ConfidenceInterval

class mlquantify.confidence.ConfidenceInterval(prev_estims, confidence_level=0.95)

Bootstrap confidence intervals for each class prevalence.

Constructs independent percentile-based confidence intervals for each class dimension from bootstrap samples.

The confidence region is defined as:

\[\begin{split}CI_α(π) = \\begin{cases} 1 & \\text{if } L_i \\le π_i \\le U_i, \\forall i=1,...,n \\\\ 0 & \\text{otherwise} \\end{cases}\end{split}\]

where \(L_i\) and \(U_i\) are the empirical α/2 and 1−α/2 quantiles for class i.

Parameters:

prev_estimsarray-like of shape (m, n)

Bootstrap prevalence estimates.

confidence_levelfloat, default=0.95

Desired confidence level.

Attributes:

I_lowndarray of shape (n,)

Lower confidence bounds.

I_highndarray of shape (n,)

Upper confidence bounds.

References

[1] Moreo, A., & Salvati, N. (2025).

An Efficient Method for Deriving Confidence Intervals in Aggregative Quantification. Section 3.3, Equation (1).

Examples

>>> X = np.random.dirichlet(np.ones(3), size=200)
>>> ci = ConfidenceInterval(X, confidence_level=0.9)
>>> ci.get_region()
(array([0.05, 0.06, 0.05]), array([0.48, 0.50, 0.48]))
>>> ci.contains([0.3, 0.4, 0.3])
array([[ True]])

contains(point)

Check whether a prevalence vector lies within the region.

get_point_estimate()

Return the point estimate of prevalence (e.g., mean of bootstrap samples).

get_region()

Return the parameters defining the confidence region.