BucketedRandomProjectionLSHModel¶

class pyspark.ml.feature.BucketedRandomProjectionLSHModel(java_model: Optional[JavaObject] = None)[source]¶

Model fitted by BucketedRandomProjectionLSH, where multiple random vectors are stored. The vectors are normalized to be unit vectors and each vector is used in a hash function: \(h_i(x) = floor(r_i \cdot x / bucketLength)\) where \(r_i\) is the i-th random unit vector. The number of buckets will be (max L2 norm of input vectors) / bucketLength.

New in version 2.2.0.

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.
`getBucketLength`()	Gets the value of bucketLength or its default value.
`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

`bucketLength`
`inputCol`
`numHashTables`
`outputCol`
`params`	Returns all params ordered by name.

Methods Documentation

approxNearestNeighbors(dataset: pyspark.sql.dataframe.DataFrame, key: pyspark.ml.linalg.Vector, numNearestNeighbors: int, distCol: str = 'distCol') → pyspark.sql.dataframe.DataFrame¶

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: pyspark.sql.dataframe.DataFrame, datasetB: pyspark.sql.dataframe.DataFrame, threshold: float, distCol: str = 'distCol') → pyspark.sql.dataframe.DataFrame¶

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: pyspark.ml.param.Param) → None¶: Clears a param from the param map if it has been explicitly set.

copy(extra: Optional[ParamMap] = None) → JP¶

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: Union[str, pyspark.ml.param.Param]) → str¶: Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.

explainParams() → str¶: Returns the documentation of all params with their optionally default values and user-supplied values.

extractParamMap(extra: Optional[ParamMap] = None) → ParamMap¶

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

getBucketLength() → float¶: Gets the value of bucketLength or its default value.

New in version 2.2.0.

getInputCol() → str¶: Gets the value of inputCol or its default value.

getNumHashTables() → int¶: Gets the value of numHashTables or its default value.

getOrDefault(param: Union[str, pyspark.ml.param.Param[T]]) → Union[Any, T]¶: Gets the value of a param in the user-supplied param map or its default value. Raises an error if neither is set.

getOutputCol() → str¶: Gets the value of outputCol or its default value.

getParam(paramName: str) → pyspark.ml.param.Param ¶: Gets a param by its name.

hasDefault(param: Union[str, pyspark.ml.param.Param[Any]]) → bool¶: Checks whether a param has a default value.

hasParam(paramName: str) → bool¶: Tests whether this instance contains a param with a given (string) name.

isDefined(param: Union[str, pyspark.ml.param.Param[Any]]) → bool¶: Checks whether a param is explicitly set by user or has a default value.

isSet(param: Union[str, pyspark.ml.param.Param[Any]]) → bool¶: Checks whether a param is explicitly set by user.

classmethod load(path: str) → RL¶: Reads an ML instance from the input path, a shortcut of read().load(path).

classmethod read() → pyspark.ml.util.JavaMLReader[RL]¶: Returns an MLReader instance for this class.

save(path: str) → None¶: Save this ML instance to the given path, a shortcut of ‘write().save(path)’.

set(param: pyspark.ml.param.Param, value: Any) → None¶: Sets a parameter in the embedded param map.

setInputCol(value: str) → P¶: Sets the value of inputCol.

setOutputCol(value: str) → P¶: Sets the value of outputCol.

transform(dataset: pyspark.sql.dataframe.DataFrame, params: Optional[ParamMap] = None) → pyspark.sql.dataframe.DataFrame¶

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() → pyspark.ml.util.JavaMLWriter¶: Returns an MLWriter instance for this ML instance.

Attributes Documentation

bucketLength = Param(parent='undefined', name='bucketLength', doc='the length of each hash bucket, a larger bucket lowers the false negative rate.')¶

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

numHashTables: pyspark.ml.param.Param[int] = 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.

BucketedRandomProjectionLSH

Bucketizer