55 lines
3.3 KiB
Plaintext
55 lines
3.3 KiB
Plaintext
Packaging:
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Documentation with sphinx
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Document methods with paper references
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unit-tests
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New features:
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Add NAE, NRAE
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Add "measures for evaluating ordinal"?
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Add datasets for topic.
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Do we want to cover cross-lingual quantification natively in QuaPy, or does it make more sense as an application on top?
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Current issues:
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SVMperf-based learners do not remove temp files in __del__?
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In binary quantification (hp, kindle, imdb) we used F1 in the minority class (which in kindle and hp happens to be the
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negative class). This is not covered in this new implementation, in which the binary case is not treated as such, but as
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an instance of single-label with 2 labels. Check
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Add automatic reindex of class labels in LabelledCollection (currently, class indexes should be ordered and with no gaps)
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OVR I believe is currently tied to aggregative methods. We should provide a general interface also for general quantifiers
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Currently, being "binary" only adds one checker; we should figure out how to impose the check to be automatically performed
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Add random seed management to support replicability (see temp_seed in util.py).
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Improvements:
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==========================================
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Explore the hyperparameter "number of bins" in HDy
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Rename EMQ to SLD ?
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Parallelize the kFCV in ACC and PACC?
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Parallelize model selection trainings
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We might want to think of (improving and) adding the class Tabular (it is defined and used on branch tweetsent). A more
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recent version is in the project ql4facct. This class is meant to generate latex tables from results (highligting
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best results, computing statistical tests, colouring cells, producing rankings, producing averages, etc.). Trying
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to generate tables is typically a bad idea, but in this specific case we do have pretty good control of what an
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experiment looks like. (Do we want to abstract experimental results? this could be useful not only for tables but
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also for plots).
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Checks:
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How many times is the system of equations for ACC and PACC not solved? How many times is it clipped? Do they sum up
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to one always?
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Re-check how hyperparameters from the quantifier and hyperparameters from the classifier (in aggregative quantifiers)
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is handled. In scikit-learn the hyperparameters from a wrapper method are indicated directly whereas the hyperparams
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from the internal learner are prefixed with "estimator__". In QuaPy, combinations having to do with the classifier
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can be computed at the begining, and then in an internal loop the hyperparams of the quantifier can be explored,
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passing fit_learner=False.
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Re-check Ensembles. As for now, they are strongly tied to aggregative quantifiers.
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Re-think the environment variables. Maybe add new ones (like, for example, parameters for the plots)
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Do we want to wrap prevalences (currently simple np.ndarray) as a class? This might be convenient for some interfaces
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(e.g., for specifying artificial prevalences in samplings, for printing them -- currently supported through
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F.strprev(), etc.). This might however add some overload, and prevent/difficult post processing with numpy.
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Would be nice to get a better integration with sklearn.
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