Python bindings for the Neo4j Graph Data Science library
Project description
gdsclient
gdsclient
is a Python wrapper API for operating and working with the Neo4j Graph Data Science (GDS) library.
It enables users to write pure Python code to project graphs, run algorithms, and define and use machine learning pipelines in GDS.
The API is designed to mimic the GDS Cypher procedure API in Python code. It abstracts the necessary operations of the Neo4j Python driver to offer a simpler surface.
Please leave any feedback as issues on the source repository. Happy coding!
NOTE
This is a work in progress and several GDS features are known to be missing or not working properly (see Known limitations below). Further, this library targets GDS versions 2.0+ (not yet released) and as such may not work with older versions.
Installation
To install the latest deployed version of gdsclient
, simply run:
pip install gdsclient
Usage
What follows is a high level description of some of the operations supported by gdsclient
.
For extensive documentation of all operations supported by GDS, please refer to the GDS Manual.
Extensive end-to-end examples in Jupyter ready-to-run notebooks can be found in the examples
source directory:
Imports and setup
The library wraps the Neo4j Python driver with a GraphDataScience
object through which most calls to GDS will be made.
from neo4j import GraphDatabase
from gdsclient import Neo4jQueryRunner, GraphDataScience
# Replace Neo4j Python driver settings according to your setup
URI = "bolt://localhost:7687"
driver = GraphDatabase.driver(URI)
gds = GraphDataScience(Neo4jQueryRunner(driver))
gds.set_database("my-db") # (Optional) Use a specific Neo4j database
Projecting a graph
Supposing that we have some graph data in our Neo4j database, we can project the graph into memory.
# Optionally we can estimate memory of the operation first
res = gds.graph.project.estimate("*", "*")
assert res[0]["requiredMemory"] < 1e12
G = gds.graph.project("graph", "*", "*")
The G
that is returned here is a Graph
which on the client side represents the projection on the server side.
The analogous calls gds.graph.project.cypher{,.estimate}
for Cypher based projection are also supported.
Running algorithms
We can take a projected graph, represented to us by a Graph
object named G
, and run algorithms on it.
# Optionally we can estimate memory of the operation first (if the algo supports it)
res = gds.pageRank.write.estimate (G, tolerance=0.5, writeProperty="pagerank")
assert res[0]["requiredMemory"] < 1e12
res = gds.pageRank.write(G, tolerance=0.5, writeProperty="pagerank")
assert res[0]["nodePropertiesWritten"] == G.node_count()
These calls take one positional argument and a number of keyword arguments depending on the algorithm.
The first (positional) argument is a Graph
, and the keyword arguments map directly to the algorithm's configuration map.
The other algorithm execution modes - mutate, stats and stream - are also supported via analogous calls.
Though most algorithms are supported this way, not all are yet. Please see Known limitations below for more on this.
The Graph object
In this library, graphs projected onto server-side memory are represented by Graph
objects.
There are convenience methods on the Graph
object that let us extract information about our projected graph.
Some examples are (where G
is a Graph
):
# Get the graph's node count
G.node_count()
# Get a list of all relationship properties present on
# relationships of the type "myRelType"
G.relationship_properties("myRelType")
# Drop the projection represented by G
G.drop()
Graph catalog utils
Apart from the project calls, some additional GDS Graph catalog operations are supported. Some notable examples are:
gds.beta.graph.subgraph
gds.graph.list
gds.graph.exists
gds.graph.drop
gds.graph.export
Further, there's a new call named gds.graph.get
which takes a name as input and returns a Graph
object if a graph projection of that name exists in the user's graph catalog.
The idea is to have a way of creating Graph
s for already projected graphs, without having to do a new projection.
Known limitations
Several operations are known to not yet work with gdsclient
:
- Path finding algorithms
- Topological link prediction
- Supervised machine learning (GraphSAGE, Link prediction, Node classification)
- Progress logging and system monitoring
- Some Graph catalog operations
License
gdsclient
is licensed under the Apache Software License version 2.0.
All content is copyright © Neo4j Sweden AB.
Acknowledgements
This work has been inspired by the great work done in the following libraries:
- pygds by stellasia
- gds-python by moxious
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