API Reference¶
blockdiagonalBMD¶
-
class
bmdcluster.
blockdiagonalBMD
(n_clusters, max_iter=100, use_bootstrap=False, b=None, init_ratio=1.0, seed=None)[source]¶ -
__init__
(n_clusters, max_iter=100, use_bootstrap=False, b=None, init_ratio=1.0, seed=None)[source]¶ Run the block-diagonal form of the BMD algorithm.
Parameters: - n_clusters (int) – number of data clusters
- max_iter (int, optional) – maximum number of optimization iterations, by default 100
- use_bootstrap (bool, optional) – use bootstrap cluster initialization, by default False
- b (int, optional) – number of bootstrapped samples to use, by default None
- init_ratio (float, optional) – fraction of points to randomly initialize, by default 1.0
- seed (int, optional) – random initialization seed, by default None
Raises: ValueError
– Ifuse_bootstrap
is set to True but andb
is not specifiedValueError
– If bothB_ident
andf_clusters
are not specified
-
fit
(W, verbose=False)[source]¶ Fit the model.
Parameters: - W (np.array) – binary data matrix
- verbose (bool, optional) – print progress during optimization, by default False
-
fit_predict
(W, verbose=False)[source]¶ Fit the model and return final value of objective function and cluster assignment labels for the data and features.
Parameters: - W (np.array) – binary data matrix
- verbose (bool, optional) – print progress during optimization, by default False
Returns: - float – final value of objective function
- np.array – data cluster labels
- np.array – feature cluster labels
-
fit_transform
(W, verbose=False)[source]¶ Fit the model and return final value of objective function and final values of the data and feature cluster assignment matrices A and B, whose entries are cluster affinity scores.
Parameters: - W (np.array) – binary data matrix
- verbose (bool, optional) – print progress during optimization, by default False
Returns: - float – final cost of objective function
- np.array – final value of data cluster assignment matrix A
- np.array – final value of feature cluster assignment matrix B
-
get_data_labels
()[source]¶ Get data cluster labels after .fit(). Outliers will be labeled -1.
Returns: data cluster labels Return type: np.array
-
get_feature_labels
()[source]¶ Get feature cluster labels after .fit(). Outliers will be labeled -1.
Returns: feature cluster labels Return type: np.array
-
generalBMD¶
-
class
bmdcluster.
generalBMD
(n_clusters, f_clusters=None, B_ident=True, max_iter=100, use_bootstrap=False, b=None, init_ratio=1.0, seed=None)[source]¶ -
__init__
(n_clusters, f_clusters=None, B_ident=True, max_iter=100, use_bootstrap=False, b=None, init_ratio=1.0, seed=None)[source]¶ Run the general form of the BMD algorithm.
Parameters: - n_clusters (int) – number of data clusters
- f_clusters (int, optional) – number of feature clusters, by default None
- B_ident (bool, optional) – initialize feature cluster assignment matrix to the identity, by default True
- max_iter (int, optional) – maximum number of optimization iterations, by default 100
- use_bootstrap (bool, optional) – use bootstrap cluster initialization, by default False
- b (int, optional) – number of bootstrapped samples to use, by default None
- init_ratio (float, optional) – fraction of points to randomly initialize, by default 1.0
- seed (int, optional) – random initialization seed, by default None
Raises: ValueError
– Ifuse_bootstrap
is set to True but andb
is not specifiedValueError
– If bothB_ident
andf_clusters
are not specifiedValueError
– If bothB_ident=True
andf_clusters
is set
Caution
Setting both
B_ident=True
andf_clusters
are mutually exclusive options and will result in an error.
-
fit
(W, verbose=False)[source]¶ Fit the model.
Parameters: - W (np.array) – binary data matrix
- verbose (bool, optional) – print progress during optimization, by default False
-
fit_predict
(W, verbose=False)[source]¶ Fit the model and return final value of objective function and cluster assignment labels for the data and features.
Parameters: - W (np.array) – binary data matrix
- verbose (bool, optional) – print progress during optimization, by default False
Returns: - float – final value of objective function
- np.array – data cluster labels
- np.array – feature cluster labels
-
fit_transform
(W, verbose)[source]¶ Fit the model and return final value of objective function and final values of the data and feature cluster assignment matrices A and B, whose entries are cluster affinity scores.
Parameters: - W (np.array) – binary data matrix
- verbose (bool, optional) – print progress during optimization, by default False
Returns: - float – final cost of objective function
- np.array – final value of data cluster assignment matrix A
- np.array – final value of feature cluster assignment matrix B
-
get_data_labels
()[source]¶ Get data cluster labels after .fit(). Outliers will be labeled -1.
Returns: data cluster labels Return type: np.array
-
get_feature_labels
()[source]¶ Get feature cluster labels after .fit(). Outliers will be labeled -1.
Returns: feature cluster labels Return type: np.array
-