{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import findspark\n",
    "findspark.init('/Users/ryanshin/Downloads/spark-2.3.1-bin-hadoop2.7')\n",
    "import pyspark\n",
    "sc = pyspark.SparkContext()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- PassengerId: string (nullable = true)\n",
      " |-- Survived: double (nullable = true)\n",
      " |-- Pclass: double (nullable = true)\n",
      " |-- Name: string (nullable = true)\n",
      " |-- Sex: string (nullable = true)\n",
      " |-- Age: integer (nullable = true)\n",
      " |-- SibSp: double (nullable = true)\n",
      " |-- Parch: double (nullable = true)\n",
      " |-- Ticket: string (nullable = true)\n",
      " |-- Fare: double (nullable = true)\n",
      " |-- Cabin: string (nullable = true)\n",
      " |-- Embarked: string (nullable = false)\n",
      " |-- label: double (nullable = true)\n",
      " |-- age_group: integer (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from pyspark.sql.session import SparkSession\n",
    "from pyspark.sql.functions import *\n",
    "from pyspark.sql.types import *\n",
    "\n",
    "def age_group_func(age):\n",
    "    return int(age / 10)\n",
    "age_group = udf(age_group_func, returnType=IntegerType())\n",
    "\n",
    "spark = SparkSession(sc)\n",
    "\n",
    "titanic = spark.read.option(\"header\", \"true\") \\\n",
    "  .csv(\"/Users/ryanshin/Downloads/train.csv\") \\\n",
    "  .withColumn(\"Survived\", col(\"Survived\").cast(\"double\")) \\\n",
    "  .withColumn(\"label\", col(\"Survived\")) \\\n",
    "  .withColumn(\"Pclass\", col(\"Pclass\").cast(\"double\"))\\\n",
    "  .withColumn(\"SibSp\", col(\"SibSp\").cast(\"double\"))\\\n",
    "  .withColumn(\"Parch\", col(\"Parch\").cast(\"double\"))\\\n",
    "  .withColumn(\"Fare\", col(\"Fare\").cast(\"double\"))\\\n",
    "  .withColumn(\"Age\", col(\"Age\").cast(\"int\"))\\\n",
    "  .na.fill(\"S\", \"Embarked\") \\\n",
    "  .na.fill(-1, \"Age\") \\\n",
    "  .withColumn(\"age_group\", age_group(col(\"Age\")))\n",
    "titanic.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----------+--------+------+--------------------+------+---+-----+-----+----------------+-------+-----+--------+-----+---------+\n",
      "|PassengerId|Survived|Pclass|                Name|   Sex|Age|SibSp|Parch|          Ticket|   Fare|Cabin|Embarked|label|age_group|\n",
      "+-----------+--------+------+--------------------+------+---+-----+-----+----------------+-------+-----+--------+-----+---------+\n",
      "|          1|     0.0|   3.0|Braund, Mr. Owen ...|  male| 22|  1.0|  0.0|       A/5 21171|   7.25| null|       S|  0.0|        2|\n",
      "|          2|     1.0|   1.0|Cumings, Mrs. Joh...|female| 38|  1.0|  0.0|        PC 17599|71.2833|  C85|       C|  1.0|        3|\n",
      "|          3|     1.0|   3.0|Heikkinen, Miss. ...|female| 26|  0.0|  0.0|STON/O2. 3101282|  7.925| null|       S|  1.0|        2|\n",
      "|          4|     1.0|   1.0|Futrelle, Mrs. Ja...|female| 35|  1.0|  0.0|          113803|   53.1| C123|       S|  1.0|        3|\n",
      "|          5|     0.0|   3.0|Allen, Mr. Willia...|  male| 35|  0.0|  0.0|          373450|   8.05| null|       S|  0.0|        3|\n",
      "|          6|     0.0|   3.0|    Moran, Mr. James|  male| -1|  0.0|  0.0|          330877| 8.4583| null|       Q|  0.0|        0|\n",
      "|          7|     0.0|   1.0|McCarthy, Mr. Tim...|  male| 54|  0.0|  0.0|           17463|51.8625|  E46|       S|  0.0|        5|\n",
      "|          8|     0.0|   3.0|Palsson, Master. ...|  male|  2|  3.0|  1.0|          349909| 21.075| null|       S|  0.0|        0|\n",
      "|          9|     1.0|   3.0|Johnson, Mrs. Osc...|female| 27|  0.0|  2.0|          347742|11.1333| null|       S|  1.0|        2|\n",
      "|         10|     1.0|   2.0|Nasser, Mrs. Nich...|female| 14|  1.0|  0.0|          237736|30.0708| null|       C|  1.0|        1|\n",
      "|         11|     1.0|   3.0|Sandstrom, Miss. ...|female|  4|  1.0|  1.0|         PP 9549|   16.7|   G6|       S|  1.0|        0|\n",
      "|         12|     1.0|   1.0|Bonnell, Miss. El...|female| 58|  0.0|  0.0|          113783|  26.55| C103|       S|  1.0|        5|\n",
      "|         13|     0.0|   3.0|Saundercock, Mr. ...|  male| 20|  0.0|  0.0|       A/5. 2151|   8.05| null|       S|  0.0|        2|\n",
