MinHashLSHModel#

class pyspark.ml.feature.MinHashLSHModel(java_model=None)[source]#

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.

uid#: A unique id for the object.