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add some benchmarks to Levenshtein distance

This commit is contained in:
Max Bachmann 2021-03-06 12:27:32 +01:00
parent 83b9fd8336
commit 779e26b6a0
7 changed files with 808 additions and 152 deletions
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1
.gitattributes vendored
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@ -1,4 +1,3 @@
extern/variant/* linguist-vendored
src/cpp_process.cpp linguist-vendored
src/cpp_fuzz.cpp linguist-vendored
src/cpp_string_metric.cpp linguist-vendored

46
bench/benchmark_indel_levenshtein.py Normal file
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# todo combine benchmarks of scorers into common code base
import timeit
import pandas
import numpy as np
def benchmark(name, func, setup, lengths, count):
print(f"starting {name}")
start = timeit.default_timer()
results = []
for length in lengths:
test = timeit.Timer(func, setup=setup.format(length, count))
results.append(min(test.timeit(number=1) for _ in range(7)) / count)
stop = timeit.default_timer()
print(f"finished {name}, Runtime: ", stop - start)
return results
setup ="""
from rapidfuzz import string_metric, process, fuzz
import Levenshtein
import string
import random
random.seed(18)
characters = string.ascii_letters + string.digits + string.whitespace + string.punctuation
a = ''.join(random.choice(characters) for _ in range({0}))
b_list = [''.join(random.choice(characters) for _ in range({0})) for _ in range({1})]
"""
lengths = list(range(1,512,2))
count = 1000
time_rapidfuzz = benchmark("rapidfuzz",
'[string_metric.levenshtein(a, b, (1,1,2)) for b in b_list]',
setup, lengths, count)
# this gets very slow, so only benchmark it for smaller values
time_python_levenshtein = benchmark("python-Levenshtein",
'[Levenshtein.ratio(a, b) for b in b_list]',
setup, list(range(1,256,2)), count) + [np.NaN] * 128
df = pandas.DataFrame(data={
"length": lengths,
"rapidfuzz": time_rapidfuzz,
"python-Levenshtein": time_python_levenshtein,
})
df.to_csv("results/levenshtein_indel.csv", sep=',',index=False)

63
bench/benchmark_uniform_levenshtein.py Normal file
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@ -0,0 +1,63 @@
import timeit
import pandas
import numpy as np
def benchmark(name, func, setup, lengths, count):
print(f"starting {name}")
start = timeit.default_timer()
results = []
for length in lengths:
test = timeit.Timer(func, setup=setup.format(length, count))
results.append(min(test.timeit(number=1) for _ in range(7)) / count)
stop = timeit.default_timer()
print(f"finished {name}, Runtime: ", stop - start)
return results
setup ="""
from rapidfuzz import string_metric
import Levenshtein
import polyleven
import edlib
import editdistance
import string
import random
random.seed(18)
characters = string.ascii_letters + string.digits + string.whitespace + string.punctuation
a = ''.join(random.choice(characters) for _ in range({0}))
b_list = [''.join(random.choice(characters) for _ in range({0})) for _ in range({1})]
"""
lengths = list(range(1,512,2))
count = 2000
time_rapidfuzz = benchmark("rapidfuzz",
'[string_metric.levenshtein(a, b) for b in b_list]',
setup, lengths, count)
time_polyleven = benchmark("polyleven",
