snowflake.ml.modeling.cluster.SpectralClustering¶

class snowflake.ml.modeling.cluster.SpectralClustering(*, n_clusters=8, eigen_solver=None, n_components=None, random_state=None, n_init=10, gamma=1.0, affinity='rbf', n_neighbors=10, eigen_tol='auto', assign_labels='kmeans', degree=3, coef0=1, kernel_params=None, n_jobs=None, verbose=False, 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

Apply clustering to a projection of the normalized Laplacian For more details on this class, see sklearn.cluster.SpectralClustering

n_clusters: int, default=8

The dimension of the projection subspace.

eigen_solver: {‘arpack’, ‘lobpcg’, ‘amg’}, default=None

The eigenvalue decomposition strategy to use. AMG requires pyamg to be installed. It can be faster on very large, sparse problems, but may also lead to instabilities. If None, then 'arpack' is used. See [4]_ for more details regarding ‘lobpcg’.

n_components: int, default=None

Number of eigenvectors to use for the spectral embedding. If None, defaults to n_clusters.

random_state: int, RandomState instance, default=None

A pseudo random number generator used for the initialization of the lobpcg eigenvectors decomposition when eigen_solver == ‘amg’, and for the K-Means initialization. Use an int to make the results deterministic across calls (See Glossary).

n_init: int, default=10

Number of time the k-means algorithm will be run with different centroid seeds. The final results will be the best output of n_init consecutive runs in terms of inertia. Only used if assign_labels='kmeans'.

gamma: float, default=1.0

Kernel coefficient for rbf, poly, sigmoid, laplacian and chi2 kernels. Ignored for affinity='nearest_neighbors'.

affinity: str or callable, default=’rbf’

How to construct the affinity matrix.

• ‘nearest_neighbors’: construct the affinity matrix by computing a graph of nearest neighbors.

• ‘rbf’: construct the affinity matrix using a radial basis function (RBF) kernel.

• ‘precomputed’: interpret X as a precomputed affinity matrix, where larger values indicate greater similarity between instances.

• ‘precomputed_nearest_neighbors’: interpret X as a sparse graph of precomputed distances, and construct a binary affinity matrix from the n_neighbors nearest neighbors of each instance.

• one of the kernels supported by pairwise_kernels().

Only kernels that produce similarity scores (non-negative values that increase with similarity) should be used. This property is not checked by the clustering algorithm.

n_neighbors: int, default=10

Number of neighbors to use when constructing the affinity matrix using the nearest neighbors method. Ignored for affinity='rbf'.

eigen_tol: float, default=”auto”

Stopping criterion for eigendecomposition of the Laplacian matrix. If eigen_tol=”auto” then the passed tolerance will depend on the eigen_solver:

• If eigen_solver=”arpack”, then eigen_tol=0.0;

• If eigen_solver=”lobpcg” or eigen_solver=”amg”, then eigen_tol=None which configures the underlying lobpcg solver to automatically resolve the value according to their heuristics. See, scipy.sparse.linalg.lobpcg() for details.

Note that when using eigen_solver=”lobpcg” or eigen_solver=”amg” values of tol<1e-5 may lead to convergence issues and should be avoided.

assign_labels: {‘kmeans’, ‘discretize’, ‘cluster_qr’}, default=’kmeans’

The strategy for assigning labels in the embedding space. There are two ways to assign labels after the Laplacian embedding. k-means is a popular choice, but it can be sensitive to initialization. Discretization is another approach which is less sensitive to random initialization [3]_. The cluster_qr method [5]_ directly extract clusters from eigenvectors in spectral clustering. In contrast to k-means and discretization, cluster_qr has no tuning parameters and runs no iterations, yet may outperform k-means and discretization in terms of both quality and speed.

degree: float, default=3

Degree of the polynomial kernel. Ignored by other kernels.

coef0: float, default=1

Zero coefficient for polynomial and sigmoid kernels. Ignored by other kernels.

kernel_params: dict of str to any, default=None

Parameters (keyword arguments) and values for kernel passed as callable object. Ignored by other kernels.

n_jobs: int, default=None

The number of parallel jobs to run when affinity=’nearest_neighbors’ or affinity=’precomputed_nearest_neighbors’. The neighbors search will be done in parallel. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.

verbose: bool, default=False

Verbosity mode.

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)	Perform spectral clustering from features, or affinity matrix For more details on this function, see sklearn.cluster.SpectralClustering.fit
score(dataset)	Method not supported for this class.
set_input_cols(input_cols)	Input columns setter.
to_sklearn()	Get sklearn.cluster.SpectralClustering object.

Attributes

model_signatures

Returns model signature of current class.