Class RuleStats

java.lang.Object
weka.classifiers.rules.RuleStats
All Implemented Interfaces:
Serializable, RevisionHandler

public class RuleStats extends Object implements Serializable, RevisionHandler
This class implements the statistics functions used in the propositional rule learner, from the simpler ones like count of true/false positive/negatives, filter data based on the ruleset, etc. to the more sophisticated ones such as MDL calculation and rule variants generation for each rule in the ruleset.

Obviously the statistics functions listed above need the specific data and the specific ruleset, which are given in order to instantiate an object of this class.

Version:
$Revision: 10153 $
Author:
Xin Xu (xx5@cs.waikato.ac.nz)
See Also:
  • Constructor Summary

    Constructors
    Constructor
    Description
    Default constructor
    Constructor that provides ruleset and data
  • Method Summary

    Modifier and Type
    Method
    Description
    void
    addAndUpdate(Rule lastRule)
    Add a rule to the ruleset and update the stats
    void
    Frees up memory after classifier has been built.
    double
    combinedDL(double expFPRate, double predicted)
    Compute the combined DL of the ruleset in this class, i.e.
    void
    Filter the data according to the ruleset and compute the basic stats: coverage/uncoverage, true/false positive/negatives of each rule
    void
    countData(int index, Instances uncovered, double[][] prevRuleStats)
    Count data from the position index in the ruleset assuming that given data are not covered by the rules in position 0...(index-1), and the statistics of these rules are provided.
    This procedure is typically useful when a temporary object of RuleStats is constructed in order to efficiently calculate the relative DL of rule in position index, thus all other stuff is not needed.
    static double
    dataDL(double expFPOverErr, double cover, double uncover, double fp, double fn)
    The description length of data given the parameters of the data based on the ruleset.
    Get the data of the stats
    double[]
    getDistributions(int index)
    Get the class distribution predicted by the rule in given position
    getFiltered(int index)
    Get the data after filtering the given rule
    Returns the revision string.
    Get the ruleset of the stats
    int
    Get the size of the ruleset in the stats
    double[]
    getSimpleStats(int index)
    Get the simple stats of one rule, including 6 parameters: 0: coverage; 1:uncoverage; 2: true positive; 3: true negatives; 4: false positives; 5: false negatives
    double
    minDataDLIfDeleted(int index, double expFPRate, boolean checkErr)
    Compute the minimal data description length of the ruleset if the rule in the given position is deleted.
    The min_data_DL_if_deleted = data_DL_if_deleted - potential
    double
    minDataDLIfExists(int index, double expFPRate, boolean checkErr)
    Compute the minimal data description length of the ruleset if the rule in the given position is NOT deleted.
    The min_data_DL_if_n_deleted = data_DL_if_n_deleted - potential
    static double
    Compute the number of all possible conditions that could appear in a rule of a given data.
    static final Instances[]
    partition(Instances data, int numFolds)
    Patition the data into 2, first of which has (numFolds-1)/numFolds of the data and the second has 1/numFolds of the data
    double
    potential(int index, double expFPOverErr, double[] rulesetStat, double[] ruleStat, boolean checkErr)
    Calculate the potential to decrease DL of the ruleset, i.e.
    void
    reduceDL(double expFPRate, boolean checkErr)
    Try to reduce the DL of the ruleset by testing removing the rules one by one in reverse order and update all the stats
    double
    relativeDL(int index, double expFPRate, boolean checkErr)
    The description length (DL) of the ruleset relative to if the rule in the given position is deleted, which is obtained by:
    MDL if the rule exists - MDL if the rule does not exist
    Note the minimal possible DL of the ruleset is calculated(i.e.
    void
    Remove the last rule in the ruleset as well as it's stats.
    static Instances
    rmCoveredBySuccessives(Instances data, ArrayList<Rule> rules, int index)
    Static utility function to count the data covered by the rules after the given index in the given rules, and then remove them.
    void
    Set the data of the stats, overwriting the old one if any
    void
    setMDLTheoryWeight(double weight)
    Set the weight of theory in MDL calcualtion
    void
    setNumAllConds(double total)
    Set the number of all conditions that could appear in a rule in this RuleStats object, if the number set is smaller than 0 (typically -1), then it calcualtes based on the data store
    void
    Set the ruleset of the stats, overwriting the old one if any
    static final Instances
    stratify(Instances data, int folds, Random rand)
    Stratify the given data into the given number of bags based on the class values.
    static double
    subsetDL(double t, double k, double p)
    Subset description length:
    S(t,k,p) = -k*log2(p)-(n-k)log2(1-p) Details see Quilan: "MDL and categorical theories (Continued)",ML95
    double
    theoryDL(int index)
    The description length of the theory for a given rule.

