bond+random+forest+regression+.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# connecting to spark\n",
    "import findspark\n",
    "findspark.init()\n",
    "from pyspark import SparkConf, SparkContext\n",
    "\n",
    "# set up spark context\n",
    "conf = SparkConf().setAppName(\"myApp\")\n",
    "sc = SparkContext(conf=conf)\n",
    "\n",
    "# create a sparksession object\n",
    "from pyspark.sql import SparkSession\n",
    "\n",
    "spark = SparkSession \\\n",
    "    .builder \\\n",
    "    .appName(\"Python Spark Random Forest Regression for Municipal Bond\") \\\n",
    "    .config(\"spark.some.config.option\", \"some-value\") \\\n",
    "    .getOrCreate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- _c0: integer (nullable = true)\n",
      " |-- tradeid: double (nullable = true)\n",
      " |-- cusip: string (nullable = true)\n",
      " |-- amount: double (nullable = true)\n",
      " |-- price: double (nullable = true)\n",
      " |-- yield: double (nullable = true)\n",
      " |-- tradedate: double (nullable = true)\n",
      " |-- tradetype: string (nullable = true)\n",
      " |-- name: string (nullable = true)\n",
      " |-- state: string (nullable = true)\n",
      " |-- coupon: double (nullable = true)\n",
      " |-- maturity: double (nullable = true)\n",
      " |-- issuesize: double (nullable = true)\n",
      " |-- issuetype: string (nullable = true)\n",
      " |-- issuesource: string (nullable = true)\n",
      " |-- bidcount: double (nullable = true)\n",
      " |-- rtg: integer (nullable = true)\n",
      " |-- dprice: double (nullable = true)\n",
      " |-- holdtime: double (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# import data\n",
    "df = spark.read.format('com.databricks.spark.csv').\\\n",
    "                        options(header='true', \\\n",
    "                        inferschema='true').load(\"./price_change_data.csv\",header=True);\n",
    "df.printSchema()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "add a column indicating whether \"dprice\" is positive or negative."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- _c0: integer (nullable = true)\n",
      " |-- tradeid: double (nullable = true)\n",
      " |-- cusip: string (nullable = true)\n",
      " |-- amount: double (nullable = true)\n",
      " |-- price: double (nullable = true)\n",
      " |-- yield: double (nullable = true)\n",
      " |-- tradedate: double (nullable = true)\n",
      " |-- tradetype: string (nullable = true)\n",
      " |-- name: string (nullable = true)\n",
      " |-- state: string (nullable = true)\n",
      " |-- coupon: double (nullable = true)\n",
      " |-- maturity: double (nullable = true)\n",
      " |-- issuesize: double (nullable = true)\n",
      " |-- issuetype: string (nullable = true)\n",
      " |-- issuesource: string (nullable = true)\n",
      " |-- bidcount: double (nullable = true)\n",
      " |-- rtg: integer (nullable = true)\n",
      " |-- dprice: double (nullable = true)\n",
      " |-- holdtime: double (nullable = true)\n",
      " |-- price_change: string (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from pyspark.sql.functions import udf\n",
    "from pyspark.sql.types import StringType\n",
    "price_change = udf(lambda dprice: \"increasing\" if dprice >=0 else \"decreasing\", StringType())\n",
    "df = df.withColumn(\"price_change\", price_change(df.dprice))\n",
    "df.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- _c0: integer (nullable = true)\n",
      " |-- tradeid: double (nullable = true)\n",
      " |-- cusip: string (nullable = true)\n",
      " |-- amount: double (nullable = true)\n",
      " |-- price: double (nullable = true)\n",
      " |-- yield: double (nullable = true)\n",
      " |-- tradedate: double (nullable = true)\n",
      " |-- tradetype: string (nullable = true)\n",
      " |-- name: string (nullable = true)\n",
      " |-- state: string (nullable = true)\n",
      " |-- coupon: double (nullable = true)\n",
      " |-- maturity: double (nullable = true)\n",
      " |-- issuesize: double (nullable = true)\n",
      " |-- issuetype: string (nullable = true)\n",
      " |-- issuesource: string (nullable = true)\n",
      " |-- bidcount: double (nullable = true)\n",
      " |-- rtg: integer (nullable = true)\n",
      " |-- dprice: double (nullable = true)\n",
      " |-- holdtime: double (nullable = true)\n",
      " |-- price_change: string (nullable = true)\n",
      " |-- name_indexed: double (nullable = true)\n",
      " |-- state_indexed: double (nullable = true)\n",
      " |-- issuetype_indexed: double (nullable = true)\n",
      " |-- issuesource_indexed: double (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from pyspark.ml.feature import StringIndexer\n",
    "stringIndexer1 = StringIndexer(inputCol=\"name\", outputCol=\"name_indexed\")\n",
    "stringIndexer2 = StringIndexer(inputCol=\"state\", outputCol=\"state_indexed\")\n",
    "stringIndexer3 = StringIndexer(inputCol=\"issuetype\", outputCol=\"issuetype_indexed\")\n",
    "stringIndexer4 = StringIndexer(inputCol=\"issuesource\", outputCol=\"issuesource_indexed\")\n",
