update tutorials, and implement plotting fixes.

sgbaird · Jan 28, 2025 · 572d157 · 572d157
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diff --git a/docs/curriculum/tutorials/batch/batch-fullybayesian.ipynb b/docs/curriculum/tutorials/batch/batch-fullybayesian.ipynb
diff --git a/docs/curriculum/tutorials/batch/batch_fullybayesian_selection.png b/docs/curriculum/tutorials/batch/batch_fullybayesian_selection.png
diff --git a/docs/curriculum/tutorials/benchmarking/benchmarking.ipynb b/docs/curriculum/tutorials/benchmarking/benchmarking.ipynb
diff --git a/docs/curriculum/tutorials/benchmarking/benchmarking_selection.png b/docs/curriculum/tutorials/benchmarking/benchmarking_selection.png
diff --git a/docs/curriculum/tutorials/featurization/featurization.ipynb b/docs/curriculum/tutorials/featurization/featurization.ipynb
diff --git a/docs/curriculum/tutorials/featurization/featurization_selection.png b/docs/curriculum/tutorials/featurization/featurization_selection.png
diff --git a/docs/curriculum/tutorials/mobo/mobo.ipynb b/docs/curriculum/tutorials/mobo/mobo.ipynb
diff --git a/docs/curriculum/tutorials/mobo/multi_objective_selection.png b/docs/curriculum/tutorials/mobo/multi_objective_selection.png
diff --git a/docs/curriculum/tutorials/multitask/multitask_selection.png b/docs/curriculum/tutorials/multitask/multitask_selection.png
diff --git a/docs/curriculum/tutorials/sobo/single_objective_selections.png b/docs/curriculum/tutorials/sobo/single_objective_selections.png
diff --git a/docs/curriculum/tutorials/sobo/sobo.ipynb b/docs/curriculum/tutorials/sobo/sobo.ipynb
diff --git a/src/honegumi/ax/main.py.jinja b/src/honegumi/ax/main.py.jinja
@@ -342,6 +342,9 @@ best_parameters, metrics = ax_client.get_best_parameters()
 # Plot results
 objectives = ax_client.objective_names
 df = ax_client.get_trials_data_frame()
+{%- if synchrony == "Batch" and objective == "Single"%}
+df.index = df.index // batch_size
+{%- endif %}
 
 fig, ax = plt.subplots(figsize=(6, 4), dpi=150)
 
@@ -374,17 +377,19 @@ pareto = pd.DataFrame(pareto_data).sort_values(objectives[0])
 ax.scatter(df[objectives[0]], df[objectives[1]], fc="None", ec="k", label="Observed")
 ax.plot(pareto[objectives[0]], pareto[objectives[1]], color="#0033FF", lw=2, label="Pareto Front")
 ax.set_title(f"Task {task}")
+ax.set_xlabel(objectives[0])
+ax.set_ylabel(objectives[1])
 {%- else %}
 pareto = ax_client.get_pareto_optimal_parameters(use_model_predictions=False)
 pareto_data = [p[1][0] for p in pareto.values()]
 pareto = pd.DataFrame(pareto_data).sort_values(objectives[0])
 
 ax.scatter(df[objectives[0]], df[objectives[1]], fc="None", ec="k", label="Observed")
 ax.plot(pareto[objectives[0]], pareto[objectives[1]], color="#0033FF", lw=2, label="Pareto Front")
-{%- endif %}
-
 ax.set_xlabel(objectives[0])
 ax.set_ylabel(objectives[1])
+{%- endif %}
+
 ax.legend()
 plt.show()
-{% endif %}
+{% endif %}