org.apache.spark.ml.classification

Class ClassificationModel<FeaturesType,M extends ClassificationModel<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>

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, Params, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasRawPredictionCol, PredictorParams, Identifiable

Direct Known Subclasses:

LinearSVCModel, ProbabilisticClassificationModel

public abstract class ClassificationModel<FeaturesType,M extends ClassificationModel<FeaturesType,M>>
extends PredictionModel<FeaturesType,M>
implements ClassifierParams

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

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
ClassificationModel()

Method Summary

Modifier and Type	Method and Description
abstract int	numClasses() Number of classes (values which the label can take).
double	predict(FeaturesType features) Predict label for the given features.
abstract Vector	predictRaw(FeaturesType features) Raw prediction for each possible label.
Param<String>	rawPredictionCol() Param for raw prediction (a.k.a.
M	setRawPredictionCol(String value)
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.
Dataset<Row>	transformImpl(Dataset<?> dataset)
StructType	transformSchema(StructType schema) Check transform validity and derive the output schema from the input schema.

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.ClassifierParams

extractInstances, validateAndTransformSchema

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

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

ClassificationModel

public ClassificationModel()

Method Detail

numClasses

public abstract int numClasses()

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

predict

public double predict(FeaturesType features)

Predict label for the given features. This method is used to implement transform() and output predictionCol.

This default implementation for classification predicts the index of the maximum value from predictRaw().

Specified by:

predict in class PredictionModel<FeaturesType,M extends ClassificationModel<FeaturesType,M>>

Parameters:

features - (undocumented)

Returns:

(undocumented)

predictRaw

public abstract Vector predictRaw(FeaturesType features)

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.

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.

rawPredictionCol

public final Param<String> rawPredictionCol()

Description copied from interface: HasRawPredictionCol

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

Specified by:

rawPredictionCol in interface HasRawPredictionCol

Returns:

(undocumented)

setRawPredictionCol

public M setRawPredictionCol(String 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.

Overrides:

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

Parameters:

dataset - input dataset

Returns:

transformed dataset

transformImpl

public final Dataset<Row> transformImpl(Dataset<?> dataset)

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 PredictionModel<FeaturesType,M extends ClassificationModel<FeaturesType,M>>

Parameters:

schema - (undocumented)

Returns:

(undocumented)