sampleclean.clean.featurize

Featurizer

abstract class Featurizer extends AnyRef

The featurizer Abstract Class takes a set of records and returns a list of numbers in R^d

We define featurization to be symmetric where the features of {a, b} == {b, a}

Note: one challenge is that the internal schema of an algorithm, namely, the schema of intermediate states may not be visible to the user. That is why the featurizer class contains a "context" variable which is a list of strings that represent the column names. Internally, algorithms can set the current context to allow for seamless use.

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Instance Constructors

  1. new Featurizer(colNames: List[String], contextIn: List[String])

    colNames

    names of columns that will be used for featurization

    contextIn

    names of all columns for the existing dataset

Abstract Value Members

  1. abstract def featurize[K, V](rows: Set[Row], params: Map[K, V] = null): (Set[Row], Array[Double])

    This function takes a set of rows, takes a set of parameters that are specified in a Map and returns a tuple.

    This function takes a set of rows, takes a set of parameters that are specified in a Map and returns a tuple.

    The first element of the tuple is a set of primary keys and the second element is an array of doubles which are the features.

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  1. final def !=(arg0: AnyRef): Boolean

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  8. var cols: List[Int]

  9. var context: List[String]

  10. final def eq(arg0: AnyRef): Boolean

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