Bayesian Optimization Tools

Use this skill when the user wants the runtime to recommend the next experiment or parameter set instead of only summarizing past results.

Typical triggers:

• suggest the next assay or reaction condition to try
• tune temperature, pH, concentration, or incubation parameters under limited budget
• optimize model or simulation hyperparameters when evaluations are expensive
• build a closed-loop experiment table from prior results and explicit bounds

Environment Check

which python3 || true
python3 - <<'PY'
mods = ["numpy", "sklearn"]
for name in mods:
    try:
        __import__(name)
        print(f"{name}: ok")
    except Exception as exc:
        print(f"{name}: missing ({exc})")
PY

Do not claim a suggestion run completed if numpy or scikit-learn is missing.

Bundled Asset

• templates/bayesian_optimize.py

Preferred Workflow

1. Confirm the objective column and whether the user wants to maximize or minimize it.
2. Confirm the numeric search-space bounds for every parameter.
3. Start from a saved history table or inline JSON records.
4. Export ranked suggestions plus a summary JSON so the next round is reproducible.
5. Treat the output as an experiment-prioritization proposal, not proof that the optimum has been found.

Quick Start

python3 templates/bayesian_optimize.py \
  --input experiments.csv \
  --objective-column yield \
  --param-column temperature \
  --param-column ph \
  --bound temperature:20:80 \
  --bound ph:5.5:8.5 \
  --direction maximize \
  --output optimization/next_conditions.csv \
  --summary optimization/next_conditions.json

Inline JSON example:

python3 templates/bayesian_optimize.py \
  --history-json '[{"temperature": 20, "ph": 7.0, "yield": 0.52}, {"temperature": 35, "ph": 6.5, "yield": 0.68}]' \
  --objective-column yield \
  --bound temperature:20:60 \
  --bound ph:5.5:8.0 \
  --direction maximize \
  --suggestions 3 \
  --output optimization/suggestions.csv \
  --summary optimization/suggestions.json

Output Expectations

Good answers should mention:

• the exact objective column and optimization direction
• which parameter bounds were used
• the acquisition policy and exploration weight
• the best observed point so far
• how many ranked suggestions were written
• where the CSV and summary JSON were saved

Related Skills

For regression or hypothesis testing on finished experiments, activate stat-modeling-tools. For study-planning artifacts or reproducibility checklists, activate scientific-workflow-tools. For chemistry, omics, or docking analyses that generate the objective values, activate the corresponding domain skill.