Question

使用PYMC3计算后期时出现此错误：

with pm.Model() as model:
    p = pm.Gamma('p', alpha=1, beta=3, shape=regions.shape[0])
    q = pm.Gamma('q', alpha=1, beta=3, shape=regions.shape[0])
    m = pm.Lognormal('m', mu=np.log(total_M), sd=.25, shape=regions.shape[0])
    t = pm.Uniform('t', lower=0, upper=100, observed=sales.t)
    cid = pm.Categorical('cid', p=np.repeat(1./sales.shape[0], sales.shape[0]), observed=sales.region )
    sigma = pm.Gamma('sigma', alpha=1, beta=3)
    mu = m[cid]*(((p[cid]+q[cid])**2)/p[cid])*((np.exp(-(p[cid]+q[cid])*t))/((1+(q[cid]/p[cid])*np.exp(-(p[cid]+q[cid])*t))**2))
    Y_obs = pm.Normal('Ft', mu= mu, sd=sigma, observed= sales.sales)
    trace = pm.sample(100000,init = 'adapt_diag', progressbar = True, tune  = 1000)

我已经尝试将mu = mu更改为mu = np.log（mu），它解决了错误但与我的其他伙伴相比给了我不好的结果。

Answer 1

我尝试过使用各种选项更改init方法并并行运行它们。它已经解决了这些问题，但自上48小时以来一直在运行。有人可以帮助提出任何建议和反馈吗？

下面给出的代码是最后几行的变化：

 1. 

with pm.Model() as model:
    p = pm.Gamma('p', alpha=1, beta=3, shape=regions.shape[0])
    q = pm.Gamma('q', alpha=1, beta=3, shape=regions.shape[0])
    m = pm.Lognormal('m', mu=np.log(total_M), sd=.375, shape=regions.shape[0])
    t = pm.Uniform('t', lower=0, upper=100, observed=sales.t)
   # p1 = pm.Deterministic('p1', np.repeat(1./sales.shape[0],sales.shape[0]))
   # cid = pm.Categorical('cid', p=p1, observed=sales.region )
    cid = pm.Categorical('cid', p=np.repeat(1./sales.shape[0], sales.shape[0]), observed=sales.region )
    sigma = pm.Gamma('sigma', alpha=1, beta=3)
    mu = m[cid]*(((p[cid]+q[cid])**2)/p[
        cid])*((np.exp(-(p[cid]+q[cid])*t))/((1+(q[cid]/p[cid])*np.exp(-(p[cid]+q[cid])*t))**2))
    Y_obs = pm.Normal('Ft', mu=mu, sd=sigma, observed=sales.sales)
    **trace = pm.sample(init = 'advi+adapt_diag', tune  = 1000)**

 2. 

with pm.Model() as model:
    p = pm.Gamma('p', alpha=1, beta=3, shape=regions.shape[0])
    q = pm.Gamma('q', alpha=1, beta=3, shape=regions.shape[0])
    m = pm.Lognormal('m', mu=np.log(total_M), sd=.375, shape=regions.shape[0])
    t = pm.Uniform('t', lower=0, upper=100, observed=sales.t)
   # p1 = pm.Deterministic('p1', np.repeat(1./sales.shape[0],sales.shape[0]))
   # cid = pm.Categorical('cid', p=p1, observed=sales.region )
    cid = pm.Categorical('cid', p=np.repeat(1./sales.shape[0], sales.shape[0]), observed=sales.region )
    sigma = pm.Gamma('sigma', alpha=1, beta=3)
    mu = m[cid]*(((p[cid]+q[cid])**2)/p[
        cid])*((np.exp(-(p[cid]+q[cid])*t))/((1+(q[cid]/p[cid])*np.exp(-(p[cid]+q[cid])*t))**2))
    Y_obs = pm.Normal('Ft', mu=mu, sd=sigma, observed=sales.sales)
    **trace = pm.sample(200000, init = 'advi', tune  = 1000)**

 3. 

with pm.Model() as model:
    p = pm.Gamma('p', alpha=1, beta=3, shape=regions.shape[0])
    q = pm.Gamma('q', alpha=1, beta=3, shape=regions.shape[0])
    m = pm.Lognormal('m', mu=np.log(total_M), sd=.375, shape=regions.shape[0])
    t = pm.Uniform('t', lower=0, upper=100, observed=sales.t)
   # p1 = pm.Deterministic('p1', np.repeat(1./sales.shape[0],sales.shape[0]))
   # cid = pm.Categorical('cid', p=p1, observed=sales.region )
    cid = pm.Categorical('cid', p=np.repeat(1./sales.shape[0], sales.shape[0]), observed=sales.region )
    sigma = pm.Gamma('sigma', alpha=1, beta=3)
    mu = m[cid]*(((p[cid]+q[cid])**2)/p[
        cid])*((np.exp(-(p[cid]+q[cid])*t))/((1+(q[cid]/p[cid])*np.exp(-(p[cid]+q[cid])*t))**2))
    Y_obs = pm.Normal('Ft', mu=mu, sd=sigma, observed=sales.sales)
    **start = pm.find_MAP() 
    step = pm.NUTS(scaling=start)
    trace = pm.sample(5000, step, start = start, progressbar=True)**

PYMC3 ValueError：质量矩阵在对角线上包含零。一些衍生品可能总是为零

1 个答案: