chunk_bindings.cpp File Reference

#include <pybind11/numpy.h>
#include <pybind11/pybind11.h>
#include <pybind11/stl.h>
#include "chunk.hpp"
#include "chunk_benchmark.hpp"
#include "chunk_compression.hpp"
#include "chunk_integrations.hpp"
#include "chunk_metrics.hpp"
#include "chunk_serialization.hpp"
#include "chunk_strategies.hpp"
#include "chunk_strategy_implementations.hpp"
#include "chunk_visualization.hpp"
#include "neural_chunking.hpp"
#include "sophisticated_chunking.hpp"

Include dependency graph for chunk_bindings.cpp:

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Functions
	PYBIND11_MODULE (chunking_cpp, m)

Function Documentation

PYBIND11_MODULE()

PYBIND11_MODULE	(	chunking_cpp	,
		m
	)

Definition at line 22 of file chunk_bindings.cpp.

                                 {
    m.doc() = "Python bindings for the C++ chunking library";
 
    // Register exception translators
    py::register_exception_translator([](std::exception_ptr p) {
        try {
            if (p)
                std::rethrow_exception(p);
        } catch (const std::invalid_argument& e) {
            PyErr_SetString(PyExc_ValueError, e.what());
        } catch (const std::runtime_error& e) {
            PyErr_SetString(PyExc_RuntimeError, e.what());
        } catch (const std::exception& e) {
            PyErr_SetString(PyExc_RuntimeError, e.what());
        }
    });
 
    // Basic Chunking
    py::class_<chunk_processing::Chunk<double>>(m, "Chunk")
        .def(py::init<size_t>())
        .def("add",
             static_cast<void (chunk_processing::Chunk<double>::*)(const double&)>(
                 &chunk_processing::Chunk<double>::add),
             "Add a single element")
        .def(
            "add",
            [](chunk_processing::Chunk<double>& self, const std::vector<double>& data) {
                if (data.empty()) {
                    throw std::invalid_argument("Cannot add empty vector");
                }
                self.add(data);
            },
            "Add multiple elements")
        .def("chunk_by_size", &chunk_processing::Chunk<double>::chunk_by_size)
        .def("chunk_by_threshold", &chunk_processing::Chunk<double>::chunk_by_threshold)
        .def("get_chunks", &chunk_processing::Chunk<double>::get_chunks);
 
    py::class_<chunk_processing::Chunk<std::vector<double>>>(m, "Chunk2D")
        .def(py::init<size_t>())
        .def("add",
             [](chunk_processing::Chunk<std::vector<double>>& self,
                py::array_t<double, py::array::c_style>& data) {
                 auto buf = data.request();
                 if (buf.ndim != 2) {
                     throw std::invalid_argument("Expected 2D array");
                 }
 
                 std::vector<std::vector<double>> nested_data;
                 nested_data.reserve(buf.shape[0]);
 
                 for (size_t i = 0; i < buf.shape[0]; ++i) {
                     std::vector<double> row(static_cast<double*>(buf.ptr) + i * buf.shape[1],
                                             static_cast<double*>(buf.ptr) +
                                                 (i + 1) * buf.shape[1]);
                     nested_data.push_back(row);
                 }
                 self.add(nested_data);
             })
        .def("get_chunks", [](chunk_processing::Chunk<std::vector<double>>& self) {
            auto chunks = self.get_chunks();
            py::list result;
            for (const auto& chunk : chunks) {
                // Convert each chunk to numpy array
                ssize_t rows = chunk.size();
                ssize_t cols = rows > 0 ? chunk[0].size() : 0;
 
                auto array = py::array_t<double>({rows, cols});
                auto buf = array.request();
                double* ptr = static_cast<double*>(buf.ptr);
 
                for (size_t i = 0; i < rows; ++i) {
                    std::copy(chunk[i].begin(), chunk[i].end(), ptr + i * cols);
                }
                result.append(array);
            }
            return result;
        });
 
    py::class_<chunk_processing::Chunk<std::vector<std::vector<double>>>>(m, "Chunk3D")
        .def(py::init<size_t>())
        .def("add",
             [](chunk_processing::Chunk<std::vector<std::vector<double>>>& self,
                py::array_t<double, py::array::c_style>& data) {
                 auto buf = data.request();
                 if (buf.ndim != 3) {
                     throw std::invalid_argument("Expected 3D array");
                 }
 
