64 lines
1.9 KiB
Python
64 lines
1.9 KiB
Python
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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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for length in 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 import string_metric
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import Levenshtein
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import polyleven
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import edlib
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import editdistance
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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(1,512,2))
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count = 2000
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time_rapidfuzz = benchmark("rapidfuzz",
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'[string_metric.levenshtein(a, b) for b in b_list]',
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setup, lengths, count)
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time_polyleven = benchmark("polyleven",
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'[polyleven.levenshtein(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_python_levenshtein = benchmark("python-Levenshtein",
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'[Levenshtein.distance(a, b) for b in b_list]',
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setup, list(range(1,256,2)), count) + [np.NaN] * 128
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time_edlib = benchmark("edlib",
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'[edlib.align(a, b) for b in b_list]',
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setup, lengths, count)
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time_editdistance = benchmark("editdistance",
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'[editdistance.eval(a, b) for b in b_list]',
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setup, lengths, count)
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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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"polyleven": time_polyleven,
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"python-Levenshtein": time_python_levenshtein,
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"edlib": time_edlib,
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"editdistance": time_editdistance
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})
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df.to_csv("results/levenshtein_uniform.csv", sep=',',index=False)
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