Description
* Overview
This homework is about working with a mixture of
Dirichlet–multinomial unigram language models for grouped
documents. The purpose of this homework is threefold:
* To understand and implement the Dirichlet–multinomial unigram
for a mixture of Dirichlet–multinomial unigram language models.
* To understand various methods for computing the evidence, the
posterior mean, and the predictive probability of new data.
* To appreciate the kinds of information that may be obtained by
examining the most probable word types for each document group
according to the mean of the posterior distribution.
To complete this homework, you will need to use Python 2.7 [1], NumPy
[2], and SciPy [3]. Although all three are installed on EdLab, I
recommend you install them on your own computer. I also recommend you
install and use IPython [4] instead of the default Python shell.
Before answering the questions below, you should familiarize yourself
with the code in hw4.py. In particular, you should try running
sample(). For example, running ‘sample(array([5, 2, 3]), 10)’
generates 10 samples from the specified distribution as follows:
>>> sample(array([5, 2, 3]), 10)
array([0, 0, 2, 2, 1, 0, 0, 0, 2, 1])
* Questions
** Question 1
Class GroupedCorpus (in corpus.py) implements a grouped corpus, i.e.,
a corpus in which each document is associated with exactly one of T
groups. You can run this code as follows:
>>> corpus = GroupedCorpus()
>>> corpus.add(‘doc 1’, ‘group 1’, [‘foo’, ‘bar’])
>>> corpus.add(‘doc 2’, ‘group 2’, [‘bar’, ‘baz’, ‘baz’])
>>> corpus.add(‘doc 3’, ‘group 1’, [‘bar’, ‘foo’])
>>> print len(corpus)
3
>>> print sum(len(doc) for doc, t in corpus)
7
>>> print len(corpus.vocab)
3
>>> print len(corpus.group_vocab)
2
>>> for doc, t in corpus:
… print doc.w
…
[0, 1]
[1, 2, 2]
[1, 0]
>>> for doc, t in corpus:
… print ‘%s: %s’ % (doc.name, doc.plaintext())
…
doc 1: foo bar
doc 2: bar baz baz
doc 3: bar foo
>>> corpus = GroupedCorpus()
>>> corpus.add(str(0), str(1), [str(x) for x in [5, 1, 1, 3]])
>>> corpus.add(str(1), str(0), [str(x) for x in [2, 4]])
>>> print len(corpus)
2
>>> print sum(len(doc) for doc, t in corpus)
6
>>> print len(corpus.vocab)
5
>>> print len(corpus.group_vocab)
2
>>> for doc, t in corpus:
… print doc.w
…
[0, 1, 1, 2]
[3, 4]
>>> for doc, t in corpus:
… print ‘%s: %s’ % (doc.name, doc.plaintext())
…
0: 5 1 1 3
1: 2 4
(Note: although you do not need to understand how this class is
implemented, you may find it helpful to do so.)
Function sample() contains code for sampling from an unnormalized
discrete distribution using the inverse transform method [5].
a) Implement the generative process for the Dirichlet–multinomial
mixture model by filling in the missing code in
generate_corpus(). Hint: you will need to use GroupedCorpus and
sample() and numpy.random.mtrand’s dirichlet() function [6].
* Question 2
CSV file ufos.csv contains descriptions of 61,067 UFO sightings. Each
sighting is represented by a unique ID, the state in which the
sighting occurred, the shape of the sighted UFO, and a description.
