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Remove commented test code until namespacing is implemented
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@markkohdev
markkohdev committed Apr 1, 2024
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285 changes: 1 addition & 284 deletions cpp/test/test_main.cpp
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#include "doctest.h"

// #include <algorithm>
// #include <cstdint>
// #include <filesystem>
// #include <memory>
// #include <random>
// #include <type_traits>
// #include <utility>

// TODO: Extract data generation as a function or as a Catch2 Generator

// template <typename T> struct AllCloseMatcher : Catch::Matchers::MatcherGenericBase {
// AllCloseMatcher(const std::vector<T> &a, const float rtol = 1e-7, const float atol = 0)
// : a_(a), rtol_(rtol), atol_(atol) {}

// bool match(const std::vector<T> &b) const {
// // Could use std::reduce, but early return is most likely faster
// if (a_.size() != b.size()) {
// return false;
// }
// // TODO: Replace with Ranges https://en.cppreference.com/w/cpp/ranges
// for (int i = 0; i < a_.size(); ++i) {
// if (!(std::fabs(a_[i] - b[i]) <= (atol_ + rtol_ * std::fabs(a_[i])))) {
// return false;
// }
// }
// return true;
// }

// std::string describe() const override { return "IsClose"; }

// private:
// const std::vector<T> &a_;
// const float atol_;
// const float rtol_;
// };

// template <typename T>
// auto AllClose(const std::vector<T> a, const float rtol = 1e-7, const float atol = 0)
// -> AllCloseMatcher<T> {
// return AllCloseMatcher{a, rtol, atol};
// }

// template <typename T> std::vector<T> flattenNDArray(NDArray<T, 2> &arr) {
// std::vector<T> res(arr.shape[0]);
// for (auto i = 0; i < arr.shape[0]; ++i) {
// res[i] = arr[i][0];
// }
// return res;
// };

TEST_CASE("Basic init") {
// auto space = GENERATE(SpaceType::Euclidean, SpaceType::InnerProduct);
// auto num_dimensions = GENERATE(4, 16, 128, 256);
// auto num_elements = GENERATE(100, 1000);

// SECTION("(num_dimensions, num_elements, space): (" + std::to_string(num_dimensions) + "," +
// std::to_string(num_elements) + "," + std::to_string(space) + ")") {
// auto index = TypedIndex<float>(space, num_dimensions);
// REQUIRE(index.getSpace() == space);
// REQUIRE(index.getStorageDataType() == StorageDataType::Float32);
// REQUIRE(index.getNumDimensions() == num_dimensions);
// }

CHECK(1 == 1);
// SpaceType space = SpaceType::Euclidean;
// int num_dimensions = 10;
// TypedIndex<float> index = TypedIndex<float>(space, num_dimensions);
// CHECK(index.getSpace() == space);
}

// // dist_t, data_t, scalefactor, tolerance

// TEMPLATE_TEST_CASE("create_and_query",
// "[index_creation]",
// (std::tuple<float, struct E4M3, std::ratio<1, 1>>),
// // (std::tuple<float, char, std::ratio<1, 127>>),
// (std::tuple<float, float, std::ratio<1, 1>>)) {
// auto num_dimensions = GENERATE(4, 16, 128, 128, 256);
// auto num_elements = GENERATE(100, 1000);
// auto space = GENERATE(SpaceType::Euclidean, SpaceType::Cosine);

// // It's a struggle to include these as std::ratio in the TEMPLATE test case so
// // we'll set distance tolerance here.
// float distance_tolerance = 0.0;
// if (std::is_same<typename std::tuple_element<1, TestType>::type, struct E4M3>::value) {
// distance_tolerance = 0.20;
// } else if (std::is_same<typename std::tuple_element<1, TestType>::type, char>::value) {
// distance_tolerance = 0.20;
// } else if (std::is_same<typename std::tuple_element<1, TestType>::type, float>::value) {
// distance_tolerance = 2e-6;
// }

// SECTION("(num_dimensions, num_elements, space): (" + std::to_string(num_dimensions) + "," +
// std::to_string(num_elements) + "," + std::to_string(space) + ")") {

