I refactored the org.apache.lucene.search.FuzzyTermEnum
edit distance implementation. It now only uses a single
pair of arrays, and those never get resized. That required
turning the order of text/target around in the loops. You'll
see that with the pair of arrays method, they get re-used
hand-over-hand, and are assigned to local variables in the
tight loops.
I removed the calculation of min distance and replaced
it with a boolean -- the min wasn't needed, only the test vs.
the max. I also flipped some variables around so there's
one less addition in the very inner loop and the arrays are
now looping in the ordinary way (starting at 0 with a < comparison).
I also commented out the redundant definition of the public close()
[which just called super.close() and had none of its own doc.]
I also just compute the max distance each time rather than
fiddling with an array -- it's just a little arithmetic done once
per term, but that could be put back.
I also rewrote min(int,int,int) to get rid of intermediate
assignments. Is there a lib for this kind of thing?
An intermediate refactoring that does the hand-over-hand
with the existing array and resizing strategy is in
FuzzyTermEnum.intermed.java.
I ran the unit tests as follows on both versions (my hat's off to the
build.xml author(s) -- this all just worked out of the box and was
really easy to follow the first through):
C:\java\lucene-2.0.0>ant -Djunit.includes="" -Dtestcase=TestFuzzyQuery test
Buildfile: build.xml
javacc-uptodate-check:
javacc-notice:
init:
common.compile-core:
[javac] Compiling 1 source file to
C:\java\lucene-2.0.0\build\classes\java
compile-core:
compile-demo:
common.compile-test:
compile-test:
test:
[junit] Testsuite: org.apache.lucene.search.TestFuzzyQuery
[junit] Tests run: 2, Failures: 0, Errors: 0, Time elapsed: 0.453 sec
BUILD SUCCESSFUL
Total time: 2 seconds
Does anyone have regression/performance test harnesses?
The unit tests were pretty minimal (which is a good thing!).
It'd be nice to test the min impl (ternary vs. if/then)
and the assumption about not allocating an
array of max distances. All told, the refactored version
should be a modest speed improvement, mainly from
unfolding the arrays so they're local one-dimensional refs.
I don't know what the protocol is for one-off contributions.
I'm happy with the Apache license, so that shouldn't
be a problem. I also don't know whether you use tabs
or spaces -- I untabified the final version and used your
two-space format in emacs.
- Bob Carpenter
package org.apache.lucene.search;
/**
* Copyright 2004 The Apache Software Foundation
*
* 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.
*/
import org.apache.lucene.index.IndexReader;
import org.apache.lucene.index.Term;
import java.io.IOException;
/** Subclass of FilteredTermEnum for enumerating all terms that are similiar
* to the specified filter term.
*
* <p>Term enumerations are always ordered by Term.compareTo(). Each term in
* the enumeration is greater than all that precede it.
*/
public final class FuzzyTermEnum extends FilteredTermEnum {
/* This should be somewhere around the average long word.
* If it is longer, we waste time and space. If it is shorter, we waste a
* little bit of time growing the array as we encounter longer words.
*/
private static final int TYPICAL_LONGEST_WORD_IN_INDEX = 19;
/* Allows us save time required to create a new array
* everytime similarity is called. These are slices that
* will be reused during dynamic programming hand-over-hand
* style.
*/
private final int[] d0;
private final int[] d1;
private float similarity;
private boolean endEnum = false;
private Term searchTerm = null;
private final String field;
private final String text;
private final String prefix;
private final float minimumSimilarity;
private final float scale_factor;
/**
* Creates a FuzzyTermEnum with an empty prefix and a minSimilarity of 0.5f.
* <p>
* After calling the constructor the enumeration is already pointing to the
first
* valid term if such a term exists.
*
* @param reader
* @param term
* @throws IOException
* @see #FuzzyTermEnum(IndexReader, Term, float, int)
*/
public FuzzyTermEnum(IndexReader reader, Term term) throws IOException {
this(reader, term, FuzzyQuery.defaultMinSimilarity,
FuzzyQuery.defaultPrefixLength);
}
/**
* Creates a FuzzyTermEnum with an empty prefix.
