add Aggregation class to aggrigate new feature

Java-Cesco/Detecting_fraud_clicks#3

+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.*;
+import static org.apache.spark.sql.functions.lit;
+import static org.apache.spark.sql.functions.when;
+
+public class Aggregation {
+
+    public static void main(String[] args) throws Exception {
+
+        //Create Session
+        SparkSession spark = SparkSession
+                .builder()
+                .appName("Detecting Fraud Clicks")
+                .master("local")
+                .getOrCreate();
+        
+        Aggregation agg = new Aggregation();
+        
+        Dataset<Row> dataset = agg.loadCSVDataSet("./train_sample.csv", spark);
+        dataset = agg.changeTimestempToLong(dataset);
+        dataset = agg.averageValidClickCount(dataset);
+        dataset = agg.clickTimeDelta(dataset);
+
+        dataset.where("ip == '5348' and app == '19'").show();
+        
+    }
+        
+        
+    private Dataset<Row> loadCSVDataSet(String path, SparkSession spark){
+        // Read SCV to DataSet
+        Dataset<Row> dataset = spark.read().format("csv")
+                .option("inferSchema", "true")
+                .option("header", "true")
+                .load("train_sample.csv");
+        return dataset;
+    }
+    
+    private Dataset<Row> changeTimestempToLong(Dataset<Row> dataset){
+        // cast timestamp to long
+        Dataset<Row> newDF = dataset.withColumn("utc_click_time", dataset.col("click_time").cast("long"));
+        newDF = newDF.withColumn("utc_attributed_time", dataset.col("attributed_time").cast("long"));
+        newDF = newDF.drop("click_time").drop("attributed_time");
+        return newDF;
+    }
+         
+    private Dataset<Row> averageValidClickCount(Dataset<Row> dataset){
+        // 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
+        Dataset<Row> newDF = dataset.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_valid_click_count", col("cum_sum_attributed").divide(col("cum_count_click")));
+        newDF = newDF.drop("cum_count_click", "cum_sum_attributed");
+        return newDF;
+    }
+
+    private Dataset<Row> clickTimeDelta(Dataset<Row> dataset){
+        WindowSpec w = Window.partitionBy ("ip")
+                .orderBy("utc_click_time");
+
+        Dataset<Row> newDF = dataset.withColumn("lag(utc_click_time)", lag("utc_click_time",1).over(w));
+        newDF = newDF.withColumn("click_time_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.drop("lag(utc_click_time)");
+        return newDF;
+    }
+}