pelt 0.3.0

Changepoint detection with Pruned Exact Linear Time

Changepoint detection with Pruned Exact Linear Time.

Usage

Python

from pelt import predict

predict(signal, penalty=20, segment_cost_function="l1", jump=10, minimum_segment_length=2)

Rust

use std::num::NonZero;
use pelt::{Pelt, SegmentCostFunction};

// Setup the structure for calculating changepoints
let pelt = Pelt::new()
  .with_jump(NonZero::new(5).expect("Invalid number"))
  .with_minimum_segment_length(NonZero::new(2).expect("Invalid number"))
  .with_segment_cost_function(SegmentCostFunction::L1);

// Do the calculation on a data set
let penalty = 10.0;
let result = pelt.predict(&signal[..], penalty)?;

Run locally

# Install maturin inside a Python environment
python3 -m venv .env
source .env/bin/activate
pip install maturin numpy

# Create a Python package from the Rust code
maturin develop --features python

# Open an interpreter
python

>>> from pelt import predict
>>> import numpy as np
>>> signal = np.array([np.sin(np.arange(0, 1000, 10))]).transpose()
>>> predict(signal, penalty=20)

Benchmarks

[!WARNING] Like all benchmarks, take these with a grain of salt.

Python

Comparison with ruptures:

Cost Function	Data Points	Data Dimension	Mean pelt	Mean ruptures	Times Faster
L2	100	1D	0.004ms	3.113ms	753.1x
L2	100	2D	0.005ms	3.251ms	608.5x
L2	1000	1D	0.419ms	188.546ms	449.8x
L2	1000	2D	0.572ms	94.201ms	164.7x
L2	10000	1D	95.851ms	12.103s	126.3x
L2	10000	2D	19.871ms	1.778s	89.5x
L1	100	1D	0.010ms	5.101ms	508.8x
L1	100	2D	0.025ms	5.323ms	213.0x
L1	1000	1D	0.641ms	180.250ms	281.3x
L1	1000	2D	3.595ms	638.049ms	177.5x
L1	10000	1D	43.444ms	15.710s	361.6x
L1	10000	2D	364.553ms	30.572s	83.9x

Command

maturin develop --features python --release
python benches/bench_compare.py

Profile

Command

cargo build --example simple --profile profiling \
 && samply record target/profiling/examples/simple tests/signals-large.csv

Credits

• fastpelt
• ruptures