Function preprocess() takes a CSV file, an optional stopword file, and
an optional field number as input and returns a grouped corpus, with
any stopwords removed. Each document’s group is determined by the
contents of the field corresponding to the specified the field
number. If no field number is specified, then all documents are
assumed to belong to a single group. You can run this code as follows:
>>> corpus = preprocess(‘ufos.csv’, ‘stopwordlist.txt’)
>>> print ‘V = %s’ % len(corpus.vocab)
V = 100456
>>> print ‘T = %s’ % len(corpus.group_vocab)
T = 1
>>> corpus = preprocess(‘ufos.csv’, ‘stopwordlist.txt’, 1)
>>> print ‘V = %s’ % len(corpus.vocab)
V = 100456
>>> print ‘T = %s’ % len(corpus.group_vocab)
T = 52
>>> print corpus.group_vocab.plaintext()
0 IA
1 WI
2 WA
3 MO
4 ND
[…]
>>> corpus = preprocess(‘ufos.csv’, ‘stopwordlist.txt’, 2)
>>> print ‘V = %s’ % len(corpus.vocab)
V = 100456
>>> print ‘T = %s’ % len(corpus.group_vocab)
T = 30
>>> print corpus.group_vocab.plaintext()
0 missing
1 cone
2 light
3 triangle
4 fireball
[…]
According to a mixture of Dirichlet–multinomial unigram language
models, the evidence for observed data w_1 through w_N and z_1 through
z_D given assumptions H is given by:
P(w_1, …, w_N, z_1, …, z_D | H) = (prod_{t=1}^T ((Gamma(beta) *
prod_{v=1}^V Gamma(N_{v|t} + beta * n_v)) / (Gamma(N_t + beta) *
prod_{v=1}^V Gamma(beta * n_v)))) * ((Gamma(alpha) * prod_{t=1}^T
Gamma(D_t + alpha * m_t)) / (Gamma(D + alpha) * prod_{t=1}^T
Gamma(alpha * m_t))),
where D_t is the number of documents belonging to group t, N_t is the
number of tokens in documents belonging to group t, of which N_{v|t}
are of type v, and alpha, m, beta, and n are hyperparameters.
a) Implement code for computing the log evidence by filling in the
missing code in log_evidence_1(). Hint: you will need to use
scipy.special’s gammaln() function [7].
b) Use log_evidence_1() as follows to compute the log evidence for
the data in ufos.csv according to a mixture of
Dirichlet–multinomial unigram language models (with uniform base
measures and concentration parameters equal to the vocabulary size
and the number of groups) with only a single group:
>>> corpus = preprocess(‘ufos.csv’, ‘stopwordlist.txt’)
>>> V = len(corpus.vocab)
>>> T = len(corpus.group_vocab)
>>> log_evidence_1(corpus, T, ones(T) / T, V, ones(V) / V)
c) Does your code give the same value as log_evidence_1() from last
week’s homework? Explain your answer.
According to a mixture of Dirichlet–multinomial unigram language
models, the predictive probability of a single new token w_{N + 1} of
type v in a new document belonging to group t (given observed data w_1
through w_N and z_1 through z_D and assumptions H) is given by
P(w_{N + 1} = v, z_{D + 1} = t | w_1, …, w_N, z_1, …, z_D, H) =
(N_v + beta * n_v) / (N + beta) * (D_t + alpha * m_t) / (D + alpha),
where alpha, m, beta, and n are hyperparameters.
d) What is the predictive probability of a single new token w_{N + 1}
of type v in an EXISTING document belonging to group t?
e) Use your answer, along with the definition of P(w_{N + 1} = v,
z_{D + 1} = t | w_1, …, w_N, z_1, …, z_D, H), the recurrence
Gamma(x + 1) = x * Gamma(x), and the definition of the chain rule
[8], to show how the evidence for observed data w_1 through w_N
and z_1 through z_D given assumptions H (as defined above) can
also be written without any gamma functions.
f) Implement this variant of log_evidence_1() by filling in the
missing code in log_evidence_2(). You should check that your code
gives the same value as log_evidence_1().
Function time_taken() computes the time taken to compute the specified
function, averaged over the specified number of repetitions.
g) Use time_taken() to compute the run times of log_evidence_1() and
log_evidence_2(), averaged over 10 repetitions, for the data in
ufos.csv with document groups determined by the UFOs’ shapes:
>>> corpus = preprocess(‘ufos.csv’, ‘stopwordlist.txt’, 2)
>>> V = len(corpus.vocab)
>>> T = len(corpus.group_vocab)
>>> alpha = T
>>> m = ones(T) / T
>>> beta = V
>>> n = ones(V) / V
>>> print time_taken(log_evidence_1, corpus, alpha, m, beta, n, 10)
>>> print time_taken(log_evidence_2, corpus, alpha, m, beta, n, 10)
h) Which function is faster? Will this algorithm always be faster
than the other one, regardless of the programming language?
