from __future__ import annotations
|
|
from typing import TYPE_CHECKING
|
|
import numpy as np
|
|
from pandas.compat._optional import import_optional_dependency
|
|
|
def generate_online_numba_ewma_func(
|
nopython: bool,
|
nogil: bool,
|
parallel: bool,
|
):
|
"""
|
Generate a numba jitted groupby ewma function specified by values
|
from engine_kwargs.
|
|
Parameters
|
----------
|
nopython : bool
|
nopython to be passed into numba.jit
|
nogil : bool
|
nogil to be passed into numba.jit
|
parallel : bool
|
parallel to be passed into numba.jit
|
|
Returns
|
-------
|
Numba function
|
"""
|
if TYPE_CHECKING:
|
import numba
|
else:
|
numba = import_optional_dependency("numba")
|
|
@numba.jit(nopython=nopython, nogil=nogil, parallel=parallel)
|
def online_ewma(
|
values: np.ndarray,
|
deltas: np.ndarray,
|
minimum_periods: int,
|
old_wt_factor: float,
|
new_wt: float,
|
old_wt: np.ndarray,
|
adjust: bool,
|
ignore_na: bool,
|
):
|
"""
|
Compute online exponentially weighted mean per column over 2D values.
|
|
Takes the first observation as is, then computes the subsequent
|
exponentially weighted mean accounting minimum periods.
|
"""
|
result = np.empty(values.shape)
|
weighted_avg = values[0]
|
nobs = (~np.isnan(weighted_avg)).astype(np.int64)
|
result[0] = np.where(nobs >= minimum_periods, weighted_avg, np.nan)
|
|
for i in range(1, len(values)):
|
cur = values[i]
|
is_observations = ~np.isnan(cur)
|
nobs += is_observations.astype(np.int64)
|
for j in numba.prange(len(cur)):
|
if not np.isnan(weighted_avg[j]):
|
if is_observations[j] or not ignore_na:
|
# note that len(deltas) = len(vals) - 1 and deltas[i] is to be
|
# used in conjunction with vals[i+1]
|
old_wt[j] *= old_wt_factor ** deltas[j - 1]
|
if is_observations[j]:
|
# avoid numerical errors on constant series
|
if weighted_avg[j] != cur[j]:
|
weighted_avg[j] = (
|
(old_wt[j] * weighted_avg[j]) + (new_wt * cur[j])
|
) / (old_wt[j] + new_wt)
|
if adjust:
|
old_wt[j] += new_wt
|
else:
|
old_wt[j] = 1.0
|
elif is_observations[j]:
|
weighted_avg[j] = cur[j]
|
|
result[i] = np.where(nobs >= minimum_periods, weighted_avg, np.nan)
|
|
return result, old_wt
|
|
return online_ewma
|
|
|
class EWMMeanState:
|
def __init__(self, com, adjust, ignore_na, axis, shape) -> None:
|
alpha = 1.0 / (1.0 + com)
|
self.axis = axis
|
self.shape = shape
|
self.adjust = adjust
|
self.ignore_na = ignore_na
|
self.new_wt = 1.0 if adjust else alpha
|
self.old_wt_factor = 1.0 - alpha
|
self.old_wt = np.ones(self.shape[self.axis - 1])
|
self.last_ewm = None
|
|
def run_ewm(self, weighted_avg, deltas, min_periods, ewm_func):
|
result, old_wt = ewm_func(
|
weighted_avg,
|
deltas,
|
min_periods,
|
self.old_wt_factor,
|
self.new_wt,
|
self.old_wt,
|
self.adjust,
|
self.ignore_na,
|
)
|
self.old_wt = old_wt
|
self.last_ewm = result[-1]
|
return result
|
|
def reset(self) -> None:
|
self.old_wt = np.ones(self.shape[self.axis - 1])
|
self.last_ewm = None
|
