pyspark.ml.feature.
BucketedRandomProjectionLSHModel
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.
BucketedRandomProjectionLSH
New in version 2.2.0.
Methods
approxNearestNeighbors(dataset, key, …[, …])
approxNearestNeighbors
Given a large dataset and an item, approximately find at most k items which have the closest distance to the item.
approxSimilarityJoin(datasetA, datasetB, …)
approxSimilarityJoin
Join two datasets to approximately find all pairs of rows whose distance are smaller than the threshold.
clear(param)
clear
Clears a param from the param map if it has been explicitly set.
copy([extra])
copy
Creates a copy of this instance with the same uid and some extra params.
explainParam(param)
explainParam
Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.
explainParams()
explainParams
Returns the documentation of all params with their optionally default values and user-supplied values.
extractParamMap([extra])
extractParamMap
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()
getBucketLength
Gets the value of bucketLength or its default value.
getInputCol()
getInputCol
Gets the value of inputCol or its default value.
getNumHashTables()
getNumHashTables
Gets the value of numHashTables or its default value.
getOrDefault(param)
getOrDefault
Gets the value of a param in the user-supplied param map or its default value.
getOutputCol()
getOutputCol
Gets the value of outputCol or its default value.
getParam(paramName)
getParam
Gets a param by its name.
hasDefault(param)
hasDefault
Checks whether a param has a default value.
hasParam(paramName)
hasParam
Tests whether this instance contains a param with a given (string) name.
isDefined(param)
isDefined
Checks whether a param is explicitly set by user or has a default value.
isSet(param)
isSet
Checks whether a param is explicitly set by user.
load(path)
load
Reads an ML instance from the input path, a shortcut of read().load(path).
read()
read
Returns an MLReader instance for this class.
save(path)
save
Save this ML instance to the given path, a shortcut of ‘write().save(path)’.
set(param, value)
set
Sets a parameter in the embedded param map.
setInputCol(value)
setInputCol
Sets the value of inputCol.
inputCol
setOutputCol(value)
setOutputCol
Sets the value of outputCol.
outputCol
transform(dataset[, params])
transform
Transforms the input dataset with optional parameters.
write()
write
Returns an MLWriter instance for this ML instance.
Attributes
bucketLength
numHashTables
params
Returns all params ordered by name.
Methods Documentation
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.
pyspark.sql.DataFrame
The dataset to search for nearest neighbors of the key.
pyspark.ml.linalg.Vector
Feature vector representing the item to search for.
The maximum number of nearest neighbors.
Output column for storing the distance between each result row and the key. Use “distCol” as default value if it’s not specified.
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.
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.
One of the datasets to join.
Another dataset to join.
The threshold for the distance of row pairs.
Output column for storing the distance between each pair of rows. Use “distCol” as default value if it’s not specified.
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.
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.
Extra parameters to copy to the new instance
JavaParams
Copy of this instance
extra param values
merged param map
Gets the value of a param in the user-supplied param map or its default value. Raises an error if neither is set.
New in version 1.3.0.
input dataset
an optional param map that overrides embedded params.
transformed dataset
Attributes Documentation
Returns all params ordered by name. The default implementation uses dir() to get all attributes of type Param.
dir()
Param