Can the features package help to study features in CLTS?

[LinguList]

The features package seems to be very convenient to explore feature systems, so I ask myself if it might be useful to see if it can be integrated, e.g., by either exporting our feature sets in clts-data to the formats required there, or by looking at an integration from within pyclts?

[LinguList]

It might even be possible to say that we store feature sets for our data in the ini-format required by the package for inter-operability in CLDF. I am also thinking here of other feature sets, like morphological features, etc., since we have them implicitly reflected so far in our data, etc., but not coded in a computer-readable form (e.g., deriving features in CLTS is a bit tedious, by iterating over sets, etc.).

[tresoldi]

I discussed this package some weeks ago with @XachaB (I think he used it in his thesis?), when I was preparing the matrix that eventually led to my hacky distfeat. The whole system is very nice and mature, but also considering this maturity I was not sure how easy or desirable it would be to integrate it with pyclts. My impression was that it would make sense the integrate the whole system for formal analysis (concepts) to the CLDF ecosystem, but it seemed something more appropriate to lexitools or equivalents.

This does not exclude the idea of adopting (preferably) a CLDF or at least a plain-tabular storage for features. It would help with code dealing with phonology and the more we demonstrate that CLDF is not only for wordlists, the better.

[LinguList]

Okay, we have about 5 if not more feature systems in CLTS datasets already. Including our CLTS data system. Do you have the time, @tresoldi, to code our CLTS feature system in Sebastian's features format? You could even write a blog post. If we have this one time, we could then do it for the other feature systems in CLTS as well. And from there, we could see what it may bring us, okay?

[tresoldi]

His format default format is a textual grid parsed in code (https://github.com/xflr6/features/blob/master/features/config.ini), but it accepts a string with the full contents of a CSV file as well.

Exporting out features into these formats should be easy, but maybe we should have our own CLDF-to-features module? Or it is best to start small, with CSV files, and later do a CLDF (as we'd also need to discuss the structure)?

[LinguList]

I do not want to overcomplicate it. I want one file for our CLTS feature system and some tests on how the algebra works. This can also be done in the form of a blog post. One could also compare the system with the system we find in phoible. The feature systems can be submitted in CSV format in CLTS, in the form of a pull request, and maybe an example, how they can be read into features. Any more complex discussions are not needed now, I think, as we are testing this.

[tresoldi]

Great, I'll do it, I have meaning to play with these libraries. 👍

[XachaB]

I did not use it for my dissertation, instead I used directly his other package concepts: https://pypi.org/project/concepts/
which is just pure FCA.

The input format is simple enough, the steps are 1. compute dummy values (pandas has a method for it from dataframes, or you can re-code it by hand, it's quite simple), to get a binary matrix which serves as a Context for FCA, 2. replace the 1s by "X" and 0s by the empty string, and 3. pass it as a csv string to the library:

context_table = pd.get_dummies(feature_table, prefix_sep="=")
context_table = context_table.applymap(lambda x: "X" if x == 1 else "")
context_str = context_table.to_csv()
context = concepts.Context.fromstring(context_str, frmat='csv')

[LinguList]

Yep, I was aware concepts is a dependency. For the purpose of understandability, etc., however, I think it is useful to start from features in CLTS, also as a test for the suitability. So can I expect some kind of a blog post for October from you on that matter,then, @tresoldi?

[tresoldi]

Yes, code and post.

[xrotwang]

Thoughts on this @xflr6 ?