Version 1.0 #581
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Version 1.0 #581
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Improves EmbeddingNet() performance time by 100% where n_agents > 10_000 and extends functionality without termination up to n_agents=100_000.
Updates code and removes omergeleft() and replaces with updated default_pars().
Updated add_pairs() to align with main branch.
Expands test coverage to 79%.
…ameter even if using a subclass. Now `self.pars.p_fertility` in `init_pre` rather than doing everything in `__init__`. Also changing `uint64` to signed `int64` in test_randomness.
…ecause `fertility_rate[max_age+1] = 0` and the fertility rate had indices of -inf and 0:
```python
index = pd.MultiIndex.from_arrays([[default_year, default_year], [-np.inf, 0]], names=['year','age'])
return pd.Series(index=index, data=[out_of_range, data])
```
I don't like this approach, too complied and confusing, but at least it seems to work now.
Update network.py
Test coverage 80
4 tasks
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Playing around with the new repr: |
daniel-klein
reviewed
Jul 8, 2024
| @@ -295,8 +295,9 @@ def make_new_cases(self): | |||
| # Tidy up | |||
| if len(new_cases) and len(sources): | |||
| new_cases = ss.uids.cat(new_cases) | |||
| sources = ss.uids.cat(sources) | |||
| networks = np.concatenate(networks) | |||
| new_cases, inds = new_cases.unique(return_index=True) | |||
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Thoughts on passing an allow_superinfection argument somewhere that would default to False? Honestly, I think taking unique is the right think to do in 99% of applications, but it's not 100%.
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| __all__ = ['__version__', '__versiondate__', '__license__'] | ||
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| __version__ = '0.5.10' | ||
| __versiondate__ = '2024-07-03' | ||
| __version__ = '1.0.0' |
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| skestimator (str): if provided, use this scikit-learn estimator instead | ||
| estimator (func): if provided, use this custom estimator instead |
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Could these be combined? Call if function and otherwise try sk?
| ), | ||
| networks = dict( | ||
| randomnet = dict( | ||
| n_contacts = [4, 2, 10], |
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What if these were ss.poisson instead of scalars?
| @@ -18,14 +18,18 @@ class Result(np.ndarray): | |||
| 'module', # The name of the module (e.g. hiv) | |||
| 'scale', # Whether or not the result scales with population size (e.g. True) | |||
| 'label', # The human-readable label for the result (e.g. Number of infections) | |||
| 'low', # The lower bound for the values (used with MultiSim) | |||
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Mention these low and high values are used for plotting confidence intervals. If not used for any other purpose, perhaps better to move into the MultiSim class?
Dev tutorials
Improving pregnancy configuration and fixing test_randomness
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Description
Calibrationclass, based on Optuna, to facilitate the calibration of Starsim models.mean(),median(), andplot()methods toMultiSim.lowandhighattributes toResultobjects.flatten()method toResults, allowing nestedResultsobjects to be turned into flat dictionaries.unique()method toss.uids.ss.lognorm_im()from using callable parameters.Simstring representation to be a single line; the more verbose version is available viasim.disp().Checklist