Alejandro Moreo Fernandez 649d412389 dataset fetch for polarity reviews (hp, kindle, imdb) and twitter sentiment (11 datasets) added		2020-12-14 18:36:19 +01:00
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classification	evaluation by artificial prevalence sampling added. New methods added. New util functions added to quapy.functional and quapy.utils	2020-12-10 19:04:33 +01:00
data	dataset fetch for polarity reviews (hp, kindle, imdb) and twitter sentiment (11 datasets) added	2020-12-14 18:36:19 +01:00
method	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	2020-12-11 19:28:17 +01:00
utils	dataset fetch for polarity reviews (hp, kindle, imdb) and twitter sentiment (11 datasets) added	2020-12-14 18:36:19 +01:00
__init__.py	evaluation by artificial prevalence sampling added. New methods added. New util functions added to quapy.functional and quapy.utils	2020-12-10 19:04:33 +01:00
error.py	merged	2020-12-10 19:08:22 +01:00
evaluation.py	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	2020-12-11 19:28:17 +01:00
functional.py	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	2020-12-11 19:28:17 +01:00