Lightweight computation graphs for Python
Project description
# Graphtik
      
   
> It’s a DAG all the way down
<img src=”docs/source/images/barebone_2ops.svg” width=100 alt=”simple graphtik computation”>
## 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 might be of use in computer vision, machine learning and other data science domains, or become the core of a custom ETL pipelne.
Graphtik is a temporary fork of [Graphkit](https://github.com/yahoo/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](https://graphviz.org) program 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 graphtik import compose, operation>>> # Computes |a|^p. >>> def abspow(a, p): ... c = abs(a) ** p ... return c>>> # Compose the mul, sub, and abspow operations into a computation graph. >>> graphop = compose(name="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"], params={"p": 3})(abspow) ... )
<img src=”docs/source/images/barebone_3ops.svg” width=100 alt=”simple graphtik computation”>
>>> # Run the graph and request all of the outputs. >>> out = graphop({'a': 2, 'b': 5}) >>> print(out) {'a': 2, 'b': 5, 'ab': 10, 'a_minus_ab': -8, 'abs_a_minus_ab_cubed': 512}>>> # Run the graph and request a subset of the outputs. >>> out = graphop({'a': 2, 'b': 5}, outputs=["a_minus_ab"]) >>> print(out) {'a_minus_ab': -8}
- <img src=”docs/source/images/executed_3ops.svg” width=120
alt=”simple graphtik computation”>
As you can see, any function can be used as an operation in Graphtik, even ones imported from system modules!
## Plotting
For debugging the above graph-operation you may plot either the newly omposed graph or the execution plan of the last computation executed, using these methods:
`python graphop.plot(show=True) # open a matplotlib window graphop.plot("graphop.svg") # other supported formats: png, jpg, pdf, ... graphop.plot() # without arguments return a pydot.DOT object graphop.plot(solution=out) # annotate graph with solution values `
> TIP: The pydot.Dot instances returned by plot() are rendered as SVG in Jupyter/IPython.
# License
Code licensed under the Apache License, Version 2.0 license. See LICENSE file for terms.
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