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 en:dydaktyka:problog:lab3 [2019/01/14 13:40]msl [Toy Problem] en:dydaktyka:problog:lab3 [2019/06/27 15:49] (current) Both sides previous revision Previous revision 2019/01/15 11:32 msl [Settings format] 2019/01/15 11:31 msl [Settings format] 2019/01/14 15:53 msl [Toy Problem] 2019/01/14 15:49 msl [Structure Learning] 2019/01/14 15:49 msl [Big Fat Assignment] 2019/01/14 15:48 msl [Settings format] 2019/01/14 15:45 msl [Structure Learning] 2019/01/14 15:12 msl [Toy Problem] 2019/01/14 13:40 msl [Toy Problem] 2019/01/14 11:31 msl [Entity Classification] 2019/01/14 11:30 msl [Toy Problem] 2019/01/14 11:13 msl created Next revision Previous revision 2019/01/15 11:32 msl [Settings format] 2019/01/15 11:31 msl [Settings format] 2019/01/14 15:53 msl [Toy Problem] 2019/01/14 15:49 msl [Structure Learning] 2019/01/14 15:49 msl [Big Fat Assignment] 2019/01/14 15:48 msl [Settings format] 2019/01/14 15:45 msl [Structure Learning] 2019/01/14 15:12 msl [Toy Problem] 2019/01/14 13:40 msl [Toy Problem] 2019/01/14 11:31 msl [Entity Classification] 2019/01/14 11:30 msl [Toy Problem] 2019/01/14 11:13 msl created Line 23: Line 23: ===== Toy Problem ===== ===== Toy Problem ===== - {{ :​en:​dydaktyka:​problog:​toy_link_1.png?​200|}}Let assume we have a very tiny network ​as shown on the right. In this problem all links are undirected and unlabeled. Nodes have labels shown using different colors. ​ + {{ :​en:​dydaktyka:​problog:​toy_link_1.png?​200|}}Let assume we have a very tiny network, similar to the one shown on the right. In this problem all links are undirected and unlabeled. Nodes have labels shown using different colors. ​ Our ask is to train a link predictor using [[https://​dtai.cs.kuleuven.be/​problog/​|Problog]]. In case somebody forgot Problog installation is fairly easy given a working Python environment (''​pip install problog''​ and optionally ''​problog install''​ on Linux). In case it wasn't simple enough, one can try to use the [[https://​dtai.cs.kuleuven.be/​problog/​editor.html|on-line interface]]. The evidence file for the problem can downloaded from {{ :​en:​dydaktyka:​problog:​link_prediction_data.pl | this link}}. Our ask is to train a link predictor using [[https://​dtai.cs.kuleuven.be/​problog/​|Problog]]. In case somebody forgot Problog installation is fairly easy given a working Python environment (''​pip install problog''​ and optionally ''​problog install''​ on Linux). In case it wasn't simple enough, one can try to use the [[https://​dtai.cs.kuleuven.be/​problog/​editor.html|on-line interface]]. The evidence file for the problem can downloaded from {{ :​en:​dydaktyka:​problog:​link_prediction_data.pl | this link}}. + You can start from {{ :​en:​dydaktyka:​problog:​link_prediction_empty.pl |this point}}. == Questions: == == Questions: == Line 56: Line 57: - What network'​s features have impact on the result? - What network'​s features have impact on the result? - - Try to learn a similar (but bigger) model from the following {{ :​en:​dydaktyka:​problog:​hypertext_classification_data.pl|data}}. Is there any issue with creating such a model? ​ + - Try to learn a similar (but bigger) model from the following ​evidence ​{{ :​en:​dydaktyka:​problog:​hypertext_classification_data.pl|data}} and {{ :​en:​dydaktyka:​problog:​hypertext_classification_network.pl|network definition}}. Is there any issue with creating such a model? ​ - Could you learn similar classifier using classic machine learning classifiers?​ - Could you learn similar classifier using classic machine learning classifiers?​ Line 81: Line 82: == Questions: == == Questions: == - ​ + - What applications of structure learning can you imagine? - What applications of structure learning can you imagine? - Do you know any related problems/​methods? ​ - Do you know any related problems/​methods? ​ Line 163: Line 164: base(parent(person,​person)). base(parent(person,​person)). base(male(person)). base(male(person)). - base(female(person)). - base(mother(person,​person)). base(grandmother(person,​person)). base(grandmother(person,​person)). - base(father(person,​person)). - base(male_ancestor(person,​person)). - base(female_ancestor(person,​person)). % Target % Target Line 176: Line 172: example_mode(auto). example_mode(auto). ​ + + You'll have to define a family using ''​male''​ and ''​parent''​ facts. + Start with simple family and then add new members as needed. + + ===== Big Fat Assignment ====== + + - Try to learn structure of the Information Retrieval model, you've done earlier by hand. + - Is the learned model satisfying? If not, what is the problem? Try to fix it by changing learning data by hand. + - Modify model to consider more than only one query. What has to be changed?
en/dydaktyka/problog/lab3.1547469649.txt.gz · Last modified: 2019/06/27 16:00 (external edit)