fullseye

sens_sobol — MULTIVAR sensitivity op

• データ種: design × signal → table
• 呼び出し: import fullseye as fs; fs.ledger.sens_sobol(design=None, y=None, n_boot=500, level=0.95, seed=0) -> 'dict' (実装を直接呼ぶなら import sensitivity; sensitivity.sens_sobol(design=None, y=None, n_boot=500, level=0.95, seed=0) -> 'dict'、台帳から引くなら opsmultivar.get("sens_sobol"))

使い方

First-order and total Sobol’ indices with bootstrap confidence intervals.

From the outputs on a :func:doe_saltelli design (blocks A, B, A_B^(i)):

• S1_i = (1/N) sum_j f(B)_j (f(A_B^(i))_j - f(A)_j) / V (Saltelli et al. 2010, Table 2 (b));
• ST_i = (1/(2N)) sum_j (f(A)_j - f(A_B^(i))_j)^2 / V (Jansen 1999; Saltelli et al. 2010, Table 2 (f));
• V = variance of the outputs at A and B together.

S1 is the share of output variance a factor explains alone; ST adds every interaction it takes part in. ST ~ 0 means the factor can be fixed anywhere in its range; ST - S1 large means it acts through interactions. Confidence intervals come from n_boot bootstrap resamples of the N rows (the same row index across A, B and A_B^(i)) at level. The sum of S1 (sum_s1) is <= 1 up to estimation error; a value far below 1 says interactions carry the rest.

詳しい使い方ガイド

• design_of_experiments ファミリ ガイド

参考(サンプルデータ・文献)

• サンプルデータ カタログ(DL URL / ライセンス) — 2-D は skimage.data(BSD/public)+ 合成、3-D は実データ源(Stanford/PDS 等)の DL URL。
• 演算子の来歴・参考文献 — この op 族の元になった研究/手法の出典。
• アルゴリズムの正典(著者・年)と用途は上記ファミリ使い方ガイドに記載。

実行できる例(この op を実際に呼ぶ検証済みサンプル)

• sensitivity_tour — py -3.11 examples/sensitivity_tour.py

型が繋がる次の op(table を入力に取れる)

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同カテゴリ(sensitivity)

doe_morris · sens_morris · doe_saltelli

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Provenance: sensitivity.py — MULTIVAR operator registry. この per-op ノートは tools/opdocs.py md が自動生成(手編集しない)。

© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.

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