GridSearch

java.lang.Object
- weka.classifiers.AbstractClassifier
- - weka.classifiers.SingleClassifierEnhancer
  - - weka.classifiers.RandomizableSingleClassifierEnhancer
    - - weka.classifiers.meta.GridSearch

All Implemented Interfaces:: java.io.Serializable, java.lang.Cloneable, weka.classifiers.Classifier, weka.core.AdditionalMeasureProducer, weka.core.BatchPredictor, weka.core.CapabilitiesHandler, weka.core.CapabilitiesIgnorer, weka.core.CommandlineRunnable, weka.core.OptionHandler, weka.core.Randomizable, weka.core.RevisionHandler, weka.core.Summarizable

public class GridSearch
extends weka.classifiers.RandomizableSingleClassifierEnhancer
implements weka.core.AdditionalMeasureProducer, weka.core.Summarizable

Performs a grid search of parameter pairs for a classifier and chooses the best pair found for the actual predicting.

The initial grid is worked on with 2-fold CV to determine the values of the parameter pairs for the selected type of evaluation (e.g., accuracy). The best point in the grid is then taken and a 10-fold CV is performed with the adjacent parameter pairs. If a better pair is found, then this will act as new center and another 10-fold CV will be performed (kind of hill-climbing). This process is repeated until no better pair is found or the best pair is on the border of the grid.
In case the best pair is on the border, one can let GridSearch automatically extend the grid and continue the search. Check out the properties 'gridIsExtendable' (option '-extend-grid') and 'maxGridExtensions' (option '-max-grid-extensions <num>').

GridSearch can handle doubles, integers (values are just cast to int) and booleans (0 is false, otherwise true). float, char and long are supported as well.

The best classifier setup can be accessed after the buildClassifier call via the getBestClassifier methods.

Note: with -num-slots/numExecutionSlots you can specify how many setups are evaluated in parallel, taking advantage of multi-cpu/core architectures.

Valid options are:

 -E <CC|RMSE|RRSE|MAE|RAE|COMB|ACC|WAUC|KAP>
  Determines the parameter used for evaluation:
  CC = Correlation coefficient
  RMSE = Root mean squared error
  RRSE = Root relative squared error
  MAE = Mean absolute error
  RAE = Root absolute error
  COMB = Combined = (1-abs(CC)) + RRSE + RAE
  ACC = Accuracy
  WAUC = Weighted AUC
  KAP = Kappa
  (default: CC)

 -y-property <option>
  The Y option to test (without leading dash).
  (default: kernel.gamma)

 -y-min <num>
  The minimum for Y.
  (default: -3)

 -y-max <num>
  The maximum for Y.
  (default: +3)

 -y-step <num>
  The step size for Y.
  (default: 1)

 -y-base <num>
  The base for Y.
  (default: 10)

 -y-expression <expr>
  The expression for Y.
  Available parameters:
   BASE
   FROM
   TO
   STEP
   I - the current iteration value
   (from 'FROM' to 'TO' with stepsize 'STEP')
  (default: 'pow(BASE,I)')

 -x-property <option>
  The X option to test (without leading dash).
  (default: C)

 -x-min <num>
  The minimum for X.
  (default: -3)

 -x-max <num>
  The maximum for X.
  (default: 3)

 -x-step <num>
  The step size for X.
  (default: 1)

 -x-base <num>
  The base for X.
  (default: 10)

 -x-expression <expr>
  The expression for the X value.
  Available parameters:
   BASE
   MIN
   MAX
   STEP
   I - the current iteration value
   (from 'FROM' to 'TO' with stepsize 'STEP')
  (default: 'pow(BASE,I)')

 -extend-grid
  Whether the grid can be extended.
  (default: no)

 -max-grid-extensions <num>
  The maximum number of grid extensions (-1 is unlimited).
  (default: 3)

 -sample-size <num>
  The size (in percent) of the sample to search the inital grid with.
  (default: 100)

 -traversal <ROW-WISE|COLUMN-WISE>
  The type of traversal for the grid.
  (default: COLUMN-WISE)

 -log-file <filename>
  The log file to log the messages to.
  (default: none)

 -num-slots <num>
  Number of execution slots.
  (default 1 - i.e. no parallelism)

 -S <num>
  Random number seed.
  (default 1)

 -W
  Full name of base classifier.
  (default: weka.classifiers.functions.SMOreg with options -K weka.classifiers.functions.supportVector.RBFKernel)

 -output-debug-info
  If set, classifier is run in debug mode and
  may output additional info to the console

 -do-not-check-capabilities
  If set, classifier capabilities are not checked before classifier is built
  (use with caution).

