120 lines
3.2 KiB
Python
120 lines
3.2 KiB
Python
# 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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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 fuzz as rfuzz
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from fuzzywuzzy import fuzz
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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 = 1000
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def scorer_benchmark(funcname):
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time_rapidfuzz = benchmark(
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"rapidfuzz", f"[rfuzz.{funcname}(a, b) for b in b_list]", setup, lengths, count
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)
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time_fuzzywuzzy = benchmark(
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"fuzzywuzzy", f"[fuzz.{funcname}(a, b) for b in b_list]", setup, lengths, count
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)
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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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"fuzzywuzzy": time_fuzzywuzzy,
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}
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)
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df.to_csv(f"results/{funcname}.csv", sep=",", index=False)
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scorer_benchmark("ratio")
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scorer_benchmark("partial_ratio")
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scorer_benchmark("token_sort_ratio")
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scorer_benchmark("token_set_ratio")
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scorer_benchmark("partial_token_sort_ratio")
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scorer_benchmark("partial_token_set_ratio")
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scorer_benchmark("WRatio")
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# token_ratio is unique to RapidFuzz
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time_token_ratio = benchmark(
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"token_ratio",
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"[rfuzz.token_ratio(a, b, processor=None) 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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# this gets very slow, so only benchmark it for smaller values
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time_token_ratio_simple = benchmark(
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"fuzzywuzzy",
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"[max(rfuzz.token_sort_ratio(a, b, processor=None), rfuzz.token_set_ratio(a, b, processor=None)) 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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df = pandas.DataFrame(
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data={
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"length": lengths,
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"token_ratio": time_token_ratio,
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"max(token_sort_ratio, token_set_ratio)": time_token_ratio_simple,
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}
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)
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df.to_csv("results/token_ratio.csv", sep=",", index=False)
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# partial_token_ratio is unique to RapidFuzz
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time_partial_token_ratio = benchmark(
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"token_ratio",
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"[rfuzz.partial_token_ratio(a, b, processor=None) 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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# this gets very slow, so only benchmark it for smaller values
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time_partial_token_ratio_simple = benchmark(
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"fuzzywuzzy",
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(
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"[max(rfuzz.partial_token_sort_ratio(a, b, processor=None), "
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"rfuzz.partial_token_set_ratio(a, b, processor=None)) for b in b_list]"
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),
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setup,
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lengths,
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count,
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)
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df = pandas.DataFrame(
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data={
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"length": lengths,
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"partial_token_ratio": time_partial_token_ratio,
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"max(partial_token_sort_ratio, partial_token_set_ratio)": time_partial_token_ratio_simple,
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}
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)
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df.to_csv("results/partial_token_ratio.csv", sep=",", index=False)
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