Added euclid_similarity UDF

scripts/ddl/define-all-as-permanent.hive

@@ -99,6 +99,9 @@ CREATE FUNCTION jaccard_similarity as 'hivemall.knn.similarity.JaccardIndexUDF'
 DROP FUNCTION IF EXISTS angular_similarity;
 CREATE FUNCTION angular_similarity as 'hivemall.knn.similarity.AngularSimilarityUDF' USING JAR '${hivemall_jar}';
 
+DROP FUNCTION IF EXISTS euclid_similarity;
+CREATE FUNCTION euclid_similarity as 'hivemall.knn.similarity.EuclidSimilarity' USING JAR '${hivemall_jar}';
+
 ------------------------
 -- distance functions --
 ------------------------

scripts/ddl/define-all-excluding-macro.hive

@@ -95,6 +95,9 @@ create temporary function jaccard_similarity as 'hivemall.knn.similarity.Jaccard
 drop temporary function angular_similarity;
 create temporary function angular_similarity as 'hivemall.knn.similarity.AngularSimilarityUDF';
 
+drop temporary function euclid_similarity;
+create temporary function euclid_similarity as 'hivemall.knn.similarity.EuclidSimilarity';
+
 ------------------------
 -- distance functions --
 ------------------------

scripts/ddl/define-all.hive

@@ -95,6 +95,9 @@ create temporary function jaccard_similarity as 'hivemall.knn.similarity.Jaccard
 drop temporary function angular_similarity;
 create temporary function angular_similarity as 'hivemall.knn.similarity.AngularSimilarityUDF';
 
+drop temporary function euclid_similarity;
+create temporary function euclid_similarity as 'hivemall.knn.similarity.EuclidSimilarity';
+
 ------------------------
 -- distance functions --
 ------------------------

src/main/java/hivemall/knn/distance/EuclidDistanceUDF.java

@@ -57,10 +57,11 @@ public ObjectInspector initialize(ObjectInspector[] argOIs) throws UDFArgumentEx
     public FloatWritable evaluate(DeferredObject[] arguments) throws HiveException {
         List<String> ftvec1 = HiveUtils.asStringList(arguments[0], arg0ListOI);
         List<String> ftvec2 = HiveUtils.asStringList(arguments[1], arg1ListOI);
-        return evaluate(ftvec1, ftvec2);
+        float d = (float) euclidDistance(ftvec1, ftvec2);
+        return new FloatWritable(d);
     }
 
-    public FloatWritable evaluate(final List<String> ftvec1, final List<String> ftvec2) {
+    public static double euclidDistance(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) {
@@ -93,7 +94,7 @@ public FloatWritable evaluate(final List<String> ftvec1, final List<String> ftve
             float v1f = e.getValue();
             d += (v1f * v1f);
         }
-        return new FloatWritable((float) Math.sqrt(d));
+        return Math.sqrt(d);
     }
 
     @Override

src/main/java/hivemall/knn/similarity/EuclidSimilarity.java

@@ -0,0 +1,69 @@
+/*
+ * Hivemall: Hive scalable Machine Learning Library
+ *
+ * Copyright (C) 2015 Makoto YUI
+ * Copyright (C) 2013-2015 National Institute of Advanced Industrial Science and Technology (AIST)
+ *
+ * Licensed 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.similarity;
+
+import hivemall.knn.distance.EuclidDistanceUDF;
+import hivemall.utils.hadoop.HiveUtils;
+
+import java.util.Arrays;
+import java.util.List;
+
+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;
+
+@Description(name = "euclid_similarity", value = "_FUNC_(ftvec1, ftvec2) - Returns a euclid distance based similarity"
+        + ", which is `1.0 / (1.0 + distance)`, of the given two vectors")
+@UDFType(deterministic = true, stateful = false)
+public final class EuclidSimilarity extends GenericUDF {
+
+    private ListObjectInspector arg0ListOI, arg1ListOI;
+
+    @Override
+    public ObjectInspector initialize(ObjectInspector[] argOIs) throws UDFArgumentException {
+        if(argOIs.length != 2) {
+            throw new UDFArgumentException("euclid_similarity 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) EuclidDistanceUDF.euclidDistance(ftvec1, ftvec2);
+        float sim = 1.0f / (1.0f + d);
+        return new FloatWritable(sim);
+    }
+
+    @Override
+    public String getDisplayString(String[] children) {
+        return "euclid_similarity(" + Arrays.toString(children) + ")";
+    }
+
+}

src/test/java/hivemall/knn/distance/EuclidDistanceUDFTest.java

@@ -1,30 +1,45 @@
+/*
+ * Hivemall: Hive scalable Machine Learning Library
+ *
+ * Copyright (C) 2015 Makoto YUI
+ * Copyright (C) 2013-2015 National Institute of Advanced Industrial Science and Technology (AIST)
+ *
+ * Licensed 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 java.util.Arrays;
 import java.util.List;
 
-import org.apache.hadoop.io.FloatWritable;
 import org.junit.Assert;
 import org.junit.Test;
 
 public class EuclidDistanceUDFTest {
 
     @Test
     public void test1() {
-        EuclidDistanceUDF udf = new EuclidDistanceUDF();
         List<String> ftvec1 = Arrays.asList("1:1.0", "2:2.0", "3:3.0");
         List<String> ftvec2 = Arrays.asList("1:2.0", "2:4.0", "3:6.0");
-        FloatWritable d = udf.evaluate(ftvec1, ftvec2);
-        Assert.assertEquals((float) Math.sqrt(1.0 + 4.0 + 9.0), d.get(), 0.f);
+        double d = EuclidDistanceUDF.euclidDistance(ftvec1, ftvec2);
+        Assert.assertEquals(Math.sqrt(1.0 + 4.0 + 9.0), d, 0.f);
     }
 
     @Test
     public void test2() {
-        EuclidDistanceUDF udf = new EuclidDistanceUDF();
         List<String> ftvec1 = Arrays.asList("1:1.0", "2:3.0", "3:3.0");
         List<String> ftvec2 = Arrays.asList("1:2.0", "3:6.0");
-        FloatWritable d = udf.evaluate(ftvec1, ftvec2);
-        Assert.assertEquals((float) Math.sqrt(1.0 + 9.0 + 9.0), d.get(), 0.f);
+        double d = EuclidDistanceUDF.euclidDistance(ftvec1, ftvec2);
+        Assert.assertEquals(Math.sqrt(1.0 + 9.0 + 9.0), d, 0.f);
     }
 
 }