Fixes #90 (axis labeling)

bayesflow/computational_utilities.py

@@ -279,7 +279,8 @@ def expected_calibration_error(m_true, m_pred, num_bins=10):
     # Extract number of models and prepare containers
     n_models = m_true.shape[1]
     cal_errs = []
-    probs = []
+    probs_true = []
+    probs_pred = []
 
     # Loop for each model and compute calibration errs per bin
     for k in range(n_models):
@@ -295,8 +296,9 @@ def expected_calibration_error(m_true, m_pred, num_bins=10):
         cal_err = np.sum(np.abs(prob_true - prob_pred) * (bin_total[nonzero] / len(y_true)))
 
         cal_errs.append(cal_err)
-        probs.append((prob_true, prob_pred))
-    return cal_errs, probs
+        probs_true.append(prob_true)
+        probs_pred.append(prob_pred)
+    return cal_errs, probs_true, probs_pred
 
 
 def maximum_mean_discrepancy(source_samples, target_samples, kernel="gaussian", mmd_weight=1.0, minimum=0.0):

bayesflow/diagnostics.py

@@ -1057,7 +1057,7 @@ def plot_calibration_curves(
     # Determine n_subplots dynamically
     n_row = int(np.ceil(num_models / 6))
     n_col = int(np.ceil(num_models / n_row))
-    cal_errs, cal_probs = expected_calibration_error(true_models, pred_models, num_bins)
+    cal_errs, probs_true, probs_pred = expected_calibration_error(true_models, pred_models, num_bins)
 
     # Initialize figure
     if fig_size is None:
@@ -1073,7 +1073,7 @@ def plot_calibration_curves(
         ax = axarr
     for j in range(num_models):
         # Plot calibration curve
-        ax[j].plot(cal_probs[j][0], cal_probs[j][1], color=color)
+        ax[j].plot(probs_pred[j], probs_true[j], color=color)
 
         # Plot AB line
         ax[j].plot(ax[j].get_xlim(), ax[j].get_xlim(), "--", color="darkgrey")