com.workfusion.vds.sdk.ml.sklearn.randomforest

Class RandomForestClassifier.Builder

java.lang.Object

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

com.workfusion.vds.sdk.ml.sklearn.randomforest.RandomForestClassifier.Builder

Enclosing class:

RandomForestClassifier

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

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
RandomForestClassifier.Builder	bootstrap(boolean bootstrap) Sets whether bootstrap samples are used when building trees.
protected RandomForestClassifier	buildModel() Build classifier.
RandomForestClassifier.Builder	classWeight(ClassWeightType classWeight) Sets class weight.
protected com.workfusion.automl.ml.bridge.python.classifier.PythonClassifierBridge	createDefaultClassifierBridge() Create default python classifier bridge.
RandomForestClassifier.Builder	criterion(CriterionType criterion) Sets the function to measure the quality of a split.
RandomForestClassifier.Builder	estimators(int estimators) Sets the number of estimators(trees) in the forest.
RandomForestClassifier.Builder	jobs(int jobs) Sets the number of jobs to run in parallel for both fit and predict.
RandomForestClassifier.Builder	maxDepth(Integer maxDepth) Sets the maximum depth of the tree.
RandomForestClassifier.Builder	maxFeatures(double maxFeatures) Sets the fraction of features to consider when looking for the best split.
RandomForestClassifier.Builder	maxFeatures(int maxFeatures) Sets the maximum number of features to consider when looking for the best split.
RandomForestClassifier.Builder	maxFeatures(MaxFeaturesType maxFeatures) Sets the maximum number of features to consider when looking for the best split.
RandomForestClassifier.Builder	maxLeafNodes(Integer maxLeafNodes) Sets max leaf nodes.
RandomForestClassifier.Builder	minImpurityDecrease(double minImpurityDecrease) Sets min impurity decrease.
RandomForestClassifier.Builder	minSamplesLeaf(double minSamplesLeaf) Sets the minimum number of samples required to be at a leaf node (the fraction of the samples).
RandomForestClassifier.Builder	minSamplesLeaf(int minSamplesLeaf) Sets the minimum number of samples required to be at a leaf node.
RandomForestClassifier.Builder	minSamplesSplit(double minSamplesSplit) Sets the minimum number of samples required to split an internal node (the fraction of the samples).
RandomForestClassifier.Builder	minSamplesSplit(int minSamplesSplit) Sets the minimum number of samples required to split an internal node.
RandomForestClassifier.Builder	minWeightFractionLeaf(double minWeightFractionLeaf) Sets the minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node.
RandomForestClassifier.Builder	modelName(String name) Set a model name.
RandomForestClassifier.Builder	oobScore(boolean oobScore) Sets whether to use out-of-bag samples to estimate the generalization accuracy.
RandomForestClassifier.Builder	seed(Integer seed) Sets the seed used by the random number generator.
RandomForestClassifier.Builder	verbose(boolean verbose) Sets whether warm start is used.
RandomForestClassifier.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<RandomForestClassifier.Builder,RandomForestClassifier>

Returns:

the python classifier bridge

estimators

public RandomForestClassifier.Builder estimators(int estimators)

Sets the number of estimators(trees) in the forest.

Parameters:

estimators - the number of estimators (default: 10)

Returns:

the builder

criterion

public RandomForestClassifier.Builder criterion(CriterionType criterion)

Sets the function to measure the quality of a split.

Parameters:

criterion - the criterion (CriterionType) (default: CriterionType.GINI)

Returns:

the builder

maxFeatures

public RandomForestClassifier.Builder maxFeatures(MaxFeaturesType maxFeatures)

Sets the maximum number of features to consider when looking for the best split. If null then maxFeatures=n_features.

Parameters:

maxFeatures - the max features (MaxFeaturesType) (default: MaxFeaturesType.AUTO)

Returns:

the builder

maxFeatures

public RandomForestClassifier.Builder maxFeatures(int maxFeatures)

Sets the maximum number of features to consider when looking for the best split.

