class CountVectorizer extends Estimator[CountVectorizerModel] with CountVectorizerParams with DefaultParamsWritable
Extracts a vocabulary from document collections and generates a CountVectorizerModel.
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- @Since( "1.5.0" )
- Source
- CountVectorizer.scala
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def
!=(arg0: Any): Boolean
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final
def
##(): Int
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final
def
$[T](param: Param[T]): T
An alias for
getOrDefault().An alias for
getOrDefault().- Attributes
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final
def
==(arg0: Any): Boolean
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def
asInstanceOf[T0]: T0
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val
binary: BooleanParam
Binary toggle to control the output vector values.
Binary toggle to control the output vector values. If True, all nonzero counts (after minTF filter applied) are set to 1. This is useful for discrete probabilistic models that model binary events rather than integer counts. Default: false
- Definition Classes
- CountVectorizerParams
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final
def
clear(param: Param[_]): CountVectorizer.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
clone(): AnyRef
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def
copy(extra: ParamMap): CountVectorizer
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
- CountVectorizer → Estimator → PipelineStage → Params
- Annotations
- @Since( "1.5.0" )
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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
- Attributes
- protected
- Definition Classes
- Params
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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.
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final
def
eq(arg0: AnyRef): Boolean
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def
equals(arg0: Any): Boolean
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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
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def
explainParams(): String
Explains all params of this instance.
Explains all params of this instance. See
explainParam().- Definition Classes
- Params
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final
def
extractParamMap(): ParamMap
extractParamMapwith no extra values.extractParamMapwith no extra values.- Definition Classes
- Params
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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
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def
finalize(): Unit
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def
fit(dataset: Dataset[_]): CountVectorizerModel
Fits a model to the input data.
Fits a model to the input data.
- Definition Classes
- CountVectorizer → Estimator
- Annotations
- @Since( "2.0.0" )
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def
fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[CountVectorizerModel]
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" )
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def
fit(dataset: Dataset[_], paramMap: ParamMap): CountVectorizerModel
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[_]*): CountVectorizerModel
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()
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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
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def
getBinary: Boolean
- Definition Classes
- CountVectorizerParams
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final
def
getClass(): Class[_]
- Definition Classes
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- @native()
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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
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final
def
getInputCol: String
- Definition Classes
- HasInputCol
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def
getMaxDF: Double
- Definition Classes
- CountVectorizerParams
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def
getMinDF: Double
- Definition Classes
- CountVectorizerParams
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def
getMinTF: Double
- Definition Classes
- CountVectorizerParams
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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
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final
def
getOutputCol: String
- Definition Classes
- HasOutputCol
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def
getParam(paramName: String): Param[Any]
Gets a param by its name.
Gets a param by its name.
- Definition Classes
- Params
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def
getVocabSize: Int
- Definition Classes
- CountVectorizerParams
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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
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def
hashCode(): Int
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def
initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
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def
initializeLogIfNecessary(isInterpreter: Boolean): Unit
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final
val
inputCol: Param[String]
Param for input column name.
Param for input column name.
- Definition Classes
- HasInputCol
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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
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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
isTraceEnabled(): Boolean
- Attributes
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def
log: Logger
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def
logDebug(msg: ⇒ String, throwable: Throwable): Unit
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def
logDebug(msg: ⇒ String): Unit
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def
logError(msg: ⇒ String, throwable: Throwable): Unit
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def
logError(msg: ⇒ String): Unit
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def
logInfo(msg: ⇒ String, throwable: Throwable): 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(msg: ⇒ String): Unit
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def
logWarning(msg: ⇒ String, throwable: Throwable): Unit
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def
logWarning(msg: ⇒ String): Unit
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val
maxDF: DoubleParam
Specifies the maximum number of different documents a term could appear in to be included in the vocabulary.
