All Implemented Interfaces:
Serializable, org.apache.spark.internal.Logging, CountVectorizerParams, Params, HasInputCol, HasOutputCol, Identifiable, MLWritable

Converts a text document to a sparse vector of token counts. param: vocabulary An Array over terms. Only the terms in the vocabulary will be counted.

See Also:

  • Nested Class Summary

    Nested Classes

    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

    binary()

    Binary toggle to control the output vector values.

    Creates a copy of this instance with the same UID and some extra params.

    inputCol()

    Param for input column name.

    maxDF()

    Specifies the maximum number of different documents a term could appear in to be included in the vocabulary.

    minDF()

    Specifies the minimum number of different documents a term must appear in to be included in the vocabulary.

    minTF()

    Filter to ignore rare words in a document.

    outputCol()

    Param for output column name.

    read()

    setBinary(boolean value)

    setMinTF(double value)

    toString()

    Transforms the input dataset.

    Check transform validity and derive the output schema from the input schema.

    uid()

    An immutable unique ID for the object and its derivatives.

    vocabSize()

    Max size of the vocabulary.

    write()

    Returns an MLWriter instance for this ML instance.

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

    initializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, isTraceEnabled, log, logBasedOnLevel, logDebug, logDebug, logDebug, logDebug, logError, logError, logError, logError, logInfo, logInfo, logInfo, logInfo, logName, LogStringContext, logTrace, logTrace, logTrace, logTrace, logWarning, logWarning, logWarning, logWarning, MDC, org$apache$spark$internal$Logging$$log_, org$apache$spark$internal$Logging$$log__$eq, withLogContext

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

    save

  • Constructor Details

    • CountVectorizerModel

      public CountVectorizerModel(String uid, String[] vocabulary)

    • CountVectorizerModel

      public CountVectorizerModel(String[] vocabulary)

  • Method Details

    • read

    • load

    • 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^

      Specified by:
      vocabSize in interface CountVectorizerParams
      Returns:
      (undocumented)

    • 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

      Specified by:
      minDF in interface CountVectorizerParams
      Returns:
      (undocumented)

    • maxDF

      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: (2^63^) - 1

      Specified by:
      maxDF in interface CountVectorizerParams
      Returns:
      (undocumented)

    • 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

      Specified by:
      minTF in interface CountVectorizerParams
      Returns:
      (undocumented)

    • 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

      Specified by:
      binary in interface CountVectorizerParams
      Returns:
      (undocumented)

    • outputCol

      Param for output column name.

      Specified by:
      outputCol in interface HasOutputCol
      Returns:
      (undocumented)

    • inputCol

      Description copied from interface: HasInputCol

      Param for input column name.

      Specified by:
      inputCol in interface HasInputCol
      Returns:
      (undocumented)

    • uid

      An immutable unique ID for the object and its derivatives.

      Specified by:
      uid in interface Identifiable
      Returns:
      (undocumented)

    • vocabulary

      public String[] vocabulary()

    • setInputCol

    • setOutputCol

    • setMinTF

    • setBinary

    • transform

      Transforms the input dataset.

      Specified by:
      transform in class Transformer
      Parameters:
      dataset - (undocumented)
      Returns:
      (undocumented)

    • transformSchema

      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

      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 Model<CountVectorizerModel>
      Parameters:
      extra - (undocumented)
      Returns:
      (undocumented)

    • write

      Description copied from interface: MLWritable

      Returns an MLWriter instance for this ML instance.

      Specified by:
      write in interface MLWritable
      Returns:
      (undocumented)

    • toString

      Specified by:
      toString in interface Identifiable
      Overrides:
      toString in class Object