When you miss declaring a node in your causal graph, it's going to throw a KeyError: 'label' error. It could be more explicit to make debugging easier. I think it would be nice to inform what is the node hough used in the graph.

The documentation of some classes/methods is severely lacking. Here's a list of methods that needs more detailed documentation as has been pointed out by users:

• TabularCPD doesn't clearly specify what the arguments are expected to be. Ref: #1036
• TabularCPD methods need to describe in more detail what each of them does.
• Make sure that Return section is available in ea

Description

Following the tutorial raises the following:

bn = bn.fit_cpds(train, method="BayesianEstimator", bayes_prior="K2")

Would be great to have a fully working jupyter notebook as an example.

Steps to Reproduce

/usr/local/lib/python3.7/site-packages/causalnex/network/network.py in fit_cpds(self, data, method, bayes_prior, equivalent_sample_size)
    344 
    345  

Learning methods should detect that the provided DAG contains variables with no attributes associated (because it is randonmly generated) and does not match the attributes of the provided data.

I would also love to have the ability to add expert knowledge to the model as described here,

http://www.bnlearn.com/examples/custom/

Though I have not been able to figure out how to update the bnlearn object from the ui as needed.

I would also love to have the ability to add expert knowledge to the model as described here,

http://www.bnlearn.com/examples/custom/

Though I have not been able to figure out how to update the bnlearn object from the ui as needed.