org.apache.spark.ml

Class PredictionModel<FeaturesType,M extends PredictionModel<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>

Type Parameters:

FeaturesType - Type of features. E.g., VectorUDT for vector features.

M - Specialization of PredictionModel. If you subclass this type, use this type parameter to specify the concrete type for the corresponding model.

All Implemented Interfaces:

java.io.Serializable, Logging, Params, Identifiable

Direct Known Subclasses:

ClassificationModel, DecisionTreeRegressionModel, GBTRegressionModel, RandomForestRegressionModel, RegressionModel

public abstract class PredictionModel<FeaturesType,M extends PredictionModel<FeaturesType,M>>
extends Model<M>

:: DeveloperApi :: Abstraction for a model for prediction tasks (regression and classification).

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
PredictionModel()

Method Summary

Modifier and Type	Method and Description
int	numFeatures() Returns the number of features the model was trained on.
abstract double	predict(FeaturesType features) Predict label for the given features.
M	setFeaturesCol(String value)
M	setPredictionCol(String value)
Dataset<Row>	transform(Dataset<?> dataset) Transforms dataset by reading from featuresCol, calling predict, and storing the predictions as a new column predictionCol.
StructType	transformSchema(StructType schema) :: DeveloperApi ::
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.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.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.internal.Logging

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

Constructor Detail

PredictionModel

public PredictionModel()

Method Detail

numFeatures

public int numFeatures()

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

predict

public abstract double predict(FeaturesType features)

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

Parameters:

features - (undocumented)

Returns:

(undocumented)

setFeaturesCol

public M setFeaturesCol(String value)

setPredictionCol

public M setPredictionCol(String value)

transform

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

Transforms dataset by reading from featuresCol, calling predict, and storing the predictions as a new column predictionCol.

Specified by:

transform in class Transformer

Parameters:

dataset - input dataset

Returns:

transformed dataset with predictionCol of type Double

transformSchema

public StructType transformSchema(StructType schema)

Description copied from class: PipelineStage

:: DeveloperApi ::

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.

Specified by:

transformSchema in class PipelineStage

Parameters:

schema - (undocumented)

Returns:

(undocumented)

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