org.apache.spark.ml.classification

Class RandomForestClassificationModel

java.lang.Object

org.apache.spark.ml.PipelineStage

org.apache.spark.ml.Transformer

org.apache.spark.ml.Model<M>

org.apache.spark.ml.PredictionModel<FeaturesType,M>

org.apache.spark.ml.classification.ClassificationModel<FeaturesType,M>

org.apache.spark.ml.classification.ProbabilisticClassificationModel<Vector,RandomForestClassificationModel>

org.apache.spark.ml.classification.RandomForestClassificationModel

All Implemented Interfaces:

java.io.Serializable, Logging, Params, Identifiable

public final class RandomForestClassificationModel
extends ProbabilisticClassificationModel<Vector,RandomForestClassificationModel>
implements scala.Serializable

:: Experimental :: Random Forest model for classification. It supports both binary and multiclass labels, as well as both continuous and categorical features. param: _trees Decision trees in the ensemble. Warning: These have null parents.

See Also:

Serialized Form

Method Summary

Methods

Modifier and Type	Method and Description
RandomForestClassificationModel	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
Vector	featureImportances() Estimate of the importance of each feature.
Param<java.lang.String>	featuresCol() Param for features column name.
static RandomForestClassificationModel	fromOld(RandomForestModel oldModel, RandomForestClassifier parent, scala.collection.immutable.Map<java.lang.Object,java.lang.Object> categoricalFeatures, int numClasses, int numFeatures) (private[ml]) Convert a model from the old API
java.lang.String	getFeaturesCol()
java.lang.String	getLabelCol()
java.lang.String	getPredictionCol()
java.lang.String	getRawPredictionCol()
Param<java.lang.String>	labelCol() Param for label column name.
int	numClasses() Number of classes (values which the label can take).
int	numFeatures() Returns the number of features the model was trained on.
Param<java.lang.String>	predictionCol() Param for prediction column name.
protected Vector	predictRaw(Vector features) Raw prediction for each possible label.
protected Vector	raw2probabilityInPlace(Vector rawPrediction) Estimate the probability of each class given the raw prediction, doing the computation in-place.
Param<java.lang.String>	rawPredictionCol() Param for raw prediction (a.k.a.
java.lang.String	toString()
protected DataFrame	transformImpl(DataFrame dataset)
org.apache.spark.ml.tree.DecisionTreeModel[]	trees()
double[]	treeWeights()
java.lang.String	uid() An immutable unique ID for the object and its derivatives.
StructType	validateAndTransformSchema(StructType schema, boolean fitting, DataType featuresDataType)
StructType	validateAndTransformSchema(StructType schema, boolean fitting, DataType featuresDataType) Validates and transforms the input schema with the provided param map.

Methods inherited from class org.apache.spark.ml.classification.ProbabilisticClassificationModel

normalizeToProbabilitiesInPlace, predictProbability, probability2prediction, raw2prediction, raw2probability, setProbabilityCol, setThresholds, transform

Methods inherited from class org.apache.spark.ml.classification.ClassificationModel

predict, setRawPredictionCol

Methods inherited from class org.apache.spark.ml.PredictionModel

featuresDataType, setFeaturesCol, setPredictionCol, transformSchema

Methods inherited from class org.apache.spark.ml.Model

hasParent, parent, setParent

Methods inherited from class org.apache.spark.ml.Transformer

transform, transform, transform

Methods inherited from class org.apache.spark.ml.PipelineStage

transformSchema

Methods inherited from class java.lang.Object

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

Methods inherited from interface org.apache.spark.ml.param.Params

clear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, paramMap, params, set, set, set, setDefault, setDefault, shouldOwn, validateParams

Methods inherited from interface org.apache.spark.Logging

initializeIfNecessary, initializeLogging, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning

Method Detail

fromOld

public static RandomForestClassificationModel fromOld(RandomForestModel oldModel,
                                      RandomForestClassifier parent,
                                      scala.collection.immutable.Map<java.lang.Object,java.lang.Object> categoricalFeatures,
                                      int numClasses,
                                      int numFeatures)

(private[ml]) Convert a model from the old API

uid

public java.lang.String uid()

Description copied from interface: Identifiable

An immutable unique ID for the object and its derivatives.

