Package smile.feature.imputation

Class SimpleImputer

java.lang.Object

smile.feature.imputation.SimpleImputer

All Implemented Interfaces:

Serializable, Function<Tuple,Tuple>, Transform

public class SimpleImputer
extends Object
implements Transform

Simple algorithm replaces missing values with the constant value along each column.

See Also:

• Serialized Form

Constructor Summary

Constructors

Constructor	Description
SimpleImputer(Map<String,Object> values)	Constructor.

Method Summary

Modifier and Type	Method	Description
DataFrame	apply(DataFrame data)	Applies this transform to the given argument.
Tuple	apply(Tuple x)
static SimpleImputer	fit(DataFrame data, double lower, double upper, String... columns)	Fits the missing value imputation values.
static SimpleImputer	fit(DataFrame data, String... columns)	Fits the missing value imputation values.
static boolean	hasMissing(Tuple x)	Return true if the tuple x has missing values.
static double[][]	impute(double[][] data)	Impute the missing values with column averages.
String	toString()

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface java.util.function.Function

andThen, compose

Methods inherited from interface smile.data.transform.Transform

andThen, compose

Constructor Details

SimpleImputer

public SimpleImputer(Map<String,Object> values)

Constructor.

Parameters:

values - the map of column name to the constant value.

Method Details

hasMissing

public static boolean hasMissing(Tuple x)

Return true if the tuple x has missing values.

Parameters:

x - a tuple.

Returns:

true if the tuple x has missing values.

apply

public Tuple apply(Tuple x)

Specified by:

apply in interface Function<Tuple,Tuple>

apply

public DataFrame apply(DataFrame data)

Description copied from interface: Transform

Applies this transform to the given argument.

Specified by:

apply in interface Transform

Parameters:

data - the input data frame.

Returns:

the transformed data frame.

toString

public String toString()

Overrides:

toString in class Object

fit

public static SimpleImputer fit(DataFrame data,
 String... columns)

Fits the missing value imputation values. Impute all the numeric columns with median, boolean/nominal columns with mode, and text columns with empty string.

Parameters:

data - the training data.

columns - the columns to impute. If empty, impute all the applicable columns.

Returns:

the imputer.

fit

public static SimpleImputer fit(DataFrame data,
 double lower,
 double upper,
 String... columns)

Fits the missing value imputation values. Impute all the numeric columns with the mean of values in the range [lower, upper], boolean/nominal columns with mode, and text columns with empty string.

Parameters:

data - the training data.

lower - the lower limit in terms of percentiles of the original distribution (e.g. 5th percentile).

upper - the upper limit in terms of percentiles of the original distribution (e.g. 95th percentile).

columns - the columns to impute. If empty, impute all the applicable columns.

Returns:

the imputer.

impute

public static double[][] impute(double[][] data)

Impute the missing values with column averages.

Parameters:

data - data with missing values.

Returns:

the imputed data.

Throws:

IllegalArgumentException - when the whole row or column is missing.