Style changes

cbg-ethz · May 14, 2024 · 369c86f · 369c86f
1 parent 878b663
commit 369c86f
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Showing 2 changed files with 15 additions and 10 deletions.
diff --git a/workflows/projects/Mixtures/figure_bmm_vs_other.smk b/workflows/projects/Mixtures/figure_bmm_vs_other.smk
@@ -119,10 +119,11 @@ rule generate_figure:
             ax.set_xlim(*task_config.xlim)
             ax.set_xticks(task_config.xticks)
             ax.set_yticks([])
+            ax.set_ylim(-0.05, 1.01)
             ax.spines[["top", "left", "right"]].set_visible(False)
 
             mi_true = data_v2.groupby("task_id")["mi_true"].mean()[task_id]
-            ax.axvline(mi_true, linestyle="--", color="black", linewidth=2)
+            ax.axvline(mi_true, linestyle=":", color="black", linewidth=2)
 
             # Plot credible intervals from the BMM
             bmm_subtable = data_bmm[(data_bmm["task_id"] == task_id)].copy() 
@@ -140,7 +141,7 @@ rule generate_figure:
                 index = (data_v2["task_id"] == task_id) & (data_v2["estimator_id"] == estimator_id) & (data_v2["n_samples"] == N_SAMPLES)
                 estimates = data_v2[index]["mi_estimate"].values
                 y = y_scaler.get_y(estimator_id=estimator_id, n_points=len(estimates))
-                ax.scatter(estimates, y, color=estimator_config.color, s=DOT_SIZE)
+                ax.scatter(estimates, y, color=estimator_config.color, s=DOT_SIZE, alpha=0.4)
 
         ax = axs[0]
         ax.set_yticks(y_scaler.get_tick_locations(), ["BMM"] + [config.name for config in POINT_ESTIMATORS])

diff --git a/workflows/projects/Mixtures/fitting_gmm.smk b/workflows/projects/Mixtures/fitting_gmm.smk
@@ -117,7 +117,7 @@ DISTRIBUTIONS = {
 rule all:
     # For the main part of the manuscript
     input:
-        expand("plots/{dist_name}-{n_points}-10.pdf", dist_name=["AI", "Galaxy"], n_points=[250])
+        expand("plots/{dist_name}-{n_points}-10.pdf", dist_name=["AI", "Galaxy"], n_points=[500])
 
 
 rule plots_all:
@@ -192,20 +192,22 @@ rule plot_pdf:
         approx_sample = "approx_samples/{dist_name}-{n_points}-{n_components}-0.npz",
     output: "plots/{dist_name}-{n_points}-{n_components}.pdf"
     run:
-        fig, axs = subplots_from_axsize(1, 4, axsize=(1.5, 1.5), top=0.3, wspace=0.3)
+        fig, axs = subplots_from_axsize(1, 4, axsize=(1.2, 1.2), top=0.3, wspace=[0.3, 0.05, 0.05], left=0.5, right=0.15)
 
         for ax in axs:
             ax.spines[['right', 'top']].set_visible(False)
 
+        FONTDICT = {'fontsize': 10}
+
         # Visualise true sample
         ax = axs[0]
-        ax.set_title("Ground-truth sample")
+        ax.set_title("Ground-truth sample", fontdict=FONTDICT)
         true_sample = np.load(input.true_sample)
         visualise_points(true_sample["xs"], true_sample["ys"], ax)
 
         # Visualise approximate sample
         ax = axs[1]
-        ax.set_title("Simulated sample")
+        ax.set_title("Simulated sample", fontdict=FONTDICT)
         approx_sample = np.load(input.approx_sample)
         visualise_points(approx_sample["xs"], approx_sample["ys"], ax)
 
@@ -214,7 +216,7 @@ rule plot_pdf:
 
         # Visualise posterior on mutual information
         ax = axs[2]
-        ax.set_title("Posterior MI")
+        ax.set_title("Posterior MI", fontdict=FONTDICT)
         mi_true = np.mean(pmi_true)
         mi_approx = np.mean(pmi_approx, axis=1)  # (num_mcmc_samples,)
         ax.set_xlabel("MI")
@@ -225,11 +227,13 @@ rule plot_pdf:
 
         # Visualise posterior on profile
         ax = axs[3]
-        ax.set_title("Posterior PMI profile")
+        ax.set_title("Posterior PMI profile", fontdict=FONTDICT)
         ax.set_xlabel("PMI")
 
-        min_val = np.min([pmi_true.min(), pmi_approx.min()])
-        max_val = np.max([pmi_true.max(), pmi_approx.max()])
+        quantile_min = 0.02
+        quantile_max = 1 - quantile_min
+        min_val = np.min([np.quantile(pmi_true, quantile_min), np.quantile(pmi_approx, quantile_min)])
+        max_val = np.max([np.quantile(pmi_true, quantile_max), np.quantile(pmi_approx, quantile_max)])
 
         bins = np.linspace(min_val, max_val, 50)
         for pmi_vals in pmi_approx: