public class KMeansModel extends Model<KMeansModel> implements KMeansParams, GeneralMLWritable
param: parentModel a model trained by spark.mllib.clustering.KMeans.
| Modifier and Type | Method and Description |
|---|---|
Vector[] |
clusterCenters() |
double |
computeCost(Dataset<?> dataset)
Deprecated.
This method is deprecated and will be removed in 3.0.0. Use ClusteringEvaluator
instead. You can also get the cost on the training dataset in the summary.
|
KMeansModel |
copy(ParamMap extra)
Creates a copy of this instance with the same UID and some extra params.
|
boolean |
hasSummary()
Return true if there exists summary of model.
|
static KMeansModel |
load(String path) |
static MLReader<KMeansModel> |
read() |
KMeansModel |
setFeaturesCol(String value) |
KMeansModel |
setPredictionCol(String value) |
KMeansSummary |
summary()
Gets summary of model on training set.
|
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.
|
GeneralMLWriter |
write()
Returns a
GeneralMLWriter instance for this ML instance. |
transform, transform, transformequals, getClass, hashCode, notify, notifyAll, toString, wait, wait, waitgetInitMode, getInitSteps, getK, initMode, initSteps, k, validateAndTransformSchemagetMaxIter, maxIterfeaturesCol, getFeaturesColgetPredictionCol, predictionColdistanceMeasure, getDistanceMeasureclear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, paramMap, params, set, set, set, setDefault, setDefault, shouldOwntoStringsaveinitializeLogging, initializeLogIfNecessary, initializeLogIfNecessary, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarningpublic static MLReader<KMeansModel> read()
public static KMeansModel load(String path)
public String uid()
Identifiableuid in interface Identifiablepublic KMeansModel copy(ParamMap extra)
ParamsdefaultCopy().copy in interface Paramscopy in class Model<KMeansModel>extra - (undocumented)public KMeansModel setFeaturesCol(String value)
public KMeansModel setPredictionCol(String value)
public Dataset<Row> transform(Dataset<?> dataset)
Transformertransform in class Transformerdataset - (undocumented)public StructType transformSchema(StructType schema)
PipelineStageCheck 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.
transformSchema in class PipelineStageschema - (undocumented)public Vector[] clusterCenters()
public double computeCost(Dataset<?> dataset)
dataset - (undocumented)public GeneralMLWriter write()
GeneralMLWriter 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.
write in interface GeneralMLWritablewrite in interface MLWritablepublic boolean hasSummary()
public KMeansSummary summary()
trainingSummary == None.