49 lines
1.2 KiB
Python
49 lines
1.2 KiB
Python
# Author: Mathieu Blondel <mathieu@mblondel.org>
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# License: BSD 3 clause
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import time
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import matplotlib.pyplot as plt
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from sklearn.utils import check_random_state
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from sklearn.metrics.pairwise import pairwise_distances
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from sklearn.metrics.pairwise import pairwise_kernels
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def plot(func):
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random_state = check_random_state(0)
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one_core = []
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multi_core = []
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sample_sizes = range(1000, 6000, 1000)
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for n_samples in sample_sizes:
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X = random_state.rand(n_samples, 300)
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start = time.time()
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func(X, n_jobs=1)
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one_core.append(time.time() - start)
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start = time.time()
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func(X, n_jobs=-1)
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multi_core.append(time.time() - start)
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plt.figure("scikit-learn parallel %s benchmark results" % func.__name__)
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plt.plot(sample_sizes, one_core, label="one core")
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plt.plot(sample_sizes, multi_core, label="multi core")
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plt.xlabel("n_samples")
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plt.ylabel("Time (s)")
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plt.title("Parallel %s" % func.__name__)
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plt.legend()
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def euclidean_distances(X, n_jobs):
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return pairwise_distances(X, metric="euclidean", n_jobs=n_jobs)
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def rbf_kernels(X, n_jobs):
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return pairwise_kernels(X, metric="rbf", n_jobs=n_jobs, gamma=0.1)
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plot(euclidean_distances)
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plot(rbf_kernels)
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plt.show()
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