| /* |
| * Licensed to the Apache Software Foundation (ASF) under one |
| * or more contributor license agreements. See the NOTICE file |
| * distributed with this work for additional information |
| * regarding copyright ownership. The ASF licenses this file |
| * to you under the Apache License, Version 2.0 (the |
| * "License"); you may not use this file except in compliance |
| * with the License. You may obtain a copy of the License at |
| * |
| * http://www.apache.org/licenses/LICENSE-2.0 |
| * |
| * Unless required by applicable law or agreed to in writing, |
| * software distributed under the License is distributed on an |
| * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY |
| * KIND, either express or implied. See the License for the |
| * specific language governing permissions and limitations |
| * under the License. |
| */ |
| |
| #include "kll_sketch.hpp" |
| |
| #include <pybind11/pybind11.h> |
| #include <pybind11/stl.h> |
| #include <pybind11/numpy.h> |
| #include <sstream> |
| #include <vector> |
| #include <stdexcept> |
| |
| namespace py = pybind11; |
| |
| namespace datasketches { |
| |
| namespace python { |
| |
| template<typename T> |
| kll_sketch<T> kll_sketch_deserialize(py::bytes skBytes) { |
| std::string skStr = skBytes; // implicit cast |
| return kll_sketch<T>::deserialize(skStr.c_str(), skStr.length()); |
| } |
| |
| template<typename T> |
| py::object kll_sketch_serialize(const kll_sketch<T>& sk) { |
| auto serResult = sk.serialize(); |
| return py::bytes((char*)serResult.data(), serResult.size()); |
| } |
| |
| // maybe possible to disambiguate the static vs method rank error calls, but |
| // this is easier for now |
| template<typename T> |
| double kll_sketch_generic_normalized_rank_error(uint16_t k, bool pmf) { |
| return kll_sketch<T>::get_normalized_rank_error(k, pmf); |
| } |
| |
| template<typename T> |
| double kll_sketch_get_rank(const kll_sketch<T>& sk, const T& item, bool inclusive) { |
| if (inclusive) |
| return sk.template get_rank<true>(item); |
| else |
| return sk.template get_rank<false>(item); |
| } |
| |
| template<typename T> |
| T kll_sketch_get_quantile(const kll_sketch<T>& sk, |
| double rank, |
| bool inclusive) { |
| if (inclusive) |
| return T(sk.template get_quantile<true>(rank)); |
| else |
| return T(sk.template get_quantile<false>(rank)); |
| } |
| |
| template<typename T> |
| py::list kll_sketch_get_quantiles(const kll_sketch<T>& sk, |
| std::vector<double>& fractions, |
| bool inclusive) { |
| size_t nQuantiles = fractions.size(); |
| auto result = inclusive ? |
| sk.template get_quantiles<true>(fractions.data(), nQuantiles) |
| : sk.template get_quantiles<false>(fractions.data(), nQuantiles); |
| |
| // returning as std::vector<> would copy values to a list anyway |
| py::list list(nQuantiles); |
| for (size_t i = 0; i < nQuantiles; ++i) { |
| list[i] = result[i]; |
| } |
| |
| return list; |
| } |
| |
| template<typename T> |
| py::list kll_sketch_get_pmf(const kll_sketch<T>& sk, |
| std::vector<T>& split_points, |
| bool inclusive) { |
| size_t nPoints = split_points.size(); |
| auto result = inclusive ? |
| sk.template get_PMF<true>(split_points.data(), nPoints) |
| : sk.template get_PMF<false>(split_points.data(), nPoints); |
| |
| py::list list(nPoints + 1); |
| for (size_t i = 0; i <= nPoints; ++i) { |
| list[i] = result[i]; |
| } |
| |
| return list; |
| } |
| |
| template<typename T> |
| py::list kll_sketch_get_cdf(const kll_sketch<T>& sk, |
| std::vector<T>& split_points, |
| bool inclusive) { |
| size_t nPoints = split_points.size(); |
| auto result = inclusive ? |
| sk.template get_CDF<true>(split_points.data(), nPoints) |
| : sk.template get_CDF<false>(split_points.data(), nPoints); |
| |
| py::list list(nPoints + 1); |
| for (size_t i = 0; i <= nPoints; ++i) { |
| list[i] = result[i]; |
| } |
| |
| return list; |
| } |
| |
| template<typename T> |
| void kll_sketch_update(kll_sketch<T>& sk, py::array_t<T, py::array::c_style | py::array::forcecast> items) { |
