python - 加快内核估计的采样

Question

这是我正在使用的更大代码的 MWE。基本上，它对位于特定阈值以下的所有值在 KDE ( kernel density estimate ) 上执行蒙特卡罗积分(在这个问题 BTW 上建议了积分方法:Integrate 2D kernel density estimate)。

import numpy as np
from scipy import stats
import time

# Generate some random two-dimensional data:
def measure(n):
    "Measurement model, return two coupled measurements."
    m1 = np.random.normal(size=n)
    m2 = np.random.normal(scale=0.5, size=n)
    return m1+m2, m1-m2

# Get data.
m1, m2 = measure(20000)
# Define limits.
xmin = m1.min()
xmax = m1.max()
ymin = m2.min()
ymax = m2.max()

# Perform a kernel density estimate on the data.
x, y = np.mgrid[xmin:xmax:100j, ymin:ymax:100j]
values = np.vstack([m1, m2])
kernel = stats.gaussian_kde(values)

# Define point below which to integrate the kernel.
x1, y1 = 0.5, 0.5

# Get kernel value for this point.
tik = time.time()
iso = kernel((x1,y1))
print 'iso: ', time.time()-tik

# Sample from KDE distribution (Monte Carlo process).
tik = time.time()
sample = kernel.resample(size=1000)
print 'resample: ', time.time()-tik

# Filter the sample leaving only values for which
# the kernel evaluates to less than what it does for
# the (x1, y1) point defined above.
tik = time.time()
insample = kernel(sample) < iso
print 'filter/sample: ', time.time()-tik

# Integrate for all values below iso.
tik = time.time()
integral = insample.sum() / float(insample.shape[0])
print 'integral: ', time.time()-tik

输出看起来像这样:

iso:  0.00259208679199
resample:  0.000817060470581
filter/sample:  2.10829401016
integral:  4.2200088501e-05

这显然意味着 filter/sample 调用几乎占用了代码运行的所有时间。我必须反复运行此代码块数千次，因此它可能会非常耗时。

有什么方法可以加快过滤/采样过程吗？

---

添加

这是我的实际代码的更现实的 MWE，其中写入了 Ophion 的多线程解决方案:

import numpy as np
from scipy import stats
from multiprocessing import Pool

def kde_integration(m_list):

    m1, m2 = [], []
    for item in m_list:
        # Color data.
        m1.append(item[0])
        # Magnitude data.
        m2.append(item[1])

    # Define limits.
    xmin, xmax = min(m1), max(m1)
    ymin, ymax = min(m2), max(m2)

    # Perform a kernel density estimate on the data:
    x, y = np.mgrid[xmin:xmax:100j, ymin:ymax:100j]
    values = np.vstack([m1, m2])
    kernel = stats.gaussian_kde(values)

    out_list = []

    for point in m_list:

        # Compute the point below which to integrate.
        iso = kernel((point[0], point[1]))

        # Sample KDE distribution
        sample = kernel.resample(size=1000)

        #Create definition.
        def calc_kernel(samp):
            return kernel(samp)

        #Choose number of cores and split input array.
        cores = 4
        torun = np.array_split(sample, cores, axis=1)

        #Calculate
        pool = Pool(processes=cores)
        results = pool.map(calc_kernel, torun)

        #Reintegrate and calculate results
        insample_mp = np.concatenate(results) < iso

        # Integrate for all values below iso.
        integral = insample_mp.sum() / float(insample_mp.shape[0])

        out_list.append(integral)

    return out_list


# Generate some random two-dimensional data:
def measure(n):
    "Measurement model, return two coupled measurements."
    m1 = np.random.normal(size=n)
    m2 = np.random.normal(scale=0.5, size=n)
    return m1+m2, m1-m2

# Create list to pass.
m_list = []
for i in range(60):
    m1, m2 = measure(5)
    m_list.append(m1.tolist())
    m_list.append(m2.tolist())

# Call KDE integration function.
print 'Integral result: ', kde_integration(m_list)

Ophion 提供的解决方案在我提供的原始代码上运行良好，但在此版本中失败并出现以下错误:

Integral result: Exception in thread Thread-3:
Traceback (most recent call last):
  File "/usr/lib/python2.7/threading.py", line 551, in __bootstrap_inner
    self.run()
  File "/usr/lib/python2.7/threading.py", line 504, in run
    self.__target(*self.__args, **self.__kwargs)
  File "/usr/lib/python2.7/multiprocessing/pool.py", line 319, in _handle_tasks
    put(task)
PicklingError: Can't pickle <type 'function'>: attribute lookup __builtin__.function failed

我尝试移动 calc_kernel 函数，因为这个问题的答案之一 Multiprocessing: How to use Pool.map on a function defined in a class?声明“您提供给 map() 的函数必须可以通过导入您的模块来访问”；但我仍然无法使这段代码正常工作。

非常感谢任何帮助。

---

加2

实现 Ophion 的建议以删除 calc_kernel 函数并简单地使用:

results = pool.map(kernel, torun)

努力摆脱 PicklingError 但现在我看到如果我创建一个初始 m_list 超过 62-63 个项目，我会得到这个错误:

Traceback (most recent call last):
  File "~/gauss_kde_temp.py", line 67, in <module>
    print 'Integral result: ', kde_integration(m_list)
  File "~/gauss_kde_temp.py", line 38, in kde_integration
    pool = Pool(processes=cores)
  File "/usr/lib/python2.7/multiprocessing/__init__.py", line 232, in Pool
    return Pool(processes, initializer, initargs, maxtasksperchild)
  File "/usr/lib/python2.7/multiprocessing/pool.py", line 161, in __init__
    self._result_handler.start()
  File "/usr/lib/python2.7/threading.py", line 494, in start
    _start_new_thread(self.__bootstrap, ())
thread.error: can't start new thread

由于我在实际执行此代码时的实际列表最多可包含 2000 个项目，因此此问题导致代码无法使用。 38 行是这一行:

pool = Pool(processes=cores)

很明显这与我使用的内核数量有关？

这个问题"Can't start a new thread error" in Python建议使用:

threading.active_count()

当我得到那个错误时检查我正在运行的线程数。我检查了一下，它总是在达到 374 线程时崩溃。我如何编写代码来解决这个问题？

---

这是处理最后一个问题的新问题:Thread error: can't start new thread

Answer 1

可能最简单的加速方法是并行化kernel(sample):

取这段代码片段:

tik = time.time()
insample = kernel(sample) < iso
print 'filter/sample: ', time.time()-tik
#filter/sample:  1.94065904617

将其更改为使用多处理:

from multiprocessing import Pool
tik = time.time()

#Create definition.
def calc_kernel(samp):
    return kernel(samp)

#Choose number of cores and split input array.
cores = 4
torun = np.array_split(sample, cores, axis=1)

#Calculate
pool = Pool(processes=cores)
results = pool.map(calc_kernel, torun)

#Reintegrate and calculate results
insample_mp = np.concatenate(results) < iso

print 'multiprocessing filter/sample: ', time.time()-tik
#multiprocessing filter/sample:  0.496874094009

仔细检查他们返回相同的答案:

print np.all(insample==insample_mp)
#True

4 核性能提升 3.9 倍。不确定你在什么上运行它，但是在大约 6 个处理器之后，你的输入数组大小不够大，无法获得相当大的 yield 。例如，使用 20 个处理器，速度仅提高了 5.8 倍。