      "|         14|     0.0|   3.0|Andersson, Mr. An...|  male| 39|  1.0|  5.0|          347082| 31.275| null|       S|  0.0|        3|\n",
      "|         15|     0.0|   3.0|Vestrom, Miss. Hu...|female| 14|  0.0|  0.0|          350406| 7.8542| null|       S|  0.0|        1|\n",
      "|         16|     1.0|   2.0|Hewlett, Mrs. (Ma...|female| 55|  0.0|  0.0|          248706|   16.0| null|       S|  1.0|        5|\n",
      "|         17|     0.0|   3.0|Rice, Master. Eugene|  male|  2|  4.0|  1.0|          382652| 29.125| null|       Q|  0.0|        0|\n",
      "|         18|     1.0|   2.0|Williams, Mr. Cha...|  male| -1|  0.0|  0.0|          244373|   13.0| null|       S|  1.0|        0|\n",
      "|         19|     0.0|   3.0|Vander Planke, Mr...|female| 31|  1.0|  0.0|          345763|   18.0| null|       S|  0.0|        3|\n",
      "|         20|     1.0|   3.0|Masselmani, Mrs. ...|female| -1|  0.0|  0.0|            2649|  7.225| null|       C|  1.0|        0|\n",
      "+-----------+--------+------+--------------------+------+---+-----+-----+----------------+-------+-----+--------+-----+---------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "titanic.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "891"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prediction1result areaUnderROC=0.500000\n",
      "prediction2result areaUnderROC=0.766873\n",
      "prediction1result areaUnderPR=0.383838\n",
      "prediction2result areaUnderPR=0.684957\n"
     ]
    }
   ],
   "source": [
    "# 다 죽었다고 예측\n",
    "def predict1_func(dummy):\n",
    "    return 0.0\n",
    "predict1 = udf(predict1_func, returnType=DoubleType())\n",
    "    \n",
    "# 여자는 다 살았다고 남자는 다 죽었다고 예측\n",
    "def predict2_func(gender):\n",
    "    if gender == \"female\":\n",
    "        return 1.0\n",
    "    else:\n",
    "        return 0.0 \n",
    "predict2 = udf(predict2_func, returnType=DoubleType())\n",
    "    \n",
    "# UDF 생성\n",
    "prediction1result = titanic.select(predict1(\"Sex\").alias(\"prediction\"), col(\"Survived\").cast(\"double\").alias(\"label\"))\n",
    "prediction2result = titanic.select(predict2(\"Sex\").alias(\"prediction\"), col(\"Survived\").cast(\"double\").alias(\"label\"))\n",
    "\n",
    "from pyspark.ml.evaluation import BinaryClassificationEvaluator\n",
    "\n",
    "evaluator = BinaryClassificationEvaluator()\n",
    "evaluator.setRawPredictionCol(\"prediction\").setLabelCol(\"label\")\n",
    "\n",
    "evaluator.setMetricName(\"areaUnderROC\")\n",
    "print(\"prediction1result areaUnderROC=%f\" % evaluator.evaluate(prediction1result))\n",
    "print(\"prediction2result areaUnderROC=%f\" % evaluator.evaluate(prediction2result))\n",
    "\n",
    "evaluator.setMetricName(\"areaUnderPR\")\n",
    "print(\"prediction1result areaUnderPR=%f\" % evaluator.evaluate(prediction1result))\n",
    "print(\"prediction2result areaUnderPR=%f\" % evaluator.evaluate(prediction2result))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 다 죽었다고 예측시 정확도"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6161616161616161"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction1result = prediction1result.withColumn('accuracy',col('prediction')== col('label'))\n",
    "prediction1result = prediction1result.where(prediction1result.prediction==prediction1result.label)\n",
    "prediction1result.count() / float(titanic.count())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 여자는 다 살았다고 남자는 다 죽었다고 예측시 정확도"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7867564534231201"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction2result = prediction2result.withColumn('accuracy',col('prediction')== col('label'))\n",
    "prediction2result = prediction2result.where(prediction2result.prediction==prediction2result.label)\n",
    "prediction2result.count() / float(titanic.count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------+------+-------------+---------------+---------------------------------+\n",
      "|age_group|   Sex|sum(Survived)|count(Survived)|(sum(Survived) / count(Survived))|\n",
      "+---------+------+-------------+---------------+---------------------------------+\n",
      "|        0|female|         55.0|             83|               0.6626506024096386|\n",
      "|        1|female|         34.0|             45|               0.7555555555555555|\n",
      "|        2|female|         52.0|             72|               0.7222222222222222|\n",
