public class VectorIndexerModel extends Model<VectorIndexerModel> implements VectorIndexerParams, MLWritable
VectorIndexer
. Transform categorical features to use 0-based indices
instead of their original values.
- Categorical features are mapped to indices.
- Continuous features (columns) are left unchanged.
This also appends metadata to the output column, marking features as Numeric (continuous),
Nominal (categorical), or Binary (either continuous or categorical).
Non-ML metadata is not carried over from the input to the output column.
This maintains vector sparsity.
param: numFeatures Number of features, i.e., length of Vectors which this transforms param: categoryMaps Feature value index. Keys are categorical feature indices (column indices). Values are maps from original features values to 0-based category indices. If a feature is not in this map, it is treated as continuous.
Modifier and Type | Method and Description |
---|---|
scala.collection.immutable.Map<Object,scala.collection.immutable.Map<Object,Object>> |
categoryMaps() |
VectorIndexerModel |
copy(ParamMap extra)
Creates a copy of this instance with the same UID and some extra params.
|
Param<String> |
handleInvalid()
Param for how to handle invalid data (unseen labels or NULL values).
|
Param<String> |
inputCol()
Param for input column name.
|
java.util.Map<Integer,java.util.Map<Double,Integer>> |
javaCategoryMaps()
Java-friendly version of
categoryMaps |
static VectorIndexerModel |
load(String path) |
IntParam |
maxCategories()
Threshold for the number of values a categorical feature can take.
|
int |
numFeatures() |
Param<String> |
outputCol()
Param for output column name.
|
static MLReader<VectorIndexerModel> |
read() |
VectorIndexerModel |
setInputCol(String value) |
VectorIndexerModel |
setOutputCol(String value) |
String |
toString() |
Dataset<Row> |
transform(Dataset<?> dataset)
Transforms the input dataset.
|
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
getMaxCategories
getInputCol
getOutputCol
getHandleInvalid
clear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, 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 MLReader<VectorIndexerModel> read()
public static VectorIndexerModel load(String path)
public Param<String> handleInvalid()
VectorIndexerParams
handleInvalid
in interface VectorIndexerParams
handleInvalid
in interface HasHandleInvalid
public IntParam maxCategories()
VectorIndexerParams
(default = 20)
maxCategories
in interface VectorIndexerParams
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 int numFeatures()
public scala.collection.immutable.Map<Object,scala.collection.immutable.Map<Object,Object>> categoryMaps()
public java.util.Map<Integer,java.util.Map<Double,Integer>> javaCategoryMaps()
categoryMaps
public VectorIndexerModel setInputCol(String value)
public VectorIndexerModel setOutputCol(String value)
public Dataset<Row> transform(Dataset<?> dataset)
Transformer
transform
in class Transformer
dataset
- (undocumented)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 VectorIndexerModel copy(ParamMap extra)
Params
defaultCopy()
.copy
in interface Params
copy
in class Model<VectorIndexerModel>
extra
- (undocumented)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