Segmentation fault while from castle.common.independence_tests import CI_Test

[Ethan-Chen-plus]

using by pip install gcastle==1.0.3

[shaido987]

It could be related to importing torch (e.g., pytorch/pytorch#66782). Does it work if you import other packages? What about import torch?

[nolanrbrady]

Has there been any update on this issue? I am facing a similar error when I run the DirectLiNGAM example code. I've posted the error message for that below.

from sklearn.pipeline import make_pipeline

model = make_pipeline(StandardScaler(with_mean=False), LassoLarsIC())

If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:

kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}
model.fit(X, y, **kwargs)

Set parameter alpha to: original_alpha * np.sqrt(n_samples).
  warnings.warn(
/Users/nolanbrady/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_base.py:133: FutureWarning: The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.
If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:

from sklearn.pipeline import make_pipeline

model = make_pipeline(StandardScaler(with_mean=False), LassoLarsIC())

If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:

kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}
model.fit(X, y, **kwargs)

Set parameter alpha to: original_alpha * np.sqrt(n_samples).
  warnings.warn(
/Users/nolanbrady/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_base.py:133: FutureWarning: The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.
If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:

from sklearn.pipeline import make_pipeline

model = make_pipeline(StandardScaler(with_mean=False), LassoLarsIC())

If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:

kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}
model.fit(X, y, **kwargs)

Set parameter alpha to: original_alpha * np.sqrt(n_samples).
  warnings.warn(
Segmentation fault: 11

[shaido987]

Hi @nolanrbrady ,

No progress since we have not been able to reproduce the issue. On which line does the segmentation error occur for you? As mentioned in my follow-up question to the original post, does pytorch work normally?