    Methods inherited from class java.lang.Object

    equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
  • Constructor Details

    • RuleStats

      public RuleStats()
      Default constructor
    • RuleStats

      public RuleStats(Instances data, ArrayList<Rule> rules)
      Constructor that provides ruleset and data
      Parameters:
      data - the data
      rules - the ruleset
  • Method Details

    • cleanUp

      public void cleanUp()
      Frees up memory after classifier has been built.
    • setNumAllConds

      public void setNumAllConds(double total)
      Set the number of all conditions that could appear in a rule in this RuleStats object, if the number set is smaller than 0 (typically -1), then it calcualtes based on the data store
      Parameters:
      total - the set number
    • setData

      public void setData(Instances data)
      Set the data of the stats, overwriting the old one if any
      Parameters:
      data - the data to be set
    • getData

      public Instances getData()
      Get the data of the stats
      Returns:
      the data
    • setRuleset

      public void setRuleset(ArrayList<Rule> rules)
      Set the ruleset of the stats, overwriting the old one if any
      Parameters:
      rules - the set of rules to be set
    • getRuleset

      public ArrayList<Rule> getRuleset()
      Get the ruleset of the stats
      Returns:
      the set of rules
    • getRulesetSize

      public int getRulesetSize()
      Get the size of the ruleset in the stats
      Returns:
      the size of ruleset
    • getSimpleStats

      public double[] getSimpleStats(int index)
      Get the simple stats of one rule, including 6 parameters: 0: coverage; 1:uncoverage; 2: true positive; 3: true negatives; 4: false positives; 5: false negatives
      Parameters:
      index - the index of the rule
      Returns:
      the stats
    • getFiltered

      public Instances[] getFiltered(int index)
      Get the data after filtering the given rule
      Parameters:
      index - the index of the rule
      Returns:
      the data covered and uncovered by the rule
    • getDistributions

      public double[] getDistributions(int index)
      Get the class distribution predicted by the rule in given position
      Parameters:
      index - the position index of the rule
      Returns:
      the class distributions
    • setMDLTheoryWeight

      public void setMDLTheoryWeight(double weight)
      Set the weight of theory in MDL calcualtion
      Parameters:
      weight - the weight to be set
    • numAllConditions

      public static double numAllConditions(Instances data)
      Compute the number of all possible conditions that could appear in a rule of a given data. For nominal attributes, it's the number of values that could appear; for numeric attributes, it's the number of values * 2, i.e. <= and >= are counted as different possible conditions.
      Parameters:
      data - the given data
      Returns:
      number of all conditions of the data
    • countData

      public void countData()
      Filter the data according to the ruleset and compute the basic stats: coverage/uncoverage, true/false positive/negatives of each rule
    • countData

      public void countData(int index, Instances uncovered, double[][] prevRuleStats)
      Count data from the position index in the ruleset assuming that given data are not covered by the rules in position 0...(index-1), and the statistics of these rules are provided.
      This procedure is typically useful when a temporary object of RuleStats is constructed in order to efficiently calculate the relative DL of rule in position index, thus all other stuff is not needed.
      Parameters:
      index - the given position
      uncovered - the data not covered by rules before index
      prevRuleStats - the provided stats of previous rules
    • addAndUpdate

      public void addAndUpdate(Rule lastRule)
      Add a rule to the ruleset and update the stats
      Parameters:
      lastRule - the rule to be added
    • subsetDL

      public static double subsetDL(double t, double k, double p)
      Subset description length:
      S(t,k,p) = -k*log2(p)-(n-k)log2(1-p) Details see Quilan: "MDL and categorical theories (Continued)",ML95
      Parameters:
      t - the number of elements in a known set
      k - the number of elements in a subset
      p - the expected proportion of subset known by recipient
      Returns:
      the subset description length
    • theoryDL

      public double theoryDL(int index)
      The description length of the theory for a given rule. Computed as:
      0.5* [||k||+ S(t, k, k/t)]
      where k is the number of antecedents of the rule; t is the total possible antecedents that could appear in a rule; ||K|| is the universal prior for k , log2*(k) and S(t,k,p) = -k*log2(p)-(n-k)log2(1-p) is the subset encoding length.

      Details see Quilan: "MDL and categorical theories (Continued)",ML95

      Parameters:
      index - the index of the given rule (assuming correct)
      Returns:
      the theory DL, weighted if weight != 1.0
    • dataDL

      public static double dataDL(double expFPOverErr, double cover, double uncover, double fp, double fn)
      The description length of data given the parameters of the data based on the ruleset.

