final class Bucketizer extends Model[Bucketizer] with HasHandleInvalid with HasInputCol with HasOutputCol with HasInputCols with HasOutputCols with DefaultParamsWritable
Bucketizer
maps a column of continuous features to a column of feature buckets.
Since 2.3.0,
Bucketizer
can map multiple columns at once by setting the inputCols
parameter. Note that
when both the inputCol
and inputCols
parameters are set, an Exception will be thrown. The
splits
parameter is only used for single column usage, and splitsArray
is for multiple
columns.
- Annotations
- @Since( "1.4.0" )
- Source
- Bucketizer.scala
- Grouped
- Alphabetic
- By Inheritance
- Bucketizer
- DefaultParamsWritable
- MLWritable
- HasOutputCols
- HasInputCols
- HasOutputCol
- HasInputCol
- HasHandleInvalid
- Model
- Transformer
- PipelineStage
- Logging
- Params
- Serializable
- Serializable
- Identifiable
- AnyRef
- Any
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Parameters
A list of (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.
-
val
handleInvalid: Param[String]
Param for how to handle invalid entries containing NaN values.
Param for how to handle invalid entries containing NaN values. Values outside the splits will always be treated as errors. Options are 'skip' (filter out rows with invalid values), 'error' (throw an error), or 'keep' (keep invalid values in a special additional bucket). Note that in the multiple column case, the invalid handling is applied to all columns. That said for 'error' it will throw an error if any invalids are found in any column, for 'skip' it will skip rows with any invalids in any columns, etc. Default: "error"
- Definition Classes
- Bucketizer → HasHandleInvalid
- Annotations
- @Since( "2.1.0" )
-
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
-
val
splits: DoubleArrayParam
Parameter for mapping continuous features into buckets.
Parameter for mapping continuous features into buckets. With n+1 splits, there are n buckets. A bucket defined by splits x,y holds values in the range [x,y) except the last bucket, which also includes y. Splits should be of length greater than or equal to 3 and strictly increasing. Values at -inf, inf must be explicitly provided to cover all Double values; otherwise, values outside the splits specified will be treated as errors.
See also handleInvalid, which can optionally create an additional bucket for NaN values.
- Annotations
- @Since( "1.4.0" )
-
val
splitsArray: DoubleArrayArrayParam
Parameter for specifying multiple splits parameters.
Parameter for specifying multiple splits parameters. Each element in this array can be used to map continuous features into buckets.
- Annotations
- @Since( "2.3.0" )
Members
-
final
def
clear(param: Param[_]): Bucketizer.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): Bucketizer
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
- Bucketizer → Model → Transformer → PipelineStage → Params
- Annotations
- @Since( "1.4.1" )
-
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
-
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
-
def
hasParent: Boolean
Indicates whether this Model has a corresponding parent.
-
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.
-
var
parent: Estimator[Bucketizer]
The parent estimator that produced this model.
The parent estimator that produced this model.
- Definition Classes
- Model
- Note
For ensembles' component Models, this value can be null.
-
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): Bucketizer.this.type
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.
- Definition Classes
- Params
-
def
setParent(parent: Estimator[Bucketizer]): Bucketizer
Sets the parent of this model (Java API).
Sets the parent of this model (Java API).
- Definition Classes
- Model
-
def
toString(): String
- Definition Classes
- Bucketizer → Identifiable → AnyRef → Any
- Annotations
- @Since( "3.0.0" )
-
def
transform(dataset: Dataset[_]): DataFrame
Transforms the input dataset.
Transforms the input dataset.
- Definition Classes
- Bucketizer → Transformer
- Annotations
- @Since( "2.0.0" )
-
def
transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
Transforms the dataset with provided parameter map as additional parameters.
Transforms the dataset with provided parameter map as additional parameters.
- dataset
input dataset
- paramMap
additional parameters, overwrite embedded params
- returns
transformed dataset
- Definition Classes
- Transformer
- Annotations
- @Since( "2.0.0" )
-
def
transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
Transforms the dataset with optional parameters
Transforms the dataset with optional parameters
- dataset
input dataset
- firstParamPair
the first param pair, overwrite embedded params
- otherParamPairs
other param pairs, overwrite embedded params
- returns
transformed dataset
- Definition Classes
- Transformer
- Annotations
- @Since( "2.0.0" ) @varargs()
-
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
- Bucketizer → PipelineStage
- Annotations
- @Since( "1.4.0" )
-
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
- Bucketizer → Identifiable
- Annotations
- @Since( "1.4.0" )
-
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
setHandleInvalid(value: String): Bucketizer.this.type
- Annotations
- @Since( "2.1.0" )
-
def
setInputCol(value: String): Bucketizer.this.type
- Annotations
- @Since( "1.4.0" )
-
def
setInputCols(value: Array[String]): Bucketizer.this.type
- Annotations
- @Since( "2.3.0" )
-
def
setOutputCol(value: String): Bucketizer.this.type
- Annotations
- @Since( "1.4.0" )
-
def
setOutputCols(value: Array[String]): Bucketizer.this.type
- Annotations
- @Since( "2.3.0" )
-
def
setSplits(value: Array[Double]): Bucketizer.this.type
- Annotations
- @Since( "1.4.0" )
-
def
setSplitsArray(value: Array[Array[Double]]): Bucketizer.this.type
- Annotations
- @Since( "2.3.0" )
Parameter getters
-
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
-
def
getSplits: Array[Double]
- Annotations
- @Since( "1.4.0" )
-
def
getSplitsArray: Array[Array[Double]]
- Annotations
- @Since( "2.3.0" )