Merge branch 'Development' of https://github.com/stefanradev93/BayesFlow into Development

Author: stefanradev93

bayesflow/diagnostics.py

@@ -51,6 +51,8 @@ def plot_recovery(
     color="#8f2727",
     n_col=None,
     n_row=None,
+    xlabel="Ground truth",
+    ylabel="Estimated",
 ):
     """Creates and plots publication-ready recovery plot with true vs. point estimate + uncertainty.
     The point estimate can be controlled with the ``point_agg`` argument, and the uncertainty estimate
@@ -96,7 +98,11 @@ def plot_recovery(
         A flag for adding R^2 between true and estimates to the plot
     color             : str, optional, default: '#8f2727'
         The color for the true vs. estimated scatter points and error bars
-
+    xlabel            : str, optional, default: 'Ground truth'
+        The label on the x-axis of the plot
+    ylabel            : str, optional, default: 'Estimated'
+        The label on the y-axis of the plot
+        
     Returns
     -------
     f : plt.Figure - the figure instance for optional saving
@@ -198,15 +204,15 @@ def plot_recovery(
     # Only add x-labels to the bottom row
     bottom_row = axarr if n_row == 1 else axarr[0] if n_col == 1 else axarr[n_row - 1, :]
     for _ax in bottom_row:
-        _ax.set_xlabel("Ground truth", fontsize=label_fontsize)
+        _ax.set_xlabel(xlabel, fontsize=label_fontsize)
 
     # Only add y-labels to right left-most row
     if n_row == 1:  # if there is only one row, the ax array is 1D
-        axarr[0].set_ylabel("Estimated", fontsize=label_fontsize)
+        axarr[0].set_ylabel(ylabel, fontsize=label_fontsize)
     # If there is more than one row, the ax array is 2D
     else:
         for _ax in axarr[:, 0]:
-            _ax.set_ylabel("Estimated", fontsize=label_fontsize)
+            _ax.set_ylabel(ylabel, fontsize=label_fontsize)
 
     # Remove unused axes entirely
     for _ax in axarr_it[n_params:]:

bayesflow/summary_networks.py

@@ -148,6 +148,7 @@ def __init__(
 
         # Final output reduces representation into a vector of length summary_dim
         self.output_layer = Dense(summary_dim)
+        self.summary_dim = summary_dim
 
     def call(self, x, **kwargs):
         """Performs the forward pass through the transformer.
@@ -269,6 +270,8 @@ def __init__(
             summary_dim, attention_settings, num_dense_fc, dense_settings, use_layer_norm, num_seeds
         )
 
+        self.summary_dim = summary_dim
+
     def call(self, x, **kwargs):
         """Performs the forward pass through the set-transformer.
 

examples/Model_Comparison_MPT.ipynb

@@ -699,7 +699,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Our simple simulators are extremly fast, so we can use online training (simulating the data on the fly during training). Here, we use 5 epochs with 500 iterations each and a batch size of 64 simulations. This means that we use $10 \\times 500 \\times 64 = 320,000$ unique simulations in total for training our neural network. We can do this, because the simulators are trivial to implement and thus very efficient to run. Training should take a couple of seconds to complete."
+    "Our simple simulators are extremly fast, so we can use online training (simulating the data on the fly during training). Here, we use $10$ epochs with $500$ iterations each and a batch size of $64$ simulations. This means that we use $10 \\times 500 \\times 64 = 320,000$ unique simulations in total for training our neural network. We can do this because the simulators are trivial to implement and thus very efficient to run. Training should take a couple of seconds to complete."
    ]
   },
   {
@@ -937,7 +937,7 @@
    ],
    "source": [
     "# Way 1: Amortizer with dictionary input\n",
-    "amortizer.posterior_probs({\"summary_conditions\" : fake_data})[0]"
+    "amortizer.posterior_probs({\"summary_conditions\": fake_data})[0]"
    ]
   },
   {
@@ -1016,7 +1016,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.10.10"
   },
   "toc": {
    "base_numbering": 1,