 Options specific to classifier weka.classifiers.functions.SMOreg:

 -C <double>
  The complexity constant C.
  (default 1)

 -N
  Whether to 0=normalize/1=standardize/2=neither.
  (default 0=normalize)

 -I <classname and parameters>
  Optimizer class used for solving quadratic optimization problem
  (default weka.classifiers.functions.supportVector.RegSMOImproved)

 -K <classname and parameters>
  The Kernel to use.
  (default: weka.classifiers.functions.supportVector.PolyKernel)

 -output-debug-info
  If set, classifier is run in debug mode and
  may output additional info to the console

 -do-not-check-capabilities
  If set, classifier capabilities are not checked before classifier is built
  (use with caution).

 Options specific to optimizer ('-I') weka.classifiers.functions.supportVector.RegSMOImproved:

 -T <double>
  The tolerance parameter for checking the stopping criterion.
  (default 0.001)

 -V
  Use variant 1 of the algorithm when true, otherwise use variant 2.
  (default true)

 -P <double>
  The epsilon for round-off error.
  (default 1.0e-12)

 -L <double>
  The epsilon parameter in epsilon-insensitive loss function.
  (default 1.0e-3)

 -W <double>
  The random number seed.
  (default 1)

 Options specific to kernel ('-K') weka.classifiers.functions.supportVector.RBFKernel:

 -G <num>
  The Gamma parameter.
  (default: 0.01)

 -C <num>
  The size of the cache (a prime number), 0 for full cache and 
  -1 to turn it off.
  (default: 250007)

 -output-debug-info
  Enables debugging output (if available) to be printed.
  (default: off)

 -no-checks
  Turns off all checks - use with caution!
  (default: checks on)

Examples:

Optimizing SMO with RBFKernel (C and gamma)
- Set the evaluation to Accuracy.
- Set weka.classifiers.functions.SMO as classifier with weka.classifiers.functions.supportVector.RBFKernel as kernel.
- Set the XProperty to "C", XMin to "1", XMax to "16", XStep to "1" and the XExpression to "I". This will test the "C" parameter of SMO for the values from 1 to 16.
- Set the YProperty to "kernel.gamma", YMin to "-5", YMax to "2", YStep to "1" YBase to "10" and YExpression to "pow(BASE,I)". This will test the gamma of the RBFKernel with the values 10^-5, 10^-4,..,10^2.
Optimizing PLSFilter with LinearRegression (# of components and ridge) - default setup
- Set the evaluation to Correlation coefficient.
- Set the classifier to weka.classifiers.meta.FilteredClassifier.
- Set the filter in the classifier to weka.filters.supervised.attribute.PLSFilter.
- Set the base classifier in FilteredClassifier to weka.classifiers.functions.LinearRegression and use no attribute selection and no elimination of colinear attributes.
- Set the XProperty to "filter.numComponents", XMin to "5", XMax to "20" (this depends heavily on your dataset, should be no more than the number of attributes!), XStep to "1" and XExpression to "I". This will test the number of components the PLSFilter will produce from 5 to 20.
- Set the YProperty to "classifier.ridge", YMin to "-10", YMax to "5", YStep to "1" and YExpression to "pow(BASE,I)". This will try ridge parameters from 10^-10 to 10^5.

General notes:

Turn the debug flag on in order to see some progress output in the console
If you want to view the fitness landscape that GridSearch explores, select a log file. This log will then contain Gnuplot data and a script block for viewing the landscape. Just copy paste those blocks into files named accordingly and run Gnuplot with them.

Version:: $Revision: 15005 $
Author:: Bernhard Pfahringer (bernhard at cs dot waikato dot ac dot nz), Geoff Holmes (geoff at cs dot waikato dot ac dot nz), fracpete (fracpete at waikato dot ac dot nz)
See Also:: Serialized Form

Field Summary

Fields
Modifier and Type	Field and Description
`static int`	`EVALUATION_ACC` evaluation via: Accuracy.
`static int`	`EVALUATION_CC` evaluation via: Correlation coefficient.
`static int`	`EVALUATION_COMBINED` evaluation via: Combined = (1-CC) + RRSE + RAE.
`static int`	`EVALUATION_KAPPA` evaluation via: kappa statistic.
`static int`	`EVALUATION_MAE` evaluation via: Mean absolute error.
`static int`	`EVALUATION_RAE` evaluation via: Relative absolute error.
`static int`	`EVALUATION_RMSE` evaluation via: Root mean squared error.
`static int`	`EVALUATION_RRSE` evaluation via: Root relative squared error.
`static int`	`EVALUATION_WAUC` evaluation via: weighted AUC
`static weka.core.Tag[]`	`TAGS_EVALUATION` evaluation.
`static weka.core.Tag[]`	`TAGS_TRAVERSAL` traversal.
`static int`	`TRAVERSAL_BY_COLUMN` column-wise grid traversal.
`static int`	`TRAVERSAL_BY_ROW` row-wise grid traversal.