    "\n",
    "model1 = stringIndexer1.fit(df)\n",
    "indexed1 = model1.transform(df)\n",
    "\n",
    "model2 = stringIndexer2.fit(indexed1)\n",
    "indexed2 = model2.transform(indexed1)\n",
    "\n",
    "model3 = stringIndexer3.fit(indexed2)\n",
    "indexed3 = model3.transform(indexed2)\n",
    "\n",
    "model4 = stringIndexer4.fit(indexed3)\n",
    "indexed4 = model4.transform(indexed3)\n",
    "\n",
    "indexed4.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "Py4JJavaError",
     "evalue": "An error occurred while calling z:org.apache.spark.api.python.PythonRDD.runJob.\n: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 12.0 failed 1 times, most recent failure: Lost task 0.0 in stage 12.0 (TID 37, localhost, executor driver): org.apache.spark.api.python.PythonException: Traceback (most recent call last):\n  File \"/usr/lib/spark/python/lib/pyspark.zip/pyspark/worker.py\", line 174, in main\n    process()\n  File \"/usr/lib/spark/python/lib/pyspark.zip/pyspark/worker.py\", line 169, in process\n    serializer.dump_stream(func(split_index, iterator), outfile)\n  File \"/usr/lib/spark/python/lib/pyspark.zip/pyspark/serializers.py\", line 268, in dump_stream\n    vs = list(itertools.islice(iterator, batch))\n  File \"/usr/lib/spark/python/pyspark/rdd.py\", line 1339, in takeUpToNumLeft\n    yield next(iterator)\n  File \"<ipython-input-7-c347698e3c0f>\", line 11, in <lambda>\n  File \"/usr/lib/spark/python/lib/pyspark.zip/pyspark/sql/types.py\", line 1489, in __getitem__\n    raise ValueError(item)\nValueError: dtradedate\n\n\tat org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRDD.scala:193)\n\tat org.apache.spark.api.python.PythonRunner$$anon$1.<init>(PythonRDD.scala:234)\n\tat org.apache.spark.api.python.PythonRunner.compute(PythonRDD.scala:152)\n\tat org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:63)\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:287)\n\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)\n\tat org.apache.spark.scheduler.Task.run(Task.scala:99)\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:282)\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)\n\tat java.lang.Thread.run(Thread.java:745)\n\nDriver stacktrace:\n\tat org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1435)\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1423)\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1422)\n\tat scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)\n\tat scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)\n\tat org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1422)\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:802)\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:802)\n\tat scala.Option.foreach(Option.scala:257)\n\tat org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:802)\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1650)\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1605)\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1594)\n\tat org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)\n\tat org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:628)\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:1918)\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:1931)\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:1944)\n\tat org.apache.spark.api.python.PythonRDD$.runJob(PythonRDD.scala:441)\n\tat org.apache.spark.api.python.PythonRDD.runJob(PythonRDD.scala)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\n\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\n\tat java.lang.reflect.Method.invoke(Method.java:498)\n\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\n\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)\n\tat py4j.Gateway.invoke(Gateway.java:280)\n\tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\n\tat py4j.commands.CallCommand.execute(CallCommand.java:79)\n\tat py4j.GatewayConnection.run(GatewayConnection.java:214)\n\tat java.lang.Thread.run(Thread.java:745)\nCaused by: org.apache.spark.api.python.PythonException: Traceback (most recent call last):\n  File \"/usr/lib/spark/python/lib/pyspark.zip/pyspark/worker.py\", line 174, in main\n    process()\n  File \"/usr/lib/spark/python/lib/pyspark.zip/pyspark/worker.py\", line 169, in process\n    serializer.dump_stream(func(split_index, iterator), outfile)\n  File \"/usr/lib/spark/python/lib/pyspark.zip/pyspark/serializers.py\", line 268, in dump_stream\n    vs = list(itertools.islice(iterator, batch))\n  File \"/usr/lib/spark/python/pyspark/rdd.py\", line 1339, in takeUpToNumLeft\n    yield next(iterator)\n  File \"<ipython-input-7-c347698e3c0f>\", line 11, in <lambda>\n  File \"/usr/lib/spark/python/lib/pyspark.zip/pyspark/sql/types.py\", line 1489, in __getitem__\n    raise ValueError(item)\nValueError: dtradedate\n\n\tat org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRDD.scala:193)\n\tat org.apache.spark.api.python.PythonRunner$$anon$1.