knncolle 0.3.0

Nearest Neighbor Detection for Bioconductor

Python bindings to knncolle

Overview

The knncolle Python package implements Python bindings to the C++ library of the same name for nearest neighbor (NN) searches. Downstream packages can re-use the NN search algorithms in knncolle, either via Python or by directly calling C++ through shared pointers. This is inspired by the BiocNeighbors Bioconductor package, which does the same thing for R packages.

Quick start

Install it:

pip install knncolle

And run the desired search:

# Mocking up data with 20 dimensions, 1000 observations
import numpy
y = numpy.random.rand(1000, 20) 

# Building a search index with vantage point trees:
import knncolle
params = knncolle.VptreeParameters()
idx = knncolle.build_index(params, y)

# Performing the search:
res = knncolle.find_knn(idx, num_neighbors=10)

res.index # each row is an observation, each column is a neighbor
## array([[881,  74, 959, ..., 917, 385, 522],
##        [586,   8, 874, ..., 895,  52, 591],
##        [290, 215, 298, ..., 148, 627, 443],
##        ...,
##        [773,  44, 669, ..., 775, 287, 819],
##        [658, 847, 691, ..., 630, 861, 434],
##        [796, 158,  11, ..., 606, 815, 882]],
##       shape=(1000, 10), dtype=uint32)

res.distance # distances to the neighbors in 'index'
## array([[1.12512471, 1.12792771, 1.15229055, ..., 1.21499808, 1.2176659 ,
##         1.23952456],
##        [0.9988856 , 1.03782045, 1.08870223, ..., 1.16899062, 1.17007634,
##         1.17147675],
##        [1.2471501 , 1.26328659, 1.2643019 , ..., 1.32229768, 1.32679721,
##         1.33451926],
##        ...,
##        [1.05765983, 1.08981287, 1.11295647, ..., 1.18395012, 1.1976068 ,
##         1.21577234],
##        [0.96758957, 1.02363497, 1.05326212, ..., 1.21518925, 1.22847612,
##         1.24106054],
##        [1.17846147, 1.22299985, 1.2248128 , ..., 1.35088373, 1.39274142,
##         1.40207528]], shape=(1000, 10))

Check out the reference documentation for details.

Switching algorithms

We can easily switch to a different NN search algorithm by supplying a different params object. For example, we could use the Approximate Nearest Neighbors Oh Yeah (Annoy) algorithm:

an_params = knncolle.AnnoyParameters()
an_idx = knncolle.build_index(an_params, y)

We can also tweak the search parameters in our Parameters object during or after its construction. For example, with the hierarchical navigable small worlds (HNSW) algorithm:

h_params = knncolle.HnswParameters(num_links=20, distance="Manhattan")
h_params.ef_construction = 150
h_idx = knncolle.build_index(h_params, y)

Currently, we support Annoy, HNSW, vantage point trees, k-means k-nearest neighbors, and an exhaustive brute-force search. More algorithms can be added by extending knncolle as described below without any change to end-user code.

Other searches

Given a separate query dataset of the same dimensionality, we can find the nearest neighbors in the prebuilt NN search index:

q = numpy.random.rand(50, 20)
qres = knncolle.query_knn(idx, q, num_neighbors=10)

qres.index.shape # each row is an observation in 'q'
## (50, 10)
qres.distance.shape
## (50, 10)

qres.index[0,:]
## array([712, 947, 924, 506, 640, 228, 424, 662, 299, 473], dtype=uint32)

qres.distance[0,:]
## array([0.9846863 , 0.99493741, 1.01642662, 1.02303339, 1.02915264,
##        1.05241022, 1.0690309 , 1.09889404, 1.1327715 , 1.14832321])

We can ask find_knn() to report variable numbers of neighbors for each observation:

variable_k = (numpy.random.rand(y.shape[0]) * 10).astype(numpy.uint32)
var_res = knncolle.find_knn(idx, num_neighbors=variable_k)

len(var_res.index)
## 1000

len(var_res.distance)
## 1000

variable_k[0]
## np.uint32(7)

var_res.index[0]
## array([881,  74, 959, 135, 148, 946, 276], dtype=uint32)

var_res.distance[0]
## array([1.12512471, 1.12792771, 1.15229055, 1.16210922, 1.19067866,
##        1.19773984, 1.21375003])

