snowflake.ml.modeling.cluster.SpectralClusteringΒΆ
- 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)ΒΆ
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: {βarpackβ, βlobpcgβ, βamgβ}, 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 βlobpcgβ.- 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 == βamgβ, 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=βrbfβ
- How to construct the affinity matrix.
βnearest_neighborsβ: construct the affinity matrix by computing a graph of nearest neighbors.
βrbfβ: construct the affinity matrix using a radial basis function (RBF) kernel.
βprecomputedβ: interpret
X
as a precomputed affinity matrix, where larger values indicate greater similarity between instances.βprecomputed_nearest_neighborsβ: interpret
X
as a sparse graph of precomputed distances, and construct a binary affinity matrix from then_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=βautoβ
Stopping criterion for eigendecomposition of the Laplacian matrix. If eigen_tol=βautoβ then the passed tolerance will depend on the eigen_solver:
If eigen_solver=βarpackβ, then eigen_tol=0.0;
If eigen_solver=βlobpcgβ or eigen_solver=βamgβ, 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=βlobpcgβ or eigen_solver=βamgβ values of tol<1e-5 may lead to convergence issues and should be avoided.
- assign_labels: {βkmeansβ, βdiscretizeβ, βcluster_qrβ}, default=βkmeansβ
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=βnearest_neighborsβ or affinity=βprecomputed_nearest_neighborsβ. The neighbors search will be done in parallel.
None
means 1 unless in ajoblib.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 estimatorβs predict() method, but output_cols must be set explicitly for transformers.
- sample_weight_col: Optional[str]
A string representing the column name containing the examplesβ 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.