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Showing content from http://spark.apache.org/docs/latest/api/java/org/apache/spark/ml/feature/MinHashLSHModel.html below:

MinHashLSHModel (Spark 4.0.0 JavaDoc)

All Implemented Interfaces:: Serializable, org.apache.spark.internal.Logging, LSHParams, Params, HasInputCol, HasOutputCol, Identifiable, MLWritable

public class MinHashLSHModel extends Model<T>

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.

param: randCoefficients Pairs of random coefficients. Each pair is used by one hash function.

See Also:

Serialized Form

Nested Class Summary
Nested classes/interfaces inherited from interface org.apache.spark.internal.Loggingorg.apache.spark.internal.Logging.LogStringContext, org.apache.spark.internal.Logging.SparkShellLoggingFilter
Method Summary
Overloaded method for approxNearestNeighbors.

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

Overloaded method for approxSimilarityJoin.

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

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

Param for input column name.

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

Param for output column name.

Transforms the input dataset.

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

An immutable unique ID for the object and its derivatives.

Returns an MLWriter instance for this ML instance.

Methods inherited from interface org.apache.spark.internal.LogginginitializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, isTraceEnabled, log, logDebug, logDebug, logDebug, logDebug, logError, logError, logError, logError, logInfo, logInfo, logInfo, logInfo, logName, LogStringContext, logTrace, logTrace, logTrace, logTrace, logWarning, logWarning, logWarning, logWarning, org$apache$spark$internal$Logging$$log_, org$apache$spark$internal$Logging$$log__$eq, withLogContext
Methods inherited from interface org.apache.spark.ml.util.MLWritablesave Methods inherited from interface org.apache.spark.ml.param.Paramsclear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, onParamChange, paramMap, params, set, set, set, setDefault, setDefault, shouldOwn

Method Details
- read
- load
- uid
  An immutable unique ID for the object and its derivatives.
  
  Returns:
  
  (undocumented)
- setInputCol
- setOutputCol
- copy 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<MinHashLSHModel>
  
  Parameters:
  
  extra - (undocumented)
  
  Returns:
  
  (undocumented)
- write
  Returns an MLWriter instance for this ML instance.
  
  Returns:
  
  (undocumented)
- toString
  
  Specified by:
  
  toString in interface Identifiable
  
  Overrides:
  
  toString in class Object
- approxNearestNeighbors
  
  Parameters:
  
  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.
  
  Note:
  
  This method is experimental and will likely change behavior in the next release.
- approxNearestNeighbors
  Overloaded method for approxNearestNeighbors. Use "distCol" as default distCol.
  
  Parameters:
  
  dataset - (undocumented)
  
  key - (undocumented)
  
  numNearestNeighbors - (undocumented)
  
  Returns:
  
  (undocumented)
- approxSimilarityJoin
  
  Parameters:
  
  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.
- approxSimilarityJoin
  Overloaded method for approxSimilarityJoin. Use "distCol" as default distCol.
  
  Parameters:
  
  datasetA - (undocumented)
  
  datasetB - (undocumented)
  
  threshold - (undocumented)
  
  Returns:
  
  (undocumented)
- inputCol
  Param for input column name.
  
  Specified by:
  
  inputCol in interface HasInputCol
  
  Returns:
  
  (undocumented)
- numHashTables
  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.
  
  Specified by:
  
  numHashTables in interface LSHParams
  
  Returns:
  
  (undocumented)
- outputCol
  Param for output column name.
  
  Specified by:
  
  outputCol in interface HasOutputCol
  
  Returns:
  
  (undocumented)
- transform
  Transforms the input dataset.
  
  Specified by:
  
  transform in class Transformer
  
  Parameters:
  
  dataset - (undocumented)
  
  Returns:
  
  (undocumented)
- transformSchema
  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)

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