magpie.models.regression

Class MetallurgicalHeuristicRegression

java.lang.Object

magpie.models.BaseModel

magpie.models.regression.BaseRegression

magpie.models.regression.MetallurgicalHeuristicRegression

All Implemented Interfaces:

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

public class MetallurgicalHeuristicRegression
extends BaseRegression

Estimate formation energy for ternary+ alloys using a metallurgical heuristic. Works by estimating multi-component formation energy based on formation energies of binaries. See Hillert's text for a detailed description.

You can also compute correction factors using the technique demonstrated by Meredig and Agrawal et al., where:
ΔH_modified = a * ΔH_heuristic + b

Usage: $<hull data> [-correction]
hull data: A CompositionDataset containing formation energies for all known binary compounds.
-correction: Compute correction factors

See Also:

Serialized Form

Field Summary

Fields inherited from class magpie.models.BaseModel

AttributeSelector, trained, TrainingStats, validated, ValidationStats

Constructor Summary

Constructors

Constructor and Description
MetallurgicalHeuristicRegression()

Method Summary

Modifier and Type	Method and Description
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.
protected 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	setBinaryConvexHulls(CompositionDataset hullData) Define binary convex hull data.
void	setOptions(java.util.List<java.lang.Object> Options) Set any options for this object.
void	setUseCorrection(boolean x) Set whether to employ correction factors
protected void	train_protected(Dataset TrainData) Train a model without evaluating performance

Methods inherited from class magpie.models.regression.BaseRegression

clone, 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

MetallurgicalHeuristicRegression

public MetallurgicalHeuristicRegression()

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

setBinaryConvexHulls

public void setBinaryConvexHulls(CompositionDataset hullData)

Define binary convex hull data. Should contain compositions and formation energies ("delta_e") for each known binary and elemental compounds that are on the convex hull. You also should output the formation energy for each element (which may be non-zero if your dataset uses fitted reference states).

This code does not currently have the capability to calculate convex hulls. Since, this class is intended to be used with data from qmpy I figured that you can just use that code's capabilities.

Parameters:

hullData - Dataset holding all known compounds to be used for calculating delta_e

setUseCorrection

public void setUseCorrection(boolean x)

Set whether to employ correction factors

Parameters:

x - Desired setting

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

protected 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