watermill

Blazingly fast, generic, and serializable online statistics

2 releases

 0.1.1 Feb 6, 2023 Sep 12, 2022

#1069 in Algorithms

Used in simple_accumulator

74KB
1.5K SLoC

Online statistics in Rust 🦀

`watermill` is crate 🦀 for Blazingly fast, generic and serializable online statistics.

Quickstart

Let's compute the online median and then serialize it:

``````use watermill::quantile::Quantile;
use watermill::stats::Univariate;
let data: Vec<f64> = vec![9., 7., 3., 2., 6., 1., 8., 5., 4.];
let mut running_median: Quantile<f64> = Quantile::new(0.5_f64).unwrap();
for x in data.into_iter() {
running_median.update(x); // update the current statistics
println!("The actual median value is: {}", running_median.get());
}
assert_eq!(running_median.get(), 5.0);

// Convert the statistic to a JSON string.
let serialized = serde_json::to_string(&running_median).unwrap();

// Convert the JSON string back to a statistic.
let deserialized: Quantile<f64> = serde_json::from_str(&serialized).unwrap();

``````

Now let's compute the online sum using the iterators:

``````use watermill::iter::IterStatisticsExtend;
let data: Vec<f64> = vec![1., 2., 3.];
let vec_true: Vec<f64> = vec![1., 3., 6.];
for (d, t) in data.into_iter().online_sum().zip(vec_true.into_iter()) {
assert_eq!(d, t); //       ^^^^^^^^^^
}
``````

You can also compute rolling statistics; in the following example let's compute the rolling sum on 2 previous data:

``````
use watermill::rolling::Rolling;
use watermill::stats::Univariate;
use watermill::variance::Variance;
let data: Vec<f64> = vec![9., 7., 3., 2., 6., 1., 8., 5., 4.];
let mut running_var: Variance<f64> = Variance::default();
// We wrap `running_var` inside the `Rolling` struct.
let mut rolling_var: Rolling<f64> = Rolling::new(&mut running_var, 2).unwrap();
for x in data.into_iter() {
rolling_var.update(x);
}
assert_eq!(rolling_var.get(), 0.5);
``````

Installation

Add the following line to your `cargo.toml`:

``````[dependencies]
watermill = "0.1.0"
``````

Statistics available

Statistics Rollable ?
Mean
Variance
Sum
Min
Max
Count
Quantile
Peak to peak
Exponentially weighted mean
Exponentially weighted variance
Interquartile range
Kurtosis
Skewness
Covariance

Inspiration

The `stats` module of the `river` library in `Python` greatly inspired this crate.

~1.4–2.3MB
~50K SLoC