add python wrapper (w/o merging as that's not implemented yet)

python/CMakeLists.txt

@@ -40,6 +40,7 @@ target_link_libraries(python
     theta
     sampling
     req
+    quantiles
     pybind11::module
 )
 
@@ -63,5 +64,6 @@ target_sources(python
     src/theta_wrapper.cpp
     src/vo_wrapper.cpp
     src/req_wrapper.cpp
+    src/quantiles_wrapper.cpp
     src/vector_of_kll.cpp
 )

python/src/quantiles_wrapper.cpp

@@ -0,0 +1,240 @@
+/*
+ * 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 "quantiles_sketch.hpp"
+
+#include <pybind11/pybind11.h>
+#include <pybind11/stl.h>
+#include <pybind11/numpy.h>
+#include <vector>
+
+namespace py = pybind11;
+
+namespace datasketches {
+
+namespace python {
+
+template<typename T>
+quantiles_sketch<T> quantiles_sketch_deserialize(py::bytes sk_bytes) {
+  std::string sk_str = sk_bytes; // implicit cast  
+  return quantiles_sketch<T>::deserialize(sk_str.c_str(), sk_str.length());
+}
+
+template<typename T>
+py::object quantiles_sketch_serialize(const quantiles_sketch<T>& sk) {
+  auto ser_result = sk.serialize();
+  return py::bytes((char*)ser_result.data(), ser_result.size());
+}
+
+// maybe possible to disambiguate the static vs method rank error calls, but
+// this is easier for now
+template<typename T>
+double quantiles_sketch_generic_normalized_rank_error(uint16_t k, bool pmf) {
+  return quantiles_sketch<T>::get_normalized_rank_error(k, pmf);
+}
+
+template<typename T>
+double quantiles_sketch_get_rank(const quantiles_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 quantiles_sketch_get_quantile(const quantiles_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 quantiles_sketch_get_quantiles(const quantiles_sketch<T>& sk,
+                                  std::vector<double>& fractions,
+                                  bool inclusive) {
+  size_t n_quantiles = fractions.size();
+  auto result = inclusive
+     ? sk.template get_quantiles<true>(&fractions[0], n_quantiles)
+     : sk.template get_quantiles<false>(&fractions[0], n_quantiles);
+
+  // returning as std::vector<> would copy values to a list anyway
+  py::list list(n_quantiles);
+  for (size_t i = 0; i < n_quantiles; ++i) {
+      list[i] = result[i];
+  }
+
+  return list;
+}
+
+template<typename T>
+py::list quantiles_sketch_get_pmf(const quantiles_sketch<T>& sk,
+                            std::vector<T>& split_points,
+                            bool inclusive) {
+  size_t n_points = split_points.size();
+  auto result = inclusive
+     ? sk.template get_PMF<true>(&split_points[0], n_points)
+     : sk.template get_PMF<false>(&split_points[0], n_points);
+
+  py::list list(n_points + 1);
+  for (size_t i = 0; i <= n_points; ++i) {
+    list[i] = result[i];
+  }
+
+  return list;
+}
+
+template<typename T>
+py::list quantiles_sketch_get_cdf(const quantiles_sketch<T>& sk,
+                            std::vector<T>& split_points,
+                            bool inclusive) {
+  size_t n_points = split_points.size();
+  auto result = inclusive
+     ? sk.template get_CDF<true>(&split_points[0], n_points)
+     : sk.template get_CDF<false>(&split_points[0], n_points);
+
+  py::list list(n_points + 1);
+  for (size_t i = 0; i <= n_points; ++i) {
+    list[i] = result[i];
+  }
+
+  return list;
+}
+
+template<typename T>
+void quantiles_sketch_update(quantiles_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_quantiles_sketch(py::module &m, const char* name) {
+  using namespace datasketches;
+
+  py::class_<quantiles_sketch<T>>(m, name)
+    .def(py::init<uint16_t>(), py::arg("k")=quantiles_constants::DEFAULT_K)
+    .def(py::init<const quantiles_sketch<T>&>())
+    .def("update", (void (quantiles_sketch<T>::*)(const T&)) &quantiles_sketch<T>::update, py::arg("item"),
+         "Updates the sketch with the given value")
+    .def("update", &dspy::quantiles_sketch_update<T>, py::arg("array"),
+         "Updates the sketch with the values in the given array")
+    //.def("merge", (void (quantiles_sketch<T>::*)(const quantiles_sketch<T>&)) &quantiles_sketch<T>::merge, py::arg("sketch"),
+    //     "Merges the provided sketch into the this one")
