org.apache.spark.ml.feature

Class ImputerModel

Object

org.apache.spark.ml.PipelineStage

org.apache.spark.ml.Transformer

org.apache.spark.ml.Model<ImputerModel>

org.apache.spark.ml.feature.ImputerModel

All Implemented Interfaces:

java.io.Serializable, org.apache.spark.internal.Logging, ImputerParams, Params, HasInputCol, HasInputCols, HasOutputCol, HasOutputCols, HasRelativeError, Identifiable, MLWritable

public class ImputerModel
extends Model<ImputerModel>
implements ImputerParams, MLWritable

Model fitted by Imputer.

param: surrogateDF a DataFrame containing inputCols and their corresponding surrogates, which are used to replace the missing values in the input DataFrame.

See Also:

Serialized Form

Method Summary

Modifier and Type	Method and Description
ImputerModel	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
Param<String>	inputCol() Param for input column name.
StringArrayParam	inputCols() Param for input column names.
static ImputerModel	load(String path)
DoubleParam	missingValue() The placeholder for the missing values.
Param<String>	outputCol() Param for output column name.
StringArrayParam	outputCols() Param for output column names.
static MLReader<ImputerModel>	read()
DoubleParam	relativeError() Param for the relative target precision for the approximate quantile algorithm.
ImputerModel	setInputCol(String value)
ImputerModel	setInputCols(String[] value)
ImputerModel	setOutputCol(String value)
ImputerModel	setOutputCols(String[] value)
Param<String>	strategy() The imputation strategy.
Dataset<Row>	surrogateDF()
String	toString()
Dataset<Row>	transform(Dataset<?> dataset) Transforms the input dataset.
StructType	transformSchema(StructType schema) Check transform validity and derive the output schema from the input schema.
String	uid() An immutable unique ID for the object and its derivatives.
MLWriter	write() Returns an MLWriter instance for this ML instance.

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

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, wait, wait, wait

Methods inherited from interface org.apache.spark.ml.feature.ImputerParams

getInOutCols, getMissingValue, getStrategy, validateAndTransformSchema

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

getInputCol

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

getInputCols

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

getOutputCol

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

getOutputCols

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

getRelativeError

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

clear, 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.MLWritable

save

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

Method Detail

read

public static MLReader<ImputerModel> read()

load

public static ImputerModel load(String path)

strategy

public final Param<String> strategy()

Description copied from interface: ImputerParams

The imputation strategy. Currently only "mean" and "median" are supported. If "mean", then replace missing values using the mean value of the feature. If "median", then replace missing values using the approximate median value of the feature. If "mode", then replace missing using the most frequent value of the feature. Default: mean

Specified by:

strategy in interface ImputerParams

Returns:

(undocumented)

missingValue

public final DoubleParam missingValue()

Description copied from interface: ImputerParams

The placeholder for the missing values. All occurrences of missingValue will be imputed. Note that null values are always treated as missing. Default: Double.NaN

Specified by:

missingValue in interface ImputerParams

Returns:

(undocumented)

relativeError

public final DoubleParam relativeError()

Description copied from interface: HasRelativeError

Param for the relative target precision for the approximate quantile algorithm. Must be in the range [0, 1].

Specified by:

relativeError in interface HasRelativeError

Returns:

(undocumented)

outputCols

public final StringArrayParam outputCols()

Description copied from interface: HasOutputCols

Param for output column names.

Specified by:

outputCols in interface HasOutputCols

Returns:

(undocumented)

outputCol

public final Param<String> outputCol()

Description copied from interface: HasOutputCol

Param for output column name.

Specified by:

outputCol in interface HasOutputCol

Returns:

(undocumented)

inputCols

public final StringArrayParam inputCols()

Description copied from interface: HasInputCols

Param for input column names.

Specified by:

inputCols in interface HasInputCols

Returns:

(undocumented)

inputCol

public final Param<String> inputCol()

Description copied from interface: HasInputCol

Param for input column name.

Specified by:

inputCol in interface HasInputCol

Returns:

(undocumented)

uid

public String uid()

Description copied from interface: Identifiable

An immutable unique ID for the object and its derivatives.

Specified by:

uid in interface Identifiable

Returns:

(undocumented)

surrogateDF

public Dataset<Row> surrogateDF()

setInputCol

public ImputerModel setInputCol(String value)

setOutputCol

public ImputerModel setOutputCol(String value)

setInputCols

public ImputerModel setInputCols(String[] value)

setOutputCols

public ImputerModel setOutputCols(String[] value)

transform

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

Description copied from class: Transformer

Transforms the input dataset.

Specified by:

transform in class Transformer

Parameters:

dataset - (undocumented)

Returns:

(undocumented)

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.

Specified by:

transformSchema in class PipelineStage

Parameters:

schema - (undocumented)

Returns:

(undocumented)

copy

public ImputerModel copy(ParamMap extra)

Description copied from interface: Params

Creates a copy of this instance with the same UID and some extra params. Subclasses should implement this method and set the return type properly. See defaultCopy().

Specified by:

copy in interface Params

Specified by:

copy in class Model<ImputerModel>

Parameters:

extra - (undocumented)

Returns:

(undocumented)

write

public MLWriter write()

Description copied from interface: MLWritable

Returns an MLWriter instance for this ML instance.

Specified by:

write in interface MLWritable

Returns:

(undocumented)

toString

public String toString()

Specified by:

toString in interface Identifiable

Overrides:

toString in class Object