Commit 6b31a8f4 authored by Antoine RICHARD's avatar Antoine RICHARD
Browse files

init test plan

parent bf158472
......@@ -157,7 +157,7 @@ public class App
// Init Learners
HashMap<String, MultiLabelLearnerBase> learners = new HashMap<>();
learners.put("FuzzyBayes", new FuzzyBayes());
//learners.put("FuzzyBayes", new FuzzyBayes());
learners.put("HistBayes", new HistBayes());
//learners.put("MLkNN", new MLkNN()); // k-Nearest Neighboors
//learners.put("BPMLL", new BPMLL());
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title: "Transparency vs Performances"
toc: true
code_folding: "hide"
bibliography: bibliography.bib
```{r include=FALSE}
# Motivations
In these experiments we aimed to observe
variations of performances
of different multi-label classification
according to their degree of transparency.
These experiments was made using
the Mulan Java Library, developped by @tsoumakas2011mulan,
which contains plethora of classification algorithms,
but also tools to evaluate them.
# Performances
```{r loadCsv}
results <- read.csv("results/crossvalidation.csv", sep=";")
## Hamming Loss
According to @schapire2000, Hamming Loss of a
classifier $H$ on a dataset $D$ is defined as follow :
$$HammingLoss(H,D) = \frac{1}{|D|}\sum_{i=1}^{|D|}\frac{Y_i \Delta Z_i}{|L|}$$
Where $\Delta$ is corresponding to symmetric difference of
two sets of labels (equivalent to XOR operator).
The hamming loss is corresponding to the mean of errors by labels.
```{r hammingLoss}
) +
stat = "identity",
color = "black",
position = position_dodge()
) +
ymin=Hamming_Loss - Hamming_Loss_std,
ymax=Hamming_Loss + Hamming_Loss_std
position = position_dodge(.9)
) +
ylim(0.0,1.0) +
ggtitle("Hamming loss of multi-label classifier systems by dataset") +
theme(plot.title = element_text(size=14, face="bold", hjust=0.5))
# References
#!/usr/bin/env Rscript
#!/usr/bin/env sh
cd api
mvn clean package
java -cp target/api-1.0-SNAPSHOT.jar com.lamsade.App
cd ../
......@@ -8,6 +8,7 @@ let
# rmarkdown related packages
# Rstudio related packages
# servr
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