outlier_detector

The module outlier_detector.py has two classes: (1) Detector and (2) ScoreModel. The Detector class has multiple functions to perform noise detection. The entry point function to use this class is “purify”

Test Examples:

All benchmark tests and used dataset can be found in “outlier_detector/tests” To run test problems run the scripts listed below. These scripts conduct multiple numerical experiments and save results in a folder named “results”. The datasets include 2 synthetic problems (1D and 6D datasets), and a real-world problem (public supply in the southwest USA).

Synthetic Test Problems

• Run one_dimension_case.py
• Run hartmann_6d.py
• run evaluating_data_model.py
• fig_effect_of_seed_number.py
• fig_effect_of_noise_signal_ratio.py
• run fig_compare_smapler_functions.py

Public Supply Problem (outlier_detector/tests/ca_wu)

A complete copy of the dataset for the Public Supply can be found at https://doi.org/10.5066/P9FUL880. A subset of the data for California, Arizona, and Nevda are extracted and used for the testing (south_westh.csv)

• Run ca_case.py
• Run evaluate_equifinality.py
• run long_run.py