<init>(PythonRDD.scala:234)\n\tat org.apache.spark.api.python.PythonRunner.compute(PythonRDD.scala:152)\n\tat org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:63)\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:287)\n\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)\n\tat org.apache.spark.scheduler.Task.run(Task.scala:99)\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:282)\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)\n\t... 1 more\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mPy4JJavaError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-c347698e3c0f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpyspark\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mml\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinalg\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mVectors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpyspark\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msql\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mRow\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m df_temp = indexed4.rdd.map(lambda data: Row(label=data['dprice'],\n\u001b[0m\u001b[1;32m      5\u001b[0m                                             features=Vectors.dense([\n\u001b[1;32m      6\u001b[0m                                             \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'amount'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/spark/python/pyspark/sql/session.pyc\u001b[0m in \u001b[0;36mtoDF\u001b[0;34m(self, schema, sampleRatio)\u001b[0m\n\u001b[1;32m     55\u001b[0m         \u001b[0;34m[\u001b[0m\u001b[0mRow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34mu'Alice'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mage\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m         \"\"\"\n\u001b[0;32m---> 57\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msparkSession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreateDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mschema\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msampleRatio\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     58\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m     \u001b[0mRDD\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoDF\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtoDF\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/spark/python/pyspark/sql/session.pyc\u001b[0m in \u001b[0;36mcreateDataFrame\u001b[0;34m(self, data, schema, samplingRatio, verifySchema)\u001b[0m\n\u001b[1;32m    518\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    519\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mRDD\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 520\u001b[0;31m             \u001b[0mrdd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mschema\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_createFromRDD\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprepare\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mschema\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msamplingRatio\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    521\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    522\u001b[0m             \u001b[0mrdd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mschema\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_createFromLocal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprepare\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mschema\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/spark/python/pyspark/sql/session.pyc\u001b[0m in \u001b[0;36m_createFromRDD\u001b[0;34m(self, rdd, schema, samplingRatio)\u001b[0m\n\u001b[1;32m    358\u001b[0m         \"\"\"\n\u001b[1;32m    359\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mschema\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mschema\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 360\u001b[0;31m             \u001b[0mstruct\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_inferSchema\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrdd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msamplingRatio\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    361\u001b[0m             \u001b[0mconverter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_create_converter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstruct\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    362\u001b[0m             \u001b[0mrdd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconverter\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/spark/python/pyspark/sql/session.pyc\u001b[0m in \u001b[0;36m_inferSchema\u001b[0;34m(self, rdd, samplingRatio)\u001b[0m\n\u001b[1;32m    329\u001b[0m         \u001b[0;34m:\u001b[0m\u001b[0;32mreturn\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;32mclass\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m`\u001b[0m\u001b[0mpyspark\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msql\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtypes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mStructType\u001b[0m\u001b[0;34m`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    330\u001b[0m         \"\"\"\n\u001b[0;32m--> 331\u001b[0;31m         \u001b[0mfirst\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfirst\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    332\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mfirst\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    333\u001b[0m             raise ValueError(\"The first row in RDD is empty, \"\n",