'[polyleven.levenshtein(a, b) for b in b_list]',
setup, lengths, count)
# this gets very slow, so only benchmark it for smaller values
time_python_levenshtein = benchmark("python-Levenshtein",
'[Levenshtein.distance(a, b) for b in b_list]',
setup, list(range(1,256,2)), count) + [np.NaN] * 128
time_edlib = benchmark("edlib",
'[edlib.align(a, b) for b in b_list]',
setup, lengths, count)
time_editdistance = benchmark("editdistance",
'[editdistance.eval(a, b) for b in b_list]',
setup, lengths, count)
df = pandas.DataFrame(data={
"length": lengths,
"rapidfuzz": time_rapidfuzz,
"polyleven": time_polyleven,
"python-Levenshtein": time_python_levenshtein,
"edlib": time_edlib,
"editdistance": time_editdistance
})
df.to_csv("results/levenshtein_uniform.csv", sep=',',index=False)

257
bench/results/levenshtein_indel.csv Normal file
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@ -0,0 +1,257 @@
length,rapidfuzz,python-Levenshtein
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1 length rapidfuzz python-Levenshtein
2 1 1.2468099885154517e-07 1.0524900062591769e-07
3 3 1.3070699787931517e-07 1.4172100054565818e-07
4 5 1.376649997837376e-07 1.6557599883526564e-07
5 7 1.423840003553778e-07 1.9732100190594793e-07
6 9 1.4730299881193786e-07 2.3082799816620536e-07
7 11 1.5495200204895808e-07 2.8296399977989493e-07
8 13 1.6093800149974413e-07 3.063600015593693e-07
9 15 1.6728799892007374e-07 4.2989499706891366e-07
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11 19 1.8033800006378442e-07 5.746030001319014e-07
12 21 1.869509978860151e-07 5.730039993068204e-07
13 23 1.9280099877505563e-07 6.615389975195284e-07
14 25 1.9892199998139403e-07 7.551790004072245e-07
15 27 2.0476099962252193e-07 8.575210013077595e-07
16 29 2.1009199917898513e-07 9.70551001955755e-07
17 31 2.1750599989900364e-07 1.1884669984283391e-06
18 33 2.2445500144385732e-07 1.3859749997209292e-06
19 35 2.465420002408791e-07 1.5269529976649209e-06
20 37 2.3682500250288286e-07 1.694680999207776e-06
21 39 2.4279399804072457e-07 1.7842809975263662e-06
22 41 2.494870022928808e-07 2.0377930013637525e-06
23 43 2.5495099907857365e-07 2.22583299910184e-06
24 45 2.6129300022148525e-07 2.4161880028259475e-06
25 47 2.674850002222229e-07 2.5204009980370755e-06
26 49 2.7427799795987084e-07 2.8303500002948568e-06
27 51 2.8008099980070255e-07 2.9500000018742867e-06
28 53 2.858629995898809e-07 3.175680001731962e-06
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34 65 1.0491019966138993e-06 4.725435999716865e-06
35 67 1.0886290001508314e-06 5.015237999032252e-06
36 69 1.1150500031362752e-06 5.302932000631699e-06
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257
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@ -0,0 +1,257 @@
length,rapidfuzz,polyleven,python-Levenshtein,edlib,editdistance
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389,2.3369731000002506e-05,3.4528282999872314e-05,,6.118583639999998e-05,4.9973804500041296e-05
391,2.35083629999906e-05,3.4675143000185934e-05,,6.136202719990251e-05,4.989375500008464e-05
393,2.365131999999903e-05,3.510468300009961e-05,,6.148874350001278e-05,5.0763263000590085e-05
395,2.375129300000367e-05,3.5219742000208505e-05,,6.153614220002055e-05,5.070214050010691e-05
