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Class weka.classifiers.rules.RuleStats 

java.lang.Object
    |
    +----weka.classifiers.rules.RuleStats
public class RuleStats
extends java.lang.Object
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: 1.2 $
Author:
Xin Xu (xx5@cs.waikato.ac.nz)

Constructor Index

o RuleStats()
Default constructor
o RuleStats(Instances, FastVector)
Constructor that provides ruleset and data

Method Index

o addAndUpdate(Rule)
Add a rule to the ruleset and update the stats
o combinedDL(double, double)
Compute the combined DL of the ruleset in this class, i.e.
o countData()
Filter the data according to the ruleset and compute the basic stats: coverage/uncoverage, true/false positive/negatives of each rule
o countData(int, Instances, double[][])
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.
o dataDL(double, double, double, double, double)
The description length of data given the parameters of the data based on the ruleset.
o getData()
Get the data of the stats
o getDistributions(int)
Get the class distribution predicted by the rule in given position
o getFiltered(int)
Get the data after filtering the given rule
o getRuleset()
Get the ruleset of the stats
o getRulesetSize()
Get the size of the ruleset in the stats
o getSimpleStats(int)
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
o minDataDLIfDeleted(int, double, boolean)
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
o minDataDLIfExists(int, double, boolean)
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
o numAllConditions(Instances)
Compute the number of all possible conditions that could appear in a rule of a given data.
o partition(Instances, int)
Patition the data into 2, first of which has (numFolds-1)/numFolds of the data and the second has 1/numFolds of the data
o potential(int, double, double[], double[], boolean)
Calculate the potential to decrease DL of the ruleset, i.e.
o reduceDL(double, boolean)
Try to reduce the DL of the ruleset by testing removing the rules one by one in reverse order and update all the stats
o relativeDL(int, double, boolean)
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.
o removeLast()
Remove the last rule in the ruleset as well as it's stats.
o rmCoveredBySuccessives(Instances, FastVector, int)
Static utility function to count the data covered by the rules after the given index in the given rules, and then remove them.
o setData(Instances)
Set the data of the stats, overwriting the old one if any
o setMDLTheoryWeight(double)
Set the weight of theory in MDL calcualtion
o setNumAllConds(double)
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
o setRuleset(FastVector)
Set the ruleset of the stats, overwriting the old one if any
o stratify(Instances, int, Random)
Stratify the given data into the given number of bags based on the class values.
o subsetDL(double, double, double)
Subset description length:
S(t,k,p) = -k*log2(p)-(n-k)log2(1-p) Details see Quilan: "MDL and categorical theories (Continued)",ML95
o theoryDL(int)
The description length of the theory for a given rule.

Constructor Detail

o RuleStats
public RuleStats()
          Default constructor
o RuleStats
public RuleStats(Instances data,
                 FastVector rules)
          Constructor that provides ruleset and data
Parameters:
data - the data
rules - the ruleset

Method Detail

o 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
o 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
o getData
public Instances getData()
          Get the data of the stats
Returns:
the data
o 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
o getRuleset
public FastVector getRuleset()
          Get the ruleset of the stats
Returns:
the set of rules
o getRulesetSize
public int getRulesetSize()
          Get the size of the ruleset in the stats
Returns:
the size of ruleset
o 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
o 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
o 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
o setMDLTheoryWeight
public void setMDLTheoryWeight(double weight)
          Set the weight of theory in MDL calcualtion
Parameters:
weight - the weight to be set
o 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
o 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
o 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
o addAndUpdate
public void addAndUpdate(Rule lastRule)
          Add a rule to the ruleset and update the stats
Parameters:
the - rule to be added
o 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
o 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
Throws:
if - index out of range or object not initialized yet
o 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
o 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
o 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
return - the minDataDL
o 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
return - the minDataDL
o 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
o 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
o 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
o 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
o 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
o 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
o 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
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