Pynomaly

Changed
- 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
- 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.

Changed
- Removed Numba JIT compilation from the `_standard_distance` and
`_prob_distance` calculations. Using Numba JIT compilation there does
not result in a speed improvement and only add compilation overhead.
- Integrated [pull request 33](https://github.com/vc1492a/PyNomaly/pull/33)
which decreases runtime about 30 to more than 90 percent in some cases, in
particular on repeated calls with larger datasets.
Added
- Type hinting for unit tests in `tests/test_loop.py`.

Changed
- 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](https://rushter.com/blog/python-3-adoption/)).
- Raised the minimum numpy version requirement from 1.12.0 to 1.16.3.
Added
- 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 `0.43.1`
in anticipation of [this deprecation](http://numba.pydata.org/numba-doc/latest/reference/deprecation.htmldeprecation-of-reflection-for-list-and-set-types) -
which does not yet have an implemented solution.

Changed
- Integrated various performance enhancements as described in
[pull request 30](https://github.com/vc1492a/PyNomaly/pull/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.
Added
- [Issue 27](https://github.com/vc1492a/PyNomaly/issues/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%.

Fixed
- [Issue 25](https://github.com/vc1492a/PyNomaly/issues/25) - Fixed an issue
that caused zero division errors when all the values in a neighborhood are
duplicate samples.
Changed
- 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 to `fit` (assumes data or a
distance matrix is available) and then later calls `stream`. If no
data or distance matrix is provided, a warning is raised.
Added
- [Issue 24](https://github.com/vc1492a/PyNomaly/issues/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.py` for examples.
- [Issue 23](https://github.com/vc1492a/PyNomaly/issues/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](https://github.com/vc1492a/PyNomaly/issues/24) and
[25](https://github.com/vc1492a/PyNomaly/issues/25).

Fixed
- [Issue 20](https://github.com/vc1492a/PyNomaly/issues/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](https://github.com/vc1492a/PyNomaly/pull/21) -
This pull request addressed the issue noted above.
Changed
- 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.