Runtime & Support Scaling¶
The case study in cases/runtime_support_scaling/ investigates computational efficiency and memory scaling as the Cartesian support of baseline covariates \(X\) expands.
The Combinatorial Challenge¶
Evaluating calibration constraints requires computing:
\[
\sum_{x \in \mathcal{S}_k} P(Y = 1 \mid X = x; \theta) P(X = x)
\]
As the number of discrete variables \(p\) or the number of levels per variable increases, the total Cartesian support \(\prod_{j=1}^p \lvert \mathcal{X}_j \rvert\) grows exponentially.
The scaling harness systematically benchmarks: - Total wall-clock time per iteration. - Peak memory consumption. - Vectorized integration throughput. - Robust rejection thresholds when cardinality exceeds safety bounds.
Execution¶
Smoke Scaling Benchmark¶
Full Protocol Profile¶
Outputs are saved in data/runtime_support_scaling/runtime_protocol.csv and support_scaling.csv.