SOLR-11890: Add multiKmeans Stream Evaluator

solr/core/src/java/org/apache/solr/handler/StreamHandler.java

@@ -312,6 +312,7 @@ public class StreamHandler extends RequestHandlerBase implements SolrCoreAware,
         .withFunctionName("rowCount", RowCountEvaluator.class)
         .withFunctionName("fuzzyKmeans", FuzzyKmeansEvaluator.class)
         .withFunctionName("getMembershipMatrix", GetMembershipMatrixEvaluator.class)
+        .withFunctionName("multiKmeans", MultiKmeansEvaluator.class)
 
         // Boolean Stream Evaluators
 

solr/solrj/src/java/org/apache/solr/client/solrj/io/eval/MultiKmeansEvaluator.java

@@ -0,0 +1,108 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.solr.client.solrj.io.eval;
+
+import java.io.IOException;
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Map;
+import java.util.HashMap;
+
+import org.apache.commons.math3.ml.clustering.KMeansPlusPlusClusterer;
+import org.apache.commons.math3.ml.clustering.MultiKMeansPlusPlusClusterer;
+import org.apache.solr.client.solrj.io.stream.expr.StreamExpression;
+import org.apache.solr.client.solrj.io.stream.expr.StreamExpressionNamedParameter;
+import org.apache.solr.client.solrj.io.stream.expr.StreamFactory;
+
+public class MultiKmeansEvaluator extends RecursiveObjectEvaluator implements ManyValueWorker {
+  protected static final long serialVersionUID = 1L;
+
+  private int maxIterations = 1000;
+
+  public MultiKmeansEvaluator(StreamExpression expression, StreamFactory factory) throws IOException{
+    super(expression, factory);
+
+    List<StreamExpressionNamedParameter> namedParams = factory.getNamedOperands(expression);
+
+    for(StreamExpressionNamedParameter namedParam : namedParams){
+      if(namedParam.getName().equals("maxIterations")) {
+        this.maxIterations = Integer.parseInt(namedParam.getParameter().toString().trim());
+      } else {
+        throw new IOException("Unexpected named parameter:"+namedParam.getName());
+      }
+    }
+  }
+
+  @Override
+  public Object doWork(Object... values) throws IOException {
+
+    if(values.length != 3) {
+      throw new IOException("The multiKmeans function expects three parameters; a matrix to cluster, k and number of trials.");
+    }
+
+    Object value1 = values[0];
+    Object value2 = values[1];
+    Object value3 = values[2];
+
+    Matrix matrix = null;
+    int k = 0;
+    int trials=0;
+
+    if(value1 instanceof Matrix) {
+      matrix = (Matrix)value1;
+    } else {
+      throw new IOException("The first parameter for multiKmeans should be the observation matrix.");
+    }
+
+    if(value2 instanceof Number) {
+      k = ((Number)value2).intValue();
+    } else {
+      throw new IOException("The second parameter for multiKmeans should be k.");
+    }
+
+    if(value3 instanceof Number) {
+      trials= ((Number)value3).intValue();
+    } else {
+      throw new IOException("The third parameter for multiKmeans should be trials.");
+    }
+
+    KMeansPlusPlusClusterer<KmeansEvaluator.ClusterPoint> kmeans = new KMeansPlusPlusClusterer(k, maxIterations);
+    MultiKMeansPlusPlusClusterer multiKmeans = new MultiKMeansPlusPlusClusterer(kmeans, trials);
+
+    List<KmeansEvaluator.ClusterPoint> points = new ArrayList();
+    double[][] data = matrix.getData();
+
+    List<String> ids = matrix.getRowLabels();
+
+    for(int i=0; i<data.length; i++) {
+      double[] vec = data[i];
+      points.add(new KmeansEvaluator.ClusterPoint(ids.get(i), vec));
+    }
+
+    Map fields = new HashMap();
+
+    fields.put("k", k);
+    fields.put("trials", trials);
+    fields.put("distance", "euclidean");
+    fields.put("maxIterations", maxIterations);
+
+    return new KmeansEvaluator.ClusterTuple(fields, multiKmeans.cluster(points), matrix.getColumnLabels());
+  }
+
+}
+

solr/solrj/src/test/org/apache/solr/client/solrj/io/stream/StreamExpressionTest.java

@@ -7076,6 +7076,87 @@ public class StreamExpressionTest extends SolrCloudTestCase {
     }
   }
 