* Question 3
According to a mixture of Dirichlet–multinomial unigram language
models, the evidence for observed tokens w_1 through w_N given z_1
through z_D and assumptions H is given by:
P(w_1, …, w_N | z_1, …, z_D, H) = prod_{t=1}^T ((Gamma(beta) *
prod_{v=1}^V Gamma(N_{v|t} + beta * n_v)) / (Gamma(N_t + beta) *
prod_{v=1}^V Gamma(beta * n_v))).
a) Implement code for computing the log evidence for observed tokens
w_1 through w_N given z_1 through z_D and assumptions H by filling
in the missing code in log_evidence_tokens_1().
b) Run your code to compute the log evidence for the text data in
ufos.csv given document groups determined by the UFOs’ shapes and
given document groups determined by the sighting locations:
>>> corpus = preprocess(‘ufos.csv’, ‘stopwordlist.txt’, 2)
>>> V = len(corpus.vocab)
>>> log_evidence_tokens_1(corpus, V, ones(V) / V)
>>> corpus = preprocess(‘ufos.csv’, ‘stopwordlist.txt’, 1)
>>> V = len(corpus.vocab)
>>> log_evidence_tokens_1(corpus, V, ones(V) / V)
c) Use your answer to explain which set of document groups results in
a better model of the text and why this might be the case.
* Question 4
Function posterior_mean() returns the mean of the posterior
distribution over phi_1 through phi_T (the categorical distributions
over word types corresponding to document groups 1 through T) and
theta (the categorical distribution over document groups).
a) Implement code for computing the mean of the posterior
distribution over phi_1 through phi_T by filling in the missing
code in posterior_mean_phis(). Hint: you will need to use your
definition of P(w_{N + 1} = v | w_1, …, w_N, z_1, …, z_D, H),
where d_{N + 1} = 1, 2, …, or D, from question 2d.
b) Implement code for computing the posterior distribution over theta
by filling in the missing code in posterior_mean_theta().
Function print_top_types() contains code for printing the most
probable word types for document groups 1 through T according to the
posterior distribution over phi_1 through phi_T.
b) Use print_top_types() to generate lists of the most probable word
types according to the posterior distribution over phi_1 through
phi_T for document groups corresponding to the UFOs’ shapes:
>>> corpus = preprocess(‘ufos.csv’, ‘stopwordlist.txt’, 2)
>>> V = len(corpus.vocab)
>>> print_top_types(corpus, V, ones(V) / V)
Almost all of these lists contain the words ‘light’, ‘lights’,
‘object’, ‘objects’, and ‘sky’. In the context of UFO sightings, these
words can therefore be treated as uninformative stopwords.
c) Create a new stopword list (new_stopwordlist.txt) by appending
these words to the list of words in stopwordlist.txt and
regenerate your lists of the most probable word types:
>>> corpus = preprocess(‘ufos.csv’, ‘new_stopwordlist.txt’, 2)
>>> V = len(corpus.vocab)
>>> print_top_types(corpus, V, ones(V) / V)
d) Do these lists look more useful/informative? Why?
e) Use print_top_types() to generate lists of the most probable word
types according to the posterior distribution over phi_1 through
phi_T for document groups corresponding to the sighting locations:
>>> corpus = preprocess(‘ufos.csv’, ‘new_stopwordlist.txt’, 1)
>>> V = len(corpus.vocab)
>>> print_top_types(corpus, V, ones(V) / V)
f) Do these lists look more useful/informative than those obtained
for document groups corresponding to the UFOs’ shapes? Why?
g) How does your answer relate to your answer to question 3c?
* References
[1] http://www.python.org/
[2] http://numpy.scipy.org/
[3] http://www.scipy.org/
[4] http://ipython.org/
[5] http://en.wikipedia.org/wiki/Inverse_transform_sampling
[6] http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.dirichlet.html
[7] http://docs.scipy.org/doc/scipy/reference/generated/scipy.special.gammaln.html
[8] http://en.wikipedia.org/wiki/Chain_rule_%28probability%29
