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Posted to commits@datasketches.apache.org by al...@apache.org on 2024/10/23 01:07:54 UTC
(datasketches-cpp) 01/01: implemented get_PMF() and get_CDF()
This is an automated email from the ASF dual-hosted git repository.
alsay pushed a commit to branch tdigest_pmf_and_cdf
in repository https://gitbox.apache.org/repos/asf/datasketches-cpp.git
commit f0d4cb7c7113ca461801ca5d52934a61872f9ab6
Author: AlexanderSaydakov <Al...@users.noreply.github.com>
AuthorDate: Tue Oct 22 18:07:47 2024 -0700
implemented get_PMF() and get_CDF()
---
tdigest/include/tdigest.hpp | 50 ++++++++++++++++++++++++++++++++++++++++
tdigest/include/tdigest_impl.hpp | 36 +++++++++++++++++++++++++++++
tdigest/test/tdigest_test.cpp | 15 +++++++++---
3 files changed, 98 insertions(+), 3 deletions(-)
diff --git a/tdigest/include/tdigest.hpp b/tdigest/include/tdigest.hpp
index bb4f737..d33084e 100644
--- a/tdigest/include/tdigest.hpp
+++ b/tdigest/include/tdigest.hpp
@@ -89,6 +89,7 @@ public:
using vector_t = std::vector<T, Allocator>;
using vector_centroid = std::vector<centroid, typename std::allocator_traits<Allocator>::template rebind_alloc<centroid>>;
using vector_bytes = std::vector<uint8_t, typename std::allocator_traits<Allocator>::template rebind_alloc<uint8_t>>;
+ using vector_double = std::vector<double, typename std::allocator_traits<Allocator>::template rebind_alloc<double>>;
struct centroid_cmp {
centroid_cmp() {}
@@ -142,8 +143,17 @@ public:
*/
uint64_t get_total_weight() const;
+ /**
+ * Returns an instance of the allocator for this t-Digest.
+ * @return allocator
+ */
+ Allocator get_allocator() const;
+
/**
* Compute approximate normalized rank of the given value.
+ *
+ * <p>If the sketch is empty this throws std::runtime_error.
+ *
* @param value to be ranked
* @return normalized rank (from 0 to 1 inclusive)
*/
@@ -151,11 +161,49 @@ public:
/**
* Compute approximate quantile value corresponding to the given normalized rank
+ *
+ * <p>If the sketch is empty this throws std::runtime_error.
+ *
* @param rank normalized rank (from 0 to 1 inclusive)
* @return quantile value corresponding to the given rank
*/
T get_quantile(double rank) const;
+ /**
+ * Returns an approximation to the Probability Mass Function (PMF) of the input stream
+ * given a set of split points.
+ *
+ * <p>If the sketch is empty this throws std::runtime_error.
+ *
+ * @param split_points an array of <i>m</i> unique, monotonically increasing values
+ * that divide the input domain into <i>m+1</i> consecutive disjoint intervals (bins).
+ *
+ * @param size the number of split points in the array
+ *
+ * @return an array of m+1 doubles each of which is an approximation
+ * to the fraction of the input stream values (the mass) that fall into one of those intervals.
+ */
+ vector_double get_PMF(const T* split_points, uint32_t size) const;
+
+ /**
+ * 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.
+ *
+ * <p>If the sketch is empty this throws std::runtime_error.
+ *
+ * @param split_points an array of <i>m</i> unique, monotonically increasing values
+ * that divide the input domain into <i>m+1</i> consecutive disjoint intervals.
+ *
+ * @param size the number of split points in the array
+ *
+ * @return an array of m+1 doubles, which are a consecutive approximation to the CDF
+ * of the input stream given the split_points. The value at array position j of the returned
+ * CDF array is the sum of the returned values in positions 0 through j of the returned PMF
+ * array. This can be viewed as array of ranks of the given split points plus one more value
+ * that is always 1.
