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- 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)
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- fastai routine needs always a validation dataset, but it can be empty.
- set fastai defaults to cpu
- compare two sampling strategies
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Streamline comparison and add new ways of sampling simulated missing values.
fixed that DAE and VAE can also be trained without a validation split
cleaned up model classes, started to get ride of PeptideAnalyzer (also moving code)
no dependency on Thermo specific metadata (basic quality control by number of features in sample)