신은섭(Shin Eun Seop) / Detecting_fraud_clicks

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b40afe35 1 parent 76cfbe89

Calculating Backward/Forward DELTA Prob2

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import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import scala.Tuple2;
import java.util.Arrays;
import java.util.List;
public class MapExample {
static SparkConf conf = new SparkConf().setMaster("local[*]").setAppName("Cesco");
static JavaSparkContext sc = new JavaSparkContext(conf);
public static void main(String[] args) throws Exception {
// Parallelized with 2 partitions
JavaRDD<String> x = sc.parallelize(
Arrays.asList("spark", "rdd", "example", "sample", "example"),
2);
// Word Count Map Example
JavaRDD<Tuple2<String, Integer>> y1 = x.map(e -> new Tuple2<>(e, 1));
List<Tuple2<String, Integer>> list1 = y1.collect();
// Another example of making tuple with string and it's length
JavaRDD<Tuple2<String, Integer>> y2 = x.map(e -> new Tuple2<>(e, e.length()));
List<Tuple2<String, Integer>> list2 = y2.collect();
System.out.println(list1);
}
}
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.expressions.Window;
import org.apache.spark.sql.expressions.WindowSpec;
import javax.xml.crypto.Data;
import static org.apache.spark.sql.functions.*;
public class calForwardTimeDelta {
static SparkConf conf = new SparkConf().setMaster("local[*]").setAppName("Cesco");
static JavaSparkContext sc = new JavaSparkContext(conf);
public static void main(String[] args) throws Exception{
//Create Session
SparkSession spark = SparkSession
.builder()
.appName("Detecting Fraud Clicks")
.getOrCreate();
//run methods here
calcDelta(spark);
}
private static void calcDelta(SparkSession spark){
// put the path the file you gonna deal with being placed
String filepath = "/home/chris/.kaggle/competitions/talkingdata-adtracking-fraud-detection/mnt/ssd/kaggle-talkingdata2/competition_files/train_sample.csv";
// create Dataset from files
Dataset<Row> logDF = spark.read()
.format("csv")
.option("inferSchema", "true")
.option("header","true")
.load(filepath);
// cast timestamp(click_time, attributed_time) type to long type
//add column for long(click_time)
Dataset<Row> newDF = logDF.withColumn("utc_click_time", logDF.col("click_time").cast("long"));
//add column for long(attributed_time)
newDF = newDF.withColumn("utc_attributed_time", logDF.col("attributed_time").cast("long"));
//drop timestamp type columns
newDF = newDF.drop("click_time").drop("attributed_time");
newDF.createOrReplaceTempView("logs");
WindowSpec w = Window.partitionBy ("ip")
.orderBy("utc_click_time");
newDF = newDF.withColumn("lag(utc_click_time)", lag("utc_click_time",1).over(w));
newDF.where("ip=10").show();
newDF = newDF.withColumn("delta", when(col("lag(utc_click_time)").isNull(),lit(0)).otherwise(col("utc_click_time")).minus(when(col("lag(utc_click_time)").isNull(),lit(0)).otherwise(col("lag(utc_click_time)"))));
//newDF = newDF.withColumn("delta", datediff());
newDF = newDF.drop("lag(utc_click_time)");
newDF = newDF.orderBy("ip");
newDF.show();
}
}
public class valid {
private int x;
valid() {
x = 0;
}
void printX(){
System.out.println(x);
}
public static void main(String[] args){
valid v = new valid();
v.printX();
}
}
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