Fix mc_calibration plot

bayesflow/diagnostics/plots/mc_calibration.py

@@ -68,41 +68,42 @@ def mc_calibration(
 
     # Gather plot data and metadata into a dictionary
     plot_data = prepare_plot_data(
-        estimates=pred_models,
-        ground_truths=true_models,
+        targets=pred_models,
+        references=true_models,
         variable_names=model_names,
         num_col=num_col,
         num_row=num_row,
         figsize=figsize,
+        default_name="M",
     )
 
     # Compute calibration
     cal_errors, true_probs, pred_probs = expected_calibration_error(
-        plot_data["ground_truths"], plot_data["estimates"], num_bins
+        plot_data["references"], plot_data["targets"], num_bins
     )
 
     for j, ax in enumerate(plot_data["axes"].flat):
         # Plot calibration curve
-        ax[j].plot(pred_probs[j], true_probs[j], "o-", color=color)
+        ax.plot(pred_probs[j], true_probs[j], "o-", color=color)
 
         # Plot PMP distribution over bins
         uniform_bins = np.linspace(0.0, 1.0, num_bins + 1)
-        norm_weights = np.ones_like(plot_data["estimates"]) / len(plot_data["estimates"])
-        ax[j].hist(plot_data["estimates"][:, j], bins=uniform_bins, weights=norm_weights[:, j], color="grey", alpha=0.3)
+        norm_weights = np.ones_like(plot_data["targets"]) / len(plot_data["targets"])
+        ax.hist(plot_data["targets"][:, j], bins=uniform_bins, weights=norm_weights[:, j], color="grey", alpha=0.3)
 
         # Plot AB line
-        ax[j].plot((0, 1), (0, 1), "--", color="black", alpha=0.9)
+        ax.plot((0, 1), (0, 1), "--", color="black", alpha=0.9)
 
         # Tweak plot
-        ax[j].set_xlim([0 - epsilon, 1 + epsilon])
-        ax[j].set_ylim([0 - epsilon, 1 + epsilon])
-        ax[j].set_xticks([0.0, 0.2, 0.4, 0.6, 0.8, 1.0])
-        ax[j].set_yticks([0.0, 0.2, 0.4, 0.6, 0.8, 1.0])
+        ax.set_xlim([0 - epsilon, 1 + epsilon])
+        ax.set_ylim([0 - epsilon, 1 + epsilon])
+        ax.set_xticks([0.0, 0.2, 0.4, 0.6, 0.8, 1.0])
+        ax.set_yticks([0.0, 0.2, 0.4, 0.6, 0.8, 1.0])
 
         # Add ECE label
         add_metric(
-            ax[j],
-            metric_text=r"$\widehat{{\mathrm{{ECE}}}}$ = {0:.3f}",
+            ax,
+            metric_text=r"$\widehat{{\mathrm{{ECE}}}}$",
             metric_value=cal_errors[j],
             metric_fontsize=metric_fontsize,
         )

bayesflow/utils/__init__.py

@@ -35,7 +35,7 @@
 )
 from .optimal_transport import optimal_transport
 from .plot_utils import (
-    check_posterior_prior_shapes,
+    check_estimates_prior_shapes,
     prepare_plot_data,
     add_titles_and_labels,
     prettify_subplots,

bayesflow/utils/plot_utils.py

@@ -4,7 +4,7 @@
 import matplotlib.pyplot as plt
 import seaborn as sns
 
-from .validators import check_posterior_prior_shapes
+from .validators import check_estimates_prior_shapes
 from .dict_utils import dicts_to_arrays
 
 
@@ -52,7 +52,7 @@ def prepare_plot_data(
     plot_data = dicts_to_arrays(
         targets=targets, references=references, variable_names=variable_names, default_name=default_name
     )
-    check_posterior_prior_shapes(plot_data["targets"], plot_data["references"])
+    check_estimates_prior_shapes(plot_data["targets"], plot_data["references"])
 
     # Configure layout
     num_row, num_col = set_layout(plot_data["num_variables"], num_row, num_col, stacked)

bayesflow/utils/validators.py

@@ -8,48 +8,82 @@ def check_lengths_same(*args):
         raise ValueError(f"All tuple arguments must have the same length, but lengths are {tuple(map(len, args))}.")
 
