OPENAI_API_KEYenvironment variable before using it.
import lynx.kite # Start LynxKite in this SparkSession. lk = lynx.kite.LynxKite(spark=spark) # Turn a LynxKite table into a Spark DataFrame. df = lk.createExampleGraph().sql('select name, age from vertices').spark() df = df.filter('age < 30') # Turn a Spark DataFrame into a LynxKite table. g = lk.from_spark(df).useTableAsVertices()
lk = lynx.kite.LynxKite(spark=spark)), LynxKite will run in that SparkSession. #294 Useful if you want to run LynxKite as part of a pipeline, rather than as permanent fixture.
LynxKite()constructor in the Python API now defaults to connecting to
spark-submit. #269 This makes deployment much simpler in Hadoop environments.
.kiterc. The list of algorithms includes PageRank, connected components, betweenness and Katz centrality, the Louvain method, k-core decomposition, and ForceAtlas2, a new option in Place vertices with edge lengths.
$KITE_DATA/partitioned. Everything will be recomputed when accessed, and will be stored in the new format.
workspaceName) that can be used to force recomputation in wizards. (#131)
We’ve open-sourced LynxKite!
We took this opportunity to make many changes that break compatibility with the LynxKite 3.x series. We can help migrate existing workspaces to LynxKite 4.0 if necessary.
Doubleattribute types with
(Double, Double)attribute type, 2D positions are now represented as
Vector[number]. This type is widely supported and more flexible. Use “Bundle vertex attributes into a Vector” instead of “Convert vertex attributes to position”, which is now gone.
kitercconfiguration options to allow public access LynxKite instances.
edgescan be accessed as