opendp 0.14.2-dev.20260401.2

A library of differential privacy algorithms for the statistical analysis of sensitive private data.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84

use polars::df;
use polars::lazy::frame::IntoLazy;
use polars::prelude::{NamedFrom, col, lit};
use polars::series::Series;

use crate::domains::{AtomDomain, LazyFrameDomain, OptionDomain, SeriesDomain};
use crate::metrics::{FrameDistance, SymmetricDistance};

use super::*;

#[test]
fn test_make_expr_fill_nan_literal() -> Fallible<()> {
    let lf_domain = LazyFrameDomain::new(vec![SeriesDomain::new(
        "f64",
        OptionDomain::new(AtomDomain::<f64>::default()),
    )])?;

    let lf = df!("f64" => [None, Some(1.), Some(f64::NAN)])?.lazy();

    let t_fill_nan = col("f64").fill_nan(0.0).make_stable(
        lf_domain.clone().row_by_row(),
        FrameDistance(SymmetricDistance),
    )?;
    let expr_fill_nan = t_fill_nan.invoke(&lf.logical_plan)?.expr;
    let actual = lf.with_column(expr_fill_nan).collect()?;

    assert_eq!(actual, df!("f64" => [None, Some(1.), Some(0.)])?);

    assert!(!t_fill_nan.output_domain.column.atom_domain::<f64>()?.nan());

    Ok(())
}

#[test]
fn test_make_expr_fill_nan_expr() -> Fallible<()> {
    let f64_nan = SeriesDomain::new("f64_nan", OptionDomain::new(AtomDomain::<f64>::default()));
    let f64_nonnan = SeriesDomain::new("f64_nonnan", AtomDomain::<f64>::new_non_nan());
    let lf_domain = LazyFrameDomain::new(vec![f64_nan, f64_nonnan])?;

    let lf = df!(
        "f64_nan" => [None, Some(1.), Some(f64::NAN)],
        "f64_nonnan" => [0f64; 3]
    )?
    .lazy();

    let t_fill_nan = col("f64_nan").fill_nan(col("f64_nonnan")).make_stable(
        lf_domain.clone().row_by_row(),
        FrameDistance(SymmetricDistance),
    )?;

    let expr_fill_nan = t_fill_nan.invoke(&lf.logical_plan)?.expr;
    let actual = lf.with_column(expr_fill_nan).collect()?;
    let actual_f64_nan = actual.column("f64_nan")?.as_materialized_series();

    assert_eq!(
        actual_f64_nan,
        &Series::new("f64_nan".into(), [None, Some(1.), Some(0.)])
    );

    assert!(!t_fill_nan.output_domain.column.atom_domain::<f64>()?.nan());

    Ok(())
}

#[test]
fn test_make_expr_fill_nan_expr_filter_fail() -> Fallible<()> {
    let f64_nan = SeriesDomain::new("f64_nan", OptionDomain::new(AtomDomain::<f64>::default()));
    let f64_nonnan = SeriesDomain::new("f64_nonnan", AtomDomain::<f64>::new_non_nan());
    let lf_domain = LazyFrameDomain::new(vec![f64_nan, f64_nonnan])?;

    let err = col("f64_nan")
        .fill_nan(lit(0.0).filter(col("f64_nonnan").gt_eq(0.0)))
        .make_stable(
            lf_domain.clone().row_by_row(),
            FrameDistance(SymmetricDistance),
        )
        .unwrap_err();

    assert_eq!(
        err.message,
        Some("filter is not allowed in a row-by-row context".to_string())
    );
    Ok(())
}