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QuaPy v0.1.9

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@AlexMoreo AlexMoreo released this 17 Sep 10:12
· 6 commits to master since this release
0.1.9
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Learn about vigilant mode.

QuaPy v0.1.9 released!

Major changes can be consuled here:

  • Added LeQua 2024 datasets and normalized match distance to qp.error

  • Improved data loaders for UCI binary and UCI multiclass datasets (thanks to Lorenzo Volpi!); these datasets
    can be loaded with standardised covariates (default)

  • Added a default classifier for aggregative quantifiers, which now can be instantiated without specifying
    the classifier. The default classifier can be accessed in qp.environ['DEFAULT_CLS'] and is assigned to
    sklearn.linear_model.LogisticRegression(max_iter=3000). If the classifier is not specified, then a clone
    of said classifier is returned. E.g.:

    pacc = PACC()
    is equivalent to:
    pacc = PACC(classifier=LogisticRegression(max_iter=3000))

  • Improved error loging in model selection. In v0.1.8 only Status.INVALID was reported; in v0.1.9 it is
    now accompanied by a textual description of the error

  • The number of parallel workers can now be set via an environment variable by running, e.g.:

    N_JOBS=10 python3 your_script.py
    which has the same effect as writing the following code at the beginning of your_script.py:
    import quapy as qp
    qp.environ["N_JOBS"] = 10

  • Some examples have been added to the ./examples/ dir, which now contains numbered examples from basics (0)
    to advanced topics (higher numbers)

  • Moved the wiki documents to the ./docs/ folder so that they become editable via PR for the community

  • Added Composable methods from Mirko Bunse's qunfold library! (thanks to Mirko Bunse!)

  • Added Continuous Integration with GitHub Actions (thanks to Mirko Bunse!)

  • Added Bayesian CC method (thanks to Pawel Czyz!). The method is described in detail in the paper
    Ziegler, Albert, and Paweł Czyż. "Bayesian Quantification with Black-Box Estimators."
    arXiv preprint arXiv:2302.09159 (2023).

  • Removed binary UCI datasets {acute.a, acute.b, balance.2} from the list qp.data.datasets.UCI_BINARY_DATASETS
    (the datasets are still loadable from the fetch_UCIBinaryLabelledCollection and fetch_UCIBinaryDataset
    functions, though). The reason is that these datasets tend to yield results (for all methods) that are
    one or two orders of magnitude greater than for other datasets, and this has a disproportionate impact in
    methods average (I suspect there is something wrong in those datasets).

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