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scikit-learn Binary Tree benchmark script -- used in scikit-learn/scikit-learn#19473
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| import numpy as np | |
| from sklearn.neighbors import KDTree, BallTree | |
| from .common import Benchmark | |
| class BinaryTreeBenchmark(Benchmark): | |
| """ | |
| Base class for BinaryTree benchmarks. | |
| """ | |
| param_names = ['BinaryTree', 'n', 'matrix_type', 'leaf_size'] | |
| params = [ | |
| [KDTree, BallTree], | |
| [1000, 10000], | |
| ['random', 'ordered', 'reverse_ordered', 'duplicated'], | |
| [1 , 20, 40, 100], | |
| ] | |
| test_cases = { | |
| "random": lambda n: np.random.rand(n), | |
| "ordered": lambda n: np.arange(n, dtype=float), | |
| "reverse_ordered": lambda n: np.arange(n, 0, -1, dtype=float), | |
| "duplicated": lambda n: np.zeros([n], dtype=float) | |
| } | |
| class BinaryTreeCreation(BinaryTreeBenchmark): | |
| """ | |
| Benchmarks for BinaryTree creation. | |
| """ | |
| def setup(self, BinaryTree, n, matrix_type, leaf_size): | |
| self.data = np.expand_dims(self.test_cases[matrix_type](n), -1) | |
| self.leaf_size = leaf_size | |
| self.BinaryTree = BinaryTree | |
| def time_creation(self, BinaryTree, n, matrix_type, leaf_size): | |
| self.BinaryTree(self.data, leaf_size=self.leaf_size) | |
| class BinaryTreeQuery(BinaryTreeBenchmark): | |
| """ | |
| Benchmarks for BinaryTree queries. | |
| """ | |
| param_names = BinaryTreeBenchmark.param_names + ['breadth_first'] | |
| params = BinaryTreeBenchmark.params + [[True, False]] | |
| def setup(self, BinaryTree, n, matrix_type, leaf_size, breadth_first): | |
| self.data = np.expand_dims(self.test_cases[matrix_type](n), -1) | |
| self.tree = BinaryTree(self.data, leaf_size=leaf_size) | |
| self.query_points = np.expand_dims(np.random.rand(10000), -1) | |
| def time_query(self, BinaryTree, n, matrix_type, leaf_size, breadth_first): | |
| self.tree.query( | |
| self.query_points, | |
| dualtree=False, | |
| breadth_first=breadth_first, | |
| sort_results=False) | |
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See scikit-learn/scikit-learn#13331 for reference.