CountVectorizer (Spark 2.3.0 JavaDoc)

Object
- org.apache.spark.ml.PipelineStage
- - org.apache.spark.ml.Estimator<CountVectorizerModel>
  - - org.apache.spark.ml.feature.CountVectorizer

All Implemented Interfaces:

java.io.Serializable, Logging, Params, DefaultParamsWritable, Identifiable, MLWritable
```
public class CountVectorizer
extends Estimator<CountVectorizerModel>
implements DefaultParamsWritable
```
Extracts a vocabulary from document collections and generates a CountVectorizerModel.

See Also:

Serialized Form

Constructor Summary

Constructors
Constructor and Description

CountVectorizer()

CountVectorizer(String uid)

Constructors
Constructor and Description
`CountVectorizer()`
`CountVectorizer(String uid)`

Method Summary

All Methods Static Methods Instance Methods Abstract Methods Concrete Methods
Modifier and Type	Method and Description
`static BooleanParam`	`binary()`
`BooleanParam`	`binary()` Binary toggle to control the output vector values.
`static Params`	`clear(Param<?> param)`
`CountVectorizer`	`copy(ParamMap extra)` Creates a copy of this instance with the same UID and some extra params.
`static String`	`explainParam(Param<?> param)`
`static String`	`explainParams()`
`static ParamMap`	`extractParamMap()`
`static ParamMap`	`extractParamMap(ParamMap extra)`
`CountVectorizerModel`	`fit(Dataset<?> dataset)` Fits a model to the input data.
`static <T> scala.Option<T>`	`get(Param<T> param)`
`static boolean`	`getBinary()`
`boolean`	`getBinary()`
`static <T> scala.Option<T>`	`getDefault(Param<T> param)`
`static String`	`getInputCol()`
`static double`	`getMinDF()`
`double`	`getMinDF()`
`static double`	`getMinTF()`
`double`	`getMinTF()`
`static <T> T`	`getOrDefault(Param<T> param)`
`static String`	`getOutputCol()`
`static Param<Object>`	`getParam(String paramName)`
`static int`	`getVocabSize()`
`int`	`getVocabSize()`
`static <T> boolean`	`hasDefault(Param<T> param)`
`static boolean`	`hasParam(String paramName)`
`static Param<String>`	`inputCol()`
`static boolean`	`isDefined(Param<?> param)`
`static boolean`	`isSet(Param<?> param)`
`static CountVectorizer`	`load(String path)`
`static DoubleParam`	`minDF()`
`DoubleParam`	`minDF()` Specifies the minimum number of different documents a term must appear in to be included in the vocabulary.
`static DoubleParam`	`minTF()`
`DoubleParam`	`minTF()` Filter to ignore rare words in a document.
`static Param<String>`	`outputCol()`
`static Param<?>[]`	`params()`
`static void`	`save(String path)`
`static <T> Params`	`set(Param<T> param, T value)`
`CountVectorizer`	`setBinary(boolean value)`
`CountVectorizer`	`setInputCol(String value)`
`CountVectorizer`	`setMinDF(double value)`
`CountVectorizer`	`setMinTF(double value)`
`CountVectorizer`	`setOutputCol(String value)`
`CountVectorizer`	`setVocabSize(int value)`
`static String`	`toString()`
`StructType`	`transformSchema(StructType schema)` :: DeveloperApi ::
`String`	`uid()` An immutable unique ID for the object and its derivatives.
`StructType`	`validateAndTransformSchema(StructType schema)` Validates and transforms the input schema.
`static IntParam`	`vocabSize()`
`IntParam`	`vocabSize()` Max size of the vocabulary.
`static MLWriter`	`write()`

Methods inherited from class org.apache.spark.ml.Estimator
fit, fit, fit, fit

Methods inherited from class Object
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface org.apache.spark.ml.param.shared.HasInputCol
getInputCol, inputCol

Methods inherited from interface org.apache.spark.ml.param.shared.HasOutputCol
getOutputCol, outputCol

Methods 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, paramMap, params, set, set, set, setDefault, setDefault, shouldOwn

Methods inherited from interface org.apache.spark.ml.util.Identifiable
toString

Methods inherited from interface org.apache.spark.ml.util.DefaultParamsWritable
write

Methods inherited from interface org.apache.spark.ml.util.MLWritable
save

Methods inherited from interface org.apache.spark.internal.Logging
initializeLogging, initializeLogIfNecessary, initializeLogIfNecessary, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning

