stringkernel.java

Java 编写的多种数据挖掘算法 包括聚类、分类、预处理等

package weka.classifiers.functions.supportVector;

import weka.core.Capabilities;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.SelectedTag;
import weka.core.Tag;
import weka.core.TechnicalInformation;
import weka.core.Utils;
import weka.core.Capabilities.Capability;
import weka.core.TechnicalInformation.Type;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformationHandler;

import java.util.Enumeration;
import java.util.Vector;

/**
 <!-- globalinfo-start -->
 * Implementation of the subsequence kernel (SSK) as described in [1] and of the subsequence kernel with lambda pruning (SSK-LP) as described in [2].<br/>
 * <br/>
 * For more information, see<br/>
 * <br/>
 * Huma Lodhi, Craig Saunders, John Shawe-Taylor, Nello Cristianini, Christopher J. C. H. Watkins (2002). Text Classification using String Kernels. Journal of Machine Learning Research. 2:419-444.<br/>
 * <br/>
 * F. Kleedorfer, A. Seewald (2005). Implementation of a String Kernel for WEKA. Wien, Austria.
 * <p/>
 <!-- globalinfo-end -->
 *
 <!-- technical-bibtex-start -->
 * BibTeX:
 * <pre>
 * &#64;article{Lodhi2002,
 *    author = {Huma Lodhi and Craig Saunders and John Shawe-Taylor and Nello Cristianini and Christopher J. C. H. Watkins},
 *    journal = {Journal of Machine Learning Research},
 *    pages = {419-444},
 *    title = {Text Classification using String Kernels},
 *    volume = {2},
 *    year = {2002},
 *    HTTP = {http://www.jmlr.org/papers/v2/lodhi02a.html}
 * }
 * 
 * &#64;techreport{Kleedorfer2005,
 *    address = {Wien, Austria},
 *    author = {F. Kleedorfer and A. Seewald},
 *    institution = {Oesterreichisches Forschungsinstitut fuer Artificial Intelligence},
 *    number = {TR-2005-13},
 *    title = {Implementation of a String Kernel for WEKA},
 *    year = {2005}
 * }
 * </pre>
 * <p/>
 <!-- technical-bibtex-end -->
 * 
 <!-- options-start -->
 * Valid options are: <p/>
 * 
 * <pre> -D
 *  Enables debugging output (if available) to be printed.
 *  (default: off)</pre>
 * 
 * <pre> -no-checks
 *  Turns off all checks - use with caution!
 *  (default: checks on)</pre>
 * 
 * <pre> -P &lt;0|1&gt;
 *  The pruning method to use:
 *  0 = No pruning
 *  1 = Lambda pruning
 *  (default: 0)</pre>
 * 
 * <pre> -C &lt;num&gt;
 *  The size of the cache (a prime number).
 *  (default: 250007)</pre>
 * 
 * <pre> -IC &lt;num&gt;
 *  The size of the internal cache (a prime number).
 *  (default: 200003)</pre>
 * 
 * <pre> -L &lt;num&gt;
 *  The lambda constant. Penalizes non-continuous subsequence
 *  matches. Must be in (0,1).
 *  (default: 0.5)</pre>
 * 
 * <pre> -ssl &lt;num&gt;
 *  The length of the subsequence.
 *  (default: 3)</pre>
 * 
 * <pre> -ssl-max &lt;num&gt;
 *  The maximum length of the subsequence.
 *  (default: 9)</pre>
 * 
 * <pre> -N
 *  Use normalization.
 *  (default: no)</pre>
 * 
 <!-- options-end -->
 * 
 * <h1>Theory</h1>
 * <h2>Overview</h2>
 * The algorithm computes a measure of similarity between two texts based on
 * the number and form of their common subsequences, which need not be
 * contiguous. This method can be parametrized by specifying the subsequence
 * length k, the penalty factor lambda, which penalizes non-contiguous matches,
 * and optional 'lambda pruning', which takes maxLambdaExponent,
 * <code>m</code>, as parameter. Lambda pruning causes very 'stretched'
 * substring matches not to be counted, thus speeding up the computation. The
 * functionality of SSK and SSK-LP is explained in the following using simple
 * examples.
 * 
 * <h2>Explanation &amp; Examples</h2>
 * for all of the following examples, we assume these parameter values: 
 *<pre> 
 *k=2
 *lambda=0.5
 *m=8 (for SSK-LP examples)
 *</pre>
 * 
 * <h3>SSK</h3>
 * 
 * <h4>Example 1</h4>
 * 
 * <pre>
 *SSK(2,"ab","axb")=0.5^5 = 0,03125
 *</pre>
 * There is one subsequence of the length of 2 that both strings have in
 * common, "ab".  The result of SSK is computed by raising lambda to the power
 * of L, where L is the length of the subsequence match in the one string plus
 * the length of the subsequence match in the other, in our case:
 * <pre>
 *&nbsp;  ab    axb
 *L= 2  +   3 = 5
 * </pre>
 * hence, the kernel yields 0.5^5 = 0,03125
 *
 * <h4>Example 2</h4>
 * <pre>
 *SSK(2,"ab","abb")=0.5^5 + 0.5^4 = 0,09375
 *</pre>
 * Here, we also have one subsequence of the length of 2 that both strings have
 * in common, "ab".  The result of SSK is actually computed by summing over all
 * values computed for each occurrence of a common subsequence match. In this
 * example, there are two possible cases:
 * <pre>
 *ab    abb
 *--    --  L=4
 *--    - - L=5
 * </pre>
