improvements to diagnostic plots

bayesflow/diagnostics/plots/calibration_ecdf_from_quantiles.py

@@ -26,6 +26,7 @@ def calibration_ecdf_from_quantiles(
     fill_color: str = "grey",
     num_row: int = None,
     num_col: int = None,
+    markersize: float = None,
     **kwargs,
 ) -> plt.Figure:
     """
@@ -97,6 +98,8 @@ def calibration_ecdf_from_quantiles(
     num_col           : int, optional, default: None
         The number of columns for the subplots.
         Dynamically determined if None.
+    markersize        : float, optional, default: None
+        The marker size in points.
     **kwargs          : dict, optional, default: {}
         Keyword arguments can be passed to control the behavior of
         ECDF simultaneous band computation through the ``ecdf_bands_kwargs``
@@ -142,11 +145,15 @@ def calibration_ecdf_from_quantiles(
 
         if stacked:
             if j == 0:
-                plot_data["axes"][0].plot(xx, yy, marker="o", color=rank_ecdf_color, alpha=0.95, label="Rank ECDFs")
+                plot_data["axes"][0].plot(
+                    xx, yy, marker="o", color=rank_ecdf_color, markersize=markersize, alpha=0.95, label="Rank ECDFs"
+                )
             else:
-                plot_data["axes"][0].plot(xx, yy, marker="o", color=rank_ecdf_color, alpha=0.95)
+                plot_data["axes"][0].plot(xx, yy, marker="o", color=rank_ecdf_color, markersize=markersize, alpha=0.95)
         else:
-            plot_data["axes"].flat[j].plot(xx, yy, marker="o", color=rank_ecdf_color, alpha=0.95, label="Rank ECDF")
+            plot_data["axes"].flat[j].plot(
+                xx, yy, marker="o", color=rank_ecdf_color, markersize=markersize, alpha=0.95, label="Rank ECDF"
+            )
 
     # Compute uniform ECDF and bands
     alpha, z, L, U = pointwise_ecdf_bands(estimates.shape[0], **kwargs.pop("ecdf_bands_kwargs", {}))

bayesflow/diagnostics/plots/mc_calibration.py

@@ -27,6 +27,7 @@ def mc_calibration(
     color: str = "#132a70",
     num_col: int = None,
     num_row: int = None,
+    markersize: float = None,
 ) -> plt.Figure:
     """Plots the calibration curves, the ECEs and the marginal histograms of predicted posterior model probabilities
     for a model comparison problem. The marginal histograms inform about the fraction of predictions in each bin.
@@ -60,6 +61,8 @@ def mc_calibration(
         The number of rows for the subplots. Dynamically determined if None.
     num_col             : int, optional, default: None
         The number of columns for the subplots. Dynamically determined if None.
+    markersize          : float, optional, default: None
+        The marker size in points.
 
     Returns
     -------
@@ -88,7 +91,7 @@ def mc_calibration(
 
     for j, ax in enumerate(plot_data["axes"].flat):
         # Plot calibration curve
-        ax.plot(ece["probs_pred"][j], ece["probs_true"][j], "o-", color=color)
+        ax.plot(ece["probs_pred"][j], ece["probs_true"][j], "o-", color=color, markersize=markersize)
 
         # Plot PMP distribution over bins
         uniform_bins = np.linspace(0.0, 1.0, num_bins + 1)

bayesflow/diagnostics/plots/pairs_posterior.py

@@ -24,6 +24,8 @@ def pairs_posterior(
     prior_color: str | tuple = "gray",
     target_color: str | tuple = "red",
     alpha: float = 0.9,
+    markersize: float = 40,
+    target_markersize: float = 40,
     label_fontsize: int = 14,
     tick_fontsize: int = 12,
     legend_fontsize: int = 14,
@@ -62,6 +64,10 @@ def pairs_posterior(
         The color for the optional true parameter lines and points
     alpha             : float in [0, 1], optional, default: 0.9
         The opacity of the posterior plots
+    markersize        : float, optional, default: 40
+        The marker size in points**2 of the scatter plots
+    target_markersize : float, optional, default: 40
+        The marker size in points**2 of the target marker
 
     **kwargs          : dict, optional, default: {}
         Further optional keyword arguments propagated to `_pairs_samples`
@@ -101,6 +107,9 @@ def pairs_posterior(
         label_fontsize=label_fontsize,
         tick_fontsize=tick_fontsize,
         legend_fontsize=legend_fontsize,
+        markersize=markersize,
+        target_markersize=target_markersize,
+        target_color=target_color,
         **kwargs,
     )
 