                 std::vector<std::vector<std::vector<double>>> nested_data;
                 nested_data.reserve(buf.shape[0]);
 
                 double* ptr = static_cast<double*>(buf.ptr);
                 for (size_t i = 0; i < buf.shape[0]; ++i) {
                     std::vector<std::vector<double>> matrix;
                     matrix.reserve(buf.shape[1]);
                     for (size_t j = 0; j < buf.shape[1]; ++j) {
                         std::vector<double> row(
                             ptr + (i * buf.shape[1] * buf.shape[2]) + (j * buf.shape[2]),
                             ptr + (i * buf.shape[1] * buf.shape[2]) + ((j + 1) * buf.shape[2]));
                         matrix.push_back(row);
                     }
                     nested_data.push_back(matrix);
                 }
                 self.add(nested_data);
             })
        .def("get_chunks", [](chunk_processing::Chunk<std::vector<std::vector<double>>>& self) {
            auto chunks = self.get_chunks();
            py::list result;
            for (const auto& chunk : chunks) {
                // Convert each chunk to numpy array
                if (chunk.empty() || chunk[0].empty())
                    continue;
 
                ssize_t depth = chunk.size();
                ssize_t rows = chunk[0].size();
                ssize_t cols = chunk[0][0].size();
 
                auto array = py::array_t<double>({depth, rows, cols});
                auto buf = array.request();
                double* ptr = static_cast<double*>(buf.ptr);
 
                for (size_t i = 0; i < depth; ++i) {
                    for (size_t j = 0; j < rows; ++j) {
                        std::copy(chunk[i][j].begin(), chunk[i][j].end(),
                                  ptr + (i * rows * cols) + (j * cols));
                    }
                }
                result.append(array);
            }
            return result;
        });
 
    // Neural Chunking
    py::class_<neural_chunking::NeuralChunking<double>>(m, "NeuralChunking")
        .def(py::init<size_t, double>())
        .def("chunk",
             [](neural_chunking::NeuralChunking<double>& self, const std::vector<double>& data) {
                 auto chunks = self.chunk(data);
                 py::list result;
                 for (const auto& chunk : chunks) {
                     result.append(py::array_t<double>(chunk.size(), chunk.data()));
                 }
                 return result;
             })
        .def("get_window_size", &neural_chunking::NeuralChunking<double>::get_window_size)
        .def("get_threshold", &neural_chunking::NeuralChunking<double>::get_threshold)
        .def("set_window_size", &neural_chunking::NeuralChunking<double>::set_window_size)
        .def("set_threshold", &neural_chunking::NeuralChunking<double>::set_threshold)
        .def("set_learning_rate", &neural_chunking::NeuralChunking<double>::set_learning_rate)
        .def("get_learning_rate", &neural_chunking::NeuralChunking<double>::get_learning_rate)
        .def("set_batch_size", &neural_chunking::NeuralChunking<double>::set_batch_size)
        .def("get_batch_size", &neural_chunking::NeuralChunking<double>::get_batch_size)
        .def("set_activation", &neural_chunking::NeuralChunking<double>::set_activation)
        .def("get_activation", &neural_chunking::NeuralChunking<double>::get_activation)
        .def("set_epochs", &neural_chunking::NeuralChunking<double>::set_epochs)
        .def("get_epochs", &neural_chunking::NeuralChunking<double>::get_epochs)
        .def("train",
             [](neural_chunking::NeuralChunking<double>& self, const std::vector<double>& data) {
                 auto losses = self.train(data);
                 return py::array_t<double>(losses.size(), losses.data());
             });
 
    // GPU Chunking
#ifdef HAVE_CUDA
    py::class_<gpu_chunking::GPUChunkProcessor<double>>(m, "GPUChunkProcessor")
        .def(py::init<>())
        .def("process_on_gpu", &gpu_chunking::GPUChunkProcessor<double>::process_on_gpu);
#endif
 
