Bayesian Optimization Tools

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.