397,2.394928600000413e-05,3.558097800032556e-05,,6.171069350002652e-05,5.092248950040812e-05
399,2.4057702999982666e-05,3.561898599991764e-05,,6.164522889994259e-05,5.131039649950253e-05
401,2.41848169999912e-05,3.601219400025002e-05,,6.183610249991034e-05,5.155986950012448e-05
403,2.428869899998176e-05,3.598346599983415e-05,,6.190940690012212e-05,5.1552625500335125e-05
405,2.4464819999991504e-05,3.651467700001376e-05,,6.181639510014065e-05,5.188454000017373e-05
407,2.4506658000007066e-05,3.6466360999838786e-05,,6.185912109995116e-05,5.2302760000202394e-05
409,2.472715900000821e-05,3.7058816999888224e-05,,6.200726739989478e-05,5.251504299940279e-05
411,2.486268899997413e-05,3.7179432999892016e-05,,6.1936995600081e-05,5.268927899942355e-05
413,2.498605400000997e-05,3.717658100003973e-05,,6.206541179999476e-05,5.2807500500421155e-05
415,2.510999599999764e-05,3.764449099980993e-05,,6.209758780005357e-05,5.3076476000569525e-05
417,2.523934100000247e-05,3.787568000007013e-05,,6.216939059995638e-05,5.366225400030089e-05
419,2.535589299998264e-05,3.810839500010843e-05,,6.232799490007892e-05,5.372596600045654e-05
421,2.547493399998757e-05,3.8095099000202025e-05,,6.223583269984375e-05,5.411172399999487e-05
423,2.5608584000025306e-05,3.86273600001914e-05,,6.240265129999898e-05,5.415566050032794e-05
425,2.568974700000126e-05,3.88332510001419e-05,,6.241767870014883e-05,5.448702749981749e-05
427,2.5847646999977772e-05,3.874143500024729e-05,,6.257362340002146e-05,5.466328900001827e-05
429,2.5969867000014803e-05,3.934265300040352e-05,,6.259217699989677e-05,5.500244850009039e-05
431,2.6048656000000393e-05,3.964892500016504e-05,,6.27965536999909e-05,5.530678549985168e-05
433,2.620645399997557e-05,3.981365600020581e-05,,6.300613069997779e-05,5.576178600040294e-05
435,2.6289307000013195e-05,4.0031074000125956e-05,,6.297886829997879e-05,5.583795450002072e-05
437,2.649120099999891e-05,4.02854210001351e-05,,6.321917140012374e-05,5.613353099943197e-05
439,2.656470699997726e-05,4.038523600002008e-05,,6.327236580000319e-05,5.614412900013122e-05
441,2.6730138999994325e-05,4.068574800021452e-05,,6.337684199988871e-05,5.64931244998661e-05
443,2.6830756000038035e-05,4.0837893000116306e-05,,6.348795819994847e-05,5.650883850012178e-05
445,2.6994894999972987e-05,4.098672600002829e-05,,6.359127490013634e-05,5.698729999949137e-05
447,2.7193979999992732e-05,4.136010000001989e-05,,6.419779259995268e-05,5.711569349932688e-05
449,3.037892100002182e-05,4.452321000007942e-05,,6.886446019998402e-05,6.105917399963801e-05
451,3.0662639000013316e-05,4.487769099978323e-05,,6.909542619996501e-05,6.114174800040928e-05
453,3.082121899996082e-05,4.533448699976361e-05,,6.912210760001472e-05,6.142202899991389e-05
455,3.1004032999987884e-05,4.556388099990727e-05,,6.925471899994591e-05,6.174264350011072e-05
457,3.113929500000268e-05,4.5910112000001395e-05,,6.937000569996599e-05,6.220378250054636e-05
459,3.1341420999979165e-05,4.596033999996507e-05,,6.946433940011047e-05,6.179874149984244e-05
461,3.144988900004364e-05,4.650317699997686e-05,,6.965121060002276e-05,6.212368599972251e-05
463,3.16053399999987e-05,4.701457400005893e-05,,6.968682690003332e-05,6.258292799975606e-05
465,3.173726699998269e-05,4.712764399982916e-05,,6.959654330003104e-05,6.295023300026514e-05
467,3.1881915999974814e-05,4.717966399994112e-05,,6.957229130002816e-05,6.339502149967303e-05
469,3.206901999999445e-05,4.769093899994914e-05,,6.966278240015527e-05,6.341633099964384e-05
471,3.218361699998695e-05,4.785621100018034e-05,,6.975435130007099e-05,6.365726749936584e-05
473,3.233288399997036e-05,4.830055599995832e-05,,6.970227929996326e-05,6.34794609995879e-05
475,3.24865780000323e-05,4.838001899997835e-05,,6.991995490006957e-05,6.372634500075947e-05
477,3.2596264000005705e-05,4.856058399991525e-05,,6.995761250000214e-05,6.410864650024451e-05
479,3.2742199999972854e-05,4.9151995000102036e-05,,7.001146660004451e-05,6.438287150012911e-05
481,3.292377299999316e-05,4.931360600039626e-05,,7.019085499996436e-05,6.483184849912505e-05
483,3.299801399998614e-05,4.964082600008623e-05,,7.02185993999592e-05,6.502312300017365e-05
485,3.3141932000035016e-05,4.995582800029297e-05,,7.036133999990852e-05,6.523342099990259e-05
487,3.3302762999994675e-05,5.000761600012993e-05,,7.031938869986334e-05,6.551909599966166e-05
489,3.3464127999991435e-05,5.029823399991074e-05,,7.058074020005734e-05,6.557789449925621e-05
491,3.3553594000011344e-05,5.069046600010552e-05,,7.054368720000638e-05,6.617511449985614e-05
493,3.370228500000394e-05,5.104721299994708e-05,,7.054038859987485e-05,6.650590249955712e-05
495,3.388215100000025e-05,5.124169000009715e-05,,7.089727659986238e-05,6.64643125001021e-05
497,3.3965432000002235e-05,5.137543999990157e-05,,7.088362819995381e-05,6.644193000011e-05
499,3.412805900001104e-05,5.169260000002396e-05,,7.093735140006176e-05,6.65725585004111e-05
501,3.431646100000307e-05,5.228185199985092e-05,,7.116033019992756e-05,6.70534860000771e-05
503,3.438569799999414e-05,5.204082600039328e-05,,7.136115960001916e-05,6.732430500051124e-05
505,3.456698700000516e-05,5.2604458000132584e-05,,7.163654120013235e-05,6.769553200047085e-05
507,3.474381199998788e-05,5.280265400006101e-05,,7.160754239994276e-05,6.763535150003008e-05
509,3.4888624000018346e-05,5.305266799996389e-05,,7.179135480000695e-05,6.808248850029485e-05
511,3.512258299997484e-05,5.325088099971253e-05,,7.254744040001242e-05,6.862808899950324e-05
1 length rapidfuzz polyleven python-Levenshtein edlib editdistance
2 1 1.1868500000389304e-07 1.3419399965641786e-07 8.809399969322838e-08 1.173390500025562e-06 1.8403750073048286e-07
3 3 1.2571299998853648e-07 1.4306399998531561e-07 1.2584300020535009e-07 1.4431384000090475e-06 3.3988450013566764e-07
4 5 1.3181900000347463e-07 1.4947499994377722e-07 1.436219999959576e-07 1.8077366999932568e-06 5.73024000004807e-07
5 7 1.406230000071673e-07 1.5495699972234432e-07 1.7044500009433249e-07 2.098987499994109e-06 8.09755999398476e-07
6 9 1.476800000119738e-07 1.6333499979737098e-07 2.0917000028930488e-07 2.3607205999724103e-06 1.0465779996593482e-06
7 11 1.5763999999762746e-07 1.720569998724386e-07 2.5574199980837874e-07 2.608982299989293e-06 1.30297400028212e-06
8 13 1.6653200000860125e-07 1.8100299985235323e-07 3.209369997421163e-07 2.906105999954889e-06 1.675448999776563e-06
9 15 1.7319299999485338e-07 1.902110002447444e-07 3.7229500003377325e-07 3.191073200014216e-06 1.7951925001398194e-06
