support fit and transform

support fit and transform

https://github.com/apache/incubator-wayang/blob/897797899866f373f93e5672b36d5e34611faece/wayang-platforms/wayang-spark/code/main/java/org/apache/wayang/spark/operators/ml/SparkKMeansOperator.java#L94

```java

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package org.apache.wayang.spark.operators.ml;

import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.ml.clustering.KMeans;
import org.apache.spark.ml.clustering.KMeansModel;
import org.apache.spark.ml.linalg.Vector;
import org.apache.spark.ml.linalg.Vectors;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SparkSession;
import org.apache.wayang.basic.data.Tuple2;
import org.apache.wayang.basic.operators.KMeansOperator;
import org.apache.wayang.core.optimizer.OptimizationContext;
import org.apache.wayang.core.plan.wayangplan.ExecutionOperator;
import org.apache.wayang.core.platform.ChannelDescriptor;
import org.apache.wayang.core.platform.ChannelInstance;
import org.apache.wayang.core.platform.lineage.ExecutionLineageNode;
import org.apache.wayang.core.util.Tuple;
import org.apache.wayang.spark.channels.RddChannel;
import org.apache.wayang.spark.execution.SparkExecutor;
import org.apache.wayang.spark.operators.SparkExecutionOperator;

import java.util.*;

public class SparkKMeansOperator extends KMeansOperator implements SparkExecutionOperator {

public SparkKMeansOperator(int k) {
super(k);
}

public SparkKMeansOperator(KMeansOperator that) {
super(that);
}

@Override
public List getSupportedInputChannels(int index) {
// TODO need DataFrameChannel?
return Arrays.asList(RddChannel.UNCACHED_DESCRIPTOR, RddChannel.CACHED_DESCRIPTOR);
}

@Override
public List getSupportedOutputChannels(int index) {
// TODO need DataFrameChannel?
return Collections.singletonList(RddChannel.UNCACHED_DESCRIPTOR);
}

@Override
public Tuple, Collection> evaluate(
ChannelInstance[] inputs,
ChannelInstance[] outputs,
SparkExecutor sparkExecutor,
OptimizationContext.OperatorContext operatorContext) {
assert inputs.length == this.getNumInputs();
assert outputs.length == this.getNumInputs();

final RddChannel.Instance input = (RddChannel.Instance) inputs[0];
final RddChannel.Instance output = (RddChannel.Instance) outputs[0];

final JavaRDD inputRdd = input.provideRdd();
final JavaRDD dataRdd = inputRdd.map(Data::new);
final Dataset df = SparkSession.builder().getOrCreate().createDataFrame(dataRdd, Data.class);
final KMeansModel model = new KMeans()
.setK(this.k)
.fit(df);

final Dataset transform = model.transform(df);
final JavaRDD> outputRdd = transform.toJavaRDD()
.map(row -> new Tuple2<>(((Vector) row.get(0)).toArray(), (Integer) row.get(1)));

this.name(outputRdd);
output.accept(outputRdd, sparkExecutor);

return ExecutionOperator.modelLazyExecution(inputs, outputs, operatorContext);
}

// TODO support fit and transform

@Override
public boolean containsAction() {
return false;
}

public static class Data {
private final Vector features;

public Data(Vector features) {
this.features = features;
}

public Data(double[] features) {
this.features = Vectors.dense(features);
}

public Vector getFeatures() {
return features;
}

@Override
public String toString() {
return "Data{" +
"features=" + features +
'}';
}

@Override
public boolean equals(Object o) {
if (this == o) return true;
if (!(o instanceof Data)) return false;
Data data = (Data) o;
return Objects.equals(features, data.features);
}

@Override
public int hashCode() {
return Objects.hash(features);
}
}
}

```

3f31a326ba75f6759cc6fd58baf76d28ad75c033

Start in wayang-platforms/wayang-spark/code/main/java/org/apache/wayang/spark/operators/ml/SparkKMeansOperator.java around evaluate and the TODO about fit and transform. Read the current Dataset creation, KMeans fit, and model transform flow to clarify the required support. Done means the Spark KMeans operator supports the requested fit-and-transform behavior and produces the expected output.

Written by the indexing model from the issue text.