 Alejandro Moreo Fernandez
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0a6185d908
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refactoring the aggregative quantifiers
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2023-11-12 14:45:03 +01:00 |
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Alejandro Moreo Fernandez
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505d2de823
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elm examples
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2023-02-13 12:01:52 +01:00 |
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Alejandro Moreo Fernandez
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952cf5e767
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fixing bugs in one-vs-all
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2023-02-10 19:02:17 +01:00 |
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Alejandro Moreo Fernandez
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e28abfc362
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more examples, one-vs-all fixed
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2023-02-09 19:39:16 +01:00 |
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Alejandro Moreo Fernandez
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2485117f05
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adding documentation and adding one new example
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2023-02-08 19:06:53 +01:00 |
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Alejandro Moreo Fernandez
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f9a199d859
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fixing hyperparameters with prefixes, and replacing learner with classifier in aggregative quantifiers
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2023-01-27 18:13:23 +01:00 |
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Alejandro Moreo Fernandez
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2cc7db60cc
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updating parallel policy to take n_jobs from environment (not yet tested)
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2022-06-14 09:35:39 +02:00 |
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Alejandro Moreo Fernandez
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eba6fd8123
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optimization conditional in the prediction function
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2022-05-26 17:59:23 +02:00 |
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Alejandro Moreo Fernandez
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4bc9d19635
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many changes, see change log
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2022-05-25 19:14:33 +02:00 |
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Alejandro Moreo Fernandez
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3835f89e9d
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adding documentation
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2021-12-15 15:27:43 +01:00 |
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Alejandro Moreo Fernandez
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8239947746
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refit=True default value in GridSearchQ
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2021-06-16 13:53:54 +02:00 |
 Andrea Esuli
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5b772c7eda
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Bug fixes on use of classes_. Tests.
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2021-05-05 17:12:44 +02:00 |
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Andrea Esuli
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bfbfe08116
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Added classes_ property to all quantifiers.
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2021-05-04 17:09:13 +02:00 |
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Alejandro Moreo Fernandez
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b30c40b7a0
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some refactor made in order to accomodate OneVsAll to operate with aggregative probabilistic quantifiers; launching OneVsAll(HDy)
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2021-01-18 16:52:19 +01:00 |
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Alejandro Moreo Fernandez
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5e64d2588a
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import fixes
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2021-01-15 18:32:32 +01:00 |
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Alejandro Moreo Fernandez
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d1b449d2e9
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plot functionality added
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2021-01-07 17:58:48 +01:00 |
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Alejandro Moreo Fernandez
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326a8ab803
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added Ensemble methods (methods ALL, ACC, Ptr, DS from Pérez-Gallego et al 2017 and 2019) and some UCI ML datasets used in those articles (only 5 datasets out of 32 they used)
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2021-01-06 14:58:29 +01:00 |
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Alejandro Moreo Fernandez
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c8a1a70c8a
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refactoring aggregative methods as methods that not only implement 'classify' and 'quantify', but that also implement 'aggregate' and that, by default, have a default implementation of 'quantify' as a pipeline of 'classify' and 'aggregate'; this helps speeding up evaluations A LOT, since the documents can be pre-classified and the samples are carried out across pre-classified values (labels, or posterior probabilities), and thus only aggregate is called many times within the artificial sampling protocol
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2020-12-11 19:28:17 +01:00 |
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Alejandro Moreo Fernandez
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9bc3a9f28a
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evaluation by artificial prevalence sampling added. New methods added. New util functions added to quapy.functional and quapy.utils
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2020-12-10 19:04:33 +01:00 |
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Alejandro Moreo Fernandez
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a882424eeb
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many aggregative methods added
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2020-12-03 18:12:28 +01:00 |