Better pairplots

bayesflow/diagnostics/plots/pairs_posterior.py

@@ -7,6 +7,7 @@
 import seaborn as sns
 
 from bayesflow.utils.dict_utils import dicts_to_arrays
+from bayesflow.utils.plot_utils import create_legends
 
 from .pairs_samples import _pairs_samples
 
@@ -21,6 +22,7 @@ def pairs_posterior(
     height: int = 3,
     post_color: str | tuple = "#132a70",
     prior_color: str | tuple = "gray",
+    target_color: str | tuple = "red",
     alpha: float = 0.9,
     label_fontsize: int = 14,
     tick_fontsize: int = 12,
@@ -37,25 +39,27 @@ def pairs_posterior(
         Optional true parameter values that have generated the observed dataset.
     priors       : np.ndarray of shape (n_prior_draws, n_params) or None, optional (default: None)
         Optional prior samples obtained from the prior.
-    dataset_id: Optional ID of the dataset for whose posterior the pairs plot shall be generated.
-        Should only be specified if estimates contains posterior draws from multiple datasets.
+    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.
     variable_names       : list or None, optional, default: None
         The parameter names for nice plot titles. Inferred if None
     height            : float, optional, default: 3
-        The height of the pairplot
+        The height of the pair plots
     label_fontsize    : int, optional, default: 14
         The font size of the x and y-label texts (parameter names)
     tick_fontsize     : int, optional, default: 12
-        The font size of the axis ticklabels
+        The font size of the axis tick labels
     legend_fontsize   : int, optional, default: 16
         The font size of the legend text
     post_color        : str, optional, default: '#132a70'
         The color for the posterior histograms and KDEs
     prior_color      : str, optional, default: gray
         The color for the optional prior histograms and KDEs
+    target_color      : str, optional, default: red
+        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
 
@@ -81,7 +85,7 @@ def pairs_posterior(
         variable_names=variable_names,
     )
 
-    # dicts_to_arrays will keep dataset axis even if it is of length 1
+    # 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:
@@ -109,14 +113,30 @@ def pairs_posterior(
         # Create DataFrame with variable names as columns
         g.data = pd.DataFrame(targets, columns=targets.variable_names)
         g.data["_source"] = "True Parameter"
-        g.map_diag(plot_true_params)
+        g.map_diag(plot_true_params_as_lines, color=target_color)
+        g.map_offdiag(plot_true_params_as_points, color=target_color)
+
+        create_legends(
+            g,
+            plot_data,
+            color=post_color,
+            color2=prior_color,
+            legend_fontsize=legend_fontsize,
+            show_single_legend=False,
+        )
 
     return g
 
 
-def plot_true_params(x, hue=None, **kwargs):
-    """Custom function to plot true parameters on the diagonal."""
+def plot_true_params_as_lines(x, hue=None, color=None, **kwargs):
+    """Custom function to plot true parameters on the diagonal as dashed lines."""
     # hue needs to be added to handle the case of plotting both posterior and prior
     param = x.iloc[0]  # Get the single true value for the diagonal
     # only plot on the diagonal a vertical line for the true parameter
-    plt.axvline(param, color="black", linestyle="--")
+    plt.axvline(param, color=color, linestyle="--")
+
+
+def plot_true_params_as_points(x, y, color=None, marker="x", **kwargs):
+    """Custom function to plot true parameters on the off-diagonal as a single point."""
+    if len(x) > 0 and len(y) > 0:
+        plt.scatter(x.iloc[0], y.iloc[0], color=color, marker=marker, **kwargs)

bayesflow/diagnostics/plots/pairs_samples.py

@@ -8,6 +8,7 @@
 
 from bayesflow.utils import logging
 from bayesflow.utils.dict_utils import dicts_to_arrays
+from bayesflow.utils.plot_utils import create_legends
 
