MinHashLSHModel

class pyspark.ml.feature.MinHashLSHModel(java_model=None)

Model produced by MinHashLSH, where 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.

New in version 2.2.0.

Notes

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

Methods

approxNearestNeighbors(dataset, key, …[, …])	Given a large dataset and an item, approximately find at most k items which have the closest distance to the item.
approxSimilarityJoin(datasetA, datasetB, …)	Join two datasets to approximately find all pairs of rows whose distance are smaller than the threshold.
clear(param)	Clears a param from the param map if it has been explicitly set.
copy([extra])	Creates a copy of this instance with the same uid and some extra params.
explainParam(param)	Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.
explainParams()	Returns the documentation of all params with their optionally default values and user-supplied values.
extractParamMap([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 < user-supplied values < extra.
getInputCol()	Gets the value of inputCol or its default value.
getNumHashTables()	Gets the value of numHashTables or its default value.
getOrDefault(param)	Gets the value of a param in the user-supplied param map or its default value.
getOutputCol()	Gets the value of outputCol or its default value.
getParam(paramName)	Gets a param by its name.
hasDefault(param)	Checks whether a param has a default value.
hasParam(paramName)	Tests whether this instance contains a param with a given (string) name.
isDefined(param)	Checks whether a param is explicitly set by user or has a default value.
isSet(param)	Checks whether a param is explicitly set by user.
load(path)	Reads an ML instance from the input path, a shortcut of read().load(path).
read()	Returns an MLReader instance for this class.
save(path)	Save this ML instance to the given path, a shortcut of ‘write().save(path)’.
set(param, value)	Sets a parameter in the embedded param map.
setInputCol(value)	Sets the value of inputCol.
setOutputCol(value)	Sets the value of outputCol.
transform(dataset[, params])	Transforms the input dataset with optional parameters.
write()	Returns an MLWriter instance for this ML instance.

Attributes

inputCol
numHashTables
outputCol
params	Returns all params ordered by name.

Methods Documentation

approxNearestNeighbors(dataset, key, numNearestNeighbors, distCol='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 that. This allows caching of the transformed data when necessary.

Parameters:

datasetpyspark.sql.DataFrame

The dataset to search for nearest neighbors of the key.

keypyspark.ml.linalg.Vector

Feature vector representing the item to search for.

numNearestNeighborsint

The maximum number of nearest neighbors.

distColstr

Output column for storing the distance between each result row and the key. Use “distCol” as default value if it’s not specified.

Returns:

pyspark.sql.DataFrame

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.

Notes

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

approxSimilarityJoin(datasetA, datasetB, threshold, distCol='distCol')

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 that. This allows caching of the transformed data when necessary.

Parameters:

datasetApyspark.sql.DataFrame

One of the datasets to join.

datasetBpyspark.sql.DataFrame

Another dataset to join.

thresholdfloat

The threshold for the distance of row pairs.

distColstr, optional

Output column for storing the distance between each pair of rows. Use “distCol” as default value if it’s not specified.

Returns:

pyspark.sql.DataFrame

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.

clear(param)

Clears a param from the param map if it has been explicitly set.

copy(extra=None)

Creates a copy of this instance with the same uid and some extra params. This implementation first calls Params.copy and then make a copy of the companion Java pipeline component with extra params. So both the Python wrapper and the Java pipeline component get copied.

Parameters:

extradict, optional

Extra parameters to copy to the new instance

Returns:

JavaParams

Copy of this instance

explainParam(param)

Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.

explainParams()

Returns the documentation of all params with their optionally default values and user-supplied values.

extractParamMap(extra=None)

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 < user-supplied values < extra.

Parameters:

extradict, optional

extra param values

Returns:

dict

merged param map

getInputCol()

Gets the value of inputCol or its default value.

getNumHashTables()

Gets the value of numHashTables or its default value.

getOrDefault(param)

Gets the value of a param in the user-supplied param map or its default value. Raises an error if neither is set.

getOutputCol()

Gets the value of outputCol or its default value.

getParam(paramName)

Gets a param by its name.

hasDefault(param)

Checks whether a param has a default value.

hasParam(paramName)

Tests whether this instance contains a param with a given (string) name.

isDefined(param)

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

isSet(param)

Checks whether a param is explicitly set by user.

classmethod load(path)

Reads an ML instance from the input path, a shortcut of read().load(path).

classmethod read()

Returns an MLReader instance for this class.

save(path)

Save this ML instance to the given path, a shortcut of ‘write().save(path)’.

set(param, value)

Sets a parameter in the embedded param map.

setInputCol(value)

Sets the value of inputCol.

setOutputCol(value)

Sets the value of outputCol.

transform(dataset, params=None)

Transforms the input dataset with optional parameters.

New in version 1.3.0.

Parameters:

datasetpyspark.sql.DataFrame

input dataset

paramsdict, optional

an optional param map that overrides embedded params.

Returns:

pyspark.sql.DataFrame

transformed dataset

write()

Returns an MLWriter instance for this ML instance.

Attributes Documentation

inputCol = Param(parent='undefined', name='inputCol', doc='input column name.')

numHashTables = Param(parent='undefined', name='numHashTables', doc='number of hash tables, where increasing number of hash tables lowers the false negative rate, and decreasing it improves the running performance.')

outputCol = Param(parent='undefined', name='outputCol', doc='output column name.')

params

Returns all params ordered by name. The default implementation uses dir() to get all attributes of type Param.

MinHashLSH MinMaxScaler