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SpectralEmbedding#

class cuml.manifold.SpectralEmbedding( n_components=2, affinity='nearest_neighbors', random_state=None, n_neighbors=None, verbose=False, output_type=None, )#

Spectral embedding for non-linear dimensionality reduction.

Forms an affinity matrix given by the specified function and applies spectral decomposition to the corresponding graph laplacian. The resulting transformation is given by the value of the eigenvectors for each data point.

Note : Laplacian Eigenmaps is the actual algorithm implemented here.

Parameters:

n_componentsint, default=2

The dimension of the projected subspace.

affinity{‘nearest_neighbors’, ‘precomputed’}, default=’nearest_neighbors’

How to construct the affinity matrix.

‘nearest_neighbors’ : construct the affinity matrix by computing a graph of nearest neighbors.
‘precomputed’ : interpret X as a precomputed affinity matrix.

random_stateint, RandomState instance or None, default=None

A pseudo random number generator used for the initialization. Use an int to make the results deterministic across calls.

n_neighborsint or None, default=2

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

verboseint or boolean, default=False

Sets logging level. It must be one of cuml.common.logger.level_*. See Verbosity Levels for more info.

output_type{None, ‘input’, ‘cupy’, ‘numpy’, ‘cudf’, ‘pandas’}, default=None

Return results and set estimator attributes to the indicated output type. If None, the output type set at the module level (cuml.global_settings.output_type) will be used. See Output Data Type Configuration for more info.

Attributes:

embedding_cupy.ndarray of shape (n_samples, n_components): Spectral embedding of the training matrix.
n_neighbors_int: Number of nearest neighbors effectively used.

Methods

`fit`(self, X[, y])	Fit the model from data in X.
`fit_transform`(self, X[, y])	Fit the model from data in X and transform X.

Notes

Spectral Embedding (Laplacian Eigenmaps) is most useful when the graph has one connected component. If there graph has many components, the first few eigenvectors will simply uncover the connected components of the graph.

Examples

>>> import cupy as cp
>>> from cuml.manifold import SpectralEmbedding
>>> X = cp.random.rand(100, 20, dtype=cp.float32)
>>> embedding = SpectralEmbedding(n_components=2, random_state=42)
>>> X_transformed = embedding.fit_transform(X)
>>> X_transformed.shape
(100, 2)

as_sklearn()[source]#

Convert this estimator into an equivalent scikit-learn (or scikit-learn extension) estimator.

Returns:

sklearn.base.BaseEstimator: A scikit-learn compatible estimator instance that mirrors the trained state of the current estimator.

fit(self, X, y=None) → 'SpectralEmbedding'[source]#

Fit the model from data in X.

Parameters:

Xarray-like or sparse matrix of shape (n_samples, n_features) or (n_samples, n_samples): Training vector, where n_samples is the number of samples and n_features is the number of features. If affinity is ‘precomputed’, X is the affinity matrix. Supported formats for precomputed affinity: scipy sparse (CSR, CSC, COO), cupy sparse (CSR, CSC, COO), dense numpy arrays, or dense cupy arrays.
yIgnored: Not used, present for API consistency by convention.

Returns:

selfobject: Returns the instance itself.

fit_transform(self, X, y=None)[source]#

Fit the model from data in X and transform X.

Parameters:

Xarray-like or sparse matrix of shape (n_samples, n_features) or (n_samples, n_samples): Training vector, where n_samples is the number of samples and n_features is the number of features. If affinity is ‘precomputed’, X is the affinity matrix. Supported formats for precomputed affinity: scipy sparse (CSR, CSC, COO), cupy sparse (CSR, CSC, COO), dense numpy arrays, or dense cupy arrays.
yIgnored: Not used, present for API consistency by convention.

Returns:

X_newcupy.ndarray of shape (n_samples, n_components): Spectral embedding of the training matrix.

classmethod from_sklearn(model)[source]#

Create a cuml estimator from a scikit-learn estimator.

Parameters:

modelsklearn.base.BaseEstimator: A compatible scikit-learn (or scikit-learn extension) estimator.

Returns:

cls: A new instance of this cuml estimator class that mirrors the state of the input estimator.

Notes

output_type of the estimator is set to “numpy” by default, as these cannot be inferred from training arguments. If something different is required, then please use cuml’s output_type configuration utilities.

get_feature_names_out(input_features=None)[source]#

Get output feature names for transformation.

The feature names out will prefixed by the lowercased class name. For example, if the transformer outputs 3 features, then the feature names out are: ["class_name0", "class_name1", "class_name2"].

Parameters:

input_featuresarray-like of str or None, default=None: Only used to validate feature names with the names seen in fit.

Returns:

feature_names_outndarray of str objects: Transformed feature names.

get_params(deep=True)[source]#: Returns a dict of all params owned by this class. If the child class has appropriately overridden the _get_param_names method and does not need anything other than what is there in this method, then it doesn’t have to override this method

set_params(**params)[source]#: Accepts a dict of params and updates the corresponding ones owned by this class. If the child class has appropriately overridden the _get_param_names method and does not need anything other than what is, there in this method, then it doesn’t have to override this method