ModelMaterialsScience.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=\"materialsciencemodel\", 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": [
    "materialsscienceFOSID = spark.read.options(header = True, inferSchema = True,delimiter=',').csv(\"FOSDomains.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "materialssciFOSID = materialsscienceFOSID.filter(materialsscienceFOSID[\"Domain\"]==\"MaterialScience\").collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "papers = spark.read.options(header = True, inferSchema = True,delimiter=',').csv(\"AllPapersFOS1.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.sql.types import ArrayType, StructField, StructType, StringType, IntegerType,LongType"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "schema = StructType([\n",
    "    StructField('FOS_ID', LongType(), True),\n",
    "    StructField('Domain', StringType(), True)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert list to RDD\n",
    "rdd = spark.sparkContext.parallelize(materialssciFOSID)\n",
    "\n",
    "# Create data frame\n",
    "fos = spark.createDataFrame(rdd,schema)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------+---------------+\n",
      "|   FOS_ID|         Domain|\n",
      "+---------+---------------+\n",
      "|188027245|MaterialScience|\n",
      "|159985019|MaterialScience|\n",
      "+---------+---------------+\n",
      "only showing top 2 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "fos.show(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "papers_filtered = papers.join(fos,on=\"FOS_ID\",how=\"inner\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "papers_filtered = papers_filtered.withColumnRenamed(\"CitationCount\",\"label\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "papers_filtered1 = papers_filtered.select([\"PaperID\",\"EstimatedCount\",\"Rank\",\"ReferenceCount\",\"PaperAge\",\\\n",
    "                        \"IsJournal\",\"min_FOSRank\",\"max_FOSRank\",\"max_FOSPaperCount\",\"min_FOSPaperCount\",\"min_FOSCitationCount\",\\\n",
    "                        \"max_FOSCitationCount\",\"PaperRefCnt\",\"label\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4799426"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "papers_filtered1.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "(sample1,sample2) = papers_filtered1.randomSplit([0.3,0.7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1439680"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample1.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample1_pd = sample1.toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'PaperID', u'EstimatedCount', u'Rank', u'ReferenceCount', 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', u'label'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample1_pd.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_est = np.array(sample1_pd[\"EstimatedCount\"])\n",
    "y_act = np.array(sample1_pd[\"label\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Mean Absolute Error of EstimatedCount:', 1.1620526783729719)\n",
      "('Mean Squared Error of EstimatedCount:', 662.4387822293843)\n",
      "('Root Mean Squared of with EstimatedCount:', 25.737886125892008)\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": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = sample1_pd.iloc[:, 0:11].values\n",
    "y = sample1_pd.iloc[:, 11].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 2, 7, 4, 8], dtype=int32)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>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",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23586</td>\n",
       "      <td>4</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>7838</td>\n",
       "      <td>14213</td>\n",
       "      <td>8960343</td>\n",
       "      <td>1471</td>\n",
       "      <td>4225</td>\n",
       "      <td>43049958</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23276</td>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>7838</td>\n",
       "      <td>11794</td>\n",
       "      <td>8960343</td>\n",
       "      <td>5570</td>\n",
       "      <td>33564</td>\n",
       "      <td>49764336</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Rank  ReferenceCount  PaperAge  IsJournal  min_FOSRank  max_FOSRank  \\\n",
       "0  23586               4        34          0         7838        14213   \n",
       "1  23276               3        30          0         7838        11794   \n",
       "\n",
       "   max_FOSPaperCount  min_FOSPaperCount  min_FOSCitationCount  \\\n",
       "0            8960343               1471                  4225   \n",
       "1            8960343               5570                 33564   \n",
       "\n",
       "   max_FOSCitationCount  PaperRefCnt  label  \n",
       "0              43049958            0      3  \n",
       "1              49764336            0      2  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample1_pd.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kf = KFold(n_splits=5)\n",
    "kf.get_n_splits(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('TRAIN:', array([ 287936,  287937,  287938, ..., 1439677, 1439678, 1439679]), 'TEST:', array([     0,      1,      2, ..., 287933, 287934, 287935]))\n",
      "('Mean Absolute Error:', 2.408161866189057)\n",
      "('Mean Squared Error:', 91.07079717976261)\n",
      "('Root Mean Squared Error:', 9.543102073213019)\n",
      "('TRAIN:', array([      0,       1,       2, ..., 1439677, 1439678, 1439679]), 'TEST:', array([287936, 287937, 287938, ..., 575869, 575870, 575871]))\n",
