HighDimDirectLiNGAM

class lingam.HighDimDirectLiNGAM(J=3, K=4, alpha=0.5, estimate_adj_mat=True, random_state=None)[source]

An implementation of the high-dimensional LiNGAM algorithm. [1]

References

__init__(J=3, K=4, alpha=0.5, estimate_adj_mat=True, random_state=None)[source]

Construct a HighDimDirectLiNGAM model.

Parameters:
  • J (int, optional (default=3)) – Assumed largest in-degree.

  • K (int, optional (default=4)) – The degree of the moment which is non-Gaussianity.

  • alpha (float, optional (default=0.5)) – The value for pruning away false parents.

  • estimate_adj_mat (bool, optional (default=True)) – If Fase, skip the estimation of the adjacency matrix.

  • random_state (int, optional (default=None)) – random_state is the seed used by the random number generator.

property 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

property 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)[source]

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 – Fitted model.

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)