final class QuantileDiscretizer extends Estimator[Bucketizer] with QuantileDiscretizerBase with DefaultParamsWritable
QuantileDiscretizer takes a column with continuous features and outputs a column with binned
categorical features. The number of bins can be set using the numBuckets parameter. It is
possible that the number of buckets used will be smaller than this value, for example, if there
are too few distinct values of the input to create enough distinct quantiles.
Since 2.3.0, QuantileDiscretizer can map multiple columns at once by setting the inputCols
parameter. If both of the inputCol and inputCols parameters are set, an Exception will be
thrown. To specify the number of buckets for each column, the numBucketsArray parameter can
be set, or if the number of buckets should be the same across columns, numBuckets can be
set as a convenience. Note that in multiple columns case, relative error is applied to all
columns.
NaN handling:
null and NaN values will be ignored from the column during QuantileDiscretizer fitting. This
will produce a Bucketizer model for making predictions. During the transformation,
Bucketizer will raise an error when it finds NaN values in the dataset, but the user can
also choose to either keep or remove NaN values within the dataset by setting handleInvalid.
If the user chooses to keep NaN values, they will be handled specially and placed into their own
bucket, for example, if 4 buckets are used, then non-NaN data will be put into buckets[0-3],
but NaNs will be counted in a special bucket[4].
Algorithm: The bin ranges are chosen using an approximate algorithm (see the documentation for
org.apache.spark.sql.DataFrameStatFunctions.approxQuantile
for a detailed description). The precision of the approximation can be controlled with the
relativeError parameter. The lower and upper bin bounds will be -Infinity and +Infinity,
covering all real values.
- Annotations
- @Since("1.6.0")
- Source
- QuantileDiscretizer.scala
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- QuantileDiscretizer
- DefaultParamsWritable
- MLWritable
- QuantileDiscretizerBase
- HasRelativeError
- HasOutputCols
- HasInputCols
- HasOutputCol
- HasInputCol
- HasHandleInvalid
- Estimator
- PipelineStage
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- implicit class LogStringContext extends AnyRef
- Definition Classes
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Value Members
- final def !=(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
- final def ##: Int
- Definition Classes
- AnyRef → Any
- final def $[T](param: Param[T]): T
An alias for
getOrDefault().An alias for
getOrDefault().- Attributes
- protected
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- final def ==(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
- final def asInstanceOf[T0]: T0
- Definition Classes
- Any
- final def clear(param: Param[_]): QuantileDiscretizer.this.type
Clears the user-supplied value for the input param.
Clears the user-supplied value for the input param.
- Definition Classes
- Params
- def clone(): AnyRef
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.CloneNotSupportedException]) @IntrinsicCandidate() @native()
- def copy(extra: ParamMap): QuantileDiscretizer
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
- QuantileDiscretizer → Estimator → PipelineStage → Params
- Annotations
- @Since("1.6.0")
- def copyValues[T <: Params](to: T, extra: ParamMap = ParamMap.empty): T
Copies param values from this instance to another instance for params shared by them.
Copies param values from this instance to another instance for params shared by them.
This handles default Params and explicitly set Params separately. Default Params are copied from and to
defaultParamMap, and explicitly set Params are copied from and toparamMap. Warning: This implicitly assumes that this Params instance and the target instance share the same set of default Params.- to
the target instance, which should work with the same set of default Params as this source instance
- extra
extra params to be copied to the target's
paramMap- returns
the target instance with param values copied
- Attributes
- protected
- Definition Classes
- Params
- final def defaultCopy[T <: Params](extra: ParamMap): T
Default implementation of copy with extra params.
Default implementation of copy with extra params. It tries to create a new instance with the same UID. Then it copies the embedded and extra parameters over and returns the new instance.
- Attributes
- protected
- Definition Classes
- Params
- final def eq(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
- def equals(arg0: AnyRef): Boolean
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- AnyRef → Any
- 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
extractParamMapwith no extra values.extractParamMapwith 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[_]): Bucketizer
Fits a model to the input data.
