46 lines
1.4 KiB
Python
46 lines
1.4 KiB
Python
from sklearn.metrics.pairwise import pairwise_distances
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from .common import Benchmark
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from .datasets import _random_dataset
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class PairwiseDistancesBenchmark(Benchmark):
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"""
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Benchmarks for pairwise distances.
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"""
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param_names = ['representation', 'metric', 'n_jobs']
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params = (['dense', 'sparse'],
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['cosine', 'euclidean', 'manhattan', 'correlation'],
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Benchmark.n_jobs_vals)
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def setup(self, *params):
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representation, metric, n_jobs = params
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if representation == 'sparse' and metric == 'correlation':
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raise NotImplementedError
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if Benchmark.data_size == 'large':
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if metric in ('manhattan', 'correlation'):
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n_samples = 8000
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else:
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n_samples = 24000
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else:
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if metric in ('manhattan', 'correlation'):
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n_samples = 4000
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else:
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n_samples = 12000
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data = _random_dataset(n_samples=n_samples,
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representation=representation)
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self.X, self.X_val, self.y, self.y_val = data
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self.pdist_params = {'metric': metric,
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'n_jobs': n_jobs}
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def time_pairwise_distances(self, *args):
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pairwise_distances(self.X, **self.pdist_params)
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def peakmem_pairwise_distances(self, *args):
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pairwise_distances(self.X, **self.pdist_params)
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