

In [1]:

    
# This notebook is used to decide on a tolerable level of corruptableness.
%matplotlib inline

import numpy as np
from scipy.stats import entropy as KL_divergence
import pandas as pd
import matplotlib.pyplot as plt

from slda.topic_models import RTM
from modules.helpers import plot_images



In [2]:

    
# Generate topics
# We assume a vocabulary of 'rows'^2 terms, and create 'rows'*2 "topics",
# where each topic assigns exactly 'rows' consecutive terms equal probability.
rows = 3
V = rows * rows
K = rows * 2
N = K * K
D = 10000
seed = 42
topics = []
topic_base = np.concatenate((np.ones((1, rows)) * (1/rows),
                             np.zeros((rows-1, rows))), axis=0).ravel()
for i in range(rows):
    topics.append(np.roll(topic_base, i * rows))
topic_base = np.concatenate((np.ones((rows, 1)) * (1/rows),
                             np.zeros((rows, rows-1))), axis=1).ravel()
for i in range(rows):
    topics.append(np.roll(topic_base, i))
topics = np.array(topics)

# Generate documents from topics
# We generate D documents from these V topics by sampling D topic
# distributions, one for each document, from a Dirichlet distribution with
# parameter α=(1,…,1)
alpha = np.ones(K)
np.random.seed(seed)
thetas = np.random.dirichlet(alpha, size=D)
topic_assignments = np.array([np.random.choice(range(K), size=N, p=theta)
                              for theta in thetas])
word_assignments = np.array([[np.random.choice(range(V), size=1,
                                               p=topics[topic_assignments[d, n]])[0]
                              for n in range(N)] for d in range(D)])
doc_term_matrix = np.array([np.histogram(word_assignments[d], bins=V,
                                         range=(0, V - 1))[0] for d in range(D)])

# choose parameter values
mu = 0.
sigma2 = 1.
nu = 1.
np.random.seed(14)

# Estimate parameters
_K = K
_alpha = alpha[:_K]
_beta = np.repeat(0.01, V)
_mu = mu
_sigma2 = sigma2
_nu = nu
n_iter = 1000
rtm = RTM(_K, _alpha, _beta, _mu, _sigma2, _nu, n_iter, seed=42)

rtm.fit(doc_term_matrix)
results = grtm.phi









    



---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-2-e654d367102d> in <module>()
     50 rtm = RTM(_K, _alpha, _beta, _mu, _sigma2, _nu, n_iter, seed=42)
     51 
---> 52 rtm.fit(doc_term_matrix)
     53 results = grtm.phi

/Users/mattburbidge/github/ss/lda-cython/lda_cython/topic_models.py in fit(self, X, y)
    466             self.n_topics, self.n_docs, self.n_terms, self.n_tokens,
    467             self.alpha, self.beta, self.mu, self.sigma2, self.nu,
--> 468             doc_lookup, term_lookup, self.adjacency_matrix, self.seed)
    469 
    470 class BLHSLDA(TopicModelBase):

/Users/mattburbidge/github/ss/lda-cython/lda_cython/_topic_models.pyx in lda_cython._topic_models.gibbs_sampler_rtm (lda_cython/_topic_models.c:14586)()

AttributeError: 'NoneType' object has no attribute 'getnnz'