Fields inherited from class weka.classifiers.AbstractClassifier
BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT

Constructor Summary

Constructors
Constructor and Description

GridSearch()
the default constructor.

Constructors
Constructor and Description
`GridSearch()` the default constructor.

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`void`	`buildClassifier(weka.core.Instances data)` builds the classifier.
`double[]`	`distributionForInstance(weka.core.Instance instance)` Computes the distribution for a given instance
`java.util.Enumeration<java.lang.String>`	`enumerateMeasures()` Returns an enumeration of the measure names.
`java.lang.String`	`evaluationTipText()` Returns the tip text for this property.
`weka.classifiers.Classifier`	`getBestClassifier()` returns the best Classifier setup.
`weka.core.Capabilities`	`getCapabilities()` Returns default capabilities of the classifier.
`weka.core.SelectedTag`	`getEvaluation()` Gets the criterion used for evaluating the classifier performance.
`int`	`getGridExtensionsPerformed()` returns the number of grid extensions that took place during the search (only applicable if the grid was extendable).
`boolean`	`getGridIsExtendable()` Get whether the grid can be extended dynamically.
`java.io.File`	`getLogFile()` Gets current log file.
`int`	`getMaxGridExtensions()` Gets the maximum number of grid extensions, -1 for unlimited.
`double`	`getMeasure(java.lang.String measureName)` Returns the value of the named measure.
`int`	`getNumExecutionSlots()` Get the number of execution slots (threads) to use for finding optimal parameters.
`java.lang.String[]`	`getOptions()` returns the options of the current setup.
`java.lang.String`	`getRevision()` Returns the revision string.
`double`	`getSampleSizePercent()` Gets the sample size for the initial grid search.
`weka.core.SelectedTag`	`getTraversal()` Gets the type of traversal for the grid.
`weka.classifiers.meta.GridSearch.PointDouble`	`getValues()` returns the parameter pair that was found to work best.
`double`	`getXBase()` Get the value of the base for X.
`java.lang.String`	`getXExpression()` Get the expression for the X value.
`double`	`getXMax()` Get the value of the Maximum of X.
`double`	`getXMin()` Get the value of the minimum of X.
`java.lang.String`	`getXProperty()` Get the X property to test.
`double`	`getXStep()` Get the value of the step size for X.
`double`	`getYBase()` Get the value of the base for Y.
`java.lang.String`	`getYExpression()` Get the expression for the Y value.
`double`	`getYMax()` Get the value of the Maximum of Y.
`double`	`getYMin()` Get the value of the minimum of Y.
`java.lang.String`	`getYProperty()` Get the Y property (normally the classifier).
`double`	`getYStep()` Get the value of the step size for Y.
`java.lang.String`	`globalInfo()` Returns a string describing classifier.
`java.lang.String`	`gridIsExtendableTipText()` Returns the tip text for this property.
`java.util.Enumeration<weka.core.Option>`	`listOptions()` Gets an enumeration describing the available options.
`java.lang.String`	`logFileTipText()` Returns the tip text for this property.
`static void`	`main(java.lang.String[] args)` Main method for running this classifier from commandline.
`java.lang.String`	`maxGridExtensionsTipText()` Returns the tip text for this property.
`java.lang.String`	`numExecutionSlotsTipText()` Returns the tip text for this property.
`java.lang.String`	`sampleSizePercentTipText()` Returns the tip text for this property.
`void`	`setClassifier(weka.classifiers.Classifier newClassifier)` Set the base learner.
`void`	`setEvaluation(weka.core.SelectedTag value)` Sets the criterion to use for evaluating the classifier performance.
`void`	`setGridIsExtendable(boolean value)` Set whether the grid can be extended dynamically.
`void`	`setLogFile(java.io.File value)` Sets the log file to use.
`void`	`setMaxGridExtensions(int value)` Sets the maximum number of grid extensions, -1 for unlimited.
`void`	`setNumExecutionSlots(int value)` Set the number of execution slots (threads) to use for finding optimal parameters.
`void`	`setOptions(java.lang.String[] options)` Parses the options for this object.
`void`	`setSampleSizePercent(double value)` Sets the sample size for the initial grid search.
`void`	`setTraversal(weka.core.SelectedTag value)` Sets the type of traversal for the grid.
`void`	`setXBase(double value)` Set the value of the base for X.
`void`	`setXExpression(java.lang.String value)` Set the expression for the X value.
`void`	`setXMax(double value)` Set the value of the Maximum of X.
`void`	`setXMin(double value)` Set the value of the minimum of X.
`void`	`setXProperty(java.lang.String value)` Set the X property.
`void`	`setXStep(double value)` Set the value of the step size for X.
`void`	`setYBase(double value)` Set the value of the base for Y.
`void`	`setYExpression(java.lang.String value)` Set the expression for the Y value.
`void`	`setYMax(double value)` Set the value of the Maximum of Y.
`void`	`setYMin(double value)` Set the value of the minimum of Y.
`void`	`setYProperty(java.lang.String value)` Set the Y property (normally the classifier).
`void`	`setYStep(double value)` Set the value of the step size for Y.
`java.lang.String`	`toString()` returns a string representation of the classifier.
`java.lang.String`	`toSummaryString()` Returns a string that summarizes the object.
`java.lang.String`	`traversalTipText()` Returns the tip text for this property.
`java.lang.String`	`XBaseTipText()` Returns the tip text for this property.
`java.lang.String`	`XExpressionTipText()` Returns the tip text for this property.
`java.lang.String`	`XMaxTipText()` Returns the tip text for this property.
`java.lang.String`	`XMinTipText()` Returns the tip text for this property.
`java.lang.String`	`XPropertyTipText()` Returns the tip text for this property.
`java.lang.String`	`XStepTipText()` Returns the tip text for this property.
`java.lang.String`	`YBaseTipText()` Returns the tip text for this property.
`java.lang.String`	`YExpressionTipText()` Returns the tip text for this property.
`java.lang.String`	`YMaxTipText()` Returns the tip text for this property.
`java.lang.String`	`YMinTipText()` Returns the tip text for this property.
`java.lang.String`	`YPropertyTipText()` Returns the tip text for this property.
`java.lang.String`	`YStepTipText()` Returns the tip text for this property.

