Add Scala RankingMetrics example

scala/src/main/scala/WhylogsScalaRankingMetricsExample.scala

@@ -0,0 +1,70 @@
+// Tested on Databricks cluster running as scala notebook:
+// * cluster version: 13.3 (includes Apache Spark 3.4.1, Scala 2.12)
+// * installed whylogs jar: https://oss.sonatype.org/service/local/repositories/snapshots/content/ai/whylabs/whylogs-spark-bundle_3.1.1-scala_2.12/0.2.0-b2-SNAPSHOT/whylogs-spark-bundle_3.1.1-scala_2.12-0.2.0-b2-20240429.142812-1-all.jar
+
+
+import java.time.LocalDateTime
+import java.time.ZonedDateTime
+
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.{SaveMode, SparkSession}
+import com.whylogs.spark.WhyLogs._
+
+// COMMAND ----------
+
+// For demo purposes we will create a time column with yesterday's date, so that Whylabs ingestion sees this as a recent dataset profile
+// and it shows up in default dashboard of last 7 days on Whylabs.
+
+def unixTimestampForNumberOfDaysAgo(numDaysAgo: Int): ZonedDateTime = {
+    import java.time._
+    val numDaysAgoDateTime: LocalDateTime = LocalDateTime.now().minusDays(numDaysAgo)
+    val zdt: ZonedDateTime = numDaysAgoDateTime.atZone(ZoneId.of("America/Los_Angeles"))
+    zdt
+}
+
+val timestamp_two_days_ago = unixTimestampForNumberOfDaysAgo(2)
+
+
+
+// COMMAND ----------
+
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.types.DataTypes
+val spark = SparkSession
+  .builder()
+  .master("local[*, 3]")
+  .appName("SparkTesting-" + LocalDateTime.now().toString)
+  .config("spark.ui.enabled", "false")
+  .getOrCreate()
+
+// Using some examples from the Apache support for RankingMetrics: 
+// See https://spark.apache.org/docs/2.3.0/mllib-evaluation-metrics.html#ranking-systems
+val predictionAndLabelsRDD = spark.sparkContext.parallelize(
+      Seq(
+        (Array(1, 6, 2, 7, 8, 3, 9, 10, 4, 5), Array(1, 2, 3, 4, 5)),
+        (Array(4, 1, 5, 6, 2, 7, 3, 8, 9, 10), Array(1, 2, 3)),
+        (Array(1, 2, 3, 4, 5), Array(0, 0, 0, 0, 0))),
+      2)
+
+// Now we have an example DataFrame with columns for the predictions and targets
+val df = predictionAndLabelsRDD.toDF("predictions", "targets")
+df.printSchema()
+
+// Next we create a profiling session to compute RankingMetrics
+// This must be a stand alone profiling session that does not compute
+// other whylogs metrics. The default of k is 10 if not specified
+val session = df.newProfilingSession("RankingMetricsTest") // start a new WhyLogs profiling job
+  .withRankingMetrics(predictionField="predictions", targetField="targets", k=Some(2))
+
+// COMMAND ----------
+
+// Replace the following parameters below with your values after signing up for an account at https://whylabs.ai/
+// You can find Organization Id on https://hub.whylabsapp.com/settings/access-tokens and the value looks something like: org-123abc
+// also the settings page allows you t create new apiKeys which you will need an apiKey to upload to your account in Whylabs
+// The modelId below specifies which model this profile is for, by default an initial model-1 is created but you will update this
+// if you create a new model here https://hub.whylabsapp.com/settings/model-management
+// Note: if you don't specify the timestamp current local time is used as a default.
+session.logRankingMetrics(timestamp=timestamp_two_days_ago.toInstant,
+          orgId = "replace-with-org-id",
+          modelId = "replace-with-model-id",
+          apiKey = "replace-with-api-key")