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From "ASF GitHub Bot (JIRA)" <j...@apache.org>
Subject [jira] [Work logged] (TEXT-126) Dice's Coefficient Algorithm in String similarity
Date Sat, 09 Mar 2019 09:47:00 GMT

     [ https://issues.apache.org/jira/browse/TEXT-126?focusedWorklogId=210507&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-210507
]

ASF GitHub Bot logged work on TEXT-126:
---------------------------------------

                Author: ASF GitHub Bot
            Created on: 09/Mar/19 09:46
            Start Date: 09/Mar/19 09:46
    Worklog Time Spent: 10m 
      Work Description: kinow commented on pull request #103: TEXT-126: Adding Sorensen-Dice
similarity algoritham
URL: https://github.com/apache/commons-text/pull/103#discussion_r263993346
 
 

 ##########
 File path: src/main/java/org/apache/commons/text/similarity/SorensenDiceSimilarity.java
 ##########
 @@ -0,0 +1,127 @@
+/*
+ * 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.commons.text.similarity;
+
+import java.util.HashSet;
+import java.util.Set;
+import java.util.stream.Collectors;
+
+import org.apache.commons.lang3.StringUtils;
+
+/**
+ * A similarity algorithm indicating the percentage of matched characters
+ * between two character sequences.
+ *
+ * <p>
+ * The S&#248;rensen–Dice coefficient is a statistic used for comparing the
+ * similarity of two samples. It was independently developed by the botanists
+ * Thorvald S&#248;rensen and Lee Raymond Dice, who published in 1948 and 1945
+ * respectively. The index is known by several other names, especially
+ * S&#248;rensen–Dice index, S&#248;rensen index and Dice's coefficient. Other
+ * variations include the "similarity coefficient" or "index", such as Dice
+ * similarity coefficient (DSC).
+ * </p>
+ *
+ * <p>
+ * This implementation is based on the S&#248;rensen–Dice similarity algorithm
+ * from <a href=
+ * "http://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient">
+ * http://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient</a>.
+ *
+ *
+ * </p>
+ *
+ * @since 1.7
+ */
+public class SorensenDiceSimilarity implements SimilarityScore<Double> {
+
+    /**
+     * Calculates Sorensen-Dice Similarity of two character sequences passed as
+     * input.
+     *
+     * <pre>
+     * similarity.apply(null, null)                 = IllegalArgumentException
+     * similarity.apply("foo", null)                = IllegalArgumentException
+     * similarity.apply(null, "foo")                = IllegalArgumentException
+     * similarity.apply("night", "nacht")           = 0.25
+     * similarity.apply("", "")                     = 1.0
+     * similarity.apply("foo", "foo")               = 1.0
+     * similarity.apply("foo", "foo ")              = 0.8
+     * similarity.apply("foo", "foo ")              = 0.66
+     * similarity.apply("foo", " foo ")             = 0.66
+     * similarity.apply("foo", " foo")              = 0.66
+     * similarity.apply("", "a")                    = 0.0
+     * similarity.apply("aaapppp", "")              = 0.0
+     * similarity.apply("frog", "fog")              = 0.4
+     * similarity.apply("fly", "ant")               = 0.0
+     * similarity.apply("elephant", "hippo")        = 0.0
+     * similarity.apply("hippo", "elephant")        = 0.0
+     * similarity.apply("hippo", "zzzzzzzz")        = 0.0
+     * similarity.apply("hello", "hallo")           = 0.5
+     * similarity.apply("ABC Corporation", "ABC Corp") = 0.7
+     * similarity.apply("D N H Enterprises Inc", "D &amp; H Enterprises, Inc.") = 0.74
+     * similarity.apply("My Gym Children's Fitness Center", "My Gym. Childrens Fitness")
= 0.81
+     * similarity.apply("PENNSYLVANIA", "PENNCISYLVNIA") = 0.69
+     * </pre>
+     *
+     * @param left  the first CharSequence, must not be null
+     * @param right the second CharSequence, must not be null
+     * @return result similarity
+     * @throws IllegalArgumentException if either CharSequence input is {@code null}
+     */
+    @Override
+    public Double apply(final CharSequence left, final CharSequence right) {
+
+        if (left == null || right == null) {
+            throw new IllegalArgumentException("CharSequences must not be null");
+        }
+
+        if (StringUtils.equals(left, right)) {
 
