Question

我正在尝试使用Hierarchical Dirichlet Process实施PyMC3（HDP）主题模型。 HDP图形模型如下所示：

我想出了以下代码：

import numpy as np
import scipy as sp
import pandas as pd

import seaborn as sns
import matplotlib.pyplot as plt

import pymc3 as pm
from theano import tensor as tt

np.random.seed(0)

def stick_breaking(beta):
    portion_remaining = tt.concatenate([[1], tt.extra_ops.cumprod(1 - beta)[:-1]])
    return beta * portion_remaining

def main():

    #load data    
    data = np.array([[1, 1, 1, 1], [1, 1, 1, 1], [0, 0, 0, 0]])    
    Wd = [len(doc) for doc in data]

    #HDP parameters    
    T = 10   # top-level truncation
    K = 2    # group-level truncation    
    V = 4    # number of words
    D = 3    # number of documents    

    with pm.Model() as model:    

        #top-level stick breaking
        gamma = pm.Gamma('gamma', 1., 1.)
        beta_prime = pm.Beta('beta_prime', 1., gamma, shape=T)
        beta = pm.Deterministic('beta', stick_breaking(beta_prime))

        #group-level stick breaking                                                
        alpha = pm.Gamma('alpha', 1., 1.)        
        pi_prime = pm.Beta("pi_prime", 1, alpha, shape=K) #Sethuraman's stick breaking
        #pi_prime = [pm.Beta("pi_prime_%s_%s" %(j,k), alpha*(beta[k]), alpha*(1-np.sum(beta[:k+1])), shape=1)
        #            for j in range(K) for k in range(T)]  #Teh's stick breaking
        pi = pm.Deterministic('pi', stick_breaking(pi_prime))

        #top-level DP
        H = pm.Dirichlet("H", a=np.ones(V), shape=V)
        phi_top = pm.Multinomial('phi_top', n=np.sum(Wd), p=H, shape=(T,V))                        
        G0 = pm.Mixture('G0', w=beta, comp_dists=phi_top)

        #group-level DP
        phi_group = [pm.Multinomial('phi_group_%s' %j, n=Wd[j], p=G0) for j in range(D)]
        Gj = [pm.Mixture('G_%s' %j, w=pi, comp_dists=phi_group[j]) for j in range(D)]

        #likelihood                
        w = [pm.Categorical("w_%s_%s" %(j,n), p = Gj[j], observed=data[j][n]) for j in range(D) for n in range(Wd[j])]      

    with model:
        trace = pm.sample(2000, n_init=1000, random_seed=42)


    pm.traceplot(trace)
    plt.show()


if __name__ == '__main__':
    main()

但是，我目前正在使用AssertionError阻止我调试模型的其余部分，它发生在以下行：

phi_top = pm.Multinomial('phi_top', n=np.sum(Wd), p=H, shape=(T,V))

没有关于错误的其他信息。有谁知道如何解决这个问题？

PyMC3中的分层Dirichlet过程

0 个答案: