Pymc线性回归开始问题（缩放输入参数？）

继续使用this example做相当简单的贝叶斯线性回归使用PYMC3（学习，我希望）我得到的初始示例运行，但然后尝试使用我自己的数据，并得到：Pymc线性回归开始问题（缩放输入参数？）

ValueError: Optimization error: max, logp or dlogp at max have non-finite values. 
Some values may be outside of distribution support. max: {'alpha': array(50000.0), 
'beta': array(50000.0), 'sigma': array(25000.0)} logp: array(nan) dlogp: array([ nan, 
nan, nan])Check that 1) you don't have hierarchical parameters, these will lead to 
points with infinite density. 2) your distribution logp's are properly specified. 
Specific issues:

这是可疑的是由于我的数据范围，但它可能是我不明白其他参数。数据和代码如下：这应该只是运行在我希望的IPython笔记本上。该lastqu应该预测单元，当一切都说过和做过..

import pandas as pd 
import io 
content2 = '''\ 
Units lastqu 
2000-12-31 19391 NaN 
2001-12-31 35068 5925 
2002-12-31 39279 8063 
2003-12-31 47517 9473 
2004-12-31 51439 11226 
2005-12-31 59674 11667 
2006-12-31 58664 14016 
2007-12-31 55698 13186 
2008-12-31 42235 11343 
2009-12-31 40478 7867 
2010-12-31 38722 8114 
2011-12-31 36965 8361 
2012-12-31 39132 8608 
2013-12-31 43160 9016 
2014-12-31 NaN  9785 
''' 
df2 = pd.read_table(io.BytesIO(content2)) 
#make sure that the columns are int, it is all a DataFrame 
df2['Units']=df2['Units'][:-1].astype('int') 
df2['lastqu']=df2['lastqu'][1:].astype('int') 
df2

我试着型号代码：

import pymc as pm 
#import numpy as np 
x=df2['lastqu']    <<<< my best guess as to how to specify my data 
y=df2['Units'] 
trace = None 
with pm.Model() as model: 
    alpha = pm.Normal('alpha', mu=0, sd=20) 
    beta = pm.Normal('beta', mu=0, sd=20) 
    sigma = pm.Uniform('sigma', lower=0, upper=50000) 

    y_est = alpha + beta * x 

    likelihood = pm.Normal('y', mu=y_est, sd=sigma, observed=y) 

    start = pm.find_MAP() 
    step = pm.NUTS(state=start) 
    trace = pm.sample(2000, step, start=start, progressbar=False) 

    pm.traceplot(trace);

来源

2014-02-12 dartdog

最初的错误，它似乎是我包含NaN值，我削减框架以排除NaN在这两列。它跑了一下，但得到了一些Theano错误，我现在正在欣赏...... /usr/local/lib/python2.7/dist-packages/theano/scan_module/scan_perform_ext.py:85：RuntimeWarning ：numpy.ndarray大小已更改，可能表示来自scan_perform.scan_perform的二进制不兼容 import *想知道是否需要访问Theano的Git版本？ – dartdog

代码适用于添加％matplotlib inline Doh！ – dartdog

这工作：

df2=df2[1:-1]   <<<< gets rid of NaN from example data 
df2 
%matplotlib inline 
import pymc as pm 
#import numpy as np 
x=df2['lastqu']    <<<< my best guess as to how to specify my data 
y=df2['Units'] 
trace = None 
with pm.Model() as model: 
    alpha = pm.Normal('alpha', mu=0, sd=20) 
    beta = pm.Normal('beta', mu=0, sd=20) 
    sigma = pm.Uniform('sigma', lower=0, upper=50000) 

    y_est = alpha + beta * x 

    likelihood = pm.Normal('y', mu=y_est, sd=sigma, observed=y) 

    start = pm.find_MAP() 
    step = pm.NUTS(state=start) 
    trace = pm.sample(2000, step, start=start, progressbar=False) 

    pm.traceplot(trace);

再次非常感谢到@fonnesbeck！

来源

2014-02-13 16:05:21 dartdog

Pymc线性回归开始问题（缩放输入参数？）

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