Comments (5)
Hard to say. If you put future::plan(sequential), parallelization is disabled so at least the "Error in unserialize(node$con)" should not occur. The chol() warning suggests that some of your columns are (close to) linearly dependent on other columns (you may check this by checking whether some of the eigenvalues of your training data matrix are negative or close to zero.
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Hi Martin,
1- I remove this library "future" and try to run it on HPC with 12 cores and 200Gb memory. It cannot give me an output. Do you suggest we should have HPCs with more than 200GB memory?
I think n_combinations
has more effect on my code. I consider it 10000 according to your suggestion.
2- Furthermore, I try to solve problem according issue #226 . Please see my comments in #226 .
3- Also, I have another problem, I don't have NA data, but I see this note when I running code. If it is possible I send my data as private?
Note: Feature classes extracted from the model contains NA.
Assuming feature classes from the data are correct.
Thanks in advance
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200gb RAM is more than enough. There is probably some other issue with your data. You can send them to me on [email protected] and might be able to take a look at it some time next week. I promises, though.
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Dear @martinju,
Hope you are doing well;
Please check your email. I have sent you my email with the attached dataset.
Kind regards,
Az
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I can't reproduce this with the latest version of shapr from github. Everything works as intended. Results and script sent by personal email.
The note is nothing to worry about, it is not an error.
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Related Issues (20)
- Adding support for TensorFlow/Keras - anticipated challenges? HOT 1
- TypeError when running examples HOT 3
- Shap loss values HOT 2
- GroupShapley in python HOT 5
- Build Fail with Mac M1 / Ventura 13.3 HOT 2
- Error during "explain" HOT 5
- explain_forecast runs unnecessarily many predictions when multiple xreg variables are present HOT 2
- `explain()` crashes when using combined approaches on categorical and mixed data. HOT 3
- Possible bug in explain_forecast without lag explanation
- Run shapr on HPC with a large size for x_explain HOT 8
- shapr on xgboost classifier: `group` must be NULL or a list HOT 1
- Feature classes extracted from the model contains NA HOT 2
- Multiclass Dataset by Applying SHAPR Package HOT 1
- VAEAC for Abalone dataset HOT 3
- Machine Learning Model for Mixed Data using VAEAC Approach HOT 2
- Comparing different dependency-aware approaches when the size of x_explain is large HOT 2
- Dependency-aware approaches with one-hot encoded data HOT 3
- VAEAC with old Cuda
- Documentation claims shaprpy can explain Keras models, but code claims it cannot
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