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 -
Constructor Summary
Constructors -
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 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.Transformer
transform, transform, transformMethods inherited from class org.apache.spark.ml.PipelineStage
paramsMethods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, wait, wait, waitMethods inherited from interface org.apache.spark.ml.util.DefaultParamsWritable
writeMethods inherited from interface org.apache.spark.ml.param.shared.HasHandleInvalid
getHandleInvalidMethods inherited from interface org.apache.spark.ml.param.shared.HasInputCol
getInputColMethods inherited from interface org.apache.spark.ml.param.shared.HasInputCols
getInputColsMethods inherited from interface org.apache.spark.ml.param.shared.HasOutputCol
getOutputColMethods inherited from interface org.apache.spark.ml.param.shared.HasOutputCols
getOutputColsMethods 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, withLogContextMethods inherited from interface org.apache.spark.ml.util.MLWritable
saveMethods 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:HasOutputColsParam for output column names.- Specified by:
outputColsin interfaceHasOutputCols- Returns:
- (undocumented)
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inputCols
Description copied from interface:HasInputColsParam for input column names.- Specified by:
inputColsin interfaceHasInputCols- Returns:
- (undocumented)
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outputCol
Description copied from interface:HasOutputColParam for output column name.- Specified by:
outputColin interfaceHasOutputCol- Returns:
- (undocumented)
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inputCol
Description copied from interface:HasInputColParam for input column name.- Specified by:
inputColin interfaceHasInputCol- Returns:
- (undocumented)
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uid
Description copied from interface:IdentifiableAn immutable unique ID for the object and its derivatives.- Specified by:
uidin interfaceIdentifiable- Returns:
- (undocumented)
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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:
handleInvalidin 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:TransformerTransforms the input dataset.- Specified by:
transformin classTransformer- Parameters:
dataset- (undocumented)- Returns:
- (undocumented)
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transformSchema
Description 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 classPipelineStage- Parameters:
schema- (undocumented)- Returns:
- (undocumented)
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copy
Description 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 interfaceParams- Specified by:
copyin classModel<Bucketizer>- Parameters:
extra- (undocumented)- Returns:
- (undocumented)
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toString
- Specified by:
toStringin interfaceIdentifiable- Overrides:
toStringin classObject
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