All notable changes to PyNomaly will be documented in this Changelog.
The format is based on Keep a Changelog and adheres to Semantic Versioning.
- The implementation of the progress bar to support use when the number of observations is less than the width of the Python console in which the code is being executed (tracked in this issue).
- Docstring to the testing functions to provide some additional documentation of the testing (tracked in this issue).
- Removed numba as a strict dependency, which is now an optional dependency that is not needed to use PyNomaly but which provides performance enhancements when functions are called repeatedly, such as when the number of observations is large. This relaxes the numba requirement introduced in version 0.3.0.
- Added progress bar functionality that can be called using
LocalOutlierProbability(progress_bar=True)in both native Python and numba just-in-time (JIT) compiled modes. This is helpful in cases where PyNomaly is processing a large amount of observations.
- Removed Numba JIT compilation from the
_standard_distanceand_prob_distancecalculations. Using Numba JIT compilation there does not result in a speed improvement and only add compilation overhead. - Integrated pull request #33 which decreases runtime about 30 to more than 90 percent in some cases, in particular on repeated calls with larger datasets.
- Type hinting for unit tests in
tests/test_loop.py.
- The manner in which the standard distance is calculated from list comprehension to a vectorized Numpy implementation, reducing compute time for that specific calculation by approximately 75%.
- Removed formal testing and support for Python 3.4 (Python 3 adoption rates).
- Raised the minimum numpy version requirement from 1.12.0 to 1.16.3.
- Numba just in time (JIT) compilation to improve the speed of some
of the core functionality, consistently achieving a further 20% reduction
in compute time when n = 1000. Future optimizations could yield
further reductions in computation time. For now, requiring a strict numba version of
0.43.1in anticipation of this deprecation - which does not yet have an implemented solution.
- Integrated various performance enhancements as described in pull request #30 that increase PyNomaly's performance by at least up to 50% in some cases.
- The Validate classes functions from public to private, as they are only used in validating specification and data input into PyNomaly.
- Issue #27 - Added docstring to key functions in PyNomaly to ease future development and provide additional information.
- Additional unit tests to raise code coverage from 96% to 100%.
- Issue #25 - Fixed an issue that caused zero division errors when all the values in a neighborhood are duplicate samples.
- The error behavior when attempting to use the stream approach
before calling
fit. While the previous implementation resulted in a warning and system exit, PyNomaly now attempts tofit(assumes data or a distance matrix is available) and then later callsstream. If no data or distance matrix is provided, a warning is raised.
- Issue #24 - Added
the ability to use one's own distance matrix,
provided a neighbor index matrix is also provided. This ensures
PyNomaly can be used with distances other than the euclidean.
See the file
iris_dist_grid.pyfor examples. - Issue #23 - Added Python 3.7 to the tested distributions in Travis CI and passed tests.
- Unit tests to monitor the issues and features covered in issues 24 and 25.
- Issue #20 - Fixed a bug that inadvertently used global means of the probabilistic distance as the expected value of the probabilistic distance, as opposed to the expected value of the probabilistic distance within a neighborhood of a point.
- Integrated pull request #21 - This pull request addressed the issue noted above.
- Changed the default behavior to strictly not supporting the use of missing values in the input data, as opposed to the soft enforcement (a simple user warning) used in the previous behavior.
- Issue #17 - Fixed a bug that allowed for a column of empty values in the primary data store.
- Integrated pull request #18 - Fixed a bug that was not causing dependencies such as numpy to skip installation when installing PyNomaly via pip.
- Issue #14 - Fixed an issue that was causing a ZeroDivisionError when the specified neighborhood size is larger than the total number of observations in the smallest cluster.
- This implementation to align more closely with the specification of the approach in the original paper. The extent parameter now takes an integer value of 1, 2, or 3 that corresponds to the lambda parameter specified in the paper. See the readme for more details.
- Refactored the code base and created the Validate class, which includes checks for data type, correct specification, and other dependencies.
- Automated tests to ensure the desired functionality is being met can now be
found in the
PyNomaly/testsdirectory. - Code for the examples in the readme can now be found in the
examplesdirectory. - Additional information for parameter selection in the readme.
- Issue #10 - Fixed error on line 142 which was causing the class to fail. More explicit examples were also included in the readme for using numpy arrays.
- An improvement to the Euclidean distance calculation by MichaelSchreier which brings a over a 50% reduction in computation time.
- Added new functionality to PyNomaly by integrating a modified LoOP approach introduced by Hamlet et al. which can be used for streaming data applications or in the case where computational expense is a concern. Data is first fit to a "training set", with any additional observations considered for outlierness against this initial set.
- Fixed an issue which allowed the number of neighbors considered to exceed the number of observations. Added a check to ensure this is no longer possible.
- Fixed an issue inadvertently introduced in 0.1.6 that caused distance calculations to be incorrect, thus resulting in incorrect LoOP values.
- Updated the distance calculation such that the euclidean distance calculation has been separated from the main distance calculation function.
- Fixed an error in the calculation of the standard distance.
- .fit() now returns a fitted object instead of local_outlier_probabilities. Local outlier probabilities can be now be retrieved by calling .local_outlier_probabilities. See the readme for an example.
- Some private functions have been renamed.
- Issue #4 - Separated parameter type checks
from checks for invalid parameter values.
- @accepts decorator verifies LocalOutlierProbability parameters are of correct type.
- Parameter value checks moved from .fit() to init.
- Fixed parameter check to ensure extent value is in the range (0., 1.] instead of [0, 1] (extent cannot be zero).
- Issue #1 - Added type check using @accepts decorator for cluster_labels.
- Issue #3 - .fit() fails if the sum of squared distances sums to 0.
- Added check to ensure the sum of square distances is greater than zero.
- Added UserWarning to increase the neighborhood size if all neighbors in n_neighbors are zero distance from an observation.
- Added UserWarning to check for integer type n_neighbor conditions versus float type.
- Changed calculation of the probabilistic local outlier factor expected value to Numpy operation from base Python.
- Altered the distance matrix computation to return a triangular matrix instead of a fully populated matrix. This was made to ensure no duplicate neighbors were present in computing the neighborhood distance for each observation.
- LICENSE.txt file of Apache License, Version 2.0.
- setup.py, setup.cfg files configured for release to PyPi.
- Changed name throughout code base from PyLoOP to PyNomaly.
- Initial release to PyPi.
- A bad push to PyPi necessitated the need to skip a version number.
- Chosen name of PyLoOP not present on test index but present on production PyPi index.
- Issue not known until push was made to the test index.
- Skipped version number to align test and production PyPi indices.
- readme.md file documenting methodology, package dependencies, use cases, how to contribute, and acknowledgements.
- Initial open release of PyNomaly codebase on Github.