Usa il in una pipeline Spark SageMakerEstimator

Puoi utilizzare gli strumenti di valutazione org.apache.spark.ml.Estimator e i modelli org.apache.spark.ml.Model e gli strumenti di valutazione SageMakerEstimator e i modelli SageMakerModel nelle pipeline org.apache.spark.ml.Pipeline, come mostrato nel seguente esempio:

import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.feature.PCA
import org.apache.spark.sql.SparkSession
import com.amazonaws.services.sagemaker.sparksdk.IAMRole
import com.amazonaws.services.sagemaker.sparksdk.algorithms
import com.amazonaws.services.sagemaker.sparksdk.algorithms.KMeansSageMakerEstimator

val spark = SparkSession.builder.getOrCreate

// load mnist data as a dataframe from libsvm
val region = "us-east-1"
val trainingData = spark.read.format("libsvm")
  .option("numFeatures", "784")
  .load(s"s3://sagemaker-sample-data-$region/spark/mnist/train/")
val testData = spark.read.format("libsvm")
  .option("numFeatures", "784")
  .load(s"s3://sagemaker-sample-data-$region/spark/mnist/test/")

// substitute your SageMaker IAM role here
val roleArn = "arn:aws:iam::account-id:role/rolename"

val pcaEstimator = new PCA()
  .setInputCol("features")
  .setOutputCol("projectedFeatures")
  .setK(50)

val kMeansSageMakerEstimator = new KMeansSageMakerEstimator(
  sagemakerRole = IAMRole(integTestingRole),
  requestRowSerializer =
    new ProtobufRequestRowSerializer(featuresColumnName = "projectedFeatures"),
  trainingSparkDataFormatOptions = Map("featuresColumnName" -> "projectedFeatures"),
  trainingInstanceType = "ml.p2.xlarge",
  trainingInstanceCount = 1,
  endpointInstanceType = "ml.c4.xlarge",
  endpointInitialInstanceCount = 1)
  .setK(10).setFeatureDim(50)

val pipeline = new Pipeline().setStages(Array(pcaEstimator, kMeansSageMakerEstimator))

// train
val pipelineModel = pipeline.fit(trainingData)

val transformedData = pipelineModel.transform(testData)
transformedData.show()

Il parametro trainingSparkDataFormatOptions configura Spark per la serializzazione in protobuf della colonna "" per l'addestramento dei modelli. projectedFeatures Inoltre, per impostazione predefinita Spark serializza in protobuf la colonna "label".

Poiché vogliamo fare inferenze usando la colonna "projectedFeatures", passiamo il nome della colonna nella. ProtobufRequestRowSerializer

Il seguente esempio mostra un DataFrame trasformato:

+-----+--------------------+--------------------+-------------------+---------------+
|label|            features|   projectedFeatures|distance_to_cluster|closest_cluster|
+-----+--------------------+--------------------+-------------------+---------------+
|  5.0|(784,[152,153,154...|[880.731433034386...|     1500.470703125|            0.0|
|  0.0|(784,[127,128,129...|[1768.51722024166...|      1142.18359375|            4.0|
|  4.0|(784,[160,161,162...|[704.949236329314...|  1386.246826171875|            9.0|
|  1.0|(784,[158,159,160...|[-42.328192193771...| 1277.0736083984375|            5.0|
|  9.0|(784,[208,209,210...|[374.043902028333...|   1211.00927734375|            3.0|
|  2.0|(784,[155,156,157...|[941.267714528850...|  1496.157958984375|            8.0|
|  1.0|(784,[124,125,126...|[30.2848596410594...| 1327.6766357421875|            5.0|
|  3.0|(784,[151,152,153...|[1270.14374062052...| 1570.7674560546875|            0.0|
|  1.0|(784,[152,153,154...|[-112.10792566485...|     1037.568359375|            5.0|
|  4.0|(784,[134,135,161...|[452.068280676606...| 1165.1236572265625|            3.0|
|  3.0|(784,[123,124,125...|[610.596447285397...|  1325.953369140625|            7.0|
|  5.0|(784,[216,217,218...|[142.959601818422...| 1353.4930419921875|            5.0|
|  3.0|(784,[143,144,145...|[1036.71862533658...| 1460.4315185546875|            7.0|
|  6.0|(784,[72,73,74,99...|[996.740157435754...| 1159.8631591796875|            2.0|
|  1.0|(784,[151,152,153...|[-107.26076167417...|   960.963623046875|            5.0|
|  7.0|(784,[211,212,213...|[619.771820430940...|   1245.13623046875|            6.0|
|  2.0|(784,[151,152,153...|[850.152101817161...|  1304.437744140625|            8.0|
|  8.0|(784,[159,160,161...|[370.041887230547...| 1192.4781494140625|            0.0|
|  6.0|(784,[100,101,102...|[546.674328209335...|    1277.0908203125|            2.0|
|  9.0|(784,[209,210,211...|[-29.259112927426...| 1245.8182373046875|            6.0|
+-----+--------------------+--------------------+-------------------+---------------+