org.apache.spark.ml.feature

Class MinHashLSHModel

Object

org.apache.spark.ml.PipelineStage

org.apache.spark.ml.Transformer

org.apache.spark.ml.Model<T>

org.apache.spark.ml.feature.MinHashLSHModel

All Implemented Interfaces:

java.io.Serializable, Params, Identifiable, MLWritable

public class MinHashLSHModel
extends Model<T>

:: Experimental ::

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

Method Summary

Methods

Modifier and Type	Method and Description
static Dataset<?>	approxNearestNeighbors(Dataset<?> dataset, Vector key, int numNearestNeighbors)
Dataset<?>	approxNearestNeighbors(Dataset<?> dataset, Vector key, int numNearestNeighbors) Overloaded method for approxNearestNeighbors.
static Dataset<?>	approxNearestNeighbors(Dataset<?> dataset, Vector key, int numNearestNeighbors, String distCol)
Dataset<?>	approxNearestNeighbors(Dataset<?> dataset, Vector key, int numNearestNeighbors, String distCol) Given a large dataset and an item, approximately find at most k items which have the closest distance to the item.
static Dataset<?>	approxSimilarityJoin(Dataset<?> datasetA, Dataset<?> datasetB, double threshold)
Dataset<?>	approxSimilarityJoin(Dataset<?> datasetA, Dataset<?> datasetB, double threshold) Overloaded method for approxSimilarityJoin.
static Dataset<?>	approxSimilarityJoin(Dataset<?> datasetA, Dataset<?> datasetB, double threshold, String distCol)
Dataset<?>	approxSimilarityJoin(Dataset<?> datasetA, Dataset<?> datasetB, double threshold, String distCol) Join two dataset to approximately find all pairs of rows whose distance are smaller than the threshold.
static Params	clear(Param<?> param)
MinHashLSHModel	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
static String	explainParam(Param<?> param)
static String	explainParams()
static ParamMap	extractParamMap()
static ParamMap	extractParamMap(ParamMap extra)
static <T> scala.Option<T>	get(Param<T> param)
static <T> scala.Option<T>	getDefault(Param<T> param)
static String	getInputCol()
String	getInputCol()
static int	getNumHashTables()
int	getNumHashTables()
static <T> T	getOrDefault(Param<T> param)
static String	getOutputCol()
String	getOutputCol()
static Param<Object>	getParam(String paramName)
static <T> boolean	hasDefault(Param<T> param)
static boolean	hasParam(String paramName)
static boolean	hasParent()
static Param<String>	inputCol()
Param<String>	inputCol() Param for input column name.
static boolean	isDefined(Param<?> param)
static boolean	isSet(Param<?> param)
static MinHashLSHModel	load(String path)
static IntParam	numHashTables()
IntParam	numHashTables() Param for the number of hash tables used in LSH OR-amplification.
static Param<String>	outputCol()
Param<String>	outputCol() Param for output column name.
static Param<?>[]	params()
static void	parent_$eq(Estimator<M> x$1)
static Estimator<M>	parent()
static MLReader<MinHashLSHModel>	read()
static void	save(String path)
static <T> Params	set(Param<T> param, T value)
static M	setParent(Estimator<M> parent)
static String	toString()
static Dataset<Row>	transform(Dataset<?> dataset)
Dataset<Row>	transform(Dataset<?> dataset) Transforms the input dataset.
static Dataset<Row>	transform(Dataset<?> dataset, ParamMap paramMap)
static Dataset<Row>	transform(Dataset<?> dataset, ParamPair<?> firstParamPair, ParamPair<?>... otherParamPairs)
static Dataset<Row>	transform(Dataset<?> dataset, ParamPair<?> firstParamPair, scala.collection.Seq<ParamPair<?>> otherParamPairs)
static StructType	transformSchema(StructType schema)
StructType	transformSchema(StructType schema) :: DeveloperApi ::
String	uid() An immutable unique ID for the object and its derivatives.
StructType	validateAndTransformSchema(StructType schema) Transform the Schema for LSH
MLWriter	write() Returns an MLWriter instance for this ML instance.

Methods inherited from class org.apache.spark.ml.Model

hasParent, parent, setParent

Methods inherited from class org.apache.spark.ml.Transformer

transform, transform, transform

Methods inherited from class Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface org.apache.spark.ml.param.Params

clear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, paramMap, params, set, set, set, setDefault, setDefault, shouldOwn

