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.

MinHashLSH

MinMaxScaler