      "\u001b[0;32m/usr/lib/spark/python/pyspark/rdd.pyc\u001b[0m in \u001b[0;36mfirst\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1359\u001b[0m         \u001b[0mValueError\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mRDD\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mempty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1360\u001b[0m         \"\"\"\n\u001b[0;32m-> 1361\u001b[0;31m         \u001b[0mrs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1362\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1363\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mrs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/spark/python/pyspark/rdd.pyc\u001b[0m in \u001b[0;36mtake\u001b[0;34m(self, num)\u001b[0m\n\u001b[1;32m   1341\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1342\u001b[0m             \u001b[0mp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpartsScanned\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpartsScanned\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mnumPartsToTry\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtotalParts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1343\u001b[0;31m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrunJob\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtakeUpToNumLeft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1344\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1345\u001b[0m             \u001b[0mitems\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/spark/python/pyspark/context.pyc\u001b[0m in \u001b[0;36mrunJob\u001b[0;34m(self, rdd, partitionFunc, partitions, allowLocal)\u001b[0m\n\u001b[1;32m    963\u001b[0m         \u001b[0;31m# SparkContext#runJob.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    964\u001b[0m         \u001b[0mmappedRDD\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmapPartitions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpartitionFunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 965\u001b[0;31m         \u001b[0mport\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jvm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPythonRDD\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrunJob\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jsc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmappedRDD\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jrdd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpartitions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    966\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_load_from_socket\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mport\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmappedRDD\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jrdd_deserializer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    967\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/spark/python/lib/py4j-0.10.4-src.zip/py4j/java_gateway.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m   1131\u001b[0m         \u001b[0manswer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgateway_client\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend_command\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcommand\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1132\u001b[0m         return_value = get_return_value(\n\u001b[0;32m-> 1133\u001b[0;31m             answer, self.gateway_client, self.target_id, self.name)\n\u001b[0m\u001b[1;32m   1134\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1135\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mtemp_arg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtemp_args\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/spark/python/pyspark/sql/utils.pyc\u001b[0m in \u001b[0;36mdeco\u001b[0;34m(*a, **kw)\u001b[0m\n\u001b[1;32m     61\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdeco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     64\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mpy4j\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprotocol\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPy4JJavaError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m             \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjava_exception\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoString\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/spark/python/lib/py4j-0.10.4-src.zip/py4j/protocol.py\u001b[0m in \u001b[0;36mget_return_value\u001b[0;34m(answer, gateway_client, target_id, name)\u001b[0m\n\u001b[1;32m    317\u001b[0m                 raise Py4JJavaError(\n\u001b[1;32m    318\u001b[0m                     \u001b[0;34m\"An error occurred while calling {0}{1}{2}.\\n\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 319\u001b[0;31m                     format(target_id, \".\", name), value)\n\u001b[0m\u001b[1;32m    320\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    321\u001b[0m                 raise Py4JError(\n",
