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bindings.cpp
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#include <pybind11/pybind11.h>
#include <pybind11/stl.h>
#include "src/c_clause/api/Handler.h"
#include "src/c_clause/api/RulesHandler.h"
#include "src/c_clause/api/RankingHandler.h"
#include "src/c_clause/api/QAHandler.h"
#include "src/c_clause/api/Loader.h"
#include "src/c_clause/api/PredictionHandler.h"
#include "src/c_clause/core/Types.h"
#include "src/c_clause/tests.h"
#include <string>
#include <array>
// **********************************************************************
// ***************** c_clause Python API ********************************
// **********************************************************************
namespace py = pybind11;
PYBIND11_MODULE(c_clause, m) {
// ***exposed backend functions that are usable in the frontend***
// RankingHandler()
py::class_<RankingHandler>(m, "RankingHandler")
.def(py::init<std::map<std::string, std::string>>(), py::arg("options"))
.def("calculate_ranking", &RankingHandler::calculateRanking, py::arg("loader"))
.def("write_ranking", &RankingHandler::writeRanking, py::arg("path"), py::arg("loader"))
.def("write_rules", &RankingHandler::writeRules, py::arg("path"), py::arg("loader"), py::arg("direction"), py::arg("as_string"))
.def("set_options", &RankingHandler::setOptionsFrontend, py::arg("options"))
.def(
"get_ranking",
[](RankingHandler& self, std::string headOrTail, bool return_strings)->py::object{
if (return_strings){
return py::cast(self.getStrRanking(headOrTail));
}else{
return py::cast(self.getRanking(headOrTail));
}
},
py::arg("direction"), py::arg("as_string")
)
.def(
"get_rules",
[](RankingHandler& self, std::string headOrTail, bool return_strings)->py::object{
if (return_strings){
return py::cast(self.getStrRules(headOrTail));
}else{
return py::cast(self.getIdxRules(headOrTail));
}
},
py::arg("direction"), py::arg("as_string")
)
; //class end
// QAHandler()
py::class_<QAHandler>(m, "QAHandler")
.def(py::init<std::map<std::string, std::string>>(), py::arg("options"))
.def("set_options", &QAHandler::setOptionsFrontend, py::arg("options"))
.def(
"calculate_answers",
py::overload_cast<std::vector<std::pair<int, int>>&, std::shared_ptr<Loader>, std::string>(&QAHandler::calculate_answers),
py::arg("queries"), py::arg("loader"), py::arg("direction")
)
.def(
"calculate_answers",
py::overload_cast<std::vector<std::pair<std::string, std::string>>&, std::shared_ptr<Loader>, std::string>(&QAHandler::calculate_answers),
py::arg("queries"), py::arg("loader"), py::arg("direction")
)
.def(
"calculate_answers",
py::overload_cast<std::string&, std::shared_ptr<Loader>, std::string>(&QAHandler::calculate_answers),
py::arg("queries"), py::arg("loader"), py::arg("direction")
)
.def(
"get_answers",
[](QAHandler& self, bool return_strings)->py::object{
if (return_strings){
return py::cast(self.getStrAnswers());
}else{
return py::cast(self.getIdxAnswers());
}
},
py::arg("as_string")
)
.def("write_answers", &QAHandler::writeAnswers, py::arg("path"), py::arg("as_string"))
.def(
"get_rules",
[](QAHandler& self, bool return_strings)->py::object{
if (return_strings){
return py::cast(self.getStrRules());
}else{
return py::cast(self.getIdxRules());
}
},
py::arg("as_string")
)
.def("write_rules", &QAHandler::writeRules, py::arg("path"), py::arg("as_string"))
.def("set_options", &QAHandler::setOptions)
; //class end
// RulesHandler()
py::class_<RulesHandler>(m, "RulesHandler")
.def(py::init<std::map<std::string, std::string>>(), py::arg("options"))
.def("set_options", &RulesHandler::setOptionsFrontend, py::arg("options"))
.def(
"calculate_predictions", py::overload_cast<std::vector<std::string>&, std::shared_ptr<Loader>>(&RulesHandler::calcRulesPredictions),
py::arg("rules"), py::arg("loader"),
R"pbdoc(
Given a list of string rules calculate predictions and rule statistics (num_pred, num_true_pred).
Option parameters can specify if predictions are stored or if statistics are stored. If only statistics
need to be computed, turn off collect_predictions option for efficiency.
)pbdoc"
)
.def(
"calculate_predictions", py::overload_cast<std::string&, std::shared_ptr<Loader>>(&RulesHandler::calcRulesPredictions),
py::arg("rules"), py::arg("loader"),
R"pbdoc(
Given a list of rules in a file (list of rules or AnyBURL format) calculate predictions and rule statistics (num_pred, num_true_pred).
Option parameters can specify if predictions are stored or if statistics are stored. If only statistics
need to be computed, turn off collect_predictions option for efficiency.