Specified by:

uid in interface Identifiable

Returns:

(undocumented)

numFeatures

public int numFeatures()

Description copied from class: PredictionModel

Returns the number of features the model was trained on. If unknown, returns -1

Overrides:

numFeatures in class PredictionModel<Vector,RandomForestClassificationModel>

numClasses

public int numClasses()

Description copied from class: ClassificationModel

Number of classes (values which the label can take).

Specified by:

numClasses in class ClassificationModel<Vector,RandomForestClassificationModel>

trees

public org.apache.spark.ml.tree.DecisionTreeModel[] trees()

treeWeights

public double[] treeWeights()

transformImpl

protected DataFrame transformImpl(DataFrame dataset)

Overrides:

transformImpl in class PredictionModel<Vector,RandomForestClassificationModel>

predictRaw

protected Vector predictRaw(Vector features)

Description copied from class: ClassificationModel

Raw prediction for each possible label. The meaning of a "raw" prediction may vary between algorithms, but it intuitively gives a measure of confidence in each possible label (where larger = more confident). This internal method is used to implement transform() and output rawPredictionCol.

Specified by:

predictRaw in class ClassificationModel<Vector,RandomForestClassificationModel>

Parameters:

features - (undocumented)

Returns:

vector where element i is the raw prediction for label i. This raw prediction may be any real number, where a larger value indicates greater confidence for that label.

raw2probabilityInPlace

protected Vector raw2probabilityInPlace(Vector rawPrediction)

Description copied from class: ProbabilisticClassificationModel

Estimate the probability of each class given the raw prediction, doing the computation in-place. These predictions are also called class conditional probabilities.

This internal method is used to implement transform() and output probabilityCol.

Specified by:

raw2probabilityInPlace in class ProbabilisticClassificationModel<Vector,RandomForestClassificationModel>

Parameters:

rawPrediction - (undocumented)

Returns:

Estimated class conditional probabilities (modified input vector)

copy

public RandomForestClassificationModel copy(ParamMap extra)

Description copied from interface: Params

Creates a copy of this instance with the same UID and some extra params. Subclasses should implement this method and set the return type properly.

Specified by:

copy in interface Params

Specified by:

copy in class Model<RandomForestClassificationModel>

Parameters:

extra - (undocumented)

Returns:

(undocumented)

See Also:

defaultCopy()

toString

public java.lang.String toString()

Specified by:

toString in interface Identifiable

Overrides:

toString in class java.lang.Object

featureImportances

public Vector featureImportances()

Estimate of the importance of each feature.

This generalizes the idea of "Gini" importance to other losses, following the explanation of Gini importance from "Random Forests" documentation by Leo Breiman and Adele Cutler, and following the implementation from scikit-learn.

This feature importance is calculated as follows: - Average over trees: - importance(feature j) = sum (over nodes which split on feature j) of the gain, where gain is scaled by the number of instances passing through node - Normalize importances for tree based on total number of training instances used to build tree. - Normalize feature importance vector to sum to 1.

Returns:

(undocumented)

validateAndTransformSchema

public StructType validateAndTransformSchema(StructType schema,
                                    boolean fitting,
                                    DataType featuresDataType)

rawPredictionCol

public Param<java.lang.String> rawPredictionCol()

Param for raw prediction (a.k.a. confidence) column name.

Returns:

(undocumented)

getRawPredictionCol

public java.lang.String getRawPredictionCol()

validateAndTransformSchema

public StructType validateAndTransformSchema(StructType schema,
                                    boolean fitting,
                                    DataType featuresDataType)

Validates and transforms the input schema with the provided param map.

Parameters:

schema - input schema

fitting - whether this is in fitting

featuresDataType - SQL DataType for FeaturesType. E.g., VectorUDT for vector features.

Returns:

output schema

labelCol

public Param<java.lang.String> labelCol()

Param for label column name.

Returns:

(undocumented)

getLabelCol

public java.lang.String getLabelCol()

featuresCol

public Param<java.lang.String> featuresCol()

Param for features column name.

Returns:

(undocumented)

getFeaturesCol

public java.lang.String getFeaturesCol()

predictionCol

public Param<java.lang.String> predictionCol()

Param for prediction column name.

Returns:

(undocumented)

getPredictionCol

public java.lang.String getPredictionCol()