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: 5783, 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
fit``method and ``predict
function like scikitlearn’s model.  prior_knowledge (arraylike, 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.
 random_state (int, optional (default=None)) –

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: arraylike, shape (n_features, n_features)

bootstrap
(X, n_sampling)[source]¶ Evaluate the statistical reliability of DAG based on the bootstrapping.
Parameters:  X (arraylike, shape (n_samples, n_features)) – Training data, where
n_samples
is the number of samples andn_features
is the number of features.  n_sampling (int) – Number of bootstrapping samples.
Returns: result – Returns the result of bootstrapping.
Return type:  X (arraylike, shape (n_samples, n_features)) – Training data, where

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: arraylike, shape (n_features)

estimate_total_effect
(X, from_index, to_index)[source]¶ Estimate total effect using causal model.
Parameters:  X (arraylike, 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 (arraylike, shape (n_samples, n_features)) – Training data, where n_samples
is the number of samples andn_features
is the number of features.Returns: self – Returns the instance itself. Return type: object

get_error_independence_p_values
(X)[source]¶ Calculate the pvalue matrix of independence between error variables.
Parameters: X (arraylike, 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 – pvalue matrix of independence between error variables. Return type: arraylike, shape (n_features, n_features)
