https://github.com/online-ml/watermill.rs

🦀 Online statistics in Rust
https://github.com/online-ml/watermill.rs

crate online-statistics rust statistics stream-processing streaming

Last synced: 2 days ago
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🦀 Online statistics in Rust

• Host: GitHub
• URL: https://github.com/online-ml/watermill.rs
• Owner: online-ml
• License: mit
• Created: 2022-06-14T14:41:32.000Z (over 2 years ago)
• Default Branch: master
• Last Pushed: 2023-02-10T08:57:32.000Z (almost 2 years ago)
• Last Synced: 2024-11-10T06:11:36.078Z (5 days ago)
• Topics: crate, online-statistics, rust, statistics, stream-processing, streaming
• Language: Rust
• Homepage: https://docs.rs/watermill/
• Size: 106 KB
• Stars: 58
• Watchers: 6
• Forks: 1
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • License: LICENSE

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README

        
# 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:

```rust

use watermill::quantile::Quantile;

use watermill::stats::Univariate;

let data: Vec = vec![9., 7., 3., 2., 6., 1., 8., 5., 4.];

let mut running_median: Quantile = 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 = serde_json::from_str(&serialized).unwrap();

```

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

```rust

use watermill::iter::IterStatisticsExtend;

let data: Vec = vec![1., 2., 3.];

let vec_true: Vec = 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: 

```rust

use watermill::rolling::Rolling;

use watermill::stats::Univariate;

use watermill::variance::Variance;

let data: Vec = vec![9., 7., 3., 2., 6., 1., 8., 5., 4.];

let mut running_var: Variance = Variance::default();

// We wrap `running_var` inside the `Rolling` struct.

let mut rolling_var: Rolling = 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`](https://github.com/online-ml/river) library in `Python` greatly inspired this crate.