Merge remote-tracking branch 'origin/Development' into Development

# Conflicts:
#	bayesflow/diagnostics.py

README.md

@@ -8,9 +8,11 @@ Welcome to our BayesFlow library for efficient simulation-based Bayesian workflo
 For starters, check out some of our walk-through notebooks:
 
 1. [Quickstart amortized posterior estimation](docs/source/tutorial_notebooks/Intro_Amortized_Posterior_Estimation.ipynb)
-2. [Principled Bayesian workflow for cognitive models](docs/source/tutorial_notebooks/LCA_Model_Posterior_Estimation.ipynb)
-3. [Posterior estimation for ODEs](docs/source/tutorial_notebooks/Linear_ODE_system.ipynb)
-4. [Posterior estimation for SIR-like models](docs/source/tutorial_notebooks/Covid19_Initial_Posterior_Estimation.ipynb)
+3. [Principled Bayesian workflow for cognitive models](docs/source/tutorial_notebooks/LCA_Model_Posterior_Estimation.ipynb)
+4. [Posterior estimation for ODEs](docs/source/tutorial_notebooks/Linear_ODE_system.ipynb)
+5. [Posterior estimation for SIR-like models](docs/source/tutorial_notebooks/Covid19_Initial_Posterior_Estimation.ipynb)
+6. [Model comparison for cognitive models](docs/source/tutorial_notebooks/Model_Comparison_MPT.ipynb)
+7. [Hierarchical model comparison for cognitive models](docs/source/tutorial_notebooks/Hierarchical_Model_Comparison_MPT.ipynb)
 
