35 lines
816 B
Python
35 lines
816 B
Python
from sklearn.manifold import TSNE
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from .common import Benchmark, Estimator
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from .datasets import _digits_dataset
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class TSNEBenchmark(Estimator, Benchmark):
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"""
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Benchmarks for t-SNE.
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"""
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param_names = ['method']
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params = (['exact', 'barnes_hut'],)
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def setup_cache(self):
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super().setup_cache()
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def make_data(self, params):
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method, = params
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n_samples = 500 if method == 'exact' else None
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return _digits_dataset(n_samples=n_samples)
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def make_estimator(self, params):
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method, = params
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estimator = TSNE(random_state=0, method=method)
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return estimator
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def make_scorers(self):
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self.train_scorer = lambda _, __: self.estimator.kl_divergence_
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self.test_scorer = lambda _, __: self.estimator.kl_divergence_
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