This module provides functions for efficiently checking if elements in one array are present in another array. It includes a Cython implementation for improved performance.
Note: The Cython implementation is compiled during the first import, and the compiled extension module is stored in the same directory. Subsequent imports will use the precompiled module for improved performance.
import timeit
from isincython import generate_random_arrays, fast_isin
import numpy as np
size = 10000000
low = 0
high = 254
arras = [
(size, "float32", low, high),
(size, "float64", low, high),
(size, np.uint8, low, high),
(size, np.int8, low, high),
(size, np.int16, low, high),
(size, np.int32, low, high),
(size, np.int64, low, high),
(size, np.uint16, low, high),
(size, np.uint32, low, high),
(size, np.uint64, low, high),
]
reps = 1
for a in arras:
arr = generate_random_arrays(*a)
seq = generate_random_arrays(size // 10, *a[1:])
s = """u=fast_isin(arr,seq)"""
u = fast_isin(arr, seq)
print("c++", arr[u])
t1 = timeit.timeit(s, globals=globals(), number=reps) / reps
print(t1)
s2 = """q=np.isin(arr,seq)"""
q = np.isin(arr, seq)
print("numpy", arr[q])
t2 = timeit.timeit(s2, globals=globals(), number=reps) / reps
print(t2)
print(np.all(q == u))
print("-----------------")
haystack = np.array(
[
b"Cumings",
b"Heikkinen",
b"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",
b"aaa",
b"bbbb()",
b"Futrelle",
b"Allen",
b"Cumings, Mrs. John Bradley (Florence Briggs Thayer)q",
b"Braund, Mr. Owen Harris",
b"Heikkinen, Miss. Laina",
b"Futrelle, Mrs. Jacques Heath (Lily May Peel)",
b"Allen, Mr. William Henry",
b"Braund",
],
dtype="S",
)
needels = np.array(
[
b"Braund, Mr. Owen Harris",
b"Cumings, Mrs. John Bradley (Florence Briggs Th",
b"Heikkinen, Miss. Lxxaina",
b"Futrelle, Mrs. Jacqxues Heath (Lily May Peel)",
b"Allen, Mxr. William Henry",
b"sdfsdd",
b"aaa",
b"bbbb()",
],
dtype="S",
)
haystack = np.ascontiguousarray(np.concatenate([haystack for _ in range(200000)]))
needels = np.ascontiguousarray(np.concatenate([needels for _ in range(10000)]))
s = "o = fast_isin(haystack, needels)"
t1 = timeit.timeit(s, globals=globals(), number=reps) / reps
s1 = "o = np.isin(haystack, needels)"
t2 = timeit.timeit(s1, globals=globals(), number=reps) / reps
print(f"c++ {t1}")
print(f"numpy {t2}")
o1 = fast_isin(haystack, needels)
o2 = np.isin(haystack, needels)
print(np.all(o1 == o2))
needels = needels.astype("U")
haystack = haystack.astype("U")
s = "o = fast_isin(haystack, needels)"
t1 = timeit.timeit(s, globals=globals(), number=reps) / reps
s1 = "o = np.isin(haystack, needels)"
t2 = timeit.timeit(s1, globals=globals(), number=reps) / reps
print(f"c++ {t1}")
print(f"numpy {t2}")
o1 = fast_isin(haystack, needels)
o2 = np.isin(haystack, needels)
print(np.all(o1 == o2))
# c++ [136.03264 62.5741 156.39038 ... 78.545906 229.14676 186.44472 ]
# 0.39614199999778066
# numpy [136.03264 62.5741 156.39038 ... 78.545906 229.14676 186.44472 ]
# 2.1623376999996253
# True
# -----------------
# c++ []
# 0.4184691000045859
# numpy []
# 2.189824300003238
# True
# -----------------
# c++ [126 128 31 ... 113 190 146]
# 0.011114299995824695
# numpy [126 128 31 ... 113 190 146]
# 0.05381579999811947
# True
# -----------------
# c++ [ 23 35 52 ... 54 98 -125]
# 0.010347299998102244
# numpy [ 23 35 52 ... 54 98 -125]
# 0.8121466000011424
# True
# -----------------
# c++ [144 29 89 ... 90 34 202]
# 0.012101899999834131
# numpy [144 29 89 ... 90 34 202]
# 0.05841199999849778
# True
# -----------------
# c++ [ 93 51 131 ... 231 147 140]
# 0.013264799999888055
# numpy [ 93 51 131 ... 231 147 140]
# 0.07822610000584973
# True
# -----------------
# c++ [138 158 233 ... 64 82 160]
# 0.018734699995548
# numpy [138 158 233 ... 64 82 160]
# 0.09425780000310624
# True
# -----------------
# c++ [158 17 126 ... 55 7 116]
# 0.011595800002396572
# numpy [158 17 126 ... 55 7 116]
# 0.06014610000420362
# True
# -----------------
# c++ [ 60 12 226 ... 152 190 155]
# 0.013999900002090726
# numpy [ 60 12 226 ... 152 190 155]
# 0.07416449999436736
# True
# -----------------
# c++ [239 84 81 ... 146 85 63]
# 0.026196500002697576
# numpy [239 84 81 ... 146 85 63]
# 0.11476380000385689
# True
# -----------------
# c++ 0.7991062000000966
# numpy 2.1993997000026866
# True
# c++ 1.7051588000031188
# numpy 3.0464809000040987
# True