+    .def("__str__", &quantiles_sketch<T>::to_string, py::arg("print_levels")=false, py::arg("print_items")=false,
+         "Produces a string summary of the sketch")
+    .def("to_string", &quantiles_sketch<T>::to_string, py::arg("print_levels")=false, py::arg("print_items")=false,
+         "Produces a string summary of the sketch")
+    .def("is_empty", &quantiles_sketch<T>::is_empty,
+         "Returns True if the sketch is empty, otherwise False")
+    .def("get_k", &quantiles_sketch<T>::get_k,
+         "Returns the configured parameter k")
+    .def("get_n", &quantiles_sketch<T>::get_n,
+         "Returns the length of the input stream")
+    .def("get_num_retained", &quantiles_sketch<T>::get_num_retained,
+         "Returns the number of retained items (samples) in the sketch")
+    .def("is_estimation_mode", &quantiles_sketch<T>::is_estimation_mode,
+         "Returns True if the sketch is in estimation mode, otherwise False")
+    .def("get_min_value", &quantiles_sketch<T>::get_min_value,
+         "Returns the minimum value from the stream. If empty, quantiles_floats_sketch returns nan; quantiles_ints_sketch throws a RuntimeError")
+    .def("get_max_value", &quantiles_sketch<T>::get_max_value,
+         "Returns the maximum value from the stream. If empty, quantiles_floats_sketch returns nan; quantiles_ints_sketch throws a RuntimeError")
+    .def("get_quantile", &dspy::quantiles_sketch_get_quantile<T>, py::arg("rank"), 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 quantiles_floats_sketch: if the sketch is empty this returns nan. "
+         "For quantiles_ints_sketch: if the sketch is empty this throws a RuntimeError.")
+    .def("get_quantiles", &dspy::quantiles_sketch_get_quantiles<T>, py::arg("ranks"), 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::quantiles_sketch_get_rank<T>, py::arg("item"), 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"
+         "If the sketch is empty this returns nan.")
+    .def("get_pmf", &dspy::quantiles_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"
+         "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"
+         "It is not necessary to include either the min or max values in these split points.")
+    .def("get_cdf", &dspy::quantiles_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"
+         "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"
+         "It is not necessary to include either the min or max values in these split points.")
+    .def("normalized_rank_error", (double (quantiles_sketch<T>::*)(bool) const) &quantiles_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::quantiles_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::quantiles_sketch_serialize<T>, "Serializes the sketch into a bytes object")
+    .def_static("deserialize", &dspy::quantiles_sketch_deserialize<T>, "Deserializes the sketch from a bytes object")
+    ;
+}
+
+void init_quantiles(py::module &m) {
+  bind_quantiles_sketch<int>(m, "quantiles_ints_sketch");
+  bind_quantiles_sketch<float>(m, "quantiles_floats_sketch");
+  bind_quantiles_sketch<double>(m, "quantiles_doubles_sketch");
+}

quantiles/include/quantiles_sketch_impl.hpp

@@ -24,6 +24,7 @@
 #include <algorithm>
 #include <stdexcept>
 #include <iomanip>
+#include <sstream>
 
 #include "common_defs.hpp"
 #include "count_zeros.hpp"
@@ -267,7 +268,7 @@ auto quantiles_sketch<T, C, A>::deserialize(std::istream &is, const SerDe& serde
   read<uint16_t>(is); // unused
 
   check_k(k);
-  check_serial_version(serial_version); // a little redundant with the next line, but explicit checks
+  check_serial_version(serial_version); // a little redundant with the header check
   check_family_id(family_id);
   check_header_validity(preamble_longs, flags_byte, serial_version);
 
@@ -376,7 +377,7 @@ auto quantiles_sketch<T, C, A>::deserialize(const void* bytes, size_t size, cons
   ptr += copy_from_mem(ptr, unused);
 
   check_k(k);
-  check_serial_version(serial_version); // a little redundant with the next line, but explicit checks
+  check_serial_version(serial_version); // a little redundant with the header check
   check_family_id(family_id);
   check_header_validity(preamble_longs, flags_byte, serial_version);