我没有使用PyMc3将属于类实例的方法作为确定性函数.你能告诉我怎么做吗？
为简单起见,我的案例总结如下,并附有一个简单的例子.实际上,我的约束是一切都是通过GUI完成的,像'find_MAP'这样的动作应该在链接到pyqt按钮的方法中.
我想在数据点上安装函数'FunctionIWantToFit'.问题,以下代码:
import numpy as np
import pymc3 as pm3
from scipy.interpolate import interp1d
import theano.tensor as tt
import theano.compile
class cprofile:
def __init__(self):
self.observed_x = np.array([0.3,1.4,3.1,5,6.8,9,13.4,17.1])
self.observations = np.array([6.25,2.75,1.25,1.25,1.5,1.75,1.5,1])
self.x = np.arange(0,18,0.5)
@theano.compile.ops.as_op(itypes=[tt.dscalar,tt.dscalar,tt.dscalar],
otypes=[tt.dvector])
def FunctionIWantToFit(self,t,y,z):
# can be complicated but simple in this example
# among other things, this FunctionIWantToFit depends on a bunch of
# variables and methods that belong to this instance of the class cprofile,
# so it cannot simply be put outside the class ! (like in the following example)
val=t+y*self.x+z*self.x**2
interp_values = interp1d(self.x,val)
return interp_values(self.observed_x)
def doMAP(self):
model = pm3.Model()
with model:
t = pm3.Uniform("t",0,5)
y = pm3.Uniform("y",0,5)
z = pm3.Uniform("z",0,5)
MyModel = pm3.Deterministic('MyModel',self.FunctionIWantToFit(t,y,z))
obs = pm3.Normal('obs',mu=MyModel,sd=0.1,observed=self.observations)
start = pm3.find_MAP()
print('start: ',start)
test=cprofile()
test.doMAP()
给出以下错误:
Traceback (most recent call last):
File "<ipython-input-15-3dfb7aa09f84>", line 1, in <module>
runfile('/Users/steph/work/profiles/GUI/pymc3/so.py', wdir='/Users/steph/work/profiles/GUI/pymc3')
File "/Users/steph/anaconda/lib/python3.5/site-packages/spyder/utils/site/sitecustomize.py", line 866, in runfile
execfile(filename, namespace)
File "/Users/steph/anaconda/lib/python3.5/site-packages/spyder/utils/site/sitecustomize.py", line 102, in execfile
exec(compile(f.read(), filename, 'exec'), namespace)
File "/Users/steph/work/profiles/GUI/pymc3/so.py", line 44, in <module>
test.doMAP()
File "/Users/steph/work/profiles/GUI/pymc3/so.py", line 38, in doMAP
MyModel = pm3.Deterministic('MyModel',self.FunctionIWantToFit(x,y,z))
File "/Users/steph/anaconda/lib/python3.5/site-packages/theano/gof/op.py", line 668, in __call__
required = thunk()
File "/Users/steph/anaconda/lib/python3.5/site-packages/theano/gof/op.py", line 912, in rval
r = p(n, [x[0] for x in i], o)
File "/Users/steph/anaconda/lib/python3.5/site-packages/theano/compile/ops.py", line 522, in perform
outs = self.__fn(*inputs)
TypeError: FunctionIWantToFit() missing 1 required positional argument: 'z'
怎么了 ？
备注1:我系统地收到有关'FunctionIWantToFit'的最后一个参数的错误消息.这里是'z'但是如果我从签名中删除z,则错误消息涉及'y'(除了变量名称之外相同).如果我在签名中添加第四个变量'w',则错误消息涉及'w'(除了变量名称之外相同).
rk2:看起来我在'theano'或'pymc3'中遗漏了一些非常基本的东西,因为当我把'FunctionIWantToFit'放在课外时,它可以工作.请参阅以下示例.
class cprofile:
def __init__(self):
self.observations = np.array([6.25,2.75,1.25,1.25,1.5,1.75,1.5,1])
def doMAP(self):
model = pm3.Model()
with model:
t = pm3.Uniform("t",0,5)
y = pm3.Uniform("y",0,5)
z = pm3.Uniform("z",0,5)
MyModel = pm3.Deterministic('MyModel',FunctionIWantToFit(t,y,z))
obs = pm3.Normal('obs',mu=MyModel,sd=0.1,observed=self.observations)
start = pm3.find_MAP()
print('start: ',start)
@theano.compile.ops.as_op(itypes=[tt.dscalar,tt.dscalar,tt.dscalar],
otypes=[tt.dvector])
def FunctionIWantToFit(t,y,z):
observed_x = np.array([0.3,1.4,3.1,5,6.8,9,13.4,17.1])
x = np.arange(0,18,0.5)
val=t+y*x+z*x**2
interp_values = interp1d(x,val)
return interp_values(observed_x)
test=cprofile()
test.doMAP()
得到:
Warning: gradient not available.(E.g. vars contains discrete variables). MAP estimates may not be accurate for the default parameters. Defaulting to non-gradient minimization fmin_powell.
WARNING:pymc3:Warning: gradient not available.(E.g. vars contains discrete variables). MAP estimates may not be accurate for the default parameters. Defaulting to non-gradient minimization fmin_powell.
Optimization terminated successfully.
Current function value: 1070.673818
Iterations: 4
Function evaluations: 179
start: {'t_interval_': array(-0.27924150484602733), 'y_interval_': array(-9.940000425802811), 'z_interval_': array(-12.524909223913992)}
除了我不知道如何在几个模块中进行大的修改之后如何做到这一点,因为真正的'FunctionIWantToFit'依赖于属于类配置文件的这个实例的一堆变量和方法.
