Releases · mckinsey/causalnex

Changelog:

Add expectation-maximisation (EM) algorithm to learn with latent variables
Add a new tutorial on adding latent variable as well as identifying its candidate location
Allow users to provide self-defined CPD, as per #18 and #99
Generalise the utility function to get Markov blanket and incorporate it within StructureModel (cf. #136)
Add a link to PyGraphviz installation guide under the installation prerequisites
Add GPU support to Pytorch implementation, as requested in #56 and #114 (some issues remain)
Add an example for structure model exporting into first causalnex tutorial, as per #124 and #129
Fix infinite loop when querying InferenceEngine after a do-intervention that splits
the graph into two or more subgraphs, as per #45 and #100
Fix decision tree and mdlp discretisations bug when input data is shuffled
Fix broken URLs in FAQ documentation, as per #113 and #125
Fix integer index type checking for timeseries data, as per #74 and #86
Fix bug where inputs to the DAGRegressor/Classifier yielded different predictions between float and int dtypes, as per #140

Functionality:

Add BayesianNetworkClassifier an sklearn compatible class for fitting and predicting probabilities in a BN.
Add supervised discretisation strategies using Decision Tree and MDLP algorithms.
Support receiving a list of inputs for InferenceEngine with a multiprocessing option
Add utility function to extract Markov blanket from a Bayesian Network

Minor fixes and housekeeping:

Fix estimator issues with sklearn ("unofficial python 3.9 support", doesn't work with discretiser option)
Fixes cyclical import of causalnex.plots, as per #106.
Added manifest files to ensure requirements and licenses are packaged
Minor bumps in dependency versions, remove prettytable as dependency

Provide feedback