BucketedRandomProjectionLSHModel¶
-
class
pyspark.ml.feature.
BucketedRandomProjectionLSHModel
(java_model=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
Attributes
Methods Documentation
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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 theoutputCol
exists, it will use that. This allows caching of the transformed data when necessary.Note
This method is experimental and will likely change behavior in the next release.
- Parameters
dataset – The dataset to search for nearest neighbors of the key.
key – Feature vector representing the item to search for.
numNearestNeighbors – The maximum number of nearest neighbors.
distCol – Output column for storing the distance between each result row and the key. Use “distCol” as default value if it’s not specified.
- Returns
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.
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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 theoutputCol
exists, it will use that. This allows caching of the transformed data when necessary.- Parameters
datasetA – One of the datasets to join.
datasetB – Another dataset to join.
threshold – The threshold for the distance of row pairs.
distCol – Output column for storing the distance between each pair of rows. Use “distCol” as default value if it’s not specified.
- Returns
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.
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clear
(param)¶ Clears a param from the param map if it has been explicitly set.
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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
extra – Extra parameters to copy to the new instance
- Returns
Copy of this instance
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explainParam
(param)¶ Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.
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explainParams
()¶ Returns the documentation of all params with their optionally default values and user-supplied values.
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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
extra – extra param values
- Returns
merged param map
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getBucketLength
()¶ Gets the value of bucketLength or its default value.
New in version 2.2.0.
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getInputCol
()¶ Gets the value of inputCol or its default value.
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getNumHashTables
()¶ Gets the value of numHashTables or its default value.
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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.
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getOutputCol
()¶ Gets the value of outputCol or its default value.
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getParam
(paramName)¶ Gets a param by its name.
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hasDefault
(param)¶ Checks whether a param has a default value.
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hasParam
(paramName)¶ Tests whether this instance contains a param with a given (string) name.
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isDefined
(param)¶ Checks whether a param is explicitly set by user or has a default value.
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isSet
(param)¶ Checks whether a param is explicitly set by user.
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classmethod
load
(path)¶ Reads an ML instance from the input path, a shortcut of read().load(path).
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classmethod
read
()¶ Returns an MLReader instance for this class.
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save
(path)¶ Save this ML instance to the given path, a shortcut of ‘write().save(path)’.
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set
(param, value)¶ Sets a parameter in the embedded param map.
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transform
(dataset, params=None)¶ Transforms the input dataset with optional parameters.
- Parameters
dataset – input dataset, which is an instance of
pyspark.sql.DataFrame
params – an optional param map that overrides embedded params.
- Returns
transformed dataset
New in version 1.3.0.
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write
()¶ Returns an MLWriter instance for this ML instance.
Attributes Documentation
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bucketLength
= Param(parent='undefined', name='bucketLength', doc='the length of each hash bucket, a larger bucket lowers the false negative rate.')¶
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inputCol
= Param(parent='undefined', name='inputCol', doc='input column name.')¶
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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.')¶
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outputCol
= Param(parent='undefined', name='outputCol', doc='output column name.')¶
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params
¶ Returns all params ordered by name. The default implementation uses
dir()
to get all attributes of typeParam
.
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