      "|        3|female|         50.0|             60|               0.8333333333333334|\n",
      "|        4|female|         22.0|             32|                           0.6875|\n",
      "|        5|female|         16.0|             18|               0.8888888888888888|\n",
      "|        6|female|          4.0|              4|                              1.0|\n",
      "|        0|  male|         35.0|            156|              0.22435897435897437|\n",
      "|        1|  male|          7.0|             57|              0.12280701754385964|\n",
      "|        2|  male|         25.0|            148|              0.16891891891891891|\n",
      "|        3|  male|         23.0|            107|              0.21495327102803738|\n",
      "|        4|  male|         12.0|             57|              0.21052631578947367|\n",
      "|        5|  male|          4.0|             30|              0.13333333333333333|\n",
      "|        6|  male|          2.0|             15|              0.13333333333333333|\n",
      "|        7|  male|          0.0|              6|                              0.0|\n",
      "|        8|  male|          1.0|              1|                              1.0|\n",
      "+---------+------+-------------+---------------+---------------------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "titanic.withColumn(\"age_group\", age_group(col(\"Age\"))).groupBy(\"age_group\", \"Sex\") \\\n",
    "  .agg(sum(\"Survived\"), count(\"Survived\"), sum(\"Survived\")/count(\"Survived\")) \\\n",
    "  .orderBy(\"Sex\", \"age_group\").show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_data, test_data = titanic.randomSplit([0.7, 0.3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml.classification import *\n",
    "from pyspark.ml.feature import *\n",
    "\n",
    "# Train a RandomForest model.\n",
    "rf = RandomForestClassifier() \\\n",
    "  .setNumTrees(20)\n",
    "\n",
    "sex_indexer = StringIndexer().setInputCol(\"Sex\").setOutputCol(\"sex_idx\").fit(titanic)\n",
    "embark_indexer = StringIndexer().setInputCol(\"Embarked\").setOutputCol(\"embark_idx\").fit(titanic)\n",
    "\n",
    "assembler = VectorAssembler() \\\n",
    "  .setInputCols([\"Pclass\", \"SibSp\", \"Parch\", \"age_group\", \"sex_idx\"]) \\\n",
    "  .setOutputCol(\"features\")\n",
    "\n",
    "\n",
    "from pyspark.ml import Pipeline\n",
    "\n",
    "# Chain indexers and forest in a Pipeline.\n",
    "pipeline = Pipeline().setStages([sex_indexer, embark_indexer, assembler, rf])\n",
    "\n",
    "pipeline_model = pipeline.fit(training_data)\n",
    "\n",
    "prediction4result = pipeline_model.transform(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7497701149425287\n",
      " \n",
      "0.7079536774334352\n"
     ]
    }
   ],
   "source": [
    "evaluator.setMetricName(\"areaUnderROC\")\n",
    "print(evaluator.evaluate(prediction4result))\n",
    "print(\" \")\n",
    "evaluator.setMetricName(\"areaUnderPR\")\n",
    "print(evaluator.evaluate(prediction4result))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----------+------+--------------------+------+---+-----+-----+----------------+-------+-----+--------+---------+\n",
      "|PassengerId|Pclass|                Name|   Sex|Age|SibSp|Parch|          Ticket|   Fare|Cabin|Embarked|age_group|\n",
      "+-----------+------+--------------------+------+---+-----+-----+----------------+-------+-----+--------+---------+\n",
      "|        892|   3.0|    Kelly, Mr. James|  male| 34|  0.0|  0.0|          330911| 7.8292| null|       Q|        3|\n",
      "|        893|   3.0|Wilkes, Mrs. Jame...|female| 47|  1.0|  0.0|          363272|    7.0| null|       S|        4|\n",
      "|        894|   2.0|Myles, Mr. Thomas...|  male| 62|  0.0|  0.0|          240276| 9.6875| null|       Q|        6|\n",
      "|        895|   3.0|    Wirz, Mr. Albert|  male| 27|  0.0|  0.0|          315154| 8.6625| null|       S|        2|\n",
      "|        896|   3.0|Hirvonen, Mrs. Al...|female| 22|  1.0|  1.0|         3101298|12.2875| null|       S|        2|\n",
      "|        897|   3.0|Svensson, Mr. Joh...|  male| 14|  0.0|  0.0|            7538|  9.225| null|       S|        1|\n",
      "|        898|   3.0|Connolly, Miss. Kate|female| 30|  0.0|  0.0|          330972| 7.6292| null|       Q|        3|\n",
