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>

Type Parameters:

FeaturesType - Type of input features. E.g., Vector

M - Concrete Model type

All Implemented Interfaces:

java.io.Serializable, org.apache.spark.internal.Logging, ClassifierParams, ProbabilisticClassifierParams, Params, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasProbabilityCol, HasRawPredictionCol, HasThresholds, PredictorParams, Identifiable

Direct Known Subclasses:

DecisionTreeClassificationModel, FMClassificationModel, GBTClassificationModel, LogisticRegressionModel, MultilayerPerceptronClassificationModel, NaiveBayesModel, RandomForestClassificationModel

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

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 void	normalizeToProbabilitiesInPlace(DenseVector v) Normalize a vector of raw predictions to be a multinomial probability vector, in place.
Vector	predictProbability(FeaturesType features) Predict the probability of each class given the features.
Param<String>	probabilityCol() Param for Column name for predicted class conditional probabilities.
M	setProbabilityCol(String value)
M	setThresholds(double[] value)
DoubleArrayParam	thresholds() Param for Thresholds in multi-class classification to adjust the probability of predicting each class.
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.
StructType	transformSchema(StructType schema) Check transform validity and derive the output schema from the input schema.

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

numClasses, predict, predictRaw, rawPredictionCol, setRawPredictionCol, transformImpl

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

featuresCol, labelCol, numFeatures, predictionCol, setFeaturesCol, setPredictionCol

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 org.apache.spark.ml.PipelineStage

params

Methods inherited from class Object

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

Methods inherited from interface org.apache.spark.ml.classification.ProbabilisticClassifierParams

validateAndTransformSchema

Methods inherited from interface org.apache.spark.ml.classification.ClassifierParams

extractInstances

Methods inherited from interface org.apache.spark.ml.PredictorParams

extractInstances, extractInstances

Methods inherited from interface org.apache.spark.ml.param.shared.HasLabelCol

getLabelCol, labelCol

Methods inherited from interface org.apache.spark.ml.param.shared.HasFeaturesCol

featuresCol, getFeaturesCol

Methods inherited from interface org.apache.spark.ml.param.shared.HasPredictionCol

getPredictionCol, predictionCol

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.ml.param.shared.HasRawPredictionCol

getRawPredictionCol, rawPredictionCol

Methods inherited from interface org.apache.spark.ml.param.shared.HasProbabilityCol

getProbabilityCol

Methods inherited from interface org.apache.spark.ml.param.shared.HasThresholds

getThresholds

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

$init$, initializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, initLock, isTraceEnabled, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning, org$apache$spark$internal$Logging$$log__$eq, org$apache$spark$internal$Logging$$log_, uninitialize

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.

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

Parameters:

v - (undocumented)

Throws:

IllegalArgumentException - if the input vector is all-0 or including negative values

thresholds

public DoubleArrayParam thresholds()

Description copied from interface: HasThresholds

Param for Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values &gt; 0 excepting that at most one value may be 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class's threshold.

Specified by:

thresholds in interface HasThresholds

Returns:

(undocumented)

probabilityCol

public final Param<String> probabilityCol()

Description copied from interface: HasProbabilityCol

Param for Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities.

Specified by:

probabilityCol in interface HasProbabilityCol

Returns:

(undocumented)

setProbabilityCol

public M setProbabilityCol(String value)

setThresholds

public M setThresholds(double[] value)

transformSchema

public StructType transformSchema(StructType schema)

Description copied from class: PipelineStage

Check transform validity and derive the output schema from the input schema.

We check validity for interactions between parameters during transformSchema and raise an exception if any parameter value is invalid. Parameter value checks which do not depend on other parameters are handled by Param.validate().

Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.

Overrides:

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

Parameters:

schema - (undocumented)

Returns:

(undocumented)

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

predictProbability

public Vector predictProbability(FeaturesType features)

Predict the probability of each class given the features. These predictions are also called class conditional probabilities.

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

Parameters:

features - (undocumented)

Returns:

Estimated class conditional probabilities