public class PCAModel extends Model<PCAModel> implements PCAParams, MLWritable
PCA
. Transforms vectors to a lower dimensional space.
param: pc A principal components Matrix. Each column is one principal component. param: explainedVariance A vector of proportions of variance explained by each principal component.
Modifier and Type | Method and Description |
---|---|
PCAModel |
copy(ParamMap extra)
Creates a copy of this instance with the same UID and some extra params.
|
DenseVector |
explainedVariance() |
Param<String> |
inputCol()
Param for input column name.
|
IntParam |
k()
The number of principal components.
|
static PCAModel |
load(String path) |
Param<String> |
outputCol()
Param for output column name.
|
DenseMatrix |
pc() |
static MLReader<PCAModel> |
read() |
PCAModel |
setInputCol(String value) |
PCAModel |
setOutputCol(String value) |
String |
toString() |
Dataset<Row> |
transform(Dataset<?> dataset)
Transform a vector by computed Principal Components.
|
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.
|
MLWriter |
write()
Returns an
MLWriter instance for this ML instance. |
transform, transform, transform
params
getK, validateAndTransformSchema
getInputCol
getOutputCol
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
save
$init$, 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
public static PCAModel load(String path)
public final IntParam k()
PCAParams
public final Param<String> outputCol()
HasOutputCol
outputCol
in interface HasOutputCol
public final Param<String> inputCol()
HasInputCol
inputCol
in interface HasInputCol
public String uid()
Identifiable
uid
in interface Identifiable
public DenseMatrix pc()
public DenseVector explainedVariance()
public PCAModel setInputCol(String value)
public PCAModel setOutputCol(String value)
public Dataset<Row> transform(Dataset<?> dataset)
transform
in class Transformer
dataset
- (undocumented)PCA.fit()
.public StructType transformSchema(StructType schema)
PipelineStage
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 PipelineStage
schema
- (undocumented)public PCAModel copy(ParamMap extra)
Params
defaultCopy()
.public MLWriter write()
MLWritable
MLWriter
instance for this ML instance.write
in interface MLWritable
public String toString()
toString
in interface Identifiable
toString
in class Object