 ## Project Documentation
 
@@ -167,7 +169,7 @@ to the `AmortizedPosterior` instance:
 amortizer = bf.amortizers.AmortizedPosterior(inference_net, summary_net, summary_loss_fun='MMD')
 ```
 
-The amortizer knows how to combine its losses.
+The amortizer knows how to combine its losses and you can inspect the summary space for outliers during inference.
 
 ### References and Further Reading
 
@@ -177,7 +179,74 @@ preprint</em>, available for free at: https://arxiv.org/abs/2112.08866
 
 ## Model Comparison
 
-Example coming soon...
+BayesFlow can not only be used for parameter estimation, but also to perform approximate Bayesian model comparison via posterior model probabilities or Bayes factors.
+Let's extend the minimal example from before with a second model $M_2$ that we want to compare with our original model $M_1$:
+
+```python
+def simulator(theta, n_obs=50, scale=1.0):
+ return np.random.default_rng().normal(loc=theta, scale=scale, size=(n_obs, theta.shape[0]))
+
+def prior_m1(D=2, mu=0., sigma=1.0):
+ return np.random.default_rng().normal(loc=mu, scale=sigma, size=D)
+
+def prior_m2(D=2, mu=2., sigma=1.0):
+ return np.random.default_rng().normal(loc=mu, scale=sigma, size=D)
+```
+
+For the purpose of this illustration, the two toy models only differ with respect to their prior specification ($M_1: \mu = 0, M_2: \mu = 2$). We create both models as before and use a `MultiGenerativeModel` wrapper to combine them in a `meta_model`:
+
+```python
+model_m1 = bf.simulation.GenerativeModel(prior_m1, simulator, simulator_is_batched=False)
+model_m2 = bf.simulation.GenerativeModel(prior_m2, simulator, simulator_is_batched=False)
+meta_model = bf.simulation.MultiGenerativeModel([model_m1, model_m2])
+```
+
+Next, we construct our neural network with a `PMPNetwork` for approximating posterior model probabilities:
+
+```python
+summary_net = bf.networks.DeepSet()
+probability_net = bf.networks.PMPNetwork(num_models=2)
+amortizer = bf.amortizers.AmortizedModelComparison(probability_net, summary_net)
+```
+
+We combine all previous steps with a `Trainer` instance and train the neural approximator:
+
+```python
+trainer = bf.trainers.Trainer(amortizer=amortizer, generative_model=meta_model)
+losses = trainer.train_online(epochs=3, iterations_per_epoch=100, batch_size=32)
+```
+
+Let's simulate data sets from our models to check our networks' performance:
+
+```python
+sims = trainer.configurator(meta_model(5000))
+```
+
+When feeding the data to our trained network, we almost immediately obtain posterior model probabilities for each of the 5000 data sets:
+
+```python
+model_probs = amortizer.posterior_probs(sims)
+```
+
+How good are these predicted probabilities in the closed world? We can have a look at the calibration:
+
+```python
+cal_curves = bf.diagnostics.plot_calibration_curves(sims["model_indices"], model_probs)
+```
+
+<img src="img/showcase_calibration_curves.png" width=65% height=65%>
+
+Our approximator shows excellent calibration, with the calibration curve being closely aligned to the diagonal, an expected calibration error (ECE) near 0 and most predicted probabilities being certain of the model underlying a data set. We can further assess patterns of misclassification with a confusion matrix:
+
+```python
+conf_matrix = bf.diagnostics.plot_confusion_matrix(sims["model_indices"], model_probs)
+```
+
+<img src="img/showcase_confusion_matrix.png" width=44% height=44%>
+
+For the vast majority of simulated data sets, the "true" data-generating model is correctly identified. With these diagnostic results backing us up, we can proceed and apply our trained network to empirical data.
+
+BayesFlow is also able to conduct model comparison for hierarchical models. See this [tutorial notebook](docs/source/tutorial_notebooks/Hierarchical_Model_Comparison_MPT.ipynb) for an introduction to the associated workflow.
 
 ### References and Further Reading
 
@@ -190,6 +259,10 @@ doi:10.1109/TNNLS.2021.3124052 available for free at: https://arxiv.org/abs/2004
 Bayesian Model Comparison. <em>ArXiv preprint</em>, available for free at:
 https://arxiv.org/abs/2210.07278
 
+- Elsemüller, L., Schnuerch, M., Bürkner, P. C., & Radev, S. T. (2023). A Deep
+Learning Method for Comparing Bayesian Hierarchical Models. <em>ArXiv preprint</em>,
+available for free at: https://arxiv.org/abs/2301.11873
+
 ## Likelihood emulation
 
 Example coming soon...

bayesflow/diagnostics.py

@@ -1036,7 +1036,7 @@ def plot_calibration_curves(
 
  Returns
  -------
- f : plt.Figure - the figure instance for optional saving
+ fig : plt.Figure - the figure instance for optional saving
  """
 
  num_models = true_models.shape[-1]
@@ -1051,7 +1051,7 @@ def plot_calibration_curves(
  # Initialize figure
  if fig_size is None:
  fig_size = (int(5 * n_col), int(5 * n_row))
- f, axarr = plt.subplots(n_row, n_col, figsize=fig_size)
+ fig, axarr = plt.subplots(n_row, n_col, figsize=fig_size)
  if n_row > 1:
  ax = axarr.flat
 
@@ -1096,8 +1096,8 @@ def plot_calibration_curves(
 
  # Set title
  ax[j].set_title(model_names[j], fontsize=title_fontsize)
- f.tight_layout()
- return f
+ fig.tight_layout()
+ return fig
 
 
 def plot_confusion_matrix(
@@ -1107,6 +1107,8 @@ def plot_confusion_matrix(
  fig_size=(5, 5),
  title_fontsize=18,
  tick_fontsize=12,
+ xtick_rotation=None,
+ ytick_rotation=None,
  normalize=True,
  cmap=None,
  title=True,
@@ -1124,9 +1126,13 @@ def plot_confusion_matrix(
  fig_size : tuple or None, optional, default: (5, 5)
  The figure size passed to the ``matplotlib`` constructor. Inferred if ``None``
  title_fontsize : int, optional, default: 18
- The font size of the axis label texts.
+ The font size of the title text.
  tick_fontsize : int, optional, default: 12
- The font size of the axis label texts.
+ The font size of the axis label and model name texts.
+ xtick_rotation: int, optional, default: None
+ Rotation of x-axis tick labels (helps with long model names).
+ ytick_rotation: int, optional, default: None
+ Rotation of y-axis tick labels (helps with long model names).
  normalize : bool, optional, default: True
  A flag for normalization of the confusion matrix.
  If True, each row of the confusion matrix is normalized to sum to 1.
@@ -1135,6 +1141,10 @@ def plot_confusion_matrix(
  e.g., 'viridis'. Default colormap matches the BayesFlow defaults by ranging from white to red.
  title : bool, optional, default True
  A flag for adding 'Confusion Matrix' above the matrix.
+
+ Returns
+ -------
+ fig : plt.Figure - the figure instance for optional saving
  """
 
  if model_names is None:
@@ -1154,14 +1164,18 @@ def plot_confusion_matrix(
  cm = cm.astype("float") / cm.sum(axis=1)[:, np.newaxis]
 
  # Initialize figure
- f, ax = plt.subplots(1, 1, figsize=fig_size)
+ fig, ax = plt.subplots(1, 1, figsize=fig_size)
 
  im = ax.imshow(cm, interpolation="nearest", cmap=cmap)
  ax.figure.colorbar(im, ax=ax, shrink=0.7)
 
  ax.set(xticks=np.arange(cm.shape[1]), yticks=np.arange(cm.shape[0]))
  ax.set_xticklabels(model_names, fontsize=tick_fontsize)
+ if xtick_rotation:
+ plt.xticks(rotation=xtick_rotation, ha="right")
  ax.set_yticklabels(model_names, fontsize=tick_fontsize)
+ if ytick_rotation:
+ plt.yticks(rotation=ytick_rotation)
  ax.set_xlabel("Predicted model", fontsize=tick_fontsize)
  ax.set_ylabel("True model", fontsize=tick_fontsize)
 
@@ -1175,6 +1189,7 @@ def plot_confusion_matrix(
  )
  if title:
  ax.set_title("Confusion Matrix", fontsize=title_fontsize)
+ return fig
 
 
 def plot_mmd_hypothesis_test(mmd_null,