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 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.
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: <built-in function mul>, <built-in function 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
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