# BottomUpParceLiNGAM¶

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

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)[source]

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 fitmethod 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_

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. array-like, shape (n_features, n_features)
bootstrap(X, 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. n_sampling (int) – Number of bootstrapping samples. result – Returns the result of bootstrapping. 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. array-like, shape (n_features)
estimate_total_effect(X, from_index, to_index)[source]

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. total_effect – Estimated total effect. 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. self – Returns the instance itself. object
get_error_independence_p_values(X)[source]

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. independence_p_values – p-value matrix of independence between error variables. array-like, shape (n_features, n_features)