tree
API Definition
def tree(num_leaves: int, num_leaves_to_search: int, training_sample_size: int, min_partition_size: int, training_iterations: int, spherical: bool, quantize_centroids: bool, random_init: bool, soar_lambda: float, overretrieve_factor: float, distance_measure: string) -> scann.scann_ops.py.scann_builder.ScannBuilder
Function
IVF partitioning configuration (consistent with the open source algorithm).
Parameters
Parameter |
Description |
Data Type |
Value Range |
|---|---|---|---|
num_leaves |
Total subspace number in the IVF partition. |
int |
≥ 1 |
num_leaves_to_search |
Default number of subspaces to be searched. |
int |
[1, num_leaves], where num_leaves indicates the total number of subspaces in the IVF index partition. |
training_sample_size |
Number of samples in the base library during partitioned training. |
int |
[0, number_of_base_libraries] |
min_partition_size |
Number of base library vectors contained in the smallest partition. |
int |
[0, number_of_base_libraries] |
training_iterations |
Number of training iterations. |
int |
≥ 1 |
spherical |
Indicates whether the partition type is spherical. |
Boolean |
- |
quantize_centroids |
Indicates whether to quantize the bucket center. |
Boolean |
- |
random_init |
Indicates whether to start training randomly. |
Boolean |
- |
soar_lambda |
Controls orthogonality. This parameter takes effect only for the IP (dot_product) dataset. |
float |
> 0. Set the value to −1 to disable the function. |
overretrieve_factor |
Used together with soar_lambda to specify the over-retrieval factor. This parameter takes effect only for the IP (dot_product) dataset. |
float |
[1, 2]. Set the value to −1 to disable the function. |
distance_measure |
Distance type of the vector. |
Character string |
dot_product or squared_l2. |
Return Value
Data Type |
Description |
|---|---|
scann.scann_ops.py.scann_builder.ScannBuilder |
ScannBuilder is used to receive build parameters. |