 Alejandro Moreo Fernandez
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40cb8f78fe
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pytests before release
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2024-02-14 12:27:19 +01:00 |
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Alejandro Moreo Fernandez
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49fc486c53
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preparing to merge
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2023-02-14 17:00:50 +01:00 |
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Alejandro Moreo Fernandez
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25a829996e
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evaluation updated
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2023-02-14 11:14:38 +01:00 |
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Alejandro Moreo Fernandez
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c608647475
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some bug fixes here and there
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2023-02-13 19:27:48 +01:00 |
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Alejandro Moreo Fernandez
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f2550fdb82
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full example of training, model selection, and evaluation using the lequa2022 dataset with the new protocols
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2022-11-04 15:04:36 +01:00 |
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Alejandro Moreo Fernandez
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45642ad778
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lequa as dataset
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2022-06-01 18:28:59 +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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46e3632200
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ongoing protocols
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2022-05-23 00:20:08 +02:00 |
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Alejandro Moreo Fernandez
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5deb92b457
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update doc
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2021-12-07 17:16:39 +01:00 |
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Alejandro Moreo Fernandez
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8368c467dc
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adapting new format
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2021-11-26 10:57:49 +01:00 |
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Alejandro Moreo Fernandez
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238a30520c
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adapting everything to the new format
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2021-11-08 18:01:49 +01:00 |
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Alejandro Moreo Fernandez
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a7e87e41f8
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GridSearchQ adapted to work with generator functions and integrated for the baselines of LeQua2022; some tests with SVD
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2021-10-26 18:41:10 +02:00 |
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Alejandro Moreo Fernandez
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be2f54de9c
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renaming functions to match the app and npp nomenclature; adding npp as an option for GridSearchQ
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2021-06-16 11:45:40 +02:00 |
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Alejandro Moreo Fernandez
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be1fa757d6
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cleaning
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2021-05-27 16:56:09 +02:00 |
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Alejandro Moreo Fernandez
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731b54c5ba
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adding natural sampling protocol
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2021-05-27 16:53:58 +02:00 |
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Alejandro Moreo Fernandez
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a2ec72496a
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adding eval_budget to evaluation functions
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2021-02-09 11:48:16 +01:00 |
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Alejandro Moreo Fernandez
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3aaf57f2f3
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all uci datasets from Pérez-Gállego added, quantification report added
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2021-01-28 18:22:43 +01:00 |
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Alejandro Moreo Fernandez
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e609c262b4
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parallel functionality added to quapy in order to allow for multiprocess parallelization (and not threading) handling quapy's environment variables
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2021-01-27 09:54:41 +01:00 |
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Alejandro Moreo Fernandez
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03cf73aff6
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refactor: methods requiring a val_split can now declare a default value in the __init__ method that will be used in case the fit method is called without specifying the val_split, which now is by default None in the fit, i.e., by default takes the value of the init, that is generally set to 0.4; some uci datasets added; ensembles can now be optimized for quantification, and can be trained on samples of smaller size
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2021-01-22 18:01:51 +01:00 |
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Alejandro Moreo Fernandez
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bf1cc74ba1
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quapy fixed
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2021-01-22 09:58:12 +01:00 |
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Alejandro Moreo Fernandez
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99132c8166
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fixing quanet
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2021-01-20 12:35:14 +01:00 |
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Alejandro Moreo Fernandez
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482e4453a8
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refactor of ensembles, launching EPACC with Ptr policy
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2021-01-19 18:26:40 +01:00 |
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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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3c5a53bdec
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testing quapy via replicating Tweet Quantification experiments
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2021-01-12 17:39:00 +01:00 |
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Alejandro Moreo Fernandez
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3e07feda3c
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import bug fixed
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2021-01-12 09:35:49 +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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7bed93dcbf
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added model selection for quantification
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2020-12-22 17:43:23 +01:00 |
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Alejandro Moreo Fernandez
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7d6f523e4b
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uniform sampling added if *prevs is empty
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2020-12-17 18:17:17 +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 |