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ai-content-maker/.venv/Lib/site-packages/pandas/_libs/algos.pyx

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2024-05-03 04:18:51 +03:00
cimport cython
from cython cimport Py_ssize_t
from libc.math cimport (
fabs,
sqrt,
)
from libc.stdlib cimport (
free,
malloc,
)
from libc.string cimport memmove
import numpy as np
cimport numpy as cnp
from numpy cimport (
NPY_COMPLEX64,
NPY_COMPLEX128,
NPY_FLOAT32,
NPY_FLOAT64,
NPY_INT8,
NPY_INT16,
NPY_INT32,
NPY_INT64,
NPY_OBJECT,
NPY_UINT8,
NPY_UINT16,
NPY_UINT32,
NPY_UINT64,
float32_t,
float64_t,
int8_t,
int16_t,
int32_t,
int64_t,
intp_t,
ndarray,
uint8_t,
uint16_t,
uint32_t,
uint64_t,
)
cnp.import_array()
cimport pandas._libs.util as util
from pandas._libs.dtypes cimport (
numeric_object_t,
numeric_t,
)
from pandas._libs.khash cimport (
kh_destroy_int64,
kh_get_int64,
kh_init_int64,
kh_int64_t,
kh_put_int64,
kh_resize_int64,
khiter_t,
)
from pandas._libs.util cimport get_nat
import pandas._libs.missing as missing
cdef:
float64_t FP_ERR = 1e-13
float64_t NaN = <float64_t>np.NaN
int64_t NPY_NAT = get_nat()
tiebreakers = {
"average": TIEBREAK_AVERAGE,
"min": TIEBREAK_MIN,
"max": TIEBREAK_MAX,
"first": TIEBREAK_FIRST,
"dense": TIEBREAK_DENSE,
}
cdef inline bint are_diff(object left, object right):
try:
return fabs(left - right) > FP_ERR
except TypeError:
return left != right
class Infinity:
"""
Provide a positive Infinity comparison method for ranking.
"""
__lt__ = lambda self, other: False
__le__ = lambda self, other: isinstance(other, Infinity)
__eq__ = lambda self, other: isinstance(other, Infinity)
__ne__ = lambda self, other: not isinstance(other, Infinity)
__gt__ = lambda self, other: (not isinstance(other, Infinity) and
not missing.checknull(other))
__ge__ = lambda self, other: not missing.checknull(other)
class NegInfinity:
"""
Provide a negative Infinity comparison method for ranking.
"""
__lt__ = lambda self, other: (not isinstance(other, NegInfinity) and
not missing.checknull(other))
__le__ = lambda self, other: not missing.checknull(other)
__eq__ = lambda self, other: isinstance(other, NegInfinity)
__ne__ = lambda self, other: not isinstance(other, NegInfinity)
__gt__ = lambda self, other: False
__ge__ = lambda self, other: isinstance(other, NegInfinity)
@cython.wraparound(False)
@cython.boundscheck(False)
cpdef ndarray[int64_t, ndim=1] unique_deltas(const int64_t[:] arr):
"""
Efficiently find the unique first-differences of the given array.
Parameters
----------
arr : ndarray[int64_t]
Returns
-------
ndarray[int64_t]
An ordered ndarray[int64_t]
"""
cdef:
Py_ssize_t i, n = len(arr)
int64_t val
khiter_t k
kh_int64_t *table
int ret = 0
list uniques = []
ndarray[int64_t, ndim=1] result
table = kh_init_int64()
kh_resize_int64(table, 10)
for i in range(n - 1):
val = arr[i + 1] - arr[i]
k = kh_get_int64(table, val)
if k == table.n_buckets:
kh_put_int64(table, val, &ret)
uniques.append(val)
kh_destroy_int64(table)
result = np.array(uniques, dtype=np.int64)
result.sort()
return result
@cython.wraparound(False)
@cython.boundscheck(False)
def is_lexsorted(list_of_arrays: list) -> bint:
cdef:
Py_ssize_t i
Py_ssize_t n, nlevels
int64_t k, cur, pre
ndarray arr
bint result = True
nlevels = len(list_of_arrays)
n = len(list_of_arrays[0])
cdef int64_t **vecs = <int64_t**>malloc(nlevels * sizeof(int64_t*))
for i in range(nlevels):
arr = list_of_arrays[i]
assert arr.dtype.name == 'int64'
vecs[i] = <int64_t*>cnp.PyArray_DATA(arr)
# Assume uniqueness??
with nogil:
for i in range(1, n):
for k in range(nlevels):
cur = vecs[k][i]
pre = vecs[k][i -1]
if cur == pre:
continue
elif cur > pre:
break
else:
result = False
break
if not result:
break
free(vecs)
return result
@cython.boundscheck(False)
@cython.wraparound(False)
def groupsort_indexer(const intp_t[:] index, Py_ssize_t ngroups):
"""
Compute a 1-d indexer.
The indexer is an ordering of the passed index,
ordered by the groups.
Parameters
----------
index: np.ndarray[np.intp]
Mappings from group -> position.
ngroups: int64
Number of groups.
Returns
-------
ndarray[intp_t, ndim=1]
Indexer
ndarray[intp_t, ndim=1]
Group Counts
Notes
-----
This is a reverse of the label factorization process.
