org.apache.spark.ml.feature

Class Imputer

Object

org.apache.spark.ml.PipelineStage

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

org.apache.spark.ml.feature.Imputer

All Implemented Interfaces:

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

public class Imputer
extends Estimator<ImputerModel>
implements ImputerParams, DefaultParamsWritable

Imputation estimator for completing missing values, using the mean, median or mode of the columns in which the missing values are located. The input columns should be of numeric type. Currently Imputer does not support categorical features (SPARK-15041) and possibly creates incorrect values for a categorical feature.

Note when an input column is integer, the imputed value is casted (truncated) to an integer type. For example, if the input column is IntegerType (1, 2, 4, null), the output will be IntegerType (1, 2, 4, 2) after mean imputation.

Note that the mean/median/mode value is computed after filtering out missing values. All Null values in the input columns are treated as missing, and so are also imputed. For computing median, DataFrameStatFunctions.approxQuantile is used with a relative error of 0.001.

See Also:

Serialized Form

Nested Class Summary

Nested classes/interfaces inherited from interface org.apache.spark.internal.Logging

org.apache.spark.internal.Logging.SparkShellLoggingFilter

Constructor Summary

Constructors

Constructor and Description
Imputer()
Imputer(String uid)

Method Summary

Modifier and Type	Method and Description
Imputer	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
ImputerModel	fit(Dataset<?> dataset) Fits a model to the input data.
Param<String>	inputCol() Param for input column name.
StringArrayParam	inputCols() Param for input column names.
static Imputer	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<T>	read()
DoubleParam	relativeError() Param for the relative target precision for the approximate quantile algorithm.
Imputer	setInputCol(String value)
Imputer	setInputCols(String[] value)
Imputer	setMissingValue(double value)
Imputer	setOutputCol(String value)
Imputer	setOutputCols(String[] value)
Imputer	setRelativeError(double value)
Imputer	setStrategy(String value) Imputation strategy.
Param<String>	strategy() The imputation strategy.
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.

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

fit, fit, fit, fit

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.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, onParamChange, paramMap, params, set, set, set, setDefault, setDefault, shouldOwn

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

toString

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

write

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

Constructor Detail

Imputer

public Imputer(String uid)

Imputer

public Imputer()

Method Detail

load

public static Imputer load(String path)

read

public static MLReader<T> read()

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)

setInputCol

public Imputer setInputCol(String value)

setOutputCol

public Imputer setOutputCol(String value)

setInputCols

public Imputer setInputCols(String[] value)

setOutputCols

public Imputer setOutputCols(String[] value)

setStrategy

public Imputer setStrategy(String value)

Imputation strategy. Available options are ["mean", "median", "mode"].

Parameters:

value - (undocumented)

Returns:

(undocumented)

setMissingValue

public Imputer setMissingValue(double value)

setRelativeError

public Imputer setRelativeError(double value)

fit

public ImputerModel fit(Dataset<?> dataset)

Description copied from class: Estimator

Fits a model to the input data.

Specified by:

fit in class Estimator<ImputerModel>

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 Imputer 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 Estimator<ImputerModel>

Parameters:

extra - (undocumented)

Returns:

(undocumented)