2021-03-06 11:27:32 +00:00
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import timeit
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2022-10-02 08:06:27 +00:00
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2021-03-06 11:27:32 +00:00
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import numpy as np
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2022-10-02 08:06:27 +00:00
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import pandas
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2021-03-06 11:27:32 +00:00
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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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2022-10-02 08:24:00 +00:00
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setup = """
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2021-03-06 11:27:32 +00:00
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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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2022-10-02 08:24:00 +00:00
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lengths = list(range(1, 512, 2))
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2021-03-06 11:27:32 +00:00
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count = 2000
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2022-10-02 08:24:00 +00:00
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time_rapidfuzz = benchmark(
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"rapidfuzz",
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"[string_metric.levenshtein(a, b) for b in b_list]",
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setup,
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lengths,
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count,
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)
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2021-03-06 11:27:32 +00:00
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2022-10-02 08:24:00 +00:00
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time_polyleven = benchmark(
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"polyleven", "[polyleven.levenshtein(a, b) for b in b_list]", setup, lengths, count
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)
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2021-03-06 11:27:32 +00:00
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# this gets very slow, so only benchmark it for smaller values
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2022-10-02 08:24:00 +00:00
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time_python_levenshtein = (
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benchmark(
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"python-Levenshtein",
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"[Levenshtein.distance(a, b) for b in b_list]",
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setup,
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list(range(1, 256, 2)),
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count,
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)
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+ [np.NaN] * 128
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)
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2021-03-06 11:27:32 +00:00
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2022-10-02 08:24:00 +00:00
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time_edlib = benchmark(
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"edlib", "[edlib.align(a, b) for b in b_list]", setup, lengths, count
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)
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2021-03-06 11:27:32 +00:00
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2022-10-02 08:24:00 +00:00
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time_editdistance = benchmark(
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"editdistance", "[editdistance.eval(a, b) for b in b_list]", setup, lengths, count
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)
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2021-03-06 11:27:32 +00:00
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2022-10-02 08:24:00 +00:00
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df = pandas.DataFrame(
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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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)
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2021-03-06 11:27:32 +00:00
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2022-10-02 08:24:00 +00:00
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df.to_csv("results/levenshtein_uniform.csv", sep=",", index=False)
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