com.workfusion.vds.sdk.ml.sklearn.logisticregression

Class LogisticRegression.Builder

java.lang.Object

com.workfusion.vds.sdk.ml.python.BasePythonClassifier.Builder<LogisticRegression.Builder,LogisticRegression>

com.workfusion.vds.sdk.ml.sklearn.logisticregression.LogisticRegression.Builder

Enclosing class:

LogisticRegression

public static class LogisticRegression.Builder
extends BasePythonClassifier.Builder<LogisticRegression.Builder,LogisticRegression>

The type Builder.

Field Summary

Fields inherited from class com.workfusion.vds.sdk.ml.python.BasePythonClassifier.Builder

modelName, workingDir

Constructor Summary

Constructors

Constructor and Description
Builder() Instantiates a new Builder.

Method Summary

Modifier and Type	Method and Description
protected LogisticRegression	buildModel() Build classifier.
LogisticRegression.Builder	classWeight(ClassWeightType classWeight) Sets class weight.
protected com.workfusion.automl.ml.bridge.python.classifier.PythonClassifierBridge	createDefaultClassifierBridge() Create default python classifier bridge.
LogisticRegression.Builder	dual(boolean dual) Sets the algorithm to either solve the dual or primal optimization problem.
LogisticRegression.Builder	fitIntercept(boolean fitIntercept) Sets whether to calculate the intercept for this model.
LogisticRegression.Builder	interceptScaling(double interceptScaling) Sets the scaling coefficient of the synthetic features.
LogisticRegression.Builder	jobs(int jobs) Sets the number of jobs to run in parallel for both fit and predict.
LogisticRegression.Builder	maxIterations(int maxIterations) Sets the maximum number of iterations to be run.
LogisticRegression.Builder	modelName(String name) Set a model name.
LogisticRegression.Builder	multiClass(MultiClassType multiClass) Sets the multi-class strategy if y contains more than two classes.
LogisticRegression.Builder	penalty(PenaltyType penalty) Sets the norm used in the penalization.
LogisticRegression.Builder	penaltyCoefficient(double penaltyCoefficient) Sets penalty coefficient of the error term.
LogisticRegression.Builder	seed(Integer seed) Sets the seed used by the random number generator.
LogisticRegression.Builder	solver(SolverType solver) Sets the algorithm to use in the optimization problem.
LogisticRegression.Builder	tolerance(double tolerance) Sets the tolerance for stopping criteria.
LogisticRegression.Builder	verbose(boolean verbose) Sets whether verbose mode is used.
LogisticRegression.Builder	warmStart(boolean warmStart) Sets whether warm start is used.

Methods inherited from class com.workfusion.vds.sdk.ml.python.BasePythonClassifier.Builder

build, workingDir

Methods inherited from class java.lang.Object

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

Constructor Detail

Builder

public Builder()

Instantiates a new Builder.

Method Detail

createDefaultClassifierBridge

protected com.workfusion.automl.ml.bridge.python.classifier.PythonClassifierBridge createDefaultClassifierBridge()

Description copied from class: BasePythonClassifier.Builder

Create default python classifier bridge.

Specified by:

createDefaultClassifierBridge in class BasePythonClassifier.Builder<LogisticRegression.Builder,LogisticRegression>

Returns:

the python classifier bridge

penalty

public LogisticRegression.Builder penalty(PenaltyType penalty)

Sets the norm used in the penalization.

Parameters:

penalty - the penalty (PenaltyType) (default: PenaltyType.L2)

Returns:

the builder

dual

public LogisticRegression.Builder dual(boolean dual)

Sets the algorithm to either solve the dual or primal optimization problem.

Parameters:

dual - the dual flag (default: true)

Returns:

the builder

tolerance

public LogisticRegression.Builder tolerance(double tolerance)

Sets the tolerance for stopping criteria.

Parameters:

tolerance - the tolerance (default: 1e-4)

Returns:

the builder

penaltyCoefficient

public LogisticRegression.Builder penaltyCoefficient(double penaltyCoefficient)

Sets penalty coefficient of the error term.

Parameters:

penaltyCoefficient - the penalty coefficient (default: 1.0)

Returns:

the builder

fitIntercept

public LogisticRegression.Builder fitIntercept(boolean fitIntercept)

Sets whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (i.e. data is expected to be already centered).

Parameters:

fitIntercept - the fit intercept flag (default: true)

Returns:

the builder

interceptScaling

public LogisticRegression.Builder interceptScaling(double interceptScaling)

Sets the scaling coefficient of the synthetic features.

Parameters:

interceptScaling - the scaling coefficient (default: 1.0)

Returns:

the builder

classWeight

public LogisticRegression.Builder classWeight(ClassWeightType classWeight)

Sets class weight. If null then all classes are supposed to have weight one.

Parameters:

classWeight - the class weight (ClassWeightType) (default: null)

Returns:

the builder

seed

public LogisticRegression.Builder seed(Integer seed)

Sets the seed used by the random number generator. If null then default random number generator is used (np.random).

Parameters:

seed - the seed (default: null)

Returns:

the builder

solver

public LogisticRegression.Builder solver(SolverType solver)

Sets the algorithm to use in the optimization problem.

Parameters:

solver - the solver (SolverType) (default:SolverType.LIBLINEAR)

Returns:

the builder

maxIterations

public LogisticRegression.Builder maxIterations(int maxIterations)

Sets the maximum number of iterations to be run.

Parameters:

maxIterations - the number of iterations (default: 100)

Returns:

the builder

multiClass

public LogisticRegression.Builder multiClass(MultiClassType multiClass)

Sets the multi-class strategy if y contains more than two classes.

Parameters:

multiClass - the multi class strategy (MultiClassType) (default: MultiClassType.OVR)

Returns:

the builder

verbose

public LogisticRegression.Builder verbose(boolean verbose)

Sets whether verbose mode is used.

Parameters:

verbose - the verbose mode flag (default: false)

Returns:

the builder

warmStart

public LogisticRegression.Builder warmStart(boolean warmStart)

Sets whether warm start is used.

Parameters:

warmStart - the warm start flag (default: false)

Returns:

the builder

jobs

public LogisticRegression.Builder jobs(int jobs)

Sets the number of jobs to run in parallel for both fit and predict. If -1, then the number of jobs is set to the number of cores.

Parameters:

jobs - the number of jobs (default: 1)

Returns:

the builder

modelName

public LogisticRegression.Builder modelName(String name)

Description copied from class: BasePythonClassifier.Builder

Set a model name.

Overrides:

modelName in class BasePythonClassifier.Builder<LogisticRegression.Builder,LogisticRegression>

Parameters:

name - the model name (String)

Returns:

the builder

buildModel

protected LogisticRegression buildModel()

Description copied from class: BasePythonClassifier.Builder

Build classifier.

Specified by:

buildModel in class BasePythonClassifier.Builder<LogisticRegression.Builder,LogisticRegression>

Returns:

the classifier instance