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cstrees.scoring as sc + + +def learn_synth_cstree(data): + # estimate possible context variables and create score tables + pcgraph = pc(data.values[1:], 0.05, "gsq", node_names=data.columns) + poss_cvars = ctl.causallearn_graph_to_posscvars(pcgraph, labels=data.columns) + + score_table, context_scores, _ = sc.order_score_tables( + data, max_cvars=2, alpha_tot=1.0, method="BDeu", poss_cvars=poss_cvars + ) + + # run Gibbs sampler to get MAP order + orders, scores = ctl.gibbs_order_sampler(5000, score_table) + map_order = orders[scores.index(max(scores))] + + # estimate CStree + opt_tree = ctl._optimal_cstree_given_order(map_order, context_scores) + + return opt_tree + + +data = pd.read_csv("dataset-1707210860.6860206.csv", index_col=0) +est = learn_synth_cstree(data)