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, GBTClassificationModel, 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

Modifier and Type	Method and Description
static Params	clear(Param<?> param)
abstract static M	copy(ParamMap extra)
static String	explainParam(Param<?> param)
static String	explainParams()
static ParamMap	extractParamMap()
static ParamMap	extractParamMap(ParamMap extra)
static Param<String>	featuresCol()
Param<String>	featuresCol() Param for features column name.
static <T> scala.Option<T>	get(Param<T> param)
static <T> scala.Option<T>	getDefault(Param<T> param)
static String	getFeaturesCol()
String	getFeaturesCol()
static String	getLabelCol()
String	getLabelCol()
static <T> T	getOrDefault(Param<T> param)
static Param<Object>	getParam(String paramName)
static String	getPredictionCol()
String	getPredictionCol()
static String	getProbabilityCol()
static String	getRawPredictionCol()
String	getRawPredictionCol()
static double[]	getThresholds()
static <T> boolean	hasDefault(Param<T> param)
static boolean	hasParam(String paramName)
static boolean	hasParent()
static boolean	isDefined(Param<?> param)
static boolean	isSet(Param<?> param)
static Param<String>	labelCol()
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.
abstract static int	numClasses()
static int	numFeatures()
static Param<?>[]	params()
static void	parent_$eq(Estimator<M> x$1)
static Estimator<M>	parent()
static Param<String>	predictionCol()
Param<String>	predictionCol() Param for prediction column name.
static Param<String>	probabilityCol()
static Param<String>	rawPredictionCol()
Param<String>	rawPredictionCol() Param for raw prediction (a.k.a.
static <T> Params	set(Param<T> param, T value)
static M	setFeaturesCol(String value)
static M	setParent(Estimator<M> parent)
static M	setPredictionCol(String value)
M	setProbabilityCol(String value)
static M	setRawPredictionCol(String value)
M	setThresholds(double[] value)
static DoubleArrayParam	thresholds()
static String	toString()
Dataset<Row>	transform(Dataset<?> 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.
static StructType	transformSchema(StructType schema)
abstract static String	uid()
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

Methods inherited from interface org.apache.spark.ml.util.Identifiable

toString, uid

Methods inherited from interface org.apache.spark.internal.Logging

initializeLogging, initializeLogIfNecessary, 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 nonnegative. 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)

uid

public abstract static String uid()

toString

public static String toString()

params

public static Param<?>[] params()

explainParam

public static String explainParam(Param<?> param)

explainParams

public static String explainParams()

isSet

public static final boolean isSet(Param<?> param)

isDefined

public static final boolean isDefined(Param<?> param)

hasParam

public static boolean hasParam(String paramName)

getParam

public static Param<Object> getParam(String paramName)

set

public static final <T> Params set(Param<T> param,
                                   T value)

get

public static final <T> scala.Option<T> get(Param<T> param)

clear

public static final Params clear(Param<?> param)

getOrDefault

public static final <T> T getOrDefault(Param<T> param)

getDefault

public static final <T> scala.Option<T> getDefault(Param<T> param)

hasDefault

public static final <T> boolean hasDefault(Param<T> param)

extractParamMap

public static final ParamMap extractParamMap(ParamMap extra)

extractParamMap

public static final ParamMap extractParamMap()

parent

public static Estimator<M> parent()

parent_$eq

public static void parent_$eq(Estimator<M> x$1)

setParent

public static M setParent(Estimator<M> parent)

hasParent

public static boolean hasParent()

copy

public abstract static M copy(ParamMap extra)

labelCol

public static final Param<String> labelCol()

getLabelCol

public static final String getLabelCol()

featuresCol

public static final Param<String> featuresCol()

getFeaturesCol

public static final String getFeaturesCol()

predictionCol

public static final Param<String> predictionCol()

getPredictionCol

public static final String getPredictionCol()

setFeaturesCol

public static M setFeaturesCol(String value)

setPredictionCol

public static M setPredictionCol(String value)

numFeatures

public static int numFeatures()

transformSchema

public static StructType transformSchema(StructType schema)

rawPredictionCol

public static final Param<String> rawPredictionCol()

getRawPredictionCol

public static final String getRawPredictionCol()

setRawPredictionCol

public static M setRawPredictionCol(String value)

numClasses

public abstract static int numClasses()

probabilityCol

public static final Param<String> probabilityCol()

getProbabilityCol

public static final String getProbabilityCol()

thresholds

public static final DoubleArrayParam thresholds()

getThresholds

public static double[] getThresholds()

setProbabilityCol

public M setProbabilityCol(String value)

setThresholds

public M setThresholds(double[] value)

transform

public Dataset<Row> transform(Dataset<?> 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()