changelog

Author: dilpath

changes.md

@@ -4,7 +4,7 @@
 There are some **major breaking changes**, to support users providing previous calibration results, e.g. from previous model selection runs. The following changes are reflected in the notebook examples.
 - **breaking change** previously, calibration tools would call `candidates` at each iteration of model selection. `candidates` has now been renamed to `start_iteration`, and tools are now expected to run `end_iteration` after calibrating the iteration's models. This structure also simplifies the codebase for other features of PEtab Select.
 - **breaking change** previously, calibration tools would determine whether to continue model selection based on whether the candidate space contains any models. Now, calibration tools should rely on the `TERMINATE` signal provided by `end_iteration` to determine whether to continue model selection.
-- **breaking change** PEtab Select hides user-calibrated models from the calibration tool, until `end_iteration` is called. Hence, if a calibration tool does some analysis on the calibrated models of the current iteration, the tool should use the `MODELS` provided by `end_iteration`, and not the MODELS provided by `start_iteration`.
+- **breaking change** PEtab Select hides user-calibrated models from the calibration tool, until `end_iteration` is called. Hence, if a calibration tool does some analysis on the calibrated models of the current iteration, the tool should use the `MODELS` provided by `end_iteration`, and not the `MODELS` provided by `start_iteration`.
 In summary, here's some pseudocode showing the old way.
 ```python
 from petab_select.ui import candidates
@@ -17,7 +17,7 @@ while True:
     # Calibrate iteration models
     for model in models:
         calibrate(model)
-    # Print a summary/analysis of current iteration models
+    # Print a summary/analysis of current iteration models (dummy code)
     print_summary_of_iteration_models(models)
 ```
 And here's the new way. Full working examples are given in the updated notebooks, including how to handle the candidate space.
@@ -32,29 +32,25 @@ while True:
         calibrate(model)
     # Finalize iteration, get all iteration models and results
     iteration_results = end_iteration(...)
-    # Print a summary/analysis of all iteration models
+    # Print a summary/analysis of all iteration models (dummy code)
     print_summary_of_iteration_models(iteration_results[MODELS])
     # Terminate if indicated
     if iteration_results[TERMINATE]:
         break
 ```
-- Other changes
-    - fixed a buy introduced in 0.1.8, where FAMoS "jump to most distant" moves were not handled correctly
+- Other **major changes**
+    - Many thanks to @dweindl for:
+        - documentation! https://petab-select.readthedocs.io/
+        - GitHub CI fixes and GHA deployments to PyPI and Zenodo
+    - fixed a bug introduced in 0.1.8, where FAMoS "jump to most distant" moves were not handled correctly
     - the renamed `candidates`->`start_iteration`:
         - no longer accepts `calibrated_models`, as they are automatically stored in the `CandidateSpace` now with each `end_iteration`
-        - exclusions via `exclude_models` is no longer supported. exclusions can be supplied with `set_excluded_hashes`
         - `calibrated_models` and `newly_calibrated_models` no longer need to be tracked between iterations. They are now tracked by the candidate space.
-    - some refactoring
-    - PEtab hashes are now computed for each model, to determine whether they are unique, e.g. for assessing whether a model is already excluded.
-      Two models are considered equivalent if their PEtab hashes match. The PEtab hash is composed of the location of the PEtab YAML in the filesystem,
-      the nominal values of the parameters in the model's PEtab problem, and the estimated parameters of the model's PEtab problem. The PEtab hash
-      digest size is automatically computed to ensure a collision probability of <2^{-64}, given some assumptions. Users can also manually set the digest size.
-      More details are available at the documentation for `petab_select.model.ModelHash`.
-    - model hashes are more readable and composed of three parts:
+        - exclusions via `exclude_models` is no longer supported. exclusions can be supplied with `set_excluded_hashes`
+    - model hashes are more readable and composed of two parts:
         1. the model subspace ID
         2. the location of the model in its subspace (the model subspace indices)
-        3. the PEtab hash
-    - users can now specify a "PEtab Select problem ID" in their YAML files
+    - users can now provide model calibrations from previous model selection runs. This enables them to skip re-calibration of the same models.
 
 ## 0.1.13
 - fixed bug when no predecessor model is provided, introduced in 0.1.11 (#83)