* <p>
* After calling the constructor the enumeration is already pointing to the
first
* valid term if such a term exists.
*
* @param reader
* @param term
* @param minSimilarity
* @throws IOException
* @see #FuzzyTermEnum(IndexReader, Term, float, int)
*/
public FuzzyTermEnum(IndexReader reader, Term term, float minSimilarity)
throws IOException {
this(reader, term, minSimilarity, FuzzyQuery.defaultPrefixLength);
}
/**
* Constructor for enumeration of all terms from specified
<code>reader</code> which share a prefix of
* length <code>prefixLength</code> with <code>term</code> and which have a
fuzzy similarity >
* <code>minSimilarity</code>.
* <p>
* After calling the constructor the enumeration is already pointing to the
first
* valid term if such a term exists.
*
* @param reader Delivers terms.
* @param term Pattern term.
* @param minSimilarity Minimum required similarity for terms from the
reader. Default value is 0.5f.
* @param prefixLength Length of required common prefix. Default value is 0.
* @throws IOException
*/
public FuzzyTermEnum(IndexReader reader, Term term, final float
minSimilarity, final int prefixLength) throws IOException {
super();
if (minSimilarity >= 1.0f)
throw new IllegalArgumentException("minimumSimilarity cannot be greater
than or equal to 1");
else if (minSimilarity < 0.0f)
throw new IllegalArgumentException("minimumSimilarity cannot be less than
0");
if(prefixLength < 0)
throw new IllegalArgumentException("prefixLength cannot be less than 0");
this.minimumSimilarity = minSimilarity;
this.scale_factor = 1.0f / (1.0f - minimumSimilarity);
this.searchTerm = term;
this.field = searchTerm.field();
//The prefix could be longer than the word.
//It's kind of silly though. It means we must match the entire word.
final int fullSearchTermLength = searchTerm.text().length();
final int realPrefixLength = prefixLength > fullSearchTermLength ?
fullSearchTermLength : prefixLength;
this.text = searchTerm.text().substring(realPrefixLength);
this.prefix = searchTerm.text().substring(0, realPrefixLength);
this.d0 = new int[this.text.length()+1];
this.d1 = new int[this.d0.length];
setEnum(reader.terms(new Term(searchTerm.field(), prefix)));
}
/**
* The termCompare method in FuzzyTermEnum uses Levenshtein distance to
* calculate the distance between the given term and the comparing term.
*/
protected final boolean termCompare(Term term) {
if (field == term.field() && term.text().startsWith(prefix)) {
final String target = term.text().substring(prefix.length());
this.similarity = similarity(target);
return (similarity > minimumSimilarity);
}
endEnum = true;
return false;
}
public final float difference() {
return (float)((similarity - minimumSimilarity) * scale_factor);
}
public final boolean endEnum() {
return endEnum;
}
/******************************
* Compute Levenshtein distance
******************************/
/**
* Finds and returns the smallest of three integers
*/
private static final int min(int a, int b, int c) {
// removed assignments to use double ternary
return (a < b)
? ((a < c) ? a : c)
: ((b < c) ? b: c);
// alt form is:
// if (a < b) { if (a < c) return a; else return c; }
// if (b < c) return b; else return c;
}
/**
* <p>Similarity returns a number that is 1.0f or less (including negative
numbers)
* based on how similar the Term is compared to a target term. It returns
* exactly 0.0f when
* <pre>
* editDistance < maximumEditDistance</pre>
* Otherwise it returns:
* <pre>
* 1 - (editDistance / length)</pre>
* where length is the length of the shortest term (text or target) including
a
* prefix that are identical and editDistance is the Levenshtein distance for
* the two words.</p>
*
* <p>Embedded within this algorithm is a fail-fast Levenshtein distance
* algorithm. The fail-fast algorithm differs from the standard Levenshtein
* distance algorithm in that it is aborted if it is discovered that the
* mimimum distance between the words is greater than some threshold.
*
* <p>To calculate the maximum distance threshold we use the following
formula:
* <pre>
* (1 - minimumSimilarity) * length</pre>
* where length is the shortest term including any prefix that is not part of
the
* similarity comparision. This formula was derived by solving for what
maximum value
* of distance returns false for the following statements:
* <pre>
* similarity = 1 - ((float)distance / (float) (prefixLength +
Math.min(textlen, targetlen)));
* return (similarity > minimumSimilarity);</pre>
* where distance is the Levenshtein distance for the two words.
