CompositeRegression (magpie API)

java.lang.Object
- magpie.models.BaseModel
- - magpie.models.regression.BaseRegression
  - - magpie.models.regression.CompositeRegression

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, MultiModel, AbstractRegressionModel, Citable, Commandable, Options, Printable, Savable
```
public class CompositeRegression
extends BaseRegression
implements MultiModel
```
Ensemble of regression models.
Has the ability to return the variance in predictions between submodels, which can be used as a measure of their composite reliability.
Usage: *No options to set*
Implemented Commands:
submodel - Print the number of submodels
submodel set generic $<model> - Define a model template to use for all submodels
model: An instance of BaseModel. Note: Do not use this command for CompositeRegression unless each model automatically uses a different random number seed. Otherwise, each submodel will be identical.
submodel set <number> $<model - Set a specific submodel
number: Index of the submodel to set (list starts with 0)
model: An instance of BaseModel to use for that model
<output> = submodel get generic - Retrieve the template for any unassigned submodels
<output = submodel get <number> - Retrieve a specific submodel
number: Index of submodel to retrieve (list starts with 0) Returns a clone of the model - you cannot use this to edit the model.

See Also:

Serialized Form

Field Summary

Fields
Modifier and Type	Field and Description
`BaseRegression`	`GenericModel` Generic composite regression model (use wisely)
`java.util.List<BaseRegression>`	`Model` Collections of models used by this class

Fields inherited from class magpie.models.BaseModel
AttributeSelector, trained, TrainingStats, validated, ValidationStats

Constructor Summary

Constructors
Constructor and Description

CompositeRegression()

