KMeansModel (Spark 2.1.1 JavaDoc)

Object
- org.apache.spark.ml.PipelineStage
- - org.apache.spark.ml.Transformer
  - - org.apache.spark.ml.Model<KMeansModel>
    - - org.apache.spark.ml.clustering.KMeansModel

All Implemented Interfaces:

java.io.Serializable, Params, Identifiable, MLWritable
```
public class KMeansModel
extends Model<KMeansModel>
implements MLWritable
```
Model fitted by KMeans.
param: parentModel a model trained by spark.mllib.clustering.KMeans.

See Also:
Serialized Form

Method Summary

Methods
Modifier and Type	Method and Description
`static Params`	`clear(Param<?> param)`
`Vector[]`	`clusterCenters()`
`double`	`computeCost(Dataset<?> dataset)` Return the K-means cost (sum of squared distances of points to their nearest center) for this model on the given data.
`KMeansModel`	`copy(ParamMap extra)` Creates a copy of this instance with the same UID and some extra params.
`static String`	`explainParam(Param<?> param)`
`static String`	`explainParams()`
`static ParamMap`	`extractParamMap()`
`static ParamMap`	`extractParamMap(ParamMap extra)`
`static Param<String>`	`featuresCol()`
`static <T> scala.Option<T>`	`get(Param<T> param)`
`static <T> scala.Option<T>`	`getDefault(Param<T> param)`
`static String`	`getFeaturesCol()`
`static String`	`getInitMode()`
`String`	`getInitMode()`
`static int`	`getInitSteps()`
`int`	`getInitSteps()`
`static int`	`getK()`
`int`	`getK()`
`static int`	`getMaxIter()`
`static <T> T`	`getOrDefault(Param<T> param)`
`static Param<Object>`	`getParam(String paramName)`
`static String`	`getPredictionCol()`
`static long`	`getSeed()`
`static double`	`getTol()`
`static <T> boolean`	`hasDefault(Param<T> param)`
`static boolean`	`hasParam(String paramName)`
`static boolean`	`hasParent()`
`boolean`	`hasSummary()` Return true if there exists summary of model.
`static Param<String>`	`initMode()`
`Param<String>`	`initMode()` Param for the initialization algorithm.
`static IntParam`	`initSteps()`
`IntParam`	`initSteps()` Param for the number of steps for the k-means\|\| initialization mode.
`static boolean`	`isDefined(Param<?> param)`
`static boolean`	`isSet(Param<?> param)`
`static IntParam`	`k()`
`IntParam`	`k()` The number of clusters to create (k).
`static KMeansModel`	`load(String path)`
`static IntParam`	`maxIter()`
`static Param<?>[]`	`params()`
`static void`	`parent_$eq(Estimator<M> x$1)`
`static Estimator<M>`	`parent()`
`static Param<String>`	`predictionCol()`
`static MLReader<KMeansModel>`	`read()`
`static void`	`save(String path)`
`static LongParam`	`seed()`
`static <T> Params`	`set(Param<T> param, T value)`
`KMeansModel`	`setFeaturesCol(String value)`
`static M`	`setParent(Estimator<M> parent)`
`KMeansModel`	`setPredictionCol(String value)`
`KMeansSummary`	`summary()` Gets summary of model on training set.
`static DoubleParam`	`tol()`
`static String`	`toString()`
`Dataset<Row>`	`transform(Dataset<?> dataset)` Transforms the input dataset.
`StructType`	`transformSchema(StructType schema)` :: DeveloperApi ::
`String`	`uid()` An immutable unique ID for the object and its derivatives.
`StructType`	`validateAndTransformSchema(StructType schema)` Validates and transforms the input schema.
`MLWriter`	`write()` Returns a `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 Object
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

