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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) |
===== Toy Problem ===== | ===== Toy Problem ===== |
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{{ :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}}. |
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| You can start from {{ :en:dydaktyka:problog:link_prediction_empty.pl |this point}}. |
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== 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: == |
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- 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? |
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)). | |
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% Target | % Target |
example_mode(auto). | example_mode(auto). |
</code> | </code> |
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| You'll have to define a family using ''male'' and ''parent'' facts. |
| Start with simple family and then add new members as needed. |
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| ===== Big Fat Assignment ====== |
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| - 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? |
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