_index]\n",
    "    regressor = xgb.XGBRegressor(\n",
    "                n_estimators=100,\n",
    "                reg_lambda=1,\n",
    "                gamma=0,\n",
    "                max_depth=40\n",
    "                )\n",
    "    regressor.fit(X_train, y_train)\n",
    "    y_pred = regressor.predict(X_test)\n",
    "    rmse_list.append(np.sqrt(metrics.mean_squared_error(y_test, y_pred)))\n",
    "    print('Mean Absolute Error of XGBoost Kfold:', metrics.mean_absolute_error(y_test, y_pred))\n",
    "    print('Mean Squared Error of XGBoost Kfold:', metrics.mean_squared_error(y_test, y_pred))\n",
    "    print('Root Mean Squared Error of XGBoost Kfold:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Mean RMSE is :', 29.58391632148738)\n"
     ]
    }
   ],
   "source": [
    "rmse_sum = 0\n",
    "for index in range(0,5):\n",
    "    rmse_sum = rmse_sum + rmse_list[index]\n",
    "mean_rmse = rmse_sum/5\n",
    "print(\"Mean RMSE is :\",mean_rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "importances = regressor.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature: 0, Score: 0.50283\n",
      "Feature: 1, Score: 0.06808\n",
      "Feature: 2, Score: 0.14265\n",
      "Feature: 3, Score: 0.03175\n",
      "Feature: 4, Score: 0.09474\n",
      "Feature: 5, Score: 0.02423\n",
      "Feature: 6, Score: 0.00162\n",
      "Feature: 7, Score: 0.08217\n",
      "Feature: 8, Score: 0.03641\n",
      "Feature: 9, Score: 0.00942\n",
      "Feature: 10, Score: 0.00610\n"
     ]
    }
   ],
   "source": [
    "for i,v in enumerate(importances):\n",
    "\tprint('Feature: %0d, Score: %.5f' % (i,v))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = papers_pd_new.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f18e8d7f6d0>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(pd.Series(importances, index=cols[0:11]).plot(kind='barh'))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "2.7.15rc1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}