class NGram extends UnaryTransformer[Seq[String], Seq[String], NGram] with DefaultParamsWritable
A feature transformer that converts the input array of strings into an array of n-grams. Null values in the input array are ignored. It returns an array of n-grams where each n-gram is represented by a space-separated string of words.
When the input is empty, an empty array is returned. When the input array length is less than n (number of elements per n-gram), no n-grams are returned.
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 - @Since( "1.5.0" )
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 - NGram.scala
 
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An alias for
getOrDefault().An alias for
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        clear(param: Param[_]): NGram.this.type
      
      
      
Clears the user-supplied value for the input param.
Clears the user-supplied value for the input param.
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        def
      
      
        copy(extra: ParamMap): NGram
      
      
      
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
 - UnaryTransformer → Transformer → PipelineStage → Params
 
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        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
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        createTransformFunc: (Seq[String]) ⇒ Seq[String]
      
      
      
Creates the transform function using the given param map.
Creates the transform function using the given param map. The input param map already takes account of the embedded param map. So the param values should be determined solely by the input param map.
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        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.
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        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
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Explains all params of this instance.
Explains all params of this instance. See
explainParam().- Definition Classes
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extractParamMapwith no extra values.extractParamMapwith no extra values.- Definition Classes
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        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.
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Optionally returns the user-supplied value of a param.
Optionally returns the user-supplied value of a param.
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Gets the default value of a parameter.
Gets the default value of a parameter.
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        getInputCol: String
      
      
      
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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.
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        getOutputCol: String
      
      
      
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        getParam(paramName: String): Param[Any]
      
      
      
Gets a param by its name.
Gets a param by its name.
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Tests whether the input param has a default value set.
Tests whether the input param has a default value set.
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        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.
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        inputCol: Param[String]
      
      
      
Param for input column name.
Param for input column name.
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Checks whether a param is explicitly set or has a default value.
Checks whether a param is explicitly set or has a default value.
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Checks whether a param is explicitly set.
Checks whether a param is explicitly set.
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        val
      
      
        n: IntParam
      
      
      
Minimum n-gram length, greater than or equal to 1.
Minimum n-gram length, greater than or equal to 1. Default: 2, bigram features
- Annotations
 - @Since( "1.5.0" )
 
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        outputCol: Param[String]
      
      
      
Param for output column name.
Param for output column name.
- Definition Classes
 - HasOutputCol
 
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        def
      
      
        outputDataType: DataType
      
      
      
Returns the data type of the output column.
Returns the data type of the output column.
- Attributes
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 - NGram → UnaryTransformer
 
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        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.
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        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
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 - @Since( "1.6.0" ) @throws( ... )
 
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        final 
        def
      
      
        set(paramPair: ParamPair[_]): NGram.this.type
      
      
      
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.
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        set(param: String, value: Any): NGram.this.type
      
      
      
Sets a parameter (by name) in the embedded param map.
Sets a parameter (by name) in the embedded param map.
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        final 
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        set[T](param: Param[T], value: T): NGram.this.type
      
      
      
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.
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        setDefault(paramPairs: ParamPair[_]*): NGram.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.
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        setDefault[T](param: Param[T], value: T): NGram.this.type
      
      
      
Sets a default value for a param.
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        setInputCol(value: String): NGram
      
      
      
- Definition Classes
 - UnaryTransformer
 
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        def
      
      
        setN(value: Int): NGram.this.type
      
      
      
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        def
      
      
        setOutputCol(value: String): NGram
      
      
      
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        final 
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        synchronized[T0](arg0: ⇒ T0): T0
      
      
      
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        def
      
      
        toString(): String
      
      
      
- Definition Classes
 - NGram → Identifiable → AnyRef → Any
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        def
      
      
        transform(dataset: Dataset[_]): DataFrame
      
      
      
Transforms the input dataset.
Transforms the input dataset.
- Definition Classes
 - UnaryTransformer → Transformer
 
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        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
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        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
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        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
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        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
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 - PipelineStage
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        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
 - NGram → Identifiable
 - Annotations
 - @Since( "1.5.0" )
 
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        def
      
      
        validateInputType(inputType: DataType): Unit
      
      
      
Validates the input type.
Validates the input type. Throw an exception if it is invalid.
- Attributes
 - protected
 - Definition Classes
 - NGram → UnaryTransformer
 
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        final 
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        wait(): Unit
      
      
      
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        final 
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        wait(arg0: Long, arg1: Int): Unit
      
      
      
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        final 
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        wait(arg0: Long): Unit
      
      
      
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        def
      
      
        write: MLWriter
      
      
      
Returns an
MLWriterinstance for this ML instance.Returns an
MLWriterinstance for this ML instance.- Definition Classes
 - DefaultParamsWritable → MLWritable
 
 
Inherited from DefaultParamsWritable
Inherited from MLWritable
Inherited from UnaryTransformer[Seq[String], Seq[String], NGram]
Inherited from HasOutputCol
Inherited from HasInputCol
Inherited from Transformer
Inherited from PipelineStage
Inherited from Logging
Inherited from Params
Inherited from Serializable
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.