* </p>
* <p>Levenshtein distance (also known as edit distance) is a measure of
similiarity
* between two strings where the distance is measured as the number of
character
* deletions, insertions or substitutions required to transform one string to
* the other string.
* @param target the target word or phrase
* @return the similarity, 0.0 or less indicates that it matches less than
the required
* threshold and 1.0 indicates that the text and target are identical
*/
private synchronized final float similarity(final String target) {
final int m = target.length();
final int n = text.length();
if (n == 0) {
//we don't have anything to compare. That means if we just add
//the letters for m we get the new word
return prefix.length() == 0 ? 0.0f : 1.0f - ((float) m / prefix.length());
}
if (m == 0) {
return prefix.length() == 0 ? 0.0f : 1.0f - ((float) n / prefix.length());
}
final int maxDistance = calculateMaxDistance(m);
if (maxDistance < Math.abs(m-n)) {
//just adding the characters of m to n or vice-versa results in
//too many edits
//for example "pre" length is 3 and "prefixes" length is 8. We can see
that
//given this optimal circumstance, the edit distance cannot be less than
5.
//which is 8-3 or more precisesly Math.abs(3-8).
//if our maximum edit distance is 4, then we can discard this word
//without looking at it.
return 0.0f;
}
int[] dLast = d0; // set locals for efficiency
int[] dCurrent = d1;
for (int j = 0; j <= n; j++) dCurrent[j] = j;
for (int i = 0; i < m; ) {
final char s_i = target.charAt(i);
int[] dTemp = dLast;
dLast = dCurrent; // previously: d[i-i]
dCurrent = dTemp; // previously: d[i]
boolean prune = (dCurrent[0] = ++i) > maxDistance; // true if d[i][0] is
too large
for (int j = 0; j < n; j++) {
dCurrent[j+1] = (s_i == text.charAt(j))
? min(dLast[j+1]+1, dCurrent[j]+1, dLast[j])
: min(dLast[j+1], dCurrent[j], dLast[j])+1;
if (prune && dCurrent[j+1] <= maxDistance)
prune = false;
}
// (prune==false) iff (dCurrent[j] < maxDistance) for some j
if (prune) {
return 0.0f;
}
}
// this will return less than 0.0 when the edit distance is
// greater than the number of characters in the shorter word.
// but this was the formula that was previously used in FuzzyTermEnum,
// so it has not been changed (even though minimumSimilarity must be
// greater than 0.0)
return 1.0F - dCurrent[n]/(float)(prefix.length() + Math.min(n,m));
}
private int calculateMaxDistance(int m) {
return (int) ((1-minimumSimilarity) * (Math.min(text.length(), m) +
prefix.length()));
}
/* This is redundant
public void close() throws IOException {
super.close(); //call super.close() and let the garbage collector do its
work.
}
*/
}
package org.apache.lucene.search;
/**
* Copyright 2004 The Apache Software Foundation
*
* 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.
*/
import org.apache.lucene.index.IndexReader;
import org.apache.lucene.index.Term;
import java.io.IOException;
/** Subclass of FilteredTermEnum for enumerating all terms that are similiar
* to the specified filter term.
*
* <p>Term enumerations are always ordered by Term.compareTo(). Each term in
* the enumeration is greater than all that precede it.
*/
public final class FuzzyTermEnum extends FilteredTermEnum {
/* This should be somewhere around the average long word.
* If it is longer, we waste time and space. If it is shorter, we waste a
* little bit of time growing the array as we encounter longer words.
*/
private static final int TYPICAL_LONGEST_WORD_IN_INDEX = 19;
/* Allows us save time required to create a new array
* everytime similarity is called. These are slices that
* will be reused during dynamic programming hand-over-hand
* style. They get resized, if necessary, by growDistanceArrays(int).