"""
cdef:
Py_ssize_t i, label, n
intp_t[::1] indexer, where, counts
counts = np.zeros(ngroups + 1, dtype=np.intp)
n = len(index)
indexer = np.zeros(n, dtype=np.intp)
where = np.zeros(ngroups + 1, dtype=np.intp)
with nogil:
# count group sizes, location 0 for NA
for i in range(n):
counts[index[i] + 1] += 1
# mark the start of each contiguous group of like-indexed data
for i in range(1, ngroups + 1):
where[i] = where[i - 1] + counts[i - 1]
# this is our indexer
for i in range(n):
label = index[i] + 1
indexer[where[label]] = i
where[label] += 1
return indexer.base, counts.base
cdef inline Py_ssize_t swap(numeric_t *a, numeric_t *b) nogil:
cdef:
numeric_t t
# cython doesn't allow pointer dereference so use array syntax
t = a[0]
a[0] = b[0]
b[0] = t
return 0
cdef inline numeric_t kth_smallest_c(numeric_t* arr, Py_ssize_t k, Py_ssize_t n) nogil:
"""
See kth_smallest.__doc__. The additional parameter n specifies the maximum
number of elements considered in arr, needed for compatibility with usage
in groupby.pyx
"""
cdef:
Py_ssize_t i, j, left, m
numeric_t x
left = 0
m = n - 1
while left < m:
x = arr[k]
i = left
j = m
while 1:
while arr[i] < x:
i += 1
while x < arr[j]:
j -= 1
if i <= j:
swap(&arr[i], &arr[j])
i += 1
j -= 1
if i > j:
break
if j < k:
left = i
if k < i:
m = j
return arr[k]
@cython.boundscheck(False)
@cython.wraparound(False)
def kth_smallest(numeric_t[::1] arr, Py_ssize_t k) -> numeric_t:
"""
Compute the kth smallest value in arr. Note that the input
array will be modified.
Parameters
----------
arr : numeric[::1]
Array to compute the kth smallest value for, must be
contiguous
k : Py_ssize_t
Returns
-------
numeric
The kth smallest value in arr
"""
cdef:
numeric_t result
with nogil:
result = kth_smallest_c(&arr[0], k, arr.shape[0])
return result
# ----------------------------------------------------------------------
# Pairwise correlation/covariance
@cython.boundscheck(False)
@cython.wraparound(False)
@cython.cdivision(True)
def nancorr(const float64_t[:, :] mat, bint cov=False, minp=None):
cdef:
Py_ssize_t i, j, xi, yi, N, K
bint minpv
float64_t[:, ::1] result
ndarray[uint8_t, ndim=2] mask
int64_t nobs = 0
float64_t vx, vy, dx, dy, meanx, meany, divisor, ssqdmx, ssqdmy, covxy
N, K = (<object>mat).shape
if minp is None:
minpv = 1
else:
minpv = <int>minp
result = np.empty((K, K), dtype=np.float64)
mask = np.isfinite(mat).view(np.uint8)
with nogil:
for xi in range(K):
for yi in range(xi + 1):
# Welford's method for the variance-calculation
# https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance
nobs = ssqdmx = ssqdmy = covxy = meanx = meany = 0
for i in range(N):
if mask[i, xi] and mask[i, yi]:
vx = mat[i, xi]
vy = mat[i, yi]
nobs += 1
dx = vx - meanx
dy = vy - meany
meanx += 1. / nobs * dx
meany += 1. / nobs * dy
ssqdmx += (vx - meanx) * dx
ssqdmy += (vy - meany) * dy
covxy += (vx - meanx) * dy
if nobs < minpv:
result[xi, yi] = result[yi, xi] = NaN
else:
divisor = (nobs - 1.0) if cov else sqrt(ssqdmx * ssqdmy)
if divisor != 0:
result[xi, yi] = result[yi, xi] = covxy / divisor
else:
result[xi, yi] = result[yi, xi] = NaN
return result.base
# ----------------------------------------------------------------------
# Pairwise Spearman correlation
@cython.boundscheck(False)
@cython.wraparound(False)
def nancorr_spearman(ndarray[float64_t, ndim=2] mat, Py_ssize_t minp=1) -> ndarray:
cdef:
Py_ssize_t i, j, xi, yi, N, K
ndarray[float64_t, ndim=2] result
ndarray[float64_t, ndim=2] ranked_mat
ndarray[float64_t, ndim=1] rankedx, rankedy
float64_t[::1] maskedx, maskedy
ndarray[uint8_t, ndim=2] mask
int64_t nobs = 0
bint no_nans
float64_t vx, vy, sumx, sumxx, sumyy, mean, divisor
N, K = (<object>mat).shape
# Handle the edge case where we know all results will be nan
# to keep conditional logic inside loop simpler
if N < minp:
result = np.full((K, K), np.nan, dtype=np.float64)
return result
result = np.empty((K, K), dtype=np.float64)
mask = np.isfinite(mat).view(np.uint8)
no_nans = mask.all()
ranked_mat = np.empty((N, K), dtype=np.float64)
# Note: we index into maskedx, maskedy in loops up to nobs, but using N is safe
# here since N >= nobs and values are stored contiguously
maskedx = np.empty(N, dtype=np.float64)
maskedy = np.empty(N, dtype=np.float64)
for i in range(K):
ranked_mat[:, i] = rank_1d(mat[:, i])
with nogil:
for xi in range(K):
for yi in range(xi + 1):
sumx = sumxx = sumyy = 0
# Fastpath for data with no nans/infs, allows avoiding mask checks
# and array reassignments
if no_nans:
mean = (N + 1) / 2.