 Review comment:
   I think we have a bug in `Jaccard` for empty strings @ameyjadiye . Still need to confirm
with the algorithm definition, just to make sure. But the following code:
   
   ```java
   package br.eti.kinoshita.tests.text;
   
   import java.util.Collections;
   
   public class EditDistances {
   
       public static void main(String[] args) {
           System.out.println("Testing jaccard sim/dis with empty strings");
           System.out.println("---");
           org.simmetrics.metrics.Jaccard<String> j1 = new org.simmetrics.metrics.Jaccard<>();
           float s1 = j1.compare(Collections.emptySet(), Collections.emptySet());
           System.out.println("Simmetrics Jaccard similarity: " + s1);
           float d1 = j1.distance(Collections.emptySet(), Collections.emptySet());
           System.out.println("Simmetrics Jaccard distance: " + d1);
           
           System.out.println("---");
           
           info.debatty.java.stringsimilarity.Jaccard j2 = new info.debatty.java.stringsimilarity.Jaccard();
           double s2 = j2.similarity("", "");
           System.out.println("javastringsimilarity Jaccard similarity: " + s2);
           double d2 = j2.distance("", "");
           System.out.println("javastringsimilarity Jaccard distance: " + d2);
           
           System.out.println("---");
           
           org.apache.commons.text.similarity.JaccardSimilarity j3_1 = new org.apache.commons.text.similarity.JaccardSimilarity();
           double s3 = j3_1.apply("", "");
           System.out.println("commons-text Jaccard similarity: " + s3);
           org.apache.commons.text.similarity.JaccardDistance j3_2 = new org.apache.commons.text.similarity.JaccardDistance();
           double d3 = j3_2.apply("", "");
           System.out.println("commons-text Jaccard distance: " + d3);
       }
   }
   ```
   
   Produces:
   
   ```
   Testing jaccard sim/dis with empty strings
   ---
   Simmetrics Jaccard similarity: 1.0
   Simmetrics Jaccard distance: 0.0
   ---
   javastringsimilarity Jaccard similarity: 1.0
   javastringsimilarity Jaccard distance: 0.0
   ---
   commons-text Jaccard similarity: 0.0
   commons-text Jaccard distance: 1.0
   ```
   
   >I checked few other libraries to confirm.
   
   Did you compare Jaccard and Sorensen (the two discussed here) too?
   
   >This check should be moved after the check of the lengths. Thus any input with no bigrams
gets a similarity of 0 since there is effectively nothing to compare.
   
   @aherbert, the Python `textdistance` is also returning 1 for both single chars and bigrams.
   
   ```python
   >>> textdistance.sorensen("", "")
   1
   >>> textdistance.Sorensen(qval=2)("", "")
   1
   ```
   
   Same for java-string-similarity's `SorensenDice#similarity`. So I think @ameyjadiye 's
code is actually OK for the current approach? (might be that for sets that's the opposite,
but for text we need to consider some special cases?)
 
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Issue Time Tracking
-------------------

    Worklog Id:     (was: 210507)
    Time Spent: 11h  (was: 10h 50m)

> Dice's Coefficient Algorithm in String similarity
> -------------------------------------------------
>
>                 Key: TEXT-126
>                 URL: https://issues.apache.org/jira/browse/TEXT-126
>             Project: Commons Text
>          Issue Type: Improvement
>            Reporter: Vicky Chawda
>            Priority: Major
>          Time Spent: 11h
>  Remaining Estimate: 0h
>
> I'd like to propose an extension to the algorithms for string similarity in *commons-text/src/main/java/org/apache/commons/text/similarity/*
>  Dice's Coefficient Algorithm can be helpful for many who are looking for ranking similarities
in strings.
> *Inspired from* - [http://www.catalysoft.com/articles/StrikeAMatch.html]



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