Fits a model to the input data.
- Definition Classes
- QuantileDiscretizer → Estimator
- Annotations
- @Since("2.0.0")
- def fit(dataset: Dataset[_], paramMaps: Seq[ParamMap]): Seq[Bucketizer]
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): Bucketizer
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[_]*): Bucketizer
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 getClass(): Class[_ <: AnyRef]
- Definition Classes
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- Annotations
- @IntrinsicCandidate() @native()
- 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 getHandleInvalid: String
- Definition Classes
- HasHandleInvalid
- final def getInputCol: String
- Definition Classes
- HasInputCol
- final def getInputCols: Array[String]
- Definition Classes
- HasInputCols
- def getNumBuckets: Int
- Definition Classes
- QuantileDiscretizerBase
- def getNumBucketsArray: Array[Int]
- Definition Classes
- QuantileDiscretizerBase
- 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
- final def getOutputCol: String
- Definition Classes
- HasOutputCol
- final def getOutputCols: Array[String]
- Definition Classes
- HasOutputCols
- def getParam(paramName: String): Param[Any]
Gets a param by its name.
Gets a param by its name.
- Definition Classes
- Params
- final def getRelativeError: Double
- Definition Classes
- HasRelativeError
- val handleInvalid: Param[String]
Param for how to handle invalid entries.
Param for how to handle invalid entries. 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 columns 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
- QuantileDiscretizerBase → HasHandleInvalid
- Annotations
- @Since("2.1.0")
- 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 hashCode(): Int
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- AnyRef → Any
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- @IntrinsicCandidate() @native()
- def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
- Attributes
- protected
- Definition Classes
- Logging
- def initializeLogIfNecessary(isInterpreter: Boolean): Unit
- Attributes
- protected
- Definition Classes
- Logging
- 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 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 isInstanceOf[T0]: Boolean
- Definition Classes
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- final def isSet(param: Param[_]): Boolean
Checks whether a param is explicitly set.
Checks whether a param is explicitly set.
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- def logError(msg: => String, throwable: Throwable): Unit
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- def logError(entry: LogEntry, throwable: Throwable): Unit
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- def logError(entry: LogEntry): Unit
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- def logError(msg: => String): Unit
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- def logInfo(msg: => String): Unit
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- def logName: String
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- def logTrace(msg: => String, throwable: Throwable): Unit
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- def logTrace(entry: LogEntry, throwable: Throwable): Unit
- Attributes
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- def logTrace(entry: LogEntry): Unit
- Attributes
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- def logTrace(msg: => String): Unit
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- def logWarning(msg: => String): Unit
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- final def ne(arg0: AnyRef): Boolean
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- final def notify(): Unit
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- @IntrinsicCandidate() @native()
- final def notifyAll(): Unit
- Definition Classes
- AnyRef
- Annotations
- @IntrinsicCandidate() @native()
- val numBuckets: IntParam
Number of buckets (quantiles, or categories) into which data points are grouped.
Number of buckets (quantiles, or categories) into which data points are grouped. Must be greater than or equal to 2.
See also handleInvalid, which can optionally create an additional bucket for NaN values.
default: 2
- Definition Classes
- QuantileDiscretizerBase
- val numBucketsArray: IntArrayParam
Array of number of buckets (quantiles, or categories) into which data points are grouped.
Array of number of buckets (quantiles, or categories) into which data points are grouped. Each value must be greater than or equal to 2
See also handleInvalid, which can optionally create an additional bucket for NaN values.
- Definition Classes
- QuantileDiscretizerBase
- 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
- 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.
- final val relativeError: DoubleParam
Param for the relative target precision for the approximate quantile algorithm.
Param for the relative target precision for the approximate quantile algorithm. Must be in the range [0, 1].
- Definition Classes
- HasRelativeError
- 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("If the input path already exists but overwrite is not enabled.")
- final def set(paramPair: ParamPair[_]): QuantileDiscretizer.this.type
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.