# now the cov numerator
for i in range(N):
vx = ranked_mat[i, xi] - mean
vy = ranked_mat[i, yi] - mean
sumx += vx * vy
sumxx += vx * vx
sumyy += vy * vy
else:
nobs = 0
# Keep track of whether we need to recompute ranks
all_ranks = True
for i in range(N):
all_ranks &= not (mask[i, xi] ^ mask[i, yi])
if mask[i, xi] and mask[i, yi]:
maskedx[nobs] = ranked_mat[i, xi]
maskedy[nobs] = ranked_mat[i, yi]
nobs += 1
if nobs < minp:
result[xi, yi] = result[yi, xi] = NaN
continue
else:
if not all_ranks:
with gil:
# We need to slice back to nobs because rank_1d will
# require arrays of nobs length
rankedx = rank_1d(np.asarray(maskedx)[:nobs])
rankedy = rank_1d(np.asarray(maskedy)[:nobs])
for i in range(nobs):
maskedx[i] = rankedx[i]
maskedy[i] = rankedy[i]
mean = (nobs + 1) / 2.
# now the cov numerator
for i in range(nobs):
vx = maskedx[i] - mean
vy = maskedy[i] - mean
sumx += vx * vy
sumxx += vx * vx
sumyy += vy * vy
divisor = sqrt(sumxx * sumyy)
if divisor != 0:
result[xi, yi] = result[yi, xi] = sumx / divisor
else:
result[xi, yi] = result[yi, xi] = NaN
return result
# ----------------------------------------------------------------------
def validate_limit(nobs: int | None, limit=None) -> int:
"""
Check that the `limit` argument is a positive integer.
Parameters
----------
nobs : int
limit : object
Returns
-------
int
The limit.
"""
if limit is None:
lim = nobs
else:
if not util.is_integer_object(limit):
raise ValueError('Limit must be an integer')
if limit < 1:
raise ValueError('Limit must be greater than 0')
lim = limit
return lim
@cython.boundscheck(False)
@cython.wraparound(False)
def pad(
ndarray[numeric_object_t] old,
ndarray[numeric_object_t] new,
limit=None
) -> ndarray:
# -> ndarray[intp_t, ndim=1]
cdef:
Py_ssize_t i, j, nleft, nright
ndarray[intp_t, ndim=1] indexer
numeric_object_t cur, next_val
int lim, fill_count = 0
nleft = len(old)
nright = len(new)
indexer = np.empty(nright, dtype=np.intp)
indexer[:] = -1
lim = validate_limit(nright, limit)
if nleft == 0 or nright == 0 or new[nright - 1] < old[0]:
return indexer
i = j = 0
cur = old[0]
while j <= nright - 1 and new[j] < cur:
j += 1
while True:
if j == nright:
break
if i == nleft - 1:
while j < nright:
if new[j] == cur:
indexer[j] = i
elif new[j] > cur and fill_count < lim:
indexer[j] = i
fill_count += 1
j += 1
break
next_val = old[i + 1]
while j < nright and cur <= new[j] < next_val:
if new[j] == cur:
indexer[j] = i
elif fill_count < lim:
indexer[j] = i
fill_count += 1
j += 1
fill_count = 0
i += 1
cur = next_val
return indexer
@cython.boundscheck(False)
@cython.wraparound(False)
def pad_inplace(numeric_object_t[:] values, uint8_t[:] mask, limit=None):
cdef:
Py_ssize_t i, N
numeric_object_t val
uint8_t prev_mask
int lim, fill_count = 0
N = len(values)
# GH#2778
if N == 0:
return
lim = validate_limit(N, limit)
val = values[0]
prev_mask = mask[0]
for i in range(N):
if mask[i]:
if fill_count >= lim:
continue
fill_count += 1
values[i] = val
mask[i] = prev_mask
else:
fill_count = 0
val = values[i]
prev_mask = mask[i]
@cython.boundscheck(False)
@cython.wraparound(False)
def pad_2d_inplace(numeric_object_t[:, :] values, uint8_t[:, :] mask, limit=None):
cdef:
Py_ssize_t i, j, N, K
numeric_object_t val
int lim, fill_count = 0
K, N = (<object>values).shape
# GH#2778
if N == 0:
return
lim = validate_limit(N, limit)
for j in range(K):
fill_count = 0
val = values[j, 0]
for i in range(N):
if mask[j, i]:
if fill_count >= lim or i == 0:
continue
fill_count += 1
values[j, i] = val
mask[j, i] = False
else:
fill_count = 0
val = values[j, i]
"""
Backfilling logic for generating fill vector
Diagram of what's going on
Old New Fill vector Mask
. 0 1
. 0 1
. 0 1
A A 0 1
. 1 1
. 1 1
. 1 1
. 1 1
. 1 1
B B 1 1
. 2 1
. 2 1
. 2 1
C C 2 1
. 0
. 0
D
"""
@cython.boundscheck(False)
@cython.wraparound(False)
def backfill(
ndarray[numeric_object_t] old,
ndarray[numeric_object_t] new,
limit=None
) -> ndarray:
# -> ndarray[intp_t, ndim=1]
cdef:
Py_ssize_t i, j, nleft, nright
ndarray[intp_t, ndim=1] indexer
numeric_object_t cur, prev
int lim, fill_count = 0
nleft = len(old)
nright = len(new)
indexer = np.empty(nright, dtype=np.intp)
indexer[:] = -1
lim = validate_limit(nright, limit)
if nleft == 0 or nright == 0 or new[0] > old[nleft - 1]:
return indexer
i = nleft - 1
j = nright - 1
cur = old[nleft - 1]
while j >= 0 and new[j] > cur:
j -= 1
while True:
if j < 0:
break
if i == 0:
while j >= 0:
if new[j] == cur:
indexer[j] = i
elif new[j] < cur and fill_count < lim:
indexer[j] = i
fill_count += 1
j -= 1
break
prev = old[i - 1]
while j >= 0 and prev < new[j] <= cur:
if new[j] == cur:
indexer[j] = i
elif new[j] < cur and fill_count < lim:
indexer[j] = i
fill_count += 1
j -= 1
fill_count = 0
i -= 1
cur = prev
return indexer
def backfill_inplace(numeric_object_t[:] values, uint8_t[:] mask, limit=None):
pad_inplace(values[::-1], mask[::-1], limit=limit)
def backfill_2d_inplace(numeric_object_t[:, :] values,
uint8_t[:, :] mask,
limit=None):
pad_2d_inplace(values[:, ::-1], mask[:, ::-1], limit)
@cython.boundscheck(False)
@cython.wraparound(False)
def is_monotonic(ndarray[numeric_object_t, ndim=1] arr, bint timelike):
"""
Returns
-------
tuple
is_monotonic_inc : bool
is_monotonic_dec : bool
is_unique : bool
"""
cdef:
Py_ssize_t i, n
numeric_object_t prev, cur
bint is_monotonic_inc = 1
bint is_monotonic_dec = 1
bint is_unique = 1
bint is_strict_monotonic = 1
n = len(arr)
if n == 1:
if arr[0] != arr[0] or (numeric_object_t is int64_t and timelike and arr[0] == NPY_NAT):
# single value is NaN
return False, False, True
else:
return True, True, True
elif n < 2:
return True, True, True
if timelike and <int64_t>arr[0] == NPY_NAT:
return False, False, True
if numeric_object_t is not object:
with nogil:
prev = arr[0]
for i in range(1, n):
cur = arr[i]
if timelike and <int64_t>cur == NPY_NAT:
is_monotonic_inc = 0
is_monotonic_dec = 0
break
if cur < prev:
is_monotonic_inc = 0
elif cur > prev:
is_monotonic_dec = 0
elif cur == prev:
is_unique = 0
else:
# cur or prev is NaN
is_monotonic_inc = 0
is_monotonic_dec = 0
break
if not is_monotonic_inc and not is_monotonic_dec:
is_monotonic_inc = 0
is_monotonic_dec = 0
break
prev = cur
else:
# object-dtype, identical to above except we cannot use `with nogil`
prev = arr[0]
for i in range(1, n):
cur = arr[i]
if timelike and <int64_t>cur == NPY_NAT:
is_monotonic_inc = 0
is_monotonic_dec = 0
break
if cur < prev:
is_monotonic_inc = 0
elif cur > prev:
is_monotonic_dec = 0
elif cur == prev:
is_unique = 0
else:
# cur or prev is NaN
is_monotonic_inc = 0
is_monotonic_dec = 0
break
if not is_monotonic_inc and not is_monotonic_dec:
is_monotonic_inc = 0
is_monotonic_dec = 0
break
prev = cur
is_strict_monotonic = is_unique and (is_monotonic_inc or is_monotonic_dec)
return is_monotonic_inc, is_monotonic_dec, is_strict_monotonic
# ----------------------------------------------------------------------
# rank_1d, rank_2d
# ----------------------------------------------------------------------
cdef numeric_object_t get_rank_nan_fill_val(
bint rank_nans_highest,
numeric_object_t val,
bint is_datetimelike=False,
):
"""
Return the value we'll use to represent missing values when sorting depending
on if we'd like missing values to end up at the top/bottom. (The second parameter
is unused, but needed for fused type specialization)
"""
if numeric_object_t is int64_t and is_datetimelike and not rank_nans_highest:
return NPY_NAT + 1
if rank_nans_highest:
if numeric_object_t is object:
return Infinity()
elif numeric_object_t is int64_t:
return util.INT64_MAX
elif numeric_object_t is int32_t:
return util.INT32_MAX
elif numeric_object_t is int16_t:
return util.INT16_MAX
elif numeric_object_t is int8_t:
return util.INT8_MAX
elif numeric_object_t is uint64_t:
return util.UINT64_MAX
elif numeric_object_t is uint32_t:
return util.UINT32_MAX
elif numeric_object_t is uint16_t:
return util.UINT16_MAX
elif numeric_object_t is uint8_t:
return util.UINT8_MAX
else:
return np.inf
else:
if numeric_object_t is object:
return NegInfinity()
elif numeric_object_t is int64_t:
# Note(jbrockmendel) 2022-03-15 for reasons unknown, using util.INT64_MIN
# instead of NPY_NAT here causes build warnings and failure in
# test_cummax_i8_at_implementation_bound
return NPY_NAT
elif numeric_object_t is int32_t:
return util.INT32_MIN
elif numeric_object_t is int16_t:
return util.INT16_MIN
elif numeric_object_t is int8_t:
return util.INT8_MIN
elif numeric_object_t is uint64_t:
return 0
elif numeric_object_t is uint32_t:
return 0
elif numeric_object_t is uint16_t:
return 0
elif numeric_object_t is uint8_t:
return 0
else:
return -np.inf
@cython.wraparound(False)
@cython.boundscheck(False)
def rank_1d(
ndarray[numeric_object_t, ndim=1] values,
const intp_t[:] labels=None,
bint is_datetimelike=False,
ties_method="average",
bint ascending=True,
bint pct=False,
na_option="keep",
const uint8_t[:] mask=None,
):
"""
Fast NaN-friendly version of ``scipy.stats.rankdata``.
