PyNomaly
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vc1492a
Anomaly detection using LoOP: Local Outlier Probabilities, a local density based outlier detection method providing an outlier score in the range of [0,1].
It would be great if there's an option for embarrassingly parallel computations, especially if all N^2 distances are calculated.
Hi Dear Author, I wonder does this package contains an API to do independent testing after fitting? For instance, something like: `m = loop.LocalOutlierProbability(data).fit()` `scores_of_test_data = m.local_outlier_probabilities(test_data)` where the "data"...
Any functions with the `@jit` decorator are not read by `pytest-cov` as having been executed despite them running successfully within the unit tests, as they are compiled by numba as...
This feature addresses #36 and adds parallelization to the distance calculation between observations through the optional [Numba](https://numba.pydata.org/) library (which JIT compiles the code for faster run times). While parallelization is...
It may be nice for both developers and users to have a better understanding of how PyNomaly is being used. See the usage charts put out by [seaborn](https://pypistats.org/packages/seaborn).
Provide parameters in LocalOutlierProbability() and provide data in fit() as opposed to providing data in LocalOutlierProbability() along with params. This is s.t. PyNomaly is more in line with scikit-learn and...
As we get closer to a release of [Python 3.9](https://docs.python.org/3.9/whatsnew/3.9.html), it would be prudent to go ahead and begin testing the new version. Travis CI - the continuous integration service...
Could just be loop().fit() (so you can have lof().fit(), etc). E.g. from PyNomaly import loop, loop().fit().
As the current capabilities of PyNomaly are solidified and new capabilities added, it would be beneficial to have dedicated documentation that is hosted and available to users outside of the...
It looks like all distances are currently being calculated, which is expensive. Borrowing from sklearn, BallTree and KDTree could be used to speed up nearest neighbor calculations.