8000 BENCH Benchmark 32bit implementation · scikit-learn/scikit-learn@2c842bd · GitHub
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BENCH Benchmark 32bit implementation
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‎asv_benchmarks/asv_scalability.sh

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#!/usr/bin/env bash
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#
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# Launch a ASV benchmark on groups of CPU
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# for scalability inspection
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#
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# Results are saved in given folders and files.
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#
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for i in 128 64 32 16 8 4 2 1;
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do
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last_core=$(($i-1))
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taskset -c 0-$last_core \
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asv continuous -b PairwiseDistancesArgKmin \
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-e main feat/pdr-32bit | tee pairwise_distances_argkmin_asv_${i}_cores.txt
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cp -R results results_${i}_cores
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done
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‎asv_benchmarks/benchmarks/pairwise_argkmin_estimator.py

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import numpy as np
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from .common import Benchmark
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from sklearn.metrics import (
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pairwise_distances_argmin,
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pairwise_distances_argmin_min
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)
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from sklearn.cluster import (
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AffinityPropagation,
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Birch,
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MeanShift,
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SpectralClustering,
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)
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from sklearn.neighbors import NearestNeighbors
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from sklearn.manifold import (
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Isomap,
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LocallyLinearEmbedding,
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TSNE,
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)
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from sklearn.semi_supervised import (
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LabelPropagation,
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LabelSpreading,
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)
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class PairwiseDistancesArgKminBenchmark(Benchmark):
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param_names = ["n_train", "n_test", "n_features"]
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params = [
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[1000, 10_000, int(1e7)],
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[1000, 10_000, 100_000],
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[100],
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]
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def setup(self, n_train, n_test, n_features):
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rng = np.random.RandomState(0)
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self.X_train = rng.rand(n_train, n_features).astype(np.float32)
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self.X_test = rng.rand(n_test, n_features).astype(np.float32)
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self.y_train = rng.randint(low=-1, high=1, size=(n_train,))
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def time_nearest_neighbors(self, n_train, n_test, n_features):
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est = NearestNeighbors(n_neighbors=10).fit(X=self.X_train)
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est.kneighbors(self.X_test)

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