Inzynierka_Gwiazdy/machine_learning/Lib/site-packages/pandas/_libs/hashing.pyx
2023-09-20 19:46:58 +02:00

195 lines
4.6 KiB
Cython

# Translated from the reference implementation
# at https://github.com/veorq/SipHash
cimport cython
from libc.stdlib cimport (
free,
malloc,
)
import numpy as np
from numpy cimport (
import_array,
ndarray,
uint8_t,
uint64_t,
)
import_array()
from pandas._libs.util cimport is_nan
@cython.boundscheck(False)
def hash_object_array(
ndarray[object] arr, str key, str encoding="utf8"
) -> np.ndarray[np.uint64]:
"""
Parameters
----------
arr : 1-d object ndarray of objects
key : hash key, must be 16 byte len encoded
encoding : encoding for key & arr, default to 'utf8'
Returns
-------
1-d uint64 ndarray of hashes.
Raises
------
TypeError
If the array contains mixed types.
Notes
-----
Allowed values must be strings, or nulls
mixed array types will raise TypeError.
"""
cdef:
Py_ssize_t i, n
uint64_t[::1] result
bytes data, k
uint8_t *kb
uint64_t *lens
char **vecs
char *cdata
object val
list datas = []
k = <bytes>key.encode(encoding)
kb = <uint8_t *>k
if len(k) != 16:
raise ValueError(
f"key should be a 16-byte string encoded, got {k} (len {len(k)})"
)
n = len(arr)
# create an array of bytes
vecs = <char **>malloc(n * sizeof(char *))
lens = <uint64_t*>malloc(n * sizeof(uint64_t))
for i in range(n):
val = arr[i]
if isinstance(val, bytes):
data = <bytes>val
elif isinstance(val, str):
data = <bytes>val.encode(encoding)
elif val is None or is_nan(val):
# null, stringify and encode
data = <bytes>str(val).encode(encoding)
elif isinstance(val, tuple):
# GH#28969 we could have a tuple, but need to ensure that
# the tuple entries are themselves hashable before converting
# to str
hash(val)
data = <bytes>str(val).encode(encoding)
else:
raise TypeError(
f"{val} of type {type(val)} is not a valid type for hashing, "
"must be string or null"
)
lens[i] = len(data)
cdata = data
# keep the references alive through the end of the
# function
datas.append(data)
vecs[i] = cdata
result = np.empty(n, dtype=np.uint64)
with nogil:
for i in range(n):
result[i] = low_level_siphash(<uint8_t *>vecs[i], lens[i], kb)
free(vecs)
free(lens)
return result.base # .base to retrieve underlying np.ndarray
cdef uint64_t _rotl(uint64_t x, uint64_t b) nogil:
return (x << b) | (x >> (64 - b))
cdef uint64_t u8to64_le(uint8_t* p) nogil:
return (<uint64_t>p[0] |
<uint64_t>p[1] << 8 |
<uint64_t>p[2] << 16 |
<uint64_t>p[3] << 24 |
<uint64_t>p[4] << 32 |
<uint64_t>p[5] << 40 |
<uint64_t>p[6] << 48 |
<uint64_t>p[7] << 56)
cdef void _sipround(uint64_t* v0, uint64_t* v1,
uint64_t* v2, uint64_t* v3) nogil:
v0[0] += v1[0]
v1[0] = _rotl(v1[0], 13)
v1[0] ^= v0[0]
v0[0] = _rotl(v0[0], 32)
v2[0] += v3[0]
v3[0] = _rotl(v3[0], 16)
v3[0] ^= v2[0]
v0[0] += v3[0]
v3[0] = _rotl(v3[0], 21)
v3[0] ^= v0[0]
v2[0] += v1[0]
v1[0] = _rotl(v1[0], 17)
v1[0] ^= v2[0]
v2[0] = _rotl(v2[0], 32)
@cython.cdivision(True)
cdef uint64_t low_level_siphash(uint8_t* data, size_t datalen,
uint8_t* key) nogil:
cdef uint64_t v0 = 0x736f6d6570736575ULL
cdef uint64_t v1 = 0x646f72616e646f6dULL
cdef uint64_t v2 = 0x6c7967656e657261ULL
cdef uint64_t v3 = 0x7465646279746573ULL
cdef uint64_t b
cdef uint64_t k0 = u8to64_le(key)
cdef uint64_t k1 = u8to64_le(key + 8)
cdef uint64_t m
cdef int i
cdef uint8_t* end = data + datalen - (datalen % sizeof(uint64_t))
cdef int left = datalen & 7
cdef int cROUNDS = 2
cdef int dROUNDS = 4
b = (<uint64_t>datalen) << 56
v3 ^= k1
v2 ^= k0
v1 ^= k1
v0 ^= k0
while (data != end):
m = u8to64_le(data)
v3 ^= m
for i in range(cROUNDS):
_sipround(&v0, &v1, &v2, &v3)
v0 ^= m
data += sizeof(uint64_t)
for i in range(left-1, -1, -1):
b |= (<uint64_t>data[i]) << (i * 8)
v3 ^= b
for i in range(cROUNDS):
_sipround(&v0, &v1, &v2, &v3)
v0 ^= b
v2 ^= 0xff
for i in range(dROUNDS):
_sipround(&v0, &v1, &v2, &v3)
b = v0 ^ v1 ^ v2 ^ v3
return b