|
|||||||||
PREV CLASS NEXT CLASS | FRAMES NO FRAMES | ||||||||
SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.ObjectgeneticWedge.gp.postProcess.PostProcessor
public class PostProcessor
PostProcessor allows access to various properties after an evolutionary run. Available properties include:
Nested Class Summary | |
---|---|
static class |
PostProcessor.SELECT_MODE
SELECT_MODE sets the method by which the 'best' individual is selected. |
Constructor Summary | |
---|---|
PostProcessor()
|
Method Summary | |
---|---|
static double[] |
getAllClassificationData(double[][] data,
double threshold)
getAllClassificationData returns the numbers of true positives, false positives, true negatives and false negatives, given a data array and a threshold (cut-off). |
static double[] |
getAllFitnesses(java.lang.String filename,
boolean getLowest)
getAllFitnesses returns the training, validation and test fitnesses for the 'best' individual, selected on validation fitness. |
static double[] |
getAllFitnesses(java.lang.String filename,
boolean getLowest,
PostProcessor.SELECT_MODE mode)
getAllFitnesses returns the training, validation and test fitnesses for the 'best' individual, selected using the criterion mode. |
static double[] |
getAllRSquared(java.lang.String filename,
PostProcessor.SELECT_MODE mode)
getAllRSquared returns an array containing R-squared values (Pearson coefficients) for training, validation and test data. |
static java.lang.String |
getBestIndividualAsString(java.lang.String filename,
boolean getLowest,
PostProcessor.SELECT_MODE mode)
getBestIndividualAsString returns the 'best' individual as a string, containing all nodes as a comma-separated list. |
static double[] |
getBestValidationAndTestFitness(java.lang.String filename,
boolean getLowest)
getBestValidationAndTestFitness returns the validation and test fitnesses for the 'best' individual, selected on validation fitness. |
static double[] |
setThreshold(int targetNumber,
boolean targetIsPositive,
double[][] data)
|
static double[] |
setThresholdForPPV(double targetPPV,
double[][] data)
setThresholdForPPV attamepts to set a threshold (cut-off) value that will yield the desired PPV (positive predictive value). |
Methods inherited from class java.lang.Object |
---|
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
Constructor Detail |
---|
public PostProcessor()
Method Detail |
---|
public static double[] getAllRSquared(java.lang.String filename, PostProcessor.SELECT_MODE mode) throws java.io.IOException
java.io.IOException
public static double[] getAllFitnesses(java.lang.String filename, boolean getLowest) throws java.io.IOException
getLowest
- if true, returns the individual with the lowest validation fitness, otherwise the individual with the highest validation fitness.
java.io.IOException
public static double[] getAllFitnesses(java.lang.String filename, boolean getLowest, PostProcessor.SELECT_MODE mode) throws java.io.IOException
getLowest
- if true, returns the individual with the lowest fitness, otherwise the individual with the highest fitness.
java.io.IOException
public static java.lang.String getBestIndividualAsString(java.lang.String filename, boolean getLowest, PostProcessor.SELECT_MODE mode) throws java.io.IOException
getLowest
- if true, a string representation of the individual with the lowest fitness is returend, otherwise a string representation of the individual with the highest fitness.mode
- The criterion used to select the 'best' individual
java.io.IOException
public static double[] getBestValidationAndTestFitness(java.lang.String filename, boolean getLowest) throws java.io.IOException
getLowest
- if true, returns the individual with the lowest validation fitness, otherwise the individual with the highest validation fitness.
java.io.IOException
public static double[] setThreshold(int targetNumber, boolean targetIsPositive, double[][] data)
targetNumber
- The number of predictions that will have the desired output (positive or negative) once the threshold has been set.targetIsPositive
- Whether targetNumber refers to the number of positive outputs (true) or negative outputs (false).data
- An array containing the raw outputs and the target (binary) outputs.
public static double[] setThresholdForPPV(double targetPPV, double[][] data)
data
- should be a 2-by-n array containing data values in the first dimension and target outputs (+/-1) in the second dimension.public static double[] getAllClassificationData(double[][] data, double threshold)
data
- should be a 2-by-n array containing data values in the first dimension and target outputs (+/-1) in the second dimension.
|
|||||||||
PREV CLASS NEXT CLASS | FRAMES NO FRAMES | ||||||||
SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |