Question

我正在尝试使用pymc3重建这个贝叶斯PK / PD建模的例子。

该视频显示了WinBUGS代码，我正在尝试转换为pymc3

https://www.youtube.com/watch?v=AQDXRoBan6Y

在这里模特....

WinBUGS代码在这里......

我的代码是......

from pymc3 import Model, Normal, Lognormal, Uniform
import numpy as np
import pandas as pd

data = pd.read_csv('/Users/Home/Documents/pymc3/fxa.data.csv' )

cobs = np.array(data['cobs'])
fxa = np.array(data['fxa.inh.obs'])

pkpd_model = Model()

with pkpd_model:

    # Priors for unknown model parameters
    emax = Uniform ('emax', lower =0, upper =100)
    ec50 = Lognormal('ec50', mu=0, tau = 100000)
    gamma = Uniform('gamma', lower=0, upper =10)
    sigma = Uniform('sigma', lower = 0, upper = 1000 )


    # Expected value of outcome
    fxaMean = emax*(np.power(cobs, gamma)) / (np.power(ec50, gamma) + np.power(cobs, gamma))

    # Likelihood (sampling distribution) of observations
    fxa = Normal('fxa', mu=fxaMean, sd=sigma, observed=fxa )

但是当我运行代码时，我得到以下错误，这似乎与theano解释np.power函数的方式有关。

我不知道如何继续，因为我是pymc3和theano以及PK / PD建模的菜鸟！

提前致谢

Applied interval-transform to emax and added transformed emax_interval to model.
Applied log-transform to ec50 and added transformed ec50_log to model.
Applied interval-transform to gamma and added transformed gamma_interval to model.
Applied interval-transform to sigma and added transformed sigma_interval to model.
---------------------------------------------------------------------------
AsTensorError                             Traceback (most recent call last)
<ipython-input-28-1fa311a15ed0> in <module>()
     14 
     15     # Likelihood (sampling distribution) of observations
---> 16     fxa = Normal('fxa', mu=fxaMean, sd=sigma, observed=fxa )

//anaconda/lib/python2.7/site-packages/pymc3/distributions/distribution.pyc in __new__(cls, name, *args, **kwargs)
     23             data = kwargs.pop('observed', None)
     24             dist = cls.dist(*args, **kwargs)
---> 25             return model.Var(name, dist, data)
     26         elif name is None:
     27             return object.__new__(cls)  # for pickle

//anaconda/lib/python2.7/site-packages/pymc3/model.pyc in Var(self, name, dist, data)
    282                     self.named_vars[v.name] = v
    283         else:
--> 284             var = ObservedRV(name=name, data=data, distribution=dist, model=self)
    285             self.observed_RVs.append(var)
    286             if var.missing_values:

//anaconda/lib/python2.7/site-packages/pymc3/model.pyc in __init__(self, type, owner, index, name, data, distribution, model)
    556             self.missing_values = data.missing_values
    557 
--> 558             self.logp_elemwiset = distribution.logp(data)
    559             self.model = model
    560             self.distribution = distribution

//anaconda/lib/python2.7/site-packages/pymc3/distributions/continuous.pyc in logp(self, value)
    191         sd = self.sd
    192         mu = self.mu
--> 193         return bound((-tau * (value - mu)**2 + T.log(tau / np.pi / 2.)) / 2.,
    194                      tau > 0, sd > 0)
    195 

//anaconda/lib/python2.7/site-packages/theano/tensor/var.pyc in __radd__(self, other)
    232     # ARITHMETIC - RIGHT-OPERAND
    233     def __radd__(self, other):
--> 234         return theano.tensor.basic.add(other, self)
    235 
    236     def __rsub__(self, other):

//anaconda/lib/python2.7/site-packages/theano/gof/op.pyc in __call__(self, *inputs, **kwargs)
    609         """
    610         return_list = kwargs.pop('return_list', False)
--> 611         node = self.make_node(*inputs, **kwargs)
    612 
    613         if config.compute_test_value != 'off':

//anaconda/lib/python2.7/site-packages/theano/tensor/elemwise.pyc in make_node(self, *inputs)
    541         using DimShuffle.
    542         """
--> 543         inputs = list(map(as_tensor_variable, inputs))
    544         shadow = self.scalar_op.make_node(
    545             *[get_scalar_type(dtype=i.type.dtype).make_variable()

//anaconda/lib/python2.7/site-packages/theano/tensor/basic.pyc in as_tensor_variable(x, name, ndim)
    206         except Exception:
    207             str_x = repr(x)
--> 208         raise AsTensorError("Cannot convert %s to TensorType" % str_x, type(x))
    209 
    210 # this has a different name, because _as_tensor_variable is the

AsTensorError: ('Cannot convert [Elemwise{mul,no_inplace}.0 Elemwise{mul,no_inplace}.0\n Elemwise{mul,no_inplace}.0 ..., Elemwise{mul,no_inplace}.0\n Elemwise{mul,no_inplace}.0 Elemwise{mul,no_inplace}.0] to TensorType', <type 'numpy.ndarray'>)

Answer 1

Doh - 用**取代了np.power！工作得很好！

使用幂函数

1 个答案: