Benchmarks ========== This library is written with motivation of providing decent performance in Python world. This goal can be hard especially in world of Formal Concept Analysis. In this part I would like to publish some results from small benchmarks on known datasets. During all test, concepts are stored in the memory in list. That means that concepts are not only calculated, but also stored in allocated memory. Calculation of all concepts from context ---------------------------------------- One typical task is to calculate all concepts from given context. You can see results in the following table. +----------------+------------+-------------+ | Algorithm | Mushroom | Bob Ross | +================+============+=============+ | CloseByOne | 146s | 1s | +----------------+------------+-------------+ | FastCloseByOne | 76s | 800ms | +----------------+------------+-------------+ Calculation of concept lattice ------------------------------ Another typical task is to calculate complete lattice (all concepts and all relations between concepts). You can see results in the following table. +----------------+------------+-------------+ | Algorithm | Mushroom | Bob Ross | +================+============+=============+ | Lindig's | N/A | 284s | +----------------+------------+-------------+ Datasets -------- **mushroom.csv** The Mushroom dataset describes samples corresponding to 23 species of gilled mushrooms in the Agaricus and Lepiota family. The entries are taken from The Audubon Society Field Guide to North American Mushrooms. More info: http://fimi.ua.ac.be/data/. **Dimension:** 8124 objects, 119 attributes, number of values in the scale of grades: 2 **Cite:** Lichman, M: UCI Machine Learning Repository http://archive.ics.uci.edu/ml. Irvine, CA: University of California, School of Information and Computer Science, 2013. **bob_ross.csv** The Bob Ross dataset describes occurrence of visual elements in each episody of Bob Ross art show. **Dimension:** 403 objects, 67 attributes, number of values in the scale of grades: 2 **Cite:** https://github.com/fivethirtyeight/data/tree/master/bob-ross **zoo.csv** A simple database containing 17 Boolean-valued attributes. The "type" attribute appears to be the class attribute. Here is a breakdown of which animals are in which type: (I find it unusual that there are 2 instances of "frog" and one of "girl"!). **Dimension:** dimension: 101 objects, 28 attributes, number of values in the scale of grades: 2 **Cite:** Lichman, M: UCI Machine Learning Repository http://archive.ics.uci.edu/ml. Irvine, CA: University of California, School of Information and Computer Science, 2013. Hardware -------- All benchmarks runs on laptop with following specs. :Type: MacBook Pro (Retina, 13-inch, Early 2015) :CPU: 2,7 GHz Intel Core i5 :RAM: 8 GB 1867 MHz DDR3