10 17 1.8051499999671703e-07 1.9893099988621547e-07 4.4757600016964724e-07 3.412432500044815e-06 1.9984339996881317e-06
11 19 1.8910199999311322e-07 2.0717100005640527e-07 5.366800000956573e-07 3.5666306000166517e-06 2.1883409999645664e-06
12 21 1.9711999999572074e-07 2.1562500023719625e-07 6.417539998437861e-07 3.7767147000522523e-06 2.4684364998392994e-06
13 23 2.058199999908084e-07 2.2377799996320392e-07 7.551230000899522e-07 4.053098700023838e-06 2.643716499733273e-06
14 25 2.134079999933647e-07 2.3179399977379952e-07 8.772119999775896e-07 4.257835400039766e-06 2.8498005003712024e-06
15 27 2.2175399999468933e-07 2.400359999228385e-07 1.014301999930467e-06 4.469676400003664e-06 3.0511919994751224e-06
16 29 2.3199199999623947e-07 2.4800300025162874e-07 1.1565340000743165e-06 4.308660399965447e-06 3.2511130002603748e-06
17 31 2.4083000000985064e-07 2.5590399991415317e-07 1.3454350000756675e-06 4.535376800049562e-06 3.4754979997160262e-06
18 33 2.4826300000313487e-07 2.65549999767245e-07 1.509656000052928e-06 4.675932100053615e-06 3.6364609995871432e-06
19 35 2.5635700001203075e-07 2.734009999585396e-07 1.6890759998204883e-06 4.768740300005446e-06 3.831261999948765e-06
20 37 2.6371199999175585e-07 2.815040002133173e-07 1.890184999865596e-06 4.906963299981726e-06 4.03323550017376e-06
21 39 2.7284499999780113e-07 2.894290000767796e-07 2.083845000015572e-06 5.093585799932043e-06 4.2792960002771e-06
22 41 2.794010000002345e-07 3.084219997617765e-07 2.34501300019474e-06 5.2612680000493125e-06 4.461288999664248e-06
23 43 2.892069999944624e-07 3.057859998989443e-07 2.57303399985176e-06 5.478563199994824e-06 4.628238500117732e-06
24 45 2.96037999987675e-07 3.140540002277703e-07 2.8007300002173e-06 5.701822799983347e-06 4.81603800017183e-06
25 47 3.0560300000104234e-07 3.2302299996445074e-07 3.0499849999614526e-06 6.218689399975119e-06 5.034949999753735e-06
26 49 3.126829999899883e-07 3.306520002297475e-07 3.314623999813193e-06 6.5288057000543626e-06 5.1712425001824155e-06
27 51 3.2278000000474093e-07 3.3951300019907644e-07 3.5934249999627367e-06 6.6469786999732595e-06 5.366241000047012e-06
28 53 3.2946000000322326e-07 3.4659700031625104e-07 3.875131000313558e-06 6.670764599948598e-06 5.472506999467441e-06
29 55 3.3899800000369847e-07 3.5553899988371994e-07 4.241133000050467e-06 6.910302599953866e-06 5.631097999867052e-06
30 57 3.4524999999518964e-07 3.626060001806764e-07 4.625921999831917e-06 7.145072399998752e-06 6.501988999843888e-06
31 59 3.5378299999422325e-07 3.7095000016051933e-07 5.163817999800813e-06 7.431629999973665e-06 6.486221500381362e-06
32 61 3.622729999932517e-07 3.8035199986552467e-07 5.525670999759313e-06 7.566961300017282e-06 6.520599999930709e-06
33 63 3.711600000002591e-07 3.8820600002509314e-07 6.038356000317435e-06 7.725835500059476e-06 6.897806500091974e-06
34 65 1.1409370000023955e-06 1.6252649998023114e-06 6.646771999839984e-06 1.2074574799953552e-05 7.180683999649773e-06
35 67 1.210356999990836e-06 1.7160059996967903e-06 6.865887999992993e-06 1.3006635000056122e-05 7.4547684998833575e-06
36 69 1.2598089999897863e-06 1.7802259999371017e-06 7.0848210002623094e-06 1.3396385499981987e-05 7.388586000161012e-06
