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polars-plugins

Demonstrates how to implement custom expressions on polars Series using their plugins framework. This is super convenient for executing compute intensive operations fast by leveraging the rust runtime and all the higher level optimizations pola-rs provides.

Getting Started

We implement two custom operations:

  • a discounted cumulative sum that basically computes $y_{t+1} = \gamma * y_t + x_t$ with $y_0 = x_0$. This operation is sequential by nature and so cannot really be parallelized (expect if it runs on independent groups).
  • a feature hasher that applies the hashing trick to map strings to integers: $h(x) \text{ mod } N_{bucket}$. This is very useful for online and large scale machine learning systems as it's usually a cheap operation, stateless and elegantly handle out of vocabulary modalities and missing values.

As recommended by the polars documentation, repos implementing extensions should have the following structure:

├ my_lib # python package binding the rust code
|  └ __init__.py
|
├ src # rust package implementing the logic
|  ├ my_lib_implem.rs
|  └ lib.rs
|
├ Cargo.toml # describes the rust crate (name, rust version, dependencies, ...)
└ pyproject.toml # describes the python package

It's important that the package and lib names declared in Cargo.toml are the same than the python package name (here my_lib)!

[package]
name = "polars_plugins" # <- here
version = "0.1.0"
edition = "2021"

[lib]
name = "polars_plugins" # <- and here
crate-type = ["cdylib"]

__init__.py declares methods that register new polars expressions and returns them. This binds the rust lib to python so that it can be called by the interpreter at runtime. lib.rs exposes the rust modules to make the binding work. The binding is handled by pyo3 under the hood.

def discounted_cum_sum(expr: pl.Expr, gamma: float) -> pl.Expr:
    return register_plugin_function(
        plugin_path=Path(__file__).parent,
        function_name="discounted_cum_sum",
        args=[expr],
        kwargs={"gamma": gamma},
        is_elementwise=False, # <- we can control how this expression should behave on window and aggregations
    )

The rust code need to be compiled and we use maturin for that by running maturing build to build the crate into python package, or maturing dev --uv to build and install the crate in the local venv (using uv here).

As far as I explored, developing custom expressions in rust is quite easy. Most of the time you will implement a method with the signature

pub fn my_func(inputs: &[Series], kwargs: StructKwargs) -> PolarsResult<Series> {}

taking by reference multiple Series and optionally a struct storing arguments passed from the python caller and returning a new Series. You will need to decorate it with a macro to specify the type of the output data and to generate a bunch of code at compile time.

#[polars_expr(output_type=UInt64)]

As seen in discounted_cum_sum.rs you can handle multiple types quite easily by implementing a generic kernel and simply matching on the input dtype in the bound method.

Usage

Once installed, we only need to import the new expressions and voilà!

import numpy as np
import polars as pl

from polars_plugins import discounted_cum_sum, feature_hasher


print(
    pl.DataFrame({"some_values": np.random.randint(0, 10, size=1000).tolist()}).with_columns(
        cum_sum=pl.all().cum_sum(),
        discounted_cum_sum=discounted_cum_sum(pl.all(), gamma=0.9),
    )
)
out:
┌─────────────┬─────────┬────────────────────┐
│ some_values ┆ cum_sum ┆ discounted_cum_sum │
│ ---         ┆ ---     ┆ ---                │
│ i64         ┆ i64     ┆ f64                │
╞═════════════╪═════════╪════════════════════╡
│ 0           ┆ 0       ┆ 0.0                │
│ 3           ┆ 3       ┆ 3.0                │
│ 2           ┆ 5       ┆ 4.7                │
│ 5           ┆ 10      ┆ 9.23               │
│ 7           ┆ 17      ┆ 15.307             │
│ 9           ┆ 26      ┆ 22.7763            │
│ 1           ┆ 27      ┆ 21.49867           │
│ 0           ┆ 27      ┆ 19.348803          │
│ 5           ┆ 32      ┆ 22.413923          │
│ 6           ┆ 38      ┆ 26.17253           │
└─────────────┴─────────┴────────────────────┘

vocab = ["chrome", "firefox", "safari", "bing", None]
(
    pl.DataFrame({"feature": np.random.choice(vocab, size=1000).tolist()})
    .with_columns(hashed=feature_hasher("feature", num_buckets=11))
)
out:
┌─────────┬────────┐
│ feature ┆ hashed │
│ ---     ┆ ---    │
│ str     ┆ u64    │
╞═════════╪════════╡
│ bing    ┆ 6      │
│ null    ┆ 0      │
│ bing    ┆ 6      │
│ firefox ┆ 2      │
│ null    ┆ 0      │
│ firefox ┆ 2      │
│ chrome  ┆ 9      │
│ bing    ┆ 6      │
│ firefox ┆ 2      │
│ firefox ┆ 2      │
└─────────┴────────┘