Github user Yunni commented on a diff in the pull request:
https://github.com/apache/spark/pull/16715#discussion_r100192402
--- Diff: python/pyspark/ml/feature.py ---
@@ -120,6 +122,200 @@ def getThreshold(self):
return self.getOrDefault(self.threshold)
+class LSHParams(Params):
+ """
+ Mixin for Locality Sensitive Hashing(LSH) algorithm parameters.
+ """
+
+ numHashTables = Param(Params._dummy(), "numHashTables", "number of
hash tables, where " +
+ "increasing number of hash tables lowers the
false negative rate, " +
+ "and decreasing it improves the running
performance.",
+ typeConverter=TypeConverters.toInt)
+
+ def __init__(self):
+ super(LSHParams, self).__init__()
+
+ @since("2.2.0")
+ def setNumHashTables(self, value):
+ """
+ Sets the value of :py:attr:`numHashTables`.
+ """
+ return self._set(numHashTables=value)
+
+ @since("2.2.0")
+ def getNumHashTables(self):
+ """
+ Gets the value of numHashTables or its default value.
+ """
+ return self.getOrDefault(self.numHashTables)
+
+
+class LSHModel():
+ """
+ Mixin for Locality Sensitive Hashing(LSH) models.
+ """
+
+ @since("2.2.0")
+ def approxNearestNeighbors(self, dataset, key, numNearestNeighbors,
singleProbing=True,
+ 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 :py:attr:`outputCol` is
missing, the method will
+ transform the data; if the :py:attr:`outputCol` exists, it will
use that. This allows
+ caching of the transformed data when necessary.
+
+ :param dataset: The dataset to search for nearest neighbors of the
key.
+ :param key: Feature vector representing the item to search for.
+ :param numNearestNeighbors: The maximum number of nearest
neighbors.
+ :param distCol: Output column for storing the distance between
each result row and the key.
+ Use "distCol" as default value if it's not
specified.
+ :return: A dataset containing at most k items closest to the key.
A distCol is added
+ to show the distance between each row and the key.
+ """
+ return self._call_java("approxNearestNeighbors", dataset, key,
numNearestNeighbors,
+ distCol)
+
+ @since("2.2.0")
+ def approxSimilarityJoin(self, datasetA, datasetB, threshold,
distCol="distCol"):
+ """
+ Join two dataset to approximately find all pairs of rows whose
distance are smaller than
+ the threshold. If the :py:attr:`outputCol` is missing, the method
will transform the data;
+ if the :py:attr:`outputCol` exists, it will use that. This allows
caching of the
+ transformed data when necessary.
+
+ :param datasetA: One of the datasets to join.
+ :param datasetB: Another dataset to join.
+ :param threshold: The threshold for the distance of row pairs.
+ :param distCol: Output column for storing the distance between
each result row and the key.
+ Use "distCol" as default value if it's not
specified.
+ :return: A joined dataset containing pairs of rows. The original
rows are in columns
+ "datasetA" and "datasetB", and a distCol is added to show
the distance of
+ each pair.
+ """
+ return self._call_java("approxSimilarityJoin", datasetA, datasetB,
threshold, distCol)
+
+
+@inherit_doc
+class BucketedRandomProjectionLSH(JavaEstimator, LSHParams, HasInputCol,
HasOutputCol, HasSeed,
+ JavaMLReadable, JavaMLWritable):
+ """
+ .. note:: Experimental
+
+ LSH class for Euclidean distance metrics.
+ The input is dense or sparse vectors, each of which represents a point
in the Euclidean
+ distance space. The output will be vectors of configurable dimension.
Hash value in the
--- End diff --
Done in Scala/Java doc as well.
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