class OneHotEncoder extends Estimator[OneHotEncoderModel] with OneHotEncoderBase with DefaultParamsWritable
A one-hot encoder that maps a column of category indices to a column of binary vectors, with
at most a single one-value per row that indicates the input category index.
For example with 5 categories, an input value of 2.0 would map to an output vector of
[0.0, 0.0, 1.0, 0.0]
.
The last category is not included by default (configurable via dropLast
),
because it makes the vector entries sum up to one, and hence linearly dependent.
So an input value of 4.0 maps to [0.0, 0.0, 0.0, 0.0]
.
- Annotations
- @Since( "3.0.0" )
- Source
- OneHotEncoder.scala
- Note
This is different from scikit-learn's OneHotEncoder, which keeps all categories. The output vectors are sparse. When
,handleInvalid
is configured to 'keep', an extra "category" indicating invalid values is added as last category. So whendropLast
is true, invalid values are encoded as all-zeros vector.When encoding multi-column by using
inputCols
andoutputCols
params, input/output cols come in pairs, specified by the order in the arrays, and each pair is treated independently.- See also
StringIndexer
for converting categorical values into category indices
- Grouped
- Alphabetic
- By Inheritance
- OneHotEncoder
- DefaultParamsWritable
- MLWritable
- OneHotEncoderBase
- HasOutputCols
- HasOutputCol
- HasInputCols
- HasInputCol
- HasHandleInvalid
- Estimator
- PipelineStage
- Logging
- Params
- Serializable
- Serializable
- Identifiable
- AnyRef
- Any
- Hide All
- Show All
- Public
- All
Parameters
A list of (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.
-
final
val
dropLast: BooleanParam
Whether to drop the last category in the encoded vector (default: true)
Whether to drop the last category in the encoded vector (default: true)
- Definition Classes
- OneHotEncoderBase
- Annotations
- @Since( "2.3.0" )
-
val
handleInvalid: Param[String]
Param for how to handle invalid data during transform().
Param for how to handle invalid data during transform(). Options are 'keep' (invalid data presented as an extra categorical feature) or 'error' (throw an error). Note that this Param is only used during transform; during fitting, invalid data will result in an error. Default: "error"
- Definition Classes
- OneHotEncoderBase → HasHandleInvalid
- Annotations
- @Since( "2.3.0" )
-
final
val
inputCol: Param[String]
Param for input column name.
Param for input column name.
- Definition Classes
- HasInputCol
-
final
val
inputCols: StringArrayParam
Param for input column names.
Param for input column names.
- Definition Classes
- HasInputCols
-
final
val
outputCol: Param[String]
Param for output column name.
Param for output column name.
- Definition Classes
- HasOutputCol
-
final
val
outputCols: StringArrayParam
Param for output column names.
Param for output column names.
- Definition Classes
- HasOutputCols
Members
-
final
def
clear(param: Param[_]): OneHotEncoder.this.type
Clears the user-supplied value for the input param.
Clears the user-supplied value for the input param.
- Definition Classes
- Params
-
def
copy(extra: ParamMap): OneHotEncoder
Creates a copy of this instance with the same UID and some extra params.
Creates a copy of this instance with the same UID and some extra params. Subclasses should implement this method and set the return type properly. See
defaultCopy()
.- Definition Classes
- OneHotEncoder → Estimator → PipelineStage → Params
- Annotations
- @Since( "3.0.0" )
-
def
explainParam(param: Param[_]): String
Explains a param.
Explains a param.
- param
input param, must belong to this instance.
- returns
a string that contains the input param name, doc, and optionally its default value and the user-supplied value
- Definition Classes
- Params
-
def
explainParams(): String
Explains all params of this instance.
Explains all params of this instance. See
explainParam()
.- Definition Classes
- Params
-
final
def
extractParamMap(): ParamMap
extractParamMap
with no extra values.extractParamMap
with no extra values.- Definition Classes
- Params
-
final
def
extractParamMap(extra: ParamMap): ParamMap
Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.
Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.
- Definition Classes
- Params
-
def
fit(dataset: Dataset[_]): OneHotEncoderModel
Fits a model to the input data.
Fits a model to the input data.
- Definition Classes
- OneHotEncoder → Estimator
- Annotations
- @Since( "3.0.0" )
-
def
fit(dataset: Dataset[_], paramMaps: Seq[ParamMap]): Seq[OneHotEncoderModel]
Fits multiple models to the input data with multiple sets of parameters.
Fits multiple models to the input data with multiple sets of parameters. The default implementation uses a for loop on each parameter map. Subclasses could override this to optimize multi-model training.
- dataset
input dataset
- paramMaps
An array of parameter maps. These values override any specified in this Estimator's embedded ParamMap.
- returns
fitted models, matching the input parameter maps
- Definition Classes
- Estimator
- Annotations
- @Since( "2.0.0" )
-
def
fit(dataset: Dataset[_], paramMap: ParamMap): OneHotEncoderModel
Fits a single model to the input data with provided parameter map.
