v0.2.5 -- DAG losses, factor graphs, neighborhood selection, and sklearn-style graph baselines #190
cnellington
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NOTMAD
using the NOTEARS, Poly, or DAGMA losses, and use factor graphs to infer high dimensional DAGs!contextualized.baselines
to infer traditional correlation networks and Bayesian networks using a simple sklearn-style interface (fit, predict, measure_mses), and create "grouped" versions of these models using any grouping or discrete context (e.g. clustering, feature splits, age groups, cell types) with theGroupedNetworks
class -- and follow up withContextualizedCorrelationNetworks
orContextualizedBayesianNetworks
in theeasy
module to see how much more accurate contextualized models can be!metamodel_type='Naive'
in the regression and dags lightning_modules to remove archetypes and estimate models directly from a neural network (not yet in the sklearn-style easy models).fit_intercept=False
in the regression lightning_modules to remove contextualized intercepts, and only infer models with context-varying coefficients (not yet in the sklearn-style easy models).This discussion was created from the release v0.2.5 -- DAG losses, factor graphs, neighborhood selection, and sklearn-style graph baselines.
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