+  @Test
+  public void testMultiKmeans() throws Exception {
+    String cexpr = "let(echo=true," +
+        "               a=array(1,1,1,0,0,0)," +
+        "               b=array(1,1,1,0,0,0)," +
+        "               c=array(0,0,0,1,1,1)," +
+        "               d=array(0,0,0,1,1,1)," +
+        "               e=setRowLabels(matrix(a,b,c,d), " +
+        "                              array(doc1, doc2, doc3, doc4))," +
+        "               f=multiKmeans(e, 2, 5)," +
+        "               g=getCluster(f, 0)," +
+        "               h=getCluster(f, 1)," +
+        "               i=getCentroids(f)," +
+        "               j=getRowLabels(g)," +
+        "               k=getRowLabels(h))";
+    ModifiableSolrParams paramsLoc = new ModifiableSolrParams();
+    paramsLoc.set("expr", cexpr);
+    paramsLoc.set("qt", "/stream");
+    String url = cluster.getJettySolrRunners().get(0).getBaseUrl().toString()+"/"+COLLECTIONORALIAS;
+    TupleStream solrStream = new SolrStream(url, paramsLoc);
+    StreamContext context = new StreamContext();
+    solrStream.setStreamContext(context);
+    List<Tuple> tuples = getTuples(solrStream);
+    assertTrue(tuples.size() == 1);
+    List<List<Number>> cluster1 = (List<List<Number>>)tuples.get(0).get("g");
+    List<List<Number>> cluster2 = (List<List<Number>>)tuples.get(0).get("h");
+    List<List<Number>> centroids = (List<List<Number>>)tuples.get(0).get("i");
+    List<String> labels1 = (List<String>)tuples.get(0).get("j");
+    List<String> labels2 = (List<String>)tuples.get(0).get("k");
+
+    assertEquals(cluster1.size(), 2);
+    assertEquals(cluster2.size(), 2);
+    assertEquals(centroids.size(), 2);
+
+    //Assert that the docs are not in both clusters
+    assertTrue(!(labels1.contains("doc1") && labels2.contains("doc1")));
+    assertTrue(!(labels1.contains("doc2") && labels2.contains("doc2")));
+    assertTrue(!(labels1.contains("doc3") && labels2.contains("doc3")));
+    assertTrue(!(labels1.contains("doc4") && labels2.contains("doc4")));
+
+    //Assert that (doc1 and doc2) or (doc3 and doc4) are in labels1
+    assertTrue((labels1.contains("doc1") && labels1.contains("doc2")) ||
+        ((labels1.contains("doc3") && labels1.contains("doc4"))));
+
+    //Assert that (doc1 and doc2) or (doc3 and doc4) are in labels2
+    assertTrue((labels2.contains("doc1") && labels2.contains("doc2")) ||
+        ((labels2.contains("doc3") && labels2.contains("doc4"))));
+
+    if(labels1.contains("doc1")) {
+      assertEquals(centroids.get(0).get(0).doubleValue(), 1.0, 0.0);
+      assertEquals(centroids.get(0).get(1).doubleValue(), 1.0, 0.0);
+      assertEquals(centroids.get(0).get(2).doubleValue(), 1.0, 0.0);
+      assertEquals(centroids.get(0).get(3).doubleValue(), 0.0, 0.0);
+      assertEquals(centroids.get(0).get(4).doubleValue(), 0.0, 0.0);
+      assertEquals(centroids.get(0).get(5).doubleValue(), 0.0, 0.0);
+
+      assertEquals(centroids.get(1).get(0).doubleValue(), 0.0, 0.0);
+      assertEquals(centroids.get(1).get(1).doubleValue(), 0.0, 0.0);
+      assertEquals(centroids.get(1).get(2).doubleValue(), 0.0, 0.0);
+      assertEquals(centroids.get(1).get(3).doubleValue(), 1.0, 0.0);
+      assertEquals(centroids.get(1).get(4).doubleValue(), 1.0, 0.0);
+      assertEquals(centroids.get(1).get(5).doubleValue(), 1.0, 0.0);
+    } else {
+      assertEquals(centroids.get(0).get(0).doubleValue(), 0.0, 0.0);
+      assertEquals(centroids.get(0).get(1).doubleValue(), 0.0, 0.0);
+      assertEquals(centroids.get(0).get(2).doubleValue(), 0.0, 0.0);
+      assertEquals(centroids.get(0).get(3).doubleValue(), 1.0, 0.0);
+      assertEquals(centroids.get(0).get(4).doubleValue(), 1.0, 0.0);
+      assertEquals(centroids.get(0).get(5).doubleValue(), 1.0, 0.0);
+
+      assertEquals(centroids.get(1).get(0).doubleValue(), 1.0, 0.0);
+      assertEquals(centroids.get(1).get(1).doubleValue(), 1.0, 0.0);
+      assertEquals(centroids.get(1).get(2).doubleValue(), 1.0, 0.0);
+      assertEquals(centroids.get(1).get(3).doubleValue(), 0.0, 0.0);
+      assertEquals(centroids.get(1).get(4).doubleValue(), 0.0, 0.0);
+      assertEquals(centroids.get(1).get(5).doubleValue(), 0.0, 0.0);
+    }
+  }
+
+
+
 
   @Test
   public void testFuzzyKmeans() throws Exception {