public class StringIndexerModel extends Model<StringIndexerModel> implements StringIndexerBase, MLWritable
StringIndexer
.
param: labels Ordered list of labels, corresponding to indices to be assigned.
StringIndexerModel.transform
would return the input dataset unmodified.
This is a temporary fix for the case when target labels do not exist during prediction.Constructor and Description |
---|
StringIndexerModel(String[] labels) |
StringIndexerModel(String uid,
String[] labels) |
Modifier and Type | Method and Description |
---|---|
StringIndexerModel |
copy(ParamMap extra)
Creates a copy of this instance with the same UID and some extra params.
|
String[] |
labels() |
static StringIndexerModel |
load(String path) |
static MLReader<StringIndexerModel> |
read() |
StringIndexerModel |
setHandleInvalid(String value) |
StringIndexerModel |
setInputCol(String value) |
StringIndexerModel |
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.
|
org.apache.spark.ml.feature.StringIndexerModel.StringIndexModelWriter |
write()
Returns an
MLWriter instance for this ML instance. |
transform, transform, transform
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
getStringOrderType, handleInvalid, stringOrderType, validateAndTransformSchema
getHandleInvalid
getInputCol, inputCol
getOutputCol, outputCol
clear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, paramMap, params, set, set, set, setDefault, setDefault, shouldOwn
toString
save
initializeLogging, initializeLogIfNecessary, initializeLogIfNecessary, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning
public StringIndexerModel(String uid, String[] labels)
public StringIndexerModel(String[] labels)
public static MLReader<StringIndexerModel> read()
public static StringIndexerModel load(String path)
public String uid()
Identifiable
uid
in interface Identifiable
public String[] labels()
public StringIndexerModel setHandleInvalid(String value)
public StringIndexerModel setInputCol(String value)
public StringIndexerModel setOutputCol(String value)
public Dataset<Row> transform(Dataset<?> dataset)
Transformer
transform
in class Transformer
dataset
- (undocumented)public StructType transformSchema(StructType schema)
PipelineStage
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.
transformSchema
in class PipelineStage
schema
- (undocumented)public StringIndexerModel copy(ParamMap extra)
Params
defaultCopy()
.copy
in interface Params
copy
in class Model<StringIndexerModel>
extra
- (undocumented)public org.apache.spark.ml.feature.StringIndexerModel.StringIndexModelWriter write()
MLWritable
MLWriter
instance for this ML instance.write
in interface MLWritable