snowflake.ml.modeling.manifold.SpectralEmbedding¶

class snowflake.ml.modeling.manifold.SpectralEmbedding(*, n_components=2, affinity='nearest_neighbors', gamma=None, random_state=None, eigen_solver=None, eigen_tol='auto', n_neighbors=None, n_jobs=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 embedding for non-linear dimensionality reduction For more details on this class, see sklearn.manifold.SpectralEmbedding

n_components: int, default=2

The dimension of the projected subspace.

affinity: {‘nearest_neighbors’, ‘rbf’, ‘precomputed’, ‘precomputed_nearest_neighbors’} or callable, default=’nearest_neighbors’

How to construct the affinity matrix.

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

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

• ‘precomputed’: interpret X as a precomputed affinity matrix.

• ‘precomputed_nearest_neighbors’: interpret X as a sparse graph of precomputed nearest neighbors, and constructs the affinity matrix by selecting the n_neighbors nearest neighbors.

• callable: use passed in function as affinity the function takes in data matrix (n_samples, n_features) and return affinity matrix (n_samples, n_samples).

gamma: float, default=None

Kernel coefficient for rbf kernel. If None, gamma will be set to 1/n_features.

random_state: int, RandomState instance or None, default=None

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

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. If None, then 'arpack' is used.

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.

n_neighbors: int, default=None

Number of nearest neighbors for nearest_neighbors graph building. If None, n_neighbors will be set to max(n_samples/10, 1).

n_jobs: int, default=None

The number of parallel jobs to run. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.

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)	Fit the model from data in X For more details on this function, see sklearn.manifold.SpectralEmbedding.fit
score(dataset)	Method not supported for this class.
set_input_cols(input_cols)	Input columns setter.
to_sklearn()	Get sklearn.manifold.SpectralEmbedding object.

Attributes

model_signatures

Returns model signature of current class.