Documentation is missing many important details about mltk HOT 4 OPEN

Hey @lukehutch, thanks for the feedback. I'm still working on the wiki pages (they are not complete yet).

1. In dense format, class (or target) attribute can appear at any position since for most use cases on dense format, we have reasonable features to handle and therefore providing an additional attribute file can help. In sparse format, usually we have many features and the input is highly sparse. Therefore I use the standard libsvm-like format. In this case, an attribute file is not provided and therefore we need to follow some convention to get the target position.
2. I don't think that's the case. Can you email me a data file with attribute file so that I can test? It's actually perfectly fine if the number of attributes in attribute file is less than the number of columns in data file. The remaining columns will be read but when building models, all those columns will be ignored.
3. I will make this clear in the wiki.
4. This is something that I'll improve in the future. Here're the cases where attribute file is null and how it is parsed by mltk. For dense format, that means there would be no target value (NaN is assigned). This might be used in the future for unsupervised learning algorithms (similarly, if there's no target attribute specified in attribute file, there will be no target value). For sparse format, current the first one has to be the target.

Thanks again for the feedback.

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On #2 (assuming you're referring to my third bullet point?): Here is the relevant code:

https://github.com/yinlou/mltk/blob/master/src/main/java/mltk/core/io/InstancesReader.java#L72

In the case of dense instances with the wrong number of attributes, the line is skipped by the if-statement in line 76.

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Now I remember. In the input, number of features in the attribute file does need to match the number of columns in the data file. I wanted to enforce that so that feature description in the attribute file always matches the corresponding data file. In which scenario do you want to have a smaller attribute file?

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The situation arose because I misunderstood the docs. I didn't know that "(class)" implied the target, because I was doing regression, not classification, and this tag was not described in the text. As I tried to portray in the original bug report, it is not clear from the examples as to how you should specify the target. In the case where you show "(class)" in the attributes file, you have the class occurring last. However, under "Sparse input format", you show the target occurring first. Because I didn't see a mention of target in the attributes, and because I didn't know "(class)" meant "target, whether regression or classification", I assumed that the target was always supposed to be in the first column. Thus my attributes file had one fewer attribute than there were columns.

I think the way to fix the documentation is to have two complete examples: One for classification, one for regression. Or at least make all the examples of attribute and datafiles refer to a single consistent example, and explain that "(class)" must be present and means the target, whether you're doing regression or classification.

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