@@ -114,7 +123,7 @@ def pairs_posterior(
         g.data = pd.DataFrame(targets, columns=targets.variable_names)
         g.data["_source"] = "True Parameter"
         g.map_diag(plot_true_params_as_lines, color=target_color)
-        g.map_offdiag(plot_true_params_as_points, color=target_color)
+        g.map_offdiag(plot_true_params_as_points, color=target_color, s=target_markersize)
 
         create_legends(
             g,
@@ -124,6 +133,7 @@ def pairs_posterior(
             legend_fontsize=legend_fontsize,
             show_single_legend=False,
             target_color=target_color,
+            target_markersize=target_markersize,
         )
 
     return g

bayesflow/diagnostics/plots/pairs_samples.py

@@ -13,6 +13,7 @@
 
 def pairs_samples(
     samples: Mapping[str, np.ndarray] | np.ndarray = None,
+    dataset_id: int = None,
     variable_keys: Sequence[str] = None,
     variable_names: Sequence[str] = None,
     height: float = 2.5,
@@ -22,6 +23,7 @@ def pairs_samples(
     label_fontsize: int = 14,
     tick_fontsize: int = 12,
     show_single_legend: bool = False,
+    markersize: float = 40,
     **kwargs,
 ) -> sns.PairGrid:
     """
@@ -32,6 +34,8 @@ def pairs_samples(
     ----------
     samples     : dict[str, Tensor], default: None
         Sample draws from any dataset
+    dataset_id: Optional ID of the dataset for whose posterior the pair plots shall be generated.
+        Should only be specified if estimates contain posterior draws from multiple datasets.
     variable_keys       : list or None, optional, default: None
        Select keys from the dictionary provided in samples.
        By default, select all keys.
@@ -52,15 +56,23 @@ def pairs_samples(
     show_single_legend : bool, optional, default: False
         Optional toggle for the user to choose whether a single dataset
         should also display legend
+    markersize  : float, optional, default: 40
+        Marker size in points**2 of the scatter plot.
     **kwargs    : dict, optional
         Additional keyword arguments passed to the sns.PairGrid constructor
     """
 
     plot_data = dicts_to_arrays(
         estimates=samples,
+        dataset_ids=dataset_id,
         variable_keys=variable_keys,
         variable_names=variable_names,
     )
+    # dicts_to_arrays will keep the dataset axis even if it is of length 1
+    # however, pairs plotting requires the dataset axis to be removed
+    estimates_shape = plot_data["estimates"].shape
+    if len(estimates_shape) == 3 and estimates_shape[0] == 1:
+        plot_data["estimates"] = np.squeeze(plot_data["estimates"], axis=0)
 
     g = _pairs_samples(
         plot_data=plot_data,
@@ -71,6 +83,7 @@ def pairs_samples(
         label_fontsize=label_fontsize,
         tick_fontsize=tick_fontsize,
         show_single_legend=show_single_legend,
+        markersize=markersize,
         **kwargs,
     )
 
@@ -88,6 +101,9 @@ def _pairs_samples(
     tick_fontsize: int = 12,
     legend_fontsize: int = 14,
     show_single_legend: bool = False,
+    markersize: float = 40,
+    target_markersize: float = 40,
+    target_color: str = "red",
     **kwargs,
 ) -> sns.PairGrid:
     """
@@ -101,6 +117,12 @@ def _pairs_samples(
     color2      : str, optional, default: 'gray'
         Secondary color for the pair plots.
         This is the color used for the prior draws.
+    markersize  : float, optional, default: 40
+        Marker size in points**2 of the scatter plot.
+    target_markersize  : float, optional, default: 40
+        Target marker size in points**2 of the scatter plot.
+    target_color : str, optional, default: "red"
+        Target marker color for the legend.
 
     Other arguments are documented in pairs_samples
     """
@@ -159,14 +181,14 @@ def _pairs_samples(
     )
 
     # add scatter plots to the upper diagonal
-    g.map_upper(sns.scatterplot, alpha=0.6, s=40, edgecolor="k", color=color, lw=0)
+    g.map_upper(sns.scatterplot, alpha=0.6, s=markersize, edgecolor="k", color=color, lw=0)
 
     # add KDEs to the lower diagonal
     try:
         g.map_lower(sns.kdeplot, fill=True, color=color, alpha=alpha, common_norm=False)
     except Exception as e:
         logging.exception("KDE failed due to the following exception:\n" + repr(e) + "\nSubstituting scatter plot.")
-        g.map_lower(sns.scatterplot, alpha=0.6, s=40, edgecolor="k", color=color, lw=0)
+        g.map_lower(sns.scatterplot, alpha=0.6, s=markersize, edgecolor="k", color=color, lw=0)
 
     # Generate grids
     dim = g.axes.shape[0]
@@ -200,6 +222,9 @@ def _pairs_samples(
         legend_fontsize=legend_fontsize,
         label=label,
         show_single_legend=show_single_legend,
+        markersize=markersize,
+        target_markersize=target_markersize,
+        target_color=target_color,
     )
 
     # Return figure

bayesflow/diagnostics/plots/recovery.py

@@ -26,6 +26,7 @@ def recovery(
     num_row: int = None,
     xlabel: str = "Ground truth",
     ylabel: str = "Estimate",
+    markersize: float = None,
     **kwargs,
 ) -> plt.Figure:
     """
@@ -76,8 +77,10 @@ def recovery(
         The number of rows for the subplots. Dynamically determined if None.
     num_col           : int, optional, default: None
         The number of columns for the subplots. Dynamically determined if None.
-    xlabel:
-    ylabel:
+    xlabel :
+    ylabel :
+    markersize        : float, optional, default: None
+        The marker size in points.
 
