python-logreduce
Log file anomaly extractor
Based on success logs, logreduce highlights useful text in failed logs. The goal is to save time in finding a failure's root cause. On average, learning run at 2000 lines per second, and testing run at 1300 lines per seconds. logreduce uses a *model* to learn successful logs and detect novelties in failed logs: * Random words are manually removed using regular expression * Then lines are converted to a matrix of token occurrences (using **HashingVectorizer**), * An unsupervised learner implements neighbor searches (using **NearestNeighbors**).
Il n'y a pas de paquet officiel disponible pour openSUSE Leap 16.0Distributions
openSUSE Tumbleweed
openSUSE Leap 16.0
openSUSE Leap 15.6
openSUSE Leap 15.5
openSUSE Backports for SLE 15 SP7
openSUSE Backports for SLE 15 SP4
SUSE SLE-15-SP1