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Intrinsic Multi-scale Evaluation of Generative Models

This repository provides a reference implementation of MSID, a metric for comparing underlying intrinsic geometry of data manifolds (paper).

Installation

Installation is as simple as python setup.py install.

Requirements

  • Python 2.7 or Python 3.3+
  • SciPy
  • NumPy
  • [optional] pykgraph, for Anaconda users, just conda install pykgraph

Example usage

import numpy as np
from msid import msid_score

np.random.seed(1)

x0 = np.random.randn(1000, 10)
x1 = np.random.randn(1000, 9) # MSID can compare two data distributions with different dimensionalities
y0 = np.random.beta(0.5, 0.5, (1000, 10))

print('x0=N(0, 1), shape=', x0.shape)
print('x1=N(0, 1), shape=', x1.shape)
print('y0=beta(0.5, 0.5), shape=', y0.shape)

print('MSID(x0, x1)', msid_score(x0, x1))
print('MSID(x0, y0)', msid_score(x0, y0))

Contact

echo "%7=87@=<2=<>5.27" | tr '#-)/->' '_-|'

About

Code for MSID, a Multi-Scale Intrinsic Distance for comparing generative models, studying neural networks, and more!

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