I am currently testing various community detection algorithms in the igraph package to compare against my implementation.
I am able to run the algorithms on different graphs but I was wondering if there was a way for me to write the clustering to a file, where all nodes in one community are written to one line and so on. I am able to obtain the membership of each node using membership(communities_object) and write that to a file using dput() but I don't know how to write it the way I want.
This is the first time I am working with R as well. I apologize if this has been asked before.
This does not have to do much with igraph, the clustering is given by a simple numeric vector. See ?write.
write(membership(communities_object), file="myfile", ncolumns=1)
write(communities_object$membership, file="myfile", ncolumns=1) also work
Related
For a production planning software I need a specificly structured Excel input. Therefore, I was looking for a way to make it easy for non-tech people to generate such an input. To achieve that, I thought it might be easiest for users to draw their production network as graph and I could use the resulting graphml output to derive the necessary information and transform them to the format needed.
When looking for an appropriate and easy software I found yEd. To start with I generated a very simple network with 2 sites and 1 part being transfered and exported the graphml.
When I try to read the graphml in R using igraph or casualeffect package the names are lost and I get a lot of warnings.
For igraph the plot also looks incomplete.
What am I doing wrong? Should I use a different software or package? Any recommendations?
I hope my description is sufficient (that is my first post on stackoverflow)
Thank you a lot.
### 1st try
library(igraph)
net1<-read.graph("Graph test.graphml", format = "graphml")
plot(net1)
### 2nd try
library(causaleffect)
net2 <- parse.graphml("Graph test.graphml", use.names = TRUE)
plot(net2)
Link for Graph Test on yEd
Link for graphml file
In GBM model, following parameters are used -
col_sample_rate
col_sample_rate_per_tree
col_sample_rate_change_per_level
I understand how the sampling works and how many variables get considered for splitting at each level for every tree. I am trying to understand how many times each feature gets considered for making a decision. Is there a way to easily extract all sample of features used for making a splitting decision from the model object?
Referring to the explanation provided by H2O, http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/algo-params/col_sample_rate.html, is there a way to know 60 randomly chosen features for each split?
Thank you for your help!
If you want to see which features were used at a given split in a give tree you can navigate the H2OTree object.
For R see documentation here and here
For Python see documentation here
You can also take a look at this Blog (if this link ever dies just do a google search for H2OTree class)
I don’t know if I would call this easy, but the MOJO tree visualizer spits out a graphviz dot data file which is turned into a visualization. This has the information you are interested in.
http://docs.h2o.ai/h2o/latest-stable/h2o-genmodel/javadoc/overview-summary.html#viewing-a-mojo
I've been searching paper about method review in graph clustering but not satisfied me,
please tell me what is best method (according to you) in graph clustering, so sorry if my question very general
Thanks
With such an open question, I guess I can recommend you to try WEKA.
It has a nice set of user interfaces to let you import your dataset and then try and compare various classification and clustering algorithms on your data, without writing even one line of code.
After you identified an algorithm that works for your problem, you can then search for a nice and fast implementation in the programming language of your choice.
EDIT: since you mentioned the graph tag, maybe you should have a look at Markov Cluster Algorithm, or else, you will have a hard time trying to represent your graph data in a format suitable for the distance based clustering algorithms in WEKA.
It is my first time using R for phylogenetics work and I was wondering if I could do that. It seems a rather trivial job and I think there must be a very small code for this, but I am unable to get it done. Any help appreciated!
I frequently find myself doing some analysis in R and then wanting to make a quick map. The standard plot() function does a reasonable job of quick, but I quickly find that I need to go to ggplot2 when I want to make something that looks nice or has more complex symbology requirements. Ggplot2 is great, but is sometimes cumbersome to convert a SpatialPolygonsDataFrame into the format required by Ggplot2. Ggplot2 can also be a tad slow when dealing with large maps that require specific projections.
It seems like I should be able to use Mapnik to plot spatial objects directly from R, but after exhausting my Google-fu, I cannot find any evidence of bindings. Rather than assume that such a thing doesn't exist, I thought I'd check here to see if anyone knows of an R - Mapnik binding.
The Mapnik FAQ explicitly mentions Python bindings -- as does the wiki -- with no mention of R, so I think you are correct that no (Mapnik-sponsored, at least) R bindings currently exist for Mapnik.
You might get a more satisfying (or at least more detailed) answer by asking on the Mapnik users list. They will know for certain if any projects exist to make R bindings for Mapnik, and if not, your interest may incite someone to investigate the possibility of generating bindings for R.
I would write the SpatialWotsitDataFrames to Shapefiles and then launch a Python Mapnik script. You could even use R to generate the Python script (package 'brew' is handy for making files from templates and inserting values form R).