python - 将 PyMC3 traceplot 子图保存到图像文件

Question

我尝试非常简单地将 PyMC3 traceplot 函数(参见 here)生成的子图绘制到一个文件中。

该函数生成子图的 numpy.ndarray (2d)。

我需要将这些子图移动或复制到 matplotlib.figure 中以保存图像文件。我能找到的所有内容都显示了如何首先生成图形的子图，然后构建它们。

作为一个最小示例，我从 Here 中提取了示例 PyMC3 代码, 并向其中添加了几行以尝试处理子图。

from pymc3 import *
import theano.tensor as tt
from theano import as_op
from numpy import arange, array, empty

### Added these three lines relative to source #######################
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt

__all__ = ['disasters_data', 'switchpoint', 'early_mean', 'late_mean', 'rate', 'disasters']

# Time series of recorded coal mining disasters in the UK from 1851 to 1962
disasters_data = array([4, 5, 4, 0, 1, 4, 3, 4, 0, 6, 3, 3, 4, 0, 2, 6,
                        3, 3, 5, 4, 5, 3, 1, 4, 4, 1, 5, 5, 3, 4, 2, 5,
                        2, 2, 3, 4, 2, 1, 3, 2, 2, 1, 1, 1, 1, 3, 0, 0,
                        1, 0, 1, 1, 0, 0, 3, 1, 0, 3, 2, 2, 0, 1, 1, 1,
                        0, 1, 0, 1, 0, 0, 0, 2, 1, 0, 0, 0, 1, 1, 0, 2,
                        3, 3, 1, 1, 2, 1, 1, 1, 1, 2, 4, 2, 0, 0, 1, 4,
                        0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1])
years = len(disasters_data)

@as_op(itypes=[tt.lscalar, tt.dscalar, tt.dscalar], otypes=[tt.dvector])
def rateFunc(switchpoint, early_mean, late_mean):
    out = empty(years)
    out[:switchpoint] = early_mean
    out[switchpoint:] = late_mean
    return out


with Model() as model:

    # Prior for distribution of switchpoint location
    switchpoint = DiscreteUniform('switchpoint', lower=0, upper=years)
    # Priors for pre- and post-switch mean number of disasters
    early_mean = Exponential('early_mean', lam=1.)
    late_mean = Exponential('late_mean', lam=1.)

    # Allocate appropriate Poisson rates to years before and after current switchpoint location
    rate = rateFunc(switchpoint, early_mean, late_mean)

    # Data likelihood
    disasters = Poisson('disasters', rate, observed=disasters_data)

    # Initial values for stochastic nodes
    start = {'early_mean': 2., 'late_mean': 3.}

    # Use slice sampler for means
    step1 = Slice([early_mean, late_mean])
    # Use Metropolis for switchpoint, since it accomodates discrete variables
    step2 = Metropolis([switchpoint])

    # njobs>1 works only with most recent (mid August 2014) Thenao version:
    # https://github.com/Theano/Theano/pull/2021
    tr = sample(1000, tune=500, start=start, step=[step1, step2], njobs=1)

    ### gnashing of teeth starts here ################################
    fig, axarr = plt.subplots(3,2)

    # This gives a KeyError
    # axarr = traceplot(tr, axarr)

    # This finishes without error
    trarr = traceplot(tr)

    # doesn't work
    # axarr[0, 0] = trarr[0, 0]

    fig.savefig("disaster.png")

我尝试了 subplot() 和 add_subplot() 行的一些变体，但无济于事——所有错误都指向一个事实，即必须首先为图形创建空子图，而不是分配给预先存在的子图.

一个不同的例子(参见 here ，大约 80% 的下降，从

开始

### Mysterious code to be explained in Chapter 3.

) 完全避免了实用程序并手动构建子图，所以也许对此没有好的答案？ pymc3.traceplot 输出确实是无法使用的孤立的子图的 ndarray 吗？

Answer 1

我遇到了同样的问题。我正在使用 pymc3 3.5 和 matplotlib 2.1.2。

我意识到可以通过以下方式导出跟踪图:

trarr = traceplot(tr)

fig = plt.gcf() # to get the current figure...
fig.savefig("disaster.png") # and save it directly