      "|        899|   2.0|Caldwell, Mr. Alb...|  male| 26|  1.0|  1.0|          248738|   29.0| null|       S|        2|\n",
      "|        900|   3.0|Abrahim, Mrs. Jos...|female| 18|  0.0|  0.0|            2657| 7.2292| null|       C|        1|\n",
      "|        901|   3.0|Davies, Mr. John ...|  male| 21|  2.0|  0.0|       A/4 48871|  24.15| null|       S|        2|\n",
      "|        902|   3.0|    Ilieff, Mr. Ylio|  male| -1|  0.0|  0.0|          349220| 7.8958| null|       S|        0|\n",
      "|        903|   1.0|Jones, Mr. Charle...|  male| 46|  0.0|  0.0|             694|   26.0| null|       S|        4|\n",
      "|        904|   1.0|Snyder, Mrs. John...|female| 23|  1.0|  0.0|           21228|82.2667|  B45|       S|        2|\n",
      "|        905|   2.0|Howard, Mr. Benjamin|  male| 63|  1.0|  0.0|           24065|   26.0| null|       S|        6|\n",
      "|        906|   1.0|Chaffee, Mrs. Her...|female| 47|  1.0|  0.0|     W.E.P. 5734| 61.175|  E31|       S|        4|\n",
      "|        907|   2.0|del Carlo, Mrs. S...|female| 24|  1.0|  0.0|   SC/PARIS 2167|27.7208| null|       C|        2|\n",
      "|        908|   2.0|   Keane, Mr. Daniel|  male| 35|  0.0|  0.0|          233734|  12.35| null|       Q|        3|\n",
      "|        909|   3.0|   Assaf, Mr. Gerios|  male| 21|  0.0|  0.0|            2692|  7.225| null|       C|        2|\n",
      "|        910|   3.0|Ilmakangas, Miss....|female| 27|  1.0|  0.0|STON/O2. 3101270|  7.925| null|       S|        2|\n",
      "|        911|   3.0|\"Assaf Khalil, Mr...|female| 45|  0.0|  0.0|            2696|  7.225| null|       C|        4|\n",
      "+-----------+------+--------------------+------+---+-----+-----+----------------+-------+-----+--------+---------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "titanic_test = spark.read.option(\"header\", \"true\") \\\n",
    "  .csv(\"/Users/ryanshin/Downloads/test.csv\") \\\n",
    "  .withColumn(\"Pclass\", col(\"Pclass\").cast(\"double\"))\\\n",
    "  .withColumn(\"SibSp\", col(\"SibSp\").cast(\"double\"))\\\n",
    "  .withColumn(\"Parch\", col(\"Parch\").cast(\"double\"))\\\n",
    "  .withColumn(\"Fare\", col(\"Fare\").cast(\"double\"))\\\n",
    "  .withColumn(\"Age\", col(\"Age\").cast(\"int\"))\\\n",
    "  .na.fill(\"S\", \"Embarked\") \\\n",
    "  .na.fill(-1, \"Age\") \\\n",
    "  .withColumn(\"age_group\", age_group(col(\"Age\")))\n",
    "titanic_test.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----------+--------+\n",
      "|PassengerId|Survived|\n",
      "+-----------+--------+\n",
      "|        892|       0|\n",
      "|        893|       0|\n",
      "|        894|       0|\n",
      "|        895|       0|\n",
      "|        896|       0|\n",
      "|        897|       0|\n",
      "|        898|       0|\n",
      "|        899|       0|\n",
      "|        900|       1|\n",
      "|        901|       0|\n",
      "|        902|       0|\n",
      "|        903|       0|\n",
      "|        904|       1|\n",
      "|        905|       0|\n",
      "|        906|       1|\n",
      "|        907|       1|\n",
      "|        908|       0|\n",
      "|        909|       0|\n",
      "|        910|       1|\n",
      "|        911|       0|\n",
      "+-----------+--------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "result = pipeline_model.transform(titanic_test).select(col('PassengerId'), col('prediction').alias(\"Survived\").cast('int'))\n",
    "result.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "rm -rf answer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "result.repartition(1).write.csv('answer', header='true')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PassengerId,Survived\n",
      "892,0\n",
      "893,0\n",
      "894,0\n",
      "895,0\n",
      "896,0\n",
      "897,0\n",
      "898,0\n",
      "899,0\n",
      "900,1\n",
      "901,0\n",
      "902,0\n",
      "903,0\n",
      "904,1\n",
      "905,0\n",
      "906,1\n",
      "907,1\n",
      "908,0\n",
      "909,0\n",
      "910,1\n",
      "911,0\n",
      "912,0\n",
      "913,1\n",
      "914,1\n",
      "915,0\n",
      "916,1\n",
      "917,0\n",
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      "1308,0\n",
      "1309,1\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "cat answer/part*"
   ]
  }
 ],
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