Methods inherited from interface org.apache.spark.ml.util.Identifiable

toString

Methods inherited from interface org.apache.spark.ml.util.MLWritable

save

Method Detail

read

public static MLReader<MinHashLSHModel> read()

load

public static MinHashLSHModel load(String path)

toString

public static String toString()

params

public static Param<?>[] params()

explainParam

public static String explainParam(Param<?> param)

explainParams

public static String explainParams()

isSet

public static final boolean isSet(Param<?> param)

isDefined

public static final boolean isDefined(Param<?> param)

hasParam

public static boolean hasParam(String paramName)

getParam

public static Param<Object> getParam(String paramName)

set

public static final <T> Params set(Param<T> param,
             T value)

get

public static final <T> scala.Option<T> get(Param<T> param)

clear

public static final Params clear(Param<?> param)

getOrDefault

public static final <T> T getOrDefault(Param<T> param)

getDefault

public static final <T> scala.Option<T> getDefault(Param<T> param)

hasDefault

public static final <T> boolean hasDefault(Param<T> param)

extractParamMap

public static final ParamMap extractParamMap(ParamMap extra)

extractParamMap

public static final ParamMap extractParamMap()

transform

public static Dataset<Row> transform(Dataset<?> dataset,
                     ParamPair<?> firstParamPair,
                     scala.collection.Seq<ParamPair<?>> otherParamPairs)

transform

public static Dataset<Row> transform(Dataset<?> dataset,
                     ParamMap paramMap)

transform

public static Dataset<Row> transform(Dataset<?> dataset,
                     ParamPair<?> firstParamPair,
                     ParamPair<?>... otherParamPairs)

parent

public static Estimator<M> parent()

parent_$eq

public static void parent_$eq(Estimator<M> x$1)

setParent

public static M setParent(Estimator<M> parent)

hasParent

public static boolean hasParent()

inputCol

public static final Param<String> inputCol()

getInputCol

public static final String getInputCol()

outputCol

public static final Param<String> outputCol()

getOutputCol

public static final String getOutputCol()

numHashTables

public static final IntParam numHashTables()

getNumHashTables

public static final int getNumHashTables()

save

public static void save(String path)
                 throws java.io.IOException

Throws:

java.io.IOException

transform

public static Dataset<Row> transform(Dataset<?> dataset)

transformSchema

public static StructType transformSchema(StructType schema)

approxNearestNeighbors

public static Dataset<?> approxNearestNeighbors(Dataset<?> dataset,
                                Vector key,
                                int numNearestNeighbors,
                                String distCol)

approxNearestNeighbors

public static Dataset<?> approxNearestNeighbors(Dataset<?> dataset,
                                Vector key,
                                int numNearestNeighbors)

approxSimilarityJoin

public static Dataset<?> approxSimilarityJoin(Dataset<?> datasetA,
                              Dataset<?> datasetB,
                              double threshold,
                              String distCol)

approxSimilarityJoin

public static Dataset<?> approxSimilarityJoin(Dataset<?> datasetA,
                              Dataset<?> datasetB,
                              double threshold)

uid

public String uid()

Description copied from interface: Identifiable

An immutable unique ID for the object and its derivatives.

Returns:

(undocumented)

copy

public MinHashLSHModel 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 Model<MinHashLSHModel>

Parameters:

extra - (undocumented)

Returns:

(undocumented)

write

public MLWriter write()

Description copied from interface: MLWritable

Returns an MLWriter instance for this ML instance.

Returns:

(undocumented)

transform

public Dataset<Row> transform(Dataset<?> dataset)

Description copied from class: Transformer

Transforms the input dataset.

Specified by:

transform in class Transformer

Parameters:

dataset - (undocumented)

Returns:

(undocumented)

transformSchema

public StructType transformSchema(StructType schema)

Description copied from class: PipelineStage

:: DeveloperApi ::

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)

approxNearestNeighbors

public Dataset<?> approxNearestNeighbors(Dataset<?> dataset,
                                Vector key,
                                int numNearestNeighbors,
                                String distCol)

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.

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

public Dataset<?> approxNearestNeighbors(Dataset<?> dataset,
                                Vector key,
                                int numNearestNeighbors)

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

Parameters:

dataset - (undocumented)

key - (undocumented)

numNearestNeighbors - (undocumented)

Returns:

(undocumented)

approxSimilarityJoin

public Dataset<?> approxSimilarityJoin(Dataset<?> datasetA,
                              Dataset<?> datasetB,
                              double threshold,
                              String distCol)

Join two dataset 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.

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 result row and the key.

Returns:

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

approxSimilarityJoin

public Dataset<?> approxSimilarityJoin(Dataset<?> datasetA,
                              Dataset<?> datasetB,
                              double threshold)

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

Parameters:

datasetA - (undocumented)

datasetB - (undocumented)

threshold - (undocumented)

Returns:

(undocumented)

numHashTables

public IntParam numHashTables()

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.

Returns:

(undocumented)

getNumHashTables

public int getNumHashTables()

validateAndTransformSchema

public StructType validateAndTransformSchema(StructType schema)

Transform the Schema for LSH

Parameters:

schema - The schema of the input dataset without outputCol.

Returns:

A derived schema with outputCol added.

inputCol

public Param<String> inputCol()

Param for input column name.

Returns:

(undocumented)

getInputCol

public String getInputCol()

outputCol

public Param<String> outputCol()

Param for output column name.

Returns:

(undocumented)

getOutputCol

public String getOutputCol()