Class RuleStats

  • All Implemented Interfaces:
    java.io.Serializable, RevisionHandler

    public class RuleStats
    extends java.lang.Object
    implements java.io.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: 4608 $
    Author:
    Xin Xu (xx5@cs.waikato.ac.nz)
    See Also:
    Serialized Form
    • Method Summary

      All Methods Static Methods Instance Methods Concrete Methods 
      Modifier and Type Method Description
      void addAndUpdate​(Rule lastRule)
      Add a rule to the ruleset and update the stats
      void cleanUp()
      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 countData()
      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.
      Instances getData()
      Get the data of the stats
      double[] getDistributions​(int index)
      Get the class distribution predicted by the rule in given position
      Instances[] getFiltered​(int index)
      Get the data after filtering the given rule
      java.lang.String getRevision()
      Returns the revision string.
      FastVector getRuleset()
      Get the ruleset of the stats
      int getRulesetSize()
      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 numAllConditions​(Instances data)
      Compute the number of all possible conditions that could appear in a rule of a given data.
      static 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 removeLast()
      Remove the last rule in the ruleset as well as it's stats.
      static Instances rmCoveredBySuccessives​(Instances data, FastVector 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 setData​(Instances data)
      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 setRuleset​(FastVector rules)
      Set the ruleset of the stats, overwriting the old one if any
      static Instances stratify​(Instances data, int folds, java.util.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 Detail

      • RuleStats

        public RuleStats()
        Default constructor
      • RuleStats

        public RuleStats​(Instances data,
                         FastVector rules)
        Constructor that provides ruleset and data
        Parameters:
        data - the data
        rules - the ruleset
    • Method Detail

      • 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​(FastVector rules)
        Set the ruleset of the stats, overwriting the old one if any
        Parameters:
        rules - the set of rules to be set
      • getRuleset

        public FastVector 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,
                                                       FastVector 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,
                                               java.util.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 java.lang.String getRevision()
        Returns the revision string.
        Specified by:
        getRevision in interface RevisionHandler
        Returns:
        the revision