A lightweight Python-3.6+ lib for solving & executing graphs of functions
Project description
It’s a DAG all the way down!
Lightweight computation graphs for Python
Graphtik is an an understandable and lightweight Python module for building, running and plotting graphs of functions (a.k.a pipelines).
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, a workflow-processor for interdependent files like GNU Make, or an Excel-like spreadsheet.
Quick start
Here’s how to install:
pip install graphtik
OR with various “extras” dependencies, such as, for plotting:
pip install graphtik[plot]
- . Tip::
Supported extras:
- plot
for plotting with Graphviz,
- matplot
for plotting in maplotlib windows
- sphinx
for embedding plots in sphinx-generated sites,
- test
for running pytests,
- dill
may help for pickling parallel tasks - see marshalling term and set_marshal_tasks() configuration.
- all
all of the above, plus development libraries, eg black formatter.
- dev
like all
Let’s build a graphtik computation graph that produces x3 outputs out of 2 inputs a and b:
a x b
a - a x b
|a - a x b| ^ 3
>>> from graphtik import compose, operation >>> from operator import mul, sub
>>> @operation(name="abs qubed", ... needs=["a_minus_ab"], ... provides=["abs_a_minus_ab_cubed"]) ... def abs_qubed(a): ... return abs(a) ** 3
Compose the abspow function along the mul & sub built-ins into a computation graph:
>>> graphop = compose("graphop",
... operation(needs=["a", "b"], provides=["ab"])(mul),
... operation(needs=["a", "ab"], provides=["a_minus_ab"])(sub),
... abs_qubed,
... )
>>> graphop
NetworkOperation('graphop', needs=['a', 'b', 'ab', 'a_minus_ab'],
provides=['ab', 'a_minus_ab', 'abs_a_minus_ab_cubed'],
x3 ops: mul, sub, abs qubed)
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
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file graphtik-7.1.1.tar.gz.
File metadata
- Download URL: graphtik-7.1.1.tar.gz
- Upload date:
- Size: 122.7 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.23.0 setuptools/46.1.3 requests-toolbelt/0.9.1 tqdm/4.46.0 CPython/3.8.0
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | d304e129577a1cad97ad6f361a627c8c84ff13414832f597f0452b59b3993868 |
|
| MD5 | d9e566c260d035988b106df4b075ffba |
|
| BLAKE2b-256 | 0e736279f34ce42c12e1aa34b4e9263216c6582c46c52870d4e4a819d7bdd7f6 |
File details
Details for the file graphtik-7.1.1-py2.py3-none-any.whl.
File metadata
- Download URL: graphtik-7.1.1-py2.py3-none-any.whl
- Upload date:
- Size: 86.3 kB
- Tags: Python 2, Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.23.0 setuptools/46.1.3 requests-toolbelt/0.9.1 tqdm/4.46.0 CPython/3.8.0
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | ecf59c7e5ee2031f49c2f210817500089e5eb4db09199b2eebf54359c29e23bb |
|
| MD5 | cc14dff91be69103981465073041fad3 |
|
| BLAKE2b-256 | df9924a6dd8199693095b7dda7ec9027cd4d2a9b8995b37c83d377802668202a |
