# pacmap
[](https://crates.io/crates/pacmap)
[](https://docs.rs/pacmap)
[](https://github.com/beamform/pacmap-rs/actions/workflows/ci.yml)
[](LICENSE)
A Rust implementation of PaCMAP (Pairwise Controlled Manifold Approximation) for dimensionality reduction.
## Overview
Dimensionality reduction transforms high-dimensional data into a lower-dimensional representation while preserving
important relationships between points. This is useful for visualization, analysis, and as preprocessing for other
algorithms.
PaCMAP is a relatively recent dimensionality reduction technique that preserves both local and global structure through
three types of point relationships:
- Nearest neighbor pairs preserve local structure
- Mid-near pairs preserve intermediate structure
- Far pairs prevent collapse and maintain separation
For details on the algorithm, see the [original paper](https://jmlr.org/papers/v22/20-1061.html).
## Features
- Fast approximate nearest neighbors for large datasets using USearch
- SIMD-optimized distance calculations
- Parallel processing with Rayon
- Optional PCA initialization using various BLAS backends
- Reproducible results with optional seeding
## Usage
Basic usage with default parameters:
```rust
use anyhow::Result;
use ndarray::Array2;
use ndarray_rand::RandomExt;
use ndarray_rand::rand_distr::Uniform;
use pacmap::{Configuration, fit_transform};
fn main() -> Result<()> {
// Your high-dimensional data as an n × d array
let n_samples = 1000;
let n_features = 1000;
let mut data = Array2::random((n_samples, n_features), Uniform::new(-1.0, 1.0));
let config = Configuration::default();
let (embedding, _) = fit_transform(data.view(), config)?;
// embedding is now an n × 2 array
Ok(())
}
```
Customized embedding:
```rust
use anyhow::Result;
use pacmap::{Configuration, Initialization};
fn main() -> Result<()> {
let config = Configuration::builder()
.embedding_dimensions(3)
.initialization(Initialization::Random(Some(42)))
.learning_rate(0.8)
.num_iters((50, 50, 100))
.mid_near_ratio(0.3)
.far_pair_ratio(2.0)
.approx_threshold(8_000) // Use approximate neighbors above this size
.build();
let (embedding, _) = fit_transform(data.view(), config)?;
Ok(())
}
```
Capturing intermediate states:
```rust
use anyhow::Result;
use pacmap::Configuration;
fn main() -> Result<()> {
let config = Configuration::builder()
.snapshots(vec![100, 200, 300])
.build();
let (embedding, Some(states)) = fit_transform(data.view(), config)?;
// states is now an s × n × d array where s is the number of snapshots
Ok(())
}
```
For a standalone example, see the [pacmap-rs-example repository](https://github.com/beamform/pacmap-rs-example).
## Configuration
### Core Parameters
- `embedding_dimensions`: Output dimensionality (default: 2)
- `initialization`: How to initialize coordinates:
- `Pca` - Project data using PCA (default)
- `Value(array)` - Use provided coordinates
- `Random(seed)` - Random initialization with optional seed
- `learning_rate`: Learning rate for Adam optimizer (default: 1.0)
- `num_iters`: Iteration counts for three optimization phases (default: (100, 100, 250)):
1. Mid-near weight reduction phase
2. Balanced weight phase
3. Local structure focus phase
- `snapshots`: Optional vector of iterations at which to save embedding states
- `approx_threshold`: Number of samples above which to use approximate nearest neighbors (default: 8,000)
### Pair Sampling Parameters
- `mid_near_ratio`: Ratio of mid-near to nearest neighbor pairs (default: 0.5)
- `far_pair_ratio`: Ratio of far to nearest neighbor pairs (default: 2.0)
- `override_neighbors`: Optional fixed neighbor count override (default: None, auto-scaled with dataset size)
- `seed`: Optional random seed for reproducible sampling and initialization
### Pair Configuration
- `PairConfiguration::Generate` - Generate all pairs from scratch (default)
- `PairConfiguration::NeighborsProvided { pair_neighbors }` - Use provided nearest neighbors, generate remaining pairs
- `PairConfiguration::AllProvided { pair_neighbors, pair_mn, pair_fp }` - Use all provided pairs
## BLAS/LAPACK Requirements
This crate requires a BLAS/LAPACK backend for PCA. Because BLAS/LAPACK implementations are complex system dependencies,
you must explicitly choose one when building on non-macOS platforms:
- `intel-mkl-static` or `intel-mkl-system` for Intel MKL
- `netlib-static` or `netlib-system` for Netlib
- `openblas-static` or `openblas-system` for OpenBLAS
For example:
```toml
[dependencies]
pacmap = { version = "0.1", features = ["openblas-static"] }
```
**Note:** On macOS, the Accelerate Framework is used by default, so these features are not needed.
See [ndarray-linalg's documentation](https://github.com/rust-ndarray/ndarray-linalg#backend-features) for detailed
information about BLAS/LAPACK backend configuration and performance considerations.
## Limitations
This implementation currently:
- Only supports Euclidean distances
- Does not support incremental transform
## References
[Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMap, and PaCMAP for Data Visualization](https://jmlr.org/papers/v22/20-1061.html).
Wang, Y., Huang, H., Rudin, C., & Shaposhnik, Y. (2021). Journal of Machine Learning Research, 22(201), 1-73.
## License
Apache License, Version 2.0