| if (items.ndim() != 1) { |
| throw std::invalid_argument("input data must have only one dimension. Found: " |
| + std::to_string(items.ndim())); |
| } |
| |
| auto data = items.template unchecked<1>(); |
| for (uint32_t i = 0; i < data.size(); ++i) { |
| sk.update(data(i)); |
| } |
| } |
| |
| } |
| } |
| |
| namespace dspy = datasketches::python; |
| |
| template<typename T> |
| void bind_kll_sketch(py::module &m, const char* name) { |
| using namespace datasketches; |
| |
| py::class_<kll_sketch<T>>(m, name) |
| .def(py::init<uint16_t>(), py::arg("k")=kll_constants::DEFAULT_K) |
| .def(py::init<const kll_sketch<T>&>()) |
| .def("update", (void (kll_sketch<T>::*)(const T&)) &kll_sketch<T>::update, py::arg("item"), |
| "Updates the sketch with the given value") |
| .def("update", &dspy::kll_sketch_update<T>, py::arg("array"), |
| "Updates the sketch with the values in the given array") |
| .def("merge", (void (kll_sketch<T>::*)(const kll_sketch<T>&)) &kll_sketch<T>::merge, py::arg("sketch"), |
| "Merges the provided sketch into the this one") |
| .def("__str__", &kll_sketch<T>::to_string, py::arg("print_levels")=false, py::arg("print_items")=false, |
| "Produces a string summary of the sketch") |
| .def("to_string", &kll_sketch<T>::to_string, py::arg("print_levels")=false, py::arg("print_items")=false, |
| "Produces a string summary of the sketch") |
| .def("is_empty", &kll_sketch<T>::is_empty, |
| "Returns True if the sketch is empty, otherwise False") |
| .def("get_k", &kll_sketch<T>::get_k, |
| "Returns the configured parameter k") |
| .def("get_n", &kll_sketch<T>::get_n, |
| "Returns the length of the input stream") |
| .def("get_num_retained", &kll_sketch<T>::get_num_retained, |
| "Returns the number of retained items (samples) in the sketch") |
| .def("is_estimation_mode", &kll_sketch<T>::is_estimation_mode, |
| "Returns True if the sketch is in estimation mode, otherwise False") |
| .def("get_min_value", &kll_sketch<T>::get_min_value, |
| "Returns the minimum value from the stream. If empty, kll_floats_sketch retursn nan; kll_ints_sketch throws a RuntimeError") |
| .def("get_max_value", &kll_sketch<T>::get_max_value, |
| "Returns the maximum value from the stream. If empty, kll_floats_sketch retursn nan; kll_ints_sketch throws a RuntimeError") |
| .def("get_quantile", &dspy::kll_sketch_get_quantile<T>, py::arg("fraction"), py::arg("inclusive")=false, |
| "Returns an approximation to the value of the data item " |
| "that would be preceded by the given fraction of a hypothetical sorted " |
| "version of the input stream so far.\n" |
| "Note that this method has a fairly large overhead (microseconds instead of nanoseconds) " |
| "so it should not be called multiple times to get different quantiles from the same " |
| "sketch. Instead use get_quantiles(), which pays the overhead only once.\n" |
| "For kll_floats_sketch: if the sketch is empty this returns nan. " |
| "For kll_ints_sketch: if the sketch is empty this throws a RuntimeError.") |
| .def("get_quantiles", &dspy::kll_sketch_get_quantiles<T>, py::arg("fractions"), py::arg("inclusive")=false, |
| "This is a more efficient multiple-query version of get_quantile().\n" |
| "This returns an array that could have been generated by using get_quantile() for each " |
| "fractional rank separately, but would be very inefficient. " |
| "This method incurs the internal set-up overhead once and obtains multiple quantile values in " |
| "a single query. It is strongly recommend that this method be used instead of multiple calls " |
| "to get_quantile().\n" |
| "If the sketch is empty this returns an empty vector.") |
| .def("get_rank", &dspy::kll_sketch_get_rank<T>, py::arg("value"), py::arg("inclusive")=false, |
| "Returns an approximation to the normalized (fractional) rank of the given value from 0 to 1, inclusive.\n" |
| "The resulting approximation has a probabilistic guarantee that can be obtained from the " |
| "get_normalized_rank_error(False) function.\n" |