      Details see Quinlan: "MDL and categorical theories (Continued)",ML95

      Parameters:
      expFPOverErr - expected FP/(FP+FN)
      cover - coverage
      uncover - uncoverage
      fp - False Positive
      fn - False Negative
      Returns:
      the description length
    • potential

      public double potential(int index, double expFPOverErr, double[] rulesetStat, double[] ruleStat, boolean checkErr)
      Calculate the potential to decrease DL of the ruleset, i.e. the possible DL that could be decreased by deleting the rule whose index and simple statstics are given. If there's no potentials (i.e. smOrEq 0 && error rate < 0.5), it returns NaN.

      The way this procedure does is copied from original RIPPER implementation and is quite bizzare because it does not update the following rules' stats recursively any more when testing each rule, which means it assumes after deletion no data covered by the following rules (or regards the deleted rule as the last rule). Reasonable assumption?

      Parameters:
      index - the index of the rule in m_Ruleset to be deleted
      expFPOverErr - expected FP/(FP+FN)
      rulesetStat - the simple statistics of the ruleset, updated if the rule should be deleted
      ruleStat - the simple statistics of the rule to be deleted
      checkErr - whether check if error rate >= 0.5
      Returns:
      the potential DL that could be decreased
    • minDataDLIfDeleted

      public double minDataDLIfDeleted(int index, double expFPRate, boolean checkErr)
      Compute the minimal data description length of the ruleset if the rule in the given position is deleted.
      The min_data_DL_if_deleted = data_DL_if_deleted - potential
      Parameters:
      index - the index of the rule in question
      expFPRate - expected FP/(FP+FN), used in dataDL calculation
      checkErr - whether check if error rate >= 0.5
      Returns:
      the minDataDL
    • minDataDLIfExists

      public double minDataDLIfExists(int index, double expFPRate, boolean checkErr)
      Compute the minimal data description length of the ruleset if the rule in the given position is NOT deleted.
      The min_data_DL_if_n_deleted = data_DL_if_n_deleted - potential
      Parameters:
      index - the index of the rule in question
      expFPRate - expected FP/(FP+FN), used in dataDL calculation
      checkErr - whether check if error rate >= 0.5
      Returns:
      the minDataDL
    • relativeDL

      public double relativeDL(int index, double expFPRate, boolean checkErr)
      The description length (DL) of the ruleset relative to if the rule in the given position is deleted, which is obtained by:
      MDL if the rule exists - MDL if the rule does not exist
      Note the minimal possible DL of the ruleset is calculated(i.e. some other rules may also be deleted) instead of the DL of the current ruleset.

      Parameters:
      index - the given position of the rule in question (assuming correct)
      expFPRate - expected FP/(FP+FN), used in dataDL calculation
      checkErr - whether check if error rate >= 0.5
      Returns:
      the relative DL
    • reduceDL

      public void reduceDL(double expFPRate, boolean checkErr)
      Try to reduce the DL of the ruleset by testing removing the rules one by one in reverse order and update all the stats
      Parameters:
      expFPRate - expected FP/(FP+FN), used in dataDL calculation
      checkErr - whether check if error rate >= 0.5
    • removeLast

      public void removeLast()
      Remove the last rule in the ruleset as well as it's stats. It might be useful when the last rule was added for testing purpose and then the test failed
    • rmCoveredBySuccessives

      public static Instances rmCoveredBySuccessives(Instances data, ArrayList<Rule> rules, int index)
      Static utility function to count the data covered by the rules after the given index in the given rules, and then remove them. It returns the data not covered by the successive rules.
      Parameters:
      data - the data to be processed
      rules - the ruleset
      index - the given index
      Returns:
      the data after processing
    • stratify

      public static final Instances stratify(Instances data, int folds, Random rand)
      Stratify the given data into the given number of bags based on the class values. It differs from the Instances.stratify(int fold) that before stratification it sorts the instances according to the class order in the header file. It assumes no missing values in the class.
      Parameters:
      data - the given data
      folds - the given number of folds
      rand - the random object used to randomize the instances
      Returns:
      the stratified instances
    • combinedDL

      public double combinedDL(double expFPRate, double predicted)
      Compute the combined DL of the ruleset in this class, i.e. theory DL and data DL. Note this procedure computes the combined DL according to the current status of the ruleset in this class
      Parameters:
      expFPRate - expected FP/(FP+FN), used in dataDL calculation
      predicted - the default classification if ruleset covers null
      Returns:
      the combined class
    • partition

      public static final Instances[] partition(Instances data, int numFolds)
      Patition the data into 2, first of which has (numFolds-1)/numFolds of the data and the second has 1/numFolds of the data
      Parameters:
      data - the given data
      numFolds - the given number of folds
      Returns:
      the patitioned instances
    • getRevision

      public String getRevision()
      Returns the revision string.
      Specified by:
      getRevision in interface RevisionHandler
      Returns:
      the revision