Methods inherited from class weka.classifiers.RandomizableSingleClassifierEnhancer
getSeed, seedTipText, setSeed

Methods inherited from class weka.classifiers.SingleClassifierEnhancer
classifierTipText, getClassifier, postExecution, preExecution

Methods inherited from class weka.classifiers.AbstractClassifier
batchSizeTipText, classifyInstance, debugTipText, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, implementsMoreEfficientBatchPrediction, makeCopies, makeCopy, numDecimalPlacesTipText, run, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces

Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface weka.core.OptionHandler
makeCopy

- Field Detail
  - EVALUATION_CC
```
public static final int EVALUATION_CC
```
    evaluation via: Correlation coefficient.
    
    See Also:
    
    Constant Field Values
  - EVALUATION_RMSE
```
public static final int EVALUATION_RMSE
```
    evaluation via: Root mean squared error.
    
    See Also:
    
    Constant Field Values
  - EVALUATION_RRSE
```
public static final int EVALUATION_RRSE
```
    evaluation via: Root relative squared error.
    
    See Also:
    
    Constant Field Values
  - EVALUATION_MAE
```
public static final int EVALUATION_MAE
```
    evaluation via: Mean absolute error.
    
    See Also:
    
    Constant Field Values
  - EVALUATION_RAE
```
public static final int EVALUATION_RAE
```
    evaluation via: Relative absolute error.
    
    See Also:
    
    Constant Field Values
  - EVALUATION_COMBINED
```
public static final int EVALUATION_COMBINED
```
    evaluation via: Combined = (1-CC) + RRSE + RAE.
    
    See Also:
    
    Constant Field Values
  - EVALUATION_ACC
```
public static final int EVALUATION_ACC
```
    evaluation via: Accuracy.
    
    See Also:
    
    Constant Field Values
  - EVALUATION_KAPPA
```
public static final int EVALUATION_KAPPA
```
    evaluation via: kappa statistic.
    
    See Also:
    
    Constant Field Values
  - EVALUATION_WAUC
```
public static final int EVALUATION_WAUC
```
    evaluation via: weighted AUC
    
    See Also:
    
    Constant Field Values
  - TAGS_EVALUATION
```
public static final weka.core.Tag[] TAGS_EVALUATION
```
    evaluation.
  - TRAVERSAL_BY_ROW
```
public static final int TRAVERSAL_BY_ROW
```
    row-wise grid traversal.
    
    See Also:
    
    Constant Field Values
  - TRAVERSAL_BY_COLUMN
```
public static final int TRAVERSAL_BY_COLUMN
```
    column-wise grid traversal.
    
    See Also:
    
    Constant Field Values
  - TAGS_TRAVERSAL
```
public static final weka.core.Tag[] TAGS_TRAVERSAL
```
    traversal.
- Constructor Detail
  - GridSearch
```
public GridSearch()
```
    the default constructor.
- Method Detail
  - globalInfo
```
public java.lang.String globalInfo()
```
    Returns a string describing classifier.
    
    Returns:
    
    a description suitable for displaying in the explorer/experimenter gui
  - listOptions
```
public java.util.Enumeration<weka.core.Option> listOptions()
```
    Gets an enumeration describing the available options.
    
