org.apache.spark.ml.classification

Class ProbabilisticClassificationModel<FeaturesType,M extends ProbabilisticClassificationModel<FeaturesType,M>>

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<FeaturesType,M>

All Implemented Interfaces:

java.io.Serializable, Logging, Params, Identifiable

Direct Known Subclasses:

DecisionTreeClassificationModel, LogisticRegressionModel, NaiveBayesModel, RandomForestClassificationModel

public abstract class ProbabilisticClassificationModel<FeaturesType,M extends ProbabilisticClassificationModel<FeaturesType,M>>
extends ClassificationModel<FeaturesType,M>

:: DeveloperApi ::

Model produced by a ProbabilisticClassifier. Classes are indexed {0, 1, ..., numClasses - 1}.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
ProbabilisticClassificationModel()

Method Summary

Methods

Modifier and Type	Method and Description
Param<String>	featuresCol() Param for features column name.
String	getFeaturesCol()
String	getLabelCol()
String	getPredictionCol()
String	getRawPredictionCol()
Param<String>	labelCol() Param for label column name.
static void	normalizeToProbabilitiesInPlace(DenseVector v) Normalize a vector of raw predictions to be a multinomial probability vector, in place.
Param<String>	predictionCol() Param for prediction column name.
Param<String>	rawPredictionCol() Param for raw prediction (a.k.a.
M	setProbabilityCol(String value)
M	setThresholds(double[] value)
DataFrame	transform(DataFrame dataset) Transforms dataset by reading from featuresCol, and appending new columns as specified by parameters: - predicted labels as predictionCol of type Double - raw predictions (confidences) as rawPredictionCol of type Vector - probability of each class as probabilityCol of type Vector.
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.ClassificationModel

numClasses, setRawPredictionCol

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

numFeatures, setFeaturesCol, setPredictionCol, transformSchema

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

copy, hasParent, parent, setParent

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

transform, transform, transform

Methods inherited from class Object

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

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

clear, copy, 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.ml.util.Identifiable

toString, uid

Methods inherited from interface org.apache.spark.Logging

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

Constructor Detail

ProbabilisticClassificationModel

public ProbabilisticClassificationModel()

Method Detail

normalizeToProbabilitiesInPlace

public static void normalizeToProbabilitiesInPlace(DenseVector v)

Normalize a vector of raw predictions to be a multinomial probability vector, in place.

The input raw predictions should be >= 0. The output vector sums to 1, unless the input vector is all-0 (in which case the output is all-0 too).

NOTE: This is NOT applicable to all models, only ones which effectively use class instance counts for raw predictions.

Parameters:

v - (undocumented)

setProbabilityCol

public M setProbabilityCol(String value)

setThresholds

public M setThresholds(double[] value)

transform

public DataFrame transform(DataFrame dataset)

Transforms dataset by reading from featuresCol, and appending new columns as specified by parameters: - predicted labels as predictionCol of type Double - raw predictions (confidences) as rawPredictionCol of type Vector - probability of each class as probabilityCol of type Vector.

Overrides:

transform in class ClassificationModel<FeaturesType,M extends ProbabilisticClassificationModel<FeaturesType,M>>

Parameters:

dataset - input dataset

Returns:

transformed dataset

validateAndTransformSchema

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

rawPredictionCol

public Param<String> rawPredictionCol()

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

Returns:

(undocumented)

getRawPredictionCol

public 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<String> labelCol()

Param for label column name.

Returns:

(undocumented)

getLabelCol

public String getLabelCol()

featuresCol

public Param<String> featuresCol()

Param for features column name.

Returns:

(undocumented)

getFeaturesCol

public String getFeaturesCol()

predictionCol

public Param<String> predictionCol()

Param for prediction column name.

Returns:

(undocumented)

getPredictionCol

public String getPredictionCol()