46 lines
1.3 KiB
Python
46 lines
1.3 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 = (
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["dense", "sparse"],
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["cosine", "euclidean", "manhattan", "correlation"],
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Benchmark.n_jobs_vals,
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)
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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, 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, "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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