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KGraph Parameter Tuning

KGraph supports a number of parameters for both indexing and searching so as to fine tune the performance.

In the C++ API, these parameters are passed in as fields of the KGraph::IndexParams and KGraph::SearchParams. The constructors of these two structs sets all the parameters to default values that will work reasonably well for many datasets.

In the Python API, these parameters are passed in as optional keyword arguments to the build and search methods. When not set, the same default values are assumed.

General Guidelines for Parameter Tuning

Online k-NN Search

SearchParams::K should be determined by the application. Enlarging any of P, M, S, T has the effect of increasing recall and slowing down speed at the same time.

Enlarging P is the primary way of increasing recall at the cost of slowing down speed.

If the index was created with reverse = 0, changing M between IndexParams::K and IndexParams::L is the secondary way of tuning accuracy and speed.

Enlarging T typically does not work. The effect of S is typically not significant.

The online search parameters always change accuracy and speed at the same time. To speed up search without sacrificing accuracy, one has to re-construct the index, typically with a larger IndexParams::K and larger IndexParams::L, as discussed below.

Indexing/k-NN Graph Construction

For indexing purpose, it is always recommended to set reverse to -1. If the goal is to extract the k-NN graph, then reserse has to be 0.

Increasing any of K, L, S and R has the effect of improving accuracy and slowing down speed at the same time.

In a simplified view, KGraph constructs a M-NN graph, with K <= M <= L, and M being different for each object depending on its local intrinsinc dimension. The M-NN graph, with varying M, is also the actual index when reverse is set to 0 or -1. According to this, K is the lower-bound of the per-object cost and L is the upper-bound, with KGraph to freely pick a suitable value between K and L for each object. L is set to at least K + 50 to give KGraph some wiggling space. Typical settings are (K = 25, L = 100), (K=50~100, L=150), (K=200, L=300), etc.

Enlarging S slightly increases accuracy, but slows down computation significantly, and is typically set below 30. R typically does not have to be changed.

Index Parameters

Name Default Description
K 25
L 100 >= K + 50
S 10 Use default.
R 100 Use default.
iterations 30 See 2.
controls 100 See 1.
recall 0.99 See 1, 2.
delta 0.002 See 2.
reverse 0 See 3.
seed 1998 Random seed.

1. On-the-fly accuracy estimation

When constructing a k-NN graph, KGraph estimate its accuracy after each iteration, and stops iterating when the estimated accuracy exceeds the given recall.

For this purpose, KGraph randomly sample a number of control points, whose k-NN are found with brutal force search. The number of control points can be adjusted with the parameter controls, but this is typically not needed.

Accuracy is measured in recall, which is the number of k-NN actually found divided by k, averaged across all query objects.

2. Iteration stop criteria

KGraph stops iteration when at least one of the following criteria is met:

  • Number of iterations reach iterations.
  • Estimated recall exceeds recall.
  • Number of entries updated becomes less than deltaKN.

3. Adding Reverse Edges

For indexing purpose, it usually helps to add the reverse edges of the k-NN graph. This can be enabled by setting reverse to a non-zero value. If reverse is set to a positive value Kp, then the graph is first trimmed from the original K-NN graph to a Kp-NN graph, and all reverse edges are added.

If reverse is set to -1, the recommended setting for indexing purpose, the graph is automatically trimmed to a suitable size, and then all reverse edges are added.

Search Parameters

Name Default Description
K 25 Desired K.
M 0 Use default.
P 100 See 1.
S 10 Use default.
T 1 See 1.
epsilon +1e30 See 2.
init 0 See 3.
seed 1998 Random seed.

1. Computation and Accuracy

P is the main parameter to control the amount of computation. Increase P will leads to higher recall as well as more computation. The same search process is repeated T times with results merged. Typically there is no need for a T > 1, but for some datasets it helps.

2. epsilon-NN search

Entries of a similarity value bigger than epsilon are removed, so there may be less than K items returned.

3. User-Provided Starting Points

KGraph can the index to refine k-NN search results obtained from another algorithm. To use this, set init to the number of initial k-NN items, and pass in the items via the ids parameter of KGraph::search. The input items in the buffer are overwritten when search is done.

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