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en:dydaktyka:problog:lab3 [2019/01/14 12:40]
msl [Toy Problem]
en:dydaktyka:problog:lab3 [2019/01/15 10:32]
msl [Settings format]
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 ===== 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: ==
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   - 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?​
  
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 == 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? ​
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 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
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 example_mode(auto). example_mode(auto).
 </​code>​ </​code>​
 +
 +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.txt · Last modified: 2019/01/15 10:32 by msl
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