def mdbn(data, f_cols, c_cols, f_struct='DAG', c_struct='DAG', wrapper=False): """ Learn the structure of a Multi-Dimensional Bayesian Network - typically used for Classification. Note that this structure does not have to be used for classification, since it simply returns a Bayesian Network - albeit with a more unqiue structure than tradiitonally found. If there are any other applications of this bipartite-like BN structure learning, this algorithm can certainly be used. """ f_data = data[:,f_cols] c_data = data[:,c_cols] f_bn = hc_rr(f_data) c_bn = hc_rr(c_data) mbc_bn = bridge(c_bn=c_bn, f_bn=f_bn, data=data) return mbc_bn
def bridge(c_bn, f_bn, data): """ Make a Multi-Dimensional Bayesian Network by bridging two Bayesian network structures. This happens by placing edges from c_bn -> f_bn using a heuristic optimization procedure. This can be used to create a Multi-Dimensional Bayesian Network classifier from two already-learned Bayesian networks - one of which is a BN containing all the class variables, the other containing all the feature variables. Arguments --------- *c_bn* : a BayesNet object with known structure *f_bn* : a BayesNet object with known structure. Returns ------- *m_bn* : a merged/bridge BayesNet object, whose structure contains *c_bn*, *f_bn*, and some bridge edges between them. """ restrict = [] for u in c_bn: for v in f_bn: restrict.append((u,v)) # only allow edges from c_bn -> f_bn bridge_bn = hc_rr(data, restriction=restrict) m_bn = bridge_bn.E m_bn.update(c_bn.E) m_bn.update(f_bn.E) mbc_bn = BayesNet(E=m_bn)