37 71 1.308710999992968e-06 1.8511069997657608e-06 7.65203700029815e-06 1.370664050000414e-05 7.888875999924495e-06
38 73 1.3478010000085303e-06 1.9054930003221669e-06 7.918969999991533e-06 1.3841394300015964e-05 7.939535000332398e-06
39 75 1.520093999999972e-06 1.9843579998450884e-06 8.163473999957204e-06 1.4223668600061499e-05 8.12262700037536e-06
40 77 1.6031359999999497e-06 2.0496810002441638e-06 8.728575000077395e-06 1.4264137999998638e-05 8.276972999738064e-06
41 79 1.6138769999969324e-06 2.1083169999656084e-06 9.063925000191375e-06 1.4691883900013636e-05 8.264962500106776e-06
42 81 1.6521539999985182e-06 2.186359000006633e-06 9.564869000314502e-06 1.4475649099949805e-05 8.42381500024203e-06
43 83 1.7847780000010972e-06 2.2352390001287857e-06 9.994752000238802e-06 1.468018799996571e-05 8.575667499826522e-06
44 85 1.7791410000000954e-06 2.309813000010764e-06 1.046966199965027e-05 1.4694576599958967e-05 8.770901999923807e-06
45 87 1.772411000004581e-06 2.3600920003445936e-06 1.096699899972009e-05 1.4836430300056236e-05 8.900858500055619e-06
46 89 1.798901000000796e-06 2.4182890001611668e-06 1.1469862000012656e-05 1.502379599996857e-05 9.024816999954055e-06
47 91 1.8439629999988936e-06 2.4808409998513526e-06 1.1987027000031958e-05 1.5219286699993972e-05 9.391939999659371e-06
48 93 1.8715719999988773e-06 2.551053999923169e-06 1.2587943999733395e-05 1.5345884899943485e-05 9.358370500194724e-06
49 95 1.905643000000623e-06 2.603822999844852e-06 1.3137269000253582e-05 1.547855899998467e-05 9.567823499310179e-06
50 97 1.9452800000010483e-06 2.6780799998959997e-06 1.3715703999878316e-05 1.563046579994989e-05 9.836441500738147e-06
51 99 1.971807000003878e-06 2.727709999817308e-06 1.4294856000105936e-05 1.561794440003723e-05 9.895374500047182e-06
52 101 2.0100020000057843e-06 2.8158400000393164e-06 1.4862864999940939e-05 1.571535250004672e-05 1.026750350047223e-05
53 103 2.0546340000038297e-06 2.872343999570148e-06 1.5465368999684868e-05 1.5828928900009488e-05 1.0430514000290713e-05
54 105 2.085660999995298e-06 2.9513709996535907e-06 1.6059492000294994e-05 1.5912442999979252e-05 1.0626714500176604e-05
55 107 2.1173180000033654e-06 3.0016430000614488e-06 1.666508900007102e-05 1.599805309997464e-05 1.0656286999619624e-05
56 109 2.1542160000080918e-06 3.074543999900925e-06 1.7276913999921818e-05 1.6094980300022142e-05 1.0867476999919745e-05
57 111 2.1918850000020027e-06 3.1456659999093977e-06 1.7893164999804875e-05 1.6154133700001694e-05 1.0865902499972435e-05
58 113 2.233150999998656e-06 3.2208540001192884e-06 1.8548525999904087e-05 1.636539740002263e-05 1.1213955000130226e-05
59 115 2.2712720000015444e-06 3.2817430001159664e-06 1.918714400017052e-05 1.6334746400025323e-05 1.1453830000391462e-05
60 117 2.2984609999809893e-06 3.343966000102228e-06 1.9843149000280392e-05 1.654471279998688e-05 1.1611259000346763e-05
61 119 2.3540550000120675e-06 3.416932000163797e-06 2.0531699000002845e-05 1.6769442499935394e-05 1.1683505500514003e-05
62 121 2.3876219999863225e-06 3.5052809998887827e-06 2.1235878999959824e-05 1.7061671699957518e-05 1.1758080499930657e-05
63 123 2.4399799999912377e-06 3.5566229998948984e-06 2.1899153000049413e-05 1.6963570400002937e-05 1.20360194996465e-05