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.Identifiable
toString

Methods inherited from interface org.apache.spark.ml.util.MLWritable
save

- Method Detail
  - read
```
public static MLReader<KMeansModel> read()
```
  - load
```
public static KMeansModel load(String path)
```
  - toString
```
public static String toString()
```
  - params
```
public static Param<?>[] params()
```
  - explainParam
```
public static String explainParam(Param<?> param)
```
  - explainParams
```
public static String explainParams()
```
  - isSet
```
public static final boolean isSet(Param<?> param)
```
  - isDefined
```
public static final boolean isDefined(Param<?> param)
```
  - hasParam
```
public static boolean hasParam(String paramName)
```
  - getParam
```
public static Param<Object> getParam(String paramName)
```
  - set
```
public static final <T> Params set(Param<T> param,
             T value)
```
  - get
```
public static final <T> scala.Option<T> get(Param<T> param)
```
  - clear
```
public static final Params clear(Param<?> param)
```
  - getOrDefault
```
public static final <T> T getOrDefault(Param<T> param)
```
  - getDefault
```
public static final <T> scala.Option<T> getDefault(Param<T> param)
```
  - hasDefault
```
public static final <T> boolean hasDefault(Param<T> param)
```
  - extractParamMap
```
public static final ParamMap extractParamMap(ParamMap extra)
```
  - extractParamMap
```
public static final ParamMap extractParamMap()
```
  - parent
```
public static Estimator<M> parent()
```
  - parent_$eq
```
public static void parent_$eq(Estimator<M> x$1)
```
  - setParent
```
public static M setParent(Estimator<M> parent)
```
  - hasParent
```
public static boolean hasParent()
```
  - maxIter
```
public static final IntParam maxIter()
```
  - getMaxIter
```
public static final int getMaxIter()
```
  - featuresCol
```
public static final Param<String> featuresCol()
```
  - getFeaturesCol
```
public static final String getFeaturesCol()
```
  - seed
```
public static final LongParam seed()
```
  - getSeed
```
public static final long getSeed()
```
  - predictionCol
```
public static final Param<String> predictionCol()
```
  - getPredictionCol
```
public static final String getPredictionCol()
```
  - tol
```
public static final DoubleParam tol()
```
  - getTol
```
public static final double getTol()
```
  - k
```
public static final IntParam k()
```
  - getK
```
public static int getK()
```
  - initMode
```
public static final Param<String> initMode()
```
  - getInitMode
```
public static String getInitMode()
```
  - initSteps
```
public static final IntParam initSteps()
```
  - getInitSteps
```
public static int getInitSteps()
```
  - save
```
public static void save(String path)
                 throws java.io.IOException
```
    Throws:
    
    java.io.IOException
  - 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)
  - copy
```
public KMeansModel 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<KMeansModel>
    
    Parameters:
    extra - (undocumented)
    
    Returns:
    (undocumented)
  - setFeaturesCol
```
public KMeansModel setFeaturesCol(String value)
```
  - setPredictionCol
```
public KMeansModel setPredictionCol(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
    
    :: 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)
  - clusterCenters
```
public Vector[] clusterCenters()
```
  - computeCost
```
public double computeCost(Dataset<?> dataset)
```
    Return the K-means cost (sum of squared distances of points to their nearest center) for this model on the given data.
    
    Parameters:
    dataset - (undocumented)
    
    Returns:
    (undocumented)
  - write
```
public MLWriter write()
```
    Returns a MLWriter instance for this ML instance.
    For KMeansModel, this does NOT currently save the training summary. An option to save summary may be added in the future.
    
    Specified by:
    
    write in interface MLWritable
    
    Returns:
    (undocumented)
  - hasSummary
```
public boolean hasSummary()
```
    Return true if there exists summary of model.
    
    Returns:
    (undocumented)
  - summary
```
public KMeansSummary summary()
```
    Gets summary of model on training set. An exception is thrown if trainingSummary == None.
    
    Returns:
    (undocumented)
  - k
```
public IntParam k()
```
    The number of clusters to create (k). Must be > 1. Note that it is possible for fewer than k clusters to be returned, for example, if there are fewer than k distinct points to cluster. Default: 2.
    
    Returns:
    (undocumented)
  - getK
```
public int getK()
```
  - initMode
```
public Param<String> initMode()
```
    Param for the initialization algorithm. This can be either "random" to choose random points as initial cluster centers, or "k-means||" to use a parallel variant of k-means++ (Bahmani et al., Scalable K-Means++, VLDB 2012). Default: k-means||.
    
    Returns:
    (undocumented)
  - getInitMode
```
public String getInitMode()
```
  - initSteps
```
public IntParam initSteps()
```
    Param for the number of steps for the k-means|| initialization mode. This is an advanced setting -- the default of 2 is almost always enough. Must be > 0. Default: 2.
    
    Returns:
    (undocumented)
  - getInitSteps
```
public int getInitSteps()
```
  - validateAndTransformSchema
```
public StructType validateAndTransformSchema(StructType schema)
```
    Validates and transforms the input schema.
    
    Parameters:
    schema - input schema
    
    Returns:
    output schema

Class KMeansModel

Method Summary

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

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

Methods inherited from class Object

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

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

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

Method Detail

read

load

toString

params

explainParam

explainParams

isSet

isDefined

hasParam

getParam

set

get

clear

getOrDefault

getDefault

hasDefault

extractParamMap

extractParamMap

parent

parent_$eq

setParent

hasParent

maxIter

getMaxIter

featuresCol

getFeaturesCol

seed

getSeed

predictionCol

getPredictionCol

tol

getTol

k

getK

initMode

getInitMode

initSteps

getInitSteps

save

uid

copy

setFeaturesCol

setPredictionCol

transform

transformSchema

clusterCenters

computeCost

write

hasSummary

summary

k

getK

initMode

getInitMode

initSteps

getInitSteps

validateAndTransformSchema