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Update version #64
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Update version #64
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- update documentation: restructure, add tutorial ToDo: Figure out how to resolve "pd" in docstrings and some type annotations
- easier and clearer for documentation
- nb_execution_timeout not picked up from conf.py - 🔥 remove legacy code (depenency is missing and doctest warns)
- currently tutorial is not running through on READTHEDOCS - see: executablebooks/MyST-NB#583 - 🐛 fix table formatting
- 🎨 make args consistent with all other notebooks - 🎨 move MNAR/MCAR core logic to function (which could be split up further)
- added FEATURES_CUTOFF (to restrict it to a reasonable size for quadratic computations as e.g. correlation matrices) - added titles to note sampling - use dots to show data better in some (line) plots Added brief execution hint for notebook
- let preparation of data be up to the user
- examplify format using a random dataframe
- see if this increases maximum runtime when included in sphinx docs (and execute on READTHEDOCS)
- start to get rid of AnalyzePeptides class
- base filtering on number of quantified samples (sample completness)
- 🔥 remove old funtions which are obsolete - 🎨 rename fake_na to simulated_na (to do everywhere) - clean-up fastai imports and assign parts explicitly for linting
- 🔥 remove one subclassing level, commented code - 🎨 rename fake to simulated NA and clean-up notebook
- if there will be a command line interface, then it will be defined in __main__.py
- 🎨 remove one level of inheritence - 🎨 rename fake to simulated NA
Idea: Only use a subset of samples to generate simulated validation and test data from. Performance is then split by validation and test samples, although the remaining data can be used at training time. (will be compared to setup where simulated missing values are sampled from all samples)
- fastai routine needs always a validation dataset, but it can be empty.
- set fastai defaults to cpu
- compare two sampling strategies
Add comp and clean-up PIMMS models
* 🎨🔥 format, delete old code, isort imports - tested over the last few weeks on local branch * 🎨 change default axis names in plot of available features * 🐛 missing import * 🐛 locally the import of sklearn.pipeline works. - might be that sklearn.pipeline is not loaded automatically in scikit-learn 1.3.2?
- 🐛 if namespaces are used by default, this can lead to subpackages being installed...
Pyproject toml
- format instructions nicely
- only on ubuntu, only for one python version, but also with long running packages and full medium dataset
- recent pandas version change formatting. Patched in njab (maybe there will be an option in pandas 3), see pandas-dev/pandas#57177 reference: https://njab.readthedocs.io/en/latest/reference/njab.pandas.html#njab.pandas.set_pandas_number_formatting
build workflow website only once, but on full example, test more python versions
Cicd update
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njab
as a dependency