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July 24, 2023 10:57
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RadiusNeighborsClassMode Updated Benchmark
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| import numpy as np | |
| from scipy.spatial.distance import cdist | |
| from scipy.sparse import rand as sparse_rand | |
| from .common import Benchmark | |
| from sklearn.neighbors import RadiusNeighborsClassifier | |
| class BruteRadiusNeighborsClass(Benchmark): | |
| param_names = [ | |
| "n_train", | |
| "n_test", | |
| "n_features", | |
| "metric", | |
| "dtype", | |
| "X_train", | |
| "X_test", | |
| ] | |
| params = [ | |
| [1000, 10_000], | |
| [1000, 10_000], | |
| [100], | |
| ["manhattan"], | |
| [np.float64], | |
| ["dense"], | |
| ["dense"], | |
| ] | |
| def setup(self, *params): | |
| n_train, n_test, n_features, metric, dtype, X_train, X_test = params | |
| rng = np.random.RandomState(0) | |
| self.X_train = ( | |
| rng.rand(n_train, n_features).astype(dtype) | |
| if X_train == "dense" | |
| else sparse_rand( | |
| n_train, | |
| n_features, | |
| density=0.05, | |
| format="csr", | |
| dtype=dtype, | |
| random_state=rng, | |
| ) | |
| ) | |
| self.X_test = ( | |
| rng.rand(n_test, n_features).astype(dtype) | |
| if X_test == "dense" | |
| else sparse_rand( | |
| n_test, | |
| n_features, | |
| density=0.05, | |
| format="csr", | |
| dtype=dtype, | |
| random_state=rng, | |
| ) | |
| ) | |
| self.y_train = rng.randint(low=-1, high=1, size=(n_train,)) | |
| self.metric = metric | |
| dist_mat = cdist( | |
| (self.X_train if X_train == "dense" else self.X_train.toarray())[:1000], | |
| (self.X_test if X_test == "dense" else self.X_test.toarray())[:10], | |
| ) | |
| self.radius = np.quantile(a=dist_mat.ravel(), q=0.01) * 20 | |
| self.rc = RadiusNeighborsClassifier( | |
| radius=self.radius, algorithm="brute", metric=self.metric, | |
| ) | |
| self.rc.fit(X=self.X_train, y=self.y_train) | |
| def time_predict_proba(self, *params): | |
| self.rc.predict_proba(self.X_test) |
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