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QuaPy/TODO.txt

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svmperf wrapper added
2020-12-03 16:59:13 +01:00
Documentation with sphinx
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
Add evaluation - artificial sampling
Add quantification_report (akin to classification_report from sklearn)
Add optimization - artificial sampling
Add prediction - artificial sampling
Add readers for typical datasets used in Quantification
Add NAE, NRAE
Add "measures for evaluating ordinal"?
aggregative methods adapted. Explicit loss minimization methods (SVMQ, SVMKLD, ...) added and with support to binary or single-label. HDy added
2020-12-04 19:32:08 +01:00
Document methods with paper references
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
The parallel training in svmperf seems not to work (not sure...)
merged
2020-12-10 19:08:22 +01:00