Testing

Testing transitionMatrix has two major components:

• normal code testing aiming to certify the correctness of code execution
• algorithm testing aiming to validate the correctness of algorithmic implementation

Note

In general algorithmic testing is not as precise as code testing and may be more subject to uncertainties such as numerical accuracy. To make those tests as revealing as possible transitionMatrix implements a number of standardized round-trip tests:

• starting with a matrix
• generating compatible data
• estimate a matrix from the data
• comparing the values of input and estimated matrices

Running all the examples

Running all the examples is a quick way to check that everything is installed properly, all paths are defined etc. At the root of the distribution:

python3 run_examples.py

The file simply iterates and executes a standalone list of Usage Examples.

filelist = ['adjust_nr_state', 'credit_curves', 'empirical_transition_matrix', 'fix_multiperiod_matrix', 'generate_synthetic_data', 'generate_visuals', 'matrix_from_cohort_data', 'matrix_from_duration_data', 'matrix_lendingclub', 'matrix_set_lendingclub', 'matrix_operations', 'matrix_set_operations']

Warning

The script might generate a number of files / images at random places within the distribution

Test Suite

The testing framework is based on unittest. Before you get started and depending on how you obtained / installed the library check:

• If required adjust the source directory path in transitionMatrix/__init__
• Unzip the data files in the datasets directory

Then run all tests

python3 test.py

For an individual test:

pytest tests/test_TESTNAME.py