*/
private int[] d0;
private int[] d1;
private float similarity;
private boolean endEnum = false;
private Term searchTerm = null;
private final String field;
private final String text;
private final String prefix;
private final float minimumSimilarity;
private final float scale_factor;
/**
* Creates a FuzzyTermEnum with an empty prefix and a minSimilarity of 0.5f.
* <p>
* After calling the constructor the enumeration is already pointing to the
first
* valid term if such a term exists.
*
* @param reader
* @param term
* @throws IOException
* @see #FuzzyTermEnum(IndexReader, Term, float, int)
*/
public FuzzyTermEnum(IndexReader reader, Term term) throws IOException {
this(reader, term, FuzzyQuery.defaultMinSimilarity,
FuzzyQuery.defaultPrefixLength);
}
/**
* Creates a FuzzyTermEnum with an empty prefix.
* <p>
* After calling the constructor the enumeration is already pointing to the
first
* valid term if such a term exists.
*
* @param reader
* @param term
* @param minSimilarity
* @throws IOException
* @see #FuzzyTermEnum(IndexReader, Term, float, int)
*/
public FuzzyTermEnum(IndexReader reader, Term term, float minSimilarity)
throws IOException {
this(reader, term, minSimilarity, FuzzyQuery.defaultPrefixLength);
}
/**
* Constructor for enumeration of all terms from specified
<code>reader</code> which share a prefix of
* length <code>prefixLength</code> with <code>term</code> and which have a
fuzzy similarity >
* <code>minSimilarity</code>.
* <p>
* After calling the constructor the enumeration is already pointing to the
first
* valid term if such a term exists.
*
* @param reader Delivers terms.
* @param term Pattern term.
* @param minSimilarity Minimum required similarity for terms from the
reader. Default value is 0.5f.
* @param prefixLength Length of required common prefix. Default value is 0.
* @throws IOException
*/
public FuzzyTermEnum(IndexReader reader, Term term, final float
minSimilarity, final int prefixLength) throws IOException {
super();
if (minSimilarity >= 1.0f)
throw new IllegalArgumentException("minimumSimilarity cannot be greater
than or equal to 1");
else if (minSimilarity < 0.0f)
throw new IllegalArgumentException("minimumSimilarity cannot be less than
0");
if(prefixLength < 0)
throw new IllegalArgumentException("prefixLength cannot be less than 0");
this.minimumSimilarity = minSimilarity;
this.scale_factor = 1.0f / (1.0f - minimumSimilarity);
this.searchTerm = term;
this.field = searchTerm.field();
//The prefix could be longer than the word.
//It's kind of silly though. It means we must match the entire word.
final int fullSearchTermLength = searchTerm.text().length();
final int realPrefixLength = prefixLength > fullSearchTermLength ?
fullSearchTermLength : prefixLength;
this.text = searchTerm.text().substring(realPrefixLength);
this.prefix = searchTerm.text().substring(0, realPrefixLength);
growDistanceArrays(TYPICAL_LONGEST_WORD_IN_INDEX);
setEnum(reader.terms(new Term(searchTerm.field(), prefix)));
}
/**
* The termCompare method in FuzzyTermEnum uses Levenshtein distance to
* calculate the distance between the given term and the comparing term.
*/
protected final boolean termCompare(Term term) {
if (field == term.field() && term.text().startsWith(prefix)) {
final String target = term.text().substring(prefix.length());
this.similarity = similarity(target);
return (similarity > minimumSimilarity);
}
endEnum = true;
return false;
}
public final float difference() {
return (float)((similarity - minimumSimilarity) * scale_factor);
}
public final boolean endEnum() {
return endEnum;
}
/******************************
* Compute Levenshtein distance
******************************/
/**
* Finds and returns the smallest of three integers
*/
private static final int min(int a, int b, int c) {
// removed assignments to use double ternary
return (a < b)
? ((a < c) ? a : c)
: ((b < c) ? b: c);
// alt form is:
// if (a < b) { if (a < c) return a; else return c; }
// if (b < c) return b; else return c;
}
/**
* <p>Similarity returns a number that is 1.0f or less (including negative
numbers)
* based on how similar the Term is compared to a target term. It returns
* exactly 0.0f when
* <pre>
* editDistance < maximumEditDistance</pre>
* Otherwise it returns:
* <pre>
* 1 - (editDistance / length)</pre>
* where length is the length of the shortest term (text or target) including
a
* prefix that are identical and editDistance is the Levenshtein distance for
* the two words.</p>
*
* <p>Embedded within this algorithm is a fail-fast Levenshtein distance
* algorithm. The fail-fast algorithm differs from the standard Levenshtein
* distance algorithm in that it is aborted if it is discovered that the
* mimimum distance between the words is greater than some threshold.
