logo
down
shadow

Spark implementation for Locality Sensitive Hashing


Spark implementation for Locality Sensitive Hashing

By : user2956791
Date : November 22 2020, 03:03 PM
this will help Try this implementation:
https://github.com/mrsqueeze/spark-hash
code :


Share : facebook icon twitter icon
Implementation of locality-sensitive hashing with min-hash

Implementation of locality-sensitive hashing with min-hash


By : milmar ondo
Date : March 29 2020, 07:55 AM
I wish did fix the issue. You want to implement the min-hash algorithm but not LSH per se. Min-hashing is an LSH technique. Thus, LSH, in general, does not approximate the Jaccard coefficient, the particular method of min-hashing does.
An introduction is given in Mining of Massive Datasets, Chapter 3 by Anand Rajaraman and Jeff Ullman.
How Locality Sensitive Hashing (LSH) works?

How Locality Sensitive Hashing (LSH) works?


By : Bakslat
Date : March 29 2020, 07:55 AM
will help you The first described method explains an approximate nearest neighbors search. Yes you'd get the best performance by just checking those 100 other items in the bin c, but you've got a higher risk on missing good candidates in other neighboring buckets.
A simple hashing scheme for lat/lon coordinates is the Geohash. You could find nearest shop by looking at items within the same Geohash block, but inaccurate results are possible near grid boundaries.
Locality Sensitive Hashing in Spark for single DataFrame

Locality Sensitive Hashing in Spark for single DataFrame


By : Le Ngoc Hoang Tran
Date : March 29 2020, 07:55 AM
wish helps you Here is a bit of scala code that performs a LSH. Basically, the lsh needs an assembled vector that you can construct with a VectorAssembler.
code :
// contructing the dataframe
val data= """1   11.6133  48.1075
2   11.6142  48.1066
3   11.6108  48.1061
4   11.6207  48.1192
5   11.6221  48.1223
6   11.5969  48.1276
7   11.5995  48.1258
8   11.6127  48.1066
9   11.6430  48.1275
10  11.6368  48.1278
11  11.5930  48.1156"""
val df = data
    .split("\\s*\\n\\s*")
    .map( _.split("\\s+") match {
        case Array(a, b, c) => (a.toInt,b.toDouble,c.toDouble)
    })
    .toSeq
    .toDF("id", "X", "Y")

val assembler = new VectorAssembler()
    .setInputCols(Array("X", "Y"))
    .setOutputCol("v")
val df2 = assembler.transform(df)
val lsh = new BucketedRandomProjectionLSH()
    .setInputCol("v")
    .setBucketLength(1e-3) // change that according to your use case
    .setOutputCol("lsh")
val result = lsh.fit(df2).transform(df2).orderBy("lsh")

// the lsh is in an array of vectors. To extract the double, we can use
// getItem for the array and a UDF for the vector.
val extract = udf((vector : org.apache.spark.ml.linalg.Vector) => vector(0))
result.withColumn("lsh", extract(col("lsh").getItem(0))).show(false)
Distributed LSH (locality sensitive hashing)

Distributed LSH (locality sensitive hashing)


By : Elton D'Souza
Date : March 29 2020, 07:55 AM
I hope this helps you . First, you want to consider the keys by which the data is to be accessed. It is these keys that you'd want to hash - and, if you know the exact keys you want to access, you can hash them to determine which server to query - eliminating the need to query every server.
Things get harder if you don't know the exact keys (as I suspect to be your situation) - the LSH generates a total ordering for your records - where similar records are likely (but not guaranteed) to have the same hash. I think of this as, for example, a mapping of hyperplanes to the length of their normal vector from the origin... hence, for example, if searching for a similar (but non-identical) hyperplane to one that's between 4 and 5 units from the origin, a good place to start looking is among other hyperplanes between 4 and 5 units from the origin. Hence, if this 'distance from origin' is your locality sensitive hash function, you can shard your data using it, and, in doing so - you could reduce load (while increasing worst case latency) by searching only the shard with a matching 'distance from origin' LCH. With this specific LCH, where similarity is linearly correlated with the hash, it would be possible to get an definitive result while only accessing a subset of the distributed servers. This is not the case for all LSH functions.
What does `locality-sensitive` stands for in locality-sensitive hashing?

What does `locality-sensitive` stands for in locality-sensitive hashing?


By : Bill Brown
Date : March 29 2020, 07:55 AM
Does that help LSH maps high dimension vectors to buckets and tries to ensure that vectors that are "near" to each other are mapped to the same bucket. The definition of "near" is just the neighborhood with respect to some distance function (e.g. Euclidean).
"Locality" refers to region in space; and "sensitive" means that the nearby locations are mapped to same bucket. In other words, the output of the hashing function depends on (is sensitive to) the location in space (the locality).
shadow
Privacy Policy - Terms - Contact Us © ourworld-yourmove.org