opendp 0.14.2-dev.20260401.2

A library of differential privacy algorithms for the statistical analysis of sensitive private data.
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use polars::{
    df,
    lazy::frame::IntoLazy,
    prelude::{LazyFrame, col, lit},
};

use crate::{
    domains::{AtomDomain, Margin, SeriesDomain},
    error::ErrorVariant,
    measurements::make_private_lazyframe,
    measures::MaxDivergence,
    metrics::{FrameDistance, SymmetricDistance},
    polars::PrivacyNamespace,
};

use super::*;

fn lazyframe_select_postprocess_query() -> Fallible<(LazyFrameDomain, LazyFrame)> {
    let s1 = SeriesDomain::new("A", AtomDomain::new_closed((0, 1))?);
    let s2 = SeriesDomain::new("B", AtomDomain::<bool>::default());

    let lf_domain = LazyFrameDomain::new(vec![s1, s2])?.with_margin(
        Margin::by(["B"])
            .with_invariant_lengths()
            .with_max_length(5),
    )?;
    let lf = df!("A" => [0, 1, 1], "B" => [true, false, true])?
        .lazy()
        .group_by([col("B")])
        .agg([col("A").dp().sum((lit(0), lit(1)), Some(1.))]);

    Ok((lf_domain, lf))
}

#[test]
fn test_make_private_lazyframe_post_valid() -> Fallible<()> {
    let (lf_domain, lf) = lazyframe_select_postprocess_query()?;

    let query = lf.with_column(col("A").alias("C")).select([col("C")]);

    make_private_lazyframe(
        lf_domain,
        FrameDistance(SymmetricDistance),
        MaxDivergence,
        query,
        None,
        None,
    )?;

    Ok(())
}

#[test]
fn test_make_private_lazyframe_post_invalid() -> Fallible<()> {
    let (lf_domain, lf) = lazyframe_select_postprocess_query()?;
    let query = lf.select([col("A").head(None)]);

    assert_eq!(
        make_private_lazyframe(
            lf_domain,
            FrameDistance(SymmetricDistance),
            MaxDivergence,
            query,
            None,
            None,
        )
        .unwrap_err()
        .variant,
        ErrorVariant::MakeTransformation
    );

    Ok(())
}