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Python module: cuvs.preprocessing.quantize.binary

transform

@auto_sync_resources @auto_convert_output

1	def transform(dataset, output=None, resources=None)

Applies binary quantization transform to given dataset

This applies binary quantization to a dataset, changing any positive values to a bitwise 1. This is useful for searching with the BitwiseHamming distance type.

Parameters

NameTypeDescription

dataset	row major host or device dataset to transform
output	optional preallocated output memory, on host or device memory
resources	cuvs.common.Resources, optional

Returns

NameTypeDescription

output

transformed dataset quantized into a uint8

Examples

>>> import cupy as cp
>>> from cuvs.preprocessing.quantize import binary
>>> from cuvs.neighbors import cagra
>>> n_samples = 50000
>>> n_features = 50
>>> dataset = cp.random.standard_normal((n_samples, n_features),
...                                   dtype=cp.float32)
>>> transformed = binary.transform(dataset)
>>>
>>> # build a cagra index on the binarized data
>>> params = cagra.IndexParams(metric="bitwise_hamming",
...                            build_algo="iterative_cagra_search")
>>> idx = cagra.build(params, transformed)

Binary

transform

Neighbors Vamana

Preprocessing PCA

1	>>> import cupy as cp
2	>>> from cuvs.preprocessing.quantize import binary
3	>>> from cuvs.neighbors import cagra
4	>>> n_samples = 50000
5	>>> n_features = 50
6	>>> dataset = cp.random.standard_normal((n_samples, n_features),
7	... dtype=cp.float32)
8	>>> transformed = binary.transform(dataset)
9	>>>
10	>>> # build a cagra index on the binarized data
11	>>> params = cagra.IndexParams(metric="bitwise_hamming",
12	... build_algo="iterative_cagra_search")
13	>>> idx = cagra.build(params, transformed)