      "\u001b[0;31mPy4JJavaError\u001b[0m: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.runJob.\n: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 12.0 failed 1 times, most recent failure: Lost task 0.0 in stage 12.0 (TID 37, localhost, executor driver): org.apache.spark.api.python.PythonException: Traceback (most recent call last):\n  File \"/usr/lib/spark/python/lib/pyspark.zip/pyspark/worker.py\", line 174, in main\n    process()\n  File \"/usr/lib/spark/python/lib/pyspark.zip/pyspark/worker.py\", line 169, in process\n    serializer.dump_stream(func(split_index, iterator), outfile)\n  File \"/usr/lib/spark/python/lib/pyspark.zip/pyspark/serializers.py\", line 268, in dump_stream\n    vs = list(itertools.islice(iterator, batch))\n  File \"/usr/lib/spark/python/pyspark/rdd.py\", line 1339, in takeUpToNumLeft\n    yield next(iterator)\n  File \"<ipython-input-7-c347698e3c0f>\", line 11, in <lambda>\n  File \"/usr/lib/spark/python/lib/pyspark.zip/pyspark/sql/types.py\", line 1489, in __getitem__\n    raise ValueError(item)\nValueError: dtradedate\n\n\tat org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRDD.scala:193)\n\tat org.apache.spark.api.python.PythonRunner$$anon$1.<init>(PythonRDD.scala:234)\n\tat org.apache.spark.api.python.PythonRunner.compute(PythonRDD.scala:152)\n\tat org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:63)\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:287)\n\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)\n\tat org.apache.spark.scheduler.Task.run(Task.scala:99)\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:282)\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)\n\tat java.lang.Thread.run(Thread.java:745)\n\nDriver stacktrace:\n\tat org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1435)\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1423)\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1422)\n\tat scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)\n\tat scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)\n\tat org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1422)\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:802)\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:802)\n\tat scala.Option.foreach(Option.scala:257)\n\tat org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:802)\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1650)\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1605)\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1594)\n\tat org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)\n\tat org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:628)\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:1918)\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:1931)\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:1944)\n\tat org.apache.spark.api.python.PythonRDD$.runJob(PythonRDD.scala:441)\n\tat org.apache.spark.api.python.PythonRDD.runJob(PythonRDD.scala)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\n\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\n\tat java.lang.reflect.Method.invoke(Method.java:498)\n\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\n\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)\n\tat py4j.Gateway.invoke(Gateway.java:280)\n\tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\n\tat py4j.commands.CallCommand.execute(CallCommand.java:79)\n\tat py4j.GatewayConnection.run(GatewayConnection.java:214)\n\tat java.lang.Thread.run(Thread.java:745)\nCaused by: org.apache.spark.api.python.PythonException: Traceback (most recent call last):\n  File \"/usr/lib/spark/python/lib/pyspark.zip/pyspark/worker.py\", line 174, in main\n    process()\n  File \"/usr/lib/spark/python/lib/pyspark.zip/pyspark/worker.py\", line 169, in process\n    serializer.dump_stream(func(split_index, iterator), outfile)\n  File \"/usr/lib/spark/python/lib/pyspark.zip/pyspark/serializers.py\", line 268, in dump_stream\n    vs = list(itertools.islice(iterator, batch))\n  File \"/usr/lib/spark/python/pyspark/rdd.py\", line 1339, in takeUpToNumLeft\n    yield next(iterator)\n  File \"<ipython-input-7-c347698e3c0f>\", line 11, in <lambda>\n  File \"/usr/lib/spark/python/lib/pyspark.zip/pyspark/sql/types.py\", line 1489, in __getitem__\n    raise ValueError(item)\nValueError: dtradedate\n\n\tat org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRDD.scala:193)\n\tat org.apache.spark.api.python.PythonRunner$$anon$1.<init>(PythonRDD.scala:234)\n\tat org.apache.spark.api.python.PythonRunner.compute(PythonRDD.scala:152)\n\tat org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:63)\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:287)\n\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)\n\tat org.apache.spark.scheduler.Task.run(Task.scala:99)\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:282)\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)\n\t... 1 more\n"