// // Generate a 2D Matrix of test data
// std::random_device rd;
// std::mt19937 gen(rd());
// std::uniform_real_distribution<float> dis(0.0, 1.0);
// auto input_data = std::vector<float>(num_elements * num_dimensions);
// std::generate(input_data.begin(), input_data.end(), [&dis, &gen]() {
// float val = 2 * dis(gen) - 1;
// if (std::is_same<typename std::tuple_element<1, TestType>::type, char>::value) {
// val = std::round(val * 127.0f) / 127.0f;
// }
// return val;
// });

// auto input_array = NDArray<float, 2>(input_data, {num_elements, num_dimensions});

// // Create Index
// auto index = TypedIndex<typename std::tuple_element<0, TestType>::type,
// typename std::tuple_element<1, TestType>::type,
// typename std::tuple_element<2, TestType>::type>(
// space, num_dimensions, 20, num_elements);

// index.setEF(num_elements);
// index.addItems(input_array);
// SECTION("Multiple query interface") {
// auto [labels, distances] = index.query(input_array);

// if (!std::is_same<typename std::tuple_element<1, TestType>::type, float>::value) {
// auto matches = 0;
// // Could be std::reduce or std::accumulate
// for (auto row = 0; row < num_elements; ++row) {
// matches += labels[row][0] == row;
// }
// REQUIRE((double)matches / (double)num_elements > 0.5);
// } else {
// // Could be std::reduce or std::accumulate
// std::vector<hnswlib::labeltype> expected(num_elements);
// std::iota(expected.begin(), expected.end(), 0);
// REQUIRE_THAT(flattenNDArray(labels), AllClose(expected));
// }

// REQUIRE_THAT(flattenNDArray(distances),
// AllClose(std::vector<float>(num_elements, 0.0), 1e-7, distance_tolerance));
// }

// SECTION("Single query interface") {
// for (auto row = 0; row < num_elements; ++row) {
// auto [labels, distances] =
// index.query({input_array[row], input_array[row] + num_dimensions});
// if (std::is_same<typename std::tuple_element<1, TestType>::type, float>::value) {
// REQUIRE(labels[0] == row);
// }
// if(distances[0] >= distance_tolerance) {
// float a = 0;
// }
// REQUIRE(distances[0] < distance_tolerance);
// }
// }

// // SECTION("Saving an index") {
// // auto output_file = std::tmpfile();
// // index.saveIndex(std::make_shared<FileOutputStream>(output_file));
// // auto file_byte_count = std::ftell(output_file);
// // REQUIRE(file_byte_count > 0);
// // auto memory_output_stream = std::make_shared<MemoryOutputStream>();
// // index.saveIndex(memory_output_stream);
// // auto index_bytes = memory_output_stream->getValue().size();
// // REQUIRE(index_bytes > 0);
// // REQUIRE(file_byte_count == index_bytes);
// // }
// }
// }

// TEST_CASE("Spaces") {
// auto [space, expected_distances] =
// GENERATE(std::make_tuple<SpaceType, std::vector<float>>(SpaceType::Euclidean,
// {0.0, 1.0, 2.0, 2.0, 2.0}),
// std::make_tuple<SpaceType, std::vector<float>>(SpaceType::InnerProduct,
// {-2.0, -1.0, 0.0, 0.0, 0.0}),
// std::make_tuple<SpaceType, std::vector<float>>(
// SpaceType::Cosine, {0, 1.835e-1, 4.23e-1, 4.23e-1, 4.23e-1}));
// auto right_dimension = GENERATE(Catch::Generators::range(1, 128, 3));
// auto left_dimension = GENERATE(Catch::Generators::range(1, 32, 5));

// auto num_dimensions = 3;
// auto data = NDArray<float, 2>({1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1}, {5, num_dimensions});

// auto input_data = std::vector<float>();
// for (int i = 0; i < data.shape[0]; ++i) {
// std::vector<float> to_insert(left_dimension, 0);
// std::vector<float> right(right_dimension, 0);
// to_insert.insert(to_insert.end(), data[0], data[0] + data.shape[1]);
// to_insert.insert(to_insert.end(), right.begin(), right.end());
// input_data.insert(input_data.end(), to_insert.begin(), to_insert.end());
// }

// num_dimensions = right_dimension + left_dimension + data.shape[1];

// auto data_2 = NDArray<float, 2>(input_data, {data.shape[0], num_dimensions});
// auto index = TypedIndex<float>(space, num_dimensions, 16, 100);
// index.setEF(10);
// index.addItems(data_2);

