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::{lazy::frame::LazyFrame, prelude::DslPlan};

use crate::{
    core::{FfiResult, IntoAnyMeasurementFfiResultExt, Measure, Metric, MetricSpace},
    domains::{DslPlanDomain, LazyFrameDomain},
    error::Fallible,
    ffi::{
        any::{AnyDomain, AnyMeasure, AnyMeasurement, AnyMetric, AnyObject, Downcast},
        util,
    },
    measurements::PrivateDslPlan,
    measures::{Approximate, MaxDivergence, ZeroConcentratedDivergence},
    metrics::{
        ChangeOneDistance, ChangeOneIdDistance, FrameDistance, HammingDistance,
        InsertDeleteDistance, SymmetricDistance, SymmetricIdDistance,
    },
};

use super::make_private_lazyframe;

#[unsafe(no_mangle)]
pub extern "C" fn opendp_measurements__make_private_lazyframe(
    input_domain: *const AnyDomain,
    input_metric: *const AnyMetric,
    output_measure: *const AnyMeasure,
    lazyframe: *const AnyObject,
    global_scale: *const AnyObject,
    threshold: *const AnyObject,
) -> FfiResult<*mut AnyMeasurement> {
    let input_domain = try_!(try_as_ref!(input_domain).downcast_ref::<LazyFrameDomain>()).clone();
    let input_metric = try_as_ref!(input_metric);
    let output_measure = try_as_ref!(output_measure);
    let MI_ = input_metric.type_.clone();
    let MO_ = output_measure.type_.clone();

    let lazyframe = try_!(try_as_ref!(lazyframe).downcast_ref::<LazyFrame>()).clone();

    let global_scale = if let Some(param) = util::as_ref(global_scale) {
        Some(*try_!(try_as_ref!(param).downcast_ref::<f64>()))
    } else {
        None
    };

    let threshold = if let Some(param) = util::as_ref(threshold) {
        Some(*try_!(try_as_ref!(param).downcast_ref::<u32>()))
    } else {
        None
    };

    fn monomorphize<MI: 'static + Metric, MO: 'static + Measure>(
        input_domain: LazyFrameDomain,
        input_metric: &AnyMetric,
        output_measure: &AnyMeasure,
        lazyframe: LazyFrame,
        global_scale: Option<f64>,
        threshold: Option<u32>,
    ) -> Fallible<AnyMeasurement>
    where
        DslPlan: PrivateDslPlan<MI, MO>,
        (LazyFrameDomain, MI): MetricSpace,
        (DslPlanDomain, MI): MetricSpace,
    {
        let input_metric = input_metric.downcast_ref::<MI>()?.clone();
        let output_measure = output_measure.downcast_ref::<MO>()?.clone();
        Ok(make_private_lazyframe(
            input_domain,
            input_metric,
            output_measure,
            lazyframe,
            global_scale,
            threshold,
        )?
        .into_any_Q()
        .into_any_A())
        .into_any()
    }

    dispatch!(
        monomorphize,
        [
            (MI_, [
                SymmetricDistance, SymmetricIdDistance, InsertDeleteDistance,
                ChangeOneDistance, HammingDistance, ChangeOneIdDistance,
                FrameDistance<SymmetricDistance>, FrameDistance<SymmetricIdDistance>, FrameDistance<InsertDeleteDistance>
            ]),
            (MO_, [MaxDivergence, Approximate<MaxDivergence>, ZeroConcentratedDivergence, Approximate<ZeroConcentratedDivergence>])],
        (
            input_domain,
            input_metric,
            output_measure,
            lazyframe,
            global_scale,
            threshold
        )
    )
    .into()
}