事实上,我甚至不确定我是怎么做的,因为'FunctionIWantToFit'应该在参数中有对象(我目前正在使用self)并且我不确定如何使用theano装饰器.
所以我宁愿避免这种解决方案......除非必要.然后我需要解释如何实现它......
于2017年4月9日添加:
即使没有插值问题,它也不起作用,因为我必须错过了与theano和/或pymc3明显的东西.请问你能解释一下这个问题吗？我只想比较模型和数据.首先,被pymc2困扰是如此的耻辱.; 第二,我确信我不是唯一一个有这样一个基本问题的人.
例如,让我们考虑这个基本代码的变体:
import numpy as np
import theano
import pymc3
theano.config.compute_test_value = 'ignore'
theano.config.on_unused_input = 'ignore'
class testclass:
x = np.arange(0,18,0.5)
observed_x = np.array([0.3,1.4,3.1,5,6.8,9,13.4,17.1])
observations = np.array([6.25,2.75,1.25,1.25,1.5,1.75,1.5,1])
def testfunc(self,t,y,z):
t2 = theano.tensor.dscalar('t2')
y2 = theano.tensor.dscalar('y2')
z2 = theano.tensor.dscalar('z2')
val = t2 + y2 * self.observed_x + z2 * self.observed_x**2
f = theano.function([t2,y2,z2],val)
return f
test=testclass()
model = pymc3.Model()
with model:
t = pymc3.Uniform("t",0,5)
y = pymc3.Uniform("y",0,5)
z = pymc3.Uniform("z",0,5)
with model:
MyModel = pymc3.Deterministic('MyModel',test.testfunc(t,y,z))
with model:
obs = pymc3.Normal('obs',mu=MyModel,sd=0.1,observed=test.observations)
此代码在最后一行失败,并显示错误消息: TypeError: unsupported operand type(s) for -: 'TensorConstant' and 'Function'
如果我将'testfunc'改为:
def testfunc(self,t,y,z):
t2 = theano.tensor.dscalar('t2')
y2 = theano.tensor.dscalar('y2')
z2 = theano.tensor.dscalar('z2')
val = t2 + y2 * self.observed_x + z2 * self.observed_x**2
f = theano.function([t2,y2,z2],val)
fval = f(t,y,z,self.observed_x)
return fval
代码在'MyModel ='行失败并出错 TypeError: ('Bad input argument to theano function with name "/Users/steph/work/profiles/GUI/pymc3/theanotest170409.py:32" at index 0(0-based)', 'Expected an array-like object, but found a Variable: maybe you are trying to call a function on a (possibly shared) variable instead of a numeric array?')
如果我回到原来的'testfunc'但是改变了最后的'with model'行:
with model:
fval = test.testfunc(t,y,z)
obs = pymc3.Normal('obs',mu=fval,sd=0.1,observed=test.observations)
错误与第一个错误相同.
我在这里只展示了3次尝试,但我想强调一下,我尝试了很多种组合,在这几种情况下,这些组合更简单,更简单.我感觉pymc3显示了一个巨大的精神变化,与pymc2相比,我没有得到,并且记录不足...
我最终收敛到下面的成功代码：
\n\nimport numpy as np\nimport theano\nfrom scipy.interpolate import interp1d\nimport pymc3 as pm3\ntheano.config.compute_test_value = \'ignore\'\ntheano.config.on_unused_input = \'ignore\'\n\nclass cprofile:\n observations = np.array([6.25,2.75,1.25,1.25,1.5,1.75,1.5,1])\n x = np.arange(0,18,0.5)\n observed_x = np.array([0.3,1.4,3.1,5,6.8,9,13.4,17.1]) \n\n def doMAP(self):\n model = pm3.Model()\n with model:\n t = pm3.Uniform("t",0,5)\n y = pm3.Uniform("y",0,5)\n z = pm3.Uniform("z",0,5)\n obs=pm3.Normal(\'obs\',\n mu=FunctionIWantToFit(self)(t,y,z),\n sd=0.1,observed=self.observations)\n start = pm3.find_MAP()\n print(\'start: \',start)\n\nclass FunctionIWantToFit(theano.gof.Op):\n itypes=[theano.tensor.dscalar,\n theano.tensor.dscalar,\n theano.tensor.dscalar]\n otypes=[theano.tensor.dvector]\n\n def __init__(self, cp):\n self.cp = cp # note cp is an instance of the \'cprofile\' class\n\n def perform(self,node, inputs, outputs):\n t, y, z = inputs[0], inputs[1], inputs[2]\n\n xxx = self.cp.x\n temp = t+y*xxx+z*xxx**2\n interpolated_concentration = interp1d(xxx,temp) \n outputs[0][0] = interpolated_concentration(self.cp.observed_x)\n\ntestcp=cprofile()\ntestcp.doMAP()\n感谢达里奥的回答，因为我自己理解第一个答案太慢了。我回顾了一下，但我强烈认为 pymc3 文档非常不清楚。它应该包含非常简单且说明性的示例。
\n\n然而，我没有\xe2\x80\x99t 成功地按照克里斯的评论做任何有效的事情。有人可以解释和/或举个例子吗？
\n\n另一件事：我不知道我上面的例子是否有效或者可以简化。特别是它给我的印象是实例 \xe2\x80\x98testcp\xe2\x80\x99 在内存中复制了两次。欢迎更多评论/答案，以走得更远。
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