GroupDirectLiNGAM

class lingam.GroupDirectLiNGAM(prior_knowledge=None)[source]

Implementation of GroupDirectLiNGAM Algorithm [1]

References

__init__(prior_knowledge=None)[source]

Construct a GroupDirectLiNGAM model.

Parameters:

prior_knowledge (array-like, shape (n_groups, n_groups), optional (default=None)) –

Prior knowledge used for causal discovery, where n_groups is the number of groups.

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.

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, groups, n_sampling)[source]

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.
groups (array-like, shape (n_groups)) – The list of features for each group.
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_groups is the number of groups.
Return type:: array-like, shape (n_groups)

fit(X, groups)[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.
groups (array-like, shape (n_groups)) – The list of features for each group. where n_groups is the number of groups.

Returns:

self – Returns the instance itself.

Return type:

object