     Returns
     -------
@@ -122,10 +125,18 @@ def recovery(
                 fmt="o",
                 alpha=0.5,
                 color=color,
+                markersize=markersize,
                 **kwargs,
             )
         else:
-            _ = ax.scatter(targets[:, i], point_estimate[:, i], alpha=0.5, color=color, **kwargs)
+            _ = ax.scatter(
+                targets[:, i],
+                point_estimate[:, i],
+                alpha=0.5,
+                color=color,
+                s=None if markersize is None else markersize**2,
+                **kwargs,
+            )
 
         make_quadratic(ax, targets[:, i], point_estimate[:, i])
 

bayesflow/diagnostics/plots/recovery_from_estimates.py

@@ -25,6 +25,7 @@ def recovery_from_estimates(
     num_row: int = None,
     xlabel: str = "Ground truth",
     ylabel: str = "Estimate",
+    markersize: float = None,
     **kwargs,
 ) -> plt.Figure:
     """
@@ -79,8 +80,10 @@ def recovery_from_estimates(
         The number of rows for the subplots. Dynamically determined if None.
     num_col           : int, optional, default: None
         The number of columns for the subplots. Dynamically determined if None.
-    xlabel:
-    ylabel:
+    xlabel            :
+    ylabel            :
+    markersize        : float, optional, default: None
+        The marker size in points.
 
     Returns
     -------
@@ -139,6 +142,7 @@ def recovery_from_estimates(
                 marker=markers[q_idx],
                 alpha=0.5,
                 color=color,
+                s=None if markersize is None else markersize**2,
                 **kwargs,
             )
 

bayesflow/diagnostics/plots/z_score_contraction.py

@@ -18,6 +18,7 @@ def z_score_contraction(
     color: str = "#132a70",
     num_col: int = None,
     num_row: int = None,
+    markersize: float = None,
 ) -> plt.Figure:
     """
     Implements a graphical check for global model sensitivity by plotting the
@@ -76,6 +77,8 @@ def z_score_contraction(
         The number of rows for the subplots. Dynamically determined if None.
     num_col           : int, optional, default: None
         The number of columns for the subplots. Dynamically determined if None.
+    markersize        : float, optional, default: None
+        The marker size in points**2 of the scatter plot.
 
     Returns
     -------
@@ -118,7 +121,7 @@ def z_score_contraction(
         if i >= plot_data["num_variables"]:
             break
 
-        ax.scatter(contraction[:, i], z_score[:, i], color=color, alpha=0.5)
+        ax.scatter(contraction[:, i], z_score[:, i], color=color, alpha=0.5, s=markersize)
         ax.set_xlim([-0.05, 1.05])
 
     prettify_subplots(plot_data["axes"], num_subplots=plot_data["num_variables"], tick_fontsize=tick_fontsize)

bayesflow/utils/plot_utils.py

@@ -374,7 +374,9 @@ def create_legends(
     label: str = "Posterior",
     show_single_legend: bool = False,
     legend_fontsize: int = 14,
+    markersize: float = 40,
     target_color: str = "red",
+    target_markersize: float = 40,
 ):
     """
     Helper function to create legends for pairplots.
@@ -396,8 +398,12 @@ def create_legends(
         should also display legend
     legend_fontsize    : int, optional, default: 14
         fontsize for the legend
-    target_color : str, optional, default "red"
+    markersize  : float, optional, default: 40
+        The marker size in points**2
+    target_color : str, optional, default: "red"
         Color for the target label
+    target_markersize : float, optional, default: 40
+        Marker size in points**2 of the target marker
     """
     handles = []
     labels = []
@@ -414,7 +420,15 @@ def create_legends(
     labels.append(posterior_label)
 
     if plot_data.get("targets") is not None:
-        target_handle = plt.Line2D([0], [0], color=target_color, linestyle="--", marker="x", label="Targets")
+        target_handle = plt.Line2D(
+            [0],
+            [0],
+            color=target_color,
+            linestyle="--",
+            marker="x",
+            markersize=np.sqrt(target_markersize),
+            label="Targets",
+        )
         target_label = "Targets"
         handles.append(target_handle)
         labels.append(target_label)