Comprehensively searches for informative projections (2-d projections) in the data to find boxes which separate out homogeneous data points. Returns all projection boxes which match search criteria (support, purity).
For discrete output, the algorithm tries to find 2-d projection boxes which can separate out any class of data from the rest with high purity. For numeric output, the algorithm tries to find 2-d projection boxes which can separate out data points with low variance.
This package has been written in C++ with Python wrapper (Python 2.7). Uses boost dependency. Uses pthreads for multi-threading support.
Run the following command for building and installing package-
python setup.py install [--user]
In python, import find_projections.
For creating a wheel distribution, run python setup.py bdist_wheel.
API documentation is included in Python_API_DOC.pdf and usage has been shown in test_projections.py.
binsize - 10 (Should be positive integer denoting the min. number of data points for binning data at each leaf num_threads - 1 (If >1, will run multi-threaded on Linux) support - 100 (Min. no. of data points in each projection found) purity - 0.9 (Purity of each projection found) mode - 1 (For numeric output, tries to find high-output boxes with low variance)
Valid values for mode are - 0 : Tries to find low variance boxes 1 : Tries to find high mean boxes 2 : Tries to find low mean boxes