    Specified by:
    
    listOptions in interface weka.core.OptionHandler
    
    Overrides:
    
    listOptions in class weka.classifiers.RandomizableSingleClassifierEnhancer
    
    Returns:
    
    an enumeration of all the available options.
  - getOptions
```
public java.lang.String[] getOptions()
```
    returns the options of the current setup.
    
    Specified by:
    
    getOptions in interface weka.core.OptionHandler
    
    Overrides:
    
    getOptions in class weka.classifiers.RandomizableSingleClassifierEnhancer
    
    Returns:
    
    the current options
  - setOptions
```
public void setOptions(java.lang.String[] options)
                throws java.lang.Exception
```
    Parses the options for this object.
    Valid options are:
```
 -E <CC|RMSE|RRSE|MAE|RAE|COMB|ACC|WAUC|KAP>
  Determines the parameter used for evaluation:
  CC = Correlation coefficient
  RMSE = Root mean squared error
  RRSE = Root relative squared error
  MAE = Mean absolute error
  RAE = Root absolute error
  COMB = Combined = (1-abs(CC)) + RRSE + RAE
  ACC = Accuracy
  WAUC = Weighted AUC
  KAP = Kappa
  (default: CC)
 
```
```
 -y-property <option>
  The Y option to test (without leading dash).
  (default: kernel.gamma)
 
```
```
 -y-min <num>
  The minimum for Y.
  (default: -3)
 
```
```
 -y-max <num>
  The maximum for Y.
  (default: +3)
 
```
```
 -y-step <num>
  The step size for Y.
  (default: 1)
 
```
```
 -y-base <num>
  The base for Y.
  (default: 10)
 
```
```
 -y-expression <expr>
  The expression for Y.
  Available parameters:
   BASE
   FROM
   TO
   STEP
   I - the current iteration value
   (from 'FROM' to 'TO' with stepsize 'STEP')
  (default: 'pow(BASE,I)')
 
```
```
 -x-property <option>
  The X option to test (without leading dash).
  (default: C)
 
```
```
 -x-min <num>
  The minimum for X.
  (default: -3)
 
```
```
 -x-max <num>
  The maximum for X.
  (default: 3)
 
```
```
 -x-step <num>
  The step size for X.
  (default: 1)
 
```
```
 -x-base <num>
  The base for X.
  (default: 10)
 
```
```
 -x-expression <expr>
  The expression for the X value.
  Available parameters:
   BASE
   MIN
   MAX
   STEP
   I - the current iteration value
   (from 'FROM' to 'TO' with stepsize 'STEP')
  (default: 'pow(BASE,I)')
 
```
```
 -extend-grid
  Whether the grid can be extended.
  (default: no)
 
```
```
 -max-grid-extensions <num>
  The maximum number of grid extensions (-1 is unlimited).
  (default: 3)
 
```
```
 -sample-size <num>
  The size (in percent) of the sample to search the inital grid with.
  (default: 100)
 
```
```
 -traversal <ROW-WISE|COLUMN-WISE>
  The type of traversal for the grid.
  (default: COLUMN-WISE)
 
```
```
 -log-file <filename>
  The log file to log the messages to.
  (default: none)
 
```
```
 -num-slots <num>
  Number of execution slots.
  (default 1 - i.e. no parallelism)
 
```
```
 -S <num>
  Random number seed.
  (default 1)
 
```
```
 -W
  Full name of base classifier.
  (default: weka.classifiers.functions.SMOreg with options -K weka.classifiers.functions.supportVector.RBFKernel)
 
```
```
 -output-debug-info
  If set, classifier is run in debug mode and
  may output additional info to the console
 
```
```
 -do-not-check-capabilities
  If set, classifier capabilities are not checked before classifier is built
  (use with caution).
 
```
```
 Options specific to classifier weka.classifiers.functions.SMOreg:
 
```
```
 -C <double>
  The complexity constant C.
  (default 1)
 
```
```
 -N
  Whether to 0=normalize/1=standardize/2=neither.
  (default 0=normalize)
 
```
```
 -I <classname and parameters>
  Optimizer class used for solving quadratic optimization problem
  (default weka.classifiers.functions.supportVector.RegSMOImproved)
 
```
```
 -K <classname and parameters>
  The Kernel to use.
  (default: weka.classifiers.functions.supportVector.PolyKernel)
 
```
```
 -output-debug-info
  If set, classifier is run in debug mode and
  may output additional info to the console
 
```
```
 -do-not-check-capabilities
  If set, classifier capabilities are not checked before classifier is built
  (use with caution).
 
