Class Bucketizer
- All Implemented Interfaces:
Serializable
,org.apache.spark.internal.Logging
,Params
,HasHandleInvalid
,HasInputCol
,HasInputCols
,HasOutputCol
,HasOutputCols
,DefaultParamsWritable
,Identifiable
,MLWritable
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.
- See Also:
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Nested Class Summary
Nested classes/interfaces inherited from interface org.apache.spark.internal.Logging
org.apache.spark.internal.Logging.LogStringContext, org.apache.spark.internal.Logging.SparkShellLoggingFilter
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Constructor Summary
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Method Summary
Modifier and TypeMethodDescriptionCreates a copy of this instance with the same UID and some extra params.double[]
double[][]
Param for how to handle invalid entries containing NaN values.inputCol()
Param for input column name.final StringArrayParam
Param for input column names.static Bucketizer
Param for output column name.final StringArrayParam
Param for output column names.static MLReader<T>
read()
setHandleInvalid
(String value) setInputCol
(String value) setInputCols
(String[] value) setOutputCol
(String value) setOutputCols
(String[] value) setSplits
(double[] value) setSplitsArray
(double[][] value) splits()
Parameter for mapping continuous features into buckets.Parameter for specifying multiple splits parameters.toString()
Transforms the input dataset.transformSchema
(StructType schema) Check transform validity and derive the output schema from the input schema.uid()
An immutable unique ID for the object and its derivatives.Methods inherited from class org.apache.spark.ml.Transformer
transform, transform, transform
Methods inherited from class org.apache.spark.ml.PipelineStage
params
Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait
Methods inherited from interface org.apache.spark.ml.util.DefaultParamsWritable
write
Methods inherited from interface org.apache.spark.ml.param.shared.HasHandleInvalid
getHandleInvalid
Methods inherited from interface org.apache.spark.ml.param.shared.HasInputCol
getInputCol
Methods inherited from interface org.apache.spark.ml.param.shared.HasInputCols
getInputCols
Methods inherited from interface org.apache.spark.ml.param.shared.HasOutputCol
getOutputCol
Methods inherited from interface org.apache.spark.ml.param.shared.HasOutputCols
getOutputCols
Methods inherited from interface org.apache.spark.internal.Logging
initializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, isTraceEnabled, log, logDebug, logDebug, logDebug, logDebug, logError, logError, logError, logError, logInfo, logInfo, logInfo, logInfo, logName, LogStringContext, logTrace, logTrace, logTrace, logTrace, logWarning, logWarning, logWarning, logWarning, org$apache$spark$internal$Logging$$log_, org$apache$spark$internal$Logging$$log__$eq, withLogContext
Methods inherited from interface org.apache.spark.ml.util.MLWritable
save
Methods inherited from interface org.apache.spark.ml.param.Params
clear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, onParamChange, paramMap, params, set, set, set, setDefault, setDefault, shouldOwn
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Constructor Details
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Bucketizer
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Bucketizer
public Bucketizer()
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Method Details
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load
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read
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outputCols
Description copied from interface:HasOutputCols
Param for output column names.- Specified by:
outputCols
in interfaceHasOutputCols
- Returns:
- (undocumented)
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inputCols
Description copied from interface:HasInputCols
Param for input column names.- Specified by:
inputCols
in interfaceHasInputCols
- Returns:
- (undocumented)
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outputCol
Description copied from interface:HasOutputCol
Param for output column name.- Specified by:
outputCol
in interfaceHasOutputCol
- Returns:
- (undocumented)
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inputCol
Description copied from interface:HasInputCol
Param for input column name.- Specified by:
inputCol
in interfaceHasInputCol
- Returns:
- (undocumented)
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uid
Description copied from interface:Identifiable
An immutable unique ID for the object and its derivatives.- Specified by:
uid
in interfaceIdentifiable
- Returns:
- (undocumented)
-
splits
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.- Returns:
- (undocumented)
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getSplits
public double[] getSplits() -
setSplits
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setInputCol
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setOutputCol
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handleInvalid
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"- Specified by:
handleInvalid
in interfaceHasHandleInvalid
- Returns:
- (undocumented)
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setHandleInvalid
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splitsArray
Parameter for specifying multiple splits parameters. Each element in this array can be used to map continuous features into buckets.- Returns:
- (undocumented)
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getSplitsArray
public double[][] getSplitsArray() -
setSplitsArray
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setInputCols
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setOutputCols
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transform
Description copied from class:Transformer
Transforms the input dataset.- Specified by:
transform
in classTransformer
- Parameters:
dataset
- (undocumented)- Returns:
- (undocumented)
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transformSchema
Description copied from class: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 byParam.validate()
.Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.
- Specified by:
transformSchema
in classPipelineStage
- Parameters:
schema
- (undocumented)- Returns:
- (undocumented)
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copy
Description copied from interface: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. SeedefaultCopy()
.- Specified by:
copy
in interfaceParams
- Specified by:
copy
in classModel<Bucketizer>
- Parameters:
extra
- (undocumented)- Returns:
- (undocumented)
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toString
- Specified by:
toString
in interfaceIdentifiable
- Overrides:
toString
in classObject
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