    // Sophisticated Chunking
    py::class_<sophisticated_chunking::WaveletChunking<double>>(m, "WaveletChunking")
        .def(py::init<size_t, double>())
        .def("chunk",
             [](sophisticated_chunking::WaveletChunking<double>& self,
                const std::vector<double>& data) {
                 auto chunks = self.chunk(data);
                 py::list result;
                 for (const auto& chunk : chunks) {
                     result.append(py::array_t<double>(chunk.size(), chunk.data()));
                 }
                 return result;
             })
        .def("set_window_size", &sophisticated_chunking::WaveletChunking<double>::set_window_size)
        .def("get_window_size", &sophisticated_chunking::WaveletChunking<double>::get_window_size)
        .def("set_threshold", &sophisticated_chunking::WaveletChunking<double>::set_threshold)
        .def("get_threshold", &sophisticated_chunking::WaveletChunking<double>::get_threshold)
        .def("get_wavelet_type", &sophisticated_chunking::WaveletChunking<double>::get_wavelet_type)
        .def("set_wavelet_type",
             &sophisticated_chunking::WaveletChunking<double>::set_wavelet_type);
 
    py::class_<sophisticated_chunking::MutualInformationChunking<double>>(
        m, "MutualInformationChunking")
        .def(py::init<size_t, double>())
        .def("chunk", [](sophisticated_chunking::MutualInformationChunking<double>& self,
                         const std::vector<double>& data) {
            auto chunks = self.chunk(data);
            py::list result;
            for (const auto& chunk : chunks) {
                // Convert each chunk to numpy array
                result.append(py::array_t<double>(chunk.size(), chunk.data()));
            }
            return result;
        });
 
    py::class_<sophisticated_chunking::DTWChunking<double>>(m, "DTWChunking")
        .def(py::init<size_t, double>(), py::arg("window_size") = 10, py::arg("threshold") = 1.0)
        .def("chunk", &sophisticated_chunking::DTWChunking<double>::chunk)
        .def("get_window_size", &sophisticated_chunking::DTWChunking<double>::get_window_size)
        .def("get_dtw_threshold", &sophisticated_chunking::DTWChunking<double>::get_dtw_threshold)
        .def("set_window_size", &sophisticated_chunking::DTWChunking<double>::set_window_size)
        .def("set_dtw_threshold", &sophisticated_chunking::DTWChunking<double>::set_dtw_threshold)
        .def("get_distance_metric",
             &sophisticated_chunking::DTWChunking<double>::get_distance_metric)
        .def("set_distance_metric",
             &sophisticated_chunking::DTWChunking<double>::set_distance_metric);
 
    // Chunk Metrics
    py::class_<chunk_metrics::ChunkQualityAnalyzer<double>>(m, "ChunkQualityAnalyzer")
        .def(py::init<>())
        .def("compute_cohesion", &chunk_metrics::ChunkQualityAnalyzer<double>::compute_cohesion)
        .def("compute_separation", &chunk_metrics::ChunkQualityAnalyzer<double>::compute_separation)
        .def("compute_silhouette_score",
             &chunk_metrics::ChunkQualityAnalyzer<double>::compute_silhouette_score)
        .def("compute_quality_score",
             &chunk_metrics::ChunkQualityAnalyzer<double>::compute_quality_score)
        .def("compute_size_metrics",
             &chunk_metrics::ChunkQualityAnalyzer<double>::compute_size_metrics)
        .def("clear_cache", &chunk_metrics::ChunkQualityAnalyzer<double>::clear_cache);
 
    // Chunk Visualization
    py::class_<chunk_viz::ChunkVisualizer<double>>(m, "ChunkVisualizer")
        .def(py::init<const std::vector<double>&, const std::string&>())
        .def("plot_chunk_sizes", &chunk_viz::ChunkVisualizer<double>::plot_chunk_sizes)
        .def("visualize_boundaries", &chunk_viz::ChunkVisualizer<double>::visualize_boundaries)
        .def("export_to_graphviz", &chunk_viz::ChunkVisualizer<double>::export_to_graphviz)
        .def("get_output_dir", &chunk_viz::ChunkVisualizer<double>::get_output_dir);
 
    // Chunk Serialization
    py::class_<chunk_serialization::ChunkSerializer<double>>(m, "ChunkSerializer")
        .def(py::init<>())
        .def("to_json", &chunk_serialization::ChunkSerializer<double>::to_json)
        .def("to_protobuf", &chunk_serialization::ChunkSerializer<double>::to_protobuf)
        .def("to_msgpack", &chunk_serialization::ChunkSerializer<double>::to_msgpack);
 
