신은섭(Shin Eun Seop) / Detecting_fraud_clicks

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신은섭(Shin Eun Seop)
1a6fada6 2 parents b40afe35 98173ec8

Merge branch 'feature/#3' into calcDelta

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......@@ -16,7 +16,7 @@
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......
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 static org.apache.spark.sql.functions.col;
import static org.apache.spark.sql.functions.count;
import static org.apache.spark.sql.functions.sum;
public class AvgAdvTime {
public static void main(String[] args) throws Exception {
// Start Spark Session
SparkSession spark = SparkSession
.builder()
.master("local")
.appName("Java Spark SQL basic example")
.getOrCreate();
// Read SCV to DataSet
Dataset<Row> df = spark.read().format("csv")
.option("inferSchema", "true")
.option("header", "true")
.load("train_sample.csv");
// cast timestamp to long
Dataset<Row> newdf = df.withColumn("utc_click_time", df.col("click_time").cast("long"));
newdf = newdf.withColumn("utc_attributed_time", df.col("attributed_time").cast("long"));
newdf = newdf.drop("click_time").drop("attributed_time");
// set Window partition by 'ip' and 'app' order by 'utc_click_time' select rows between 1st row to current row
WindowSpec w = Window.partitionBy("ip", "app")
.orderBy("utc_click_time")
.rowsBetween(Window.unboundedPreceding(), Window.currentRow());
// aggregation
newdf = newdf.withColumn("cum_count_click", count("utc_click_time").over(w));
newdf = newdf.withColumn("cum_sum_attributed", sum("is_attributed").over(w));
newdf = newdf.withColumn("avg_efficient", col("cum_sum_attributed").divide(col("cum_count_click")));
// print example
newdf.where("ip == '5348' and app == '19'").show();
newdf.printSchema();
}
}
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