 * we have two matches, one of the length of 2+2=4, one of the length of 2+3=5, 
 * so we get the result 0.5^5 + 0.5^4 = 0,09375.
 *
 * <h3>SSK-LP</h3>
 * Without lambda pruning, the string kernel finds *all* common subsequences of
 * the given length, whereas with lambda pruning, common subsequence matches
 * that are too much stretched in both strings are not taken into account. It
 * is argued that the value yielded for such a common subsequence is too low
 * (<code>lambda ^(length[match_in_s] + length[match_in_t]</code>) . Tests have
 * shown that a tremendous speedup can be achieved using this technique while
 * suffering from very little quality loss. <br>
 * Lambda pruning is parametrized by the maximum lambda exponent. It is a good
 * idea to choose that value to be about 3 or 4 times the subsequence length as
 * a rule of thumb. YMMV.
 *
 * <h4>Example 3</h4>
 * Without lambda pruning, one common subsequence, 
 * "AB" would be found in the following two strings. (With k=2)  
 * <pre>
 *SSK(2,"ab","axb")=0.5^14 = 0,00006103515625
 *</pre>
 * lambda pruning allows for the control of the match length. So, if m 
 * (the maximum lambda exponent) is e.g. 8, these two strings would 
 * yield a kernel value of 0:
 * <pre>
 *with lambda pruning:    SSK-LP(2,8,"AxxxxxxxxxB","AyB")= 0
 *without lambda pruning: SSK(2,"AxxxxxxxxxB","AyB")= 0.5^14 = 0,00006103515625  
 *</pre>
 * This is because the exponent for lambda (=the length of the subsequence
 * match) would be 14, which is &gt; 8. In Contrast, the next result is
 * &gt; 0
 *<pre>
 *m=8
 *SSK-LP(2,8,"AxxB","AyyB")=0.5^8 = 0,00390625
 *</pre>
 * because the lambda exponent would be 8, which is just accepted by lambda
 * pruning.
 *
 * <h3>Normalization</h3>
 * When the string kernel is used for its main purpose, as the kernel of a
 * support vector machine, it is not normalized.  The normalized kernel can be
 * switched on by -F (feature space normalization) but is much slower.  Like
 * most unnormalized kernels, K(x,x) is not a fixed value, see the next
 * example.
 *
 * <h4>Example 4</h4> 
 *<pre>
 *SSK(2,"ab","ab")=0.5^4 = 0.0625
 *SSK(2,"AxxxxxxxxxB","AxxxxxxxxxB") = 12.761724710464478
 *</pre>
 * SSK is evaluated twice, each time for two identical strings. A good measure
 * of similarity would produce the same value in both cases, which should  
 * indicate the same level of similarity. The value of the normalized SSK would
 * be 1.0 in both cases. So for the purpose of computing string similarity the
 * normalized kernel should be used. For SVM the unnormalized kernel is usually
 * sufficient.
 *
 * <h2>Complexity of SSK and SSK-LP</h2>
 * The time complexity of this method (without lambda pruning and with an
 * infinitely large cache) is<br> 
 * <pre>O(k*|s|*|t|)</pre>
 * Lambda Pruning has a complexity (without caching) of<br> 
 * <pre>O(m*binom(m,k)^2*(|s|+n)*|t|)</pre> <br>  
 * <pre>
 *k...          subsequence length (ssl)
 *s,t...        strings
 *|s|...        length of string s
 *binom(x,y)... binomial coefficient (x!/[(x-y)!y!])
 *m...          maxLambdaExponent (ssl-max)
 *</pre>
 * 
 * Keep in mind that execution time can increase fast for long strings 
 * and big values for k, especially if you don't use lambda pruning.
 * With lambda pruning, computation is usually so fast that switching
 * on the cache leads to slower computation because of setup costs. Therefore
 * caching is switched off for lambda pruning.
 * <br>
 * <br>
 * For details and qualitative experiments about SSK, see [1] <br>
 * For details about lambda pruning and performance comparison of SSK 
 * and SSK-LP (SSK with lambda pruning), see [2]   
 * Note that the complexity estimation in [2] assumes no caching of
 * intermediate results, which has been implemented in the meantime and
 * greatly improves the speed of the SSK without lambda pruning.
 *<br>
 *
 *<h1>Notes for usage within Weka</h1>
 * Only instances of the following form can be processed using string kernels:
 * <pre>
 *+----------+-------------+---------------+
 *|attribute#|     0       |       1       |
 *+----------+-------------+---------------+
 *| content  | [text data] | [class label] |
 *+----------------------------------------+
 * ... or ...
 *+----------+---------------+-------------+
 *|attribute#|     0         |     1       |
 *+----------+---------------+-------------+
 *| content  | [class label] | [text data] |
 *+----------------------------------------+
 *</pre>
 *
 * @author Florian Kleedorfer ([e-mail])
 * @author Alexander K. Seewald ([e-mail])
 * @version $Revision: 1.2 $
 */
public class StringKernel 
  extends Kernel
  implements TechnicalInformationHandler {
  