 
 def pairs_samples(
@@ -17,8 +18,10 @@ def pairs_samples(
     height: float = 2.5,
     color: str | tuple = "#132a70",
     alpha: float = 0.9,
+    label: str = "Posterior",
     label_fontsize: int = 14,
     tick_fontsize: int = 12,
+    show_single_legend: bool = False,
     **kwargs,
 ) -> sns.PairGrid:
     """
@@ -37,13 +40,18 @@ def pairs_samples(
     height      : float, optional, default: 2.5
         The height of the pair plot
     color       : str, optional, default : '#8f2727'
-        The color of the plot
+        The primary color of the plot
     alpha       : float in [0, 1], optional, default: 0.9
         The opacity of the plot
+    label       : str, optional, default: "Posterior"
+        Label for the dataset to plot
     label_fontsize    : int, optional, default: 14
         The font size of the x and y-label texts (parameter names)
     tick_fontsize     : int, optional, default: 12
-        The font size of the axis ticklabels
+        The font size of the axis tick labels
+    show_single_legend : bool, optional, default: False
+        Optional toggle for the user to choose whether a single dataset
+        should also display legend
     **kwargs    : dict, optional
         Additional keyword arguments passed to the sns.PairGrid constructor
     """
@@ -59,8 +67,11 @@ def pairs_samples(
         height=height,
         color=color,
         alpha=alpha,
+        label=label,
         label_fontsize=label_fontsize,
         tick_fontsize=tick_fontsize,
+        show_single_legend=show_single_legend,
+        **kwargs,
     )
 
     return g
@@ -72,17 +83,27 @@ def _pairs_samples(
     color: str | tuple = "#132a70",
     color2: str | tuple = "gray",
     alpha: float = 0.9,
+    label: str = "Posterior",
     label_fontsize: int = 14,
     tick_fontsize: int = 12,
     legend_fontsize: int = 14,
+    show_single_legend: bool = False,
     **kwargs,
 ) -> sns.PairGrid:
-    # internal version of pairs_samples creating the seaborn plot
+    """
+    Internal version of pairs_samples creating the seaborn PairPlot
+    for both a single dataset and multiple datasets.
 
-    # Parameters
-    # ----------
-    # plot_data   : output of bayesflow.utils.dict_utils.dicts_to_arrays
-    # other arguments are documented in pairs_samples
+    Parameters
+    ----------
+    plot_data   : output of bayesflow.utils.dict_utils.dicts_to_arrays
+        Formatted data to plot from the sample dataset
+    color2      : str, optional, default: 'gray'
+        Secondary color for the pair plots.
+        This is the color used for the prior draws.
+
+    Other arguments are documented in pairs_samples
+    """
 
     estimates_shape = plot_data["estimates"].shape
     if len(estimates_shape) != 2:
@@ -136,7 +157,7 @@ def _pairs_samples(
         common_norm=False,
     )
 
-    # add scatterplots to the upper diagonal
+    # add scatter plots to the upper diagonal
     g.map_upper(sns.scatterplot, alpha=0.6, s=40, edgecolor="k", color=color, lw=0)
 
     # add KDEs to the lower diagonal
@@ -146,11 +167,6 @@ def _pairs_samples(
         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)
 
-    # need to add legend here such that colors are recognized
-    if plot_data["priors"] is not None:
-        g.add_legend(fontsize=legend_fontsize, loc="center right")
-        g._legend.set_title(None)
-
     # Generate grids
     dim = g.axes.shape[0]
     for i in range(dim):
@@ -165,32 +181,48 @@ def _pairs_samples(
             g.axes[i, j].tick_params(axis="both", which="major", labelsize=tick_fontsize)
             g.axes[i, j].tick_params(axis="both", which="minor", labelsize=tick_fontsize)
 
-        # adjust font size of labels
+        # adjust the font size of labels
         # the labels themselves remain the same as before, i.e., variable_names
         g.axes[i, 0].set_ylabel(variable_names[i], fontsize=label_fontsize)
         g.axes[dim - 1, i].set_xlabel(variable_names[i], fontsize=label_fontsize)
 
+    # need to add legend here such that colors are recognized
+    # if plot_data["priors"] is not None:
+    #     g.add_legend(fontsize=legend_fontsize, loc="center right")
+    #     g._legend.set_title(None)
+
+    create_legends(
+        g,
+        plot_data,
+        color=color,
+        color2=color2,
+        legend_fontsize=legend_fontsize,
+        label=label,
+        show_single_legend=show_single_legend,
+    )
+
     # Return figure
     g.tight_layout()
 
     return g
 
 
-# create a histogram plot on a twin y axis
-# this ensures that the y scaling of the diagonal plots
-# in independent of the y scaling of the off-diagonal plots
 def histplot_twinx(x, **kwargs):
-    # Create a twin axis
-    ax2 = plt.gca().twinx()
+    """
+    # create a histogram plot on a twin y-axis
+    # this ensures that the y scaling of the diagonal plots
+    # in independent of the y scaling of the off-diagonal plots
 
+    Parameters
+    ----------
+    x : np.ndarray
+        Data to be plotted.
+    """
     # create a histogram on the twin axis
-    sns.histplot(x, **kwargs, ax=ax2)
+    sns.histplot(x, legend=False, **kwargs)
 