    // Database Integration
#ifdef HAVE_POSTGRESQL
    py::class_<chunk_integrations::DatabaseChunkStore>(m, "DatabaseChunkStore")
        .def(
            py::init<std::unique_ptr<chunk_integrations::DatabaseConnection>, const std::string&>())
        .def("store_chunks_postgres",
             &chunk_integrations::DatabaseChunkStore::store_chunks_postgres<double>)
#ifdef HAVE_MONGODB
        .def("store_chunks_mongodb",
             &chunk_integrations::DatabaseChunkStore::store_chunks_mongodb<double>)
#endif
        ;
#endif
 
    // Message Queue Integration
#if defined(HAVE_KAFKA) || defined(HAVE_RABBITMQ)
    py::class_<chunk_integrations::ChunkMessageQueue>(m, "ChunkMessageQueue")
        .def(py::init<std::unique_ptr<chunk_integrations::MessageQueueConnection>,
                      const std::string&>())
#ifdef HAVE_KAFKA
        .def("publish_chunks_kafka",
             &chunk_integrations::ChunkMessageQueue::publish_chunks_kafka<double>)
#endif
#ifdef HAVE_RABBITMQ
        .def("publish_chunks_rabbitmq",
             &chunk_integrations::ChunkMessageQueue::publish_chunks_rabbitmq<double>)
#endif
        ;
#endif
 
    // Benchmark bindings
    py::class_<chunk_benchmark::BenchmarkResult>(m, "BenchmarkResult")
        .def_readwrite("execution_time_ms", &chunk_benchmark::BenchmarkResult::execution_time_ms)
        .def_readwrite("memory_usage_bytes", &chunk_benchmark::BenchmarkResult::memory_usage_bytes)
        .def_readwrite("num_chunks", &chunk_benchmark::BenchmarkResult::num_chunks)
        .def_readwrite("strategy_name", &chunk_benchmark::BenchmarkResult::strategy_name);
 
    py::class_<chunk_benchmark::ChunkBenchmark<double>>(m, "ChunkBenchmark")
        .def(py::init<const std::vector<double>&, size_t>(), py::arg("data"),
             py::arg("num_iterations") = 100)
        .def("add_strategy", &chunk_benchmark::ChunkBenchmark<double>::add_strategy)
        .def("benchmark_chunking", &chunk_benchmark::ChunkBenchmark<double>::benchmark_chunking)
        .def("save_results", &chunk_benchmark::ChunkBenchmark<double>::save_results);
 
    // Add exception translations
    py::register_exception<chunk_processing::ChunkingError>(m, "ChunkingError");
 
    // Strategy bindings
    py::class_<chunk_processing::ChunkStrategy<double>,
               std::shared_ptr<chunk_processing::ChunkStrategy<double>>>(m, "ChunkStrategy")
        .def("apply", &chunk_processing::ChunkStrategy<double>::apply);
 
    py::class_<chunk_processing::NeuralChunkingStrategy<double>,
               chunk_processing::ChunkStrategy<double>,
               std::shared_ptr<chunk_processing::NeuralChunkingStrategy<double>>>(
        m, "NeuralChunkingStrategy")
        .def(py::init<>())
        .def("apply", &chunk_processing::NeuralChunkingStrategy<double>::apply);
 
    py::class_<chunk_processing::SimilarityChunkingStrategy<double>,
               chunk_processing::ChunkStrategy<double>,
               std::shared_ptr<chunk_processing::SimilarityChunkingStrategy<double>>>(
        m, "SimilarityChunkingStrategy")
        .def(py::init<double>())
        .def("apply", &chunk_processing::SimilarityChunkingStrategy<double>::apply);
}

References chunk_processing::Chunk< T >::add(), chunk_benchmark::BenchmarkResult::execution_time_ms, chunk_processing::Chunk< T >::get_chunks(), chunk_benchmark::BenchmarkResult::memory_usage_bytes, chunk_benchmark::BenchmarkResult::num_chunks, and chunk_benchmark::BenchmarkResult::strategy_name.