graphtik 10.3.0

A Python lib for solving & executing graphs of functions, with `pandas` in mind

(build-version: v10.3.0, build-date: 2020-10-02T08:29:56.317584)

It’s a DAG all the way down!

Lightweight computation graphs for Python

Graphtik is a library to compose, plot & execute graphs of python functions (a.k.a pipelines) that consume and populate (possibly nested) named data (a.k.a dependencies), based on whether values for those dependencies exist in the inputs or have been calculated earlier, with pandas in mind.

• Its primary use case is building flexible algorithms for data science/machine learning projects.

• It should be extendable to implement the following:

  • an IoC dependency resolver (e.g. Java Spring);

  • an executor of interdependent tasks based on files (e.g. GNU Make);

  • a custom ETL engine;

  • a spreadsheet calculation engine.

Graphtik sprang from Graphkit (summer 2019, v1.2.2) to experiment with Python 3.6+ features, but has diverged significantly with enhancements ever since.

Features

• Deterministic pre-decided execution plan (excepting partial-outputs or endured operations, see below).

• Can assemble existing functions without modifications into pipelines.

• dependency resolution can bypass calculation cycles based on data given and asked.

• Support functions with optional <optionals> input args and/or varargs <varargish>.

• Support functions with partial outputs; keep working even if certain endured operations fail.

• Facilitate trivial conveyor operations and alias on provides.

• Support cycles, by annotating repeated updates of dependency values as sideffects, (e.g. to add columns into pandas.DataFrames).

• Hierarchical dependencies <subdoc> may access data values deep in solution with json pointer path expressions.

• Hierarchical dependencies annotated as implicit imply which subdoc dependency the function reads or writes in the parent-doc.

• Merge <operation merging> or nest <operation nesting> sub-pipelines.

• Early eviction of intermediate results from solution, to optimize memory footprint.

• Solution tracks all intermediate overwritten <overwrite> values for the same dependency.

• Parallel execution (but underdeveloped).

• Elaborate Graphviz plotting with configurable plot themes.

• Integration with Sphinx sites with the new graphtik directive.

• Authored with debugging in mind.

• Parallel execution (but underdeveloped & deprecated).

Anti-features

• It’s not an orchestrator for long-running tasks, nor a calendar scheduler - Apache Airflow, Dagster or Luigi may help for that.

• It’s not really a parallelizing optimizer, neither a map-reduce framework - look additionally at Dask, IpyParallel, Celery, Hive, Pig, Spark, Hadoop, etc.

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 α and β:

• α x β

• α - αxβ

• |α - αxβ| ^ 3

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

>>> @operation(name="abs qubed",
...            needs=["α-α×β"],
...            provides=["|α-α×β|³"])
... 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=["α", "β"], provides=["α×β"])(mul),
...     operation(needs=["α", "α×β"], provides=["α-α×β"])(sub),
...     abs_qubed,
... )
>>> graphop
Pipeline('graphop', needs=['α', 'β', 'α×β', 'α-α×β'],
                    provides=['α×β', 'α-α×β', '|α-α×β|³'],
                    x3 ops: mul, sub, abs qubed)

Run the graph and request all of the outputs (notice that unicode characters work also as Python identifiers):

>>> graphop(α=2, β=5)
{'α': 2, 'β': 5, 'α×β': 10, 'α-α×β': -8, '|α-α×β|³': 512}

… or request a subset of outputs:

>>> solution = graphop.compute({'α': 2, 'β': 5}, outputs=["α-α×β"])
>>> solution
{'α-α×β': -8}

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

>>> solution.plot('executed_3ops.svg')  # doctest: +SKIP