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Hi,
I was looking into KMeans code and found that the following can be parallelized. For example, each work in for loop can be processed independently. I expect this to reduce the runtime. Please check.
scikit-learn/sklearn/cluster/_kmeans.py
Lines 1406 to 1438 in 84f8409
| for i in range(self._n_init): | |
| # Initialize centers | |
| centers_init = self._init_centroids( | |
| X, x_squared_norms=x_squared_norms, init=init, random_state=random_state | |
| ) | |
| if self.verbose: | |
| print("Initialization complete") | |
| # run a k-means once | |
| labels, inertia, centers, n_iter_ = kmeans_single( | |
| X, | |
| sample_weight, | |
| centers_init, | |
| max_iter=self.max_iter, | |
| verbose=self.verbose, | |
| tol=self._tol, | |
| x_squared_norms=x_squared_norms, | |
| n_threads=self._n_threads, | |
| ) | |
| # determine if these results are the best so far | |
| # we chose a new run if it has a better inertia and the clustering is | |
| # different from the best so far (it's possible that the inertia is | |
| # slightly better even if the clustering is the same with potentially | |
| # permuted labels, due to rounding errors) | |
| if best_inertia is None or ( | |
| inertia < best_inertia | |
| and not _is_same_clustering(labels, best_labels, self.n_clusters) | |
| ): | |
| best_labels = labels | |
| best_centers = centers | |
| best_inertia = inertia | |
| best_n_iter = n_iter_ |
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