Kernel PCA: Python and Benchmarking Code #5988
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tomasjoh
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tomasjoh
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Jul 27, 2024
| def _build_params(self, n_rows, n_cols): | ||
| IF GPUBUILD == 1: | ||
| cdef paramsKPCA *params = new paramsKPCA() | ||
| params.n_components = min(self.n_components_ or n_rows, n_rows) |
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Sklearn reference for setting n_components
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Description
This PR relies on C++ implementation from #5987
Adds Python, and benchmarking code for Kernel PCA. This implementation of Kernel PCA support fit(), transform(), and fit_transform().
Feature request: #1317
Tests and benchmarks were performed on an EC2
g4dn.xlargeinstance with CUDA 12.2.Click here to see environment details
Notes for Reviewers
The API deviates from SKlearn by not supporting options for these fields: fit_inverse_transform, random_state, n_jobs, max_iter. If a user tries to set one of them a
NotImplementedErrorwill be raised.The Criteria of Done mentions making the class pickable in
cuml/tests/test_pickle.py. I couldn't find a PCA reference for this. Would appreciate pointers if additional work is needed.Benchmarks
From
notebooks/tools/cuml_benchmarks.ipynbBenchmark output
We see an even greater speedup when we set n_components = n_samples. Setting n_components to n_samples is the same as default behavior, except zero eigenvalues aren't removed.
Manual tests
Kernel PCA with RBF kernel
code
Kernel PCA with poly kernel
code
from sklearn.datasets import make_classification def plot_3d_projection(X, y, title, elev=30, azim=30): fig = plt.figure(figsize=(8, 6)) ax = fig.add_subplot(111, projection='3d') for target in np.unique(y): ax.scatter(X[y == target, 0], X[y == target, 1], X[y == target, 2], label=str(target)) ax.set_title(title) ax.set_xlabel('Component 1') ax.set_ylabel('Component 2') ax.set_zlabel('Component 3') ax.legend() ax.view_init(elev=elev, azim=azim) # Set viewpoint plt.show() X, y = make_classification(n_features=3, n_informative=3, n_redundant=0, n_clusters_per_class=1, n_classes=3) plot_3d_projection(X, y, 'Original Data', elev=30, azim=60) poly_kpca = cuKernelPCA(n_components=3, kernel='poly', degree=5, gamma=2, coef0=2) X_poly_kpca = linear_kpca.fit_transform(X_linear) plot_3d_projection(X_poly_kpca, y, 'cuML KernelPCA with Poly Kernel', elev=30, azim=120)Projecting testing data
Case is copied from sklearn, except it uses cuML PCA and kernelPCA
code
Definition of Done Criteria Checklist
Python Checklist
Design
cuml/tests/test_pickle.pyinput_to_cuml_arrayto accept flexible inputs and check their datatypes and usecumlArray.to_output()to return configurable outputs.CumlArrayTesting
python/cuml/benchmarks/algorithms.pyand benchmarks notebook inpython/cuml/notebooks/tools/cuml_benchmarks.ipynbUnit test results
Python Test Results