Github user sethah commented on a diff in the pull request:
https://github.com/apache/spark/pull/16715#discussion_r100427633
--- Diff: python/pyspark/ml/feature.py ---
@@ -755,6 +947,101 @@ def maxAbs(self):
@inherit_doc
+class MinHashLSH(JavaEstimator, LSHParams, HasInputCol, HasOutputCol,
HasSeed,
+ JavaMLReadable, JavaMLWritable):
+
+ """
+ .. note:: Experimental
+
+ LSH class for Jaccard distance.
+ The input can be dense or sparse vectors, but it is more efficient if
it is sparse.
+ For example, `Vectors.sparse(10, [(2, 1.0), (3, 1.0), (5, 1.0)])`
means there are 10 elements
+ in the space. This set contains elements 2, 3, and 5. Also, any input
vector must have at
+ least 1 non-zero index, and all non-zero values are treated as binary
"1" values.
+
+ .. seealso:: `Wikipedia on MinHash
<https://en.wikipedia.org/wiki/MinHash>`_
+
+ >>> from pyspark.ml.linalg import Vectors
+ >>> data = [(0, Vectors.sparse(6, [0, 1, 2], [1.0, 1.0, 1.0]),),
+ ... (1, Vectors.sparse(6, [2, 3, 4], [1.0, 1.0, 1.0]),),
+ ... (2, Vectors.sparse(6, [0, 2, 4], [1.0, 1.0, 1.0]),)]
+ >>> df = spark.createDataFrame(data, ["id", "features"])
+ >>> mh = MinHashLSH(inputCol="features", outputCol="hashes",
seed=12345)
+ >>> model = mh.fit(df)
+ >>> model.transform(df).head()
+ Row(id=0, features=SparseVector(6, {0: 1.0, 1: 1.0, 2: 1.0}),
hashes=[DenseVector([-1638925...
+ >>> data2 = [(3, Vectors.sparse(6, [1, 3, 5], [1.0, 1.0, 1.0]),),
+ ... (4, Vectors.sparse(6, [2, 3, 5], [1.0, 1.0, 1.0]),),
+ ... (5, Vectors.sparse(6, [1, 2, 4], [1.0, 1.0, 1.0]),)]
+ >>> df2 = spark.createDataFrame(data2, ["id", "features"])
+ >>> key = Vectors.sparse(6, [1, 2], [1.0, 1.0])
+ >>> model.approxNearestNeighbors(df2, key, 1).collect()
+ [Row(id=5, features=SparseVector(6, {1: 1.0, 2: 1.0, 4: 1.0}),
hashes=[DenseVector([-163892...
+ >>> model.approxSimilarityJoin(df, df2, 0.6).select("datasetA.id",
--- End diff --
same as above, except distcol could be "jaccardSimilarity" or something.
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