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Fix cross-references within the documentation

This commit is contained in:
Mirko Bunse 2024-07-01 17:07:01 +02:00
parent c668d0b3d8
commit 415c92f803
3 changed files with 8 additions and 8 deletions
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4
docs/source/manuals/evaluation.md
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@ -72,8 +72,8 @@ one specific _sample generation procotol_ to genereate many
samples, typically characterized by widely varying amounts of
_shift_ with respect to the original distribution, that are then
used to evaluate the performance of a (trained) quantifier.
These protocols are explained in more detail in a dedicated [entry
in the wiki](Protocols.md). For the moment being, let us assume we already have
These protocols are explained in more detail in a dedicated [manual](./protocols.md).
For the moment being, let us assume we already have
chosen and instantiated one specific such protocol, that we here
simply call _prot_. Let also assume our model is called
_quantifier_ and that our evaluatio measure of choice is

8
docs/source/manuals/explicit-loss-minimization.md
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@ -5,14 +5,14 @@ SVM(Q), SVM(KLD), SVM(NKLD), SVM(AE), or SVM(RAE).
These methods require to first download the
[svmperf](http://www.cs.cornell.edu/people/tj/svm_light/svm_perf.html)
package, apply the patch
[svm-perf-quantification-ext.patch](./svm-perf-quantification-ext.patch), and compile the sources.
The script [prepare_svmperf.sh](prepare_svmperf.sh) does all the job. Simply run:
[svm-perf-quantification-ext.patch](https://github.com/HLT-ISTI/QuaPy/blob/master/svm-perf-quantification-ext.patch), and compile the sources.
The script [prepare_svmperf.sh](https://github.com/HLT-ISTI/QuaPy/blob/master/prepare_svmperf.sh) does all the job. Simply run:
```
./prepare_svmperf.sh
```
The resulting directory [svm_perf_quantification](./svm_perf_quantification) contains the
The resulting directory `svm_perf_quantification/` contains the
patched version of _svmperf_ with quantification-oriented losses.
The [svm-perf-quantification-ext.patch](https://github.com/HLT-ISTI/QuaPy/blob/master/prepare_svmperf.sh) is an extension of the patch made available by
@ -22,5 +22,5 @@ the _Q_ measure as proposed by [Barranquero et al. 2015](https://www.sciencedire
and for the _KLD_ and _NKLD_ measures as proposed by [Esuli et al. 2015](https://dl.acm.org/doi/abs/10.1145/2700406?casa_token=8D2fHsGCVn0AAAAA:ZfThYOvrzWxMGfZYlQW_y8Cagg-o_l6X_PcF09mdETQ4Tu7jK98mxFbGSXp9ZSO14JkUIYuDGFG0).
This patch extends the above one by also allowing SVMperf to optimize for
_AE_ and _RAE_.
See [Methods.md](Methods.md) for more details and code examples.
See the [](./methods) manual for more details and code examples.

4
docs/source/manuals/methods.md
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@ -414,8 +414,8 @@ model.fit(dataset.training)
estim_prevalence = model.quantify(dataset.test.instances)
```
Check the examples _[explicit_loss_minimization.py](..%2Fexamples%2Fexplicit_loss_minimization.py)_
and [one_vs_all.py](..%2Fexamples%2Fone_vs_all.py) for more details.
Check the examples on [explicit_loss_minimization](https://github.com/HLT-ISTI/QuaPy/blob/devel/examples/5.explicit_loss_minimization.py)
and on [one versus all quantification](https://github.com/HLT-ISTI/QuaPy/blob/devel/examples/10.one_vs_all.py) for more details.
### Kernel Density Estimation methods (KDEy)