Skip to main content

Accelerate Your Statistical Analysis with JAX.

Project description

Stamox

PyPI version PyPI - License PyPI - Downloads GitHub stars

Hello Stamox, Why Another Wheel?

Why Another Wheel?

What stamox does is really simple, just make it possible, it aims to provide a statistic interface for JAX. But nowadays, we have so many statistic packages around the world varying from languages, for python, statsmodels just works great, for R, tidyverse derived packages are so delicate and easy to use. So why build another wheel?

Three reasons I think:

  • Personal interest, as a student of statistics, I want to learn more about statistics and machine learning, proficient knowledge comes from books but more from practice, write down the code behind the theory is a good way to learn.

  • Speed, JAX is really fast, and Equinox is a good tool to make JAX more convenient, backend of JAX is XLA, which makes it possible to compile the code to GPU or TPU, and it is really fast.

  • Easy of Use, %>% is delicate operation in R, it combines the functions to a pipe and make the code more readable, and stamox is inspired by it, and I want to take a try to make it convenient in python with >>.

And here're few benchmarks:

generate random variables

benchmark

calculate cdf

benchmark

Installation

pip install -U stamox
# or
pip install git+https://github.com/JiaYaobo/stamox.git

Documentation

More comprehensive introduction and examples can be found in the documentation.

Quick Start

Similar but faster distribution functions to R

You can simply import all functions from stamox.functions

from stamox.functions import dnorm, pnorm, qnorm, rnorm
import jax.random as jrandom

key = jrandom.PRNGKey(20010813)

# random
x = rnorm(key, sample_shape=(1000, ))
# cdf
prob = pnorm(x)
# ppf
qntl = qnorm(prob)
# pdf
dnorm(x)

Fearless Pipeable

>> is the pipe operator, which is the similar to |> in F# and Elixir or %>% in R.

  • You can simply import all pipeable functions from pipe_functions
import jax.random as jrandom
import stamox.pipe_functions as PF
from stamox import pipe_jit

key = jrandom.PRNGKey(20010813)

@pipe_jit
def f(x):
    return [3 * x[:, 0] + 2 * x[:, 1] - x[:, 2], x] # [y, X]
pipe = PF.rnorm(sample_shape=(1000, 3)) >> f >> PF.lm
state = pipe(key)
print(state.params)

Linear Regression with Formula

import pandas as pd
import numpy as np
from stamox.functions import lm # or from stamox.pipe_functions import lm


x = np.random.uniform(size=(1000, 3))
y = 2 * x[:,0] + 3 * x[:,1] + 4 * x[:,2] + np.random.normal(size=1000)
df = pd.DataFrame(x, columns=['x1', 'x2', 'x3'])
df['y'] = y

lm(df, 'y~x1+x2+x3').params
  • Custom Functions Pipeable
from stamox import make_pipe, make_partial_pipe, Pipeable
import jax.numpy as jnp
import jax.random as jrandom

x = jnp.ones((1000, ))
# single input, simply add make pipe
@make_pipe
def f(x):
    return x ** 2

# multiple input, decorate with make partial pipe
@make_partial_pipe
def g(x, y):
    return x + y

# x -> f -> g(y=2.) -> f -> g(y=3.) -> f
h = Pipeable(x) >> f >> g(y=2.) >> f >> g(y=3.) >> f
# h is a Pipeable object, you can call it to get the result
print(h())
  • Compatible With JAX and Equinox

You can use autograd features from JAX and Equinox with Stamox easily.

import jax.numpy as jnp
from stamox import make_partial_pipe
from equinox import filter_jit, filter_vmap, filter_grad

@make_partial_pipe
@filter_jit
@filter_vmap
@filter_grad
def f(x, y):
    return y * x ** 3

# df/dx = 3y * x^2
g = f(y=3.) # derive with respect to x given y=3
g(jnp.array([1., 2., 3.]))

Or vmap, pmap, jit features integrated with Stamox:

from stamox import pipe_vmap, pipe_jit

@pipe_vmap
@pipe_jit
def f(x):
    return x ** 2

g = f >> f >> f
print(g(jnp.array([1, 2, 3])))

Acceleration Support

JAX can be accelerated by GPU and TPU. So, Stamox is compatible with them.

See More

JAX

Equinox

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

stamox-0.1.5.tar.gz (44.9 kB view details)

Uploaded Source

Built Distribution

stamox-0.1.5-py3-none-any.whl (65.6 kB view details)

Uploaded Python 3

File details

Details for the file stamox-0.1.5.tar.gz.

File metadata

  • Download URL: stamox-0.1.5.tar.gz
  • Upload date:
  • Size: 44.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.0

File hashes

Hashes for stamox-0.1.5.tar.gz
Algorithm Hash digest
SHA256 137112429825d35ab20a98a0fdab5afd0ac29cf2b05db3ac5294687c32819cff
MD5 2b85d33af9af66c812857b70543900ca
BLAKE2b-256 483194cab03d852a4eb90b3584b036d4e60d8e6418cecc1508ea0409d61ea041

See more details on using hashes here.

File details

Details for the file stamox-0.1.5-py3-none-any.whl.

File metadata

  • Download URL: stamox-0.1.5-py3-none-any.whl
  • Upload date:
  • Size: 65.6 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.0

File hashes

Hashes for stamox-0.1.5-py3-none-any.whl
Algorithm Hash digest
SHA256 465aa333c9f0c21f3dc9ddf0f5e39d12f03d0ea948f1183d3d4c5c209d6f8031
MD5 5a82267590373e49c3fe14c87ae2c1c2
BLAKE2b-256 a74b1d79deb7f00c7ac3d43820a0629902428ddeb9ae8fa89c758599652781b1

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page