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GS/MMPC algorithm removed all non-related variable #60
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Hello @Cby19961020 and thanks you for noticing this point. This behavior is not what we want: even if a variable is uncorrelated, we still want it to appear in the adjacency matrix ! |
After checking, it doesn't seem to be the |
Hi Diviyan @Diviyan-Kalainathan , Thank you very much for your promot respond! I think you are right, maybe it is the algorithms that I am using. Essentially the data I am working with is very similar to the "sachs" data used in the tutorial. Here is a screenshot. I have 54 input variables. When I implement algorithms like GS or MMPC to my dataset I will only end up with 32 variables. These variables all have some sort of connection with other variables, as indiciated in the graph. The onces that are completely independent are ignored. The nx.adjacency_matrix(output).todense() function will generate a matrix with no label, in this case a 32 by 32 matrix is generated. However based on just this I cannot figure out which one is variable Task_0 and which one is Task_1 etc. Sorry for not stating the problem clearly and thank you for your hard work! Best Regards, |
Hi, |
Hi there,
This is my very first post on Github so parden me if this is a simple fix to my problem.
I am currently exploring the different graph inference algorithms on my own dataset. One thing I realized is that when implementing algorithm like GS/MMPC the uncorrelated variable is not shown(automatically removed) in the nx.adjacency_matrix(output).todense() command.
For example I fed 50 variables into the MMPC algorithm and out of which only 35 variables are correlated and 15 are not. The nx.adjacency_matrix(output).todense() will only spit out the matrix for 35 variables and I do not know the variables that are uncorrelated and removed.
While CDT does provide plotting option I do perfer to use package like Graphviz. Thus it will be helpful to acquire the matrix for all 50 input variables instead.
Is there a way for me to obtain such matrix?
Thank you in advance!
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