Fits a single model to the input data with provided parameter map.
- dataset
input dataset
- paramMap
Parameter map. These values override any specified in this Estimator's embedded ParamMap.
- returns
fitted model
- Definition Classes
- Estimator
- Annotations
- @Since( "2.0.0" )
-
def
fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): OneHotEncoderModel
Fits a single model to the input data with optional parameters.
Fits a single model to the input data with optional parameters.
- dataset
input dataset
- firstParamPair
the first param pair, overrides embedded params
- otherParamPairs
other param pairs. These values override any specified in this Estimator's embedded ParamMap.
- returns
fitted model
- Definition Classes
- Estimator
- Annotations
- @Since( "2.0.0" ) @varargs()
-
final
def
get[T](param: Param[T]): Option[T]
Optionally returns the user-supplied value of a param.
Optionally returns the user-supplied value of a param.
- Definition Classes
- Params
-
final
def
getDefault[T](param: Param[T]): Option[T]
Gets the default value of a parameter.
Gets the default value of a parameter.
- Definition Classes
- Params
-
final
def
getOrDefault[T](param: Param[T]): T
Gets the value of a param in the embedded param map or its default value.
Gets the value of a param in the embedded param map or its default value. Throws an exception if neither is set.
- Definition Classes
- Params
-
def
getParam(paramName: String): Param[Any]
Gets a param by its name.
Gets a param by its name.
- Definition Classes
- Params
-
final
def
hasDefault[T](param: Param[T]): Boolean
Tests whether the input param has a default value set.
Tests whether the input param has a default value set.
- Definition Classes
- Params
-
def
hasParam(paramName: String): Boolean
Tests whether this instance contains a param with a given name.
Tests whether this instance contains a param with a given name.
- Definition Classes
- Params
-
final
def
isDefined(param: Param[_]): Boolean
Checks whether a param is explicitly set or has a default value.
Checks whether a param is explicitly set or has a default value.
- Definition Classes
- Params
-
final
def
isSet(param: Param[_]): Boolean
Checks whether a param is explicitly set.
Checks whether a param is explicitly set.
- Definition Classes
- Params
-
lazy val
params: Array[Param[_]]
Returns all params sorted by their names.
Returns all params sorted by their names. The default implementation uses Java reflection to list all public methods that have no arguments and return Param.
- Definition Classes
- Params
- Note
Developer should not use this method in constructor because we cannot guarantee that this variable gets initialized before other params.
-
def
save(path: String): Unit
Saves this ML instance to the input path, a shortcut of
write.save(path)
.Saves this ML instance to the input path, a shortcut of
write.save(path)
.- Definition Classes
- MLWritable
- Annotations
- @Since( "1.6.0" ) @throws( ... )
-
final
def
set[T](param: Param[T], value: T): OneHotEncoder.this.type
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.
- Definition Classes
- Params
-
def
toString(): String
- Definition Classes
- Identifiable → AnyRef → Any
-
def
transformSchema(schema: StructType): StructType
Check transform validity and derive the output schema from the input schema.
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 byParam.validate()
.Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.
- Definition Classes
- OneHotEncoder → PipelineStage
- Annotations
- @Since( "3.0.0" )
-
val
uid: String
An immutable unique ID for the object and its derivatives.
An immutable unique ID for the object and its derivatives.
- Definition Classes
- OneHotEncoder → Identifiable
- Annotations
- @Since( "3.0.0" )
-
def
write: MLWriter
Returns an
MLWriter
instance for this ML instance.Returns an
MLWriter
instance for this ML instance.- Definition Classes
- DefaultParamsWritable → MLWritable
Parameter setters
-
def
setDropLast(value: Boolean): OneHotEncoder.this.type
- Annotations
- @Since( "3.0.0" )
-
def
setHandleInvalid(value: String): OneHotEncoder.this.type
- Annotations
- @Since( "3.0.0" )
-
def
setInputCol(value: String): OneHotEncoder.this.type
- Annotations
- @Since( "3.0.0" )
-
def
setInputCols(values: Array[String]): OneHotEncoder.this.type
- Annotations
- @Since( "3.0.0" )
-
def
setOutputCol(value: String): OneHotEncoder.this.type
- Annotations
- @Since( "3.0.0" )
-
def
setOutputCols(values: Array[String]): OneHotEncoder.this.type
- Annotations
- @Since( "3.0.0" )
Parameter getters
-
def
getDropLast: Boolean
- Definition Classes
- OneHotEncoderBase
- Annotations
- @Since( "2.3.0" )
-
final
def
getHandleInvalid: String
- Definition Classes
- HasHandleInvalid
-
final
def
getInputCol: String
- Definition Classes
- HasInputCol
-
final
def
getInputCols: Array[String]
- Definition Classes
- HasInputCols
-
final
def
getOutputCol: String
- Definition Classes
- HasOutputCol
-
final
def
getOutputCols: Array[String]
- Definition Classes
- HasOutputCols