void metapy_bind_stats(py::module& m)
{
auto m_stats = m.def_submodule("stats");
py::class_<py_multinomial>{m_stats, "Multinomial"}
.def("increment", &py_multinomial::increment)
.def("decrement", &py_multinomial::decrement)
.def("counts", [](const py_multinomial& dist,
py::object obj) { return dist.counts(obj); })
.def("counts", [](const py_multinomial& dist) { return dist.counts(); })
.def("unique_events", &py_multinomial::unique_events)
.def("each_seen_event", &py_multinomial::each_seen_event)
.def("clear", &py_multinomial::clear)
.def("probability", &py_multinomial::probability)
.def("__repr__", [](const py_multinomial& mult) {
const auto size = mult.unique_events();
uint64_t i = 0;
std::string result = "<metapy.stats.Multinomial {";
mult.each_seen_event([&](const py::object& obj) {
result += obj.attr("__repr__")().cast<std::string>();
result += ": ";
result += std::to_string(mult.probability(obj));
if (++i != size)
result += ", ";
});
result += "}>";
return result;
});
}
void metapy_bind_topics(py::module& m)
{
auto m_topics = m.def_submodule("topics");
py::class_<topics::lda_model>{m_topics, "LDAModel"}
.def("run",
[](topics::lda_model& model, uint64_t num_iters,
double convergence) {
py::gil_scoped_release release;
model.run(num_iters, convergence);
})
.def("save_doc_topic_distributions",
[](const topics::lda_model& model, const std::string& filename) {
std::ofstream output{filename, std::ios::binary};
model.save_doc_topic_distributions(output);
})
.def("save_topic_term_distributions",
[](const topics::lda_model& model, const std::string& filename) {
std::ofstream output{filename, std::ios::binary};
model.save_topic_term_distributions(output);
})
.def("save", &topics::lda_model::save)
.def("compute_term_topic_probability",
&topics::lda_model::compute_term_topic_probability)
.def("compute_doc_topic_probability",
&topics::lda_model::compute_doc_topic_probability)
.def("topic_distribution",
[](const topics::lda_model& model, doc_id doc) {
return py_multinomial{model.topic_distribution(doc)};
})
.def("term_distribution",
[](const topics::lda_model& model, topic_id k) {
return py_multinomial{model.term_distribution(k)};
})
.def("num_topics", &topics::lda_model::num_topics);
py::class_<topics::inferencer>{m_topics, "LDAInferencer"}
.def("term_distribution",
[](const topics::inferencer& inf, topic_id k) {
return py_multinomial{inf.term_distribution(k)};
},
py::arg("k"))
.def("num_topics", &topics::inferencer::num_topics);
py::class_<topics::lda_cvb, topics::lda_model>{m_topics, "LDACollapsedVB"}
.def(py::init<const learn::dataset&, std::size_t, double, double>(),
py::keep_alive<0, 1>(), py::arg("docs"), py::arg("num_topics"),
py::arg("alpha"), py::arg("beta"))
.def("run",
[](topics::lda_cvb& lda, uint64_t num_iters, double convergence) {
py::gil_scoped_release release;
lda.run(num_iters, convergence);
},
py::arg("num_iters"), py::arg("convergence") = 1e-3);
py::class_<topics::lda_cvb::inferencer, topics::inferencer>{m_topics,
"CVBInferencer"}
.def("__init__",
[](topics::inferencer& inf, const std::string& cfgfile) {
py::gil_scoped_release release;
auto config = cpptoml::parse_file(cfgfile);
new (&inf) topics::inferencer(*config);
},
py::arg("cfg_file"))
.def("__init__",
[](topics::inferencer& inf, const std::string& topicsfile,
double alpha) {
py::gil_scoped_release release;
std::ifstream topics_stream{topicsfile, std::ios::binary};
new (&inf) topics::inferencer(topics_stream, alpha);
},
py::arg("topics_file"), py::arg("alpha"))
.def("infer",
[](const topics::lda_cvb::inferencer& inf,
const learn::feature_vector& doc, std::size_t max_iters,
double convergence) {
return py_multinomial{inf(doc, max_iters, convergence)};
},
py::arg("doc"), py::arg("max_iters"), py::arg("convergence"));
py::class_<topics::lda_gibbs, topics::lda_model>{m_topics, "LDAGibbs"}
.def(py::init<const learn::dataset&, std::size_t, double, double>(),
py::keep_alive<0, 1>(), py::arg("docs"), py::arg("num_topics"),