Parameters
----------
values : array of numeric_object_t values to be ranked
labels : np.ndarray[np.intp] or None
Array containing unique label for each group, with its ordering
matching up to the corresponding record in `values`. If not called
from a groupby operation, will be None.
is_datetimelike : bool, default False
True if `values` contains datetime-like entries.
ties_method : {'average', 'min', 'max', 'first', 'dense'}, default
'average'
* average: average rank of group
* min: lowest rank in group
* max: highest rank in group
* first: ranks assigned in order they appear in the array
* dense: like 'min', but rank always increases by 1 between groups
ascending : bool, default True
False for ranks by high (1) to low (N)
na_option : {'keep', 'top', 'bottom'}, default 'keep'
pct : bool, default False
Compute percentage rank of data within each group
na_option : {'keep', 'top', 'bottom'}, default 'keep'
* keep: leave NA values where they are
* top: smallest rank if ascending
* bottom: smallest rank if descending
mask : np.ndarray[bool], optional, default None
Specify locations to be treated as NA, for e.g. Categorical.
"""
cdef:
TiebreakEnumType tiebreak
Py_ssize_t N
int64_t[::1] grp_sizes
intp_t[:] lexsort_indexer
float64_t[::1] out
ndarray[numeric_object_t, ndim=1] masked_vals
numeric_object_t[:] masked_vals_memview
bint keep_na, nans_rank_highest, check_labels, check_mask
numeric_object_t nan_fill_val
tiebreak = tiebreakers[ties_method]
if tiebreak == TIEBREAK_FIRST:
if not ascending:
tiebreak = TIEBREAK_FIRST_DESCENDING
keep_na = na_option == 'keep'
N = len(values)
if labels is not None:
# TODO(cython3): cast won't be necessary (#2992)
assert <Py_ssize_t>len(labels) == N
out = np.empty(N)
grp_sizes = np.ones(N, dtype=np.int64)
# If we don't care about labels, can short-circuit later label
# comparisons
check_labels = labels is not None
# For cases where a mask is not possible, we can avoid mask checks
check_mask = (
numeric_object_t is float32_t
or numeric_object_t is float64_t
or numeric_object_t is object
or (numeric_object_t is int64_t and is_datetimelike)
)
check_mask = check_mask or mask is not None
# Copy values into new array in order to fill missing data
# with mask, without obfuscating location of missing data
# in values array
if numeric_object_t is object and values.dtype != np.object_:
masked_vals = values.astype('O')
else:
masked_vals = values.copy()
if mask is not None:
pass
elif numeric_object_t is object:
mask = missing.isnaobj(masked_vals)
elif numeric_object_t is int64_t and is_datetimelike:
mask = (masked_vals == NPY_NAT).astype(np.uint8)
elif numeric_object_t is float64_t or numeric_object_t is float32_t:
mask = np.isnan(masked_vals).astype(np.uint8)
else:
mask = np.zeros(shape=len(masked_vals), dtype=np.uint8)
# If `na_option == 'top'`, we want to assign the lowest rank
# to NaN regardless of ascending/descending. So if ascending,
# fill with lowest value of type to end up with lowest rank.
# If descending, fill with highest value since descending
# will flip the ordering to still end up with lowest rank.
# Symmetric logic applies to `na_option == 'bottom'`
nans_rank_highest = ascending ^ (na_option == 'top')
nan_fill_val = get_rank_nan_fill_val(nans_rank_highest, <numeric_object_t>0)
if nans_rank_highest:
order = [masked_vals, mask]
else:
order = [masked_vals, ~(np.asarray(mask))]
if check_labels:
order.append(labels)
np.putmask(masked_vals, mask, nan_fill_val)
# putmask doesn't accept a memoryview, so we assign as a separate step
masked_vals_memview = masked_vals
# lexsort using labels, then mask, then actual values
# each label corresponds to a different group value,
# the mask helps you differentiate missing values before
# performing sort on the actual values
lexsort_indexer = np.lexsort(order).astype(np.intp, copy=False)
if not ascending:
lexsort_indexer = lexsort_indexer[::-1]
with nogil:
rank_sorted_1d(
out,
grp_sizes,
lexsort_indexer,
masked_vals_memview,
mask,
check_mask=check_mask,
N=N,
tiebreak=tiebreak,
keep_na=keep_na,
pct=pct,
labels=labels,
)
return np.asarray(out)
@cython.wraparound(False)
@cython.boundscheck(False)
cdef void rank_sorted_1d(
float64_t[::1] out,
int64_t[::1] grp_sizes,
const intp_t[:] sort_indexer,
# TODO(cython3): make const (https://github.com/cython/cython/issues/3222)
numeric_object_t[:] masked_vals,
const uint8_t[:] mask,
bint check_mask,
Py_ssize_t N,
TiebreakEnumType tiebreak=TIEBREAK_AVERAGE,
bint keep_na=True,
bint pct=False,
# https://github.com/cython/cython/issues/1630, only trailing arguments can
# currently be omitted for cdef functions, which is why we keep this at the end
const intp_t[:] labels=None,
) nogil:
"""
See rank_1d.__doc__. Handles only actual ranking, so sorting and masking should
be handled in the caller. Note that `out` and `grp_sizes` are modified inplace.