64 125 2.488616000022148e-06 3.6531010000544477e-06 2.262432099996659e-05 1.7134647599959866e-05 1.2012328000309935e-05
65 127 2.5490269999863807e-06 3.736258999651909e-06 2.333958500003064e-05 1.7361669699948832e-05 1.2281267000616935e-05
66 129 3.5103289999938173e-06 4.663819999677799e-06 2.4050677999639452e-05 2.2247314299966088e-05 1.3443910500427591e-05
67 131 3.615252999992435e-06 4.759237000143911e-06 2.4785272999906746e-05 2.356447459997071e-05 1.6381731999899786e-05
68 133 3.690820999992184e-06 4.8775949999253495e-06 2.554028799977459e-05 2.474819810004192e-05 1.6565407499911088e-05
69 135 3.7384620000011635e-06 4.954021000230569e-06 2.629304399988541e-05 2.515236550007103e-05 1.681198500045866e-05
70 137 3.8112900000157882e-06 5.080449000161026e-06 2.708617400003277e-05 2.530308839995996e-05 1.6837157500049215e-05
71 139 3.872915000016518e-06 5.179206999855524e-06 2.7870433000316553e-05 2.549675330001265e-05 1.7037541500030786e-05
72 141 3.934725000021899e-06 5.876413999885699e-06 2.8624325000237154e-05 2.5630653899952452e-05 1.71746810001423e-05
73 143 3.9858179999896485e-06 5.817812000259438e-06 2.942745599966656e-05 2.5587768300010794e-05 1.733695450002415e-05
74 145 4.0532529999950385e-06 5.941325999629042e-06 3.024347399968974e-05 2.570756410004833e-05 1.7486718000327526e-05
75 147 4.108688000002303e-06 6.020863000230747e-06 3.10270740001215e-05 2.580618920001143e-05 1.7675985500318347e-05
76 149 4.163400000010141e-06 6.128498000180116e-06 3.1857814999966654e-05 2.591060770000695e-05 1.7675048000455718e-05
77 151 4.230379000006223e-06 6.223307000254862e-06 3.271246300027997e-05 2.596277700004066e-05 1.7832233999797608e-05
78 153 4.281233999989809e-06 6.3640520002081746e-06 3.356230500003221e-05 2.6084718300080568e-05 1.8080191999615635e-05
79 155 4.3402960000094026e-06 6.4442749999216175e-06 3.4384571999908066e-05 2.624103640000612e-05 1.8250333000651152e-05
80 157 4.404476999980034e-06 6.576811999821075e-06 3.5257034000096614e-05 2.635952199998428e-05 1.834267100002762e-05
81 159 4.438622000009218e-06 6.657796000126836e-06 3.623611100010749e-05 2.635107299993252e-05 1.851089999945543e-05
82 161 4.4866679999984175e-06 6.7962600001010275e-06 3.70947299998079e-05 2.6446089799992482e-05 1.863353550015745e-05
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241 479 3.2742199999972854e-05 4.9151995000102036e-05 7.001146660004451e-05 6.438287150012911e-05
242 481 3.292377299999316e-05 4.931360600039626e-05 7.019085499996436e-05 6.483184849912505e-05
243 483 3.299801399998614e-05 4.964082600008623e-05 7.02185993999592e-05 6.502312300017365e-05
244 485 3.3141932000035016e-05 4.995582800029297e-05 7.036133999990852e-05 6.523342099990259e-05
245 487 3.3302762999994675e-05 5.000761600012993e-05 7.031938869986334e-05 6.551909599966166e-05
246 489 3.3464127999991435e-05 5.029823399991074e-05 7.058074020005734e-05 6.557789449925621e-05
247 491 3.3553594000011344e-05 5.069046600010552e-05 7.054368720000638e-05 6.617511449985614e-05
248 493 3.370228500000394e-05 5.104721299994708e-05 7.054038859987485e-05 6.650590249955712e-05
249 495 3.388215100000025e-05 5.124169000009715e-05 7.089727659986238e-05 6.64643125001021e-05
