| name | scientific-doe |
| description | 実験計画法（DOE）スキル。直交配列表（L9/L16/L27）、中心複合計画（CCD）、
Box-Behnken 設計、D-最適計画、応答曲面法（RSM）、交互作用解析、
ベイズ最適化（Gaussian Process）、効果プロット（主効果/交互作用/pareto）の
テンプレートを提供。
|
| tu_tools | [{"key":"biotools","name":"bio.tools","description":"実験計画法ツールレジストリ検索"}] |
Scientific Design of Experiments (DOE)
体系的な実験計画と最適化のためのスキル。直交表による因子スクリーニングから
RSM による最適条件探索、ベイズ最適化による逐次最適化まで、実験の各段階に
対応するテンプレートを提供する。
When to Use
- 多因子実験の計画（因子・水準の設計）が必要なとき
- 直交表やCCD で実験回数を最小化したいとき
- 主効果・交互作用の寄与率を定量化するとき
- 応答曲面で最適条件を探索するとき
- ベイズ最適化で逐次実験を行いたいとき
Quick Start
1. 因子設計テンプレート
import numpy as np
import pandas as pd
def define_factors(factor_dict):
"""
因子定義テンプレート。
factor_dict 例:
{
"Temperature": {"levels": [200, 250, 300], "unit": "°C", "type": "continuous"},
"Pressure": {"levels": [1, 5, 10], "unit": "mTorr", "type": "continuous"},
"Gas_Ratio": {"levels": [0.2, 0.5, 0.8], "unit": "-", "type": "continuous"},
"Material": {"levels": ["ZnO", "ITO", "TiO2"], "unit": "-", "type": "categorical"},
}
"""
summary = pd.DataFrame([
{"Factor": k, "Levels": len(v["levels"]), "Values": str(v["levels"]),
"Unit": v["unit"], "Type": v["type"]}
for k, v in factor_dict.items()
])
print("=== Factor Design ===")
print(summary.to_string(index=False))
return summary
2. 直交配列表
L9 = np.array([
[0, 0, 0],
[0, 1, 1],
[0, 2, 2],
[1, 0, 1],
[1, 1, 2],
[1, 2, 0],
[2, 0, 2],
[2, 1, 0],
[2, 2, 1],
])
def generate_taguchi_design(factor_dict, array="L9"):
"""
田口直交表から実験計画を生成する。
Available arrays: L4(2^3), L9(3^3-4), L16(2^15), L27(3^13)
"""
arrays = {
"L9": L9,
"L4": np.array([[0,0,0],[0,1,1],[1,0,1],[1,1,0]]),
}
oa = arrays.get(array, L9)
factors = list(factor_dict.keys())
runs = []
for row in oa:
run = {}
for i, factor in enumerate(factors[:oa.shape[1]]):
levels = factor_dict[factor][]
run[factor] = levels[row[i] % (levels)]
runs.append(run)
design_df = pd.DataFrame(runs)
design_df.index.name =
design_df.index +=
design_df
3. 中心複合計画 (CCD)
from itertools import product
def central_composite_design(factor_dict, alpha="rotatable", center_points=3):
"""
中心複合計画 (Central Composite Design) を生成する。
Components:
- 2^k 完全実施要因計画 (cube points)
- 2k 軸点 (axial/star points)
- n_c 中心点
alpha:
"rotatable" — α = (2^k)^(1/4) (回転可能)
"face" — α = 1 (面心)
float — 任意の値
"""
continuous_factors = {k: v for k, v in factor_dict.items()
if v["type"] == "continuous"}
factor_names = list(continuous_factors.keys())
k = len(factor_names)
if alpha == "rotatable":
alpha_val = (2 ** k) ** 0.25
elif alpha == "face":
alpha_val = 1.0
else:
alpha_val = float(alpha)
midpoints = {}
half_ranges = {}
for name, info in continuous_factors.items():
levels = info["levels"]
mid = (max(levels) + min(levels)) / 2
half = (max(levels) - min(levels)) / 2
midpoints[name] = mid
half_ranges[name] = half
runs = []
for combo in product([-1, 1], repeat=k):
run = {factor_names[i]: midpoints[factor_names[i]] + combo[i] * half_ranges[factor_names[i]]
for i (k)}
run[] =
runs.append(run)
i (k):
direction [-, ]:
run = {name: midpoints[name] name factor_names}
run[factor_names[i]] = midpoints[factor_names[i]] + direction * alpha_val * half_ranges[factor_names[i]]
run[] =
runs.append(run)
_ (center_points):
run = {name: midpoints[name] name factor_names}
run[] =
runs.append(run)
design_df = pd.DataFrame(runs)
design_df.index.name =
design_df.index +=
design_df
4. 分散分析 (ANOVA) — 因子効果解析
from scipy.stats import f_oneway
def anova_factor_effects(design_df, response_col, factor_cols):
"""
各因子の主効果を ANOVA で評価する。
Returns:
DataFrame with Factor, SS, DF, MS, F_value, p_value, contribution_pct
"""
ss_total = np.sum((design_df[response_col] - design_df[response_col].mean())**2)
results = []
for factor in factor_cols:
groups = [group[response_col].values
for _, group in design_df.groupby(factor)]
if len(groups) < 2:
continue
f_val, p_val = f_oneway(*groups)
grand_mean = design_df[response_col].mean()
ss_factor = sum(len(g) * (np.mean(g) - grand_mean)**2 for g in groups)
df_factor = len(groups) - 1
