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Releases: ICB-DCM/pyABC

pyABC 0.11.8

03 Dec 16:35
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  • Interface Julia simulators via pyjulia (#514)
  • Refactor PCA distance, add tests (#518)
  • Remove pyarrow as hard dependency for pandas storage (#523)
  • Hierarchically structure examples, update "Parameter Inference"
    introduction (#524)
  • Add minimum epsilon difference stopping condition (#525)

pyABC 0.11.7

10 Nov 21:32
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  • Decompose ABCSMC.run for easier outer loop (#510)

pyABC 0.11.6

05 Nov 21:37
8b8a956
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  • Unfix sphinx version for documentation (#509)
  • Streamline function wrapper objects (#511)
  • Remove rpy2 warning upon import of pyabc.external (#512)
  • Move ot distance to scipy due to bug in pot 0.8.0 (#512)

pyABC 0.11.5

29 Oct 20:25
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  • Regularly scheduled CI (#504)
  • Fix Dask for Windows (#503)
  • Apply the uncompromising Python code formatter black (#506)
  • Apply automatic import sorting via isort (#508)

pyABC 0.11.4

27 Oct 09:19
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  • Implement Wasserstein and Sliced Wasserstein distances (#500)
  • Add env variable to bound population size in tests (#501)

pyABC 0.11.3

16 Oct 16:26
47a8f84
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  • Update to amici 0.11.19 for scaled residual support (#491)
  • Add links for online execution of notebooks on Google Colab and nbviewer
    (#492)
  • Tests: Fix early stopping test for first generation (#494)

pyABC 0.11.2

07 Oct 08:42
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  • Remove codacy due to excessive permission requests
  • Tidy up example titles

pyABC 0.11.1

06 Oct 21:37
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Summary statistics:

  • Allow transformed parameters as regression targets via ParTrafo (#478)
  • Add Sankey flow plot (#484)
  • Add "informative" notebook to document regression-based summary statistics
    and weights (#484)

Sampler:

  • Speed up redis done-list checking by atomic operations (#482)

pyABC 0.11.0

31 Jul 06:26
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This release brings, besides many bug fixes and smaller improvements, in particular outlier-robust and flexible adaptive distance functions working with any inputs. Further, a preliminary framework for semi-automatic summary statistics learning is provided (fully documented implementation planned for 0.12.0).

Diverse:

  • Shorten date-time log (#456)
  • Add look-ahead example notebook (#461)
  • Fix decoration of plot_acceptance_rates_trajectory (#465)
  • Hot-fix redis clean-up (#475)

Semi-automatic summary statistics and robust sample weighting (#429)


Breaking changes:

* API of the `(Adaptive)PNormDistance` was altered substantially to allow
  cutom definition of update indices.
* Internal weighting of samples (should not affect users).

Semi-automatic summary statistics:

* Implement (Adaptive)PNormDistance with the ability to learn summary
  statistics from simulations.
* Add `sumstat` submodule for generic mappings (id, trafos), and especially a
  `PredictorSumstat` summary statistic that can make use of `Predictor` objects.
* Add subsetting routines that allow restricting predictor model training
  samples.
* Add `predictor` submodule with generic `Predictor` class and concrete
  implementations including linear regression, Lasso, Gaussian Process,
  Neural Network.
* Add `InfoWeightedPNormDistance` that allows using predictor models to weight
  data not only by scale, but also by information content.

Outlier-robust adaptive distances:

* Update documentation towards robust distances.
* Add section in the corresponding notebook.
* Implement PCMAD outlier correction scheme.

Changes to internal sample weighting:

* Do not normalize weights of in-memory particles by model; this allows to
  more easily use the sampling weights and the list of particles for
  adaptive components (e.g. distance functions)
* Normalization of population to 1 is applied on sample level in the
  sampler wrapper function
* In the database, normalization is still by sample to not break old db
  support; would be nicer to also there only normalize by total sum
  -- requires a db update though.

Changes to internal object instruction from samples:

* Pass sample instead of weighted_sum_stats to distance function.
  This is because thus the distance can choose on its own what it wants
  -- all or only accepted particles; distances; weights; parameters;
  summary statistics.

Visualization:

* Function to plot adaptive distance weights from log file.

pyABC 0.10.16

11 May 08:24
bb90794
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  • Allow color customization for plot_credible_intervals plots (#414)
  • pyABC logo to grey to fit with both black and white backgrounds (#453)
  • Add style set to global figure parameters, enabling dark mode (#454)