```
```
 Options specific to optimizer ('-I') weka.classifiers.functions.supportVector.RegSMOImproved:
 
```
```
 -T <double>
  The tolerance parameter for checking the stopping criterion.
  (default 0.001)
 
```
```
 -V
  Use variant 1 of the algorithm when true, otherwise use variant 2.
  (default true)
 
```
```
 -P <double>
  The epsilon for round-off error.
  (default 1.0e-12)
 
```
```
 -L <double>
  The epsilon parameter in epsilon-insensitive loss function.
  (default 1.0e-3)
 
```
```
 -W <double>
  The random number seed.
  (default 1)
 
```
```
 Options specific to kernel ('-K') weka.classifiers.functions.supportVector.RBFKernel:
 
```
```
 -G <num>
  The Gamma parameter.
  (default: 0.01)
 
```
```
 -C <num>
  The size of the cache (a prime number), 0 for full cache and 
  -1 to turn it off.
  (default: 250007)
 
```
```
 -output-debug-info
  Enables debugging output (if available) to be printed.
  (default: off)
 
```
```
 -no-checks
  Turns off all checks - use with caution!
  (default: checks on)
 
```
    Specified by:
    
    setOptions in interface weka.core.OptionHandler
    
    Overrides:
    
    setOptions in class weka.classifiers.RandomizableSingleClassifierEnhancer
    
    Parameters:
    
    options - the options to use
    
    Throws:
    
    java.lang.Exception - if setting of options fails
  - setClassifier
```
public void setClassifier(weka.classifiers.Classifier newClassifier)
```
    Set the base learner.
    
    Overrides:
    
    setClassifier in class weka.classifiers.SingleClassifierEnhancer
    
    Parameters:
    
    newClassifier - the classifier to use.
  - evaluationTipText
```
public java.lang.String evaluationTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setEvaluation
```
public void setEvaluation(weka.core.SelectedTag value)
```
    Sets the criterion to use for evaluating the classifier performance.
    
    Parameters:
    
    value - the evaluation criterion
  - getEvaluation
```
public weka.core.SelectedTag getEvaluation()
```
    Gets the criterion used for evaluating the classifier performance.
    
    Returns:
    
    the current evaluation criterion.
  - YPropertyTipText
```
public java.lang.String YPropertyTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getYProperty
```
public java.lang.String getYProperty()
```
    Get the Y property (normally the classifier).
    
    Returns:
    
    Value of the property.
  - setYProperty
```
public void setYProperty(java.lang.String value)
```
    Set the Y property (normally the classifier).
    
    Parameters:
    
    value - the Y property.
  - YMinTipText
```
public java.lang.String YMinTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getYMin
```
public double getYMin()
```
    Get the value of the minimum of Y.
    
    Returns:
    
    Value of the minimum of Y.
  - setYMin
```
public void setYMin(double value)
```
    Set the value of the minimum of Y.
    
    Parameters:
    
    value - Value to use as minimum of Y.
  - YMaxTipText
```
public java.lang.String YMaxTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getYMax
```
public double getYMax()
```
    Get the value of the Maximum of Y.
    
    Returns:
    
    Value of the Maximum of Y.
  - setYMax
```
public void setYMax(double value)
```
    Set the value of the Maximum of Y.
    
    Parameters:
    
    value - Value to use as Maximum of Y.
  - YStepTipText
```
public java.lang.String YStepTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getYStep
```
public double getYStep()
```
    Get the value of the step size for Y.
    
    Returns:
    
    Value of the step size for Y.
  - setYStep
```
public void setYStep(double value)
```
    Set the value of the step size for Y.
    
    Parameters:
    
    value - Value to use as the step size for Y.
  - YBaseTipText
```
public java.lang.String YBaseTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getYBase
```
public double getYBase()
```
    Get the value of the base for Y.
    
    Returns:
    
    Value of the base for Y.
  - setYBase
```
public void setYBase(double value)
```
    Set the value of the base for Y.
    
    Parameters:
    
    value - Value to use as the base for Y.
  - YExpressionTipText
```
public java.lang.String YExpressionTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getYExpression
```
public java.lang.String getYExpression()
```
    Get the expression for the Y value.
    
    Returns:
    
    Expression for the Y value.
  - setYExpression
```
public void setYExpression(java.lang.String value)
```
    Set the expression for the Y value.
    
    Parameters:
    
    value - Expression for the Y value.
  - XPropertyTipText
```
public java.lang.String XPropertyTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getXProperty
```
public java.lang.String getXProperty()
```
    Get the X property to test.
    
    Returns:
    
    Value of the X property.
  - setXProperty
```
public void setXProperty(java.lang.String value)
```
    Set the X property.
    
