Model 2.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')\n"
   ]
  },
  {
   "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=\"newApp\", conf=conf)\n",
    "spark = SparkSession(sc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sc = spark.sparkContext"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.sql.functions import isnan, count, col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = spark.read.options(header = True, inferSchema = True,delimiter=',').csv(\"PapJournalCSCI.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- ConfInstanceID: long (nullable = true)\n",
      " |-- ConfSeriesID: long (nullable = true)\n",
      " |-- JournalID: long (nullable = true)\n",
      " |-- PaperID: long (nullable = true)\n",
      " |-- Paper_Rank: integer (nullable = true)\n",
      " |-- Paper_RefCnt: integer (nullable = true)\n",
      " |-- CitationCount: integer (nullable = true)\n",
      " |-- EstimatedCount: integer (nullable = true)\n",
      " |-- Journal_Rank: integer (nullable = true)\n",
      " |-- Journal_Pap_Cnt: integer (nullable = true)\n",
      " |-- Journal_Cit_Cnt: integer (nullable = true)\n",
      " |-- CSRank: integer (nullable = true)\n",
      " |-- CSPaperCount: integer (nullable = true)\n",
      " |-- CSCitation: integer (nullable = true)\n",
      " |-- CIRank: integer (nullable = true)\n",
      " |-- CIPaperCount: integer (nullable = true)\n",
      " |-- CIPaperCitationCnt: integer (nullable = true)\n",
      " |-- DocTypeIndex: double (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df1.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_cols = ['ConfInstanceID','ConfSeriesID','JournalID']\n",
    "df1 = df1.drop(*drop_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90728"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df1.withColumnRenamed(\"CitationCount\",\"label\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------+----------+------------+-----+--------------+------------+---------------+---------------+------+------------+----------+------+------------+------------------+------------+\n",
      "|   PaperID|Paper_Rank|Paper_RefCnt|label|EstimatedCount|Journal_Rank|Journal_Pap_Cnt|Journal_Cit_Cnt|CSRank|CSPaperCount|CSCitation|CIRank|CIPaperCount|CIPaperCitationCnt|DocTypeIndex|\n",
      "+----------+----------+------------+-----+--------------+------------+---------------+---------------+------+------------+----------+------+------------+------------------+------------+\n",
      "|1819143627|     24199|           0|    0|             0|        9728|           9284|          64351| 11187|        4309|      6256| 25000|         405|               759|         0.0|\n",
      "+----------+----------+------------+-----+--------------+------------+---------------+---------------+------+------------+----------+------+------------+------------------+------------+\n",
      "only showing top 1 row\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df1.show(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml.feature import VectorAssembler, VectorIndexer\n",
    "feature_list = []\n",
    "for col in df1.columns:\n",
    "    if col == 'label':\n",
    "        continue\n",
    "    else:\n",
    "        feature_list.append(col)\n",
    "# Concatenates all feature columns into a single feature vector in a new column \"rawFeatures\"\n",
    "vectorAssembler = VectorAssembler(inputCols=feature_list, outputCol=\"features\")\n",
    "# Identifies categorical features and indexes them\n",
    "vectorIndexer = VectorIndexer(inputCol=\"rawFeatures\", outputCol=\"features\", maxCategories=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml.tuning import CrossValidator, ParamGridBuilder\n",
    "from pyspark.ml.evaluation import RegressionEvaluator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml.regression import RandomForestRegressor\n",
    "\n",
    "#rf = RandomForestRegressor(labelCol=\"CitationCount\", featuresCol=\"featuresCols\")\n",
    "rf = RandomForestRegressor(featuresCol=\"features\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml import Pipeline\n",
    "pipeline = Pipeline(stages=[vectorAssembler, rf])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml.tuning import ParamGridBuilder\n",
    "import numpy as np\n",
    "\n",
    "paramGrid = ParamGridBuilder() \\\n",
    "    .addGrid(rf.numTrees, [int(x) for x in np.linspace(start = 10, stop = 50, num = 3)]) \\\n",
    "    .addGrid(rf.maxDepth, [int(x) for x in np.linspace(start = 5, stop = 25, num = 3)]) \\\n",
    "    .build()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "crossval = CrossValidator(estimator=pipeline,\n",
    "                          estimatorParamMaps=paramGrid,\n",
    "                          evaluator=RegressionEvaluator(),\n",
    "                          numFolds=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "(train, test) = df1.randomSplit([0.8, 0.2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "cvModel = crossval.fit(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "predictions = cvModel.transform(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "evaluator = RegressionEvaluator(labelCol=\"label\", predictionCol=\"prediction\", metricName=\"rmse\")\n",
    "\n",
    "rmse = evaluator.evaluate(predictions)\n",
    "\n",
    "rfPred = cvModel.transform(df1)\n",
    "\n",
    "rfResult = rfPred.toPandas()\n",
    "\n",
    "plt.plot(rfResult.label, rfResult.prediction, 'bo')\n",
    "plt.xlabel('CitationCount')\n",
    "plt.ylabel('Prediction')\n",
    "plt.suptitle(\"Model Performance RMSE: %f\" % rmse)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Feature Importances')"
      ]
     },
     "execution_count": 25,
     "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": [
    "bestPipeline = cvModel.bestModel\n",
    "bestModel = bestPipeline.stages[1]\n",
    "\n",
    "importances = bestModel.featureImportances\n",
    "\n",
    "x_values = list(range(len(importances)))\n",
    "\n",
    "plt.bar(x_values, importances, orientation = 'vertical')\n",
    "plt.xticks(x_values, feature_list, rotation=70)\n",
    "plt.ylabel('Importance')\n",
    "plt.xlabel('Feature')\n",
    "plt.title('Feature Importances')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('numTrees - ', 30)\n",
      "('maxDepth - ', 15)\n"
     ]
    }
   ],
   "source": [
    "print('numTrees - ', bestModel.getNumTrees)\n",
    "print('maxDepth - ', bestModel.getOrDefault('maxDepth'))"
   ]
  },
  {
   "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
}