/* * 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 hivemall.knn.distance; import hivemall.model.FeatureValue; import hivemall.utils.hadoop.HiveUtils; import java.util.Arrays; import java.util.HashMap; import java.util.List; import java.util.Map; import org.apache.hadoop.hive.ql.exec.Description; import org.apache.hadoop.hive.ql.exec.UDFArgumentException; import org.apache.hadoop.hive.ql.metadata.HiveException; import org.apache.hadoop.hive.ql.udf.UDFType; import org.apache.hadoop.hive.ql.udf.generic.GenericUDF; import org.apache.hadoop.hive.serde2.objectinspector.ListObjectInspector; import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspector; import org.apache.hadoop.hive.serde2.objectinspector.primitive.PrimitiveObjectInspectorFactory; import org.apache.hadoop.io.FloatWritable; //@formatter:off @Description(name = "manhattan_distance", value = "_FUNC_(list x, list y) - Returns sum(|x - y|)", extended = "WITH docs as (\n" + " select 1 as docid, array('apple:1.0', 'orange:2.0', 'banana:1.0', 'kuwi:0') as features\n" + " union all\n" + " select 2 as docid, array('apple:1.0', 'orange:0', 'banana:2.0', 'kuwi:1.0') as features\n" + " union all\n" + " select 3 as docid, array('apple:2.0', 'orange:0', 'banana:2.0', 'kuwi:1.0') as features\n" + ") \n" + "select\n" + " l.docid as doc1,\n" + " r.docid as doc2,\n" + " manhattan_distance(l.features, r.features) as distance,\n" + " distance2similarity(angular_distance(l.features, r.features)) as similarity\n" + "from \n" + " docs l\n" + " CROSS JOIN docs r\n" + "where\n" + " l.docid != r.docid\n" + "order by \n" + " doc1 asc,\n" + " distance asc;\n" + "\n" + "doc1 doc2 distance similarity\n" + "1 2 4.0 0.75\n" + "1 3 5.0 0.75942624\n" + "2 3 1.0 0.91039914\n" + "2 1 4.0 0.75\n" + "3 2 1.0 0.91039914\n" + "3 1 5.0 0.75942624") @UDFType(deterministic = true, stateful = false) //@formatter:on public final class ManhattanDistanceUDF extends GenericUDF { private ListObjectInspector arg0ListOI, arg1ListOI; @Override public ObjectInspector initialize(ObjectInspector[] argOIs) throws UDFArgumentException { if (argOIs.length != 2) { throw new UDFArgumentException("manhattan_distance takes 2 arguments"); } this.arg0ListOI = HiveUtils.asListOI(argOIs, 0); this.arg1ListOI = HiveUtils.asListOI(argOIs, 1); return PrimitiveObjectInspectorFactory.writableFloatObjectInspector; } @Override public FloatWritable evaluate(DeferredObject[] arguments) throws HiveException { List<String> ftvec1 = HiveUtils.asStringList(arguments[0], arg0ListOI); List<String> ftvec2 = HiveUtils.asStringList(arguments[1], arg1ListOI); float d = (float) manhattanDistance(ftvec1, ftvec2); return new FloatWritable(d); } public static double manhattanDistance(final List<String> ftvec1, final List<String> ftvec2) { final FeatureValue probe = new FeatureValue(); final Map<String, Float> map = new HashMap<String, Float>(ftvec1.size() * 2 + 1); for (String ft : ftvec1) { if (ft == null) { continue; } FeatureValue.parseFeatureAsString(ft, probe); float v1 = probe.getValueAsFloat(); String f1 = probe.getFeature(); map.put(f1, v1); } double d = 0.d; for (String ft : ftvec2) { if (ft == null) { continue; } FeatureValue.parseFeatureAsString(ft, probe); String f2 = probe.getFeature(); float v2f = probe.getValueAsFloat(); Float v1 = map.remove(f2); if (v1 == null) { d += Math.abs(v2f); } else { float v1f = v1.floatValue(); float diff = v1f - v2f; d += Math.abs(diff); } } for (Map.Entry<String, Float> e : map.entrySet()) { float v1f = e.getValue(); d += Math.abs(v1f); } return d; } @Override public String getDisplayString(String[] children) { return "manhattan_distance(" + Arrays.toString(children) + ")"; } }