pted 0.0.1

Implementation of a Permutation Test using the Energy Distance for two sample tests and posterior coverage tests

PTED: Permutation Test using the Energy Distance

Think of it like a multi-dimensional KS-test! It is used for two sample testing and posterior coverage tests.

Install

To install PTED, run the following:

pip install pted

Usage

PTED (pronounced "ted") takes in x and y two datasets and determines if they come from the same underlying distribution.

Example: Two-Sample-Test

from pted import pted
import numpy as np

p = np.random.normal(size = (500, 10)) # (n_samples_x, n_dimensions)
q = np.random.normal(size = (400, 10)) # (n_samples_y, n_dimensions)

p_value = pted(p, q, permutations = 1000)
print(f"p-value: {p_value:.3f}") # expect uniform random from 0-1

Example: Coverage Test

from pted import pted_coverage_test
import numpy as np

g = np.random.normal(size = (100, 10)) # ground truth (n_simulations, n_dimensions)
s = np.random.normal(size = (200, 100, 10)) # posterior samples (n_samples, n_simulations, n_dimensions)

p_value = pted_coverage_test(g, s, permutations = 100)
print(f"p-value: {p_value:.3f}") # expect uniform random from 0-1

GPU Compatibility

PTED works on both CPU and GPU. All that is needed is to pass the x and y as PyTorch Tensors on the appropriate device.