We can find all observations within a distance threshold of each observation via find_neighbors(). The related query_neighbors() function handles querying of observations in a separate dataset. Both functions also accept a variable threshold for each observation.

range_res = knncolle.find_neighbors(idx, threshold=1.2)

len(range_res.index)
## 1000

len(range_res.distance)
## 1000

range_res.index[0]
## array([881,  74, 959, 135, 148, 946], dtype=uint32)

range_res.distance[0]
## array([1.12512471, 1.12792771, 1.15229055, 1.16210922, 1.19067866,
##        1.19773984])

Use with C++

The raison d'être of the knncolle Python package is to facilitate the re-use of the neighbor search algorithms by C++ code in other Python packages. The idea is that downstream packages will link against the knncolle C++ interface so that they can re-use the search indices created by the knncolle Python package. This allows developers to (i) save time by avoiding the need to re-compile all desired algorithms and (ii) support more algorithms in extensions to the knncolle framework. To do so:

1. Add knncolle.includes() and assorthead.includes() to the compiler's include path for the package. This can be done through include_dirs= of the Extension() definition in setup.py or by adding a target_include_directories() in CMake, depending on the build system.
2. Call knncolle.build_index() to construct a GenericIndex instance. This exposes a shared pointer to the C++-allocated index via its ptr property.
3. Pass ptr to C++ code as a uintptr_t referencing a knncolle::Prebuilt. which can be interrogated as described in the knncolle documentation.

So, for example, the C++ code in our downstream package might look like this:

#include "knncolle_py.h"

int do_something(uintptr_t ptr) {
    const auto& prebuilt = knncolle_py::cast_prebuilt(ptr)->ptr;
    // Do something with the search index interface.
    return 1;
}

PYBIND11_MODULE(lib_downstream, m) {
    m.def("do_something", &do_something);
}

Which can then be called from Python:

from . import lib_downstream as lib
from knncolle import GenericIndex

def do_something(idx: GenericIndex):
    return lib.do_something(idx.ptr)

In some scenarios, it may be more convenient to construct the search index inside C++, e.g., if the dataset to be searched is not available before the call to the C++ function. This can be accommodated by accepting a uintptr_t to a knncolle::Builder in the C++ code:

#include "knncolle_py.h"

int do_something_mk2(uintptr_t ptr) {
    const auto& builder = knncolle_py::cast_builder(ptr)->ptr;
    // The builder is a algorithm-specific factory that accepts a matrix and
    // returns a search index for that algorithm. Presumably we construct a
    // new search index inside this function and use it.
    return 1;
}

PYBIND11_MODULE(lib_downstream, m) {
    m.def("do_something_mk2", &do_something_mk2);
}

A pointer to the knncolle::Builder can be created by the define_builder() function in Python, and then passed to the C++ code:

from . import lib_downstream as lib
from knncolle import define_builder, Parameters

def do_something(param: Parameters):
    builder, cls = define_builder(param)
    return lib.do_something_mk2(builder.ptr)

Check out the included header for more definitions.

Extending to more algorithms

Via define_builder()

The best way to extend knncolle is to do so in C++. This involves writing subclasses of the interfaces in the knncolle library. Once this is done, it is a simple matter of writing the following Python bindings:

• Implement a SomeNewParameters class that inherits from Parameters.
• Implement a SomeNewIndex class that inherits from GenericIndex. This should accept a single ptr in its constructor and have a ptr property that returns the same value.
• Register a define_builder() method that dispatches on SomeNewParameters. This should call into C++ and return a tuple containing a Builder object and the SomeNewIndex constructor.

No new methods are required for find_knn(), build_index(), etc. as the default method will work automatically if a define_builder() method is available. This approach also allows the new method to be used in C++ code of downstream packages.

Without define_builder()

If it is not possible to implement the search algorithm in C++, we can still extend knncolle in Python. Each extension package should:

• Implement a SomeNewParameters class that inherits from Parameters.
• Implement a SomeNewIndex class that inherits from Index. This can have an arbitrary structure, i.e., it does not need to have a ptr property.
• Register a build_index() method that dispatches on SomeNewParameters. This should return an instance of SomeNewIndex.
• Register a method for any number of these generics: find_knn(), find_distance(), find_neighbors(), query_knn(), query_distance(), query_neighbors(). These methods should dispatch on SomeNewParameters and return the appropriate result object.

This approach will not support re-use by C++ code in other Python packages.