duvida 0.0.3.post1

Calculating exact and approximate confidence and information metrics for differentiable functions.

🧐 duvida

duvida (Portuguese for doubt) is a suite of python tools for calculating confidence and information metrics for deep learning. It provides lower-level function transforms for exact and approximate Hessian diagonals in JAX and pytorch.

• Installation
• Python API
• Issues, problems, suggestions
• Documentation

Installation

The easy way

You can install the precompiled version directly using pip. You need to specify the machine learning framework that you want to use:

$ pip install duvida[jax]
# or
$ pip install duvida[jax_cuda12]  # for JAX installing CUDA 12 for GPU support
# or
$ pip install duvida[jax_cuda12_local]  # for JAX using a locally-installed CUDA 12
# or
$ pip install duvida[torch]

We have implemented JAX and pytorch functional transformations for approximate and exact Hessian diagonals, and doubtscore and information sensitivity. These can be used with JAX- and pytorch-based frameworks.

From source

Clone the repository, then cd into it. Then run:

$ pip install -e .[torch]

Python API

duvida provides functional transforms for JAX and pytorch that calculate either exact or approximate Hessian diagonals.

You can check which backend you're using:

>>> from duvida.stateless.config import config
>>> config
Config(backend='jax', precision='double', fallback=True)

It can be changed:

>>> config.set_backend("torch")
'torch'
>>> config
Config(backend='torch', precision='double', fallback=True)

Now you can calculate exact Hessian diagonals without calculating the full matrix:

>>> from duvida.stateless.utils import hessian
>>> import duvida.stateless.numpy as dnp 
>>> f = lambda x: dnp.sum(x ** 3. + x ** 2. + 4.)
>>> a = dnp.array([1., 2.])
>>> exact_diagonal(f)(a) == dnp.diag(hessian(f)(a))
Array([ True,  True], dtype=bool)

Various approximations are also allowed.

>>> from duvida.stateless.hessians import get_approximators
>>> get_approximators()  # Use no arguments to show what's available
('squared_jacobian', 'exact_diagonal', 'bekas', 'rough_finite_difference')

Now apply:

>>> approx_hessian_diag = get_approximators("bekas")
>>> g = lambda x: dnp.sum(dnp.sum(x) ** 3. + x ** 2. + 4.)
>>> a = dnp.array([1., 2.])
>>> dnp.diag(hessian(g)(a))  # Exact
Array([38., 38.], dtype=float64)
>>> approx_hessian_diag(g, n=1000)(a)  # Less accurate when parameters interact
Array([38.52438307, 38.49679655], dtype=float64)
>>> approx_hessian_diag(g, n=1000, seed=1)(a)  # Change the seed to alter the outcome
Array([39.07878869, 38.97796601], dtype=float64)

Issues, problems, suggestions

Add to the issue tracker.

Documentation

(To come at ReadTheDocs.)