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I wrote a function that should accomplish what you need. There are likely more elegant and more R-like ways of doing this, but this approach worked in my testing:

You can't simply transform an n-dimensional label (or "target output") into a 1-dimensional one. In some cases where the output distribution is an m-dimensional manifold embedded into an n-dimensional space, you may try to do a projection first (including, if necessary, a non-linear projection), but you have to think very carefully about what you want your classifier or your regressor to learn.

One simple strategy is to have a dedicated learner per output label. This will "work" in the mechanical sense that you'll be able to forecast an output for any input that is consistent with the input distribution. But it will ignore the possible interactions between the output variables. Imagine an 2D output distribution like this:

Two learners, each seeing only one of the output variables, won't have a chance to learn about that output structure and will likely (and wrongly) forecast some output points in the positive quadrant that should be empty.

One strategy is to learn a first variable y_0 from your input X. Then learn a second variable y_1 after augmenting your input X with the forecast y_0_hat from the first classifier. And so on.

Generally speaking, check out:

• Wikipedia: Multi-label classification
• scikit-learn's multiclass, especially the "multioutput" classes which are what I believe you are seeking.

First, executor.map does not return Future instances, so your variable futures is poorly named. It does return an iterator that yields the return values of applying get_scores_dataframe to each element of file_list in turn. Second, seeing how this is used next, it would appear that these return values are input files (which may or may not be the same file as the input argument -- can't be sure from the lack of code shown). Also, using the process pool map function rather than the builtin map function to get the base name of the filename arguments seems like overkill. Finally, in your code, it would not be futures.to_csv, but rather future.to_csv. So I am confused as to how your code could have worked at all.

If you modify your function get_scores_dataframe to return a tuple consisting of a dataframe and the original passed filename argument, then we can process the results in completion order using as_competed:

A Base R one-liner would be:

Code:

I am assuming you are using get_result(). You can do an alternative in bs4 called strings. This gives an array of all strings in a soup. Then you can join them together to get properly formatted text:

Your problem may not be simple but it's worth experimenting.

Method 1:

As you have the specification, you can try looking for an ASN.1 tool (aka ASN.1 compiler) that will create a data model. In your case, because you downloaded a textual ASN.1 value, you need this tool to provide ASN.1 value decoders.

If the tool was generating Java code, it would go like this:

You could name the colors-vector once it is created:

I tried to approximate your problem with the diamonds dataset. Could you add an identifier in your data and then use facet_wrap() on it?

Best is to cd into the desktop directory, place the csv files into the import folder.

then you can do: