v0.7.11

• Fix for interactive plot. It did not show up correctly but now it does again.

v0.7.10

• sklearn is changed to scikit-learn in the setup and requirements (issue #66 )

v0.7.9

• plot function bn.plot(model) return figure axis.
• Setup file contains import of python-louvain instead of community
• docstring updates regarding this output.

Example:

model = bn.import_DAG('sprinkler', CPD=True)
df = bn.sampling(model, n=1000, methodtype='bayes')
fig = bn.plot(model)
fig['ax']

v0.7.8

• Added functionality to compute sctructure scores when using parameter of structure learning. This can help to decide which scoring type may give a better fit ('bic', 'k2', 'bdeu'). The results scores are directly stored in the output.

import bnlearn as bn

# Load example dataset
df = bn.import_example('sprinkler')

edges = [('Cloudy', 'Sprinkler'),
         ('Cloudy', 'Rain'),
         ('Sprinkler', 'Wet_Grass'),
         ('Rain', 'Wet_Grass')]

# Make the actual Bayesian DAG
DAG = bn.make_DAG(edges)
model = bn.parameter_learning.fit(DAG, df)

model['structure_scores']
{'k2': -33962.61414408797,
 'bds': -57992.919156623604,
 'bic': -94337.69274492635,
 'bdeu': -33670.95375881856}

v0.7.7

• check_model now uses Decimals to prevent Floating Point Errors in the checks: Issue #60.

v0.7.6

• CPDs are now returned from print_CPD for further usage.

v0.7.5

• Fix for issues #37 where query2df is fixed to output a structured dataframe with the query information.
• Added unit test for this issue.

v0.7.4

• Added Gibbs sampling. See the docs for examples.
• Improvements in verbosity messages.
• Some code refactoring/cleaning.

v0.7.3

• added input parameter n_jobs where possible

v0.7.2

• improved speed in predict functionality
• Add scoretype to parameter_learning