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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 samples, typically characterized by widely varying amounts of
_shift_ with respect to the original distribution, that are then _shift_ with respect to the original distribution, that are then
used to evaluate the performance of a (trained) quantifier. used to evaluate the performance of a (trained) quantifier.
These protocols are explained in more detail in a dedicated [entry These protocols are explained in more detail in a dedicated [manual](./protocols.md).
in the wiki](Protocols.md). For the moment being, let us assume we already have For the moment being, let us assume we already have
chosen and instantiated one specific such protocol, that we here chosen and instantiated one specific such protocol, that we here
simply call _prot_. Let also assume our model is called simply call _prot_. Let also assume our model is called
_quantifier_ and that our evaluatio measure of choice is _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 These methods require to first download the
[svmperf](http://www.cs.cornell.edu/people/tj/svm_light/svm_perf.html) [svmperf](http://www.cs.cornell.edu/people/tj/svm_light/svm_perf.html)
package, apply the patch package, apply the patch
[svm-perf-quantification-ext.patch](./svm-perf-quantification-ext.patch), and compile the sources. [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](prepare_svmperf.sh) does all the job. Simply run: 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 ./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. 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 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). 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 This patch extends the above one by also allowing SVMperf to optimize for
_AE_ and _RAE_. _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) estim_prevalence = model.quantify(dataset.test.instances)
``` ```
Check the examples _[explicit_loss_minimization.py](..%2Fexamples%2Fexplicit_loss_minimization.py)_ Check the examples on [explicit_loss_minimization](https://github.com/HLT-ISTI/QuaPy/blob/devel/examples/5.explicit_loss_minimization.py)
and [one_vs_all.py](..%2Fexamples%2Fone_vs_all.py) for more details. 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) ### Kernel Density Estimation methods (KDEy)