LiNA¶
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class
lingam.
LiNA
(w_threshold=0.3, lambda1=0.1, lambda2=0.1, loss_type='laplace', max_iter=100, h_tol=1e-08, rho_max=1e+16)[source]¶ Implementation of LiNA Algorithm [1]_
References
[1] Y. Zeng, S. Shimizu, R. Cai, F. Xie, M. Yamamoto and Z. Hao. Causal Discovery with Multi-Domain LiNGAM for Latent Factors. In Proc. of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI-21), 2021: 2097–2103. -
__init__
(w_threshold=0.3, lambda1=0.1, lambda2=0.1, loss_type='laplace', max_iter=100, h_tol=1e-08, rho_max=1e+16)[source]¶ Construct a LiNA model.
Parameters: - w_threshold (float (default=0.3)) – Drop edge if the weight btw. latent factors is less than w_threshold.
- lambda1 (float, optional (default=0.1)) – L1 penalty parameter.
- lambda2 (float, (default=0.1)) – L2 penalty parameter.
- loss_type (str, (default='laplace')) – Type of distribution of the noise.
- max_iter (int, (default=100)) – Maximum number of dual ascent steps.
- h_tol (float, (default=1e-8)) – Tolerance parameter of the acyclicity constraint.
- rho_max (float, (default=1e+16)) – Maximum value of the regularization parameter rho.
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adjacency_matrix_
¶ Estimated adjacency matrix between latent factors.
Returns: adjacency_matrix_ – The adjacency matrix of latent factors, where n_features_latent
is the number of latent factors.Return type: array-like, shape (n_features_latent, n_features_latent)
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fit
(X, G_sign, scale)[source]¶ Fit the model to X with measurement structure and latent factors’ scales.
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 measurement features. - G_sign (array-like, shape (n_features, n_features_latent)) – Measurement structure matrix, where
n_features_latent
is the number of latent factors andn_features
is the number of measurement features. - scale (array-like, shape (1, n_features_latent)) – Scales of the latent factors.
Returns: self – Returns the instance of self.
Return type: object
- X (array-like, shape (n_samples, n_features)) – Training data, where
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measurement_matrix_
¶ Estimated measurement matrix between measurement variables and latent factors.
Returns: measurement_matrix_ – The measurement matrix between measurement variables and latent factors, where n_features_latent
is the number of latent factors andn_features
is the number of measurement variables.Return type: array-like, shape (n_features, n_features_latent)
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class
lingam.
MDLiNA
(w_threshold=0.3, lambda1=0.1, lambda2=0.1, loss_type='laplace', max_iter=100, h_tol=1e-08, rho_max=1e+16, no_of_domain=2, no_of_latent_1domain=3)[source]¶ Implementation of MD-LiNA Algorithm [1]_
References
[1] Y. Zeng, S. Shimizu, R. Cai, F. Xie, M. Yamamoto and Z. Hao. Causal Discovery with Multi-Domain LiNGAM for Latent Factors. In Proc. of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI-21), 2021: 2097–2103. -
__init__
(w_threshold=0.3, lambda1=0.1, lambda2=0.1, loss_type='laplace', max_iter=100, h_tol=1e-08, rho_max=1e+16, no_of_domain=2, no_of_latent_1domain=3)[source]¶ Construct an MD-LiNA model.
Parameters: - w_threshold (float (default=0.3)) – Drop edge if the weight btw. latent factors is less than w_threshold.
- lambda1 (float, optional (default=0.1)) – L1 penalty parameter.
- lambda2 (float, (default=0.1)) – L2 penalty parameter.
- loss_type (str, (default='laplace')) – Type of distribution of the noise.
- max_iter (int, (default=100)) – Maximum number of dual ascent steps.
- h_tol (float, (default=1e-8)) – Tolerance parameter of the acyclicity constraint.
- rho_max (float, (default=1e+16)) – Maximum value of the regularization parameter rho.
- no_of_domain (int, (default=2)) – Number of domains.
- no_of_latent_1domain (float, (default=3)) – Number of latent factors in a domain.
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adjacency_matrix_
¶ Estimated adjacency matrix between latent factors of interest, which is shared by all domains.
Returns: adjacency_matrix_ – The adjacency matrix of latent factors of interest, where n_features_latent_1domain
is the number of latent factors of interest.Return type: array-like, shape (n_features_latent_1domain, n_features_latent_1domain)
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fit
(X, G_sign, scale)[source]¶ Fit the model to X with measurement structure and latent factors’ scales.
Parameters: - X (array-like, shape (n_samples, n_features)) – Training data, where
n_samples
is the number of samples of all domains andn_features
is the number of features of all domains. - G_sign (array-like, shape (n_features, n_features_latent)) – Measurement structure matrix, where
n_features_latent
is the number of latent factors of all domains andn_features
is the number of measurement variables of all domains. - scale (array-like, shape (1, n_features_latent)) – Scales of the latent factors.
Returns: self – Returns the instance of self.
Return type: object
- X (array-like, shape (n_samples, n_features)) – Training data, where
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measurement_matrix_
¶ Estimated measurement matrix between measurement variables and latent factors from all domains.
Returns: measurement_matrix_ – The measurement matrix between measurement variables and latent factors, where n_features_latent
is the number of latent factors andn_features
is the number of measurement variables from all domains.Return type: array-like, shape (n_features, n_features_latent)
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