)pbdoc"
)
.def(
"get_predictions",
[](RulesHandler& self, bool return_strings)->py::object{
if (return_strings){
return py::cast(self.getStrPredictions());
}else{
return py::cast(self.getIdxPredictions());
}
},
py::arg("as_string")
)
.def("write_predictions", &RulesHandler::writeRulesPredictions, py::arg("path"), py::arg("flat") = true, py::arg("as_string") = true)
.def("get_statistics", &RulesHandler::getStats)
.def("write_statistics", &RulesHandler::writeStats, py::arg("path"))
; //class end
py::class_<Loader, std::shared_ptr<Loader>>(m, "Loader")
.def(py::init<std::map<std::string, std::string>>(), py::arg("options"))
.def("load_rules", py::overload_cast<std::string>(&Loader::loadRules), py::arg("rules"))
.def("load_rules", py::overload_cast<std::vector<std::string>>(&Loader::loadRules), py::arg("rules"))
.def(
"load_rules",
py::overload_cast<std::vector<std::string>, std::vector<std::pair<int,int>>>(&Loader::loadRules),
py::arg("rules"), py::arg("stats")
)
.def(
"load_data",
[](Loader &self, const std::string &data, const std::string &filter, const std::string &target) { return self.loadData<std::string>(data, filter, target); },
py::arg("data"), py::arg("filter") = "", py::arg("target") = ""
)
.def(
"load_data",
[](Loader &self, const StringTripleSet &data, const StringTripleSet &filter, const StringTripleSet &target) { return self.loadData<StringTripleSet>(data, filter, target); },
py::arg("data"), py::arg("filter") = StringTripleSet(), py::arg("target") = StringTripleSet()
)
.def(
"load_data",
[](Loader &self, const TripleSet &data, const TripleSet &filter, const TripleSet &target) { return self.loadData<TripleSet>(data, filter, target); },
py::arg("data"), py::arg("filter") = TripleSet(), py::arg("target") = TripleSet()
)
.def("get_entity_index", &Loader::getNodeToIdx)
.def("write_rules", &Loader::writeRules, py::arg("path"), R"pbdoc(Writes rules after loading. Can be used to store subsets, e.g., load rules ignoring B-rules and then write.)pbdoc")
.def("get_rules", &Loader::getRuleLines, R"pbdoc(Returns rules after loading. Returns a list of strings: 'num_preds\t\support\tconf\trulestring'.)pbdoc")
.def("get_relation_index", &Loader::getRelationToIdx)
.def("replace_ent_strings", &Loader::subsEntityStrings, py::arg("new_tokens"))
.def("replace_rel_strings", &Loader::subsRelationStrings, py::arg("new_tokens"))
.def("set_entity_index", &Loader::setNodeIndex, py::arg("index"))
.def("set_relation_index", &Loader::setRelIndex, py::arg("index"))
.def("rule_index", &Loader::getRuleIdx)
.def("set_options", &Loader::setOptions, py::arg("options"))
; // class end
// PredictionHandler()
py::class_<PredictionHandler>(m, "PredictionHandler")
.def(py::init<std::map<std::string, std::string>>(), py::arg("options"))
.def("set_options", &PredictionHandler::setOptionsFrontend, py::arg("options"))
.def(
"calculate_scores",
py::overload_cast<std::string, std::shared_ptr<Loader>>(&PredictionHandler::scoreTriples),
py::arg("triples"), py::arg("loader"),
R"pbdoc(
Takes as input np.array/list of idx's or a list of string/token triples (tuples or lists)
or a path to a file containing tab separarated string/token triples. Entities and relation tokens must
be known, e.g., they must have be loaded in some triples in the Loader.
)pbdoc"
)
.def(
"calculate_scores",
py::overload_cast<std::vector<std::array<int,3>>, std::shared_ptr<Loader>>(&PredictionHandler::scoreTriples),
py::arg("triples"), py::arg("loader")
)
.def(
"calculate_scores",
py::overload_cast<std::vector<std::array<std::string,3>>, std::shared_ptr<Loader>>(&PredictionHandler::scoreTriples),
py::arg("triples"), py::arg("loader")
)
.def(
"get_scores",
[](PredictionHandler& self, bool return_strings)->py::object{
if (return_strings){
return py::cast(self.getStrScores());
}else{
return py::cast(self.getIdxScores());
}
},
py::arg("as_string")
)
.def(
"get_explanations",
[](PredictionHandler& self, bool return_strings)->py::object{
if (return_strings){
return py::cast(self.getStrExplanations());
}else{
return py::cast(self.getIdxExplanations());
}
},
py::arg("as_string")
)
.def("write_explanations", &PredictionHandler::writeExplanations, py::arg("path"), py::arg("as_string"))
.def("write_scores", &PredictionHandler::writeScores, py::arg("path"), py::arg("as_string"))
; // class end
// backend tests
m.def("_test_compute_strings", &test_compute_strings);
m.def("_tests_groundings", &tests_groundings);
m.def("_tests_rules", &tests);
m.def("_test_scoring", &testTripleScoring);
}