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jnothman/bench_working_memory.csv

Created January 10, 2018 02:49
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bench_working_memory.csv
elapsed n_features n_samples_Y working_memory
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270 36.95804214477539 10000 100 464
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280 41.36782932281494 10000 10000 464
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314 25.125698566436768 10000 5000 10000
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321 19.166558027267456 10000 40000 10000
322 21.00684690475464 10000 40000 10000
323 17.919042587280273 10000 40000 10000
Raw
bench_working_memory.py
import time
import numpy as np
import pandas as pd
import sklearn
from sklearn.metrics.pairwise import pairwise_distances_argmin_min
results = []
n_samples_X = 10000
for n_features in [1000, 10000]:
for working_memory in [1, 10, 22, 46, 100, 215, 464, 1000, 10000]:
with sklearn.config_context(working_memory=working_memory):
for n_samples_Y in [100, 1000, 5000, 10000, 20000, 40000]:
for i in range(3):
X = np.random.rand(n_samples_X, n_features)
Y = np.random.rand(n_samples_Y, n_features)
start = time.time()
pairwise_distances_argmin_min(X, X)
elapsed = time.time() - start
results.append({'n_samples_Y': n_samples_Y,
'n_features': n_features,
'working_memory': working_memory,
'elapsed': elapsed})
print(results[-1])
pd.DataFrame(results).to_csv('bench_working_memory.csv')
pd.DataFrame(results).to_json('bench_working_memory.json', orient='records')
Raw
plot_bench_working_memory.py
import matplotlib.pyplot as plt
import pandas as pd
bench = pd.read_csv('bench_working_memory.csv')
fig, ax = plt.subplots(1)
for (n_features, df), linestyle in zip(bench.groupby('n_features', as_index=False), ['dotted', 'dashed', 'solid']):
piv = df.pivot_table('elapsed', ['working_memory'], ['n_samples_Y', 'n_features'])
piv.plot(logy=True, logx=True, marker='x', linestyle=linestyle, ax=ax)
plt.xlabel('working_memory')
plt.ylabel('time (s)')
plt.savefig('/tmp/bench_working_memory.png')
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