96 lines
6.2 KiB
Plaintext
96 lines
6.2 KiB
Plaintext
ensembles seem to be broken; they have an internal model selection which takes the parameters, but since quapy now
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works with protocols it would need to know the validation set in order to pass something like
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"protocol: APP(val, etc.)"
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sample_size should not be mandatory when qp.environ['SAMPLE_SIZE'] has been specified
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clean all the cumbersome methods that have to be implemented for new quantifiers (e.g., n_classes_ prop, etc.)
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make truly parallel the GridSearchQ
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make more examples in the "examples" directory
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merge with master, because I had to fix some problems with QuaNet due to an issue notified via GitHub!
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added cross_val_predict in qp.model_selection (i.e., a cross_val_predict for quantification) --would be nice to have
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it parallelized
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check the OneVsAll module(s)
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check the set_params de neural.py, because the separation of estimator__<param> is not implemented; see also
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__check_params_colision
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HDy can be customized so that the number of bins is specified, instead of explored within the fit method
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Packaging:
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==========================================
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Document methods with paper references
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unit-tests
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clean wiki_examples!
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Refactor:
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==========================================
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Unify ThresholdOptimization methods, as an extension of PACC (and not ACC), the fit methods are almost identical and
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use a prob classifier (take into account that PACC uses pcc internally, whereas the threshold methods use cc
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instead). The fit method of ACC and PACC has a block for estimating the validation estimates that should be unified
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as well...
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Refactor protocols. APP and NPP related functionalities are duplicated in functional, LabelledCollection, and evaluation
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New features:
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==========================================
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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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==========================================
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Revise the class structure of quantification methods and the methods they inherit... There is some confusion regarding
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methods isbinary, isprobabilistic, and the like. The attribute "learner_" in aggregative quantifiers is also
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confusing, since there is a getter and a setter.
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Remove the "deep" in get_params. There is no real compatibility with scikit-learn as for now.
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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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GridSearchQ is not trully parallelized. It only parallelizes on the predictions.
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In the context of a quantifier (e.g., QuaNet or CC), the parameters of the learner should be prefixed with "estimator__",
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in QuaNet this is resolved with a __check_params_colision, but this should be improved. It might be cumbersome to
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impose the "estimator__" prefix for, e.g., quantifiers like CC though... This should be changed everywhere...
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QuaNet needs refactoring. The base quantifiers ACC and PACC receive val_data with instances already transformed. This
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issue is due to a bad design.
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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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Add proper logging system. Currently we use print
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It might be good to simplify the number of methods that have to be implemented for any new Quantifier. At the moment,
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there are many functions like get_params, set_params, and, specially, @property classes_, which are cumbersome to
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implement for quick experiments. A possible solution is to impose get_params and set_params only in cases in which
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the model extends some "ModelSelectable" interface only. The classes_ should have a default implementation.
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Checks:
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==========================================
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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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