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.
|
java.util.Map<Integer,java.util.Map<Double,Integer>> |
javaCategoryMaps()
Java-friendly version of
categoryMaps |
static VectorIndexerModel |
load(String path) |
int |
numFeatures() |
static MLReader<VectorIndexerModel> |
read() |
VectorIndexerModel |
setInputCol(String value) |
VectorIndexerModel |
setOutputCol(String value) |
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.
|
MLWriter |
write()
Returns an
MLWriter instance for this ML instance. |
transform, transform, transformequals, getClass, hashCode, notify, notifyAll, toString, wait, wait, waitgetMaxCategories, handleInvalid, maxCategoriesgetInputCol, inputColgetOutputCol, outputColgetHandleInvalidclear, 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<VectorIndexerModel> read()
public static VectorIndexerModel load(String path)
public String uid()
Identifiableuid in interface Identifiablepublic 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()
categoryMapspublic VectorIndexerModel setInputCol(String value)
public VectorIndexerModel setOutputCol(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 VectorIndexerModel copy(ParamMap extra)
ParamsdefaultCopy().copy in interface Paramscopy in class Model<VectorIndexerModel>extra - (undocumented)public MLWriter write()
MLWritableMLWriter instance for this ML instance.write in interface MLWritable