Spectral Embedding#871
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cpp/include/cuvs/preprocessing/spectral/spectral_embedding_types.hpp
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Depends on rapidsai/cuvs#871 Resolves #6912 # Benchmarks 1x GH200 | Dataset | n_samples | n_features | sklearn_time (s) | cuml_time (s) | Speedup | |-----------------------|-----------|-------------|---------------|-----------|-------------| | deep-image-96 | 50000 | 96 | 87.027506 | 0.037730 | 2306.607180x | | fashion-mnist-784 | 50000 | 784 | 2.912207 | 0.053853 | 54.076849x | | gist-960 | 50000 | 960 | 73.453647 | 0.046808 | 1569.253974x | | glove-25 | 50000 | 25 | 114.452536 | 0.031595 | 3622.489824x | | sift-128 | 50000 | 128 | 14.886346 | 0.039108 | 380.651352x | # Usage ```python from sklearn import datasets from cuml.manifold import SpectralEmbedding import cupy as cp # (1500, 3) -> (1500, 2) n_samples = 1500 S_points, S_color = datasets.make_s_curve(n_samples, random_state=0) spectral = SpectralEmbedding(n_components=2, n_neighbors=None, random_state=42) embedding = spectral.fit_transform(cp.asarray(S_points, order='C', dtype=cp.float32)) from sklearn.datasets import fetch_openml # (70000, 784) -> (70000, 2) mnist = fetch_openml('mnist_784', version=1) X, y = mnist.data, mnist.target.astype(int) spectral = SpectralEmbedding(n_components=2, n_neighbors=None, random_state=42) embedding = spectral.fit_transform(cp.asarray(X, order='C', dtype=cp.float32)) ```    Authors: - Anupam (https://github.com/aamijar) - Jim Crist-Harif (https://github.com/jcrist) Approvers: - Victor Lafargue (https://github.com/viclafargue) - Jim Crist-Harif (https://github.com/jcrist) - Simon Adorf (https://github.com/csadorf) - Corey J. Nolet (https://github.com/cjnolet) URL: #6581
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Depends on rapidsai/raft#2662 and Resolves #1025