Comments (3)
Yea you are right, at least for these LiNGAM-based methods we could definitely visualize them in a similar way as follows:
from causallearn.search.FCMBased import lingam
model = lingam.ICALiNGAM()
model.fit(data)
from causallearn.search.FCMBased.lingam.utils import make_dot
make_dot(model.adjacency_matrix_, labels=labels)
We will include these usages into the doc, and preferably make the visualization way consistent with other methods. For ANM or PNL, the multivariate version could be a little bit more tricky, see e.g. https://proceedings.mlr.press/v177/uemura22a/uemura22a.pdf.
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Hello,
that worked:
https://colab.research.google.com/drive/1BZ2idQWgr7Ed6d09fk9bYi4a5RoOiu3g?usp=sharing
is there a way to save it into a file?
from causal-learn.
Quick solution
from causallearn.search.FCMBased.lingam.utils import make_dot
my_dot = make_dot(model.adjacency_matrix_,labels=df.columns.to_list())
my_dot.filename="test"
my_dot.name="test"
my_dot.render(format='png')
my_dot.save(filename="test.dot")
maybe add to the docs.
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Related Issues (20)
- PNL - Test2 HOT 3
- Data correlation matrix is singular HOT 5
- when will the Causal-Learn support causal evaluation HOT 2
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- does this codebase support some recent causal discovery methods like, notears, dag-gnn,and so on HOT 2
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- Boostrap Utilities HOT 1
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- Different results with Tetrad and causal-learn implementations. HOT 6
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