# MultiGroupDirectLiNGAM¶

class lingam.MultiGroupDirectLiNGAM(random_state=None, prior_knowledge=None, apply_prior_knowledge_softly=False)[source]

Implementation of DirectLiNGAM Algorithm with multiple groups [1]

References

 [1] (1, 2) Shimizu. Joint estimation of linear non-Gaussian acyclic models. Neurocomputing, 81: 104-107, 2012.
__init__(random_state=None, prior_knowledge=None, apply_prior_knowledge_softly=False)[source]

Construct a model.

Parameters: random_state (int, optional (default=None)) – random_state is the seed used by the random number generator. prior_knowledge (array-like, shape (n_features, n_features), optional (default=None)) – Prior knowledge used for causal discovery, where n_features is the number of features. The elements of prior knowledge matrix are defined as follows [1]: 0 : $$x_i$$ does not have a directed path to $$x_j$$ 1 : $$x_i$$ has a directed path to $$x_j$$ -1 : No prior knowledge is available to know if either of the two cases above (0 or 1) is true. apply_prior_knowledge_softly (boolean, optional (default=False)) – If True, apply prior knowledge softly.

Returns: adjacency_matrices_ – The list of adjacency matrix B for multiple datasets. The shape of B is (n_features, n_features), where n_features is the number of features. array-like, shape (B, ..)

Returns: adjacency_matrix_ – The adjacency matrix B of fitted model, where n_features is the number of features. array-like, shape (n_features, n_features)
bootstrap(X_list, n_sampling)[source]

Evaluate the statistical reliability of DAG based on the bootstrapping.

Parameters: X_list (array-like, shape (X, ..)) – Multiple datasets for training, where X is an dataset. The shape of ‘’X’’ is (n_samples, n_features), where n_samples is the number of samples and n_features is the number of features. n_sampling (int) – Number of bootstrapping samples. results – Returns the results of bootstrapping for multiple datasets. array-like, shape (BootstrapResult, ..)
causal_order_

Estimated causal ordering.

Returns: causal_order_ – The causal order of fitted model, where n_features is the number of features. array-like, shape (n_features)
estimate_total_effect(X_list, from_index, to_index)[source]

Estimate total effect using causal model.

Parameters: X_list (array-like, shape (X, ..)) – Multiple datasets for training, where X is an dataset. The shape of ‘’X’’ is (n_samples, n_features), where n_samples is the number of samples and n_features is the number of features. from_index – Index of source variable to estimate total effect. to_index – Index of destination variable to estimate total effect. total_effect – Estimated total effect. float
fit(X_list)[source]

Fit the model to multiple datasets.

Parameters: X_list (list, shape [X, ..]) – Multiple datasets for training, where X is an dataset. The shape of ‘’X’’ is (n_samples, n_features), where n_samples is the number of samples and n_features is the number of features. self – Returns the instance itself. object
get_error_independence_p_values(X_list)[source]

Calculate the p-value matrix of independence between error variables.

Parameters: X_list (array-like, shape (X, ..)) – Multiple datasets for training, where X is an dataset. The shape of ‘’X’’ is (n_samples, n_features), where n_samples is the number of samples and n_features is the number of features. independence_p_values – p-value matrix of independence between error variables. array-like, shape (n_datasets, n_features, n_features)