| "With the parameter inclusive=true the weight of the given value is included into the rank." |
| "Otherwise the rank equals the sum of the weights of values less than the given value.\n" |
| "If the sketch is empty this returns nan.") |
| .def("get_pmf", &dspy::kll_sketch_get_pmf<T>, py::arg("split_points"), py::arg("inclusive")=false, |
| "Returns an approximation to the Probability Mass Function (PMF) of the input stream " |
| "given a set of split points (values).\n" |
| "The resulting approximations have a probabilistic guarantee that can be obtained from the " |
| "get_normalized_rank_error(True) function.\n" |
| "If the sketch is empty this returns an empty vector.\n" |
| "split_points is an array of m unique, monotonically increasing float values " |
| "that divide the real number line into m+1 consecutive disjoint intervals.\n" |
| "If the parameter inclusive=false, the definition of an 'interval' is inclusive of the left split point (or minimum value) and " |
| "exclusive of the right split point, with the exception that the last interval will include " |
| "the maximum value.\n" |
| "If the parameter inclusive=true, the definition of an 'interval' is exclusive of the left split point (or minimum value) and " |
| "inclusive of the right split point.\n" |
| "It is not necessary to include either the min or max values in these split points.") |
| .def("get_cdf", &dspy::kll_sketch_get_cdf<T>, py::arg("split_points"), py::arg("inclusive")=false, |
| "Returns an approximation to the Cumulative Distribution Function (CDF), which is the " |
| "cumulative analog of the PMF, of the input stream given a set of split points (values).\n" |
| "The resulting approximations have a probabilistic guarantee that can be obtained from the " |
| "get_normalized_rank_error(True) function.\n" |
| "If the sketch is empty this returns an empty vector.\n" |
| "split_points is an array of m unique, monotonically increasing float values " |
| "that divide the real number line into m+1 consecutive disjoint intervals.\n" |
| "If the parameter inclusive=false, the definition of an 'interval' is inclusive of the left split point (or minimum value) and " |
| "exclusive of the right split point, with the exception that the last interval will include " |
| "the maximum value.\n" |
| "If the parameter inclusive=true, the definition of an 'interval' is exclusive of the left split point (or minimum value) and " |
| "inclusive of the right split point.\n" |
| "It is not necessary to include either the min or max values in these split points.") |
| .def("normalized_rank_error", (double (kll_sketch<T>::*)(bool) const) &kll_sketch<T>::get_normalized_rank_error, |
| py::arg("as_pmf"), |
| "Gets the normalized rank error for this sketch.\n" |
| "If pmf is True, returns the 'double-sided' normalized rank error for the get_PMF() function.\n" |
| "Otherwise, it is the 'single-sided' normalized rank error for all the other queries.\n" |
| "Constants were derived as the best fit to 99 percentile empirically measured max error in thousands of trials") |
| .def_static("get_normalized_rank_error", &dspy::kll_sketch_generic_normalized_rank_error<T>, |
| py::arg("k"), py::arg("as_pmf"), |
| "Gets the normalized rank error given parameters k and the pmf flag.\n" |
| "If pmf is True, returns the 'double-sided' normalized rank error for the get_PMF() function.\n" |
| "Otherwise, it is the 'single-sided' normalized rank error for all the other queries.\n" |
| "Constants were derived as the best fit to 99 percentile empirically measured max error in thousands of trials") |
| .def("serialize", &dspy::kll_sketch_serialize<T>, "Serializes the sketch into a bytes object") |
| .def_static("deserialize", &dspy::kll_sketch_deserialize<T>, "Deserializes the sketch from a bytes object") |
| ; |
| } |
| |
| void init_kll(py::module &m) { |
| bind_kll_sketch<int>(m, "kll_ints_sketch"); |
| bind_kll_sketch<float>(m, "kll_floats_sketch"); |
| bind_kll_sketch<double>(m, "kll_doubles_sketch"); |
| } |
