snowflake.ml.modeling.cluster.SpectralBiclustering¶

class snowflake.ml.modeling.cluster.SpectralBiclustering(*, n_clusters=3, method='bistochastic', n_components=6, n_best=3, svd_method='randomized', n_svd_vecs=None, mini_batch=False, init='k-means++', n_init=10, random_state=None, input_cols: Optional[Union[str, Iterable[str]]] = None, output_cols: Optional[Union[str, Iterable[str]]] = None, label_cols: Optional[Union[str, Iterable[str]]] = None, drop_input_cols: Optional[bool] = False, sample_weight_col: Optional[str] = None)¶

Bases: BaseTransformer

Spectral biclustering (Kluger, 2003) For more details on this class, see sklearn.cluster.SpectralBiclustering

n_clusters: int or tuple (n_row_clusters, n_column_clusters), default=3

The number of row and column clusters in the checkerboard structure.

method: {‘bistochastic’, ‘scale’, ‘log’}, default=’bistochastic’

Method of normalizing and converting singular vectors into biclusters. May be one of ‘scale’, ‘bistochastic’, or ‘log’. The authors recommend using ‘log’. If the data is sparse, however, log normalization will not work, which is why the default is ‘bistochastic’.

n_components: int, default=6

Number of singular vectors to check.

n_best: int, default=3

Number of best singular vectors to which to project the data for clustering.

svd_method: {‘randomized’, ‘arpack’}, default=’randomized’

Selects the algorithm for finding singular vectors. May be ‘randomized’ or ‘arpack’. If ‘randomized’, uses randomized_svd(), which may be faster for large matrices. If ‘arpack’, uses scipy.sparse.linalg.svds, which is more accurate, but possibly slower in some cases.

n_svd_vecs: int, default=None

Number of vectors to use in calculating the SVD. Corresponds to ncv when svd_method=arpack and n_oversamples when svd_method is ‘randomized`.

mini_batch: bool, default=False

Whether to use mini-batch k-means, which is faster but may get different results.

init: {‘k-means++’, ‘random’} or ndarray of shape (n_clusters, n_features), default=’k-means++’

Method for initialization of k-means algorithm; defaults to ‘k-means++’.

n_init: int, default=10

Number of random initializations that are tried with the k-means algorithm.

If mini-batch k-means is used, the best initialization is chosen and the algorithm runs once. Otherwise, the algorithm is run for each initialization and the best solution chosen.

random_state: int, RandomState instance, default=None

Used for randomizing the singular value decomposition and the k-means initialization. Use an int to make the randomness deterministic. See Glossary.

input_cols: Optional[Union[str, List[str]]]

A string or list of strings representing column names that contain features. If this parameter is not specified, all columns in the input DataFrame except the columns specified by label_cols and sample-weight_col parameters are considered input columns.

label_cols: Optional[Union[str, List[str]]]

A string or list of strings representing column names that contain labels. This is a required param for estimators, as there is no way to infer these columns. If this parameter is not specified, then object is fitted without labels(Like a transformer).

output_cols: Optional[Union[str, List[str]]]

A string or list of strings representing column names that will store the output of predict and transform operations. The length of output_cols mus match the expected number of output columns from the specific estimator or transformer class used. If this parameter is not specified, output column names are derived by adding an OUTPUT_ prefix to the label column names. These inferred output column names work for estimator’s predict() method, but output_cols must be set explicitly for transformers.

sample_weight_col: Optional[str]

A string representing the column name containing the examples’ weights. This argument is only required when working with weighted datasets.

drop_input_cols: Optional[bool], default=False

If set, the response of predict(), transform() methods will not contain input columns.

Methods

fit(dataset)	Create a biclustering for X For more details on this function, see sklearn.cluster.SpectralBiclustering.fit
score(dataset)	Method not supported for this class.
set_input_cols(input_cols)	Input columns setter.
to_sklearn()	Get sklearn.cluster.SpectralBiclustering object.

Attributes

model_signatures

Returns model signature of current class.