  /** for serialization */
  private static final long serialVersionUID = -4902954211202690123L;

  /** The size of the cache (a prime number) */
  private int m_cacheSize = 250007;

  /** The size of the internal cache for intermediate results (a prime number) */
  private int m_internalCacheSize = 200003;

  /** The attribute number of the string attribute */
  private int m_strAttr;

  /** Kernel cache (i.e., cache for kernel evaluations) */
  private double[] m_storage;
  private long[] m_keys;

  /** Counts the number of kernel evaluations. */
  private int m_kernelEvals = 0;

  /** The number of instance in the dataset */
  private int m_numInsts;

  /** Pruning method: No Pruning */
  public final static int PRUNING_NONE = 0;
  /** Pruning method: Lambda See [2] for details. */
  public final static int PRUNING_LAMBDA = 1;
  /** Pruning methods */
  public static final Tag [] TAGS_PRUNING = {
    new Tag(PRUNING_NONE, "No pruning"),
    new Tag(PRUNING_LAMBDA, "Lambda pruning"),
  };
  
  /** the pruning method */
  protected int m_PruningMethod = PRUNING_NONE;

  /** the decay factor that penalizes non-continuous substring matches. See [1]
   * for details. */
  protected double m_lambda = 0.5;

  /** The substring length */
  private int m_subsequenceLength = 3;

  /** The maximum substring length for lambda pruning */
  private int m_maxSubsequenceLength = 3;

  /** powers of lambda are prepared prior to kernel evaluations.
   * all powers between 0 and this value are precalculated */
  protected static final int MAX_POWER_OF_LAMBDA = 10000;

  /** the precalculated powers of lambda */
  protected double[] m_powersOflambda = null;

  /** flag for switching normalization on or off. This defaults to false and
   * can be turned on by the switch for feature space normalization in SMO
   */
  private boolean m_normalize = false;

  /** private cache for intermediate results */	
  private int maxCache; // is set in unnormalizedKernel(s1,s2)
  private double[] cachekh;
  private int[] cachekhK;
  private double[] cachekh2;
  private int[] cachekh2K;
  /** cached indexes for private cache */
  private int m_multX;
  private int m_multY;
  private int m_multZ;
  private int m_multZZ;

  private boolean m_useRecursionCache = true;

  /**
   * default constructor
   */
  public StringKernel() {
    super();
  }
  
  /**
   * creates a new StringKernel object. Initializes the kernel cache and the
   * 'lambda cache', i.e. the precalculated powers of lambda from lambda^2 to
   * lambda^MAX_POWER_OF_LAMBDA
   *
   * @param data		the dataset to use
   * @param cacheSize		the size of the cache
   * @param subsequenceLength	the subsequence length
   * @param lambda		the lambda value
   * @param debug		whether to output debug information
   * @throws Exception		if something goes wrong
   */
  public StringKernel(Instances data, int cacheSize, int subsequenceLength,
    double lambda, boolean debug) throws Exception {

    setDebug(debug);
    setCacheSize(cacheSize);
    setInternalCacheSize(200003);
    setSubsequenceLength(subsequenceLength);
    setMaxSubsequenceLength(-1);
    setLambda(lambda);
    
    buildKernel(data);
  }
  
  /**
   * Returns a string describing the kernel
   * 
   * @return a description suitable for displaying in the
   *         explorer/experimenter gui