BottomUpParceLiNGAM
- class lingam.BottomUpParceLiNGAM(random_state=None, alpha=0.1, regressor=None, prior_knowledge=None)[source]
Implementation of ParceLiNGAM Algorithm [1]
References
- __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 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.
- property 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)[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 andn_features
is the number of features.n_sampling (int) – Number of bootstrapping samples.
- Returns:
result – Returns the result of bootstrapping.
- Return type:
- property 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)[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.
- 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 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 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)