Package org.apache.spark.ml.feature

Class CountVectorizerModel

Object
org.apache.spark.ml.PipelineStage
org.apache.spark.ml.Transformer
org.apache.spark.ml.Model<CountVectorizerModel>
org.apache.spark.ml.feature.CountVectorizerModel
All Implemented Interfaces:
Serializable, org.apache.spark.internal.Logging, CountVectorizerParams, Params, HasInputCol, HasOutputCol, Identifiable, MLWritable
public class CountVectorizerModel extends Model<CountVectorizerModel> implements CountVectorizerParams, 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:

  • Constructor Details

    • CountVectorizerModel

      public CountVectorizerModel(String uid, String[] vocabulary)

    • CountVectorizerModel

      public CountVectorizerModel(String[] vocabulary)

  • Method Details

    • read

      public static MLReader<CountVectorizerModel> read()

    • load

      public static CountVectorizerModel load(String path)

    • vocabSize

      public IntParam vocabSize()
      Description copied from interface: CountVectorizerParams
      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

      public DoubleParam minDF()
      Description copied from interface: CountVectorizerParams
      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

      public DoubleParam maxDF()
      Description copied from interface: CountVectorizerParams
      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

      public DoubleParam minTF()
      Description copied from interface: CountVectorizerParams
      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

      public BooleanParam binary()
      Description copied from interface: CountVectorizerParams
      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

      public final Param<String> outputCol()
      Description copied from interface: HasOutputCol
      Param for output column name.
      Specified by:
      outputCol in interface HasOutputCol
      Returns:
      (undocumented)

    • inputCol

      public final Param<String> inputCol()
      Description copied from interface: HasInputCol
      Param for input column name.
      Specified by:
      inputCol in interface HasInputCol
      Returns:
      (undocumented)

    • 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)

    • vocabulary

      public String[] vocabulary()

    • setInputCol

      public CountVectorizerModel setInputCol(String value)

    • setOutputCol

      public CountVectorizerModel setOutputCol(String value)

    • setMinTF

      public CountVectorizerModel setMinTF(double value)

    • setBinary

      public CountVectorizerModel setBinary(boolean value)

    • transform

      public Dataset<Row> transform(Dataset<?> dataset)
      Description copied from class: Transformer
      Transforms the input dataset.
      Specified by:
      transform in class Transformer
      Parameters:
      dataset - (undocumented)
      Returns:
      (undocumented)

    • transformSchema

      public StructType transformSchema(StructType schema)
      Description copied from class: PipelineStage
      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 CountVectorizerModel 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 Model<CountVectorizerModel>
      Parameters:
      extra - (undocumented)
      Returns:
      (undocumented)

    • write

      public MLWriter write()
      Description copied from interface: MLWritable
      Returns an MLWriter instance for this ML instance.
      Specified by:
      write in interface MLWritable
      Returns:
      (undocumented)

    • toString

      public String toString()
      Specified by:
      toString in interface Identifiable
      Overrides:
      toString in class Object