Constructors
Constructor and Description
`CompositeRegression()`

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`protected void`	`checkStatus()`
`CompositeRegression`	`clone()`
`double[]`	`getEnsembleMean(Dataset Data)` Get mean prediction by the ensemble for each entry
`double[]`	`getEnsembleMean(double[][] EnsemblePredictions)` Get mean prediction by the ensemble for each entry
`double[][]`	`getEnsemblePredictions(Dataset Data)` Calculate the predicted class using each model
`double[]`	`getEnsembleVariation(Dataset Data)` Return the variance in predictions for each entry.
`double[]`	`getEnsembleVariation(double[][] EnsemblePredictions)` Return the variance in predictions for each entry.
`BaseRegression`	`getGenericModel()` Get the model template
`BaseModel`	`getModel(int index)` Get a specific submodel.
`int`	`getNFittingParameters()` Number of fitting parameters in a model.
`int`	`NModels()` Get the number of models in ensemble
`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 TestData)` 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.
`void`	`setGenericModel(BaseModel x)` Set the model template
`void`	`setModel(int index, BaseModel x)` Set a specific submodel.
`void`	`setNumberOfModels(int n)` Defines the number of models to be trained.
`void`	`setOptions(java.util.List 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
  - Model
```
public java.util.List<BaseRegression> Model
```
    Collections of models used by this class
  - GenericModel
```
public BaseRegression GenericModel
```
    Generic composite regression model (use wisely)
- Constructor Detail
  - CompositeRegression
```
public CompositeRegression()
```
- Method Detail
  - setOptions
```
public void setOptions(java.util.List 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.
    
    Specified by:
    
    setOptions in interface Options
    
    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: ".
    
    Specified by:
    
    printUsage in interface Options
    
    Returns:
    
    Usage for this function
  - getModel
```
public BaseModel getModel(int index)
```
    Description copied from interface: MultiModel
    
    Get a specific submodel.
    
    Specified by:
    
    getModel in interface MultiModel
    
    Parameters:
    
    index - Model to retrieve
    
    Returns:
    
    Requested model or null if index >= NModels
  - NModels
```
public int NModels()
```
    Get the number of models in ensemble
    
    Specified by:
    
    NModels in interface MultiModel
    
    Returns:
    
    Number of models
  - setNumberOfModels
```
public void setNumberOfModels(int n)
```
    Description copied from interface: MultiModel
    
    Defines the number of models to be trained. If a generic model is set, it will automatically use it to defined any new models
    
    Specified by:
    
    setNumberOfModels in interface MultiModel
    
    Parameters:
    
    n - Number of models to use
  - setGenericModel
```
public void setGenericModel(BaseModel x)
```
    Description copied from interface: MultiModel
    
    Set the model template
    
    Specified by:
    
    setGenericModel in interface MultiModel
    
    Parameters:
    
    x - Template model (will be cloned)
  - getGenericModel
```
public BaseRegression getGenericModel()
```
    Description copied from interface: MultiModel
    
    Get the model template
    
    Specified by:
    
    getGenericModel in interface MultiModel
    
    Returns:
    
    Pointer to the generic model
  - setModel
```
public void setModel(int index,
                     BaseModel x)
```
    Description copied from interface: MultiModel
    
    Set a specific submodel. Will increase number of models if necessary.
    Any implementation should not clone the model. This will allow people to construct a model used already-trained models.
    
    Specified by:
    
    setModel in interface MultiModel
    
    Parameters:
    
    index - Index of submodel to be set
    
    x - Model to be used (creates a clone)
  - checkStatus
```
protected void checkStatus()
```
  - clone
```
public CompositeRegression clone()
```
    Overrides:
    
    clone in class BaseRegression
  - 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
  - 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.
    
    Specified by:
    
    getNFittingParameters in interface AbstractRegressionModel
    
    Returns:
    
    Number of fitting parameters
  - run_protected
```
public void run_protected(Dataset TestData)
```
    Description copied from class: BaseModel
    
    Run a model without checking if stuff is trained (use carefully)
    
    Specified by:
    
    run_protected in class BaseModel
    
    Parameters:
    
    TestData - Training data
  - getEnsemblePredictions
```
public double[][] getEnsemblePredictions(Dataset Data)
```
    Calculate the predicted class using each model
    
    Parameters:
    
    Data - Dataset to be evaluated
    
    Returns:
    
    Predicted class from each model for each entry
  - getEnsembleMean
```
public double[] getEnsembleMean(double[][] EnsemblePredictions)
```
    Get mean prediction by the ensemble for each entry
    
    Parameters:
    
    EnsemblePredictions - array produced using getEnsemblePredictions
    
    Returns:
    
    Variance estimate for each entry
  - getEnsembleMean
```
public double[] getEnsembleMean(Dataset Data)
```
    Get mean prediction by the ensemble for each entry
    
    Parameters:
    
    Double - Dataset to be evaluated
    
    Returns:
    
    Variance estimate for each entry
  - getEnsembleVariation
```
public double[] getEnsembleVariation(double[][] EnsemblePredictions)
```
    Return the variance in predictions for each entry. To deal with small data sizes, this is estimated using the range
    
    Parameters:
    
    EnsemblePredictions - Double array produced using getEnsemblePredictions
    
    Returns:
    
    Variance estimate for each entry
  - getEnsembleVariation
```
public double[] getEnsembleVariation(Dataset Data)
```
    Return the variance in predictions for each entry. To deal with small data sizes, this is estimated using the range.
    
    Parameters:
    
    Data - Dataset to evaluate
    
    Returns:
    
    Variance estimate for each entry
  - printModel_protected
```
public 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

Class CompositeRegression

Field Summary

Fields inherited from class magpie.models.BaseModel

Constructor Summary

Method Summary

Methods inherited from class magpie.models.regression.BaseRegression

Methods inherited from class magpie.models.BaseModel

Methods inherited from class java.lang.Object

Field Detail

Model

GenericModel

Constructor Detail

CompositeRegression

Method Detail

setOptions

printUsage

getModel

NModels

setNumberOfModels

setGenericModel

getGenericModel

setModel

checkStatus

clone

train_protected

getNFittingParameters

run_protected

getEnsemblePredictions

getEnsembleMean

getEnsembleMean

getEnsembleVariation

getEnsembleVariation

printModel_protected

printModelDescriptionDetails

runCommand