Package org.apache.spark.ml.feature

Class OneHotEncoder

Object
org.apache.spark.ml.PipelineStage
org.apache.spark.ml.Estimator<OneHotEncoderModel>
org.apache.spark.ml.feature.OneHotEncoder
All Implemented Interfaces:
Serializable, org.apache.spark.internal.Logging, OneHotEncoderBase, Params, HasHandleInvalid, HasInputCol, HasInputCols, HasOutputCol, HasOutputCols, DefaultParamsWritable, Identifiable, MLWritable, scala.Serializable
public class OneHotEncoder extends Estimator<OneHotEncoderModel> implements OneHotEncoderBase, DefaultParamsWritable
A one-hot encoder that maps a column of category indices to a column of binary vectors, with at most a single one-value per row that indicates the input category index. For example with 5 categories, an input value of 2.0 would map to an output vector of [0.0, 0.0, 1.0, 0.0]. The last category is not included by default (configurable via dropLast), because it makes the vector entries sum up to one, and hence linearly dependent. So an input value of 4.0 maps to [0.0, 0.0, 0.0, 0.0].

See Also:
  • StringIndexer for converting categorical values into category indices
  • Serialized Form
Note:
This is different from scikit-learn's OneHotEncoder, which keeps all categories. The output vectors are sparse.

When handleInvalid is configured to 'keep', an extra "category" indicating invalid values is added as last category. So when dropLast is true, invalid values are encoded as all-zeros vector.

, When encoding multi-column by using inputCols and outputCols params, input/output cols come in pairs, specified by the order in the arrays, and each pair is treated independently.

  • Constructor Details

    • OneHotEncoder

      public OneHotEncoder(String uid)

    • OneHotEncoder

      public OneHotEncoder()

  • Method Details

    • load

      public static OneHotEncoder load(String path)

    • read

      public static MLReader<T> read()

    • handleInvalid

      public Param<String> handleInvalid()
      Description copied from interface: OneHotEncoderBase
      Param for how to handle invalid data during transform(). Options are 'keep' (invalid data presented as an extra categorical feature) or 'error' (throw an error). Note that this Param is only used during transform; during fitting, invalid data will result in an error. Default: "error"
      Specified by:
      handleInvalid in interface HasHandleInvalid
      Specified by:
      handleInvalid in interface OneHotEncoderBase
      Returns:
      (undocumented)

    • dropLast

      public final BooleanParam dropLast()
      Description copied from interface: OneHotEncoderBase
      Whether to drop the last category in the encoded vector (default: true)
      Specified by:
      dropLast in interface OneHotEncoderBase
      Returns:
      (undocumented)

    • outputCols

      public final StringArrayParam outputCols()
      Description copied from interface: HasOutputCols
      Param for output column names.
      Specified by:
      outputCols in interface HasOutputCols
      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)

    • inputCols

      public final StringArrayParam inputCols()
      Description copied from interface: HasInputCols
      Param for input column names.
      Specified by:
      inputCols in interface HasInputCols
      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)

    • setInputCol

      public OneHotEncoder setInputCol(String value)

    • setOutputCol

      public OneHotEncoder setOutputCol(String value)

    • setInputCols

      public OneHotEncoder setInputCols(String[] values)

    • setOutputCols

      public OneHotEncoder setOutputCols(String[] values)

    • setDropLast

      public OneHotEncoder setDropLast(boolean value)

    • setHandleInvalid

      public OneHotEncoder setHandleInvalid(String value)

    • 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)

    • fit

      public OneHotEncoderModel fit(Dataset<?> dataset)
      Description copied from class: Estimator
      Fits a model to the input data.
      Specified by:
      fit in class Estimator<OneHotEncoderModel>
      Parameters:
      dataset - (undocumented)
      Returns:
      (undocumented)

    • copy

      public OneHotEncoder 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<OneHotEncoderModel>
      Parameters:
      extra - (undocumented)
      Returns:
      (undocumented)