Add GFD mining #465
Add GFD mining #465
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AntonChern
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| : query_(query_), iso_(iso_), res_(res_) {} | ||
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| template <typename CorrespondenceMap1To2, typename CorrespondenceMap2To1> | ||
| bool operator()(CorrespondenceMap1To2 f, CorrespondenceMap2To1) const { |
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Second parameter is not used
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Boost Graph Library requires such syntax from callback function. Unfortunately, if I change the function signature, the code will not compile
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AntonChern
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| Token name_token = Token(i, name.first); | ||
| for (auto& value : name.second) { | ||
| Token value_token = Token(-1, value); | ||
| result.push_back(Literal(name_token, value_token)); |
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result.reserve(name.second.size() * attrs_info.at(label).size())
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Unfortunately, name.second can have different number of values, depending on name.first. The reserve may not work correctly.
AntonChern
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| // Defines a specific attribute of the pattern. | ||
| // The first element is the index of the vertex, | ||
| // the second is the name of the attribute. | ||
| // An alias for user convenience. | ||
| using Token = std::pair<int, std::string>; |
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Are indexes supposed to have negative values?
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Yes, I encode the constant token with the value -1
| std::vector<Literal> premises = {}; | ||
| Gfd gfd = {pattern, premises, conclusion}; | ||
| if (Validate(graph, gfd, embeddings, sigma)) { | ||
| rules.push_back({{}, l}); |
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To avoid unnecessary temporary object creation
| rules.push_back({{}, l}); | |
| rules.emplace_back({}, l); |
Consider everywhere else
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the suggested line gives a compiler error:
no matching function for call to ‘std::vector<std::pair<std::vector<std::pair<std::pair<int, std::__cxx11::basic_string >, std::pair<int, std::__cxx11::basic_string > > >, std::pair<std::pair<int, std::__cxx11::basic_string >, std::pair<int, std::__cxx11::basic_string > > > >::emplace_back(, gfd::Literal&)’
src/tests/test_gfd_mining.cpp
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| std::unique_ptr<algos::GfdMiner> algorithm = | ||
| CreateGfdMiningInstance(graph_path, {.k = 2, .sigma = 3}); | ||
| algorithm->Execute(); | ||
| std::vector<Gfd> gfd_list = algorithm->GfdList(); | ||
| algorithm = CreateGfdMiningInstance(graph_path, {.k = 3, .sigma = 3}); | ||
| algorithm->Execute(); | ||
| std::size_t expected_size = gfd_list.size(); | ||
| gfd_list = algorithm->GfdList(); | ||
| ASSERT_EQ(expected_size, gfd_list.size()); | ||
| for (std::size_t index = 0; index < gfd_list.size(); ++index) { | ||
| ASSERT_TRUE(gfds.at(index) == gfd_list.at(index)); | ||
| } |
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Why do we call CreateGfdMiningInstance and Execute two times in a row?
Should't this be just
| std::unique_ptr<algos::GfdMiner> algorithm = | |
| CreateGfdMiningInstance(graph_path, {.k = 2, .sigma = 3}); | |
| algorithm->Execute(); | |
| std::vector<Gfd> gfd_list = algorithm->GfdList(); | |
| algorithm = CreateGfdMiningInstance(graph_path, {.k = 3, .sigma = 3}); | |
| algorithm->Execute(); | |
| std::size_t expected_size = gfd_list.size(); | |
| gfd_list = algorithm->GfdList(); | |
| ASSERT_EQ(expected_size, gfd_list.size()); | |
| for (std::size_t index = 0; index < gfd_list.size(); ++index) { | |
| ASSERT_TRUE(gfds.at(index) == gfd_list.at(index)); | |
| } | |
| std::unique_ptr<algos::GfdMiner> algorithm = CreateGfdMiningInstance(graph_path, {.k = 2, .sigma = 3}); | |
| ExecuteAndCompare(algorithm, expected_gfds); |
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This test tests the minimality of the dependencies, that is, independence from the choice of k. In this example, the algorithm should not return patterns with 3 vertices, since they are not minimal. So, the result with k = 2 and k = 3 should be the same
Add class for GFD miner
Add minimal GFD and GFD with multiple conclusion tests
Added two examples with searching for dependencies in small graphs.
This PR implements an algorithm for mining graph functional dependencies based on article "Discovering Graph Functional Dependencies" by Fan Wenfei, Hu Chunming, Liu Xueli, and Lu Ping. Algorithm, given an input graph, returns a set of dependencies satisfied on this graph. The algorithm also has two configurable parameters:
kis the maximum number of vertices in the pattern of the mined dependency andsigmais its minimum frequency.In addition, the PR implements the ability to run the algorithm in Python, and also contains examples of its use.