    Parameters:
    
    value - the X property.
  - XMinTipText
```
public java.lang.String XMinTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getXMin
```
public double getXMin()
```
    Get the value of the minimum of X.
    
    Returns:
    
    Value of the minimum of X.
  - setXMin
```
public void setXMin(double value)
```
    Set the value of the minimum of X.
    
    Parameters:
    
    value - Value to use as minimum of X.
  - XMaxTipText
```
public java.lang.String XMaxTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getXMax
```
public double getXMax()
```
    Get the value of the Maximum of X.
    
    Returns:
    
    Value of the Maximum of X.
  - setXMax
```
public void setXMax(double value)
```
    Set the value of the Maximum of X.
    
    Parameters:
    
    value - Value to use as Maximum of X.
  - XStepTipText
```
public java.lang.String XStepTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getXStep
```
public double getXStep()
```
    Get the value of the step size for X.
    
    Returns:
    
    Value of the step size for X.
  - setXStep
```
public void setXStep(double value)
```
    Set the value of the step size for X.
    
    Parameters:
    
    value - Value to use as the step size for X.
  - XBaseTipText
```
public java.lang.String XBaseTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getXBase
```
public double getXBase()
```
    Get the value of the base for X.
    
    Returns:
    
    Value of the base for X.
  - setXBase
```
public void setXBase(double value)
```
    Set the value of the base for X.
    
    Parameters:
    
    value - Value to use as the base for X.
  - XExpressionTipText
```
public java.lang.String XExpressionTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getXExpression
```
public java.lang.String getXExpression()
```
    Get the expression for the X value.
    
    Returns:
    
    Expression for the X value.
  - setXExpression
```
public void setXExpression(java.lang.String value)
```
    Set the expression for the X value.
    
    Parameters:
    
    value - Expression for the X value.
  - gridIsExtendableTipText
```
public java.lang.String gridIsExtendableTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getGridIsExtendable
```
public boolean getGridIsExtendable()
```
    Get whether the grid can be extended dynamically.
    
    Returns:
    
    true if the grid can be extended.
  - setGridIsExtendable
```
public void setGridIsExtendable(boolean value)
```
    Set whether the grid can be extended dynamically.
    
    Parameters:
    
    value - whether the grid can be extended dynamically.
  - maxGridExtensionsTipText
```
public java.lang.String maxGridExtensionsTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getMaxGridExtensions
```
public int getMaxGridExtensions()
```
    Gets the maximum number of grid extensions, -1 for unlimited.
    
    Returns:
    
    the max number of grid extensions
  - setMaxGridExtensions
```
public void setMaxGridExtensions(int value)
```
    Sets the maximum number of grid extensions, -1 for unlimited.
    
    Parameters:
    
    value - the maximum of grid extensions.
  - sampleSizePercentTipText
```
public java.lang.String sampleSizePercentTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getSampleSizePercent
```
public double getSampleSizePercent()
```
    Gets the sample size for the initial grid search.
    
    Returns:
    
    the sample size.
  - setSampleSizePercent
```
public void setSampleSizePercent(double value)
```
    Sets the sample size for the initial grid search.
    
    Parameters:
    
    value - the sample size for the initial grid search.
  - traversalTipText
```
public java.lang.String traversalTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setTraversal
```
public void setTraversal(weka.core.SelectedTag value)
```
    Sets the type of traversal for the grid.
    
    Parameters:
    
    value - the traversal type
  - getTraversal
```
public weka.core.SelectedTag getTraversal()
```
    Gets the type of traversal for the grid.
    
    Returns:
    
    the current traversal type.
  - logFileTipText
```
public java.lang.String logFileTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getLogFile
```
public java.io.File getLogFile()
```
    Gets current log file.
    
    Returns:
    
    the log file.
  - setLogFile
```
public void setLogFile(java.io.File value)
```
    Sets the log file to use.
    
    Parameters:
    
