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

Author: stefanradev93

.github/ISSUE_TEMPLATE/bug_report.md

@@ -0,0 +1,36 @@
+---
+name: Bug report
+about: Create a bug report to help us improve BayesFlow
+title: "[BUG]"
+labels: ''
+assignees: ''
+
+---
+
+**Describe the bug**
+A clear and concise description of what the bug is.
+
+**To Reproduce**
+Minimal steps to reproduce the behavior:
+1. Import '...'
+2. Create network '....'
+3. Call '....'
+4. See error
+
+**Expected behavior**
+A clear and concise description of what you expected to happen.
+
+**Traceback**
+If you encounter an error, please provide a complete traceback to help explain your problem.
+
+**Environment**
+- OS: [e.g. Ubuntu]
+- Python Version: [e.g. 3.11]
+- Backend: [e.g. jax, tensorflow, pytorch]
+- BayesFlow Version: [e.g. 2.0.2]
+
+**Additional context**
+Add any other context about the problem here.
+
+**Minimality**
+- [ ] I verify that my example is minimal, does not rely on third-party packages, and is most likely an issue in BayesFlow.

.github/ISSUE_TEMPLATE/feature_request.md

@@ -0,0 +1,20 @@
+---
+name: Feature request
+about: Suggest a new feature to be implemented in BayesFlow
+title: "[FEATURE]"
+labels: feature
+assignees: ''
+
+---
+
+**Is your feature request related to a problem? Please describe.**
+A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
+
+**Describe the solution you'd like**
+A clear and concise description of what you want to happen.
+
+**Describe alternatives you've considered**
+A clear and concise description of any alternative solutions or features you've considered.
+
+**Additional context**
+Add any other context or screenshots about the feature request here.

bayesflow/utils/optimal_transport/log_sinkhorn.py

@@ -1,7 +1,6 @@
 import keras
 
 from .. import logging
-from ..tensor_utils import is_symbolic_tensor
 
 from .euclidean import euclidean
 
@@ -27,9 +26,6 @@ def log_sinkhorn_plan(x1, x2, regularization: float = 1.0, rtol=1e-5, atol=1e-8,
 
     log_plan = cost / -(regularization * keras.ops.mean(cost) + 1e-16)
 
-    if is_symbolic_tensor(log_plan):
-        return log_plan
-
     def contains_nans(plan):
         return keras.ops.any(keras.ops.isnan(plan))
 
@@ -57,22 +53,18 @@ def do_nothing():
         pass
 
     def log_steps():
-        msg = "Log-Sinkhorn-Knopp converged after {:d} steps."
+        msg = "Log-Sinkhorn-Knopp converged after {} steps."
 
         logging.debug(msg, steps)
 
     def warn_convergence():
-        marginals = keras.ops.logsumexp(log_plan, axis=0)
-        deviations = keras.ops.abs(marginals)
-        badness = 100.0 * keras.ops.exp(keras.ops.max(deviations))
-
-        msg = "Log-Sinkhorn-Knopp did not converge after {:d} steps (badness: {:.1f}%)."
+        msg = "Log-Sinkhorn-Knopp did not converge after {} steps."
 
-        logging.warning(msg, max_steps, badness)
+        logging.warning(msg, max_steps)
 
     def warn_nans():
-        msg = "Log-Sinkhorn-Knopp produced NaNs."
-        logging.warning(msg)
+        msg = "Log-Sinkhorn-Knopp produced NaNs after {} steps."
+        logging.warning(msg, steps)
 
     keras.ops.cond(contains_nans(log_plan), warn_nans, do_nothing)
     keras.ops.cond(is_converged(log_plan), log_steps, warn_convergence)

bayesflow/utils/optimal_transport/sinkhorn.py

@@ -3,7 +3,6 @@
 from bayesflow.types import Tensor
 
 from .. import logging
-from ..tensor_utils import is_symbolic_tensor
 
 from .euclidean import euclidean
 
@@ -76,9 +75,6 @@ def sinkhorn_plan(
     # initialize the transport plan from a gaussian kernel
     plan = keras.ops.exp(cost / -(regularization * keras.ops.mean(cost) + 1e-16))
 
-    if is_symbolic_tensor(plan):
-        return plan
-
     def contains_nans(plan):
         return keras.ops.any(keras.ops.isnan(plan))
 
@@ -106,22 +102,18 @@ def do_nothing():
         pass
 
     def log_steps():
-        msg = "Sinkhorn-Knopp converged after {:d} steps."
+        msg = "Sinkhorn-Knopp converged after {} steps."
 
         logging.info(msg, max_steps)
 
     def warn_convergence():
-        marginals = keras.ops.sum(plan, axis=0)
-        deviations = keras.ops.abs(marginals - 1.0)
-        badness = 100.0 * keras.ops.max(deviations)
-
-        msg = "Sinkhorn-Knopp did not converge after {:d} steps (badness: {:.1f}%)."
+        msg = "Sinkhorn-Knopp did not converge after {}."
 
-        logging.warning(msg, max_steps, badness)
+        logging.warning(msg, max_steps)
 
     def warn_nans():
-        msg = "Sinkhorn-Knopp produced NaNs."
-        logging.warning(msg)
+        msg = "Sinkhorn-Knopp produced NaNs after {} steps."
+        logging.warning(msg, steps)
 
     keras.ops.cond(contains_nans(plan), warn_nans, do_nothing)
     keras.ops.cond(is_converged(plan), log_steps, warn_convergence)

tests/test_examples/test_examples.py

@@ -9,6 +9,7 @@ def test_bayesian_experimental_design(examples_path):
     run_notebook(examples_path / "Bayesian_Experimental_Design.ipynb")
 
 
+@pytest.mark.skip(reason="requires setting up pyabc")
 @pytest.mark.slow
 def test_from_abc_to_bayesflow(examples_path):
     run_notebook(examples_path / "From_ABC_to_BayesFlow.ipynb")