     ]
    }
   ],
   "source": [
    "# transform data into featuresCol and labelCol structure\n",
    "from pyspark.ml.linalg import Vectors\n",
    "from pyspark.sql import Row\n",
    "df_temp = indexed4.rdd.map(lambda data: Row(label=data['dprice'],\n",
    "                                            features=Vectors.dense([\n",
    "                                            data['amount'],\n",
    "                                            data['coupon'],\n",
    "                                            data['maturity'],\n",
    "                                            data['issuesize'],\n",
    "                                            data['bidcount'],\n",
    "                                            data['dtradedate'],\n",
    "                                            data['holdtime'],\n",
    "                                            data[\"name_indexed\"],\n",
    "                                            data[\"state_indexed\"],\n",
    "                                            data[\"issuetype_indexed\"],\n",
    "                                            data[\"issuesource_indexed\"]])\n",
    "                                            )).toDF()\n",
    "df_temp.show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# use VectorIndexer to index categorical predictors in featuresCol\n",
    "from pyspark.ml.feature import VectorIndexer\n",
    "\n",
    "# automatically identify categorical features, and index them.\n",
    "# Set maxCategories so features with > 6 distinct values are treated as continuous.\n",
    "featureIndexer =\\\n",
    "    VectorIndexer(maxCategories=6, inputCol='features', outputCol='indexed_features').fit(df_temp)\n",
    "\n",
    "df_indexed = featureIndexer.transform(df_temp)\n",
    "#df_indexed.take(5)\n",
    "df_indexed.show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# split the data into training and test sets (30% held out for testing)\n",
    "(trainingData, testData) = df_indexed.randomSplit([0.7, 0.3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Why couldn't we change impurity into \"gini\"???\n",
    "see this page for reference: http://takwatanabe.me/pyspark/generated/generated/pyspark.ml.regression.RandomForestRegressor.impurity.html#pyspark.ml.regression.RandomForestRegressor.impurity\n",
    "Looks like spark doesn't support gini..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# train a RandomForest model\n",
    "# numTrees = 100, mrse = 1.20581\n",
    "from pyspark.ml.regression import RandomForestRegressor\n",
    "\n",
    "rf = RandomForestRegressor(featuresCol=\"indexed_features\",  impurity=\"variance\", maxDepth=20, seed=42)\n",
    "# Q: couldn't change impurity into \"gini\"???\n",
    "# http://takwatanabe.me/pyspark/generated/generated/pyspark.ml.regression.RandomForestRegressor.impurity.html#pyspark.ml.regression.RandomForestRegressor.impurity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# chain indexer and forest in a Pipeline\n",
    "from pyspark.ml import Pipeline\n",
    "pipeline = Pipeline(stages=[featureIndexer, rf])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# train model, this also runs the indexer\n",
    "%time model_rf = pipeline.fit(trainingData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# make predictions\n",
    "predictions = model_rf.transform(testData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# select example from predictions to display\n",
    "predictions.select(\"prediction\", \"label\", \"features\").show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# select (prediction, true label) and compute test error\n",
    "from pyspark.ml.evaluation import RegressionEvaluator\n",
    "evaluator = RegressionEvaluator(\n",
    "            labelCol=\"label\", predictionCol=\"prediction\", metricName=\"rmse\")\n",
    "rmse = evaluator.evaluate(predictions)\n",
    "print(\"Root Mean Squared Error (RMSE) on test data = %g\" % rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "rfModel = model_rf.stages[1]\n",
    "print(rfModel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#a = rfModel.toDebugString\n",
    "#print a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "predictions.select(\"prediction\", \"label\").show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "predictions.show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "predictions.toPandas().to_csv('prediction.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from pyspark.sql.types import StringType\n",
    "from pyspark.sql.functions import udf\n",
    "pred_correct = udf(lambda label, prediction: \"correct\" if label*prediction >=0 else \"wrong\", StringType())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "pred = predictions.withColumn(\"correct\", pred_correct(predictions.label, predictions.prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "pred.show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "pred.groupBy(\"correct\").count().show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  }
 ],
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