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name: analysis-visualization description: "Analyze completed Optuna studies with parameter importances, trial dataframes, diagnostic plots, Plotly/Matplotlib backends, Pareto fronts, hypervolume history, and optional dependency checks." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Optuna Analysis and Visualization

Use this sub-skill after trials already exist and the task is to inspect, export, rank, or visualize study results. It covers optuna.importance, Study.trials_dataframe, optuna.visualization, and optuna.visualization.matplotlib.

Route Here For

• Computing parameter importances with optuna.importance.get_param_importances.
• Exporting trial records with Study.trials_dataframe for tabular analysis or CSV output.
• Creating optimization-history, intermediate-value, contour, slice, parallel-coordinate, EDF, rank, timeline, and importance plots.
• Visualizing multi-objective Pareto fronts and hypervolume history.
• Handling missing optional packages such as plotly, matplotlib, pandas, or scikit-learn gracefully.

Route Elsewhere

• Use ../optimization-workflows/SKILL.md for creating studies, defining objectives, running trials, ask/tell, callbacks, pruning reports, and trial generation.
• Use ../samplers-pruners/SKILL.md for choosing or configuring samplers, pruners, constraints, seeds, and pruning algorithms.
• Use ../cli-and-storage/SKILL.md for CLI usage, storage URLs, RDB-backed study loading, study creation from a shell, and persistent storage operations.

Start With These References

• Analysis APIs and data export: references/analysis-reference.md
• Plotting APIs and backend selection: references/visualization-reference.md
• Common failures and optional dependencies: references/troubleshooting.md

Safe Smoke Checks

Run these scripts from this sub-skill directory in any environment with optuna installed. They create small deterministic in-memory studies and do not use network, external datasets, or persistent storage.

python scripts/analysis_smoke.py
python scripts/visualization_smoke.py

Expected behavior in a minimal install:

• analysis_smoke.py reports a successful core importance check with PedAnovaImportanceEvaluator, and skips dataframe or default fANOVA checks if pandas or scikit-learn are missing.
• visualization_smoke.py reports Plotly and Matplotlib checks as skipped when those optional backends are not installed.

Minimal Patterns

import optuna
from optuna.importance import PedAnovaImportanceEvaluator, get_param_importances

study = optuna.create_study(direction="minimize")
study.optimize(lambda trial: (trial.suggest_float("x", -2.0, 2.0) - 0.5) ** 2, n_trials=8)

importances = get_param_importances(
    study,
    evaluator=PedAnovaImportanceEvaluator(),
    params=["x"],
)

from optuna.visualization import plot_optimization_history, plot_slice

fig = plot_optimization_history(study)
fig.write_html("optimization_history.html")

slice_fig = plot_slice(study, params=["x"])
slice_fig.write_html("slice.html")