magpie.models.regression

Class ClassificationRegression

java.lang.Object

magpie.models.BaseModel

magpie.models.regression.BaseRegression

magpie.models.regression.ClassificationRegression

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, AbstractRegressionModel, Citable, Commandable, Options, Printable, Savable

public class ClassificationRegression
extends BaseRegression

Use a classification algorithm to perform regression. This method works by spliting all training examples into two sets: one where the class variable is past a threshold and one where it is not. This threshold is set by specifying the objective algorithm (with an BaseEntryRanker) and the treshold on which to split the data.

Regression is performed by predicting that an entry 100% probability of being past the threshold to have a class variable equal to that of the training entry farthest past the threshold, 0% probability being equivalent to the entry farthest before the threshold, and all others linearly interpolated between those two.

Usage: $<classifier> <threshold> <objective function> [<o.f. options...>]
classifier: BaseModel that fills AbstractClassifier, used to make predictions
threshold: Desired threshold for the objective function value
objective function: BaseEntryRanker used to rank the entries based on their class variable.
o.f. options: Any options for the objective function

See Also:

Serialized Form

Field Summary

Fields inherited from class magpie.models.BaseModel

AttributeSelector, trained, TrainingStats, validated, ValidationStats

Constructor Summary

Constructors

Constructor and Description
ClassificationRegression() Create a instances of this model that uses ZeroR.

Method Summary

Modifier and Type	Method and Description
ClassificationRegression	clone()
int	getNFittingParameters() Number of fitting parameters in a model.
protected java.lang.String	printModel_protected() Internal method that handles printing the model as a string.
java.util.List<java.lang.String>	printModelDescriptionDetails(boolean htmlFormat) Print details of the model.
java.lang.String	printUsage() Print out required format for options.
void	run_protected(Dataset TrainData) Run a model without checking if stuff is trained (use carefully)
void	setClassifier(BaseModel Clfr) Classifier behind this regression algorithm
void	setObjectiveFunction(BaseEntryRanker ObjFunction) Define the objective function used to order entries
void	setOptions(java.util.List<java.lang.Object> Options) Set any options for this object.
void	setThreshold(double Threshold) Define threshold of objective function on which to split entries.
protected void	train_protected(Dataset TrainData) Train a model without evaluating performance

Methods inherited from class magpie.models.regression.BaseRegression

doRobustRegression, getRobustRegressionQ, robustTraining, runCommand, setRobustRegressionQ

Methods inherited from class magpie.models.BaseModel

about, crossValidate, crossValidate, crossValidate, done, externallyValidate, getAttributeSelector, getCitations, getFilter, getTrainTime, getValidationMethod, handleSetCommand, isTrained, isValidated, loadState, printCommand, printDescription, printModel, resetModel, run, saveCommand, saveState, setAttributeSelector, setComponent, setFilter, train, train

Methods inherited from class java.lang.Object

equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

ClassificationRegression

public ClassificationRegression()
                         throws java.lang.Exception

Create a instances of this model that uses ZeroR.

Throws:

java.lang.Exception

Method Detail

clone

public ClassificationRegression clone()

Overrides:

clone in class BaseRegression

setOptions

public void setOptions(java.util.List<java.lang.Object> Options)
                throws java.lang.Exception

Description copied from interface: Options

Set any options for this object. If parsing the options fails, return an Exception with the usage as its message.

Parameters:

Options - Array of options as Objects - can be null

Throws:

java.lang.Exception - if problem with inputs

printUsage

public java.lang.String printUsage()

Description copied from interface: Options

Print out required format for options. For consistency, should start with "Usage: ".

Returns:

Usage for this function

setObjectiveFunction

public void setObjectiveFunction(BaseEntryRanker ObjFunction)

Define the objective function used to order entries

Parameters:

ObjFunction - Desired objective function

setClassifier

public void setClassifier(BaseModel Clfr)
                   throws java.lang.Exception

Classifier behind this regression algorithm

Parameters:

Clfr - Untrained regression model

Throws:

java.lang.Exception

setThreshold

public void setThreshold(double Threshold)

Define threshold of objective function on which to split entries. Vales below this value will constitute one class, all others will constitute another.

Parameters:

Threshold -

train_protected

protected void train_protected(Dataset TrainData)

Description copied from class: BaseModel

Train a model without evaluating performance

Specified by:

train_protected in class BaseModel

Parameters:

TrainData - Training data

run_protected

public void run_protected(Dataset TrainData)

Description copied from class: BaseModel

Run a model without checking if stuff is trained (use carefully)

Specified by:

run_protected in class BaseModel

Parameters:

TrainData - Training data

printModel_protected

protected java.lang.String printModel_protected()

Description copied from class: BaseModel

Internal method that handles printing the model as a string. Should end with a "\n"

Specified by:

printModel_protected in class BaseModel

Returns:

String representation of model

printModelDescriptionDetails

public java.util.List<java.lang.String> printModelDescriptionDetails(boolean htmlFormat)

Description copied from class: BaseModel

Print details of the model. Used by BaseModel.printDescription(boolean).

Implementation note: No not add indentation for details. That is handled by BaseModel.printDescription(boolean). You should also call the super operation to get the Normalizer and Attribute selector settings

Overrides:

printModelDescriptionDetails in class BaseModel

Parameters:

htmlFormat - Whether to use HTML format

Returns:

List describing model details. Each entry is a different line of the description (i.e., in place of newline characters).

getNFittingParameters

public int getNFittingParameters()

Description copied from interface: AbstractRegressionModel

Number of fitting parameters in a model. Only pertinent to nonlinear regression. Otherwise, this should just return 0.

Returns:

Number of fitting parameters