py::arg("alpha"), py::arg("beta"))
.def(
"run",
[](topics::lda_gibbs& lda, uint64_t num_iters, double convergence) {
py::gil_scoped_release release;
lda.run(num_iters, convergence);
},
py::arg("num_iters"), py::arg("convergence") = 1e-6);
py::class_<topics::lda_gibbs::inferencer, topics::inferencer>{
m_topics, "GibbsInferencer"}
.def("__init__",
[](topics::inferencer& inf, const std::string& cfgfile) {
py::gil_scoped_release release;
auto config = cpptoml::parse_file(cfgfile);
new (&inf) topics::inferencer(*config);
},
py::arg("cfg_file"))
.def("__init__",
[](topics::inferencer& inf, const std::string& topicsfile,
double alpha) {
py::gil_scoped_release release;
std::ifstream topics_stream{topicsfile, std::ios::binary};
new (&inf) topics::inferencer(topics_stream, alpha);
},
py::arg("topics_file"), py::arg("alpha"))
.def("infer",
[](const topics::lda_gibbs::inferencer& inf,
const learn::feature_vector& doc, std::size_t num_iters,
std::size_t seed) {
random::xoroshiro128 rng{seed};
return py_multinomial{inf(doc, num_iters, rng)};
},
py::arg("doc"), py::arg("max_iters"), py::arg("rng_seed"));
py::class_<topics::parallel_lda_gibbs, topics::lda_gibbs>{
m_topics, "LDAParallelGibbs"}
.def(py::init<const learn::dataset&, std::size_t, double, double>(),
py::keep_alive<0, 1>(), py::arg("docs"), py::arg("num_topics"),
py::arg("alpha"), py::arg("beta"));
py::class_<topics::lda_scvb, topics::lda_model>{m_topics,
"LDAStochasticCVB"}
.def(py::init<const learn::dataset&, std::size_t, double, double,
uint64_t>(),
py::keep_alive<0, 1>(), py::arg("docs"), py::arg("num_topics"),
py::arg("alpha"), py::arg("beta"), py::arg("minibatch_size") = 100)
.def("run",
[](topics::lda_scvb& lda, uint64_t num_iters, double convergence) {
py::gil_scoped_release release;
lda.run(num_iters, convergence);
},
py::arg("num_iters"), py::arg("convergence") = 0);
py::class_<topics::topic_model>{m_topics, "TopicModel"}
.def("__init__",
[](topics::topic_model& model, const std::string& prefix) {
py::gil_scoped_release release;
std::ifstream theta{prefix + ".theta.bin", std::ios::binary};
if (!theta)
{
throw topics::topic_model_exception{
"missing document topic probabilities file: " + prefix
+ ".theta.bin"};
}
std::ifstream phi{prefix + ".phi.bin", std::ios::binary};
if (!phi)
{
throw topics::topic_model_exception{
"missing topic term probabilities file: " + prefix
+ ".phi.bin"};
}
new (&model) topics::topic_model(theta, phi);
})
.def("top_k",
[](const topics::topic_model& model, topic_id tid, std::size_t k) {
return model.top_k(tid, k);
},
py::arg("tid"), py::arg("k") = 10)
.def("top_k",
[](const topics::topic_model& model, topic_id tid, std::size_t k,
std::function<double(topic_id, term_id)> scorer) {
return model.top_k(tid, k, scorer);
},
py::arg("tid"), py::arg("k") = 10, py::arg("scorer"))
.def("top_k",
[](const topics::topic_model& model, topic_id tid, std::size_t k,
const topics::bl_term_scorer& scorer) {
return model.top_k(tid, k, scorer);
},
py::arg("tid"), py::arg("k") = 10, py::arg("scorer"))
.def("topic_distribution",
[](const topics::topic_model& self, doc_id did) {
return py_multinomial{self.topic_distribution(did)};
})
.def("term_distribution",
[](const topics::topic_model& self, topic_id k) {
return py_multinomial{self.term_distribution(k)};
})
.def("term_probability", &topics::topic_model::term_probability)
.def("topic_probability", &topics::topic_model::topic_probability)
.def("num_topics", &topics::topic_model::num_topics)
.def("num_words", &topics::topic_model::num_words)
.def("num_docs", &topics::topic_model::num_docs);
m_topics.def("load_topic_model", [](const std::string& config_path) {
py::gil_scoped_release release;
auto config = cpptoml::parse_file(config_path);
return topics::load_topic_model(*config);
});
py::class_<topics::bl_term_scorer>{m_topics, "BLTermScorer"}
.def(py::init<const topics::topic_model&>(), py::keep_alive<0, 1>())
.def("__call__", &topics::bl_term_scorer::operator());
}