Packages

package featurize

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Type Members

  1. class CleanMissingData extends Estimator[CleanMissingDataModel] with HasInputCols with HasOutputCols with Wrappable with DefaultParamsWritable with BasicLogging

    Removes missing values from input dataset.

    Removes missing values from input dataset. The following modes are supported: Mean - replaces missings with mean of fit column Median - replaces missings with approximate median of fit column Custom - replaces missings with custom value specified by user For mean and median modes, only numeric column types are supported, specifically: Int, Long, Float, Double For custom mode, the types above are supported and additionally: String, Boolean

  2. class CleanMissingDataModel extends Model[CleanMissingDataModel] with ComplexParamsWritable with Wrappable with HasInputCols with HasOutputCols with BasicLogging

    Model produced by CleanMissingData.

  3. class CountSelector extends Estimator[CountSelectorModel] with Wrappable with DefaultParamsWritable with HasInputCol with HasOutputCol with BasicLogging

    Drops vector indicies with no nonzero data.

  4. class CountSelectorModel extends Model[CountSelectorModel] with HasInputCol with HasOutputCol with DefaultParamsWritable with Wrappable with BasicLogging
  5. class DataConversion extends Transformer with Wrappable with DefaultParamsWritable with BasicLogging

    Converts the specified list of columns to the specified type.

    Converts the specified list of columns to the specified type. Returns a new DataFrame with the converted columns

  6. class Featurize extends Estimator[PipelineModel] with Wrappable with DefaultParamsWritable with HasOutputCol with HasInputCols with BasicLogging

    Featurizes a dataset.

    Featurizes a dataset. Converts the specified columns to feature columns.

  7. class IndexToValue extends Transformer with HasInputCol with HasOutputCol with Wrappable with DefaultParamsWritable with BasicLogging

    This class takes in a categorical column with MML style attributes and then transforms it back to the original values.

    This class takes in a categorical column with MML style attributes and then transforms it back to the original values. This extends sparkML IndexToString by allowing the transformation back to any types of values.

  8. class NullOrdering[T] extends Ordering[T]
  9. class ValueIndexer extends Estimator[ValueIndexerModel] with ValueIndexerParams with BasicLogging

    Fits a dictionary of values from the input column.

    Fits a dictionary of values from the input column. Model then transforms a column to a categorical column of the given array of values. Similar to StringIndexer except it can be used on any value types.

  10. class ValueIndexerModel extends Model[ValueIndexerModel] with ValueIndexerParams with ComplexParamsWritable with BasicLogging

    Model produced by ValueIndexer.

  11. trait ValueIndexerParams extends Wrappable with DefaultParamsWritable with HasInputCol with HasOutputCol

Value Members

  1. object CleanMissingData extends DefaultParamsReadable[CleanMissingData] with Serializable
  2. object CleanMissingDataModel extends ComplexParamsReadable[CleanMissingDataModel] with Serializable
  3. object CountSelector extends DefaultParamsReadable[CountSelector] with Serializable
  4. object CountSelectorModel extends DefaultParamsReadable[CountSelectorModel] with Serializable
  5. object DataConversion extends DefaultParamsReadable[DataConversion] with Serializable
  6. object Featurize extends DefaultParamsReadable[Featurize] with Serializable
  7. object IndexToValue extends DefaultParamsReadable[IndexToValue] with Serializable
  8. object NullOrdering extends Serializable
  9. object ValueIndexer extends DefaultParamsReadable[ValueIndexer] with Serializable
  10. object ValueIndexerModel extends ComplexParamsReadable[ValueIndexerModel] with Serializable

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