BisectingKMeansModel (Spark 2.2.1 JavaDoc)

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

All Implemented Interfaces:

java.io.Serializable, Logging, Params, Identifiable, MLWritable
```
public class BisectingKMeansModel
extends Model<BisectingKMeansModel>
implements MLWritable
```
Model fitted by BisectingKMeans.
param: parentModel a model trained by BisectingKMeans.

See Also:

Serialized Form

Method Summary

All Methods Static Methods Instance Methods Abstract Methods Concrete Methods
Modifier and Type	Method and Description
`static Params`	`clear(Param<?> param)`
`Vector[]`	`clusterCenters()`
`double`	`computeCost(Dataset<?> dataset)` Computes the sum of squared distances between the input points and their corresponding cluster centers.
`BisectingKMeansModel`	`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 int`	`getK()`
`int`	`getK()`
`static int`	`getMaxIter()`
`static double`	`getMinDivisibleClusterSize()`
`double`	`getMinDivisibleClusterSize()`
`static <T> T`	`getOrDefault(Param<T> param)`
`static Param<Object>`	`getParam(String paramName)`
`static String`	`getPredictionCol()`
`static long`	`getSeed()`
`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 boolean`	`isDefined(Param<?> param)`
`static boolean`	`isSet(Param<?> param)`
`static IntParam`	`k()`
`IntParam`	`k()` The desired number of leaf clusters.
`static BisectingKMeansModel`	`load(String path)`
`static IntParam`	`maxIter()`
`static DoubleParam`	`minDivisibleClusterSize()`
`DoubleParam`	`minDivisibleClusterSize()` The minimum number of points (if greater than or equal to 1.0) or the minimum proportion of points (if less than 1.0) of a divisible cluster (default: 1.0).
`static Param<?>[]`	`params()`
`static void`	`parent_$eq(Estimator<M> x$1)`
`static Estimator<M>`	`parent()`
`static Param<String>`	`predictionCol()`
`static MLReader<BisectingKMeansModel>`	`read()`
`static void`	`save(String path)`
`static LongParam`	`seed()`
`static <T> Params`	`set(Param<T> param, T value)`
`BisectingKMeansModel`	`setFeaturesCol(String value)`
`static M`	`setParent(Estimator<M> parent)`
`BisectingKMeansModel`	`setPredictionCol(String value)`
`BisectingKMeansSummary`	`summary()` Gets summary of model on training set.
`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 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 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

Methods inherited from interface org.apache.spark.internal.Logging
initializeLogging, initializeLogIfNecessary, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning

- Method Detail
  - read
```
public static MLReader<BisectingKMeansModel> read()
```
  - load
```
public static BisectingKMeansModel 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()
```
  - k
```
public static final IntParam k()
```
  - getK
```
public static int getK()
```
  - minDivisibleClusterSize
```
public static final DoubleParam minDivisibleClusterSize()
```
  - getMinDivisibleClusterSize
```
public static double getMinDivisibleClusterSize()
```
  - 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 BisectingKMeansModel 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<BisectingKMeansModel>
    
    Parameters:
    
    extra - (undocumented)
    
    Returns:
    
    (undocumented)
  - setFeaturesCol
```
public BisectingKMeansModel setFeaturesCol(String value)
```
  - setPredictionCol
```
public BisectingKMeansModel 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)
```
    Computes the sum of squared distances between the input points and their corresponding cluster centers.
    
    Parameters:
    
    dataset - (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)
  - hasSummary
```
public boolean hasSummary()
```
    Return true if there exists summary of model.
    
    Returns:
    
    (undocumented)
  - summary
```
public BisectingKMeansSummary summary()
```
    Gets summary of model on training set. An exception is thrown if trainingSummary == None.
    
    Returns:
    
    (undocumented)
  - k
```
public IntParam k()
```
    The desired number of leaf clusters. Must be > 1. Default: 4. The actual number could be smaller if there are no divisible leaf clusters.
    
    Returns:
    
    (undocumented)
  - getK
```
public int getK()
```
  - minDivisibleClusterSize
```
public DoubleParam minDivisibleClusterSize()
```
    The minimum number of points (if greater than or equal to 1.0) or the minimum proportion of points (if less than 1.0) of a divisible cluster (default: 1.0).
    
    Returns:
    
    (undocumented)
  - getMinDivisibleClusterSize
```
public double getMinDivisibleClusterSize()
```
  - validateAndTransformSchema
```
public StructType validateAndTransformSchema(StructType schema)
```
    Validates and transforms the input schema.
    
    Parameters:
    
    schema - input schema
    
    Returns:
    
    output schema

Class BisectingKMeansModel

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

Methods inherited from interface org.apache.spark.internal.Logging

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

k

getK

minDivisibleClusterSize

getMinDivisibleClusterSize

save

uid

copy

setFeaturesCol

setPredictionCol

transform

transformSchema

clusterCenters

computeCost

write

hasSummary

summary

k

getK

minDivisibleClusterSize

getMinDivisibleClusterSize

validateAndTransformSchema