以下是简单贝叶斯线性回归的代码。在获取参数的跟踪和绘图之后,有没有什么方法可以将创建绘图的数据保存到文件中,这样如果我需要再次绘制它,我可以简单地从文件中的数据绘制它而不是再次运行整个模拟?
import pymc3 as pm
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0,9,5)
y = 2*x + 5
yerr=np.random.rand(len(x))
def soln(x, p1, p2):
return p1+p2*x
with pm.Model() as model:
# Define priors
intercept = pm.Normal('Intercept', 15, sd=5)
slope = pm.Normal('Slope', 20, sd=5)
# Model solution
sol = soln(x, intercept, slope)
# Define likelihood
likelihood = pm.Normal('Y', mu=sol,
sd=yerr, observed=y)
# Sampling
trace = pm.sample(1000, nchains = 1)
pm.traceplot(trace)
print pm.summary(trace, ['Slope'])
print pm.summary(trace, ['Intercept'])
plt.show()
答案 0 :(得分:2)
有两种简单的方法可以做到这一点:
使用3.4.1之后的版本(目前这意味着从master安装,pip install git+https://github.com/pymc-devs/pymc3
)。有一项新功能可以有效地保存和加载跟踪。请注意,您需要访问创建跟踪的模型:
...
pm.save_trace(trace, 'linreg.trace')
# later
with model:
trace = pm.load_trace('linreg.trace')
在{3}中使用cPickle
(或pickle
)。请注意pickle
至少有点不安全,不要从不受信任的来源中取消数据:
import cPickle as pickle # just `import pickle` on python 3
...
with open('trace.pkl', 'wb') as buff:
pickle.dump(trace, buff)
#later
with open('trace.pkl', 'rb') as buff:
trace = pickle.load(buff)