class CountVectorizerModel extends Model[CountVectorizerModel] with CountVectorizerParams with MLWritable
Converts a text document to a sparse vector of token counts.
- Annotations
- @Since( "1.5.0" )
- Source
- CountVectorizer.scala
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- CountVectorizerModel
- MLWritable
- CountVectorizerParams
- HasOutputCol
- HasInputCol
- Model
- Transformer
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- Logging
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Parameters
A list of (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.
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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
val
inputCol: Param[String]
Param for input column name.
Param for input column name.
- Definition Classes
- HasInputCol
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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
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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
val
outputCol: Param[String]
Param for output column name.
Param for output column name.
- Definition Classes
- HasOutputCol
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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
Members
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final
def
clear(param: Param[_]): CountVectorizerModel.this.type
Clears the user-supplied value for the input param.
Clears the user-supplied value for the input param.
- Definition Classes
- Params
-
def
copy(extra: ParamMap): CountVectorizerModel
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
- CountVectorizerModel → Model → Transformer → PipelineStage → Params
- Annotations
- @Since( "1.5.0" )
-
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
-
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
extractParamMap
with no extra values.extractParamMap
with no extra values.- Definition Classes
- Params
-
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
- Params
-
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
-
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
-
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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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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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
-
def
hasParent: Boolean
Indicates whether this Model has a corresponding parent.
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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
-
final
def
isSet(param: Param[_]): Boolean
Checks whether a param is explicitly set.
Checks whether a param is explicitly set.
- Definition Classes
- Params
-
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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var
parent: Estimator[CountVectorizerModel]
The parent estimator that produced this model.
The parent estimator that produced this model.
- Definition Classes
- Model
- Note
For ensembles' component Models, this value can be null.
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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
- Annotations
- @Since( "1.6.0" ) @throws( ... )
-
final
def
set[T](param: Param[T], value: T): CountVectorizerModel.this.type
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.
- Definition Classes
- Params
-
def
setParent(parent: Estimator[CountVectorizerModel]): CountVectorizerModel
Sets the parent of this model (Java API).
Sets the parent of this model (Java API).
- Definition Classes
- Model
-
def
toString(): String
- Definition Classes
- CountVectorizerModel → Identifiable → AnyRef → Any
- Annotations
- @Since( "3.0.0" )
-
def
transform(dataset: Dataset[_]): DataFrame
Transforms the input dataset.
Transforms the input dataset.
- Definition Classes
- CountVectorizerModel → Transformer
- Annotations
- @Since( "2.0.0" )
-
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
- Annotations
- @Since( "2.0.0" )
-
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
- Transformer
- Annotations
- @Since( "2.0.0" ) @varargs()
-
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
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.
- Definition Classes
- CountVectorizerModel → PipelineStage
- Annotations
- @Since( "1.5.0" )
-
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
- CountVectorizerModel → Identifiable
- Annotations
- @Since( "1.5.0" )
-
val
vocabulary: Array[String]
- Annotations
- @Since( "1.5.0" )
-
def
write: MLWriter
Returns an
MLWriter
instance for this ML instance.Returns an
MLWriter
instance for this ML instance.- Definition Classes
- CountVectorizerModel → MLWritable
- Annotations
- @Since( "1.6.0" )
Parameter setters
-
def
setBinary(value: Boolean): CountVectorizerModel.this.type
- Annotations
- @Since( "2.0.0" )
-
def
setInputCol(value: String): CountVectorizerModel.this.type
- Annotations
- @Since( "1.5.0" )
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def
setMinTF(value: Double): CountVectorizerModel.this.type
- Annotations
- @Since( "1.5.0" )
-
def
setOutputCol(value: String): CountVectorizerModel.this.type
- Annotations
- @Since( "1.5.0" )
Parameter getters
-
def
getBinary: Boolean
- Definition Classes
- CountVectorizerParams
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final
def
getInputCol: String
- Definition Classes
- HasInputCol
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def
getMaxDF: Double
- Definition Classes
- CountVectorizerParams
-
def
getMinDF: Double
- Definition Classes
- CountVectorizerParams
-
def
getMinTF: Double
- Definition Classes
- CountVectorizerParams
-
final
def
getOutputCol: String
- Definition Classes
- HasOutputCol
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def
getVocabSize: Int
- Definition Classes
- CountVectorizerParams