# changepoint - Change point detection for Rust
Changepoint is a library for doing change point detection for streams of data.
[](https://gitlab.com/Redpoll/changepoint/pipelines)
[](https://crates.io/crates/changepoint)
[](https://gitlab.com/Redpoll/changepoint/-/blob/master/LICENSE)
[](https://docs.rs/changepoint)
## Algorithms
Includes the following change point detection algorithms:
* `Bocpd` -- Online Bayesian Change Point Detection [Reference](https://arxiv.org/pdf/0710.3742.pdf).
* `BocpdTruncated` -- Same as `Bocpd` but truncated the run-length distribution when those lengths are unlikely.
* `Argpcp` -- Gaussain Process Change Point detector [Reference](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.170.1450&rep=rep1&type=pdf)
## Example
```rust
//! A demo of the online Bayesian change point detection on
//! the 3-month Treasury Bill Secondary Market Rate from
//!
//! After this example is run, the file `trasury_bill.ipynb` can be run to generate
//! plots for this dataset.
//!
//! > Board of Governors of the Federal Reserve System (US), 3-Month Treasury Bill: Secondary
//! > Market Rate [TB3MS], retrieved from FRED, Federal Reserve Bank of St. Louis;
//! > https://fred.stlouisfed.org/series/TB3MS, August 5, 2019.
use changepoint::{utils::*, BocpdTruncated, BocpdLike};
use rv::prelude::*;
use std::io;
use std::path::PathBuf;
use std::fs::read_to_string;
fn main() -> io::Result<()> {
// Parse the data from the TB3MS dataset
let mut csv_path = PathBuf::from(env!("CARGO_MANIFEST_DIR"));
csv_path.push("resources/TB3MS.csv");
let data: String = read_to_string(&csv_path)?;
let (dates, pct_change): (Vec<&str>, Vec<f64>) = data
.lines()
.skip(1)
.map(|line| {
let split: Vec<&str> = line.splitn(2, ',').collect();
let date = split[0];
let raw_pct = split[1];
(date, raw_pct.parse::<f64>().unwrap())
})
.unzip();
// Create the Bocpd processor
let mut cpd = BocpdTruncated::new(
250.0,
NormalGamma::new_unchecked(0.0, 1.0, 1.0, 1.0),
);
// Feed data into change point detector and generate a sequence of run-length distributions
let rs: Vec<Vec<f64>> = pct_change
.iter()
.map(|d| cpd.step(d).into())
.collect();
// Determine most likely change points
let change_points: Vec<usize> = map_changepoints(&rs);
let change_dates: Vec<&str> =
change_points.iter().map(|&i| dates[i]).collect();
// Write output for processing my `trasury_bill.ipynb`.
write_data_and_r(
"treasury_bill_output",
&pct_change,
&rs,
&change_points,
)?;
println!("Most likely dates of changes = {:#?}", change_dates);
Ok(())
}
```
To run this example, from the source root, run `cargo run --example treasury_bill`.
The partner notebook can be used to generate the following plots:
