A data structure for accurate on-line accumulation of rank-based statistics.
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.
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
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)
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.
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
|Filename, size||File type||Python version||Upload date||Hashes|
|Filename, size tdigest-cffi-0.1.1.zip (13.9 kB)||File type Source||Python version None||Upload date||Hashes View|