      "('Mean Absolute Error:', 4.22766584204813)\n",
      "('Mean Squared Error:', 1008.0275077752224)\n",
      "('Root Mean Squared Error:', 31.749448936559865)\n",
      "('TRAIN:', array([      0,       1,       2, ..., 1439677, 1439678, 1439679]), 'TEST:', array([575872, 575873, 575874, ..., 863805, 863806, 863807]))\n",
      "('Mean Absolute Error:', 1.2894564096375107)\n",
      "('Mean Squared Error:', 16.415516145768127)\n",
      "('Root Mean Squared Error:', 4.051606612909023)\n",
      "('TRAIN:', array([      0,       1,       2, ..., 1439677, 1439678, 1439679]), 'TEST:', array([ 863808,  863809,  863810, ..., 1151741, 1151742, 1151743]))\n",
      "('Mean Absolute Error:', 0.811093361619794)\n",
      "('Mean Squared Error:', 62.015084306902075)\n",
      "('Root Mean Squared Error:', 7.874965670204669)\n",
      "('TRAIN:', array([      0,       1,       2, ..., 1151741, 1151742, 1151743]), 'TEST:', array([1151744, 1151745, 1151746, ..., 1439677, 1439678, 1439679]))\n",
      "('Mean Absolute Error:', 0.4092753767316373)\n",
      "('Mean Squared Error:', 47.08147550513117)\n",
      "('Root Mean Squared Error:', 6.861594239324501)\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 = 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": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Mean RMSE is :', 12.016143506442216)\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": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "featureslist=sample1_pd.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature: 0, Score: 0.58821\n",
      "Feature: 1, Score: 0.02218\n",
      "Feature: 2, Score: 0.28091\n",
      "Feature: 3, Score: 0.00287\n",
      "Feature: 4, Score: 0.01704\n",
      "Feature: 5, Score: 0.02104\n",
      "Feature: 6, Score: 0.01072\n",
      "Feature: 7, Score: 0.01809\n",
      "Feature: 8, Score: 0.01785\n",
      "Feature: 9, Score: 0.00821\n",
      "Feature: 10, Score: 0.01289\n"
     ]
    }
   ],
   "source": [
    "for i,v in enumerate(importances):\n",
    "\tprint('Feature: %0d, Score: %.5f' % (i,v))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Feature Importances')"
      ]
     },
     "execution_count": 55,
     "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": [
    "importances = regressor.feature_importances_\n",
    "x_values = list(range(len(importances)))\n",
    "\n",
    "plt.bar(x_values, importances, orientation = 'vertical')\n",
    "plt.xticks(x_values, featureslist[0:10], rotation=70)\n",
    "plt.ylabel('Importance')\n",
    "plt.xlabel('Feature')\n",
    "plt.title('Feature Importances')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f57dc9d6f10>"
      ]
     },
     "execution_count": 84,
     "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=featureslist[0:11]).plot(kind='barh')) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XGBoost Regression model using KFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kf = KFold(n_splits=5)\n",
    "kf.get_n_splits(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('TRAIN:', array([ 287936,  287937,  287938, ..., 1439677, 1439678, 1439679]), 'TEST:', array([     0,      1,      2, ..., 287933, 287934, 287935]))\n",
      "('Mean Absolute Error of XGBoost Kfold:', 2.5111461904193444)\n",
      "('Mean Squared Error of XGBoost Kfold:', 100.54498307353973)\n",
      "('Root Mean Squared Error of XGBoost Kfold:', 10.027212128679622)\n",
      "('TRAIN:', array([      0,       1,       2, ..., 1439677, 1439678, 1439679]), 'TEST:', array([287936, 287937, 287938, ..., 575869, 575870, 575871]))\n",
      "('Mean Absolute Error of XGBoost Kfold:', 4.490979483426358)\n",
      "('Mean Squared Error of XGBoost Kfold:', 1055.9890207390292)\n",
      "('Root Mean Squared Error of XGBoost Kfold:', 32.4959846864044)\n",
      "('TRAIN:', array([      0,       1,       2, ..., 1439677, 1439678, 1439679]), 'TEST:', array([575872, 575873, 575874, ..., 863805, 863806, 863807]))\n",
      "('Mean Absolute Error of XGBoost Kfold:', 1.316222335662793)\n",
      "('Mean Squared Error of XGBoost Kfold:', 18.45193791413969)\n",
      "('Root Mean Squared Error of XGBoost Kfold:', 4.2955718960505935)\n",
      "('TRAIN:', array([      0,       1,       2, ..., 1439677, 1439678, 1439679]), 'TEST:', array([ 863808,  863809,  863810, ..., 1151741, 1151742, 1151743]))\n",
      "('Mean Absolute Error of XGBoost Kfold:', 0.8062159271738351)\n",
      "('Mean Squared Error of XGBoost Kfold:', 13.979235311884267)\n",
      "('Root Mean Squared Error of XGBoost Kfold:', 3.738881558953729)\n",
      "('TRAIN:', array([      0,       1,       2, ..., 1151741, 1151742, 1151743]), 'TEST:', array([1151744, 1151745, 1151746, ..., 1439677, 1439678, 1439679]))\n",
      "('Mean Absolute Error of XGBoost Kfold:', 0.4147295071522967)\n",
      "('Mean Squared Error of XGBoost Kfold:', 59.10239051019896)\n",
      "('Root Mean Squared Error of XGBoost Kfold:', 7.687807913195995)\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=30\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": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Mean RMSE is :', 11.649091636656868)\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": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "importances = regressor.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f57d4b21590>"
      ]
     },
     "execution_count": 93,
     "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=featureslist[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
}