250 497 3.3965432000002235e-05 5.137543999990157e-05 7.088362819995381e-05 6.644193000011e-05
251 499 3.412805900001104e-05 5.169260000002396e-05 7.093735140006176e-05 6.65725585004111e-05
252 501 3.431646100000307e-05 5.228185199985092e-05 7.116033019992756e-05 6.70534860000771e-05
253 503 3.438569799999414e-05 5.204082600039328e-05 7.136115960001916e-05 6.732430500051124e-05
254 505 3.456698700000516e-05 5.2604458000132584e-05 7.163654120013235e-05 6.769553200047085e-05
255 507 3.474381199998788e-05 5.280265400006101e-05 7.160754239994276e-05 6.763535150003008e-05
256 509 3.4888624000018346e-05 5.305266799996389e-05 7.179135480000695e-05 6.808248850029485e-05
257 511 3.512258299997484e-05 5.325088099971253e-05 7.254744040001242e-05 6.862808899950324e-05

302
src/cpp_string_metric.cpp vendored
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src/cpp_string_metric.pyx
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@ -81,6 +81,15 @@ def levenshtein(s1, s2, weights=(1,1,1), max=None):
The algorithm is described by [3]_. The time complexity of this
algorithm is ``O([N/64]M)``.
The following image shows a benchmark of the Levenshtein distance in multiple
Python libraries. All of them are implemented either in C/C++ or Cython.
The graph shows, that python-Levenshtein is the only library with a time
complexity of ``O(NM)``, while all other libraries have a time complexity of
``O([N/64]M)``. Especially for long strings RapidFuzz is a lot faster than
all the other tested libraries.
.. image:: img/uniform_levenshtein.svg
Insertion = Deletion, Substitution >= Insertion + Deletion:
Since every Substitution can be performed as Insertion + Deletion, this variant
@ -120,6 +129,14 @@ def levenshtein(s1, s2, weights=(1,1,1), max=None):
The algorithm is described by [4]_. The time complexity of this
algorithm is ``O([N/64]M)``.
The following image shows a benchmark of the InDel distance in RapidFuzz
and python-Levenshtein. Similar to the normal Levenshtein distance
python-Levenshtein uses a implementation with a time complexity of ``O(NM)``,
while RapidFuzz has a time complexity of ``O([N/64]M)``.
.. image:: img/indel_levenshtein.svg
Other weights:
The implementation for other weights is based on Wagner-Fischer.
It has a performance of ``O(N * M)`` and has a memory usage of ``O(N)``.
@ -285,6 +302,9 @@ def normalized_levenshtein(s1, s2, weights=(1,1,1), processor=None, double score
def hamming(s1, s2, max=None):
"""
Calculates the Hamming distance between two strings.
The hamming distance is defined as the number of positions
where the two strings differ. It describes the minimum
amount of substitutions required to transform s1 into s2.
Parameters
----------
@ -302,6 +322,11 @@ def hamming(s1, s2, max=None):
-------
distance : int
Hamming distance between s1 and s2
Raises
------
ValueError
If s1 and s2 have a different length
"""
cdef size_t max_ = -1
@ -335,6 +360,15 @@ def normalized_hamming(s1, s2, processor=None, double score_cutoff=0.0):
ratio : float
Normalized hamming distance between s1 and s2
as a float between 0 and 100
Raises
------
ValueError
If s1 and s2 have a different length
See Also
--------
hamming : Hamming distance
"""
if s1 is None or s2 is None:
return 0