Packages

class MinHashLSHModel extends LSHModel[MinHashLSHModel]

Model produced by MinHashLSH, where multiple hash functions are stored. Each hash function is picked from the following family of hash functions, where a_i and b_i are randomly chosen integers less than prime: h_i(x) = ((x \cdot a_i + b_i) \mod prime)

This hash family is approximately min-wise independent according to the reference.

Reference: Tom Bohman, Colin Cooper, and Alan Frieze. "Min-wise independent linear permutations." Electronic Journal of Combinatorics 7 (2000): R26.

Annotations
@Since( "2.1.0" )
Source
MinHashLSH.scala
Linear Supertypes
LSHModel[MinHashLSHModel], MLWritable, LSHParams, HasOutputCol, HasInputCol, Model[MinHashLSHModel], Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. MinHashLSHModel
  2. LSHModel
  3. MLWritable
  4. LSHParams
  5. HasOutputCol
  6. HasInputCol
  7. Model
  8. Transformer
  9. PipelineStage
  10. Logging
  11. Params
  12. Serializable
  13. Serializable
  14. Identifiable
  15. AnyRef
  16. Any
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Visibility
  1. Public
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Parameters

A list of (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.

  1. final val inputCol: Param[String]

    Param for input column name.

    Param for input column name.

    Definition Classes
    HasInputCol
  2. final val numHashTables: IntParam

    Param for the number of hash tables used in LSH OR-amplification.

    Param for the number of hash tables used in LSH OR-amplification.

    LSH OR-amplification can be used to reduce the false negative rate. Higher values for this param lead to a reduced false negative rate, at the expense of added computational complexity.

    Definition Classes
    LSHParams
  3. final val outputCol: Param[String]

    Param for output column name.

    Param for output column name.

    Definition Classes
    HasOutputCol

Members

  1. def approxNearestNeighbors(dataset: Dataset[_], key: Vector, numNearestNeighbors: Int): Dataset[_]

    Overloaded method for approxNearestNeighbors.

    Overloaded method for approxNearestNeighbors. Use "distCol" as default distCol.

    Definition Classes
    LSHModel
  2. def approxNearestNeighbors(dataset: Dataset[_], key: Vector, numNearestNeighbors: Int, distCol: String): Dataset[_]

    Given a large dataset and an item, approximately find at most k items which have the closest distance to the item.

    Given a large dataset and an item, approximately find at most k items which have the closest distance to the item. If the outputCol is missing, the method will transform the data; if the outputCol exists, it will use the outputCol. This allows caching of the transformed data when necessary.

    dataset

    The dataset to search for nearest neighbors of the key.

    key

    Feature vector representing the item to search for.

    numNearestNeighbors

    The maximum number of nearest neighbors.

    distCol

    Output column for storing the distance between each result row and the key.

    returns

    A dataset containing at most k items closest to the key. A column "distCol" is added to show the distance between each row and the key.

    Definition Classes
    LSHModel
    Note

    This method is experimental and will likely change behavior in the next release.

  3. def approxSimilarityJoin(datasetA: Dataset[_], datasetB: Dataset[_], threshold: Double): Dataset[_]

    Overloaded method for approxSimilarityJoin.

    Overloaded method for approxSimilarityJoin. Use "distCol" as default distCol.

    Definition Classes
    LSHModel
  4. def approxSimilarityJoin(datasetA: Dataset[_], datasetB: Dataset[_], threshold: Double, distCol: String): Dataset[_]

    Join two datasets to approximately find all pairs of rows whose distance are smaller than the threshold.

    Join two datasets to approximately find all pairs of rows whose distance are smaller than the threshold. If the outputCol is missing, the method will transform the data; if the outputCol exists, it will use the outputCol. This allows caching of the transformed data when necessary.

    datasetA

    One of the datasets to join.

    datasetB

    Another dataset to join.

    threshold

    The threshold for the distance of row pairs.

    distCol

    Output column for storing the distance between each pair of rows.

    returns

    A joined dataset containing pairs of rows. The original rows are in columns "datasetA" and "datasetB", and a column "distCol" is added to show the distance between each pair.

    Definition Classes
    LSHModel
  5. final def clear(param: Param[_]): MinHashLSHModel.this.type

    Clears the user-supplied value for the input param.

    Clears the user-supplied value for the input param.

    Definition Classes
    Params
  6. def copy(extra: ParamMap): MinHashLSHModel

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

    Definition Classes
    MinHashLSHModelModelTransformerPipelineStageParams
    Annotations
    @Since( "2.1.0" )
  7. def explainParam(param: Param[_]): String

    Explains a param.

    Explains a param.

    param

    input param, must belong to this instance.

    returns

    a string that contains the input param name, doc, and optionally its default value and the user-supplied value

    Definition Classes
    Params
  8. def explainParams(): String

    Explains all params of this instance.

