Implemented as described in "An empirical investigation of ceteris paribus learnability" [1].
- Preference learning
- Answer set programming (ASP)
- Computing optimal stable models of logic programs with preferences
Learns acyclic conditional preference networks from noisy preferences.
Calculates transparent entailment [1].
There is a batch algorithm and an online algorithm.
User can create custom datasets by following the existing format.
Can generate a random database with noise.
The value of -1 for a variable can mean "any prefered".
movieLens Dataset (https://grouplens.org/datasets/movielens), each of the 19 variables is a movie Genre. 0 and 1 are the binary variables but in this specific case 0 denotes that it's not part of that genre, and 1 denotes that it is.
On/Off Graph Printing: Line 588:learnCPnet.py
On/Off Preference Printing: Line 588:learnCPnet.py
On/Off Debug Mode: Line 589:learnCPnet.py
When creating a new dataset, how to encode Var1 prefered over Var2: 1 1,1 0 and 0 0,0 1
When creating a new dataset, how to encode Var2 prefered over Var3: 0 0 1,0 0 0 and 0 1 0,0 1 1
On movieLensDataset_user4222 with 10-fold
| numberOfParents | non T.E. | sd | T.E. | sd |
|---|---|---|---|---|
| 0 | 55.72 | 4.46 | 100.0 | 0.0 |
| 1 | 54.18 | 4.75 | 100.0 | 0.0 |
| 4 | 53.07 | 5.50 | 70.97 | 6.74 |
| 7 | 46.92 | 5.32 | 55.89 | 6.07 |
| 10 | 42.64 | 4.34 | 45.67 | 7.17 |
| 13 | 40.85 | 4.31 | 42.72 | 7.55 |
| 16 | 40.76 | 4.24 | 42.46 | 7.86 |
| 18 | 40.76 | 4.24 | 42.46 | 7.86 |
Theory:
- https://arxiv.org/pdf/1107.0023.pdf
- https://ourspace.uregina.ca/bitstream/handle/10294/7676/Alanazi_Eisa_200277152_PHD_CS_Spring2017.pdf
Total of only 4 implementations:
- https://github.com/potassco/asprin (Python)
- https://github.com/FabienLab/CPnets-McDiarmid (Python)
- https://github.com/nmattei/GenCPnet (C++)
- https://github.com/KathrynLaing/DQ-Pruning (R)
[1] Michael, L., & Papageorgiou, E. (2013, June). An empirical investigation of ceteris paribus learnability. In Twenty-Third International Joint Conference on Artificial Intelligence.