Parameters
----------
out : float64_t[::1]
Array to store computed ranks
grp_sizes : int64_t[::1]
Array to store group counts, only used if pct=True. Should only be None
if labels is None.
sort_indexer : intp_t[:]
Array of indices which sorts masked_vals
masked_vals : numeric_object_t[:]
The values input to rank_1d, with missing values replaced by fill values
mask : uint8_t[:]
Array where entries are True if the value is missing, False otherwise.
check_mask : bool
If False, assumes the mask is all False to skip mask indexing
N : Py_ssize_t
The number of elements to rank. Note: it is not always true that
N == len(out) or N == len(masked_vals) (see `nancorr_spearman` usage for why)
tiebreak : TiebreakEnumType, default TIEBREAK_AVERAGE
See rank_1d.__doc__ for the different modes
keep_na : bool, default True
Whether or not to keep nulls
pct : bool, default False
Compute percentage rank of data within each group
labels : See rank_1d.__doc__, default None. None implies all labels are the same.
"""
cdef:
Py_ssize_t i, j, dups=0, sum_ranks=0,
Py_ssize_t grp_start=0, grp_vals_seen=1, grp_na_count=0
bint at_end, next_val_diff, group_changed, check_labels
int64_t grp_size
check_labels = labels is not None
# Loop over the length of the value array
# each incremental i value can be looked up in the lexsort_indexer
# array that we sorted previously, which gives us the location of
# that sorted value for retrieval back from the original
# values / masked_vals arrays
# TODO(cython3): de-duplicate once cython supports conditional nogil
if numeric_object_t is object:
with gil:
for i in range(N):
at_end = i == N - 1
# dups and sum_ranks will be incremented each loop where
# the value / group remains the same, and should be reset
# when either of those change. Used to calculate tiebreakers
dups += 1
sum_ranks += i - grp_start + 1
next_val_diff = at_end or are_diff(masked_vals[sort_indexer[i]],
masked_vals[sort_indexer[i+1]])
# We'll need this check later anyway to determine group size, so just
# compute it here since shortcircuiting won't help
group_changed = at_end or (check_labels and
(labels[sort_indexer[i]]
!= labels[sort_indexer[i+1]]))
# Update out only when there is a transition of values or labels.
# When a new value or group is encountered, go back #dups steps(
# the number of occurrence of current value) and assign the ranks
# based on the starting index of the current group (grp_start)
# and the current index
if (next_val_diff or group_changed or (check_mask and
(mask[sort_indexer[i]]
^ mask[sort_indexer[i+1]]))):
# If keep_na, check for missing values and assign back
# to the result where appropriate
if keep_na and check_mask and mask[sort_indexer[i]]:
grp_na_count = dups
for j in range(i - dups + 1, i + 1):
out[sort_indexer[j]] = NaN
elif tiebreak == TIEBREAK_AVERAGE:
for j in range(i - dups + 1, i + 1):
out[sort_indexer[j]] = sum_ranks / <float64_t>dups
elif tiebreak == TIEBREAK_MIN:
for j in range(i - dups + 1, i + 1):
out[sort_indexer[j]] = i - grp_start - dups + 2
elif tiebreak == TIEBREAK_MAX:
for j in range(i - dups + 1, i + 1):
out[sort_indexer[j]] = i - grp_start + 1
# With n as the previous rank in the group and m as the number
# of duplicates in this stretch, if TIEBREAK_FIRST and ascending,
# then rankings should be n + 1, n + 2 ... n + m
elif tiebreak == TIEBREAK_FIRST:
for j in range(i - dups + 1, i + 1):
out[sort_indexer[j]] = j + 1 - grp_start
# If TIEBREAK_FIRST and descending, the ranking should be
# n + m, n + (m - 1) ... n + 1. This is equivalent to
# (i - dups + 1) + (i - j + 1) - grp_start
elif tiebreak == TIEBREAK_FIRST_DESCENDING:
for j in range(i - dups + 1, i + 1):
out[sort_indexer[j]] = 2 * i - j - dups + 2 - grp_start
elif tiebreak == TIEBREAK_DENSE:
for j in range(i - dups + 1, i + 1):
out[sort_indexer[j]] = grp_vals_seen
# Look forward to the next value (using the sorting in
# lexsort_indexer). If the value does not equal the current
# value then we need to reset the dups and sum_ranks, knowing
# that a new value is coming up. The conditional also needs
# to handle nan equality and the end of iteration. If group
# changes we do not record seeing a new value in the group
if not group_changed and (next_val_diff or (check_mask and
(mask[sort_indexer[i]]
^ mask[sort_indexer[i+1]]))):
dups = sum_ranks = 0
grp_vals_seen += 1
# Similar to the previous conditional, check now if we are
# moving to a new group. If so, keep track of the index where
# the new group occurs, so the tiebreaker calculations can
# decrement that from their position. Fill in the size of each
# group encountered (used by pct calculations later). Also be
# sure to reset any of the items helping to calculate dups
if group_changed:
# If not dense tiebreak, group size used to compute
# percentile will be # of non-null elements in group
if tiebreak != TIEBREAK_DENSE:
grp_size = i - grp_start + 1 - grp_na_count
# Otherwise, it will be the number of distinct values
# in the group, subtracting 1 if NaNs are present
# since that is a distinct value we shouldn't count
else:
grp_size = grp_vals_seen - (grp_na_count > 0)
for j in range(grp_start, i + 1):
grp_sizes[sort_indexer[j]] = grp_size
dups = sum_ranks = 0
grp_na_count = 0
grp_start = i + 1
grp_vals_seen = 1
else:
for i in range(N):
at_end = i == N - 1
# dups and sum_ranks will be incremented each loop where
# the value / group remains the same, and should be reset
# when either of those change. Used to calculate tiebreakers
dups += 1
sum_ranks += i - grp_start + 1
next_val_diff = at_end or (masked_vals[sort_indexer[i]]
!= masked_vals[sort_indexer[i+1]])
# We'll need this check later anyway to determine group size, so just
# compute it here since shortcircuiting won't help
group_changed = at_end or (check_labels and
(labels[sort_indexer[i]]
!= labels[sort_indexer[i+1]]))