 
-def check_posterior_prior_shapes(post_variables: Tensor, prior_variables: Tensor):
+def check_prior_shapes(variables: Tensor):
     """
-    Checks requirements for the shapes of posterior and prior draws as
-    necessitated by most diagnostic functions.
+    Checks the shape of posterior draws as required by most diagnostic functions
 
     Parameters
     ----------
-    post_samples      : Tensor of shape (num_data_sets, num_post_draws, num_params)
-        The posterior draws obtained from num_data_sets
-    prior_samples     : Tensor of shape (num_data_sets, num_params)
-        The prior draws obtained for generating num_data_sets
-
-    Raises
-    ------
-    ShapeError
-        If there is a deviation form the expected shapes of `post_samples` and `prior_samples`.
+    variables     : Tensor of shape (num_data_sets, num_params)
+        The prior_samples from generating num_data_sets
     """
 
-    if len(post_variables.shape) != 3:
+    if len(variables.shape) != 2:
         raise ShapeError(
-            "post_samples should be a 3-dimensional array, with the "
-            "first dimension being the number of (simulated) data sets, "
-            "the second dimension being the number of posterior draws per data set, "
-            "and the third dimension being the number of parameters (marginal distributions), "
-            f"but your input has dimensions {len(post_variables.shape)}"
+            "prior_samples samples should be a 2-dimensional array, with the "
+            "first dimension being the number of (simulated) data sets / prior_samples draws "
+            "and the second dimension being the number of variables, "
+            f"but your input has dimensions {len(variables.shape)}"
         )
-    elif len(prior_variables.shape) != 2:
+
+
+def check_estimates_shapes(variables: Tensor):
+    """
+    Checks the shape of model-generated predictions (posterior draws, point estimates)
+    as required by most diagnostic functions
+
+    Parameters
+    ----------
+    variables     : Tensor of shape (num_data_sets, num_post_draws, num_params)
+        The prior_samples from generating num_data_sets
+    """
+    if len(variables.shape) != 2 and len(variables.shape) != 3:
         raise ShapeError(
-            "prior_samples should be a 2-dimensional array, with the "
-            "first dimension being the number of (simulated) data sets / prior draws "
-            "and the second dimension being the number of parameters (marginal distributions), "
-            f"but your input has dimensions {len(prior_variables.shape)}"
+            "estimates should be a 2- or 3-dimensional array, with the "
+            "first dimension being the number of data sets, "
+            "(optional) second dimension the number of posterior draws per data set, "
+            "and the last dimension the number of estimated variables, "
+            f"but your input has dimensions {len(variables.shape)}"
         )
-    elif post_variables.shape[0] != prior_variables.shape[0]:
+
+
+def check_consistent_shapes(estimates: Tensor, prior_samples: Tensor):
+    """
+    Checks whether the model-generated predictions (posterior draws, point estimates) and
+    prior_samples have consistent leading (num_data_sets) and trailing (num_params) dimensions
+    """
+    if estimates.shape[0] != prior_samples.shape[0]:
         raise ShapeError(
-            "The number of elements over the first dimension of post_samples and prior_samples"
-            f"should match, but post_samples has {post_variables.shape[0]} and prior_samples has "
-            f"{prior_variables.shape[0]} elements, respectively."
+            "The number of elements over the first dimension of estimates and prior_samples"
+            f"should match, but estimates have {estimates.shape[0]} and prior_samples has "
+            f"{prior_samples.shape[0]} elements, respectively."
         )
-    elif post_variables.shape[-1] != prior_variables.shape[-1]:
+    if estimates.shape[-1] != prior_samples.shape[-1]:
         raise ShapeError(
-            "The number of elements over the last dimension of post_samples and prior_samples"
-            f"should match, but post_samples has {post_variables.shape[1]} and prior_samples has "
-            f"{prior_variables.shape[-1]} elements, respectively."
+            "The number of elements over the last dimension of estimates and prior_samples"
+            f"should match, but estimates has {estimates.shape[0]} and prior_samples has "
+            f"{prior_samples.shape[0]} elements, respectively."
         )
+
+
+def check_estimates_prior_shapes(estimates: Tensor, prior_samples: Tensor):
+    """
+    Checks requirements for the shapes of estimates and prior_samples draws as
+    necessitated by most diagnostic functions.
+
+    Parameters
+    ----------
+    estimates      : Tensor of shape (num_data_sets, num_post_draws, num_params) or (num_data_sets, num_params)
+        The model-generated predictions (posterior draws, point estimates) obtained from num_data_sets
+    prior_samples     : Tensor of shape (num_data_sets, num_params)
+        The prior_samples draws obtained for generating num_data_sets
+
+    Raises
+    ------
+    ShapeError
+        If there is a deviation form the expected shapes of `estimates` and `estimates`.
+    """
+
+    check_estimates_shapes(estimates)
+    check_prior_shapes(prior_samples)
+    check_consistent_shapes(estimates, prior_samples)