I'm trying to visualize the result of by CatBoostClassifier in Databricks.
I have graphviz ==0.18.2 installed on my cluster.
When I use the following code (NB. model is my trained CatBoostClassifier):
model.plot_tree(tree_idx=0)
I don't get a tree, but I get the following output: <graphviz.graphs.Digraph at 0x7f86330d9640>.
This doesn't seem to happen in this post.
Any suggestions on how I can visualize my tree?
Related
On the MAGE algorithm page I've found the Graph Neural Network algorithm, but it says that it has not been implemented yet.
Is there a way to get link prediction in Memgraph?
Link prediction can be achieved using node2vec from MAGE.
For this to work, you will need:
The MAGE graph library
Memgraph Lab - the graph explorer for querying Memgraph and visualizing graphs
gqlalchemy - a Python driver and object graph mapper (OGM)
The complete procedure is described in the article Link prediction with Node2Vec in Physics Collaboration Network.
I have two graphviz graphs. Let's call them before.dot and after.dot.
I want to know the differences between them. I've opened them with regular old text/source code diff and there is a difference, it's not a subset and superset situation, there are nodes and edges only in before.dot, nodes and edges only in after.dot and nodes and edges that are in both. How can I process these two and produce before-only.dot and after-only.dot (even if this is two separate commands)
Reading the graphviz docs pointed me to the gvpr scripting/processing tool, which appears to be the ideal mechanism for solving this given that I would like to avoid installing additional tools.
How can I get this task done using gvpr?
Came across this because I am just starting learning about gvpr.
Years ago I did make a specific dot file differ in 'awk'
for just a particular flavor of dot files.
https://github.com/TomConlin/dipper/blob/master/scripts/deltadot.awk
I would not expect it to work in the general case but
if you are comparing a distilled semantic graph with
a previous version of itself you might get some ideas.
Hi I want to visualize infection algorithm on graph, graph code and changes has wrote with Python, but there is no need that Library or visualization tool (VT) contract with my code. it could be possible and more sensible that first code run and write result in file, then VT read the structure of graph and changes in time steps, so end user just can forward and backward time.
Abstract example for interface file:
a-b
a-c
b-c
all blue
----
1:a=red
2:b=red,c=red
Thanks
EDIT: graph could be visualized on web, windows panel, java applet or something else, it is not important
EDIT 2: i found igraph that seem to work with R and Python,it just results in image format so it is not possible to show over time changes.
I found dynnetwork that is Cytoscape 3.0 app, it support importing graph from CSV that is great and visualizing graph changes over time in term of variety of node and edge attribute size, label, color, ...
that is what i want
EDIT: it has problem with loading graph with about 10000 node! so i must found another solution
I have my data as a RDF graph in DB and using SPARQL i am retriving the data. Now the nodes (objects) in the graphs gets huge and the traversal/search gets much slower now.
a. Can anyone suggest the efficient traversal/search algorithm to fetch the data?
As a next step, i have federated data i.e the data from external applications like SAP. In this case, the search becomes even much slower.
b. What efficient search algorithm do i use in this case?
This seems like a common issue in an large enterprise systems, and any inputs on how these problems have been solved in such systems will also be helpful.
I had a similiar problem. I was doing a lot of graph traversal using SPARQL property paths and it was too slow using an RDF based repository. I was using Jena TDB which is supposed to be fast but still it was too slow !
Like #Mikos suggested, I tried Neo4J. It then got much faster. Like Mark Watson says on this blog entry,
RDF data stores support SPARQL queries: good for matching patterns in data.
Neo4j supports arbitrary graph structures and seems best for exploring
a neighborhood of a graph: start at a node and explore the connected
nodes. (graph traversal)
I used Neo4j but you can try any tool that is built for graph traversal. I read that Allegrograph 4 is RDF based and has good graph traversal speed.
Now Im using Neo4j but I didnt give up on RDF. I still use URIs as identifiers and try to reuse the popular rdf vocabularies and relations. Later I'll add a feature to render my gaphs as RDF. I know that with Neo4j you can also use Tinkerpop to render RDF but I havent tried it myself.
Graph traversal and efficient querying is a wide-ranging problem and the approach to use is dependent on your situation. I would suggest looking at a data-store like Neo4j and complementing it with a tool like Lucene.
Sorry if this question seems a bit complex but I think its all related so I wanted try to get the answer in one shot. Basically I have a layered graph*, that has various sets of data that are connected to only the next set of data(so set1 has vertexes that have edges to set2, and so on but set1 has nothing connecting to set3 or anything other than set2. It might be relevant not sure). Generally, you can think of my data as one massive family tree(every set I add about a billion nodes) that I keep loading new generations with every new set(families create new families and no edges go backwards).
I have an Hbase/hadoop system running and I know how to use java to add columns and values, but what I don't know how to do is:
add data to hbase in a graph type format(since its hbase, I want to load it in a way that I can add a ton of data and it'll scale..unlike other databases that limit graphs to the size of the system). I know how to add data but don't understand how to do it in a scalable graph way.
Once the graph is loaded I want to know how to apply some kind of analytics to it. Pagerank is popular so I thought I would say it, but pretty much anything that is based on processing a graph.
I guess the simplified way of asking the question is how to do I specifically get a graph into hbase and once its there how do I analyze it? Is there a tutorial? There's a lot of hbase information on the internet(I read the hbase book) but I could not find anything specific to graphs. I found, giraph, but I don't think it can connect to hbase(yet). Seeing how hadoop/hbase are versions of mapreduce/bigtables I suspect there is a way to process graphs I'm just not having luck finding anything.
*A layered graph is a directed graph with a level for different set of vertex's, like so: http://en.wikipedia.org/wiki/Layered_graph_drawing
I think this question on SO could help:
https://stackoverflow.com/questions/9865738/is-it-possible-to-store-graphs-hbase-if-so-how-do-you-model-the-database-to-sup/9867563#9867563
This part of my answer to this question might be of use.
Using HBase/Accumulo as input to giraph has been submitted recently (7
Mar 2012) as a new feature request to Giraph: HBase/Accumulo Input
and Output formats (GIRAPH-153)
We use giraph in this way, it only store minimum data in each vertex, and then run the graph algorithm with giraph, then we assemble the result with rich data using pig, for page rank algo, each vertex only needs to store vertex id, rank, thus it could scale to almost billion level.