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A data structure for accurate on-line accumulation of rank-based statistics.

Project description

t-digest CFFI

t-digest is a data structure for accurate on-line accumulation of rank-based statistics such as quantiles and trimmed means, designed by Ted Dunning.

The t-digest construction algorithm uses a variant of 1-dimensional k-means clustering to produce a data structure that is related to the Q-digest. This t-digest data structure can be used to estimate quantiles or compute other rank statistics. The advantage of the t-digest over the Q-digest is that the t-digest can handle floating point values while the Q-digest is limited to integers. With small changes, the t-digest can handle any values from any ordered set that has something akin to a mean. The accuracy of quantile estimates produced by t-digests can be orders of magnitude more accurate than those produced by Q-digests in spite of the fact that t-digests are more compact when stored on disk.

This package provides tested, performant, thread-safe Python 3 CFFI bindings to an adapted implementation of t-digest by Usman Masood originally written for redis-tdigest.

Installation

You can install this package using pip or the included setup.py script:

# Using pip
pip install tdigest-cffi

# Using setup.py
python setup.py install

Usage

from tdigest import TDigest, RawTDigest

# Thread-safe instance with default compression factor
digest = TDigest()

# Raw instance with default compression factor
digest = RawTDigest()

# Thread-safe instance with a custom compression factor
digest = TDigest(compression=500)

# Digest compression
compression = digest.compression

# Digest weight
weight = digest.weight

# Centroid count
centroid_count = digest.centroid_count

# Compression count
compression_count = digest.compression_count

# Insertion with unit weight
digest.insert(1000)

# Insertion with custom weight
digest.insert(1000, 2)

# 99th percentile calculation
quantile = digest.quantile(0.99)
percentile = digest.percentile(99)

# Cumulative distribution function
cdf = digest.cdf(1000)  # P(X <= 1000)

# Centroid extraction
for centroid in digest.centroids():
    print(centroid.mean, centroid.weight)

# Digest merging
other = TDigest()
other.insert(42)
digest.merge(other)

License

BSD 3-Clause License

Copyright (c) 2018, Phil Demetriou
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

* Redistributions of source code must retain the above copyright notice, this
  list of conditions and the following disclaimer.

* Redistributions in binary form must reproduce the above copyright notice,
  this list of conditions and the following disclaimer in the documentation
  and/or other materials provided with the distribution.

* Neither the name of the copyright holder nor the names of its
  contributors may be used to endorse or promote products derived from
  this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

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