// auto [labels, distances] = index.query(
// std::vector(data_2[data_2.shape[0] - 1], data_2[data_2.shape[0] - 1] + num_dimensions), 5);
// REQUIRE_THAT(distances, AllClose(expected_distances, 1e-7, 1e-3));
// }

// TEST_CASE("Get Vectors") {
// auto num_dimensions = GENERATE(4, 16, 128, 256);
// auto num_elements = GENERATE(100, 1000);
// auto space = GENERATE(SpaceType::Euclidean, SpaceType::InnerProduct);

// // Generate a 2D Matrix of test data
// std::random_device rd;
// std::mt19937 gen(rd());
// std::uniform_real_distribution<float> dis(0.0, 1.0);
// auto input_data = std::vector<float>(num_elements * num_dimensions);
// std::generate(input_data.begin(), input_data.end(), [&dis, &gen]() { return 2 * dis(gen) - 1; });
// auto input_array = NDArray<float, 2>(input_data, {num_elements, num_dimensions});

// auto index = TypedIndex<float>(space, num_dimensions);
// auto labels = std::vector<hnswlib::labeltype>(num_elements);
// std::iota(labels.begin(), labels.end(), 0);

// REQUIRE_THROWS(index.getVector(labels[0]));
// index.addItems(input_array);

// SECTION("Test single vector retrieval") {
// for (auto i = 0; i < labels.size(); ++i) {
// REQUIRE_THAT(index.getVector(labels[i]),
// AllClose(std::vector<float>(input_array[i], input_array[i] + num_dimensions)));
// }
// }

// SECTION("Test all vectors retrieval") {
// auto vectors = index.getVectors(labels);
// for (auto i = 0; i < labels.size(); ++i) {
// REQUIRE_THAT(std::vector<float>(vectors[i], vectors[i] + num_dimensions),
// AllClose(std::vector<float>(input_array[i], input_array[i] + num_dimensions)));
// }
// }
// }

// TEST_CASE("Query EF") {
// auto space = GENERATE(SpaceType::Euclidean, SpaceType::InnerProduct);
// auto [query_ef, rank_tolerance] =
// GENERATE(std::make_tuple(1, 100), std::make_tuple(2, 75), std::make_tuple(100, 1));
// auto num_dimensions = 32;
// auto num_elements = 1000;

// // Generate a 2D Matrix of test data
// std::random_device rd;
// std::mt19937 gen(rd());
// std::uniform_real_distribution<float> dis(0.0, 1.0);
// auto input_data = std::vector<float>(num_elements * num_dimensions);
// std::generate(input_data.begin(), input_data.end(), [&dis, &gen]() { return 2 * dis(gen) - 1; });
// auto input_array = NDArray<float, 2>(input_data, {num_elements, num_dimensions});

// auto index = TypedIndex<float>(space, num_dimensions, 20, num_elements);
// index.setEF(num_elements);
// index.addItems(input_array);

// auto [closest_labels_per_vector, _] = index.query(input_array, num_elements, -1, num_elements);
// SECTION("Multi query interface") {
// auto [labels, _] = index.query(input_array, 1, -1, query_ef);
// for (auto i = 0; i < labels.shape[0]; ++i) {
// auto returned_label = labels[0][0];
// // Consider doing this in a loop with an early break.
// auto label_iter = std::find(closest_labels_per_vector[i],
// closest_labels_per_vector[i] + closest_labels_per_vector.shape[1],
// returned_label);
// auto actual_rank = std::distance(closest_labels_per_vector[i], label_iter);
// REQUIRE(actual_rank < rank_tolerance);
// }
// }

// SECTION("Single query interface") {
// for (auto i = 0; i < input_array.shape[0]; ++i) {
// auto [returned_labels, _] =
// index.query({input_data[i], input_data[i] + num_dimensions}, 1, query_ef);
// auto returned_label = returned_labels[0];
// auto label_iter = std::find(closest_labels_per_vector[i],
// closest_labels_per_vector[i] + closest_labels_per_vector.shape[1],
// returned_label);
// auto actual_rank = std::distance(closest_labels_per_vector[i], label_iter);
// REQUIRE(actual_rank < rank_tolerance);
// }
// }
// }
TEST_CASE("Dummy test") { CHECK(1 == 1); }

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