BootstrapResult¶

class
lingam.
BootstrapResult
(adjacency_matrices, total_effects)[source]¶ The result of bootstrapping.

__init__
(adjacency_matrices, total_effects)[source]¶ Construct a BootstrapResult.
Parameters:  adjacency_matrices (arraylike, shape (n_sampling)) – The adjacency matrix list by bootstrapping.
 total_effects (arraylike, shape (n_sampling)) – The total effects list by bootstrapping.

adjacency_matrices_
¶ The adjacency matrix list by bootstrapping.
Returns: adjacency_matrices_ – The adjacency matrix list, where n_sampling
is the number of bootstrap sampling.Return type: arraylike, shape (n_sampling)

get_causal_direction_counts
(n_directions=None, min_causal_effect=None, split_by_causal_effect_sign=False)[source]¶ Get causal direction count as a result of bootstrapping.
Parameters:  n_directions (int, optional (default=None)) – If int, then The top
n_directions
items are included in the result  min_causal_effect (float, optional (default=None)) – Threshold for detecting causal direction.
If float, then causal directions with absolute values of causal effects
less than
min_causal_effect
are excluded.  split_by_causal_effect_sign (boolean, optional (default=False)) – If True, then causal directions are split depending on the sign of the causal effect.
Returns: causal_direction_counts – List of causal directions sorted by count in descending order. The dictionary has the following format:
{'from': [n_directions], 'to': [n_directions], 'count': [n_directions]}
where
n_directions
is the number of causal directions.Return type: dict
 n_directions (int, optional (default=None)) – If int, then The top

get_directed_acyclic_graph_counts
(n_dags=None, min_causal_effect=None, split_by_causal_effect_sign=False)[source]¶ Get DAGs count as a result of bootstrapping.
Parameters:  n_dags (int, optional (default=None)) – If int, then The top
n_dags
items are included in the result  min_causal_effect (float, optional (default=None)) – Threshold for detecting causal direction.
If float, then causal directions with absolute values of causal effects less than
min_causal_effect
are excluded.  split_by_causal_effect_sign (boolean, optional (default=False)) – If True, then causal directions are split depending on the sign of the causal effect.
Returns: directed_acyclic_graph_counts – List of directed acyclic graphs sorted by count in descending order. The dictionary has the following format:
{'dag': [n_dags], 'count': [n_dags]}.
where
n_dags
is the number of directed acyclic graphs.Return type: dict
 n_dags (int, optional (default=None)) – If int, then The top

get_paths
(from_index, to_index, min_causal_effect=None)[source]¶ Get all paths from the start variable to the end variable and their bootstrap probabilities.
Parameters:  from_index (int) – Index of the variable at the start of the path.
 to_index (int) – Index of the variable at the end of the path.
 min_causal_effect (float, optional (default=None)) – Threshold for detecting causal direction.
Causal directions with absolute values of causal effects less than
min_causal_effect
are excluded.
Returns: paths – List of path and bootstrap probability. The dictionary has the following format:
{'path': [n_paths], 'effect': [n_paths], 'probability': [n_paths]}
where
n_paths
is the number of paths.Return type: dict

get_probabilities
(min_causal_effect=None)[source]¶ Get bootstrap probability.
Parameters: min_causal_effect (float, optional (default=None)) – Threshold for detecting causal direction. If float, then causal directions with absolute values of causal effects less than min_causal_effect
are excluded.Returns: probabilities – List of bootstrap probability matrix. Return type: arraylike

get_total_causal_effects
(min_causal_effect=None)[source]¶ Get total effects list.
Parameters: min_causal_effect (float, optional (default=None)) – Threshold for detecting causal direction. If float, then causal directions with absolute values of causal effects less than min_causal_effect
are excluded.Returns: total_causal_effects – List of bootstrap total causal effect sorted by probability in descending order. The dictionary has the following format: {'from': [n_directions], 'to': [n_directions], 'effect': [n_directions], 'probability': [n_directions]}
where
n_directions
is the number of causal directions.Return type: dict

total_effects_
¶ The total effect list by bootstrapping.
Returns: total_effects_ – The total effect list, where n_sampling
is the number of bootstrap sampling.Return type: arraylike, shape (n_sampling)
