About Singularity matrix #12
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Could you describe your data?
…On Wed, Oct 25, 2023 at 3:53 AM zhangziyuan ***@***.***> wrote:
When I use FGES algorithm, the output has lots of *"Singularity
encountered when scoring X|X"*.
I wonder if this is normal? If it is abnormal, how can I solve this
problems?
Thank you very much.
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The data is an open source multivariate time series dataset called SMAP. Each time serie contains 55 features. More details about this dataset can be found https://www.kaggle.com/datasets/patrickfleith/nasa-anomaly-detection-dataset-smap-msl. |
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Oh, cool, a rover dataset! A long time ago, I worked on some data for an
earlier rover. This is going to require some thought, though, so give me a
few days. I'll get to it.
The basic problem with singularities is just that--some variables are
linear combinations (exactly) of other variables. If you want to use a
score that inverts correlation matrices for submatrices, you need to remove
some variables from the data since that will throw singularity exceptions.
But until I look at the data, I can't give you much useful advice.
You could treat the data all as discrete, which should, in principle, work
if the sample size is big enough. That is, add a discretization step.
I've thought of doing a Linear Gaussian BIC score that uses generalized
matrix inversion but don't think it will be available in the near future.
On Wed, Oct 25, 2023 at 9:29 AM zhangziyuan ***@***.***>
wrote:
… Could you describe your data?
… <#m_3357814697248003521_>
On Wed, Oct 25, 2023 at 3:53 AM zhangziyuan *@*.*> wrote: When I use FGES
algorithm, the output has lots of "Singularity encountered when scoring
X|X". I wonder if this is normal? If it is abnormal, how can I solve this
problems? Thank you very much. — Reply to this email directly, view it on
GitHub <#12 <#12>>, or
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The data is an open source multivariate time series dataset called SMAP.
Each time serie contains 55 features. More details about this dataset can
be found
https://www.kaggle.com/datasets/patrickfleith/nasa-anomaly-detection-dataset-smap-msl
.
I use all the train data to learn the causal graph, using FGES.
Thank you very much.
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Thank you very much for your helping. I will also try some methods based on your ideas. |
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Great! :-)
On Wed, Oct 25, 2023 at 10:13 AM zhangziyuan ***@***.***>
wrote:
… Oh, cool, a rover dataset! A long time ago, I worked on some data for an
earlier rover. This is going to require some thought, though, so give me a
few days. I'll get to it. The basic problem with singularities is just
that--some variables are linear combinations (exactly) of other variables.
If you want to use a score that inverts correlation matrices for
submatrices, you need to remove some variables from the data since that
will throw singularity exceptions. But until I look at the data, I can't
give you much useful advice. You could treat the data all as discrete,
which should, in principle, work if the sample size is big enough. That is,
add a discretization step. I've thought of doing a Linear Gaussian BIC
score that uses generalized matrix inversion but don't think it will be
available in the near future. On Wed, Oct 25, 2023 at 9:29 AM zhangziyuan
*@*.
*> wrote: … <#m_1597049013796612852_> Could you describe your data? …
<#m_3357814697248003521_> On Wed, Oct 25, 2023 at 3:53 AM zhangziyuan @.>
wrote: When I use FGES algorithm, the output has lots of "Singularity
encountered when scoring X|X". I wonder if this is normal? If it is
abnormal, how can I solve this problems? Thank you very much. — Reply to
this email directly, view it on GitHub <#12
<#12> <#12
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. You are receiving this because you are subscribed to this thread.Message
ID: @.> The data is an open source multivariate time series dataset called
SMAP. Each time serie contains 55 features. More details about this dataset
can be found
https://www.kaggle.com/datasets/patrickfleith/nasa-anomaly-detection-dataset-smap-msl
<https://www.kaggle.com/datasets/patrickfleith/nasa-anomaly-detection-dataset-smap-msl>
. I use all the train data to learn the causal graph, using FGES. Thank you
very much. — Reply to this email directly, view it on GitHub <#12 (comment)
<#12 (comment)>>,
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Thank you very much for your helping. I will also try some methods based
on your ideas.
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Hi, is this still a live issue? Can I close it? |
yes! I don't have problems, thank you very much. |
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Great, thanks! :-) |
When I use FGES algorithm, the output has lots of "Singularity encountered when scoring X|X".
I wonder if this is normal? If it is abnormal, how can I solve this problems?
Thank you very much.
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