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Number of starting networks in NetRAX #7
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How about these two experimental setups: A: Start NetRAX from 10 random + 10 parsimony trees |
I implemented the two setups I suggested above. Let's see how it behaves in our experiments now! :) |
yes that is what is was about to suggest, we need to explore broadly
first to obtain a feeling for who this behaves
…On 25.11.20 18:35, Sarah Lutteropp wrote:
I implemented the two setups I suggested above. Let's see how it behaves
in our experiments now! :)
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Turns out 10 random + 10 parsimony trees are a bit much... especially since the current NetRAX version is only single-threaded and not optimized for runtime performance yet (it wastes much time in branch-length optimization and in trying and rejecting arc insertion moves). Trying out what happens if I reduce them by a lot. |
just use brute force and the cluster for your experiments
…On 30.11.20 02:02, Sarah Lutteropp wrote:
Turns out 10 random + 10 parsimony trees are a bit much... especially
since the current NetRAX version is only single-threaded and not
optimized for runtime performance yet (it wastes much time in
branch-length optimization and in trying and rejecting arc insertion
moves). Trying out what happens if I reduce them by a lot.
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Alexandros (Alexis) Stamatakis
Research Group Leader, Heidelberg Institute for Theoretical Studies
Full Professor, Dept. of Informatics, Karlsruhe Institute of Technology
www.exelixis-lab.org
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If we don't care aabout runtime at all: Should I also switch to the slower network search algorithm? So far, I accepted the first move that improved the BIC score. But I already observed that I get a bit better results if I evaluate the entire 1-move-neighborhood and then accept the move that lead to the largest improvement in BIC score... |
we don't care about run time for the time being, however applying
several moves earlier and quicker may also lead to the desired result,
... to be explored
…On 30.11.20 11:13, Sarah Lutteropp wrote:
If we don't care aabout runtime at all: Should I also switch to the
slower network search algorithm? So far, I accepted the first moce that
improved the BIC score. But I already observed that I get a bit better
results if I evaluate the entire 1-move-neighborhood and then accept the
move that lead to the largest improvement in BIC score...
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Alexandros (Alexis) Stamatakis
Research Group Leader, Heidelberg Institute for Theoretical Studies
Full Professor, Dept. of Informatics, Karlsruhe Institute of Technology
www.exelixis-lab.org
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With the new wavesearch algorithm, I do not see any advantage in starting from multiple starting trees vs. starting from best raxml-ng tree. |
see discussion on slack, starting from several RAxML-NG optimized trees
is something we should test
…On 17.01.21 02:58, Sarah Lutteropp wrote:
With the new wavesearch algorithm, I do not see any advantage in
starting from multiple starting trees vs. starting from best raxml-ng tree.
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Alexandros (Alexis) Stamatakis
Research Group Leader, Heidelberg Institute for Theoretical Studies
Full Professor, Dept. of Informatics, Karlsruhe Institute of Technology
www.exelixis-lab.org
|
We agreed earlier that all random starting networks should be random trees.
5 random starting networks are not enough, even if the "true" network is a tree. RAxML-NG uses by default 10 parsimony + 10 random starting trees. I tried NetRAX (with only 5 random start networks) on a small (10 taxa) tree dataset and it got stuck in a local optimum.
I suggest copying the default from RAxML-NG and also using 10 random starting trees + 10 random parsimony trees in NetRAX.
For later, we could think about replacing the parsimony trees by parsimony networks.
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