Experiments on Data Sensitive Hashing
- We generate a hash family, wherein each hash function is a linear separator.
- These linear separators are stored as famliyvectors which are D+1 dimensional.
- The hash family is weak and hence we use concatenation to generate stronger hash functions.
- We finally use 'L' concatenation functions which have 'M' random functions from the hash family each.
- Concatenation function is used synonymously with hashtable.
- This indicates the optimum grid size of each point.
- In DSH, we want to ensure a good recall. So initially we start with a conservation grid size.
- If for a given grid size, the bucket is sparse, we want to take a larger bucket so that we have a better recall and this is what is done in SHselection::radius_test, we find the optimum grid size such that the recall is good.
Computing Hash of a new point
- We get the hashresults of pre-computed data points in datahashresult.
- The hashkey equals datahashresult % bucketnum.
- Now we get the bucketindex as hashkeyindex[hashkey]
- The bucketlength is hashkeylength[hashkey]
- To get the points of the bucket iterate over datahashresult[Lused][bucketindex +j]
- In DSH Entropy, we perturb the querypoint by a N(querypoint, BaseR) distribution and compute results on these surrogate querypoints as well.
- This can be enabled in SHindex.cpp::SHindex::query_execute.
How to Run:
- Compile using make.
- Run preprocess.out to generate the binary representation of input.data.
- Run main.out and wait for results.
Input file format
- Check input.data for an example input file. This is a space separated version of the forest cover dataset.
- Many temporay .dat and .txt files are generated which are intermediate representations used by the program. Run make clean if they bother you much.
In current version, there following parameters are required to be set by users:
- Datasize: the number of data points.
- Querysize: the number of queries.
- D: the number of dimensions of the data.
- K: the top-k results that are expected to return.
- R: the ratio distance, depends on the k-nn distance of your dataset
- L: the number of hash tables, this parameter is expected to be set 40 ¨C 200 to get a good performance. Usually, a larger value will improve the efficiency. However, it will induce more memory cost.
- M: the number of concatenation functions, this value is supposed to be set between 10 and 20. M should be set larger if: a) the hash function is not balanced b) a larger L is chosen c) efficiency is considered more than recall.
- Bucketnum: in our implementation, we do not build a universal hashing function. Instead, we rehash the data directly into Bucketnum buckets. Usually, Bucketnum should be set to a prime between 10K and 20K. However, when the datasize is large, or M is large, then Bucketnum should be set to a larger value. The collisions will introduce little cost, as there is a check procedure about the exact hashkey.
There are also some other parameters. However, we do NOT recommend you to change it unless you fully understand the code.
- Familysize: the number of hashing functions used in all. We do not randomly generate M hash functions for each hash table, instead we generate a set of functions for all. Thus the hash functions may not be completely random. However, this will reduce the cost of hashing procedure in both index and query part. To make sure that the hashing functions are random enough, familysize should be set at least more than the number of dimensions of the dataset. In our implementation, it is set to 200.
- thresholdpoint : to be edited in next version
- thresholdtable : to be edited in next version
- Alter: to be edited in next version
- RI: to be edited in next version
- The hash functions (which are linear classifiers).
- The M random hash functions corresponding to each hashtable or concatenation function.
- The optimum radius ratio for a give point i.
- Given the point and hashtable, this returns the hashvalue.
- Note: The hashkey is (datahashtable[L][i] % bucketnum).
- Stores the points in an order for ease of traversal using bucketindex and bucketlength.
hashkeyindex and hashkeylength
- The bucketindex and bucketlength for a data point.
- Hashvalues for the query for each setting of ratio and for each hashtable.