magpie.models.regression

Class MultiObjectiveRegression

java.lang.Object

magpie.models.BaseModel

magpie.models.regression.BaseRegression

magpie.models.regression.MultiObjectiveRegression

All Implemented Interfaces:

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

Direct Known Subclasses:

MultiPropertyRegression

public class MultiObjectiveRegression
extends BaseRegression

Employs multiple models, each designed to predict a different property that composes the class variable. Class variable is defined by a MultiObjectiveEntryRanker.

Usage: <ranker method> [<ranker options...>]
ranker method: Multi-objective function
ranker options: Any options for the objective function

Implemented Commands:

submodel - Print type and status of model associated with each property

<model> = submodel get <property> - Get a model for a certain property
property: Name of property associated with model

submodel set <property> $<model> - Set a model for a certain property
property: Name of property associated with model
model: Regression model to use

submodel set generic $<model> - Set a model for any property without an associated model
model: Regression model to use

<output> = submodel get <property> - Get a model associated with a certain property
property: Name of property associated with desired model

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
protected BaseModel	GenericModel Generic model (in case one has not already been defined)
protected java.util.Map<java.lang.String,BaseModel>	Models Map of property name to model used to predict it.
protected MultiObjectiveEntryRanker	ObjFunction Objective function used to calculate class variable.

Fields inherited from class magpie.models.BaseModel

AttributeSelector, trained, TrainingStats, validated, ValidationStats

Constructor Summary

Constructors

Constructor and Description
MultiObjectiveRegression()

Method Summary

Modifier and Type	Method and Description
MultiObjectiveRegression	clone()
BaseModel	getModel(java.lang.String propertyName) Get the model designed to predict a certain property.
int	getNFittingParameters() Number of fitting parameters in a model.
java.lang.String[]	getPropertiesBeingModeled() Get a list of properties for which a model is defined.
boolean	modelIsDefined(java.lang.String propertyName) Determine whether a model for a certain property is defined
boolean	modelIsTrained(java.lang.String propertyName) Whether the model for a certain property is trained.
boolean	modelIsValidated(java.lang.String propertyName) Whether the model for a certain property is validated
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)
java.lang.Object	runCommand(java.util.List<java.lang.Object> Command) Process some command described by a list of Objects.
protected java.lang.Object	runSubmodelCommand(java.util.List<java.lang.Object> Command) Run commands associated with operating on submodels
void	setGenericModel(BaseModel model) Define the model to be used for a property if one is not specifically defined.
void	setModel(java.lang.String propertyName, BaseModel model) Define a model for a certain property.
void	setObjectiveFunction(MultiObjectiveEntryRanker objFunction) Define the objective function used to calculate perform of each entry.
void	setOptions(java.util.List<java.lang.Object> Options) Set any options for this object.
protected void	train_protected(Dataset TrainData) Train a model without evaluating performance

Methods inherited from class magpie.models.regression.BaseRegression

doRobustRegression, getRobustRegressionQ, robustTraining, 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

Field Detail

Models

protected java.util.Map<java.lang.String,BaseModel> Models

Map of property name to model used to predict it.

ObjFunction

protected MultiObjectiveEntryRanker ObjFunction

Objective function used to calculate class variable.

GenericModel

protected BaseModel GenericModel

Generic model (in case one has not already been defined)

Constructor Detail

MultiObjectiveRegression

public MultiObjectiveRegression()

Method Detail

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(MultiObjectiveEntryRanker objFunction)

Define the objective function used to calculate perform of each entry. This value is assigned to the class variable (allows this model to be validated)

Parameters:

objFunction - Objective function to use

clone

public MultiObjectiveRegression clone()

Overrides:

clone in class BaseRegression

getPropertiesBeingModeled

public java.lang.String[] getPropertiesBeingModeled()

Get a list of properties for which a model is defined.

Returns:

List of property names

getModel

public BaseModel getModel(java.lang.String propertyName)

Get the model designed to predict a certain property.

Parameters:

propertyName - Name of property to be modeled

Returns:

Clone of that model, or null if no model exists for that property

setModel

public void setModel(java.lang.String propertyName,
                     BaseModel model)
              throws java.lang.Exception

Define a model for a certain property. Will overwrite an existing model for that property.

Parameters:

propertyName - Name of property

model - Example of model to be used

Throws:

java.lang.Exception

setGenericModel

public void setGenericModel(BaseModel model)
                     throws java.lang.Exception

Define the model to be used for a property if one is not specifically defined.

Parameters:

model - Generic regression model

Throws:

java.lang.Exception

modelIsDefined

public boolean modelIsDefined(java.lang.String propertyName)

Determine whether a model for a certain property is defined

Parameters:

propertyName - Name of property

Returns:

Whether a model is defined

modelIsTrained

public boolean modelIsTrained(java.lang.String propertyName)

Whether the model for a certain property is trained.

Parameters:

propertyName - Name of property

Returns:

Whether Model is defined and trained

modelIsValidated

public boolean modelIsValidated(java.lang.String propertyName)

Whether the model for a certain property is validated

Parameters:

propertyName - Name of property

Returns:

Whether Model is defined and validated

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

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

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).

runCommand

public java.lang.Object runCommand(java.util.List<java.lang.Object> Command)
                            throws java.lang.Exception

Description copied from interface: Commandable

Process some command described by a list of Objects.

Specified by:

runCommand in interface Commandable

Overrides:

runCommand in class BaseRegression

Parameters:

Command - Command as a list of objects

Returns:

Any output (null if no output generated by command)

Throws:

java.lang.Exception - If something goes wrong

runSubmodelCommand

protected java.lang.Object runSubmodelCommand(java.util.List<java.lang.Object> Command)
                                       throws java.lang.Exception

Run commands associated with operating on submodels

Parameters:

Command - Command to run

Returns:

Any associated output

Throws:

java.lang.Exception - If something goes wrong