BottomUpParceLiNGAM

class lingam.BottomUpParceLiNGAM(random_state=None, alpha=0.1, regressor=None, prior_knowledge=None)

Implementation of ParceLiNGAM Algorithm [1]

References

[1]	(1, 2) T. Tashiro, S. Shimizu, and A. Hyvärinen. ParceLiNGAM: a causal ordering method robust against latent confounders. Neural computation, 26.1: 57-83, 2014.

__init__(random_state=None, alpha=0.1, regressor=None, prior_knowledge=None)

Construct a BottomUpParceLiNGAM model.

Parameters:

• random_state (int, optional (default=None)) – random_state is the seed used by the random number generator.
• alpha (float, optional (default=0.1)) – Significant level of statistical test. If alpha=0.0, rejection does not occur in statistical tests.
• regressor (regressor object implementing 'fit' and 'predict' function (default=None)) – Regressor to compute residuals. This regressor object must have fit``method and ``predict function like scikit-learn’s model.
• 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.

adjacency_matrix_

Estimated adjacency matrix.

Returns:adjacency_matrix_ – The adjacency matrix B of fitted model, where n_features is the number of features. Set np.nan if order is unknown. 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. Set the features as a list if order is unknown. 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 itself. 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)