# Update out only when there is a transition of values or labels.
# When a new value or group is encountered, go back #dups steps(
# the number of occurrence of current value) and assign the ranks
# based on the starting index of the current group (grp_start)
# and the current index
if (next_val_diff or group_changed
or (check_mask and
(mask[sort_indexer[i]] ^ mask[sort_indexer[i+1]]))):
# If keep_na, check for missing values and assign back
# to the result where appropriate
if keep_na and check_mask and mask[sort_indexer[i]]:
grp_na_count = dups
for j in range(i - dups + 1, i + 1):
out[sort_indexer[j]] = NaN
elif tiebreak == TIEBREAK_AVERAGE:
for j in range(i - dups + 1, i + 1):
out[sort_indexer[j]] = sum_ranks / <float64_t>dups
elif tiebreak == TIEBREAK_MIN:
for j in range(i - dups + 1, i + 1):
out[sort_indexer[j]] = i - grp_start - dups + 2
elif tiebreak == TIEBREAK_MAX:
for j in range(i - dups + 1, i + 1):
out[sort_indexer[j]] = i - grp_start + 1
# With n as the previous rank in the group and m as the number
# of duplicates in this stretch, if TIEBREAK_FIRST and ascending,
# then rankings should be n + 1, n + 2 ... n + m
elif tiebreak == TIEBREAK_FIRST:
for j in range(i - dups + 1, i + 1):
out[sort_indexer[j]] = j + 1 - grp_start
# If TIEBREAK_FIRST and descending, the ranking should be
# n + m, n + (m - 1) ... n + 1. This is equivalent to
# (i - dups + 1) + (i - j + 1) - grp_start
elif tiebreak == TIEBREAK_FIRST_DESCENDING:
for j in range(i - dups + 1, i + 1):
out[sort_indexer[j]] = 2 * i - j - dups + 2 - grp_start
elif tiebreak == TIEBREAK_DENSE:
for j in range(i - dups + 1, i + 1):
out[sort_indexer[j]] = grp_vals_seen
# Look forward to the next value (using the sorting in
# lexsort_indexer). If the value does not equal the current
# value then we need to reset the dups and sum_ranks, knowing
# that a new value is coming up. The conditional also needs
# to handle nan equality and the end of iteration. If group
# changes we do not record seeing a new value in the group
if not group_changed and (next_val_diff
or (check_mask and
(mask[sort_indexer[i]]
^ mask[sort_indexer[i+1]]))):
dups = sum_ranks = 0
grp_vals_seen += 1
# Similar to the previous conditional, check now if we are
# moving to a new group. If so, keep track of the index where
# the new group occurs, so the tiebreaker calculations can
# decrement that from their position. Fill in the size of each
# group encountered (used by pct calculations later). Also be
# sure to reset any of the items helping to calculate dups
if group_changed:
# If not dense tiebreak, group size used to compute
# percentile will be # of non-null elements in group
if tiebreak != TIEBREAK_DENSE:
grp_size = i - grp_start + 1 - grp_na_count
# Otherwise, it will be the number of distinct values
# in the group, subtracting 1 if NaNs are present
# since that is a distinct value we shouldn't count
else:
grp_size = grp_vals_seen - (grp_na_count > 0)
for j in range(grp_start, i + 1):
grp_sizes[sort_indexer[j]] = grp_size
dups = sum_ranks = 0
grp_na_count = 0
grp_start = i + 1
grp_vals_seen = 1
if pct:
for i in range(N):
if grp_sizes[i] != 0:
out[i] = out[i] / grp_sizes[i]
def rank_2d(
ndarray[numeric_object_t, ndim=2] in_arr,
int axis=0,
bint is_datetimelike=False,
ties_method="average",
bint ascending=True,
na_option="keep",
bint pct=False,
):
"""
Fast NaN-friendly version of ``scipy.stats.rankdata``.