    value - the log file.
  - numExecutionSlotsTipText
```
public java.lang.String numExecutionSlotsTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setNumExecutionSlots
```
public void setNumExecutionSlots(int value)
```
    Set the number of execution slots (threads) to use for finding optimal parameters.
    
    Parameters:
    
    value - the number of slots to use.
  - getNumExecutionSlots
```
public int getNumExecutionSlots()
```
    Get the number of execution slots (threads) to use for finding optimal parameters.
    
    Returns:
    
    the number of slots to use
  - getBestClassifier
```
public weka.classifiers.Classifier getBestClassifier()
```
    returns the best Classifier setup.
    
    Returns:
    
    the best Classifier setup
  - enumerateMeasures
```
public java.util.Enumeration<java.lang.String> enumerateMeasures()
```
    Returns an enumeration of the measure names.
    
    Specified by:
    
    enumerateMeasures in interface weka.core.AdditionalMeasureProducer
    
    Returns:
    
    an enumeration of the measure names
  - getMeasure
```
public double getMeasure(java.lang.String measureName)
```
    Returns the value of the named measure.
    
    Specified by:
    
    getMeasure in interface weka.core.AdditionalMeasureProducer
    
    Parameters:
    
    measureName - the name of the measure to query for its value
    
    Returns:
    
    the value of the named measure
  - getValues
```
public weka.classifiers.meta.GridSearch.PointDouble getValues()
```
    returns the parameter pair that was found to work best.
    
    Returns:
    
    the best parameter combination
  - getGridExtensionsPerformed
```
public int getGridExtensionsPerformed()
```
    returns the number of grid extensions that took place during the search (only applicable if the grid was extendable).
    
    Returns:
    
    the number of grid extensions that were performed
    
    See Also:
    
    getGridIsExtendable()
  - getCapabilities
```
public weka.core.Capabilities getCapabilities()
```
    Returns default capabilities of the classifier.
    
    Specified by:
    
    getCapabilities in interface weka.classifiers.Classifier
    
    Specified by:
    
    getCapabilities in interface weka.core.CapabilitiesHandler
    
    Overrides:
    
    getCapabilities in class weka.classifiers.SingleClassifierEnhancer
    
    Returns:
    
    the capabilities of this classifier
  - buildClassifier
```
public void buildClassifier(weka.core.Instances data)
                     throws java.lang.Exception
```
    builds the classifier.
    
    Specified by:
    
    buildClassifier in interface weka.classifiers.Classifier
    
    Parameters:
    
    data - the training instances
    
    Throws:
    
    java.lang.Exception - if something goes wrong
  - distributionForInstance
```
public double[] distributionForInstance(weka.core.Instance instance)
                                 throws java.lang.Exception
```
    Computes the distribution for a given instance
    
    Specified by:
    
    distributionForInstance in interface weka.classifiers.Classifier
    
    Overrides:
    
    distributionForInstance in class weka.classifiers.AbstractClassifier
    
    Parameters:
    
    instance - the instance for which distribution is computed
    
    Returns:
    
    the distribution
    
    Throws:
    
    java.lang.Exception - if the distribution can't be computed successfully
  - toString
```
public java.lang.String toString()
```
    returns a string representation of the classifier.
    
    Overrides:
    
    toString in class java.lang.Object
    
    Returns:
    
    a string representation of the classifier
  - toSummaryString
```
public java.lang.String toSummaryString()
```
    Returns a string that summarizes the object.
    
    Specified by:
    
    toSummaryString in interface weka.core.Summarizable
    
    Returns:
    
    the object summarized as a string
  - getRevision
```
public java.lang.String getRevision()
```
    Returns the revision string.
    
    Specified by:
    
    getRevision in interface weka.core.RevisionHandler
    
    Overrides:
    
    getRevision in class weka.classifiers.AbstractClassifier
    
    Returns:
    
    the revision
  - main
```
public static void main(java.lang.String[] args)
```
    Main method for running this classifier from commandline.
    
    Parameters:
    
    args - the options

Class GridSearch

Field Summary

Fields inherited from class weka.classifiers.AbstractClassifier

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.RandomizableSingleClassifierEnhancer

Methods inherited from class weka.classifiers.SingleClassifierEnhancer

Methods inherited from class weka.classifiers.AbstractClassifier

Methods inherited from class java.lang.Object

Methods inherited from interface weka.core.OptionHandler

Field Detail

EVALUATION_CC

EVALUATION_RMSE

EVALUATION_RRSE

EVALUATION_MAE

EVALUATION_RAE

EVALUATION_COMBINED

EVALUATION_ACC

EVALUATION_KAPPA

EVALUATION_WAUC

TAGS_EVALUATION

TRAVERSAL_BY_ROW

TRAVERSAL_BY_COLUMN

TAGS_TRAVERSAL

Constructor Detail

GridSearch

Method Detail

globalInfo

listOptions

getOptions

setOptions

setClassifier

evaluationTipText

setEvaluation

getEvaluation

YPropertyTipText

getYProperty

setYProperty

YMinTipText

getYMin

setYMin

YMaxTipText

getYMax

setYMax

YStepTipText

getYStep

setYStep

YBaseTipText

getYBase

setYBase

YExpressionTipText

getYExpression

setYExpression

XPropertyTipText

getXProperty

setXProperty

XMinTipText

getXMin

setXMin

XMaxTipText

getXMax

setXMax

XStepTipText

getXStep

setXStep

XBaseTipText

getXBase

setXBase

XExpressionTipText

getXExpression

setXExpression

gridIsExtendableTipText

getGridIsExtendable

setGridIsExtendable

maxGridExtensionsTipText

getMaxGridExtensions

setMaxGridExtensions

sampleSizePercentTipText

getSampleSizePercent

setSampleSizePercent