Bnlearn

* Added functionality to save and load models!
* Examples: https://erdogant.github.io/bnlearn/pages/html/saving%20and%20loading.html

update to the latest version with:
`pip install -U bnlearn`

* added interactive plot with pyvis using the plot functionality with parameter `bn.plot(model, interactive=True)`


import bnlearn as bn
df = bn.import_example()
Structure learning
model = bn.structure_learning.fit(df)
bn.plot(model, interactive=True)

* Included fixed_edges when using `methodtype='hc'`
* Changed the bw_list_method for clarity.
* bw_list_method='enforce' into bw_list_method='edges'
* bw_list_method='filter' into bw_list_method='nodes'
* Some docstring updates
* Some code cleaning

Note that `bw_list_method='nodes'` works for all methodtypes and `bw_list_method='edges'` only when `methodtype='hc'`

* Added new tree search based structure learning method: Tree-augmented Naive Bayes (TAN)

Example


import bnlearn as bn
df = bn.import_example()
Structure learning with TAN
model = bn.structure_learning.fit(df, methodtype='tan', root_node='Cloudy', class_node='Rain')
bn.plot(model)

* Fix for scoring_method that occured in newer versions from pgmpy/python
* Fix for return_type in forward_sampling
* removed examples from library and included functionality to auto-download
* Update of requirements
* Update unit test
* Some code refactoring

* Functionally added to make predictions on dataset with the *predict* function 17
* Predict examples can be found in the readme and sphinx pages: https://erdogant.github.io/bnlearn/pages/html/Predict.html
* Functionally added that outputs a dataframe with the results for the inference that is made (*query2df*)
* Included some exceptions to catch errors
* Fixed circular imports
* Unit test updated for the new functions
* Some docstrings updated
* Some code refactoring and cleaning