 Alejandro Moreo Fernandez
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a1cdc9ef43
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fixing fit_learner=False case in QuaNet
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2021-06-21 11:13:14 +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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41fb8651b3
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Merge branch 'master' of gitea-s2i2s.isti.cnr.it:moreo/QuaPy
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2021-06-11 10:58:54 +02:00 |
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Alejandro Moreo Fernandez
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2586e6ed7d
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fix in PCALR
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2021-06-11 10:52:30 +02:00 |
 Andrea Esuli
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79fbbd9d80
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pip package
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2021-05-10 13:36:35 +02:00 |
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Andrea Esuli
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32b25146c1
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Tests
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2021-05-10 10:26:51 +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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423fccb096
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todo update
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2021-04-27 18:47:25 +02:00 |
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Alejandro Moreo Fernandez
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854d759dc4
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making everything work like in the wiki
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2021-02-17 18:05:22 +01:00 |
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Alejandro Moreo Fernandez
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91f8d8f3e1
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readme updated
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2021-02-02 12:10:57 +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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2ec3400d15
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adding tweet sent quant experiments
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2021-01-11 18:31:12 +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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d8e2f7556e
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QuaNet added, two examples of TextClassifiers added (CNN, LSTM)
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2020-12-29 20:33:59 +01:00 |
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Alejandro Moreo Fernandez
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8f543db638
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updated TODO list
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2020-12-14 18:42:18 +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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e55caf82fd
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merged
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2020-12-10 19:08:22 +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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9c8d29156c
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aggregative methods adapted. Explicit loss minimization methods (SVMQ, SVMKLD, ...) added and with support to binary or single-label. HDy added
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2020-12-04 19:32:08 +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 |
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Alejandro Moreo Fernandez
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78988c87f9
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svmperf wrapper added
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2020-12-03 16:59:13 +01:00 |