MultiGroupDirectLiNGAM

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

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)

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.

adjacency_matrices_

Estimated adjacency matrices.

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. Return type:array-like, shape (B, ..)

adjacency_matrix_

Estimated adjacency matrix.

Returns:adjacency_matrix_ – The adjacency matrix B of fitted model, where n_features is the number of features. Return type:array-like, shape (n_features, n_features)

bootstrap(X_list, n_sampling)

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.

Returns:

results – Returns the results of bootstrapping for multiple datasets.

Return type:

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. Return type:array-like, shape (n_features)

estimate_total_effect(X_list, from_index, to_index)

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.

Returns:

total_effect – Estimated total effect.

Return type:

float

fit(X_list)

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. Returns:self – Returns the instance itself. Return type:object

get_error_independence_p_values(X_list)

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. Returns:independence_p_values – p-value matrix of independence between error variables. Return type:array-like, shape (n_datasets, n_features, n_features)