ms_factor = ss_factor / df_factor
results.append({
"Factor": factor,
"SS": ss_factor,
"DF": df_factor,
"MS": ms_factor,
"F_value": f_val,
"p_value": p_val,
"Contribution_pct": ss_factor / ss_total * 100 if ss_total > 0 else 0,
})
return pd.DataFrame(results).sort_values("Contribution_pct", ascending=False)
5. 主効果プロット
import matplotlib.pyplot as plt
def main_effects_plot(design_df, response_col, factor_cols, figsize=None):
"""全因子の主効果プロットを描画する。"""
n = len(factor_cols)
if figsize is None:
figsize = (4 * n, 4)
fig, axes = plt.subplots(1, n, figsize=figsize, sharey=True)
if n == 1:
axes = [axes]
grand_mean = design_df[response_col].mean()
for ax, factor in zip(axes, factor_cols):
means = design_df.groupby(factor)[response_col].mean()
ax.plot(range(len(means)), means.values, "bo-", linewidth=2, markersize=8)
ax.axhline(grand_mean, color="gray", linestyle="--", alpha=0.5)
ax.set_xticks(range(len(means)))
ax.set_xticklabels(means.index, rotation=45)
ax.set_xlabel(factor)
ax.grid(alpha=0.3)
axes[0].set_ylabel(response_col)
plt.suptitle("Main Effects Plot", fontweight="bold", y=1.02)
plt.tight_layout()
plt.savefig("figures/main_effects_plot.png", dpi=300, bbox_inches="tight")
plt.close()
6. ベイズ最適化（Gaussian Process）
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import Matern
def bayesian_optimization(objective_func, bounds, n_initial=5,
n_iterations=20, kappa=2.576):
"""
ベイズ最適化（Gaussian Process + Expected Improvement）。
Parameters:
objective_func: callable f(x) → y (最大化)
bounds: dict {"param": (low, high)}
n_initial: 初期ランダムサンプリング数
n_iterations: 最適化ステップ数
kappa: 探索-活用トレードオフ (UCB の κ)
"""
from scipy.optimize import minimize as scipy_minimize
from scipy.stats import norm
param_names = list(bounds.keys())
lows = np.array([bounds[p][0] for p in param_names])
highs = np.array([bounds[p][1] for p in param_names])
X_init = np.random.uniform(lows, highs, size=(n_initial, len(param_names)))
y_init = np.array([objective_func(dict(zip(param_names, x))) for x in X_init])
X_observed = X_init.tolist()
y_observed = y_init.tolist()
gp = GaussianProcessRegressor(kernel=Matern(nu=2.5), n_restarts_optimizer=5,
random_state=42)
for i in range(n_iterations):
X_arr = np.array(X_observed)
y_arr = np.array(y_observed)
gp.fit(X_arr, y_arr)
def neg_ucb():
mu, sigma = gp.predict(x.reshape(, -), return_std=)
-(mu + kappa * sigma)
best_x =
best_val = ()
_ ():
x0 = np.random.uniform(lows, highs)
res = scipy_minimize(neg_ucb, x0, bounds=((lows, highs)),
method=)
res.fun < best_val:
best_val = res.fun
best_x = res.x
y_new = objective_func(((param_names, best_x)))
X_observed.append(best_x.tolist())
y_observed.append(y_new)
best_idx = np.argmax(y_observed)
best_params = ((param_names, X_observed[best_idx]))
best_y = y_observed[best_idx]
{
: best_params,
: best_y,
: np.array(X_observed),
: np.array(y_observed),
: gp,
}
7. 交互作用プロット
def interaction_plot(design_df, response_col, factor1, factor2, figsize=(8, 6)):
"""2 因子間の交互作用プロットを描画する。"""
fig, ax = plt.subplots(figsize=figsize)
for level2, group in design_df.groupby(factor2):
means = group.groupby(factor1)[response_col].mean()
ax.plot(range(len(means)), means.values, "o-", linewidth=2,
markersize=8, label=f"{factor2}={level2}")
ax.set_xticks(range(len(means)))
ax.set_xticklabels(means.index)
ax.set_xlabel(factor1)
ax.set_ylabel(response_col)
ax.set_title(f"Interaction Plot: {factor1} × {factor2}", fontweight="bold")
ax.legend()
ax.grid(alpha=0.3)
plt.tight_layout()
plt.savefig("figures/interaction_plot.png", dpi=300, bbox_inches="tight")
plt.close()
ToolUniverse 連携
| TU Key | ツール名 | 連携内容 |
|---|
biotools | bio.tools | 実験計画法ツールレジストリ検索 |
References
Output Files
| ファイル | 形式 |
|---|
results/experimental_design.csv | CSV |
results/anova_factor_effects.csv | CSV |
results/bayesian_optimization_history.csv | CSV |
figures/main_effects_plot.png | PNG |
figures/interaction_plot.png | PNG |
figures/bayesian_convergence.png | PNG |
依存パッケージ
scipy>=1.10
scikit-learn>=1.3