     # make the twin axis invisible
     plt.gca().spines["right"].set_visible(False)
     plt.gca().spines["top"].set_visible(False)
-    ax2.set_ylabel("")
-    ax2.set_yticks([])
-    ax2.set_yticklabels([])
 
     return None

bayesflow/utils/plot_utils.py

@@ -6,7 +6,7 @@
 
 from matplotlib.collections import LineCollection
 from matplotlib.colors import Normalize
-from matplotlib.patches import Rectangle
+from matplotlib.patches import Rectangle, Patch
 from matplotlib.legend_handler import HandlerPatch
 
 from .validators import check_estimates_prior_shapes
@@ -67,7 +67,7 @@ def prepare_plot_data(
     )
     check_estimates_prior_shapes(plot_data["estimates"], plot_data["targets"])
 
-    # store variable information at top level for easy access
+    # store variable information at the top level for easy access
     variable_names = plot_data["estimates"].variable_names
     num_variables = len(variable_names)
     plot_data["variable_names"] = variable_names
@@ -249,7 +249,7 @@ def prettify_subplots(axes: np.ndarray, num_subplots: int, tick: bool = True, ti
 
 def make_quadratic(ax: plt.Axes, x_data: np.ndarray, y_data: np.ndarray):
     """
-    Utility to make a subplots quadratic in order to avoid visual illusions
+    Utility to make subplots quadratic to avoid visual illusions
     in, e.g., recovery plots.
     """
 
@@ -269,7 +269,7 @@ def make_quadratic(ax: plt.Axes, x_data: np.ndarray, y_data: np.ndarray):
 
 def gradient_line(x, y, c=None, cmap: str = "viridis", lw: float = 2.0, alpha: float = 1, ax=None):
     """
-    Plot a 1D line with color gradient determined by `c` (same shape as x and y).
+    Plot a 1D line with a color gradient determined by `c` (same shape as x and y).
     """
     if ax is None:
         ax = plt.gca()
@@ -304,7 +304,7 @@ def gradient_legend(ax, label, cmap, norm, loc="upper right"):
     - loc: legend location (default 'upper right')
     """
 
-    # Custom dummy handle to represent the gradient
+    # Custom placeholder handle to represent the gradient
     class _GradientSwatch(Rectangle):
         pass
 
@@ -358,3 +358,65 @@ def add_gradient_plot(
             label=label,
             alpha=0.01,
         )
+
+
+def create_legends(
+    g,
+    plot_data: dict,
+    color: str | tuple = "#132a70",
+    color2: str | tuple = "gray",
+    label: str = "Posterior",
+    show_single_legend: bool = False,
+    legend_fontsize: int = 14,
+):
+    """
+    Helper function to create legends for pairplots.
+
+    Parameters
+    ----------
+    g : sns.PairGrid
+        Seaborn object for the pair plots
+    plot_data   : output of bayesflow.utils.dict_utils.dicts_to_arrays
+        Formatted data to plot from the sample dataset
+    color       : str, optional, default : '#8f2727'
+        The primary color of the plot
+    color2      : str, optional, default: 'gray'
+        The secondary color for the plot
+    label       : str, optional, default: "Posterior"
+        Label for the dataset to plot
+    show_single_legend : bool, optional, default: False
+        Optional toggle for the user to choose whether a single dataset
+        should also display legend
+    legend_fontsize    : int, optional, default: 14
+        fontsize for the legend
+    """
+    handles = []
+    labels = []
+
+    if plot_data.get("priors") is not None:
+        prior_handle = Patch(color=color2, label="Prior")
+        prior_label = "Prior"
+        handles.append(prior_handle)
+        labels.append(prior_label)
+
+    posterior_handle = Patch(color=color, label="Posterior")
+    posterior_label = label
+    handles.append(posterior_handle)
+    labels.append(posterior_label)
+
+    if plot_data.get("targets") is not None:
+        target_handle = plt.Line2D([0], [0], color="r", linestyle="--", marker="x", label="Targets")
+        target_label = "Targets"
+        handles.append(target_handle)
+        labels.append(target_label)
+
+    # If there are more than one dataset to plot,
+    if len(handles) > 1 or show_single_legend:
+        g.figure.legend(
+            handles=handles,
+            labels=labels,
+            loc="center left",
+            bbox_to_anchor=(1, 0.5),
+            frameon=False,
+            fontsize=legend_fontsize,
+        )