Specifies the maximum number of different documents a term could appear in to be included in the vocabulary. A term that appears more than the threshold will be ignored. If this is an integer greater than or equal to 1, this specifies the maximum number of documents the term could appear in; if this is a double in [0,1), then this specifies the maximum fraction of documents the term could appear in.
Default: (263) - 1
- Definition Classes
- CountVectorizerParams
-
val
minDF: DoubleParam
Specifies the minimum number of different documents a term must appear in to be included in the vocabulary.
Specifies the minimum number of different documents a term must appear in to be included in the vocabulary. If this is an integer greater than or equal to 1, this specifies the number of documents the term must appear in; if this is a double in [0,1), then this specifies the fraction of documents.
Default: 1.0
- Definition Classes
- CountVectorizerParams
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val
minTF: DoubleParam
Filter to ignore rare words in a document.
Filter to ignore rare words in a document. For each document, terms with frequency/count less than the given threshold are ignored. If this is an integer greater than or equal to 1, then this specifies a count (of times the term must appear in the document); if this is a double in [0,1), then this specifies a fraction (out of the document's token count).
Note that the parameter is only used in transform of CountVectorizerModel and does not affect fitting.
Default: 1.0
- Definition Classes
- CountVectorizerParams
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final
def
ne(arg0: AnyRef): Boolean
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final
def
notify(): Unit
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- @native()
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final
def
notifyAll(): Unit
- Definition Classes
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final
val
outputCol: Param[String]
Param for output column name.
Param for output column name.
- Definition Classes
- HasOutputCol
-
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.
-
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( ... )
-
final
def
set(paramPair: ParamPair[_]): CountVectorizer.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): CountVectorizer.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): CountVectorizer.this.type
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.
- Definition Classes
- Params
-
def
setBinary(value: Boolean): CountVectorizer.this.type
- Annotations
- @Since( "2.0.0" )
-
final
def
setDefault(paramPairs: ParamPair[_]*): CountVectorizer.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): CountVectorizer.this.type
Sets a default value for a param.
Sets a default value for a param.
- param
param to set the default value. Make sure that this param is initialized before this method gets called.
- value
the default value
- Attributes
- protected
- Definition Classes
- Params
-
def
setInputCol(value: String): CountVectorizer.this.type
- Annotations
- @Since( "1.5.0" )
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def
setMaxDF(value: Double): CountVectorizer.this.type
- Annotations
- @Since( "2.4.0" )
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def
setMinDF(value: Double): CountVectorizer.this.type
- Annotations
- @Since( "1.5.0" )
-
def
setMinTF(value: Double): CountVectorizer.this.type
- Annotations
- @Since( "1.5.0" )
-
def
setOutputCol(value: String): CountVectorizer.this.type
- Annotations
- @Since( "1.5.0" )
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def
setVocabSize(value: Int): CountVectorizer.this.type
- Annotations
- @Since( "1.5.0" )
-
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
- CountVectorizer → PipelineStage
- Annotations
- @Since( "1.5.0" )
-
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()
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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
- CountVectorizer → Identifiable
- Annotations
- @Since( "1.5.0" )
-
def
validateAndTransformSchema(schema: StructType): StructType
Validates and transforms the input schema.
Validates and transforms the input schema.
- Attributes
- protected
- Definition Classes
- CountVectorizerParams
-
val
vocabSize: IntParam
Max size of the vocabulary.
Max size of the vocabulary. CountVectorizer will build a vocabulary that only considers the top vocabSize terms ordered by term frequency across the corpus.
Default: 218
- Definition Classes
- CountVectorizerParams
-
final
def
wait(): Unit
- Definition Classes
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final
def
wait(arg0: Long, arg1: Int): Unit
- Definition Classes
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- @throws( ... )
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final
def
wait(arg0: Long): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... ) @native()
-
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 CountVectorizerParams
Inherited from HasOutputCol
Inherited from HasInputCol
Inherited from Estimator[CountVectorizerModel]
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.