+ */
+ vector_double get_CDF(const T* split_points, uint32_t size) const;
+
/**
* @return parameter k (compression) that was used to configure this t-Digest
*/
@@ -245,6 +293,8 @@ private:
// for compatibility with format of the reference implementation
static tdigest deserialize_compat(std::istream& is, const Allocator& allocator = Allocator());
static tdigest deserialize_compat(const void* bytes, size_t size, const Allocator& allocator = Allocator());
+
+ static inline void check_split_points(const T* values, uint32_t size);
};
} /* namespace datasketches */
diff --git a/tdigest/include/tdigest_impl.hpp b/tdigest/include/tdigest_impl.hpp
index 165bda6..6e3ae1a 100644
--- a/tdigest/include/tdigest_impl.hpp
+++ b/tdigest/include/tdigest_impl.hpp
@@ -85,6 +85,11 @@ uint64_t tdigest<T, A>::get_total_weight() const {
return centroids_weight_ + buffer_.size();
}
+template<typename T, typename A>
+A tdigest<T, A>::get_allocator() const {
+ return buffer_.get_allocator();
+}
+
template<typename T, typename A>
double tdigest<T, A>::get_rank(T value) const {
if (is_empty()) throw std::runtime_error("operation is undefined for an empty sketch");
@@ -191,6 +196,25 @@ T tdigest<T, A>::get_quantile(double rank) const {
return weighted_average(centroids_.back().get_weight(), w1, max_, w2);
}
+template<typename T, typename A>
+auto tdigest<T, A>::get_PMF(const T* split_points, uint32_t size) const -> vector_double {
+ auto buckets = get_CDF(split_points, size);
+ for (uint32_t i = size; i > 0; --i) {
+ buckets[i] -= buckets[i - 1];
+ }
+ return buckets;
+}
+
+template<typename T, typename A>
+auto tdigest<T, A>::get_CDF(const T* split_points, uint32_t size) const -> vector_double {
+ check_split_points(split_points, size);
+ vector_double ranks(get_allocator());
+ ranks.reserve(size + 1);
+ for (uint32_t i = 0; i < size; ++i) ranks.push_back(get_rank(split_points[i]));
+ ranks.push_back(1);
+ return ranks;
+}
+
template<typename T, typename A>
uint16_t tdigest<T, A>::get_k() const {
return k_;
@@ -591,6 +615,18 @@ buffer_(std::move(buffer))
buffer_.reserve(centroids_capacity_ * BUFFER_MULTIPLIER);
}
+template<typename T, typename A>
+void tdigest<T, A>::check_split_points(const T* values, uint32_t size) {
+ for (uint32_t i = 0; i < size ; i++) {
+ if (std::isnan(values[i])) {
+ throw std::invalid_argument("Values must not be NaN");
+ }
+ if ((i < (size - 1)) && !(values[i] < values[i + 1])) {
+ throw std::invalid_argument("Values must be unique and monotonically increasing");
+ }
+ }
+}
+
} /* namespace datasketches */
#endif // _TDIGEST_IMPL_HPP_
diff --git a/tdigest/test/tdigest_test.cpp b/tdigest/test/tdigest_test.cpp
index bf64dbb..fc3f5d1 100644
--- a/tdigest/test/tdigest_test.cpp
+++ b/tdigest/test/tdigest_test.cpp
@@ -35,6 +35,9 @@ TEST_CASE("empty", "[tdigest]") {
REQUIRE_THROWS_AS(td.get_max_value(), std::runtime_error);
REQUIRE_THROWS_AS(td.get_rank(0), std::runtime_error);
REQUIRE_THROWS_AS(td.get_quantile(0.5), std::runtime_error);
+ const double split_points[1] {0};
+ REQUIRE_THROWS_AS(td.get_PMF(split_points, 1), std::runtime_error);
+ REQUIRE_THROWS_AS(td.get_CDF(split_points, 1), std::runtime_error);
}
TEST_CASE("one value", "[tdigest]") {
@@ -56,9 +59,6 @@ TEST_CASE("many values", "[tdigest]") {
const size_t n = 10000;
tdigest_double td;
for (size_t i = 0; i < n; ++i) td.update(i);
-// std::cout << td.to_string(true);
-// td.compress();
-// std::cout << td.to_string(true);
REQUIRE_FALSE(td.is_empty());
REQUIRE(td.get_total_weight() == n);
REQUIRE(td.get_min_value() == 0);
@@ -73,6 +73,15 @@ TEST_CASE("many values", "[tdigest]") {
REQUIRE(td.get_quantile(0.9) == Approx(n * 0.9).epsilon(0.01));
REQUIRE(td.get_quantile(0.95) == Approx(n * 0.95).epsilon(0.01));
REQUIRE(td.get_quantile(1) == n - 1);
+ const double split_points[1] {n / 2};
+ const auto pmf = td.get_PMF(split_points, 1);
+ REQUIRE(pmf.size() == 2);
+ REQUIRE(pmf[0] == Approx(0.5).margin(0.0001));
+ REQUIRE(pmf[1] == Approx(0.5).margin(0.0001));
+ const auto cdf = td.get_CDF(split_points, 1);
+ REQUIRE(cdf.size() == 2);
+ REQUIRE(cdf[0] == Approx(0.5).margin(0.0001));
+ REQUIRE(cdf[1] == 1);
}
TEST_CASE("rank - two values", "[tdigest]") {
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