def __init__(self, n_topics=2, alpha=0.001, beta=0.001, max_iter=100, method='LLDA'):

self.method = method.lower()

self.n_topics = n_topics

self.alpha = alpha

self.beta = beta

self.iteration = max_iter

assert self.method=='llda', 'Only Labeled LDA method is implemented in thi library'

self.model = LLDA(self.n_topics, self.alpha, self.beta)

def fit(self, X, y=None, verbose=False):

'''

X: Gensim corpora form

# Gensim treat corpus input as chunks of lists

'''

assert isinstance(X[0], list), 'Not chuncks of lists for docs. Require [[doc1],[doc2],...] as gensim chunk format'

if y is not None:

assert isinstance(y[0], list), 'Not chuncks of lists for labels. Require [[label1],[label2],...] as gensim chunk format'

self.labelset = list(set(reduce(list.__add__, y)))

self.model.set_corpus(self.labelset, X, y)

for i in range(self.iteration):

if verbose:

print "-- %d : %.4f" % (i, self.model.perplexity())

self.model.inference()

if verbose:

print "Final perplexity : %.4f" % self.model.perplexity()

def load(self, filename):

with open(filename, 'rb') as input:

self.model = pickle.load(input)

def save(self, filename):

with open(filename, 'wb+') as output:

## save a class object to a file using pickle

pickle.dump(self.model, output, pickle.HIGHEST_PROTOCOL)

def encode(self, X, topk=20, normalize=True):

'''

# Gensim treat corpus input as chunks of lists

Result is normalized any way.

'''

assert isinstance(X[0], list), 'Not chuncks of lists. Require [[],[],...] as gensim chunk format'

docs = [[self.model.term_to_id(term) for term in doc] for doc in X]

phi = self.model.phi()

thetas = self.model.theta()

hist = np.zeros(self.n_topics)

for doc, theta in zip(docs, thetas):

for w in doc:

hist += phi[:,w] * theta

if normalize:

hist = hist/(np.sum(hist, axis=0) + 1e-6)

sort_ind = np.argsort(hist)[::-1] # reverse index sequence after argsort

hist[sort_ind[topk:]] = 0

return hist

def summary(self, n_topics=-1):

phi = self.model.phi()

for k in range(np.size(phi, axis=0)):

print "\n%d: " % k,

for w in numpy.argsort(-phi[k])[:20]:

print "+ %.4f*%s" % (phi[k,w], self.model.vocas[w]),