"""
cdef:
Py_ssize_t k, n, col
float64_t[::1, :] out # Column-major so columns are contiguous
int64_t[::1] grp_sizes
ndarray[numeric_object_t, ndim=2] values
numeric_object_t[:, :] masked_vals
intp_t[:, :] sort_indexer
uint8_t[:, :] mask
TiebreakEnumType tiebreak
bint check_mask, keep_na, nans_rank_highest
numeric_object_t nan_fill_val
tiebreak = tiebreakers[ties_method]
if tiebreak == TIEBREAK_FIRST:
if not ascending:
tiebreak = TIEBREAK_FIRST_DESCENDING
keep_na = na_option == 'keep'
# For cases where a mask is not possible, we can avoid mask checks
check_mask = (
numeric_object_t is float32_t
or numeric_object_t is float64_t
or numeric_object_t is object
or (numeric_object_t is int64_t and is_datetimelike)
)
if axis == 1:
values = np.asarray(in_arr).T.copy()
else:
values = np.asarray(in_arr).copy()
if numeric_object_t is object:
if values.dtype != np.object_:
values = values.astype('O')
nans_rank_highest = ascending ^ (na_option == 'top')
if check_mask:
nan_fill_val = get_rank_nan_fill_val(nans_rank_highest, <numeric_object_t>0)
if numeric_object_t is object:
mask = missing.isnaobj2d(values).view(np.uint8)
elif numeric_object_t is float64_t or numeric_object_t is float32_t:
mask = np.isnan(values).view(np.uint8)
else:
# i.e. int64 and datetimelike
mask = (values == NPY_NAT).view(np.uint8)
np.putmask(values, mask, nan_fill_val)
else:
mask = np.zeros_like(values, dtype=np.uint8)
if nans_rank_highest:
order = (values, mask)
else:
order = (values, ~np.asarray(mask))
n, k = (<object>values).shape
out = np.empty((n, k), dtype='f8', order='F')
grp_sizes = np.ones(n, dtype=np.int64)
# lexsort is slower, so only use if we need to worry about the mask
if check_mask:
sort_indexer = np.lexsort(order, axis=0).astype(np.intp, copy=False)
else:
kind = "stable" if ties_method == "first" else None
sort_indexer = values.argsort(axis=0, kind=kind).astype(np.intp, copy=False)
if not ascending:
sort_indexer = sort_indexer[::-1, :]
# putmask doesn't accept a memoryview, so we assign in a separate step
masked_vals = values
with nogil:
for col in range(k):
rank_sorted_1d(
out[:, col],
grp_sizes,
sort_indexer[:, col],
masked_vals[:, col],
mask[:, col],
check_mask=check_mask,
N=n,
tiebreak=tiebreak,
keep_na=keep_na,
pct=pct,
)
if axis == 1:
return np.asarray(out.T)
else:
return np.asarray(out)
ctypedef fused diff_t:
float64_t
float32_t
int8_t
int16_t
int32_t
int64_t
ctypedef fused out_t:
float32_t
float64_t
int64_t
@cython.boundscheck(False)
@cython.wraparound(False)
def diff_2d(
ndarray[diff_t, ndim=2] arr, # TODO(cython3) update to "const diff_t[:, :] arr"
ndarray[out_t, ndim=2] out,
Py_ssize_t periods,
int axis,
bint datetimelike=False,
):
cdef:
Py_ssize_t i, j, sx, sy, start, stop
bint f_contig = arr.flags.f_contiguous
# bint f_contig = arr.is_f_contig() # TODO(cython3)
diff_t left, right
# Disable for unsupported dtype combinations,
# see https://github.com/cython/cython/issues/2646
if (out_t is float32_t
and not (diff_t is float32_t or diff_t is int8_t or diff_t is int16_t)):
raise NotImplementedError # pragma: no cover
elif (out_t is float64_t
and (diff_t is float32_t or diff_t is int8_t or diff_t is int16_t)):
raise NotImplementedError # pragma: no cover
elif out_t is int64_t and diff_t is not int64_t:
# We only have out_t of int64_t if we have datetimelike
raise NotImplementedError # pragma: no cover
else:
# We put this inside an indented else block to avoid cython build
# warnings about unreachable code
sx, sy = (<object>arr).shape
with nogil:
if f_contig:
if axis == 0:
if periods >= 0:
start, stop = periods, sx
else:
start, stop = 0, sx + periods
for j in range(sy):
for i in range(start, stop):
left = arr[i, j]
right = arr[i - periods, j]
if out_t is int64_t and datetimelike:
if left == NPY_NAT or right == NPY_NAT:
out[i, j] = NPY_NAT
else:
out[i, j] = left - right
else:
out[i, j] = left - right
else:
if periods >= 0:
start, stop = periods, sy
else:
start, stop = 0, sy + periods
for j in range(start, stop):
for i in range(sx):
left = arr[i, j]
right = arr[i, j - periods]
if out_t is int64_t and datetimelike:
if left == NPY_NAT or right == NPY_NAT:
out[i, j] = NPY_NAT
else:
out[i, j] = left - right
else:
out[i, j] = left - right
else:
if axis == 0:
if periods >= 0:
start, stop = periods, sx
else:
start, stop = 0, sx + periods
for i in range(start, stop):
for j in range(sy):
left = arr[i, j]
right = arr[i - periods, j]
if out_t is int64_t and datetimelike:
if left == NPY_NAT or right == NPY_NAT:
out[i, j] = NPY_NAT
else:
out[i, j] = left - right
else:
out[i, j] = left - right
else:
if periods >= 0:
start, stop = periods, sy
else:
start, stop = 0, sy + periods
for i in range(sx):
for j in range(start, stop):
left = arr[i, j]
right = arr[i, j - periods]
if out_t is int64_t and datetimelike:
if left == NPY_NAT or right == NPY_NAT:
out[i, j] = NPY_NAT
else:
out[i, j] = left - right
else:
out[i, j] = left - right
# generated from template
include "algos_common_helper.pxi"
include "algos_take_helper.pxi"