snowflake.ml.modeling.manifold.SpectralEmbeddingΒΆ
- 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)ΒΆ
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: {βnearest_neighborsβ, βrbfβ, βprecomputedβ, βprecomputed_nearest_neighborsβ} or callable, default=βnearest_neighborsβ
- How to construct the affinity matrix.
βnearest_neighborsβ: construct the affinity matrix by computing a graph of nearest neighbors.
βrbfβ: construct the affinity matrix by computing a radial basis function (RBF) kernel.
βprecomputedβ: interpret
X
as a precomputed affinity matrix.βprecomputed_nearest_neighborsβ: interpret
X
as a sparse graph of precomputed nearest neighbors, and constructs the affinity matrix by selecting then_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 == βamgβ, and for the K-Means initialization. Use an int to make the results deterministic across calls (See Glossary).
- 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. If None, then
'arpack'
is used.- 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.
- 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 ajoblib.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 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)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.