MultiGroupDirectLiNGAM
- class lingam.MultiGroupDirectLiNGAM(random_state=None, prior_knowledge=None, apply_prior_knowledge_softly=False)[source]
Implementation of DirectLiNGAM Algorithm with multiple groups [1]
References
- __init__(random_state=None, prior_knowledge=None, apply_prior_knowledge_softly=False)[source]
Construct a model.
- Parameters:
random_state (int, optional (default=None)) –
random_state
is the seed used by the random number generator.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.
apply_prior_knowledge_softly (boolean, optional (default=False)) – If True, apply prior knowledge softly.
- property adjacency_matrices_
Estimated adjacency matrices.
- Returns:
adjacency_matrices_ – The list of adjacency matrix B for multiple datasets. The shape of B is (n_features, n_features), where n_features is the number of features.
- Return type:
array-like, shape (B, …)
- 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_list, n_sampling)[source]
Evaluate the statistical reliability of DAG based on the bootstrapping.
- Parameters:
X_list (array-like, shape (X, ...)) – Multiple datasets for training, where
X
is an dataset. The shape of ‘’X’’ is (n_samples, n_features), wheren_samples
is the number of samples andn_features
is the number of features.n_sampling (int) – Number of bootstrapping samples.
- Returns:
results – Returns the results of bootstrapping for multiple datasets.
- Return type:
array-like, shape (BootstrapResult, …)
- property causal_order_
Estimated causal ordering.
- Returns:
causal_order_ – The causal order of fitted model, where n_features is the number of features.
- Return type:
array-like, shape (n_features)
- estimate_total_effect(X_list, from_index, to_index)[source]
Estimate total effect using causal model.
- Parameters:
X_list (array-like, shape (X, ...)) – Multiple datasets for training, where
X
is an dataset. The shape of ‘’X’’ is (n_samples, n_features), wheren_samples
is the number of samples andn_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
- estimate_total_effect2(from_index, to_index)[source]
Estimate total effect using causal model.
- Parameters:
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_list)[source]
Fit the model to multiple datasets.
- Parameters:
X_list (list, shape [X, ...]) – Multiple datasets for training, where
X
is an dataset. The shape of ‘’X’’ is (n_samples, n_features), wheren_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_list)[source]
Calculate the p-value matrix of independence between error variables.
- Parameters:
X_list (array-like, shape (X, ...)) – Multiple datasets for training, where
X
is an dataset. The shape of ‘’X’’ is (n_samples, n_features), wheren_samples
is the number of samples andn_features
is the number of features.- Returns:
independence_p_values – p-value matrix of independence between error variables.
- Return type:
array-like, shape (n_datasets, n_features, n_features)