*
* <p>To calculate the maximum distance threshold we use the following
formula:
* <pre>
* (1 - minimumSimilarity) * length</pre>
* where length is the shortest term including any prefix that is not part of
the
* similarity comparision. This formula was derived by solving for what
maximum value
* of distance returns false for the following statements:
* <pre>
* similarity = 1 - ((float)distance / (float) (prefixLength +
Math.min(textlen, targetlen)));
* return (similarity > minimumSimilarity);</pre>
* where distance is the Levenshtein distance for the two words.
* </p>
* <p>Levenshtein distance (also known as edit distance) is a measure of
similiarity
* between two strings where the distance is measured as the number of
character
* deletions, insertions or substitutions required to transform one string to
* the other string.
* @param target the target word or phrase
* @return the similarity, 0.0 or less indicates that it matches less than
the required
* threshold and 1.0 indicates that the text and target are identical
*/
private synchronized final float similarity(final String target) {
final int m = target.length();
final int n = text.length();
if (n == 0) {
//we don't have anything to compare. That means if we just add
//the letters for m we get the new word
return prefix.length() == 0 ? 0.0f : 1.0f - ((float) m / prefix.length());
}
if (m == 0) {
return prefix.length() == 0 ? 0.0f : 1.0f - ((float) n / prefix.length());
}
final int maxDistance = calculateMaxDistance(m);
if (maxDistance < Math.abs(m-n)) {
//just adding the characters of m to n or vice-versa results in
//too many edits
//for example "pre" length is 3 and "prefixes" length is 8. We can see
that
//given this optimal circumstance, the edit distance cannot be less than
5.
//which is 8-3 or more precisesly Math.abs(3-8).
//if our maximum edit distance is 4, then we can discard this word
//without looking at it.
return 0.0f;
}
//let's make sure we have enough room in our array to do the distance
calculations.
if (d0.length <= m) {
growDistanceArrays(m);
}
int[] dLast = d0; // set local vars for efficiency ~ the old d[i-1]
int[] dCurrent = d1; // ~ the old d[i]
for (int j = 0; j <= m; j++) dCurrent[j] = j;
for (int i = 0; i < n; ) {
final char s_i = text.charAt(i);
int[] dTemp = dLast;
dLast = dCurrent; // previously: d[i-i]
dCurrent = dTemp; // previously: d[i]
boolean prune = (dCurrent[0] = ++i) > maxDistance; // true if d[i][0]
is too large
for (int j = 0; j < m; j++) {
dCurrent[j+1] = (s_i == target.charAt(j))
? min(dLast[j+1]+1, dCurrent[j]+1, dLast[j])
: min(dLast[j+1], dCurrent[j], dLast[j])+1;
if (prune && dCurrent[j+1] <= maxDistance)
prune = false;
}
// (prune==false) iff (dCurrent[j] < maxDistance) for some j
if (prune) {
return 0.0f;
}
}
// this will return less than 0.0 when the edit distance is
// greater than the number of characters in the shorter word.
// but this was the formula that was previously used in FuzzyTermEnum,
// so it has not been changed (even though minimumSimilarity must be
// greater than 0.0)
return 1.0F - dCurrent[m]/(float)(prefix.length() + Math.min(n,m));
}
/**
* Grow the second dimension of the array slices, so that we can
* calculate the Levenshtein difference.
*/
private void growDistanceArrays(int m) {
d0 = new int[m+1];
d1 = new int[m+1];
}
private int calculateMaxDistance(int m) {
return (int) ((1-minimumSimilarity) * (Math.min(text.length(), m) +
prefix.length()));
}
/* This is redundant
public void close() throws IOException {
super.close(); //call super.close() and let the garbage collector do its
work.
}
*/
}
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