- Attributes
- protected
- Definition Classes
- Params
- final def set(param: String, value: Any): QuantileDiscretizer.this.type
Sets a parameter (by name) in the embedded param map.
Sets a parameter (by name) in the embedded param map.
- Attributes
- protected
- Definition Classes
- Params
- final def set[T](param: Param[T], value: T): QuantileDiscretizer.this.type
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.
- Definition Classes
- Params
- final def setDefault(paramPairs: ParamPair[_]*): QuantileDiscretizer.this.type
Sets default values for a list of params.
Sets default values for a list of params.
Note: Java developers should use the single-parameter
setDefault. Annotating this with varargs can cause compilation failures due to a Scala compiler bug. See SPARK-9268.- paramPairs
a list of param pairs that specify params and their default values to set respectively. Make sure that the params are initialized before this method gets called.
- Attributes
- protected
- Definition Classes
- Params
- final def setDefault[T](param: Param[T], value: T): QuantileDiscretizer.this.type
Sets a default value for a param.
- def setHandleInvalid(value: String): QuantileDiscretizer.this.type
- Annotations
- @Since("2.1.0")
- def setInputCol(value: String): QuantileDiscretizer.this.type
- Annotations
- @Since("1.6.0")
- def setInputCols(value: Array[String]): QuantileDiscretizer.this.type
- Annotations
- @Since("2.3.0")
- def setNumBuckets(value: Int): QuantileDiscretizer.this.type
- Annotations
- @Since("1.6.0")
- def setNumBucketsArray(value: Array[Int]): QuantileDiscretizer.this.type
- Annotations
- @Since("2.3.0")
- def setOutputCol(value: String): QuantileDiscretizer.this.type
- Annotations
- @Since("1.6.0")
- def setOutputCols(value: Array[String]): QuantileDiscretizer.this.type
- Annotations
- @Since("2.3.0")
- def setRelativeError(value: Double): QuantileDiscretizer.this.type
- Annotations
- @Since("2.0.0")
- final def synchronized[T0](arg0: => T0): T0
- Definition Classes
- AnyRef
- 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
transformSchemaand 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
- QuantileDiscretizer → PipelineStage
- Annotations
- @Since("1.6.0")
- def transformSchema(schema: StructType, logging: Boolean): StructType
:: DeveloperApi ::
:: DeveloperApi ::
Derives the output schema from the input schema and parameters, optionally with logging.
This should be optimistic. If it is unclear whether the schema will be valid, then it should be assumed valid until proven otherwise.
- Attributes
- protected
- Definition Classes
- PipelineStage
- Annotations
- @DeveloperApi()
- 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
- QuantileDiscretizer → Identifiable
- Annotations
- @Since("1.6.0")
- final def wait(arg0: Long, arg1: Int): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException])
- final def wait(arg0: Long): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException]) @native()
- final def wait(): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException])
- def withLogContext(context: HashMap[String, String])(body: => Unit): Unit
- Attributes
- protected
- Definition Classes
- Logging
- def write: MLWriter
Returns an
MLWriterinstance for this ML instance.Returns an
MLWriterinstance for this ML instance.- Definition Classes
- DefaultParamsWritable → MLWritable
Deprecated Value Members
- def finalize(): Unit
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.Throwable]) @Deprecated
- Deprecated
(Since version 9)
Inherited from DefaultParamsWritable
Inherited from MLWritable
Inherited from QuantileDiscretizerBase
Inherited from HasRelativeError
Inherited from HasOutputCols
Inherited from HasInputCols
Inherited from HasOutputCol
Inherited from HasInputCol
Inherited from HasHandleInvalid
Inherited from Estimator[Bucketizer]
Inherited from PipelineStage
Inherited from Logging
Inherited from Params
Inherited from Serializable
Inherited from Identifiable
Inherited from AnyRef
Inherited from Any
Parameters
A list of (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.
Members
Parameter setters
Parameter getters
(expert-only) Parameters
A list of advanced, expert-only (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.