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 SummaryNested classes/interfaces inherited from interface org.apache.spark.internal.Loggingorg.apache.spark.internal.Logging.LogStringContext, org.apache.spark.internal.Logging.SparkShellLoggingFilter
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Constructor SummaryConstructors
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Method SummaryModifier 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 StringArrayParamParam for input column names.static BucketizerParam for output column name.final StringArrayParamParam 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.Transformertransform, transform, transformMethods inherited from class org.apache.spark.ml.PipelineStageparamsMethods inherited from class java.lang.Objectequals, getClass, hashCode, notify, notifyAll, wait, wait, waitMethods inherited from interface org.apache.spark.ml.util.DefaultParamsWritablewriteMethods inherited from interface org.apache.spark.ml.param.shared.HasHandleInvalidgetHandleInvalidMethods inherited from interface org.apache.spark.ml.param.shared.HasInputColgetInputColMethods inherited from interface org.apache.spark.ml.param.shared.HasInputColsgetInputColsMethods inherited from interface org.apache.spark.ml.param.shared.HasOutputColgetOutputColMethods inherited from interface org.apache.spark.ml.param.shared.HasOutputColsgetOutputColsMethods inherited from interface org.apache.spark.internal.LogginginitializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, isTraceEnabled, log, logBasedOnLevel, logDebug, logDebug, logDebug, logDebug, logError, logError, logError, logError, logInfo, logInfo, logInfo, logInfo, logName, LogStringContext, logTrace, logTrace, logTrace, logTrace, logWarning, logWarning, logWarning, logWarning, MDC, org$apache$spark$internal$Logging$$log_, org$apache$spark$internal$Logging$$log__$eq, withLogContextMethods inherited from interface org.apache.spark.ml.util.MLWritablesaveMethods inherited from interface org.apache.spark.ml.param.Paramsclear, copyValues, defaultCopy, defaultParamMap, estimateMatadataSize, 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- 
Bucketizer
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Bucketizerpublic Bucketizer()
 
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Method Details- 
load
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read
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outputColsDescription copied from interface:HasOutputColsParam for output column names.- Specified by:
- outputColsin interface- HasOutputCols
- Returns:
- (undocumented)
 
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inputColsDescription copied from interface:HasInputColsParam for input column names.- Specified by:
- inputColsin interface- HasInputCols
- Returns:
- (undocumented)
 
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outputColDescription copied from interface:HasOutputColParam for output column name.- Specified by:
- outputColin interface- HasOutputCol
- Returns:
- (undocumented)
 
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inputColDescription copied from interface:HasInputColParam for input column name.- Specified by:
- inputColin interface- HasInputCol
- Returns:
- (undocumented)
 
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uidDescription copied from interface:IdentifiableAn immutable unique ID for the object and its derivatives.- Specified by:
- uidin interface- Identifiable
- Returns:
- (undocumented)
 
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splitsParameter 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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getSplitspublic double[] getSplits()
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setSplits
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setInputCol
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setOutputCol
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handleInvalidParam 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:
- handleInvalidin interface- HasHandleInvalid
- Returns:
- (undocumented)
 
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setHandleInvalid
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splitsArrayParameter 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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getSplitsArraypublic double[][] getSplitsArray()
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setSplitsArray
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setInputCols
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setOutputCols
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transformDescription copied from class:TransformerTransforms the input dataset.- Specified by:
- transformin class- Transformer
- Parameters:
- dataset- (undocumented)
- Returns:
- (undocumented)
 
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transformSchemaDescription copied from class:PipelineStageCheck 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. - Specified by:
- transformSchemain class- PipelineStage
- Parameters:
- schema- (undocumented)
- Returns:
- (undocumented)
 
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copyDescription copied from interface:ParamsCreates 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:
- copyin interface- Params
- Specified by:
- copyin class- Model<Bucketizer>
- Parameters:
- extra- (undocumented)
- Returns:
- (undocumented)
 
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toString- Specified by:
- toStringin interface- Identifiable
- Overrides:
- toStringin class- Object
 
 
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