【发布时间】：2021-02-25 23:17:11
【问题描述】：
我需要在具有 600 万个时间步长和每个时间步长 8 个特征的时间序列上使用 python 中的滑动窗口。我使用矢量化版本和带有 for 循环的版本创建了一组滑动窗口。 for 循环要快得多。我已经强调了矢量化版本中耗时的步骤。有没有加快矢量化版本的好方法？
这是矢量化版本：
def vectorized_window(T, l: int, stride=1, start_idxs=None, output_type="data"):
"""Takes a time series, T, and breakes it into subsequences of length l.
This is a vectorized version of window creation. It should run faster because it uses
matrix operations but for very large data, this is not true because the indexing operation
at the end is slow.
Args:
T: A np.ndarray of shape (|T|, features) where |T| is the number of time steps.
l: An int designating the length of the window.
stride: The number of time steps to move the window forward by. Default is
1 time step.
start_idxs: A ndarray or None (default). If start_idxs is specified, these will be used as the start
indices for each window. stride will be ignored. Default of None will
sequentially slide the window by stride steps. Shape should be (num of indices,)
output_type: "data" or "idxs". The default of "data" will compute and return the full window (ndarray)
with the actual data values for each time step. If "idxs" is specified, it will return a ndarray
of shape (num windows, 2) where windows[:,0] are the start indices and windows[:,1] are the end indices.
Returns:
windows: a list of ndarrays that represent windows, with length l, of the time series. The shape is
either (num windows, l, num features) or (num windows, 2) depending on output_type.
"""
window_idxs = np.expand_dims(np.arange(l), 0)
if output_type != "data":
window_idxs = window_idxs[[0, -1]]
if start_idxs is None:
start_idxs = np.expand_dims(np.arange(T.shape[0]-l, step=stride), 0).T
else:
start_idxs = np.expand_dims(start_idxs, 0).T
if output_type != "data":
windows = window_idxs + start_idxs
else:
sub_windows = (window_idxs + start_idxs)
windows = T[sub_windows] # This is the slow step
return windows
这是带有 for 循环的版本：
def create_window(T, l: int, stride=1):
"""Takes a time series, T, and breakes it into subsequences of length l.
Args:
T: A list or np.ndarray representing a univariate or multivariate time series.
If it is a multivarite time series, it must be a numpy array of shape
(time steps, features). If features is in axis 0, this will not work.
l: An int designating the length of the window.
stride: The number of time steps to move the window forward by. Default is
1 time step.
Returns:
windows: a list of ndarrays that represent windows, with length l, of the time series.
"""
if "list" in str(type(T)):
T = np.asarray(T)
n_T= T.shape[0]
windows = []
for i in range(0, n_T - l, stride):
window = T[i:i+l]
windows.append(window)
return windows
这两个版本做的事情并不完全相同。如果 output_type 不是“数据”，矢量化版本还将返回每个子序列的边界索引。但是，这种差异不会显着影响整体速度。
非常感谢任何优化此代码的建议！
【问题讨论】：
标签： python algorithm performance optimization vectorization