- Constructor Detail
  - CountVectorizer
```
public CountVectorizer(String uid)
```
  - CountVectorizer
```
public CountVectorizer()
```
- Method Detail
  - load
```
public static CountVectorizer load(String path)
```
  - toString
```
public static String toString()
```
  - params
```
public static Param<?>[] params()
```
  - explainParam
```
public static String explainParam(Param<?> param)
```
  - explainParams
```
public static String explainParams()
```
  - isSet
```
public static final boolean isSet(Param<?> param)
```
  - isDefined
```
public static final boolean isDefined(Param<?> param)
```
  - hasParam
```
public static boolean hasParam(String paramName)
```
  - getParam
```
public static Param<Object> getParam(String paramName)
```
  - set
```
public static final <T> Params set(Param<T> param,
                                   T value)
```
  - get
```
public static final <T> scala.Option<T> get(Param<T> param)
```
  - clear
```
public static final Params clear(Param<?> param)
```
  - getOrDefault
```
public static final <T> T getOrDefault(Param<T> param)
```
  - getDefault
```
public static final <T> scala.Option<T> getDefault(Param<T> param)
```
  - hasDefault
```
public static final <T> boolean hasDefault(Param<T> param)
```
  - extractParamMap
```
public static final ParamMap extractParamMap(ParamMap extra)
```
  - extractParamMap
```
public static final ParamMap extractParamMap()
```
  - inputCol
```
public static final Param<String> inputCol()
```
  - getInputCol
```
public static final String getInputCol()
```
  - outputCol
```
public static final Param<String> outputCol()
```
  - getOutputCol
```
public static final String getOutputCol()
```
  - vocabSize
```
public static IntParam vocabSize()
```
  - getVocabSize
```
public static int getVocabSize()
```
  - minDF
```
public static DoubleParam minDF()
```
  - getMinDF
```
public static double getMinDF()
```
  - minTF
```
public static DoubleParam minTF()
```
  - getMinTF
```
public static double getMinTF()
```
  - binary
```
public static BooleanParam binary()
```
  - getBinary
```
public static boolean getBinary()
```
  - save
```
public static void save(String path)
                 throws java.io.IOException
```
    Throws:
    
    java.io.IOException
  - write
```
public static MLWriter write()
```
  - uid
```
public String uid()
```
    Description copied from interface: Identifiable
    
    An immutable unique ID for the object and its derivatives.
    
    Specified by:
    
    uid in interface Identifiable
    
    Returns:
    
    (undocumented)
  - setInputCol
```
public CountVectorizer setInputCol(String value)
```
  - setOutputCol
```
public CountVectorizer setOutputCol(String value)
```
  - setVocabSize
```
public CountVectorizer setVocabSize(int value)
```
  - setMinDF
```
public CountVectorizer setMinDF(double value)
```
  - setMinTF
```
public CountVectorizer setMinTF(double value)
```
  - setBinary
```
public CountVectorizer setBinary(boolean value)
```
  - fit
```
public CountVectorizerModel fit(Dataset<?> dataset)
```
    Description copied from class: Estimator
    
    Fits a model to the input data.
    
    Specified by:
    
    fit in class Estimator<CountVectorizerModel>
    
    Parameters:
    
    dataset - (undocumented)
    
    Returns:
    
    (undocumented)
  - transformSchema
```
public StructType transformSchema(StructType schema)
```
    Description copied from class: PipelineStage
    
    :: DeveloperApi ::
    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 by Param.validate().
    Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.
    
    Specified by:
    
    transformSchema in class PipelineStage
    
    Parameters:
    
    schema - (undocumented)
    
    Returns:
    
    (undocumented)
  - copy
```
public CountVectorizer copy(ParamMap extra)
```
    Description copied from interface: 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().
    
    Specified by:
    
    copy in interface Params
    
    Specified by:
    
    copy in class Estimator<CountVectorizerModel>
    
    Parameters:
    
    extra - (undocumented)
    
    Returns:
    
    (undocumented)
  - vocabSize
```
public IntParam vocabSize()
```
    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: 2^18^
    
    Returns:
    
    (undocumented)
  - getVocabSize
```
public int getVocabSize()
```
  - minDF
```
public DoubleParam minDF()
```
    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
    
    Returns:
    
    (undocumented)
  - getMinDF
```
public double getMinDF()
```
  - validateAndTransformSchema
```
public StructType validateAndTransformSchema(StructType schema)
```
    Validates and transforms the input schema.
  - minTF
```
public DoubleParam minTF()
```
    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
    
    Returns:
    
    (undocumented)
  - getMinTF
```
public double getMinTF()
```
  - binary
```
public BooleanParam binary()
```
    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
    
    Returns:
    
    (undocumented)
  - getBinary
```
public boolean getBinary()
```

Class CountVectorizer

Constructor Summary

Method Summary

Methods inherited from class org.apache.spark.ml.Estimator

Methods inherited from class Object

Methods inherited from interface org.apache.spark.ml.param.shared.HasInputCol

Methods inherited from interface org.apache.spark.ml.param.shared.HasOutputCol

Methods inherited from interface org.apache.spark.ml.param.Params

Methods inherited from interface org.apache.spark.ml.util.Identifiable

Methods inherited from interface org.apache.spark.ml.util.DefaultParamsWritable

Methods inherited from interface org.apache.spark.ml.util.MLWritable

Methods inherited from interface org.apache.spark.internal.Logging

Constructor Detail

CountVectorizer

CountVectorizer

Method Detail

load

toString

params

explainParam

explainParams

isSet

isDefined

hasParam

getParam

set

get

clear

getOrDefault

getDefault

hasDefault

extractParamMap

extractParamMap

inputCol

getInputCol

outputCol

getOutputCol

vocabSize

getVocabSize

minDF

getMinDF

minTF

getMinTF

binary

getBinary

save

write

uid

setInputCol

setOutputCol

setVocabSize

setMinDF

setMinTF

setBinary

fit

transformSchema

copy

vocabSize

getVocabSize

minDF

getMinDF

validateAndTransformSchema

minTF

getMinTF

binary

getBinary