[
https://issues.apache.org/jira/browse/TEXT-126?focusedWorklogId=210506&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-210506
]
ASF GitHub Bot logged work on TEXT-126:
---------------------------------------
Author: ASF GitHub Bot
Created on: 09/Mar/19 09:44
Start Date: 09/Mar/19 09:44
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 Sørensen–Dice coefficient is a statistic used for comparing the
+ * similarity of two samples. It was independently developed by the botanists
+ * Thorvald Sørensen and Lee Raymond Dice, who published in 1948 and 1945
+ * respectively. The index is known by several other names, especially
+ * Sørensen–Dice index, Sørensen 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 Sørensen–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 & 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?
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Issue Time Tracking
-------------------
Worklog Id: (was: 210506)
Time Spent: 10h 50m (was: 10h 40m)
> 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: 10h 50m
> 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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