tisonkun commented on code in PR #23: URL: https://github.com/apache/datasketches-rust/pull/23#discussion_r2622627729
########## src/tdigest/mod.rs: ########## @@ -0,0 +1,88 @@ +// 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. + +//! T-Digest implementation for estimating quantiles and ranks. +//! +//! The implementation in this library is based on the MergingDigest described in +//! [Computing Extremely Accurate Quantiles Using t-Digests][paper] by Ted Dunning and Otmar Ertl. +//! +//! The implementation in this library has a few differences from the reference implementation +//! associated with that paper: +//! +//! * Merge does not modify the input +//! * Deserialization similar to other sketches in this library, although reading the reference +//! implementation format is supported +//! +//! Unlike all other algorithms in the library, t-digest is empirical and has no mathematical +//! basis for estimating its error and its results are dependent on the input data. However, +//! for many common data distributions, it can produce excellent results. t-digest also operates +//! only on numeric data and, unlike the quantiles family algorithms in the library which return +//! quantile approximations from the input domain, t-digest interpolates values and will hold and +//! return data points not seen in the input. +//! +//! The closest alternative to t-digest in this library is REQ sketch. It prioritizes one chosen +//! side of the rank domain: either low rank accuracy or high rank accuracy. t-digest (in this +//! implementation) prioritizes both ends of the rank domain and has lower accuracy towards the +//! middle of the rank domain (median). +//! +//! Measurements show that t-digest is slightly biased (tends to underestimate low ranks and +//! overestimate high ranks), while still doing very well close to the extremes. The effect seems +//! to be more pronounced with more input values. +//! +//! For more information on the performance characteristics, see the +//! [Datasketches page on t-digest](https://datasketches.apache.org/docs/tdigest/tdigest.html). +//! +//! [paper]: https://arxiv.org/abs/1902.04023 + +mod serialization; +mod sketch; + +/// T-Digest sketch for estimating quantiles and ranks. +/// +/// See the [module documentation](self) for more details. +#[derive(Debug, Clone, PartialEq)] +pub struct TDigest { Review Comment: It's possible to move this struct to `sketch.rs` and merge `serialization.rs` into `sketch.rs`. Otherwise, we need to expose the field to `pub(super)` or add several internal methods for the same purpose. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected] --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