    Explains all params of this instance. See explainParam().

    Definition Classes
    Params
  9. final def extractParamMap(): ParamMap

    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes
    Params
  10. final def extractParamMap(extra: ParamMap): ParamMap

    Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.

    Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.

    Definition Classes
    Params
  11. final def get[T](param: Param[T]): Option[T]

    Optionally returns the user-supplied value of a param.

    Optionally returns the user-supplied value of a param.

    Definition Classes
    Params
  12. final def getDefault[T](param: Param[T]): Option[T]

    Gets the default value of a parameter.

    Gets the default value of a parameter.

    Definition Classes
    Params
  13. final def getOrDefault[T](param: Param[T]): T

    Gets the value of a param in the embedded param map or its default value.

    Gets the value of a param in the embedded param map or its default value. Throws an exception if neither is set.

    Definition Classes
    Params
  14. def getParam(paramName: String): Param[Any]

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  15. final def hasDefault[T](param: Param[T]): Boolean

    Tests whether the input param has a default value set.

    Tests whether the input param has a default value set.

    Definition Classes
    Params
  16. def hasParam(paramName: String): Boolean

    Tests whether this instance contains a param with a given name.

    Tests whether this instance contains a param with a given name.

    Definition Classes
    Params
  17. def hasParent: Boolean

    Indicates whether this Model has a corresponding parent.

    Indicates whether this Model has a corresponding parent.

    Definition Classes
    Model
  18. final def isDefined(param: Param[_]): Boolean

    Checks whether a param is explicitly set or has a default value.

    Checks whether a param is explicitly set or has a default value.

    Definition Classes
    Params
  19. final def isSet(param: Param[_]): Boolean

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  20. lazy val params: Array[Param[_]]

    Returns all params sorted by their names.

    Returns all params sorted by their names. The default implementation uses Java reflection to list all public methods that have no arguments and return Param.

    Definition Classes
    Params
    Note

    Developer should not use this method in constructor because we cannot guarantee that this variable gets initialized before other params.

  21. var parent: Estimator[MinHashLSHModel]

    The parent estimator that produced this model.

    The parent estimator that produced this model.

    Definition Classes
    Model
    Note

    For ensembles' component Models, this value can be null.

  22. def save(path: String): Unit

    Saves this ML instance to the input path, a shortcut of write.save(path).

    Saves this ML instance to the input path, a shortcut of write.save(path).

    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  23. final def set[T](param: Param[T], value: T): MinHashLSHModel.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Definition Classes
    Params
  24. def setParent(parent: Estimator[MinHashLSHModel]): MinHashLSHModel

    Sets the parent of this model (Java API).

    Sets the parent of this model (Java API).

    Definition Classes
    Model
  25. def toString(): String
    Definition Classes
    MinHashLSHModelIdentifiable → AnyRef → Any
    Annotations
    @Since( "3.0.0" )
  26. def transform(dataset: Dataset[_]): DataFrame

    Transforms the input dataset.

    Transforms the input dataset.

    Definition Classes
    LSHModel → Transformer
  27. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame

    Transforms the dataset with provided parameter map as additional parameters.

    Transforms the dataset with provided parameter map as additional parameters.

    dataset

    input dataset

    paramMap

    additional parameters, overwrite embedded params

    returns

    transformed dataset

    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  28. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame

    Transforms the dataset with optional parameters

    Transforms the dataset with optional parameters

    dataset

    input dataset

    firstParamPair

    the first param pair, overwrite embedded params

    otherParamPairs

    other param pairs, overwrite embedded params

    returns

    transformed dataset

    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  29. def transformSchema(schema: StructType): StructType

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

    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.

    Definition Classes
    LSHModel → PipelineStage
  30. val uid: String

    An immutable unique ID for the object and its derivatives.

    An immutable unique ID for the object and its derivatives.

    Definition Classes
    MinHashLSHModelIdentifiable
  31. def write: MLWriter

    Returns an MLWriter instance for this ML instance.

    Returns an MLWriter instance for this ML instance.

    Definition Classes
    MinHashLSHModelMLWritable
    Annotations
    @Since( "2.1.0" )

Parameter setters

  1. def setInputCol(value: String): MinHashLSHModel.this.type

    Definition Classes
    MinHashLSHModel → LSHModel
    Annotations
    @Since( "2.4.0" )
  2. def setOutputCol(value: String): MinHashLSHModel.this.type

    Definition Classes
    MinHashLSHModel → LSHModel
    Annotations
    @Since( "2.4.0" )

Parameter getters

  1. final def getInputCol: String

    Definition Classes
    HasInputCol
  2. final def getNumHashTables: Int

    Definition Classes
    LSHParams
  3. final def getOutputCol: String

    Definition Classes
    HasOutputCol