Parameters:

maxFeatures - the max features

Returns:

the builder

maxFeatures

public RandomForestClassifier.Builder maxFeatures(double maxFeatures)

Sets the fraction of features to consider when looking for the best split.

Parameters:

maxFeatures - the max features

Returns:

the builder

maxDepth

public RandomForestClassifier.Builder maxDepth(Integer maxDepth)

Sets the maximum depth of the tree. If null then nodes are expanded until all leaves are pure or until all leaves contain less than minSamplesSplit samples.

Parameters:

maxDepth - the max depth (default: null)

Returns:

the builder

minSamplesSplit

public RandomForestClassifier.Builder minSamplesSplit(double minSamplesSplit)

Sets the minimum number of samples required to split an internal node (the fraction of the samples).

Parameters:

minSamplesSplit - the min samples split

Returns:

the builder

minSamplesSplit

public RandomForestClassifier.Builder minSamplesSplit(int minSamplesSplit)

Sets the minimum number of samples required to split an internal node.

Parameters:

minSamplesSplit - the min samples split (default: 2)

Returns:

the builder

minSamplesLeaf

public RandomForestClassifier.Builder minSamplesLeaf(int minSamplesLeaf)

Sets the minimum number of samples required to be at a leaf node.

Parameters:

minSamplesLeaf - the min samples leaf (default: 1)

Returns:

the builder

minSamplesLeaf

public RandomForestClassifier.Builder minSamplesLeaf(double minSamplesLeaf)

Sets the minimum number of samples required to be at a leaf node (the fraction of the samples).

Parameters:

minSamplesLeaf - the min samples leaf

Returns:

the builder

minWeightFractionLeaf

public RandomForestClassifier.Builder minWeightFractionLeaf(double minWeightFractionLeaf)

Sets the minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node.

Parameters:

minWeightFractionLeaf - the min weight fraction leaf (default: 0.0)

Returns:

the builder

maxLeafNodes

public RandomForestClassifier.Builder maxLeafNodes(Integer maxLeafNodes)

Sets max leaf nodes. Grow trees with max_leaf_nodes in best-first fashion. Best nodes are defined as relative reduction in impurity. If null then unlimited number of leaf nodes.

Parameters:

maxLeafNodes - the max leaf nodes (default: null)

Returns:

the builder

minImpurityDecrease

public RandomForestClassifier.Builder minImpurityDecrease(double minImpurityDecrease)

Sets min impurity decrease. A node will be split if this split induces a decrease of the impurity greater than or equal to this value

Parameters:

minImpurityDecrease - the min impurity decrease (default: 0.0)

Returns:

the builder

bootstrap

public RandomForestClassifier.Builder bootstrap(boolean bootstrap)

Sets whether bootstrap samples are used when building trees.

Parameters:

bootstrap - the bootstrap (default: true)

Returns:

the builder

oobScore

public RandomForestClassifier.Builder oobScore(boolean oobScore)

Sets whether to use out-of-bag samples to estimate the generalization accuracy.

Parameters:

oobScore - the oob score (default: false)

Returns:

the builder

jobs

public RandomForestClassifier.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

seed

public RandomForestClassifier.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

verbose

public RandomForestClassifier.Builder verbose(boolean verbose)

Sets whether warm start is used.

Parameters:

verbose - the verbose mode (default: false)

Returns:

the builder

warmStart

public RandomForestClassifier.Builder warmStart(boolean warmStart)

Sets whether warm start is used.

Parameters:

warmStart - the warm start (default: false)

Returns:

the builder

classWeight

public RandomForestClassifier.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

modelName

public RandomForestClassifier.Builder modelName(String name)

Description copied from class: BasePythonClassifier.Builder

Set a model name.

Overrides:

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

Parameters:

name - the model name (String)

Returns:

the builder

buildModel

protected RandomForestClassifier buildModel()

Description copied from class: BasePythonClassifier.Builder

Build classifier.

Specified by:

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

Returns:

the classifier instance