causalnex.structure.notears.from_numpy_lasso¶
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causalnex.structure.notears.from_numpy_lasso(X, beta, max_iter=100, h_tol=1e-08, w_threshold=0.0, tabu_edges=None, tabu_parent_nodes=None, tabu_child_nodes=None)[source]¶ Learn the StructureModel, the graph structure with lasso regularisation describing conditional dependencies between variables in data presented as a numpy array.
Based on DAGs with NO TEARS. @inproceedings{zheng2018dags,
author = {Zheng, Xun and Aragam, Bryon and Ravikumar, Pradeep and Xing, Eric P.}, booktitle = {Advances in Neural Information Processing Systems}, title = {{DAGs with NO TEARS: Continuous Optimization for Structure Learning}}, year = {2018}, codebase = {https://github.com/xunzheng/notears}}
Parameters: - X (
ndarray) – 2d input data, axis=0 is data rows, axis=1 is data columns. Data must be row oriented. - beta (
float) – Constant that multiplies the lasso term. - max_iter (
int) – max number of dual ascent steps during optimisation. - h_tol (
float) – exit if h(W) < h_tol (as opposed to strict definition of 0). - w_threshold (
float) – fixed threshold for absolute edge weights. - tabu_edges (
Optional[List[Tuple[int,int]]]) – list of edges(from, to) not to be included in the graph. - tabu_parent_nodes (
Optional[List[int]]) – list of nodes banned from being a parent of any other nodes. - tabu_child_nodes (
Optional[List[int]]) – list of nodes banned from being a child of any other nodes.
Returns: a graph of conditional dependencies between data variables.
Return type: Raises: ValueError– If X does not contain data.- X (