ICA-LiNGAM

class lingam.ICALiNGAM(random_state=None, max_iter=1000)

Implementation of ICA-based LiNGAM Algorithm [1]

References

[1]	S. Shimizu, P. O. Hoyer, A. Hyvärinen, and A. J. Kerminen. A linear non-gaussian acyclic model for causal discovery. Journal of Machine Learning Research, 7:2003-2030, 2006.

__init__(random_state=None, max_iter=1000)

Construct a ICA-based LiNGAM model.

Parameters:

• random_state (int, optional (default=None)) – random_state is the seed used by the random number generator.
• max_iter (int, optional (default=1000)) – The maximum number of iterations of FastICA.

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, n_sampling)

Evaluate the statistical reliability of DAG based on the bootstrapping.

Parameters:

• X (array-like, shape (n_samples, n_features)) – Training data, where n_samples is the number of samples and n_features is the number of features.
• n_sampling (int) – Number of bootstrapping samples.

Returns:

result – Returns the result of bootstrapping.

Return type:

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, from_index, to_index)

Estimate total effect using causal model.

Parameters:

• X (array-like, shape (n_samples, n_features)) – Original data, 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)

Fit the model to X.

Parameters:X (array-like, shape (n_samples, n_features)) – Training data, where n_samples is the number of samples and n_features is the number of features. Returns:self – Returns the instance of self. Return type:object

get_error_independence_p_values(X)

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

Parameters:X (array-like, shape (n_samples, n_features)) – Original data, 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_features, n_features)