48 lines
1.4 KiB
Python
48 lines
1.4 KiB
Python
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# todo combine benchmarks of scorers into common code base
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import timeit
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import pandas
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import numpy as np
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def benchmark(name, func, setup, lengths, count):
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print(f"starting {name}")
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start = timeit.default_timer()
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results = []
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from tqdm import tqdm
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for length in tqdm(lengths):
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test = timeit.Timer(func, setup=setup.format(length, count))
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results.append(min(test.timeit(number=1) for _ in range(7)) / count)
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stop = timeit.default_timer()
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print(f"finished {name}, Runtime: ", stop - start)
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return results
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setup ="""
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from rapidfuzz.distance import Jaro
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import jellyfish
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import string
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import random
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random.seed(18)
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characters = string.ascii_letters + string.digits + string.whitespace + string.punctuation
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a = ''.join(random.choice(characters) for _ in range({0}))
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b_list = [''.join(random.choice(characters) for _ in range({0})) for _ in range({1})]
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"""
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lengths = list(range(1,256,4))
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count = 4000
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time_rapidfuzz = benchmark("rapidfuzz",
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'[Jaro.similarity(a, b) for b in b_list]',
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setup, lengths, count)
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# this gets very slow, so only benchmark it for smaller values
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time_jellyfish = benchmark("jellyfish",
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'[jellyfish.jaro_similarity(a, b) for b in b_list]',
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setup, list(range(1,128,4)), count) + [np.NaN] * 32
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df = pandas.DataFrame(data={
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"length": lengths,
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"rapidfuzz": time_rapidfuzz,
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"jellyfish": time_jellyfish,
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})
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df.to_csv("results/jaro.csv", sep=',',index=False)
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