Skip to main content

Lightweight computation graphs for Python

Project description

Supported Python versions of latest release in PyPi Development Status Latest release in GitHub Latest version in PyPI Travis continuous integration testing ok? (Linux) ReadTheDocs ok? cover-status Code Style Apache License, version 2.0

Github watchers Github stargazers Github forks Issues count

It’s a DAG all the way down!

sample graphtik plot

Lightweight computation graphs for Python

Graphtik is an an understandable and lightweight Python module for building and running ordered graphs of computations. The API posits a fair compromise between features and complexity, without precluding any. It can be used as is to build machine learning pipelines for data science projects. It should be extendable to act as the core for a custom ETL engine or a workflow-processor for interdependent files and processes.

Graphtik sprang from Graphkit to experiment with Python 3.6+ features.

Quick start

Here’s how to install:

pip install graphtik

OR with dependencies for plotting support (and you need to install Graphviz suite separately, with your OS tools):

pip install graphtik[plot]

Here’s a Python script with an example Graphtik computation graph that produces multiple outputs (a * b, a - a * b, and abs(a - a * b) ** 3):

>>> from operator import mul, sub
>>> from functools import partial
>>> from graphtik import compose, operation

>>> # Computes |a|^p.
>>> def abspow(a, p):
...     c = abs(a) ** p
...     return c

Compose the mul, sub, and abspow functions into a computation graph:

>>> graphop = compose(
...     "graphop",
...     operation(name="mul1", needs=["a", "b"], provides=["ab"])(mul),
...     operation(name="sub1", needs=["a", "ab"], provides=["a_minus_ab"])(sub),
...     operation(name="abspow1", needs=["a_minus_ab"], provides=["abs_a_minus_ab_cubed"])
...     (partial(abspow, p=3))
... )

Run the graph and request all of the outputs:

>>> graphop(a=2, b=5)
{'a': 2, 'b': 5, 'ab': 10, 'a_minus_ab': -8, 'abs_a_minus_ab_cubed': 512}

… or request a subset of outputs:

>>> solution = graphop.compute({'a': 2, 'b': 5}, outputs=["a_minus_ab"])
>>> solution
{'a_minus_ab': -8}

… and plot the results (if in jupyter, no need to create the file):

>>> solution.plot('graphop.svg')    # doctest: +SKIP

sample graphtik plot graphtik legend

Project details

Download files

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

Files for graphtik, version 5.1.0
Filename, size File type Python version Upload date Hashes
Filename, size graphtik-5.1.0-py2.py3-none-any.whl (45.7 kB) File type Wheel Python version py2.py3 Upload date Hashes View hashes
Filename, size graphtik-5.1.0.tar.gz (69.1 kB) File type Source Python version None Upload date Hashes View hashes

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page