PyNNDescent

0.6.0 · active · verified Thu Apr 09

PyNNDescent is a Python library that provides a fast and flexible implementation of Nearest Neighbor Descent for approximate nearest neighbor search and k-neighbor-graph construction. It supports a wide variety of distance metrics, sparse matrix inputs, and integrates with Scikit-learn. The current version is 0.6.0, and it maintains a regular release cadence with several minor patches and updates throughout the year.

Warnings

deprecated The `n_search_trees` parameter in `NNDescent` has been deprecated. While it may still work, it's recommended to rely on the default or other parameters for controlling initialization.
Fix: Remove the `n_search_trees` parameter from `NNDescent` initialization. The library will automatically choose an appropriate value.
gotcha For NumPy versions 2.0 and above, `np.infty` has been replaced with `np.inf`. PyNNDescent versions `0.5.13` and later include patches for compatibility. If using an older version of PyNNDescent with newer NumPy, this could lead to issues.
Fix: Upgrade `pynndescent` to version `0.5.13` or newer, or ensure `numpy` version is compatible with your `pynndescent` installation.
breaking Version `0.6.0` removed support for End-of-Life Python versions and officially added support for Python 3.12 and 3.13. If you are on an older Python version, this update may break your environment.
Fix: Ensure your Python environment is running Python 3.8 or newer. Consider upgrading to Python 3.12 or 3.13 for full compatibility.
gotcha Earlier versions (`<0.5.9`) had bugs causing infinite recursion during random projection tree generation, especially for certain datasets or configurations. This could lead to crashes or hanging processes.
Fix: Upgrade `pynndescent` to version `0.5.9` or newer, which includes fixes for these issues.
gotcha In `0.5.11`, caching for functions that take distance metrics as arguments was removed. If your application relied on this caching for performance, you might observe a change in execution time after upgrading.
Fix: Profile your application after upgrading to identify any performance regressions. Consider pre-calculating distances or optimizing custom distance functions if performance becomes an issue.

Install

pip install pynndescent Install stable version

Imports

NNDescent
```
from pynndescent import NNDescent
```
PyNNDescentTransformer
```
from pynndescent import PyNNDescentTransformer
```
Used for scikit-learn pipeline integration.

Quickstart

This quickstart demonstrates how to initialize `NNDescent` with training data, prepare the index, and then query for approximate nearest neighbors. It generates random data for demonstration purposes.

import numpy as np
from pynndescent import NNDescent

# Generate some sample data
data = np.random.rand(1000, 64).astype(np.float32)

# Build the NNDescent index
# n_neighbors specifies the number of neighbors to find for each point
# verbose=True shows progress
index = NNDescent(data, n_neighbors=15, verbose=True)

# Build the index (computes the nearest neighbor graph)
index.prepare()

# Query the index for the 5 nearest neighbors of new data
query_data = np.random.rand(10, 64).astype(np.float32)
neighbors, distances = index.query(query_data, k=5)

print("Shape of neighbors (query_points, k):"), print(neighbors.shape)
print("Shape of distances (query_points, k):"), print(distances.shape)
print("First query point's 5 nearest neighbor indices:"), print(neighbors[0])
print("First query point's 5 nearest neighbor distances:"), print(distances[0])

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